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Embedded Real-Time Vehicle and Pedestrian Detection Using a Compressed Tiny YOLO v3 Architecture
Laura Falaschetti;Lorenzo Manoni;Lorenzo Palma;Paola Pierleoni;Claudio Turchetti
Vehicle and pedestrian detection (VaPD) is one of the most critical tasks in an advanced driver assistance system which help the driver to drive safely and save the pedestrian life. VaPD is a typical object detection problem that requires a trade-off among accuracy, speed, and memory consumption. Most existing methods focus on improving detection accuracy, while ignoring VaPD requires real-time de...Show More
An Embedded Vision System for Autoimmune Skin Diseases Classification Based on Deep Learning: A Preliminary Study
Laura Falaschetti;Sara Campanella;Sofia Curina;Giorgia Martini;Paola Pierleoni;Alberto Belli;Claudio Turchetti;Lorenzo Palma
2024 IEEE Sensors Applications Symposium (SAS)
Year: 2024 | Conference Paper |
The skin can exhibit symptoms of internal patho-logic conditions. Cutaneous autoimmunity is one such instance, serving as a sign of a systemic lack of immune tolerance. It is quite challenging to appropriately identify skin conditions because of their seemingly identical clinical presentations. Thus, this work suggests an embedded vision system based on a CNN model that is put into practice on a l...Show More
EEG-Based Neurodegenerative Disease Classification using LSTM Neural Networks
Michele Alessandrini;Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Simona Luzzi;Claudio Turchetti
2023 IEEE Statistical Signal Processing Workshop (SSP)
Year: 2023 | Conference Paper |
Cited by: Papers (9)
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 reli...Show More
Complexity bounded classification of fish-eye distorted objects with micro-controllers
Danilo Pietro Pau;Alessandro Carra;Marco Garzola;Laura Falaschetti;Claudio Turchetti
2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
Since 2019, tiny machine learning has imposed itself everywhere as an innovative technology trend deployed at the edge and has been pervasive in many IoT applications. One interesting, addressed by this work, is related to the welfare of the laboratory animals that could be preserved by acquiring and classifying some image data to monitor object’s presence in their cages. For example mice and rats...Show More
A Low-Rank CNN Architecture for Real-Time Semantic Segmentation in Visual SLAM Applications
Real-time semantic segmentation on embedded devices has recently enjoyed significant gain in popularity, due to the increasing interest in smart vehicles and smart robots. In particular, with the emergence of autonomous driving, low latency and computation-intensive operations lead to new challenges for vehicles and robots, such as excessive computing power and energy consumption. The aim of this ...Show More
Tunable and Miniaturized Microwave Filters Using Carbon Nanotube-Based Variable Capacitors
Martino Aldrigo;Mircea Dragoman;Sergiu Iordanescu;George Boldeiu;Paolo Crippa;Giorgio Biagetti;Claudio Turchetti;Luca Pierantoni;Davide Mencarelli;Stephane Xavier;Laurent Gangloff;Afshin Ziaei
In this paper, we introduce a novel concept of tunable and miniaturized filtersthat 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 ...Show More
A Low-Cost, Low-Power and Real-Time Image Detector for Grape Leaf Esca Disease Based on a Compressed CNN
Laura Falaschetti;Lorenzo Manoni;Romel Calero Fuentes Rivera;Danilo Pau;Gianfranco Romanazzi;Oriana Silvestroni;Valeria Tomaselli;Claudio Turchetti
Esca is one of the most common grape leaf diseases that seriously affect grape yield, causing a loss of global production in the range of 20%–40%. Therefore, a timely and effective identification of the disease could help to develop an early treatment approach to control its spread while reducing economic losses. For this purpose the use of computer vision and machine learning techniques for recog...Show More
Tensors and multiway analysis aim to explore the relationships between the variables used to represent the data and find a summarization of the data with models of reduced dimensionality. However, although in this context a great attention was devoted to this problem, dimension reduction of high-order tensors remains a challenge. The aim of this article is to provide a nonlinear dimensionality red...Show More
Modeling data generated by physiological systems is a crucial step in many problems such as classification, signal reconstruction and data augmentation. However finding appropriate models from high-dimensional data sampled from biosignals is in general unpracticable due to the problem known as the “curse of dimensionality”. Dimensionality reduction, that is representing data in some lower-dimensio...Show More
A Machine Learning Approach to the Identification of Dynamical Nonlinear Systems
Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Claudio Turchetti
2019 27th European Signal Processing Conference (EUSIPCO)
Year: 2019 | Conference Paper |
Cited by: Papers (4)
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 in...Show More
A GPU Parallel Algorithm for Non Parametric Tensor Learning
2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
One rich source of large data sets is the high dimensionality of the data formats known as tensors. Compared to the vector use, learning with tensors is inherently more complex and requires high-performance computing. The aim of this paper is to investigate tensor-based algorithms for regression and classification, i.e. tensor learning, that are suitable to be implemented in parallel architecture ...Show More
Sigma-Delta Based Modulation Method for Matrix Converters
Simone Orcioni;Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Claudio Turchetti
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.Show More
A machine learning method to determine intrinsic dimension of time series data
2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
Year: 2017 | Conference Paper |
Cited by: Papers (1)
The estimation of Intrinsic Dimension (ID) of data is particularly crucial in the unsupervised learning of nonlinear time series, as it essentially represents the minimum number of parameters to describe the data. The aim of this paper is to give both a new theoretical contribution and a machine learning algorithm that can be used for the ID estimation of time series. Several experimental results ...Show More
Machine learning regression based on particle bernstein polynomials for nonlinear system identification
Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Claudio Turchetti
2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)
Year: 2017 | Conference Paper |
Cited by: Papers (9)
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...Show More
An Investigation on the Accuracy of Truncated DKLT Representation for Speaker Identification With Short Sequences of Speech Frames
Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Simone Orcioni;Claudio Turchetti
IEEE Transactions on Cybernetics
Cited by: Papers (24)
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 voca...Show More
Homomorphic Deconvolution for MUAP Estimation From Surface EMG Signals
Giorgio Biagetti;Paolo Crippa;Simone Orcioni;Claudio Turchetti
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 ...Show More
Discrete bessel functions for representing the class of finite duration decaying sequences
Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Claudio Turchetti
2016 24th European Signal Processing Conference (EUSIPCO)
Year: 2016 | Conference Paper |
Cited by: Papers (3)
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 fun...Show More
Wireless surface electromyograph and electrocardiograph system on 802.15.4
Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Simone Orcioni;Claudio Turchetti
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...Show More
An algorithm for automatic words extraction from a stream of phones in dictionary-based large vocabulary continuous speech recognition systems
Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Simone Orcioni;Claudio Turchetti
2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)
Year: 2015 | Conference Paper |
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 ...Show More
CARMA: A robust motion artifact reduction algorithm for heart rate monitoring from PPG signals
Alessandro Bacà;Giorgio Biagetti;Marta Camilletti;Paolo Crippa;Laura Falaschetti;Simone Orcioni;Luca Rossini;Dario Tonelli;Claudio Turchetti
2015 23rd European Signal Processing Conference (EUSIPCO)
Year: 2015 | Conference Paper |
Cited by: Papers (17)
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. ...Show More
Improvement of RS-485 performance over long distances using the ToLHnet protocol
Giorgio Biagetti;Paolo Crippa;Laura Falaschetti;Simone Orcioni;Nicola Ortolani;Claudio Turchetti
2015 12th International Workshop on Intelligent Solutions in Embedded Systems (WISES)
Year: 2015 | Conference Paper |
Cited by: Papers (1)
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 ...Show More
Analysis of the EMG Signal During Cyclic Movements Using Multicomponent AM–FM Decomposition
Giorgio Biagetti;Paolo Crippa;Alessandro Curzi;Simone Orcioni;Claudio Turchetti
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...Show More
A Multi-class ECG Beat Classifier Based on the Truncated KLT Representation
Giorgio Biagetti;Paolo Crippa;Alessandro Curzi;Simone Orcioni;Claudio Turchetti
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 sign...Show More
Multi-port de-embedding methodology based on exponential mapping
2014 9th European Microwave Integrated Circuit Conference
Year: 2014 | Conference Paper |
This paper proposes a methodology that, starting from a set of calibration measurements picked up at the external ports, allows the de-embbeding of a multi-port transition and the determination of its representative matrix. With this methodology the coupling between internal versus external ports are supposed symmetrical. This hypothesis together with the use of exponential mapping and Baker-Campb...Show More
Multi-port de-embedding methodology based on exponential mapping
2014 44th European Microwave Conference
Year: 2014 | Conference Paper |
This paper proposes a methodology that, starting from a set of calibration measurements picked up at the external ports, allows the de-embbeding of a multi-port transition and the determination of its representative matrix. With this methodology the coupling between internal versus external ports are supposed symmetrical. This hypothesis together with the use of exponential mapping and Baker-Campb...Show More
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