106 pubblicazioni classificate nel seguente modo:

Surface Electromyography Sensors for Human Activity Recognition: Recent Advancements and Perspectives
Non-Invasive Health Systems based on Advanced Biomedical Signal and Image Processing
Autore/i: Crippa, P.; Biagetti, G.; Falaschetti, L.; Turchetti, C.
Editore: CRC Press
Luogo di pubblicazione: Boca Raton
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/326911

Photoplethysmography and Inertial Sensors in Wearable Devices for Healthcare: Multimodal Signal Processing for Increasing Accuracy
Non-Invasive Health Systems based on Advanced Biomedical Signal and Image Processing
Autore/i: Biagetti, G.; Crippa, P.; Falaschetti, L.; Turchetti, C.
Editore: CRC Press
Luogo di pubblicazione: Boca Raton
Classificazione: 2 Contributo in Volume
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/326912

A Deep Learning Model for Correlation Analysis between Electroencephalography Signal and Speech Stimuli
SENSORS
Autore/i: Alessandrini, M.; Falaschetti, L.; Biagetti, G.; Crippa, P.; Luzzi, S.; Turchetti, C.
Classificazione: 1 Contributo su Rivista
Abstract: In recent years, the use of electroencephalography (EEG) has grown as a tool for diagnostic and brain function monitoring, being a simple and non-invasive method compared with other procedures like histological sampling. Typically, in order to extract functional brain responses from EEG signals, prolonged and repeated stimuli are needed because of the artifacts generated in recordings which adversely impact the stimulus-response analysis. To mitigate the artifact effect, correlation analysis (CA) methods are applied in the literature, where the predominant approaches focus on enhancing stimulus-response correlations through the use of linear analysis methods like canonical correlation analysis (CCA). This paper introduces a novel CA framework based on a neural network with a loss function specifically designed to maximize correlation between EEG and speech stimuli. Compared with other deep learning CA approaches (DCCAs) in the literature, this framework introduces a single multilayer perceptron (MLP) network instead of two networks for each stimulus. To validate the proposed approach, a comparison with linear CCA (LCCA) and DCCA was performed, using a dataset containing the EEG traces of subjects listening to speech stimuli. The experimental results show that the proposed method improves the overall Pearson correlation by 10.56% compared with the state-of-the-art DCCA method.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/323951

EEG-Based Neurodegenerative Disease Classification using LSTM Neural Networks
Proceedings of the 22nd IEEE Statistical Signal Processing Workshop (SSP 2023)
Autore/i: Alessandrini, Michele; Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Luzzi, Simona; Turchetti, Claudio
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In recent years, the use of electroencephalography (EEG) for the clinical diagnosis of neurodegenerative diseases, such as Alzheimer’s disease, frontotemporal dementia and dementia with Lewy bodies, has been extensively studied. The classification of these different neurodegenerative diseases can benefit from machine learning techniques which, compared to manual diagnosis methods, have higher reliability and higher recognition performance, being able to handle large amounts of data. The purpose of this work is to develop an automatic classification method that can recognize a number of different neurodegenerative diseases such the aforementioned ones, having similar corresponding EEGs or being difficult to discern by inspection from a human operator. We show how a recurrent neural network (RNN) based on long short-term memory (LSTM) elements can successfully perform the task of classification, when the data are properly pre-processed.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/320772

Open-Source HW/SW Co-Simulation Using QEMU and GHDL for VHDL-Based SoC Design
ELECTRONICS
Autore/i: Biagetti, Giorgio; Falaschetti, Laura; Crippa, Paolo; Alessandrini, Michele; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: Hardware/software co-simulation is a technique that can help design and validate digital circuits controlled by embedded processors. Co-simulation has largely been applied to system-level models, and tools for SystemC or SystemVerilog are readily available, but they are either not compatible or very cumbersome to use with VHDL, the most commonly used language for FPGA design. This paper presents a direct, simple-to-use solution to co-simulate a VHDL design together with the firmware (FW) that controls it. It aims to bring the power of co-simulation to every digital designer, so it uses open-source tools, and the developed code is also open. A small patch applied to the QEMU emulator allows it to communicate with a custom-written VHDL module that exposes a CPU bus to the digital design, controlled by the FW emulated in QEMU. No changes to FW code or VHDL device code are required: with our approach, it is possible to co-simulate the very same code base that would then be implemented into an FPGA, enabling debugging, verification, and tracing capabilities that would not be possible even with the real hardware.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/322651

An U-Net Semantic Segmentation Vision System on a Low-Power Embedded Microcontroller Platform
27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems (KES 2023)
Autore/i: Falaschetti, Laura; Bruschi, Sara; Alessandrini, Michele; Biagetti, Giorgio; Crippa, Paolo; Turchetti, Claudio
Editore: Elsevier
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In recent years, real-time semantic segmentation on embedded devices has become increasingly popular, largely driven by the growing interest in smart vehicles and robots. The rising of autonomous driving has brought about new challenges for these systems, such as the need for low latency and computation-intensive operations, which can lead to excessive energy consumption and computing power. To address these challenges, this paper focuses on the critical task of semantic segmentation, which is essential for accurate environment perception, and proposes an implementation that achieves high accuracy and low complexity using a U-Net as base architecture. The goal is to enable real-time semantic segmentation on low-power cores while preserving performance, which is crucial for the success of autonomous vehicles and robots. The lightweight U-Net architectures have been implemented in a STM32 microcontroller, namely STM32L4R5, as a severe benchmark to meet the low-power, low-cost requirements.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/325152

Portable High-Accuracy Wireless Acquisition System for Graphene-Based Sensors
IEEE SENSORS JOURNAL
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Alessandrini, Michele; Marino, Daniele Di
Classificazione: 1 Contributo su Rivista
Abstract: Graphene field-effect transistors (gFETs) are gaining popularity for the realization of biological sensors as their graphene active area provides a convenient basis for attaching organic substances, such as appropriately engineered receptors. The presence of a particular biological agent then translates in the modification of the electrical characteristics of the gFET. We thus developed a compact, portable system that is able to accurately measure said electrical characteristics with high accuracy, by automatically compensating its own offsets and errors. The acquisition device we here present is able to measure drain currents with a nominal accuracy of 0.1 % and with an RMS noise as low as 22 pA, up to a maximum of 125 µA (22 bits effective resolution), and gFET channel resistances with a nominal accuracy of 0.01 % ± 0.1 Ω and with an RMS noise as low as 2.13 µV in the range from 100 Ω to 1 MΩ. Due to its performance, small dimensions, long battery life, it can be used both for scientific research, where portability and ease of use are key features when operating in potentially hazardous environments due to the presence of biological agents, and as a fully-automated detector when coupled with the appropriate sensor, as it can perform thousands of measures on a single battery charge and be completely remotely controlled over a Bluetooth low energy (BLE) connection.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/320371

SARS-CoV-2 multi-variant rapid detector based on graphene transistor functionalized with an engineered dimeric ACE2 receptor
NANO TODAY
Autore/i: Romagnoli, A.; D'Agostino, M.; Pavoni, E.; Ardiccioni, C.; Motta, S.; Crippa, P.; Biagetti, G.; Notarstefano, V.; Rexha, J.; Perta, N.; Barocci, S.; Costabile, B. K.; Colasurdo, G.; Caucci, S.; Mencarelli, D.; Turchetti, C.; Farina, M.; Pierantoni, L.; La Teana, A.; Al Hadi, R.; Cicconardi, F.; Chinappi, M.; Trucchi, E.; Mancia, F.; Menzo, S.; Morozzo della Rocca, B.; D'Annessa, I.; Di Marino, D.
Classificazione: 1 Contributo su Rivista
Abstract: Reliable point-of-care (POC) rapid tests are crucial to detect infection and contain the spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The emergence of several variants of concern (VOC) can reduce binding affinity to diagnostic antibodies, limiting the efficacy of the currently adopted tests, while showing unaltered or increased affinity for the host receptor, angiotensin converting enzyme 2 (ACE2). We present a graphene field-effect transistor (gFET) biosensor design, which exploits the Spike-ACE2 interaction, the crucial step for SARS-CoV-2 infection. Extensive computational analyses show that a chimeric ACE2-Fragment crystallizable (ACE2-Fc) construct mimics the native receptor dimeric conformation. ACE2-Fc functionalized gFET allows in vitro detection of the trimeric Spike protein, outperforming functionalization with a diagnostic antibody or with the soluble ACE2 portion, resulting in a sensitivity of 20 pg/mL. Our miniaturized POC biosensor successfully detects B.1.610 (pre-VOC), Alpha, Beta, Gamma, Delta, Omicron (i.e., BA.1, BA.2, BA.4, BA.5, BA.2.75 and BQ.1) variants in isolated viruses and patient's clinical nasopharyngeal swabs. The biosensor reached a Limit Of Detection (LOD) of 65 cps/mL in swab specimens of Omicron BA.5. Our approach paves the way for a new and reusable class of highly sensitive, rapid and variant-robust SARS-CoV-2 detection systems.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/309546

A Lightweight CNN-Based Vision System for Concrete Crack Detection on a Low-Power Embedded Microcontroller Platform
Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 26th International Conference KES2022
Autore/i: Falaschetti, Laura; Beccerica, Mattia; Biagetti, Giorgio; Crippa, Paolo; Alessandrini, Michele; Turchetti, Claudio
Editore: Elsevier B.V.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The detection of cracks is a key aspect for assessing the condition of in-service structures such as road, bridges or dams. Therefore it assumes a great importance for civil infrastructures monitoring, road maintenance and traffic safety. Intelligent detection methods based on convolutional neural networks (CNNs) have been widely applied to the field of crack detection in recently years. In this work we propose a vision system based on two lightweight and accurate CNN models implemented in a low-cost, low-power platform, namely the OpenMV Cam H7 Plus, to monitor and to detect concrete cracks in real-time, suitable to realize a prototype of early warning system. In order to be useful, such a system must provide a very high accuracy, so as not to give false alarms, and be parsimonious enough on computational resources to be embedded into low-power, portable systems that can be deployed on the field. To reach this goal, firstly we analyze different state-of-the-art CNNs applied to the concrete crack detection task in order to discover the smallest network in terms of memory storage and number of parameters. Then, we compare the performance, in terms of memory occupancy and accuracy, of the proposed CNN architectures with the smallest network in the investigated literature, LeNet, all trained on two different image datasets, the Concrete Crack Images for Classification dataset and the SDNET2018 dataset, and implemented on the embedded system OpenMV Cam H7 Plus. The proposed CNN architectures perform nicely on this platform, using only a small fraction, between 6% to 26%, of the memory required by LeNet, and always providing better accuracy in all the tested cases and on both the datasets tried, with only a marginal increase in inference time.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/307282

Nonlinear Dynamic System Identification in the Spectral Domain Using Particle-Bernstein Polynomials
ELECTRONICS
Autore/i: Alessandrini, M.; Falaschetti, L.; Biagetti, G.; Crippa, P.; Turchetti, C.
Classificazione: 1 Contributo su Rivista
Abstract: System identification (SI) is the discipline of inferring mathematical models from unknown dynamic systems using the input/output observations of such systems with or without prior knowledge of some of the system parameters. Many valid algorithms are available in the literature, including Volterra series expansion, Hammerstein–Wiener models, nonlinear auto-regressive moving average model with exogenous inputs (NARMAX) and its derivatives (NARX, NARMA). Different nonlinear estimators can be used for those algorithms, such as polynomials, neural networks or wavelet networks. This paper uses a different approach, named particle-Bernstein polynomials, as an estimator for SI. Moreover, unlike the mentioned algorithms, this approach does not operate in the time domain but rather in the spectral components of the signals through the use of the discrete Karhunen–Loève transform (DKLT). Some experiments are performed to validate this approach using a publicly available dataset based on ground vibration tests recorded from a real F-16 aircraft. The experiments show better results when compared with some of the traditional algorithms, especially for large, heterogeneous datasets such as the one used. In particular, the absolute error obtained with the prosed method is 63% smaller with respect to NARX and from 42% to 62% smaller with respect to various artificial neural network-based approaches.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/307261

Next-Generation Hybrid RF Front-End with MoS2-FET Supply Management Circuit, CNT-FET Amplifiers, and Graphene Thin-Film Antennas
ELECTRONICS
Autore/i: Crippa, Paolo; Biagetti, Giorgio; Minelli, Lorenzo; Turchetti, Claudio; Aldrigo, Martino; Dragoman, Mircea; Mencarelli, Davide; Pierantoni, Luca
Classificazione: 1 Contributo su Rivista
Abstract: One-dimensional (1D) and two-dimensional (2D) materials represent the emerging technologies for transistor electronics in view of their attractive electrical (high power gain, high cut-off frequency, low power dissipation) and mechanical properties. This work investigates the integration of carbon nanotube-based field-effect transistors (CNT-FETs) and molybdenum disulphide (MoS2)-based FETs with standard CMOS technology for designing a simple analog system integrating a power switching circuit for the supply management of a 10-GHz transmitting/receiving (T/R) module that embeds a low-noise amplifier (LNA) and a high-power amplifier (HPA), both of which loaded by nanocrystalline graphene (NCG)-based patch antennas. Verilog-A models, tuned to the technology that will be used to manufacture the FETs, have been implemented to perform electrical simulations of the MoS2 and CNT devices using a commercial integrated circuit software simulator. The obtained simulation results prove the potential of hybrid CNT-MoS2-FET circuits as building blocks for next-generation integrated circuits for radio frequency (RF) applications, such as radars or IoT systems.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/308082

A Lightweight and Accurate RNN in Wearable Embedded Systems for Human Activity Recognition
Intelligent Decision Technologies
Autore/i: Falaschetti, Laura; Biagetti, Giorgio; Crippa, Paolo; Alessandrini, Michele; Giacomo, Di Filippo; Turchetti, Claudio
Editore: Springer
Luogo di pubblicazione: Singapore
Classificazione: 2 Contributo in Volume
Abstract: Human activity recognition (HAR) is an important technology for a wide range of applications including elderly people monitoring, ambient assisted living, sport and fitness activities. The aim of this paper is to address the HAR task directly on a wearable device, implementing a recurrent neural network (RNN) on a low cost, low power microcontroller, ensuring the required performance in terms of accuracy and low complexity. To reach this goal we first develop a lightweight RNN on the Human Activity Recognition Using Smartphones dataset in order to accurately detect human activity and then we port the RNN to the embedded device Cloud-JAM L4, based on an STM32 microcontroller. Experimental results show that this HAR RNN-based detector can be effectively implemented on the chosen constrained-resource system, achieving an accuracy of about 90.50% with a very low memory cost (40.883 KB) and inference time (67.131 ms), allowing the design of a wearable embedded system for human activity recognition.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/304942

Wearable Acceleration-Based Human Activity Recognition Using AM-FM Signal Decomposition
Intelligent Decision Technologies
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Alessandrini, Michele; Turchetti, Claudio
Editore: Springer
Luogo di pubblicazione: Singapore
Classificazione: 2 Contributo in Volume
Abstract: This paper presents an efficient technique for real-time recognition of human activities using accelerometer signals alone. It is based on amplitude modulation frequency modulation (AM-FM) decomposition for feature extraction and support vector machine (SVM) algorithm for classification. Due to the nature of signals, and being the proposed technique independent from the orientation of the inertial sensor, this methodology is particularly suitable for implementation in smartwatches or other wearable sensors in order to recognize the exercise being performed. In order to demonstrate the validity of this methodology, it has been successfully applied to accelerometer data related to four dynamic activities and belonging to a free available database and compared with results in the literature.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/304941

Embedded AM-FM Signal Decomposition Algorithm for Continuous Human Activity Monitoring
Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 26th International Conference KES2022
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Bocchini, Dario; Alessandrini, Michele; Falaschetti, Laura; Turchetti, Claudio
Editore: Elsevier B.V.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: AM-FM decomposition techniques have been successfully used for extracting significative features from a large variety of signals, helping realtime signal monitoring and pattern recognition, since they represent signals as a simultaneous composition of amplitude modulation and frequency modulation, where the carriers, amplitude envelopes, and the instantaneous frequencies are the features to be estimated. Human activities often involve repetitive movements, such as in running or cycling, where sinusoidal AM-FM decompositions of signals have already demonstrated to be useful to extract compact features to aid monitoring, classification, or detection. In this work we thus present the challenges and results of implementing the iterated coherent Hilbert decomposition (ICHD), a particularly effective algorithm to obtain an AM-FM decomposition, within a resource-constrained and low-power ARM Cortex-M4 microcontroller that is present in a wearable sensor we developed. We apply ICHD to the gyroscope data acquired from an inertial measurement unit (IMU) that is present in the sensor. Optimizing the implementation allowed us to achieve real-time performance using less then 16 % of the available CPU time, while consuming only about 5.4 mW of power, which results in a run-time of over 7 days using a small 250 mAh rechargeable cell.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/307262

ECG-Based Arrhythmia Classification using Recurrent Neural Networks in Embedded Systems
Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 26th International Conference KES2022
Autore/i: Falaschetti, Laura; Alessandrini, Michele; Biagetti, Giorgio; Crippa, Paolo; Turchetti, Claudio
Editore: Elsevier B.V.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Cardiac arrhythmia is one of the most important cardiovascular diseases (CVDs), causing million deaths every year. Moreover it is difficult to diagnose because it occurs intermittently and as such requires the analysis of large amount of data, collected during the daily life of patients. An important tool for CVD diagnosis is the analysis of electrocardiogram (ECG), because of its non-invasive nature and simplicity of acquisition. In this work we propose a classification algorithm for arrhythmia based on recurrent neural networks (RNNs) that operate directly on ECG data, exploring the effectiveness and efficiency of several variations of the general RNN, in particular using different types of layers implementing the network memory. We use the MIT-BIH arrhythmia database and the evaluation protocol recommended by the Association for the Advancement of Medical Instrumentation (AAMI). After designing and testing the effectiveness of the different networks, we then test its porting to an embedded platform, namely the STM32 microcontroller architecture from ST, using a specific framework to port a pre-built RNN to the embedded hardware, convert it to optimized code for the platform and evaluate its performance in terms of resource usage. Both in binary and multiclass classification, the basic RNN model outperforms the other architectures in terms of memory storage (∼117 KB), number of parameters (∼5 k) and inference time (∼150 ms), while the RNN LSTM-based achieved the best accuracy (∼90%).
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/307281

Tunable and miniaturized microwave filters using carbon nanotube-based variable capacitors
IEEE TRANSACTIONS ON NANOTECHNOLOGY
Autore/i: Aldrigo, M.; Dragoman, M.; Iordanescu, S.; Boldeiu, G.; Crippa, P.; Biagetti, G.; Turchetti, C.; Pierantoni, L.; Mencarelli, D.; Xavier, S.; Gangloff, L.; Ziaei, A.
Classificazione: 1 Contributo su Rivista
Abstract: In this paper, we introduce a novel concept of tunable and miniaturized filters which embed, as voltage-controlled elements, state-of-the-art variable capacitors, based on vertically aligned carbon nanotubes (VACNTs). Starting from a theoretical estimation of the voltage-dependent capacitance between two adjacent CNTs, we extended this physics principle to a large matrix of CNTs, suitably placed on the molybdenum electrodes of an interdigitated capacitor (IDC), since molybdenum can withstand the high temperature necessary in the plasma process for the growth of the VACNTs. The IDC is the tunable element of a microwave filter, which must fulfill the need for both reconfigurability (being either a low-pass, a high-pass or a band-pass filter, at discretion) and low-voltage frequency tuning of reflection/transmission coefficients. For all these reasons, a very compact layout made of T-type cells (comprising VACNT-based variable capacitors and distributed inductors) was designed, simulated, fabricated, and tested, targeting the C, X, and Ku bands (4-16 GHz) for wireless and radar applications. Taking as a reference the free-space wavelength lambda_0 at 10 GHz, the band-pass filter has overall dimensions of just 3.19 mm x 3.47 mm (i.e., 0.11 lambda_0 x 0.12 lambda_0), with the minimum of the reflection coefficient shifting of 1.16 GHz (within the X band) for an applied dc bias voltage of just 4 V and spanning between -24.81 dB and -36.13 dB. Furthermore, the maximum rejection is 31.65 dB, and the 3-dB fractional bandwidth is 12.44%. The proposed filters are the proof that nanomaterials can be profitably integrated into microwave components for next-generation transceivers.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/296662

EEG-Based Alzheimer’s Disease Recognition Using Robust-PCA and LSTM Recurrent Neural Network
SENSORS
Autore/i: Alessandrini, M.; Biagetti, G.; Crippa, P.; Falaschetti, L.; Luzzi, S.; Turchetti, C.
Classificazione: 1 Contributo su Rivista
Abstract: The use of electroencephalography (EEG) has recently grown as a means to diagnose neurodegenerative pathologies such as Alzheimer’s disease (AD). AD recognition can benefit from machine learning methods that, compared with traditional manual diagnosis methods, have higher reliability and improved recognition accuracy, being able to manage large amounts of data. Nevertheless, machine learning methods may exhibit lower accuracies when faced with incomplete, corrupted, or otherwise missing data, so it is important do develop robust pre-processing techniques do deal with incomplete data. The aim of this paper is to develop an automatic classification method that can still work well with EEG data affected by artifacts, as can arise during the collection with, e.g., a wireless system that can lose packets. We show that a recurrent neural network (RNN) can operate successfully even in the case of significantly corrupted data, when it is pre-filtered by the robust principal component analysis (RPCA) algorithm. RPCA was selected because of its stated ability to remove outliers from the signal. To demonstrate this idea, we first develop an RNN which operates on EEG data, properly processed through traditional PCA; then, we use corrupted data as input and process them with RPCA to filter outlier components, showing that even with data corruption causing up to 20% erasures, the RPCA was able to increase the detection accuracy by about 5% with respect to the baseline PCA.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/300685

Classification of Alzheimer’s Disease from EEG Signal Using Robust-PCA Feature Extraction
Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 25th International Conference KES2021
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Luzzi, Simona; Turchetti, Claudio
Editore: Elsevier
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The encephalographic (EEG) signal is an electrical signal that measures the brain activity. Due to its noninvasive acquisition process, it is often used to investigate the presence of Alzheimer’s disease (AD) or other common forms of neurodegerative disorders due to brain changes, that occur most frequently in older adults. Early detection of prodromal stages of AD, in which an individual has mild but measurable cognitive deficiencies with no significant effect on the functional activity of daily living, may help to reduce mortality and morbidity. This paper proposes an investigation of the classification of AD from EEG signal using robust-principal component analysis (R-PCA) feature extraction algorithm. Four widely used machine learning algorithms such as k-nearest neighbor (kNN), decision tree (DT), support vector machine (SVM), and naive Bayes have been implemented and compared by using a custom dataset composed of 13 subjects healthy or affected by AD in order to asses their classification performance.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/292415

Singular Value Decomposition in Embedded Systems Based on ARM Cortex-M Architecture
ELECTRONICS
Autore/i: Alessandrini, Michele; Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Manoni, Lorenzo; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: Singular value decomposition (SVD) is a central mathematical tool for several emerging applications in embedded systems, such as multiple-input multiple-output (MIMO) systems, data analytics, sparse representation of signals. Since SVD algorithms reduce to solve an eigenvalue problem, that is computationally expensive, both specific hardware solutions and parallel implementations have been proposed to overcome this bottleneck. However, as those solutions require additional hardware resources that are not in general available in embedded systems, optimized algorithms are demanded in this context. The aim of this paper is to present an efficient implementation of the SVD algorithm on ARM Cortex-M. To this end, we proceed to (i) present a comprehensive treatment of the most common algorithms for SVD, providing a fairly complete and deep overview of these algorithms, with a common notation, (ii) implement them on an ARM Cortex-M4F microcontroller, in order to develop a library suitable for embedded systems without an operating system, (iii) find, through a comparative study of the proposed SVD algorithms, the best implementation suitable for a low-resource bare-metal embedded system, (iv) show a practical application to Kalman filtering of an inertial measurement unit (IMU), as an example of how SVD can improve the accuracy of existing algorithms and of its usefulness on a such low-resources system. All these contributions can be used as guidelines for embedded system designers. Regarding the second point, the chosen algorithms have been implemented on ARM Cortex-M4F microcontrollers with very limited hardware resources with respect to more advanced CPUs. Several experiments have been conducted to select which algorithms guarantee the best performance in terms of speed, accuracy and energy consumption.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/286171

Energy and performance analysis of lossless compression algorithms for wireless EMG sensors
SENSORS
Autore/i: Biagetti, G.; Crippa, P.; Falaschetti, L.; Mansour, A.; Turchetti, C.
Classificazione: 1 Contributo su Rivista
Abstract: Electromyography (EMG) sensors produce a stream of data at rates that can easily saturate a low-energy wireless link such as Bluetooth Low Energy (BLE), especially if more than a few EMG channels are being transmitted simultaneously. Compressing data can thus be seen as a nice feature that could allow both longer battery life and more simultaneous channels at the same time. A lot of research has been done in lossy compression algorithms for EMG data, but being lossy, artifacts are inevitably introduced in the signal. Some artifacts can usually be tolerable for current applications. Nevertheless, for some research purposes and to enable future research on the collected data, that might need to exploit various and currently unforseen features that had been discarded by lossy algorithms, lossless compression of data may be very important, as it guarantees no extra artifacts are introduced on the digitized signal. The present paper aims at demonstrating the effectiveness of such approaches, investigating the performance of several algorithms and their implementation on a real EMG BLE wireless sensor node. It is demonstrated that the required bandwidth can be more than halved, even reduced to 1/4 on an average case, and if the complexity of the compressor is kept low, it also ensures significant power savings.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/291524

A High-Gain CNTFET-Based LNA Developed Using a Compact Design-Oriented Device Model
ELECTRONICS
Autore/i: Crippa, Paolo; Biagetti, Giorgio; Turchetti, Claudio; Falaschetti, Laura; Mencarelli, Davide; Deligeorgis, George; Pierantoni, Luca
Classificazione: 1 Contributo su Rivista
Abstract: Recently, carbon nanotube field-effect transistors (CNTFETs) have attracted wide attention as promising candidates for components in the next generation of electronic devices. In particular CNTFET-based RF devices and circuits show superior performance to those built with silicon FETs since they are able to obtain higher power-gain and cut-off frequency at lower power dissipation. The aim of this paper is to present a compact, design-oriented model of CNTFETs that is able to ease the development of a complete amplifier. As a case study, the detailed design of a high-gain CNTFET-based broadband inductorless LNA is presented.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/293221

Microwave Detection Using 2-Atom-Thick Heterojunction Diodes
IEEE MTT-S International Microwave Symposium Digest
Autore/i: Aldrigo, M.; Dragoman, M.; Iordanescu, S.; Vasilache, D.; Dinescu, A.; Biagetti, G.; Pierantoni, L.; Mencarelli, D.
Editore: Institute of Electrical and Electronics Engineers Inc.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper, a two-dimensional material-based diode for microwave detection at 2.49 GHz with photo detector capabilities is presented. The diode consists of a molybdenum disulphide monolayer/graphene monolayer heterojunction transferred onto a silicon/silicon dioxide substrate, and patterned by means of nanolithography techniques to obtain a geometrical self-switching diode. The interaction between the two monolayers gives rise to a double-stage device, which behaves as a back-to-back diode in the [-3, +3] V range, and as a tunnel diode when exceeding +10 V. The heterojunction can be reproduced on large scale due to its CMOS compatibility; it does not need any particular doping process thanks to its geometrical nature and can be used efficiently as microwave detector up to 10 GHz, with the best performance around the ISM 2.45 GHz band. Last, a rigorous equivalent circuit model based on the Foster's method is provided, which relies on the measured scattering parameters at high frequencies. This way, the device can be exploited in circuit-based numerical tools for the desian of complex microwave front-ends.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/299221

Recurrent neural network for human activity recognition in embedded systems using ppg and accelerometer data
ELECTRONICS
Autore/i: Alessandrini, M.; Biagetti, G.; Crippa, P.; Falaschetti, L.; Turchetti, C.
Classificazione: 1 Contributo su Rivista
Abstract: Photoplethysmography (PPG) is a common and practical technique to detect human activity and other physiological parameters and is commonly implemented in wearable devices. However, the PPG signal is often severely corrupted by motion artifacts. The aim of this paper is to address the human activity recognition (HAR) task directly on the device, implementing a recurrent neural network (RNN) in a low cost, low power microcontroller, ensuring the required performance in terms of accuracy and low complexity. To reach this goal, (i) we first develop an RNN, which integrates PPG and tri-axial accelerometer data, where these data can be used to compensate motion artifacts in PPG in order to accurately detect human activity; (ii) then, we port the RNN to an embedded device, Cloud-JAM L4, based on an STM32 microcontroller, optimizing it to maintain an accuracy of over 95% while requiring modest computational power and memory resources. The experimental results show that such a system can be effectively implemented on a constrained-resource system, allowing the design of a fully autonomous wearable embedded system for human activity recognition and logging.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/291439

A multi-channel electromyography, electrocardiography and inertial wireless sensor module using bluetooth low-energy
ELECTRONICS
Autore/i: Biagetti, G.; Crippa, P.; Falaschetti, L.; Turchetti, C.
Classificazione: 1 Contributo su Rivista
Abstract: This paper proposes a wireless sensor device for the real-time acquisition of bioelectrical signals such as electromyography (EMG) and electrocardiography (ECG), coupled with an inertial sensor, to provide a comprehensive stream of data suitable for human activity detection, motion analysis, and technology-assisted nursing of persons with physical or cognitive impairments. The sensor is able to acquire up to three independent bioelectrical channels (six electrodes), each with 24 bits of resolution and a sampling rate up to 3.2 kHz, and has a 6-DoF inertial platform measuring linear acceleration and angular velocity. The bluetooth low-energy wireless link was chosen because it allows easy interfacing with many consumer electronics devices, such as smartphones or tablets, that can work as data aggregators, but also imposes data rate restrictions. These restrictions are investigated in this paper as well, together with the strategy we adopted to maximize the available bandwidth and reliability of the transmission within the limits imposed by the protocol.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/282449

Dataset from PPG wireless sensor for activity monitoring
DATA IN BRIEF
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Saraceni, Leonardo; Tiranti, Andrea; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: We introduce a dataset to provide insights about the photoplethysmography (PPG) signal captured from the wrist in presence of motion artifacts and the accelerometer signal, simultaneously acquired from the same wrist. The data presented were collected by the electronics research team of the Department of Information Engineering, Polytechnic University of Marche, Ancona, Italy. This article describes data recorded from 7 subjects and includes 105 PPG signals (15 for each subject) and the corresponding 105 tri-axial accelerometer signals measured with a sampling frequency of 400 Hz. These data can be reused for testing machine learning algorithms for human activity recognition.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/273139

A Compact and Robust Technique for the Modeling and Parameter Extraction of Carbon Nanotube Field Effect Transistors
ELECTRONICS
Autore/i: Falaschetti, Laura; Mencarelli, Davide; Pelagalli, Nicola; Crippa, Paolo; Biagetti, Giorgio; Turchetti, Claudio; Deligeorgis, George; Pierantoni, Luca
Classificazione: 1 Contributo su Rivista
Abstract: Carbon nanotubes field-effect transistors (CNTFETs) have been recently studied with great interest due to the intriguing properties of the material that, in turn, lead to remarkable properties of the charge transport of the device channel. Downstream of the full-wave simulations, the construction of equivalent device models becomes the basic step for the advanced design of high-performance CNTFET-based nanoelectronics circuits and systems. In this contribution, we introduce a strategy for deriving a compact model for a CNTFET that is based on the full-wave simulation of the 3D geometry by using the finite element method, followed by the derivation of a compact circuit model and extraction of equivalent parameters. We show examples of CNTFET simulations and extract from them the fitting parameters of the model. The aim is to achieve a fully functional description in Verilog-A language and create a model library for the SPICE-like simulator environment, in order to be used by IC designers.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/286033

Modeling and electrochemical characterization of electrodes based on epoxy composite with functionalized nanocarbon fillers at high concentration
NANOMATERIALS
Autore/i: Cataldo, A.; Biagetti, G.; Mencarelli, D.; Micciulla, F.; Crippa, P.; Turchetti, C.; Pierantoni, L.; Bellucci, S.
Classificazione: 1 Contributo su Rivista
Abstract: This paper deals with the electrochemical characterization and the equivalent circuit modeling of screen-printed electrodes, modified by an epoxy composite and loaded with carbon nanotubes (CNTs), pristine and functionalized NH2, and graphene nanoplates (GNPs). The fabrication method is optimized in order to obtain a good dispersion even at high concentration, up to 10%, to increase the range of investigation. Due to the rising presence of filler on the surface, the cyclic voltammetric analysis shows an increasing of (i) electrochemical response and (ii) filler concentration as observed by the scanning electron microscopy (SEM). Epoxy/CNTs-NH2 and epoxy/GNPs, at 10% of concentration, show the best electrochemical behavior. Furthermore, epoxy/CNTs-NH2 show a lower percolation threshold than epoxy/CNT, probably due to the direct bond created by amino groups. Furthermore, the electrochemical impedance spectroscopy (EIS) is used to obtain an electrical equivalent circuit (EEC). The EEC model is a remarkable evolution of previous circuits present in the literature, by inserting an accurate description of the capacitive/inductive/resistive characteristics, thus leading to an enhanced knowledge of phenomena that occur during electrochemical processes.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/277741

Machine Learning and Data Fusion Techniques Applied to Physical Activity Classification Using Photoplethysmographic and Accelerometric Signals
Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 24th International Conference KES2020
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Focante, Edoardo; Madrid, Natividad Martinez; Seepold, Ralf; Turchetti, Claudio
Editore: Elsevier
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The evaluation of the effectiveness of different machine learning algorithms on a publicly available database of signals derived from wearable devices is presented with the goal of optimizing human activity recognition and classification. Among the wide number of body signals we choose a couple of signals, namely photoplethysmographic (optically detected subcutaneous blood volume) and tri-axis acceleration signals that are easy to be simultaneously acquired using commercial widespread devices (e.g. smartwatches) as well as custom wearable wireless devices designed for sport, healthcare, or clinical purposes. To this end, two widely used algorithms (decision tree and k-nearest neighbor) were tested, and their performance were compared to two new recent algorithms (particle Bernstein and a Monte Carlo-based regression) both in terms of accuracy and processing time. A data preprocessing phase was also considered to improve the performance of the machine learning procedures, in order to reduce the problem size and a detailed analysis of the compression strategy and results is also presented.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/284042

Correlation Between Respiratory Action and Diaphragm Surface EMG Signal
Proceedings of EMBC Workshop ”Telemedicine and Telemonitoring in AAL Home Environments”
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Turchetti, Claudio
Editore: Hochschule Reutlingen, Reutlingen, Germany
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper investigates the possibility to effectively monitor and control the respiratory action using a very simple and non invasive technique based on a single lightweight reduced-size wireless surface electromyography (sEMG) sensor placed below the sternum. The captured sEMG signal, due to the critical sensor position, is characterized by a low energy level and it is affected by motion artifacts and cardiac noise. In this work we present a preliminary study performed on adults for assessing the correlation of the spirometry signal and the sEMG signal after the removal of the superimposed heart signal. This study and the related findings could be useful in respiratory monitoring of preterm infants.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/272568

Recognition of Daily Human Activities Using Accelerometer and sEMG Signals
Intelligent Decision Technologies 2019
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Luzzi, Simona; Turchetti, Claudio
Editore: Springer, Singapore
Luogo di pubblicazione: Singapore
Classificazione: 2 Contributo in Volume
Abstract: Human activity recognition (HAR) is an important technology for ambient-assisted living, sport and fitness activities, and health care of elderly people. HAR is usually achieved in two steps: acquisition of body signals and classification of performed activities. This paper presents an investigation on the optimal setup for recognizing daily activities using a wearable system designed to acquire surface electromyography (sEMG) and accelerometer signals through wireless sensor nodes placed on the upper limbs of the human body. To evaluate the optimal number of accelerometer and sEMG signals for detecting the user’s activities, data recorded from a few subjects were used to train and test an automatic classifier for recognizing the type of exercise being performed. In this evaluation, that was performed on eight different exercises executed by four subjects, the automatic classifier achieved an overall accuracy ranging from 10.6% to 93.0% according to different selections and combinations of the signals acquired from the sensing nodes.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266885

Activity Monitoring and Phase Detection Using a Portable EMG/ECG System
Applications in Electronics Pervading Industry, Environment and Society. ApplePies 2018.
Autore/i: Scherz, Wulhelm Daniel; Seepold, Ralf; Martínez Madrid, Natividad; Crippa, Paolo; Biagetti, Giorgio; Falaschetti, Laura; Turchetti, Claudio
Editore: Springer
Luogo di pubblicazione: Cham
Classificazione: 2 Contributo in Volume
Abstract: The investigation of stress requires to distinguish between stress caused by physical activity and stress that is caused by psychosocial factors. The behaviour of the heart in response to stress and physical activity is very similar in case the set of monitored parameters is reduced to one. Currently, the differentiation remains difficult and methods which only use the heart rate are not able to differentiate between stress and physical activity, without using additional sensor data input. The approach focusses on methods which generate signals providing characteristics that are useful for detecting stress, physical activity, no activity and relaxation.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266304

Classification of Alzheimer’s Disease from Structural Magnetic Resonance Imaging using Particle-Bernstein Polynomials Algorithm
Intelligent Decision Technologies 2019
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Luzzi, Simona; Santarelli, Riccardo; Turchetti, Claudio
Editore: Springer Singapore
Luogo di pubblicazione: Singapore
Classificazione: 2 Contributo in Volume
Abstract: Automated structural magnetic resonance imaging (MRI) classification has gained popularity for the early detection of mild cognitive impairment (MCI), the first stage of dementia condition with an increased risk of eventually developing Alzheimer’s disease (AD). In general, an MRI diagnosis system requires some fundamental activities: MRI processing, features selection, data classification. The aim of this paper is twofold: (i) first, a high-performance classification algorithm based on particle-Bernstein polynomials (PBPs), recently proposed for nonlinear regression of input–output data that combines low complexity and good accuracy, has been developed, (ii) second, an MRI-based computer-aided diagnosis (CAD) system for the classification of AD has been derived. Several experiments on a dataset from Alzheimer’s Disease Neuroimaging Initiative (ADNI) and comparisons with the state-of-the-art establish the performance of the method.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266886

Dataset from spirometer and sEMG wireless sensor for diaphragmatic respiratory activity monitoring
DATA IN BRIEF
Autore/i: Biagetti, Giorgio; Carnielli, Virgilio Paolo; Crippa, Paolo; Falaschetti, Laura; Scacchia, Valentina; Scalise, Lorenzo; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: We introduce a dataset to provide insights into the relationship between the diaphragm surface electromyographic (sEMG) signal and the respiratory air flow. The data presented had been originally collected for a research project jointly developed by the Department of Information Engineering and the Department of Industrial Enginering and Mathematical Sciences, Polytechnic University of Marche, Ancona, Italy. This article describes data recorded from 8 subjects, and includes 8 air flow and 8 surface electromyographic (sEMG) signals for diaphragmatic respiratory activity monitoring, measured with a sampling frequency of 2 kHz.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/269289

Reduced complexity algorithm for heart rate monitoring from PPG signals using automatic activity intensity classifier
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: Photoplethysmography (PPG) is a well-studied and promising technique to detect heart rate (HR) using cheap, non-invasive, wrist-wearable sensors that sense the amount of light reflected by the skin, related to the blood flow beneath. Still, the main issue is the high sensitivity to motion, which produces severe artifacts in the signal, often impeding accurate HR tracking. In this paper we present a method that combines an automatic activity intensity classifier, to select the proper amount of artifact cleaning that needs to be performed on the signal, with a geometric-based signal subspace approach to estimate the HR component of the PPG signal. Experimental evaluation is performed over a widely available dataset and the results are compared to an ECG-derived golden standard.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266197

A Machine Learning Approach to the Identification of Dynamical Nonlinear Systems
2019 27th European Signal Processing Conference (EUSIPCO)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Turchetti, Claudio
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The aim of this paper is to present a general machine learning approach to the identification of nonlinear systems, using the observed input-output finite datasets. The approach is derived representing the input and output signals in the feature space by the principal component analysis (PCA), thus transforming the nonlinear time dependent identification problem to the regression of a nonlinear input-output function. To face this problem an effective machine learning technique based on particle-Bernstein polynomials has been used to model the input-output relationship that describes the system. The approach has been validated by identifying two real world nonlinear systems, in the fields of speech signals and nonlinear audio amplifiers.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/272051

A driving technique for AC-AC direct matrix converters based on sigma-delta modulation
ENERGIES
Autore/i: Orcioni, Simone; Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura
Classificazione: 1 Contributo su Rivista
Abstract: Direct conversion of AC power between three-phase systems operating at different frequencies can be achieved using solid-state circuits known as matrix converters. These converters do not need energy storage elements, but they require sophisticated control algorithms to operate the switches. In this work we propose and evaluate the use of a sigma-delta modulation approach to control the operation of a direct matrix converter, together with a revised line filter topology suited to better handle the peculiarities of the switching noise produced by the sigma-delta modulation. Simulation results show the feasibility of such an approach, which is able to generate arbitrary output waveforms and adjust its input reactive power. Comparison with a space vector modulation implementation shows also better performance about total harmonic distortion, i.e., less harmonics in the input and output.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/266196

From Microelectronics to Nanoelectronics: Fifty Years of Advancements in Electronics
The First Outstanding 50 Years of “Università Politecnica delle Marche"
Autore/i: Biagetti, Giorgio; Conti, Massimo; Crippa, Paolo; Mencarelli, Davide; Turchetti, Claudio
Editore: Springer
Luogo di pubblicazione: Cham
Classificazione: 2 Contributo in Volume
Abstract: Fifty years ago, when the Università Politecnica delle Marche (UnivPM) was founded, the minimum size of an electron device was about ten micrometers, today dimensions in the order of twenty nanometers can be reached by the current technologies. At that time silicon foundries were able to integrate about tens of components on a chip, after fifty years has passed, an integrated circuit (IC)might contain more than ten billion devices. As the need for increasing integrated density on chips continues and silicon technologies show their physical limits, the new era of nanotechnologies, that have the potentiality for circumventing these limits, is coming. The aim of this paper is to highlight some key aspects that determined this rapid advancement and to discuss the contributions given by UnivPM both in microelectronics and nanoelectronics during these five decades. In particular, in the context of microelectronics the paper focuses on research activity in the fields of device modeling, tolerance analysis, statistical analysis of ICs, statistical simulation and design of ICs. With regard to nanoelectronics, the recently discovered nanosize materials, such as atomic clusters, nanotubes/nanowires, and monoatomic layers, may constitute a newscalable platform for RF electronics, namely for switches, amplifiers, logic devices, frequency multipliers, rectifies, interconnects, and sensors. In this framework, the present contribution provides a view on the most recent developments in modelling and simulation of carbon based devices. Specifically, we describe rigorous multi-physics approaches for the analysis of quantum transport and electromagnetic fields in nanostructured materials. In addition, we show that the low profile and size of nanomaterials make them perfect candidates as test beds for novel experiments on single electron devices and quantum transistors. Finally, the paper will give a brief excursus of the activity in progress at UnivPM, taking a look at the future development in electronics.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/278648

Human activity recognition using accelerometer and photoplethysmographic signals
Smart Innovation, Systems and Technologies - 9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Springer Science and Business Media Deutschland GmbH
Luogo di pubblicazione: Heidelberg, Berlin
Classificazione: 2 Contributo in Volume
Abstract: This paper presents an efficient technique for real-time recognition of human activities by using accelerometer and photoplethysmography (PPG) data. It is based on singular value decomposition (SVD) and truncated Karhunen-Loève transform (KLT) for feature extraction and reduction, and Bayesian classification for class recognition. Due to the nature of signals, and being the algorithm independent from the orientation of the inertial sensor, this technique is particularly suitable for implementation in smartwatches in order to both recognize the exercise being performed and improve the motion artifact (MA) removal from PPG signal for accurate heart rate (HR) estimation. In order to demonstrate the validity of this methodology, it has been successfully applied to a database of accelerometer and PPG data derived from four dynamic activities.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/249349

Sigma-Delta Based Modulation Method for Matrix Converters
Conference Proceedings 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe)
Autore/i: Orcioni, Simone; Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Turchetti, Claudio
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents a new modulation method for matrix converters control, based on Sigma-Delta modulation. The method employs a Sigma-Delta modulator, equipped with a quantizer with time-variable reference levels. A new filter type is also presented that reduces filter quality factor with losses that are present only at resonance.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/261348

Speaker identification in noisy conditions using short sequences of speech frames
Smart Innovation, Systems and Technologies - 9th KES International Conference on Intelligent Decision Technologies, KES-IDT 2017
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Springer Science and Business Media Deutschland GmbH
Luogo di pubblicazione: Heidelberg, Berlin
Classificazione: 2 Contributo in Volume
Abstract: The application of speaker recognition technologies on domotic systems, cars, or mobile devices such as tablets, smartphones and smartwatches faces with the problem of ambient noise. This paper studies the robustness of a speaker identification system when the speech signal is corrupted by the environmental noise. In the everyday scenarios the noise sources are highly time-varying and potentially unknown. Therefore the noise robustness must be investigated in the absence of information about the noise. To this end the performance of speaker identification using short sequences of speech frames was evaluated using a database with simulated noisy speech data. This database is derived from the TIMIT database by rerecording the data in the presence of various noise types, and is used to test the model for speaker identification with a focus on the varieties of noise. Additionally, in order to optimize the recognition performance, in the training stage the white noise has been added as a first step towards the generation of multicondition training data to model speech corrupted by noise with unknown temporal-spectral characteristics. The experimental results demonstrated the validity of the proposed algorithm for speaker identification using short portions of speech also in realistic conditions when the ambient noise is not negligible.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/249348

Classifier level fusion of accelerometer and sEMG signals for automatic fitness activity diarization
SENSORS
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: The human activity diarization using wearable technologies is one of the most important supporting techniques for ambient assisted living, sport and fitness activities, healthcare of elderly people. The activity diarization is performed in two steps: the acquisition of body signals and the classification of activities being performed. This paper presents a technique for data fusion at classifier level of accelerometer and sEMG signals acquired by using a low-cost wearable wireless system for monitoring the human activity when performing sport and fitness activities, as well as in healthcare applications. To demonstrate the capability of the system of diarizing the user’s activities, data recorded from a few subjects were used to train and test the automatic classifier for recognizing the type of exercise being performed.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/259769

An acquisition system of in-house parameters from wireless sensors for the identification of an environmental model
Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 22nd International Conference, KES-2018, Belgrade, Serbia
Autore/i: Biagetti, Giorgio; Coccia, Diego; Crippa, Paolo; Falaschetti, Laura; Turchetti, Claudio
Editore: Elsevier B.V.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents a system for the acquisition of in-house parameters, such as temperature, pressure, humidity and so on, that can be used for the intelligent control of a building. The main objective of this work is to determine an environmental model of an in-house room using machine learning techniques. The system is based on a low data-rate network of sensing and control nodes to acquire the data, realized with a new protocol, called ToLHnet, that is able to employ both wired and wireless communication on different media. Several standard machine learning techniques, namely linear regression, classification and regression tree algorithm, support vector machine, have been used for the regression of the input-output thermal model. Additionally, a recently proposed new technique named particle-Bernstein polynomial has been successfully applied. Experimental results show that this technique outperforms the previous techniques, for both accuracy and computation time.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/261274

Human activity monitoring system based on wearable sEMG and accelerometer wireless sensor nodes
BIOMEDICAL ENGINEERING ONLINE
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: Background: The human activity monitoring technology is one of the most important technologies for ambient assisted living, surveillance-based security, sport and fitness activities, healthcare of elderly people. The activity monitoring is performed in two steps: the acquisition of body signals and the classification of activities being performed. This paper presents a low-cost wearable wireless system specifically designed to acquire surface electromyography (sEMG) and accelerometer signals for monitoring the human activity when performing sport and fitness activities, as well as in healthcare applications. Results: The proposed system consists of several ultralight wireless sensing nodes that are able to acquire, process and efficiently transmit the motion-related (biological and accelerometer) body signals to one or more base stations through a 2.4 GHz radio link using an ad-hoc communication protocol designed on top of the IEEE 802.15.4 physical layer. A user interface software for viewing, recording, and analysing the data was implemented on a control personal computer that is connected through a USB link to the base stations. To demonstrate the capability of the system of detecting the user's activity, data recorded from a few subjects were used to train and test an automatic classifier for recognizing the type of exercise being performed. The system was tested on four different exercises performed by three people, the automatic classifier achieved an overall accuracy of 85.7% combining the features extracted from acceleration and sEMG signals. Conclusions: A low cost wireless system for the acquisition of sEMG and accelerometer signals has been presented for healthcare and fitness applications. The system consists of wearable sensing nodes that wirelessly transmit the biological and accelerometer signals to one or more base stations. The signals so acquired will be combined and processed in order to detect, monitor and recognize human activities.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/262186

HMM speech synthesis based on MDCT representation
INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: Hidden Markov model (HMM) based text-to-speech (TTS) has become one of the most promising approaches, as it has proven to be a particularly flexible and robust framework to generate synthetic speech. However, several factors such as mel-cepstral vocoder and over-smoothing are responsible for causing quality degradation of synthetic speech. This paper presents an HMM speech synthesis technique based on the modified discrete cosine transform (MDCT) representation to cope with these two issues. To this end, we use an analysis/synthesis technique based on MDCT that guarantees a perfect reconstruction of the signal frame from feature vectors and allows for a 50% overlap between frames without increasing the data vector, in contrast to the conventional mel-cepstral spectral parameters that do not ensure direct speech waveform reconstruction. Experimental results show that a sound of good quality, conveniently evaluated using both objective and subjective tests, is obtained.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/261944

A comparative study of machine learning algorithms for physiological signal classification
Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 22nd International Conference, KES-2018, Belgrade, Serbia
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Tanoni, Giulia; Turchetti, Claudio
Editore: Elsevier B.V.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: The present work aims at the evaluation of the effectiveness of different machine learning algorithms on a variety of clinical data, derived from small, medium, and large publicly available databases. To this end, several algorithms were tested, and their performance, both in terms of accuracy and time required for the training and testing phases, are here reported. Sometimes a data preprocessing phase was also deemed necessary to improve the performance of the machine learning procedures, in order to reduce the problem size. In such cases a detailed analysis of the compression strategy and results is also presented.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/261277

A Portable Wireless sEMG and Inertial Acquisition System for Human Activity Monitoring
Bioinformatics and Biomedical Engineering, 5th International Work-Conference, IWBBIO 2017, Granada, Spain, April 26–28, 2017, Proceedings, Part II
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Springer International Publishing
Luogo di pubblicazione: Heidelberg
Classificazione: 2 Contributo in Volume
Abstract: This paper presents a low-cost portable wireless system specifically designed to acquire both surface electromyography (sEMG) and accelerometer signals for healthcare applications, sport, and fitness activities. The system, consists of several ultralight wireless sensing nodes that acquire, amplify, digitize, and transmit the sEMG and accelerometer signals to one or more base stations through a 2.4GHz radio link using a custom-made communication protocol designed on top of the IEEE 802.15.4 physical layer. Additionally, the system can be easily configured to capture and process many other biological signals such as the electrocardiographic (ECG) signal. Each base station is connected through a USB link to a control PC running a user interface software for viewing, recording, and analysing the data.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/246923

Machine learning regression based on particle Bernstein polynomials for nonlinear system identification
Proceedings of the 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Turchetti, Claudio
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Polynomials have shown to be useful basis functions in the identification of nonlinear systems. However estimation of the unknown coefficients requires expensive algorithms, as for instance it occurs by applying an optimal least square approach. Bernstein polynomials have the property that the coefficients are the values of the function to be approximated at points in a fixed grid, thus avoiding a time-consuming training stage. This paper presents a novel machine learning approach to regression, based on new functions named particle-Bernstein polynomials, which is particularly suitable to solve multivariate regression problems. Several experimental results show the validity of the technique for the identification of nonlinear systems and the better performance achieved with respect to the standard techniques.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/252355

Homomorphic Deconvolution for MUAP Estimation from Surface EMG Signals
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Orcioni, Simone; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: This paper presents a technique for parametric model estimation of the motor unit action potential (MUAP) from the surface electromyography (sEMG) signal by using homomorphic deconvolution. The cepstrum-based deconvolution removes the effect of the stochastic impulse train, which originates the sEMG signal, from the power spectrum of sEMG signal itself. In this way only information on MUAP shape and amplitude were maintained and then used to estimate the parameters of a time-domain model of the MUAP itself. In order to validate the effectiveness of this technique, sEMG signals recorded during several biceps curl exercises have been used for MUAP amplitude and time scale estimation. The parameters so extracted as functions of time were used to evaluate muscle fatigue showing a good agreement with previously published results.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/234480

An Investigation on the Accuracy of Truncated DKLT Representation for Speaker Identification With Short Sequences of Speech Frames
IEEE TRANSACTIONS ON CYBERNETICS
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: Speaker identification plays a crucial role in biometric person identification as systems based on human speech are increasingly used for the recognition of people. Mel frequency cepstral coefficients (MFCCs) have been widely adopted for decades in speech processing to capture the speech-specific characteristics with a reduced dimensionality. However, although their ability to decorrelate the vocal source and the vocal tract filter make them suitable for speech recognition, they greatly mitigate the speaker variability, a specific characteristic that distinguishes different speakers. This paper presents a theoretical framework and an experimental evaluation showing that reducing the dimension of features by applying the discrete Karhunen-Loève transform (DKLT) to the log-spectrum of the speech signal guarantees better performance compared to conventional MFCC features. In particular with short sequences of speech frames, with typical duration of less than 2 s, the performance of truncated DKLT representation achieved for the identification of five speakers are always better than those achieved with the MFCCs for the experiments we performed. Additionally, the framework was tested on up to 100 TIMIT speakers with sequences of less than 3.5 s showing very good recognition capabilities.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/238482

An Efficient Technique for Real-Time Human Activity Classification Using Accelerometer Data
Intelligent Decision Technologies 2016 - Proceedings of the 8th KES International Conference on Intelligent Decision Technologies (KES-IDT 2016) – Part I
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Springer International Publishing
Classificazione: 2 Contributo in Volume
Abstract: Accurate estimation of biometric parameters recorded from subjects’ wrist or waist, when the subjects are performing various physical exercises, is often a challenging problem due to the presence of motion artifacts. In order to reduce the motion artifacts, data derived from a triaxial accelerometer have been proven to be very useful. Unfortunately, wearable devices such as smartphones and smartwatches are in general differently oriented during real life activities, so the data derived from the three axes are mixed up. This paper proposes an efficient technique for real-time recognition of human activities by using accelerometer data that is based on singular value decomposition (SVD) and truncated Karhunen-Loève transform (KLT) for feature extraction and reduction, and Bayesian classification for class recognition, that is independent of the orientation of the sensor. This is particularly suitable for implementation in wearable devices. In order to demonstrate the validity of this technique, it has been successfully applied to a database of accelerometer data derived from static postures, dynamic activities, and postural transitions occurring between the static postures.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/235879

Surface EMG Fatigue Analysis by Means of Homomorphic Deconvolution
Mobile Networks for Biometric Data Analysis
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Orcioni, Simone; Turchetti, Claudio
Editore: Springer International Publishing
Luogo di pubblicazione: Cham
Classificazione: 2 Contributo in Volume
Abstract: In this paper we use homomorphic deconvolution to obtain the power spectrum of the motor unit action potential (MUAP) from the surface electromyography (sEMG) signal. This spectrum is then used to extract the parameters of a time-domain model of the MUAP itself, in particular its amplitude and time scale. The analysis of the extracted parameters leads to the estimation of cadence and muscle fatigue. The methodology is tested with a sEMG signal recorded during biceps curl exercises.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/236334

An Algorithm for Automatic Words Extraction From a Stream of Phones in Dictionary-Based Large Vocabulary Continuous Speech Recognition Systems
Proceedings of the 15th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2015)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: As improvements on acoustic modeling have rapidly progressed in recent years thanks to the impressive gains in performance obtained using deep neural networks (DNNs), language modeling remains a bottleneck for high performance large vocabulary continuous speech recognition (LVCSR) systems. In this paper an algorithm for automatic words extraction from a stream of phones is suggested to be used in a dictionary-based LVCSR system, to overcome the limitations of current LVCSR systems. Experimental results show the effectiveness of this approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/230501

Motion artifact reduction in photoplethysmography using Bayesian classification for physical exercise identification
ICPRAM 2016 - Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: SciTePress
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Accurate heart rate (HR) estimation from photoplethysmography (PPG) recorded from subjects' wrist when the subjects are performing various physical exercises is a challenging problem. This paper presents a framework that combines a robust algorithm capable of estimating HR from PPG signal with subjects performing a single exercise and a physical exercise identification algorithm capable of recognizing the exercise the subject is performing. Experimental results on subjects performing two different exercises show that an improvement of about 50% in the accuracy of HR estimation is achieved with the proposed approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/235876

Distributed Speech and Speaker Identification System for Personalized Domotic Control
Mobile Networks for Biometric Data Analysis
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Springer International Publishing
Luogo di pubblicazione: Cham
Classificazione: 2 Contributo in Volume
Abstract: This paper presents a combined speech recognition/speaker identification system that can be efficiently used for personalized domotic control. The proposed system works as a distributed framework and it is designed to identify a speaker in home environments in order to provide user access to customized options. Human speech signals contain both language and speaker dependent information. Using this information the system realizes a personalized control in home environments and this approach can also be applied in more generic scenarios such as car customization settings. The system was optimized with the aim to allow an immediate use only with the addition of small and cheap audio front-ends that will capture commands spoken by the user. Meanwhile a remote server performs the speech recognition as well as user identification and combines these informations to provides user specific settings which are sent back to the desired actuator at home.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/236331

An Analog Front-End for Combined EMG/ECG Wireless Sensors
Mobile Networks for Biometric Data Analysis
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Orcioni, Simone; Turchetti, Claudio
Editore: Springer International Publishing
Luogo di pubblicazione: Cham
Classificazione: 2 Contributo in Volume
Abstract: In this work we describe a combined wireless sensor, able to capture either the electromyographic (EMG) or the electrocardiographic (ECG) signal. Since the two signals differ mainly because of their bandwidths, with the ECG being shifted towards lower frequencies, a simple and inexpensive circuit solution has been developed to allow an optional software-based bypass of the high-pass filtering action incorporated in the EMG signal amplifier, without sacrificing neither signal quality nor bandwidth in the much more demanding EMG path.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/236332

Optimizing linear routing in the ToLHnet protocol to improve performance over long RS-485 buses
EURASIP JOURNAL ON EMBEDDED SYSTEMS
Autore/i: Alessandrini, Michele; Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: As the adoption of sensing and control networks rises to encompass the most diverse fields, the need for simple, efficient interconnection between many different devices will become ever more pressing. Though wireless communication is certainly appealing, current technological limits still prevent its usage where high reliability is needed or where the electromagnetical environment is not really apt to let radio waves through. In these cases, a wired link, based on a robust and well-consolidated standard such as an RS-485 bus, might prove to be a good choice. In this paper, we present an extension to the routing strategy originally implemented in the recently proposed “tree or linear hopping network” (ToLHnet) protocol, aimed at better handling the special but important case of linear routing over a (possibly very long) wired link, such as an RS-485 bus. The ToLHnet protocol was especially developed to suit the need of low complexity for deployments on large control networks. Indeed, using it over RS-485 already makes it possible to overcome many of the traditional limitations regarding cable length, without requiring segmenting the bus to install repeaters. With the extension here proposed, it will also be possible to simultaneously reduce latency (i.e., increase throughput, should it matter) for short-distance communications over the same cable, largely increasing the overall network efficiency, with a negligible increase in the complexity of the nodes’ firmware.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/236329

Wireless surface electromyograph and electrocardiograph system on 802.15.4
IEEE TRANSACTIONS ON CONSUMER ELECTRONICS
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: This paper presents a flexible low-cost wireless system specifically designed to acquire fitness metrics both from surface electromyographic (sEMG) and electrocardiographic (ECG) signals. The system, that can be easily extended to capture and process many other biological signals as well as the motion-related body signals, consists of several ultralight wireless sensing nodes that acquire, amplify, digitize, and transmit the biological or mechanical signals to one or more base stations through a 2.4 GHz radio link using a custom-made communication protocol designed on top of the IEEE 802.15.4 physical layer. The number of wireless nodes the base stations can handle depends on the type of signal being acquired. Each base station is connected through an USB link to a control PC running a user interface software for viewing, recording, and analyzing the data. The system for acquiring signals from wearable nodes in combination with a smartphone application provides a complete platform for monitoring fitness metrics extracted from the signals.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/238948

Multivariate Direction Scoring for Dimensionality Reduction in Classification Problems
Intelligent Decision Technologies 2016 - Proceedings of the 8th KES International Conference on Intelligent Decision Technologies (KES-IDT 2016) – Part I
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Springer International Publishing
Classificazione: 2 Contributo in Volume
Abstract: Dimensionality reduction is the process of reducing the number of features in a data set. In a classification problem, the proposed formula allows to sort a set of directions to be used for data projection, according to a score that estimates their capability of discriminating the different data classes. A reduction in the number of features can be obtained by taking a subset of these directions and projecting data on this space. The projecting vectors can be derived from a spectral representation or other choices. If the vectors are eigenvectors of the data covariance matrix, the proposed score is aimed to take the place of the eigenvalues in eigenvector ordering.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/235878

Discrete Bessel Functions for Representing the Class of Finite Duration Decaying Sequences
Proocedings of the 2016 24th European Signal Processing Conference (EUSIPCO)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Turchetti, Claudio
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Bessel functions have shown to be particularly suitable for representing certain classes of signals, since using these basis functions may results in fewer components than using sinusoids. However, as there are no closed form expressions available for such functions, approximations and numerical methods have been adopted for their computation. In this paper the functions called discrete Bessel functions that are expressed as a finite expansion are defined. It is shown that in a finite interval a finite number of such functions that perfectly match Bessel functions of integer order exist. For finite duration sequences it is proven that the subspace spanned by a set of these functions is able to represent the class of finite duration decaying sequences.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/238345

Learning HMM State Sequences from Phonemes for Speech Synthesis
Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 20th International Conference KES-2016
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Elsevier
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents a technique for learning hidden Markov model (HMM) state sequences from phonemes, that combined with modified discrete cosine transform (MDCT), is useful for speech synthesis. Mel-cepstral spectral parameters, currently adopted in the conventional methods as features for HMM acoustic modeling, do not ensure direct speech waveforms reconstruction. In contrast to these approaches, we use an analysis/synthesis technique based on MDCT that guarantees a perfect reconstruction of the signal frame feature vectors and allows for a 50% overlap between frames without increasing the data rate. Experimental results show that the spectrograms achieved with the suggested technique behave very closely to the original spectrograms, and the quality of synthesized speech is conveniently evaluated using the well known Itakura-Saito measure.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/238344

Robust Speaker Identification in a Meeting with Short Audio Segments
Intelligent Decision Technologies 2016 - Proceedings of the 8th KES International Conference on Intelligent Decision Technologies (KES-IDT 2016) – Part II
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Springer International Publishing
Classificazione: 2 Contributo in Volume
Abstract: The paper proposes a speaker identification scheme for a meeting scenario, that is able to answer the question "is somebody currently talking?", if yes, "who is it?". The suggested system has been designed to identify during a meeting conversation the current speaker from a set of pre-trained speaker models. Experimental results on two databases show the robustness of the approach to the overlapping phenomena and the ability of the algorithm to correctly identify a speaker with short audio segments.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/235877

Analysis of the EMG Signal During Cyclic Movements Using Multicomponent AM-FM Decomposition
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Curzi, Alessandro; Orcioni, Simone; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: Sport, fitness, as well as rehabilitation activities, often require the accomplishment of repetitive movements. The correctness of the exercises is often related to the capability of maintaining the required cadence and muscular force. Failure to maintain the required force, also known as muscle fatigue, is accompanied by a shift in the spectral content of the surface electromyography (EMG) signal towards lower frequencies. This paper presents a novel approach for simultaneously obtaining exercise repetition frequency and evaluating muscular fatigue, as functions of time, by only using the EMG signal. The mean frequency of the amplitude spectrum (MFA) of the EMG signal, considered as a function of time, is directly related to the dynamics of the movement performed and to the fatigue of the involved muscles. If the movement is cyclic, MFA will display the same pattern and its average will tend to decrease. These two effects have been simultaneously modeled by a two-component AM-FM model based on the Hilbert transform. The method was tested on signals recorded using a wireless system applied to healthy subjects performing dumbbell biceps curls, dumbbell lateral rises, and bodyweight squats. Experimental results show the excellent performance of the proposed technique.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/225251

Speaker Identification with Short Sequences of Speech Frames
Proceedings of the 4th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2015)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Curzi, Alessandro; Orcioni, Simone; Turchetti, Claudio
Editore: SCITEPRESS (Science and Technology Publications,Lda.)
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In biometric person identification systems, speaker identification plays a crucial role as the voice is the more natural signal to produce and the simplest to acquire. Mel frequency cepstral coefficients (MFCCs) have been widely adopted for decades in speech processing to capture the speech-specific characteristics with a reduced dimensionality. However, although their ability to de-correlate the vocal source and the vocal tract filter make them suitable for speech recognition, they show up some drawbacks in speaker recognition. This paper presents an experimental evaluation showing that reducing the dimension of features by using the discrete Karhunen-Loève transform (DKLT), guarantees better performance with respect to conventional MFCC features. In particular with short sequences of speech frames, that is with utterance duration of less than 1 s, the performance of truncated DKLT representation are always better than MFCC.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/227759

Improvement of RS-485 Performance Over Long Distances Using the ToLHnet Protocol
2015 12th International Workshop on Intelligent Solutions in Embedded Systems (WISES)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Ortolani, Nicola; Turchetti, Claudio
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents a technique to extend the transmission range over a single RS-485 cable, without requiring installation of expensive and cumbersome RS-485 repeaters, to virtually unlimited distances, while simultaneously improving transmission speeds between closer nodes. This was accomplished by leveraging the routing capabilities embedded into each node that implements the recently released and extremely lightweight ToLHnet protocol. With it, each network node can act as a sort of "smart repeater" only when there is need to, optimizing the overall network throughput. The key ideas underlying the routing strategies are here described, together with details of a prototype node and experimental results demonstrating transmission at distances well above the traditional limit.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/230502

A Rule Based Framework for Smart Training Using sEMG Signal
Intelligent Decision Technologies - Proceedings of the 7th KES International Conference on Intelligent Decision Technologies (KES-IDT 2015)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Springer International Publishing
Luogo di pubblicazione: Heidelberg
Classificazione: 2 Contributo in Volume
Abstract: The correctness of the training during sport and fitness activities involving repetitive movements is often related to the capability of maintaining the required cadence and muscular force. Muscle fatigue may induce a failure in maintaining the needed force, and can be detected by a shift towards lower frequencies in the surface electromyography (sEMG) signal. The exercise repetition frequency and the evaluation of muscular fatigue can be simultaneously obtained by using just the sEMG signal through the application of a two-component AM-FM model based on the Hilbert transform. These two features can be used as inputs of an intelligent decision making system based on fuzzy rules for optimizing the training strategy. As an application example this system was set up using signals recorded with a wireless electromyograph applied to several healthy subjects performing dumbbell biceps curls.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/227754

Distributed Speech Recognition for Lighting System Control
Intelligent Decision Technologies - Proceedings of the 7th KES International Conference on Intelligent Decision Technologies (KES-IDT 2015)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Curzi, Alessandro; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Editore: Springer International Publishing
Luogo di pubblicazione: Heidelberg
Classificazione: 2 Contributo in Volume
Abstract: This paper presents a distributed speech recognition (DSR) system for home/office lighting control by means of users' voice. In this scheme a back-end processes audio signals and transforms them into commands, so that they can be sent to the desired actuators of the lighting system. This paper discusses in detail the solutions and strategies we adopted to improve recognition accuracy and spotting command efficiency in home/office environments, i.e. in situations that involve distant speech and great amounts of background noise or unrelated sounds. Suitable solutions implemented in this recognition engine are able to detect commands also in a continuous listening context and the used DSR strategies greatly simplify the system installation and maintenance. A case study that implements the voice control of a digital addressable lighting interface (DALI) based lighting system has been selected to show the validity and the performance of the proposed system.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/227753

CARMA: A Robust Motion Artifact Reduction Algorithm for Heart Rate Monitoring from PPG Signals
Proceedings of the 2015 23rd European Signal Processing Conference (EUSIPCO 2015)
Autore/i: Bacà, Alessandro; Biagetti, Giorgio; Camilletti, Marta; Crippa, Paolo; Falaschetti, Laura; Orcioni, Simone; Rossini, Luca; Tonelli, Dario; Turchetti, Claudio
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Photoplethysmography (PPG) is a non invasive measurement of the blood flow, that can be used instead of electrocardiography to estimate heart rate (HR). Most existing techniques used for HR monitoring in fitness with PPG focus on slowly running alone, while those suitable for intensive physical exercise need an initialization stage in which wearers are required to stand still for several seconds. This paper present a novel algorithm for HR estimation from PPG signal based on motion artifact removal (MAR) and adaptive tracking (AT) that overcomes limitations of the previous techniques. Experimental evaluations performed on datasets recorded from several subjects during running show an average absolute error of HR estimation of 2.26 beats per minute, demonstrating the validity of the presented technique to monitor HR using wearable devices which use PPG signals.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/227752

A distributed speaker identification system for personalized home control
Proceedings of the International Workshop on Mobile Networks for Biometric Data Analysis (mBiDA)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Curzi, A.; Falaschetti, Laura; Orcioni, Simone; Turchetti, Claudio
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents a combined speaker identification/speech recognition system that can be efficiently used for personalized home control. The proposed system works as a distributed framework and is designed to identify a speaker in home environments in order to provide user access to customized options. Human speech signals contain both language and speaker dependent information. Using this information the system realizes a personalized control in home environment and this approach can also be applied in more generic scenarios such as car customization settings. The system was optimized with the aim to allow an immediate use only with the addition of small and cheap audio front-ends that will capture his spoken commands. Meanwhile a remote server performs the speech recognition as well as user identification and combines these informations to provides user specific settings which are sent back to the desired actuator at home.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/224517

An inexpensive circuit solution for the acquisition of the ECG signal with wireless EMG sensors
Proceedings of the International Workshop on Mobile Networks for Biometric Data Analysis (mBiDA)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Orcioni, Simone; Turchetti, Claudio
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this work we describe a combined wireless sensor, able to capture either the electromyographic (EMG) or the electrocardiographic (ECG) signal. Since the two signals differ mainly because of their bandwidths, with the ECG being shifted towards lower frequencies, a simple and inexpensive circuit solution has been developed to allow an optional software-based bypass of the high-pass filtering action incorporated in the EMG signal amplifier, without sacrificing neither signal quality nor bandwidth in the much more demanding EMG path.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/224518

Parameter estimation of surface EMG MUAP by means of power cepstrum deconvolution
Proceedings of the International Workshop on Mobile Networks for Biometric Data Analysis (mBiDA)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Orcioni, Simone; Turchetti, Claudio
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper we use power cepstrum deconvolution to obtain the power spectrum of the motor unit action potential (MUAP) from the surface electromyog- raphy (sEMG) signal. This spectrum is then used to extract the parameters of a time- domain model of the MUAP itself, in particular its amplitude and time scale. The methodology is tested with a sEMG signal recorded during biceps curl exercises. Extraction of these parameters as a function of time can lead to pace calculation and muscle fatigue estimation.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/222515

A Multi-Class ECG Beat Classifier Based on the Truncated KLT Representation
Proceedings of the 2014 UKSim-AMSS 8th European Modelling Symposium (EMS 2014)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Curzi, Alessandro; Orcioni, Simone; Turchetti, Claudio
Editore: IEEE Computer Society
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Automatic classification of electrocardiogram (ECG) signals is of paramount importance in the detection of a wide range of heartbeat abnormalities as aid to improve the diagnostic achieved by cardiologists. In this paper an effective multi-class beat classifier, based on statistical identification of a minimum-complexity model, is proposed. The classifier is trained by extracting from the ECG signal a multivariate random vector by means of a truncated Karhunen-Loève transform (KLT) representation. The resulting statistical model is thus estimated using a robust and efficient Expectation Maximization (EM) algorithm to find the optimal parameters of a Gaussian mixture model. Based on the above statistical characterization a multi-class ECG classifier was derived. The experiments, conducted on the ECG signals from the MIT-BIH arrhythmia database, demonstrated the excellent performance of this technique to classify the ECG signals into different disease categories, with a reduced model complexity.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/224519

SystemC-WMS: Wave Mixed Signal Simulator for Nonlinear Heterogeneous Systems
INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS
Autore/i: Biagetti, Giorgio; Giammarini, Marco; Ballicchia, Mauro; Conti, Massimo; Orcioni, Simone
Classificazione: 1 Contributo su Rivista
Abstract: The present paper proposes a methodology for extending SystemC to mixed signal heterogeneous systems. To that end, a method for modelling analog modules using wave quantities is proposed, and a new kind of port and channel were defined. This class library is plugged directly on top of the standard SystemC kernel, so as to allow a seamless integration with the pre-existing simulation environment, and is designed to permit total interconnection freedom between analog modules to ease the development of reusable analog libraries. Moreover, this allows for a uniform treatment of heterogeneous domains. To highlight all these aspects a buck-converter with digital control and an induction motor were simulated.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/128689

A Speech Interaction System for an Ambient Assisted Living Scenario
Ambient Assisted Living
Autore/i: Alessandrini, Michele; Biagetti, Giorgio; Curzi, Alessandro; Turchetti, Claudio
Editore: Springer International Publishing
Classificazione: 2 Contributo in Volume
Abstract: In this work we describe a speech recognition system aimed at controlling various apparatus of an intelligent home. The system is especially tailored, and ad-hoc optimizations and strategies have been implemented, to make it suitable to operate unobtrusively in the ambient, requiring that the user only installs small and cheap audio front-ends that will capture his spoken commands. A recognition back-end, running either as a network service reached over the Internet or on a PC in the user’s home, performs the hard work of processing the data and turning it into commands, which are sent back to the desired actuator in the home. A case study involving the voice control of a DALI lighting system is presented, together with ideas and results on how to improve recognition accuracy and command spotting efficiency of a system which, by its very nature, might have to deal with audio captured from a distance and great amounts of background noise and unrelated sounds.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/205829

ToLHnet: A low-complexity protocol for mixed wired and wireless low-rate control networks
Proceedings of the 2014 6th European Embedded Design in Education and Research Conference (EDERC 2014)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Alessandro, Curzi; Orcioni, Simone; Turchetti, Claudio
Editore: Texas Instruments
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: ToLHnet (which stands for 'tree or linear hopping network') is a powerful yet simple networking protocol we developed in order to support the creation of mixed networks, employing wired and wireless connections over different media among thousands of nodes. It is based on tree routing, with special care to support the degenerate case of linear routing, to keep implementation on nodes simple and protocol overhead low. This paper describes the essentials of the protocol and presents a case study detailing its implementation and performance on a Texas Instruments TM4C123GH6PMI microcontroller, with the addition of a power-line-communication modem and a 433 MHz radio.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/222514

A Speech Interaction System for an Ambient Assisted Living Scenario
Atti del 4º Forum Italiano per l'Ambient Assisted Living (FORITAAL 2013)
Autore/i: Alessandrini, Michele; Biagetti, Giorgio; Curzi, Alessandro; Turchetti, Claudio
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/163548

Iterative Constrained MLLR Approach for Speaker Adaptation
Proceedings of the 10th IASTED International Conference on Signal Processing, Pattern Recognition, and Applications (SPPRA 2013)
Autore/i: Biagetti, Giorgio; Curzi, Alessandro; M., Mercuri; Turchetti, Claudio
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper an effective technique for speaker adaptation on the feature domain is presented. This technique starts from the well known maximum-likelihood linear regression (MLLR) auxiliary function to obtain the constrained MLLR transformation in an iterative fashion. The proposed approach is particularly suitable to be implemented on the client side of a distributed speech recognition scheme, due to the reduced number of iterations required to reach convergence. Extensive experimentation using the CMU Sphinx 4 ASR system along with a preliminarily trained speaker-independent acoustic model for the Italian language, in a setting designed for large-vocabulary continuous speech recognition, demonstrates the effectiveness of the approach even with small amounts of adaptation data.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/97462

A garbage model generation technique for embedded speech recognisers
Proceedings of the 2013 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA 2013)
Autore/i: Alessandrini, Michele; Biagetti, Giorgio; Curzi, Alessandro; Turchetti, Claudio
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper we present a simple but effective technique to help the designer of a voice-operated appliance add out-of-grammar command rejection capabilities, with a minimal effort and without overly degrading the recognition accuracy. Given the desired operational grammar of the appliance, and starting from a generic pre-trained acoustic model and comprehensive dictionary, we use a speech recogniser to identify suitable decoys to be added to the target grammar. These decoys will capture most of the spoken out-of-vocabulary words, and with appropriate changes to the desired grammar, will make the rejection of unintended commands quite easy. An evaluation of the performance of the proposed approach has been carried out on a sample appliance we developed, and tested with several users, under different acoustic conditions, in a command-spotting scenario. The reported results show that the proposed approach largely outperforms the standard phone loop-based approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/158106

Semi-automatic acoustic model generation from large unsynchronized audio and text chunks
Proceedings of the 12th Annual Conference of the International Speech Communication Association
Autore/i: Alessandrini, Michele; Biagetti, Giorgio; Curzi, Alessandro; Turchetti, Claudio
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper an effective technique to train an acoustic model from large and unsynchronized audio and text chunks is presented. Given such a speech corpus, an algorithm to automatically segment each chunk into smaller fragments and to synchronize those to the corresponding text is defined. These smaller fragments are more suitable to be used in standard model training algorithms for usage in automatic speech recognition systems. The proposed approach is particularly suitable to bootstrap language models without relying neither on specialized training material nor borrowing from models trained for other similar languages. Extensive experimentation using the CMU Sphinx 4 recognizer and the SphinxTrain model generator in a setting designed for large-vocabulary continuous speech recognition shows the effectiveness of the approach.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/62801

Unsupervised identification of nonstationary dynamical systems using a Gaussian mixture model based on EM clustering of SOMs
Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Curzi, Alessandro; Turchetti, Claudio
Editore: IEEE
Luogo di pubblicazione: Piscataway
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper an effective unsupervised statistical identification technique for nonstationary nonlinear systems is presented. This technique extracts from the system outputs the multivariate relationships of the system natural modes, by means of the separation property of the Karhunen-Loève transform (KLT). Then, it applies a Self-Organizing Map (SOM) to the KLT output vectors in order to give an optimal representation of data. Finally, it exploits an optimized Expectation Maximization (EM) algorithm to find the optimal parameters of a Gaussian mixture model. The resulting statistical system identification is thus based on the estimation of the multivariate probability density function (PDF) of system outputs, whose convergence towards that computed by kernel estimation has also been proved by verifying the asymptotically vanishing of Kullback-Leibler divergences. A large number of simulations on ECG signals demonstrated the validity and the excellent performance of this technique along with its applicability to noninvasive diagnosis of a large class of medical pathologies originated by unknown, unpractical to measure, physiological factors.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/50613

Piecewise linear second moment statistical simulation of ICs affected by non-linear statistical effects
INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Curzi, Alessandro; Orcioni, Simone; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: This paper presents a methodology for statistical simulation of non-linear integrated circuits affected by device mismatch. This simulation technique is aimed at helping designers maximize yield, since it can be orders of magnitude faster than other readily available methods, e.g. Monte Carlo. Statistical analysis is performed by modeling the electrical effects of tolerances by means of stochastic current or voltage sources, which depend on both device geometry and position across the die. They alter the behavior of both linear and non-linear components according to stochastic device models, which reflect the statistical properties of circuit devices up to the second order (i.e. covariance functions). DC, AC, and transient analyses are performed by means of the stochastic modified nodal analysis, using a piecewise linear stochastic technique with respect to the stochastic sources, around a few automatically selected points. Several experimental results on significant circuits, encompassing both the analog and the digital domains, prove the effectiveness of the proposed method.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/51084

Computational intelligence for the collaborative identification of distributed systems
Computational Intelligence: Collaboration, Fusion and Emergence (Series: Intelligent Systems Reference Library, Vol. 1)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Gianfelici, F; Turchetti, Claudio
Editore: Springer-Verlag
Luogo di pubblicazione: Berlin/Heidelberg
Classificazione: 2 Contributo in Volume
Abstract: In this chapter, on the basis of a rigorous mathematical formulation, a new algorithm for the identification of distributed systems by large scale collaborative sensor networks is suggested. The algorithm extends a KLT-based identification approach to a decentralized setting, using the distributed Karhunen-Loéve transform (DKLT) recently proposed by Gastpar et al.. The proposed approach permits an arbitrarily accurate identification since it exploits both the asymptotic properties of convergence of DKLT and the universal approximation capabilities of radial basis functions neural networks. The effectiveness of the proposed approach is directly related to the reduction of total distortion in the compression performed by the single nodes of the sensor network, to the identification accuracy, as well as to the low computational complexity of the fusion algorithm performed by the fusion center to regulate the intelligent cooperation of the nodes. Some identification experiments, that have been carried out on systems whose behavior is described by partial differential equations in 2-D domains with random excitations, confirm the validity of this approach. It is worth noting the generality of the algorithm that can be applied in a wide range of applications without limitations on the type of physical phenomena, boundary conditions, sensor network used, and number of its nodes.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/50578

2.4 GHz wireless electromyograph system with statistically optimal automatic gain control: Design and performance analysis
Proceedings of the 2009 International Conference on Bio-inspired Systems and Signal Processing
Autore/i: A., Morici; Biagetti, Giorgio; Turchetti, Claudio
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/49833

Nonlinear system identification: An effective framework based on the Karhunen-Loève transform
IEEE TRANSACTIONS ON SIGNAL PROCESSING
Autore/i: Turchetti, Claudio; Biagetti, Giorgio; Gianfelici, Francesco; Crippa, Paolo
Classificazione: 1 Contributo su Rivista
Abstract: This paper proposes, on the basis of a rigorous mathematical formulation, a general framework that is able to define a large class of nonlinear system identifiers. This framework exploits all those relationships that intrinsically characterize a limited set of realizations, obtained by an ensemble of output signals and their parameterized inputs, by means of the separation property of the Karhunen-Loève transform. The generality and the flexibility of the approximating mappings (ranging from traditional approximation techniques to multiresolution decompositions and neural networks) allow the design of a large number of distinct identifiers each displaying a number of properties such as linearity with respect to the parameters, noise rejection, low computational complexity of the approximation procedure. Exhaustive experimentation on specific case studies reports high identification performance for four distinct identifiers based on polynomials, splines, wavelets and radial basis functions. Several comparisons show how these identifiers almost always have higher performance than that obtained by current best practices, as well as very good accuracy, optimal noise rejection, and fast algorithmic elaboration. As an example of a real application, the identification of a voice communication channel, comprising a digital enhanced cordless telecommunications (DECT) cordless phone for wireless communications and a telephone line, is reported and discussed.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/50178

A computational intelligence technique for the identification of non-linear non-stationary systems
IEEE International Joint Conference on Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence)
Autore/i: Turchetti, Claudio; F., Gianfelici; Biagetti, Giorgio; Crippa, Paolo
Editore: IEEE
Luogo di pubblicazione: Piscataway
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper addresses nonlinear nonstationary system identification from stimulus-response data, a problem concerning a large variety of applications, in dynamic control as well as in signal processing, communications, physiological system modelling and so on. Among the different methods suggested in the vast literature for nonlinear system modelling, the ones based on the Volterra series and the Neural Networks are the most commonly used. However, a strong limitation for the applicability of these methods lies in the necessary property of stationarity, an assumption that cannot be considered as valid in general and strongly affecting the validity of results. Another weakness of the approaches currently used is that they refer to differential systems, thus being unsuitable to model systems described by integral equations. A computational intelligence technique that exploits the potentialities of both the Karhunen-Loève Transform (KLT) and Neural Networks (NNs) representation and without any of the limitations of the previous approaches is suggested in this paper. The technique is suitable for modelling the wide class of systems described by nonlinear nonstationary functional, thus including both differential and integral systems. It takes advantage of the KLT separable kernel representation that is able to separate the dynamic and static behaviours of the system as two distinct components, and the approximation property of NNs for the identification of the nonlinear no-memory component. To validate the suggested technique comparisons with experimental results on both nonlinear nonstationary differential and integral systems are reported.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/52939

Representation of nonlinear random transformations by non-Gaussian stochastic neural networks
IEEE TRANSACTIONS ON NEURAL NETWORKS
Autore/i: Turchetti, Claudio; Crippa, Paolo; Pirani, Massimiliano; Biagetti, Giorgio
Classificazione: 1 Contributo su Rivista
Abstract: The learning capability of neural networks is equivalent to modeling physical events that occur in the real environment. Several early works have demonstrated that neural networks belonging to some classes are universal approximators of input-output deterministic functions. Recent works extend the ability of neural networks in approximating random functions using a class of networks named stochastic neural networks (SNN). In the language of system theory, the approximation of both deterministic and stochastic functions falls within the identification of nonlinear no-memory systems. However, all the results presented so far are restricted to the case of Gaussian stochastic processes (SPs) only, or to linear transformations that guarantee this property. This paper aims at investigating the ability of stochastic neural networks to approximate nonlinear input-output random transformations, thus widening the range of applicability of these networks to nonlinear systems with memory. In particular, this study shows that networks belonging to a class named non-Gaussian stochastic approximate identity neural networks (SAINNs) are capable of approximating the solutions of large classes of nonlinear random ordinary differential transformations. The effectiveness of this approach is demonstrated and discussed by some application examples.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/51047

A novel approach to statistical simulation of ICs affected by non-linear variabilities
Proceedings of 2008 IEEE International Symposium on Circuits and Systems
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Curzi, Alessandro; Orcioni, Simone; Turchetti, Claudio
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents a methodology for statistical simulation of non-linear integrated circuits affected by device mismatch. This simulation technique is aimed at helping designers maximize yield, since it can be orders of magnitude faster than other readily available methods, e.g. Monte Carlo. DC, AC, and transient analyses are performed by means of the stochastic modified nodal analysis, using a piecewise linearization technique with respect to the stochastic sources, around a few automatically selected points.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/52330

System Level Modelling of RF IC in SystemC-WMS
EURASIP JOURNAL ON EMBEDDED SYSTEMS
Autore/i: Orcioni, Simone; Ballicchia, Mauro; Biagetti, Giorgio; D'Aparo, ROCCO DAVIDE; Conti, Massimo
Classificazione: 1 Contributo su Rivista
Abstract: This paper proposes a methodology for modelling and simulation of RF systems in SystemC-WMS. Analog RF modules have been described at system level only by using their specifications. A complete Bluetooth transceiver, consisting of digital and analog blocks, has been modelled and simulated using the proposed design methodology. The developed transceiver modules have been connected to the higher levels of the Bluetooth stack described in SystemC, allowing the analysis of the performance of the Bluetooth protocol at all the different layers of the protocol stack.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/58325

Sensor network-based nonlinear system identification
12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES 2008) - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Gianfelici, F; Turchetti, Claudio
Editore: Springer-Verlag
Luogo di pubblicazione: Berlin/Heidelberg
Classificazione: 2 Contributo in Volume
Abstract: In this paper, a new algorithm for the identification of distributed systems by large scale collaborative sensor networks is suggested. The algorithm, that uses the distributed Karhunen-Loève transform, extends in a decentralized setting the KLT-based identification approach that have recently been proposed for a centralized setting. The effectiveness of the proposed methodology is directly related to the reduction of total distortion in the compression performed by the single nodes of the sensor network, to the identification accuracy as well as to the low computational complexity of the fusion algorithm performed by the fusion center to regulate the intelligent cooperation of the nodes. The results in the identification of a system whose behavior is described by a partial differential equation in a 2-D domain with random excitation confirms the effectiveness of this technique.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/43088

SiSMA: Simulator for Statistical Mismatch Analysis
presented at the University Booth at the 10th Design, Automation and Test in Europe (DATE 07)
Autore/i: Biagetti, Giorgio; Orcioni, Simone; Curzi, Alessandro; Crippa, Paolo; Turchetti, Claudio
Classificazione: 5 Altro
Abstract: Presentato a "The University Booth" della conferenza internazionale "10th Design, Automation and Test in Europe (DATE 07)", 16-20 Aprile, 2007, Nizza, Francia.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/50212

Multicomponent AM-FM representations: An asymptotically exact approach
IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING
Autore/i: Gianfelici, Francesco; Biagetti, Giorgio; Crippa, Paolo; Turchetti, Claudio
Classificazione: 1 Contributo su Rivista
Abstract: This paper presents, on the basis of a rigorous mathematical formulation, a multicomponent sinusoidal model that allows an asymptotically exact reconstruction of nonstationary speech signals, regardless of their duration and without any limitation in the modeling of voiced, unvoiced, and transitional segments. The proposed approach is based on the application of the Hilbert transform to obtain an amplitude signal from which an AM component is extracted by filtering, so that the residue can then be iteratively processed in the same way. This technique permits a multicomponent AM-FM model to be derived in which the number of components (iterations) may be arbitrarily chosen. Additionally, the instantaneous frequencies of these components can be calculated with a given accuracy by segmentation of the phase signals. The validity of the proposed approach has been proven by some applications to both synthetic signals and natural speech. Several comparisons show how this approach almost always has a higher performance than that obtained by current best practices, and does not need the complex filter optimizations required by other techniques.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53078

Efficient synthesis of piano tones with damped Bessel functions
Proceedings of the 2007 15th International Conference on Digital Signal Processing (DSP 2007)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Turchetti, Claudio; Morici, A.
Editore: IEEE
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper a novel technique for efficient synthesis of waveforms generated by musical instruments is presented. This methodology represents single tones produced by musical instruments as a series of orthogonal Bessel functions, similarly to an additive synthesis that, instead, uses sinusoidal partials. Bessel functions possess a pitch that slowly varies with time, and are thus suited to model musical tones that usually exhibit similar characteristics. A comparative listening test has been performed, and the synthetically created piano sounds have been compared to those generated by traditional additive synthesis. Bessel-based synthesis generally achieved a higher score than the sinusoidal-based approach. The limited amount of memory resources used makes this technique suitable to be implemented on a digital signal processor.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/52896

Mixed Signal SystemC modelling of a SoC architecture with Dynamic Voltage Scaling
Proc. of SPIE’07, Int. Conference VLSI Circuits and Systems 2007
Autore/i: Leoce, G.; D'Aparo, R.; Vece, G. B.; Biagetti, G.; Orcioni, S.; Conti, M.
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/45626

Modeling of speech signals based on Bessel-like orthogonal transform
Proceedings of the 9th International Conference on Spoken Language Processing (Interspeech 2006 - ICSLP)
Autore/i: Biagetti, Giorgio; Crippa, Paolo; Turchetti, Claudio
Editore: ISCA
Luogo di pubblicazione: BONN
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: In this paper a novel modeling technique for speech signals, based on the source-filter model of speech production and on orthogonal transform theory, is presented. The proposed approach models the impulse response of such filter, by projection onto a basis of damped Bessel functions, which have been chosen for their similarity to the signal to be modeled. In such a way an orthogonal transform pair is defined which provides a simple and effective methodology for the extraction of model parameters, and its effectiveness in the case of voiced speech has been demonstrated by synthesizing natural sounding speech signals with the aid of only a few extracted parameters.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53742

SystemC-WMS: Mixed Signal Simulation based on Wave exchanges
Applications of Specification and Design Languages for SoCs
Autore/i: Orcioni, Simone; Biagetti, Giorgio; Conti, Massimo
Editore: Springer
Luogo di pubblicazione: Dordrecht
Classificazione: 2 Contributo in Volume
Abstract: Selected papers from FDL 2005.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/51679

SystemC-WMS: A wave mixed signal simulator
Proceedings of the 8th ECSI Forum on specification & Design Languages (FDL '05)
Autore/i: Orcioni, Simone; Biagetti, Giorgio; Conti, Massimo
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/51454

AM-FM decomposition of speech signals: An asymptotically exact approach based on the iterated Hilbert transform
2005 IEEE/SP 13th Workshop on Statistical Signal Processing (SSP), Vols 1 and 2
Autore/i: G., Gianfelici; Biagetti, Giorgio; Crippa, Paolo; Turchetti, Claudio
Editore: IEEE
Luogo di pubblicazione: PISCATAWAY
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents a multicomponent sinusoidal model of speech signals, obtained through a rigorous mathematical formulation that ensures an asymptotically exact reconstruction of these nonstationary signals, despite the presence of transients, voiced segments, or unvoiced segments. This result has been obtained by means of the iterated use of the Hilbert transform, and the convergence properties of the proposed method have been both analytically investigated and empirically tested. Finally, an adaptive segmentation algorithm used to accurately compute instantaneous frequencies from unwrapped phases, suited to complete the proposed AM-FM model, is presented.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/52869

A novel KLT algorithm optimized for small signal sets
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05)
Autore/i: Gianfelici, F; Biagetti, Giorgio; Crippa, Paolo; Turchetti, Claudio
Editore: IEEE
Luogo di pubblicazione: Piscataway
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: Karhunen-Loève Transform, being able to represent stochastic processes under appropriate conditions, is a powerful signal processing tool. But the high computational cost incurred in the modeling of long signals has limited its use in the recognition of speech segmented at the word level. In this paper we present a novel algorithm that significantly reduces the computational cost when the number of signals to be treated is small in comparison to their samples.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/52861

Asymptotically exact AM-FM decomposition based on iterated Hilbert transform
Proceedings of 6th INTERSPEECH 2005 and 9th European Conference on Speech Communication and Technology (EUROSPEECH)
Autore/i: Gianfelici, F; Biagetti, Giorgio; Crippa, Paolo; Turchetti, Claudio
Editore: International Speech Communication Association (ISCA)
Classificazione: 4 Contributo in Atti di Convegno (Proceeding)
Abstract: This paper presents a multicomponent sinusoidal model of speech signals, obtained through a rigorous mathematical formulation that ensures an asymptotically exact reconstruction of these nonstationary signals, despite the presence of transients, voiced segments, or unvoiced segments. This result has been obtained by means of the iterated use of the Hilbert transform, and the convergence properties of the proposed method have been both analytically investigated and empirically tested. Finally, an adaptive segmentation algorithm used to accurately compute instantaneous frequencies from unwrapped phases, suited to complete the proposed AM-FM model, is presented.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53747

Tecnica per la predizione degli effetti delle variazioni costruttive sui circuiti integrati non lineari
Autore/i: Turchetti, Claudio; Orcioni, Simone; Biagetti, Giorgio; Alessandrini, M.; Crippa, Paolo
Classificazione: 6 Brevetti
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/53626

A mixed signal fuzzy controller using current mode circuits
ANALOG INTEGRATED CIRCUITS AND SIGNAL PROCESSING
Autore/i: Orcioni, Simone; Biagetti, Giorgio; Conti, Massimo
Classificazione: 1 Contributo su Rivista
Abstract: A mixed analog-digital fuzzy logic inference processor chip, designed in a 0.35-μmCMOS technology, is presented. The analog fuzzy engine is based on a novel current-mode CMOS circuit used for the implementation of fuzzy partition membership functions. The architecture consists of a 3 inputs-1 output analog fuzzy engine, internal digital registers to store the parameters of the fuzzy controller, and a digital subsystem that allows the programmability of the fuzzy controller via an I2C interface. The architecture, circuits, and some Cadence Spectre simulations are presented.
Scheda della pubblicazione: https://iris.univpm.it/handle/11566/51883