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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Fuerte, Susana Rodriguez Yu, Xinrui Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

This paper presents the current use of the Internet of Things (IoT) in fire evacuation and extinction. It examines the different approaches to the problem and technologies like Building Information Modeling (BIM) and mathematical algorithms that can be used to determine the optimal evacuation route. It also evaluates existing fire security solutions such as smoke, flame, motion, and gas sensors, LED lights, buzzers, and SMS modules. Entities that specialize in Residential and Commercial Security Systems and Home Automation are also discussed, along with the services they offer. The main objective of this study is to understand current systems and resources regarding fire evacuation and extinction systems and to analyze different developments in smart buildings to create an efficient system for fire detection and evacuation. © 2023 IEEE.

关键词： Internet of things

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Training Deep Neural Classifiers with Soft Diamond Regularizers

Training Deep Neural Classifiers with Soft Diamond Regulariz...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Olaoluwa Adigun Bart Kosko Department of Electrical and Computer Engineering Signal and Image Processing Institute Los Angeles California

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

We introduce new soft diamond regularizers that both improve synaptic sparsity and maintain classification accuracy in deep neural networks. These parametrized regularizers outperform the state-of-the-art hard-diamond Laplacian regularizer of Lasso regression and classification. They use thick-tailed symmetric alpha-stable $(\mathcal{S}\alpha \mathcal{S})$ bell-curve synaptic weight priors that are not Gaussian and so have thicker tails. The geometry of the diamond-shaped constraint set varies from a circle to a star depending on the tail thickness and dispersion of the prior probability density function. Training directly with these priors is computationally intensive because almost all $\mathcal{S}\alpha \mathcal{S}$ probability densities lack a closed form. A precomputed lookup table removed this computational bottleneck. We tested the new soft diamond regularizers with deep neural classifiers on the three datasets CIFAR-10, CIFAR-100, and Caltech-256. The regularizers improved the accuracy of the classifiers. The improvements included 4.57% on CIFAR-10, 4.27% on CIFAR-100, and 6.69% on Caltech-256. They also outperformed $L_{2}$ regularizers on all the test cases. Soft diamond regularizers also outperformed $L_{1}$ lasso or Laplace regularizers because they better increased sparsity while improving classification accuracy. Soft-diamond priors substantially improved accuracy on CIFAR-10 when combined with dropout, batch, or data-augmentation regularization.

关键词： Training Accuracy Sparse approximation Stars Tail Machine learning Probability density function Diamond Table lookup Residual neural networks

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Smart Infant Monitoring System Using Computer Vision and AI

Smart Infant Monitoring System Using Computer Vision and AI

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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Singh, Gurpreet Shekhar, Abhishek Raj Yu, Xinrui Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

The new era of technology is being greatly influenced by the field of artificial intelligence. Computer vision and deep learning have become increasingly important due to their ability to process vast amounts of data and provide insights and solutions in a variety of fields. Computer vision, deep learning and signal analysis have been used in a growing number of applications and services including smart devices, image, and speech recognition, healthcare, etc., one such device is an infant monitoring system. It monitors the daily activities of the infant such as their sleeping patterns, sounds, and movements. In this paper, deep learning and computer vision libraries were used to develop algorithms to detect whether the infant was in any uncomfortable situation such as sleeping on its back, face being covered and whether the infant was awake. The smart infant monitoring system detects the infant's unsafe resting situation in real time and sent immediate alerts to the caretaker's device. This paper presents the design flow of a smart infant monitoring system consisting of a night vision camera, a Jetson Nano, and a Wi-Fi internet connection. The pose estimation and awake detection algorithms were developed and tested successfully for different infant resting/sleeping situations. The smart infant monitoring system provides significant benefits for safety and an improved understanding of infants' sleep patterns and behavior. © 2023 IEEE.

关键词： Face recognition

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Fundamental Survey on Neuromorphic Based Audio Classification

arXiv

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arXiv 2025年

作者： Basu, Amlan Chaudhari, Pranav Di Caterina, Gaetano Neuromorphic Sensor Signal Processing Lab Centre for Image and Signal Processing Electrical and Electronic Engineering Department University of Strathclyde Glasgow United Kingdom

Audio classification is paramount in a variety of applications including surveillance, healthcare monitoring, and environmental analysis. Traditional methods frequently depend on intricate signal processing algorithms and manually crafted features, which may fall short in fully capturing the complexities of audio patterns. Neuromorphic computing, inspired by the architecture and functioning of the human brain, presents a promising alternative for audio classification tasks. This survey provides an exhaustive examination of the current state-of-the-art in neuromorphic-based audio classification. It delves into the crucial components of neuromorphic systems, such as Spiking Neural Networks (SNNs), memristors, and neuromorphic hardware platforms, highlighting their advantages in audio classification. Furthermore, the survey explores various methodologies and strategies employed in neuromorphic audio classification, including event-based processing, spike-based learning, and bio-inspired feature extraction. It examines how these approaches address the limitations of traditional audio classification methods, particularly in terms of energy efficiency, real-time processing, and robustness to environmental noise. Additionally, the paper conducts a comparative analysis of different neuromorphic audio classification models and benchmarks, evaluating their performance metrics, computational efficiency, and scalability. By providing a comprehensive guide for researchers, engineers and practitioners, this survey aims to stimulate further innovation and advancements in the evolving field of neuromorphic audio classification. © 2025, CC BY.

关键词： Benchmarking

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Overview of EMG signal Preprocessing and Classification for Bionic Hand Control

Overview of EMG Signal Preprocessing and Classification for ...

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2022 IEEE Information Technologies and Smart Industrial Systems, ITSIS 2022

作者： Ayachi, Monaam Seddik, Hassene University of Tunis Riftsi Research Laboratory: Intelligent Robotics Reliability and Image Signal Processing Electrical Department Tunis Tunisia

ISBN: (纸本)9781665477017

This paper deals with the field of Biological signal especially one of the most used signals in the field of prosthetic devices, which is the EMG signal. After introducing this kind of signal, we will talk about different techniques used for preprocessing it in order to prepare it for classification tasks. We Will then overview different noises that affect those sensible type of signals, different techniques applied for filtering them and the useful envelope detection methods that can be applied on EMG. The next part of this paper will look at some famous feature extraction and classification techniques that proved themselves useful when dealing with classification tasks of biological signals. Finally, we will present the mechanical design of a multiarticulate multifunctional prosthetic hand, the promising classification results that can gives 98.3% and our perspectives. © 2022 IEEE.

关键词： Bionics

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Dielectric Resonator Magnetoelectric Dipole Arrays with Low Cross Polarization,Backward Radiation,and Mutual Coupling for MIMO Base Station Applications

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China Communications 2024年第6期21卷 290-304页

作者： Song Simin Da Yiran Qian Bingyi Huang Xiaoyu Chen Xiaoming Li Yingsong Ahmed A.Kishk School of Information and Communications Engineering Xi’an Jiaotong UniversityXi’an 710049China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui UniversityHefei 230039China Department of Electrical and Computer Engineering Concordia UniversityMontrealQC H3G 1M8Canada

Dielectric resonator magnetoelectric dipole(DRMED)arrays with enhanced isolation,reduced cross-polarization,and backward radiation are proposed for base station(BS)*** proposed antenna comprises an elevated dielectric resonator antenna(DRA)on a small metal plate above a sizeable common ground *** DRA is designed in its T Eδ11 mode,acting like a magnetic *** surface current excited by the differential probes flowing on the small ground plane is equivalent to an electric *** these two equivalent dipoles are orthogonal,they have the magnetoelectric dipole characteristics with reduced backward ***,the small ground planes can be treated as decoupling structures to provide a neutralization path to cancel the original coupling path.A linearly-polarized 4-element prototype array was verified experimentally in previous ***,a dual-polarized DRMED antenna is presented to construct a 2-element and 4×4 array for BS *** investigate its MIMO performance,sophisticated multi-cell scenario simulations are carried *** using the proposed dualpolarized DRMED array,the cellular system capacityis improved by 118.6%compared to a conventional DRA *** significant MIMO system improvement is mainly due to the reduced backward radiation and,therefore,reduced inter-cell *** align well with the simulations.

关键词： backward radiation dielectric resonator antennas(DRAs) isolation magnetoelectric-dipole(ME-dipole)antennas

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Evaluation Techniques for PolSAR Speckle Filtering

Evaluation Techniques for PolSAR Speckle Filtering

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2023 International Conference on Earth Observation and Geo-Spatial Information, ICEOGI 2023

作者： Souhila, Boutarfa Youcef, Smara Chaimaa, Youcef Radia, Ballah Laboratory of Innovative Technologies Department of Electrical Engineering and Industrial Computing Bordj El Kiffan Algiers16087 Algeria Laboratory of Image Processing and Radiation Faculty of Electrical Engineering Department of Telecommunications Bab Ezzouar Algiers16111 Algeria

ISBN: (纸本)9798350396720

Speckle noise is the biggest drawback that affects polarimetric SAR (Synthetic Aperture Radar) images. Due to its multiplicative nature, it degrades their quality, complicates their interpretation and reduces the information accuracy. Its effect is reduced by applying polarimetric synthetic aperture radar (PolSAR) filtering methods. In the literature, there are several methods of PolSAR filtering, it is not obvious to choose the most effective filter, and among the techniques for evaluating the performance of these filters, it is difficult to find the best analysis method that leads to the best filtering quality without information loss. In this paper, we present a comparative study of evaluation methods for polarimetric SAR filters considering three criteria: smoothing of homogeneous areas, edge preservation and conservation of polarimetric information. In this work, we propose a more simplified approach to evaluate a polarimetric SAR filter. Each criterion includes study techniques that will help to identify the analysis method that will facilitate the selection of the best filter. © 2023 IEEE.

关键词： Speckle

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Effective uncertain fault diagnosis technique for wind conversion systems using improved ensemble learning algorithm

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Energy Reports 2023年 10卷 3113-3124页

作者： Attouri, Khadija Dhibi, Khaled Mansouri, Majdi Hajji, Mansour Bouzrara, Kais Nounou, Mohamed Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine1200 Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Qatar Chemical Engineering Program Texas A&M University at Qatar Qatar Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir 5019 Tunisia

This paper introduces a pioneering fault diagnosis technique termed Interval Ensemble Learning based on Sine Cosine Optimization Algorithm (IEL- SCOA), tailored to tackle uncertainties prevalent in wind energy conversion (WEC) systems. The approach unfolds in three integral phases. Firstly, the establishment of interval centers and ranges, employing upper and lower bounds, effectively manages the inherent uncertainties arising from noise and measurement errors intrinsic to the wind system. Subsequently, the dataset undergoes processing via the Sine-Cosine Optimization Algorithm (SCOA), enabling the extraction of the most pertinent attributes. The culmination of predictive precision and classification performance is achieved through the integration of the refined dataset into an ensemble learning paradigm, harmonizing bagging, boosting techniques, and an artificial neural network classifier. The principal aim of the IEL-SCOA approach is to discern the spectrum of operational conditions within WEC systems, encompassing a healthy mode alongside six distinct faulty modes. These anomalies, encompassing short circuits, open circuits, and wear-out incidents, are deliberately induced at diverse locations and facets of the system, notably the generator and grid sides. Empirical results underscore the robustness and efficiency of the proposed methodology, showcasing an exceptional accuracy rate of 99.76 %. These outcomes definitively establish the IEL-SCOA approach as a potent and efficacious tool for precise fault diagnosis in uncertain WEC systems. © 2023

关键词： Fault detection

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Robust DNN-Enabled Cooperative Spectrum Sensing 32

Robust DNN-Enabled Cooperative Spectrum Sensing

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32nd International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2024

作者： Serghini, Omar Serrano, Salvatore Semlali, Hayat Maali, Asmaa University of Messina Laboratory of Digital Signal Processing Department of Engineering Messina Italy Cadi Ayyad University Laboratory of Electrical Systems Energy Efficiency and Telecommunications Marrakesh Morocco Royal Naval School of Casablanca Casablanca Morocco

ISBN: (纸本)9789532901382

In this contribution, we investigate cooperative spectrum sensing in cognitive radio networks using a deep neural network approach. Our proposed architecture leverages the strengths of both a one dimentional convolutional neural network and a bidirectional long short-term memory network. This combined approach aims to effectively learn the activity patterns of primary users for accurate spectrum sensing. The signal generation process incorporates diverse modulation techniques commonly used in communication systems. Additionally, the channel model considers complexities encountered in real-world deployments. These complexities mimic the challenges of multipath propagation and relative motion, making the training data more realistic. The performance of the proposed deep neural network is compared to benchmark methods using only CNN or LSTM at the secondary user level. We evaluate the models using receiver operating characteristic curves and probability of detection for various signal-to-noise ratio levels. The simulation results showcase the effectiveness of the proposed DNN, achieving superior probability of detection compared to the benchmarks and potentially surpassing state-of-the-art methods, particularly demonstrating very good performance at very low SNR values. © 2024 University of Split, FESB.

关键词： signal to noise ratio

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Scalable weighted-cumulated methodology for fatigue estimation

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Research on Biomedical engineering 2022年第4期38卷 1087-1101页

作者： de Oliveira Nascimento, Francisco Assis de Araújo Rocha, Valdinar do Carmo, Jake Carvalho Group of Digital Signal Processing Department of Electrical Engineering University of Brasília DF BrasíliaCO 04465 Brazil Laboratory of Biological Signal Processing Faculty of Physical Education University of Brasília DF BrasíliaCO 04465 Brazil

Purpose: The objective measurement of muscle fatigue through the analysis of surface electromyographic signals (S-EMG) has been the object of study in recent decades. The use of S-EMG is interesting because it allows accessing the muscular structure and function through the use of a noninvasive technique. This is a subject of interest to many areas of science such as clinical and orthopedic medicine, biomechanics, physiotherapy and rehabilitation, telemedicine, control of interfaces, intelligent prosthetics, and exoskeletons control and for expert systems to support medical diagnosis to neuromuscular diseases. Changes in the spectral signature of the S-EMG signal such as spectral shift for low frequencies and the increase in the dynamic range of the signal indicate the installation of the biological phenomenon of fatigue. For instance, classical techniques such as zero-crossing, median frequency (MDF), and mean power frequency (MPF) are able to perceive the spectral shift in S-EMG signals. On the other hand, techniques such as root mean square (RMS) values can only perceive the variation in the dynamic range of the S-EMG signal. Methods: In this work, new mathematical models for the objective assessment of muscle fatigue are presented. We sought to design models for objective fatigue estimators that simultaneously perceive the spectral shift for low frequencies and the increase in the dynamic range in the S-EMG signal during the instauration of the fatigue process. The new approach is integrated to the weighted-cumulated methodology framework previously proposed. Three new objective muscle fatigue estimators were conceived: the scalable weighted-cumulated Fourier estimator, the weighted-cumulated wavelet estimator (SWCW), and the weighted-cumulated p-side lobe attenuation algorithm (p-SL). Results: To evaluate the proposed tools based on the scalable weighted-cumulated methodology, we investigated two dynamic protocols with muscle fatigue production. The S-EMG signa

关键词： Wavelet transforms

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