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mmWave Radar-Based Non-Invasive Vehicle Occupancy Detection with Insights from Vision, RF, and Physical Sensors: A Review

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IEEE Sensors Journal 2025年第11期25卷 18643-18661页

作者： Yousaf, Jawad Palmer, Alyssa Shams, Maitha Wasma, Zayna Qasymeh, Montasir Hassan, Taimur Arora, Deepak Ghazal, Mohammed Abu Dhabi University Electrical Computer and Biomedical Engineering Department Abu Dhabi United Arab Emirates Liwa College Faculty of Engineering and Computing Abu Dhabi United Arab Emirates

The potential uses of human occupancy detection (HOD) in vehicles are crucial for handling resources, passenger safety, and privacy-preserving technology. In this study, the usage of various sensor systems for non-invasive vehicle occupant detection is examined, including mmWave radar, vision-based, and physical sensors. These technologies undergo a thorough review analysis that looks at methods, performance indicators, and use cases before being assessed for robustness, accuracy, real-time capabilities, scalability, and cost-effectiveness. The impact of clutter, scalability, and privacy in car interiors on HOD is also thoroughly discussed. Based on its ability to balance cost, accuracy, and adaptability, frequency-modulated continuous wave (FMCW) radar is termed the best option for vehicle occupant identification. The study discussed the possible challenges and future direction, as well as the conclusion provides research opportunities in hybrid sensing systems and advanced machine learning integration to overcome existing constraints. © 2001-2012 IEEE.

关键词： Frequency shift keying

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Intrinsic Entropy: A Novel Adaptive Method for Measuring the Instantaneous Complexity of Time Series

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IEEE Signal Processing Letters 2023年 30卷 160-164页

作者： Huang, Po-Hsun Hsiao, Tzu-Chien Hsinchu300 Taiwan Nycu Department of Computer Science College of Cs and Institute of Biomedical Engineering College of Electrical and Computer Engineering Hsinchu300 Taiwan

The determination of appropriate parameters and an appropriate window size in most entropy-based measurements of time-series complexity is a challenging problem. Inappropriate settings can lead to the loss of intrinsic information within a time series. Therefore, two parameter-free methods, namely the intrinsic entropy (IE) and ensemble IE (eIE) methods, are proposed in this paper. The eIE method requires two parameters, which can be easily determined through an orthogonality test. The proposed approaches can measure instantaneous complexity;thus, they do not require a predetermined window size. White noise and three other varieties of colored noise were used to test the stability of the proposed methods, and five types of synthetic signals and logistic maps were applied for measuring instantaneous complexity and regularity. The results revealed that the IE and eIE methods exhibit satisfactory stability. Both methods provide point-by-point entropy measures for time series. The eIE method is useful for measuring the complexity of frequency and amplitude modulation. Furthermore, the periodicity of time series can be detected using the two proposed methods. © 1994-2012 IEEE.

关键词： Empirical mode decomposition

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A Review of Machine Learning Methods for Screening Cardiac Abnormalities using Auscultation 10

A Review of Machine Learning Methods for Screening Cardiac A...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Alhasan, Sarah Poorshamsi, Zahra Nasser, Loubna Alzaabi, Alyaziah Ghazal, Mohammed Yousaf, Jawad Hassan, Taimur Abu Dhabi University Department of Electrical Computer and Biomedical Engineering United Arab Emirates

ISBN: (纸本)9798331532895

This paper presents a thorough review of various methodologies employed in heart sound classification, combining both conventional machine learning (ML) algorithms and complex deep learning (DL) approaches. We systematically evaluate different data collection approaches, preprocessing techniques, methodologies, and classification mechanisms. Further-more, the review analyzes several feature extraction methods required for capturing relevant signal characteristics, spanning Mel-frequency cepstral coefficients (MFCC), continuous wavelet transform (CWT), and convolutional neural networks (CNNs). Additionally, we examine a variety of classification methods, from traditional methods like support vector machines (SVMs), Knearest neighbors (KNNs), and Decision Trees (DTs), to more sophisticated parametric approaches such as recurrent neural networks (RNNs), (CNNs), long-short term memory (LSTM), and even ensemble techniques, signifying the potential of combining classification approaches. Key findings reveal a trend toward employing hybrid models that merge the capabilities of multiple techniques to enhance cardiac diagnostics precision. This comprehensive overview aims to explore existing methodologies and move toward bridging the gap in today's technologies, establishing a robust diagnostic tool for medical applications. © 2024 IEEE.

关键词： Long short-term memory

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EXPLORING INFLUENTIAL FACTORS IN KNOWLEDGE SHARING WITHIN INDONESIAN PRISONS 22

EXPLORING INFLUENTIAL FACTORS IN KNOWLEDGE SHARING WITHIN IN...

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22nd International Conference on e-Society 2024, ES 2024 and 20th International Conference on Mobile Learning 2024, ML 2024

作者： Imandeka, Ejo Hidayanto, Achmad Nizar Suhartanto, Heru Pidanic, Jan Department of Computer Science Faculty of Computer Science University of Indonesia Depok Indonesia Department of Electrical Engineering Faculty of Electrical Engineering and Informatics Pardubice University Pardubice Czech Republic

The exchange of knowledge is widely recognized as a crucial aspect of effective knowledge management. When it comes to sharing knowledge within Prison settings, things get complicated due to various challenges such as limited resources, social stigma, isolation, communication barriers, and security concerns. Additionally, there's a scarcity of resources dedicated to promoting knowledge sharing in secure environments. This study aims to dig deep into the key factors that influence how knowledge is shared in Indonesian prisons. To navigate this intricate terrain, the study uses questionnaires distributed among Prison officers to understand the complex relationship between the identified critical factors and knowledge sharing in Prison. Leadership support, organizational culture, organizational structure, incentives, and individual beliefs emerge as pivotal elements in knowledge sharing. It's also noted that the connection between these critical factors and the intention to share knowledge is significantly influenced by organizational structure. Recognizing and addressing critical factors in knowledge sharing within prisons not only improves rehabilitation within prison walls but also transforms the broader criminal justice system. This approach fosters evidence-based practices, better communication, and a comprehensive understanding of factors leading to successful rehabilitation and reduced recidivism, ultimately creating a more just and effective system. © 2024 International Conferences e-Society 2024 and Mobile Learning 2024. All rights reserved.

关键词： Critical Factors Indonesia Knowledge Knowledge Sharing Prison

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Semi‑supervised contour‑driven broad learning system for autonomous segmentation of concealed prohibited baggage items

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 1-18页

作者： Divya Velayudhan Abdelfatah Ahmed Taimur Hassan Muhammad Owais Neha Gour Mohammed Bennamoun Ernesto Damiani Naoufel Werghi Department of Electrical Engineering and Computer Sciences Center for Cyber‑Physical SystemsKhalifa University of Science and TechnologyAbu Dhabi 127788United Arab Emirates Department of Electrical Computer and Biomedical EngineeringAbu Dhabi UniversityAbu Dhabi 59911United Arab Emirates Department of Computer Science and Software Engineering the University of Western AustraliaPerthWA 6009Australia

With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation *** X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger *** address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation ***,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset ***,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as *** research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage *** proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage *** specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage *** multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign *** contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation *** proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,***,the lim

关键词： Baggage X-ray imagery Broad learning systems Threat detection Threat segmentation

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Multi-bit Receivers for High-Speed Communication over CAN Bus 30

Multi-bit Receivers for High-Speed Communication over CAN Bu...

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30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023

作者： Gallone, Andrea Malcovati, Piero University of Pavia Computer and Biomedical Engineering Department of Electrical Italy

ISBN: (纸本)9798350326499

This paper presents a new CAN (Controller Area Network) receiver, which is suited for multi-bit communication. Two alternative designs are described. The former provides better performance in terms of threshold voltage accuracy and pulse width symmetry, the latter is optimized in terms of area, but provides worse results regarding all the other key parameters. In both receivers, flash analog-to-digital conversion is used, which is performed during the data transmission phase and gives 4 pulses at the output that can be converted to a binary representation by an encoder. During arbitration the receivers behave conventionally. A chip of the second design described was produced using a 0.18- μm high voltage BCD technology. The circuit draws an average current of 5 mA from a 5-V supply. © 2023 IEEE.

关键词： Threshold voltage

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A learning-based approach to fault detection and fault-tolerant control of permanent magnet DC motors

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Journal of engineering and Applied Science 2023年第1期70卷 109页

作者： Sardashti, Abolghasem Nazari, Jamal Department of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran Faculty of Electrical Biomedical and Mechatronics Engineering Qazvin Branch Islamic Azad University Qazvin Iran

In the context of Industry 4.0, which prioritizes intelligent and efficient solutions for industrial systems, this paper introduces an innovative methodology for fault detection and fault-tolerant control of DC motors. Leveraging the capabilities of machine learning and reinforcement learning, our approach aims to achieve optimal performance while maintaining a low computational burden. At the heart of our strategy lies a reinforcement learning-enhanced proportional-integral controller meticulously designed for precise positioning of DC motors. Through extensive comparative analysis, we establish the superiority of this controller in terms of precision, efficiency, and user accessibility when compared to traditional techniques. To ensure robust fault detection, we synergize a model-based observer with Mahalanobis distance-based outlier analysis, creating a swift and accurate diagnostic method for sensor faults. In cases of sensor malfunctions, an internal model-based control strategy comes into play, enabling the system to uphold its effectiveness despite disruptions. The effectiveness of our proposed methods is vividly demonstrated through simulations in the MATLAB environment, utilizing a DC motor subjected to sensor failures. The results unequivocally highlight the advantages of our approach, showcasing improved precision, faster operation, cost-effectiveness, and streamlined simplicity. As such, our approach finds suitability for industrial applications. In our quest to strike a delicate balance between performance and complexity, our techniques are purposefully crafted to provide intelligent yet pragmatic solutions that promote reliability, safety, and sustainability. This paper contributes to the evolving landscape of intelligent industrial solutions by offering a comprehensive framework that optimizes performance while minimizing complexity and costs. In doing so, we lay the foundation for a more efficient and resilient industrial ecosystem. © 2023, Faculty o

关键词： Fault detection

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High-Performance and Low-Cost Approximation of ANN Sigmoid Activation Functions on FPGAs 12

High-Performance and Low-Cost Approximation of ANN Sigmoid A...

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12th International Conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Tatas, Kostantinos Gemenaris, Michalis Frederick University Department of Electrical Engineering Computer Engineering and Informatics Nicosia Cyprus

ISBN: (纸本)9798350321074

Neural network implementations on FPGAs have received significant attention from the research community due to their superior performance in tasks such as computer vision and the need to perform them on edge devices. Most efforts have focused on architecture optimizations such as pruning and operations such as the multiplications and additions with less attention on the ANN activation function. In this paper a hardware implementation of an artificial neural network on Field Programmable Gate Arrays (FPGA) is presented. A digital system architecture is designed with the purpose of reducing the delay of implementation and minimizing the LUTs in the FPGA. The designed architecture is described using Very High-Speed Integrated Circuits Hardware Description Language (VHDL). The proposed method utilizes a known piecewise linear approximation of the sigmoid function, and additionally exploits properties of fixed-point binary number representation to efficiently reduce delay and the amount of LUTS required Compared with previously published implementations of the same function and FPGA technology, the proposed method is shown to achieve a reduction between 33% and 95% in LUTs with no reduction in accuracy. © 2023 IEEE.

关键词： Neural networks

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Penetration of Distributed Generation in Microgrids: Characteristics and Challenges 58

Penetration of Distributed Generation in Microgrids: Charact...

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58th International Universities Power engineering Conference, UPEC 2023

作者： Tillyros, Kypros Christofides, Nicholas G. Komodromos, Michael Frederick University Computer Engineering and Informatics Department of Electrical Engineering Nicosia Cyprus

ISBN: (纸本)9798350316834

The exploitation of sustainable distributed energy sources is associated with the energy resilience and power optimisation of power grids. This study divides the energy sector of urban areas into isolated and non-isolated topologies and attempts to review the application of microgrids within the two. In addition, it investigates methods to optimise power quality with the integration of multi-renewable generation to the system and discusses on the feasibility towards islanded operating microgrids. The proposed work is a result of a careful evaluation of the current literature on the topic. Consequently, the outcome of the given study is anticipated to facilitate future work on Microgrid implementation functioning in islanded mode. © 2023 IEEE.

关键词： Power quality

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A Compact-Area Low-Power Temperature Sensor Featuring High Supply Voltage Scalability 30

A Compact-Area Low-Power Temperature Sensor Featuring High S...

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30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023

作者： Moisello, Elisabetta Malcovati, Piero Bonizzoni, Edoardo University of Pavia Computer and Biomedical Engineering Department of Electrical Italy

ISBN: (纸本)9798350326499

This paper proposes a highly supply voltage-scalable, low-power and compact-area temperature sensor, which satisfies the requirements for Systems-on-Chip (SoCs) and microprocessors hotspots monitoring. The proposed novel sensor structure employs diode-connected NMOS devices operating in sub-thresold, featuring different sizes, as sensing elements, in order to implement a proportional-to-absolute-temperature (PTAT) sensor output voltage. The proposed sensor does not require any additional biasing or start-up circuits and, since it mostly processes the signals generated by the sensing elements in the current domain, enables high supply voltage scalability. Furthermore, a low-cost 1-point temperature calibration algorithm can be employed for trimming the sensor sensitivity. The proposed sensor circuit was designed in a 130-nm CMOS process and its performance was verified through extensive simulations in the Cadence Virtuoso environment. © 2023 IEEE.

关键词： Temperature sensors

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