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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Rezaee, Sayeh Nieto, Cesar Singh, Abhyudai University of Delaware Department of Electrical and Computer Engineering NewarkDE United States University of Delaware Biomedical Engineering Mathematical Sciences Center of Bioinformatic and Computational Biology Department of Electrical and Computer Engineering NewarkDE United States

ISBN: (纸本)9798350316339

Numerous biological timing mechanisms are associated with the duration required for key regulatory molecules to reach critical threshold levels. Recent studies have mainly explored how random fluctuations affect the timing of events as molecules accumulate to reach a high threshold. This study examines the complementary scenario: decrease in protein levels until they reach a low critical threshold. We aim to study the timing precision resulting from different degradation pathways and their inherent stochastic nature, especially under low-copy number conditions. More specifically, we explore how integer-valued protein molecular counts decrease stochastically following Michaelis-Menten kinetics. This approach encompasses both zero-order (where the net degradation rate is independent of protein levels) and first-order (where the rate is proportional to protein levels) decay processes. Our results show that, while zero-order decay can be sluggish in terms of mean timing, it provides the highest precision in timing with the lowest noise in threshold-crossing time. Conversely, upon considering randomness in the initial protein levels, first-order decay demonstrates better precision compared to zero-order. Interestingly, in the presence of multiple noise sources, timing stochasticity can be minimized when Michaelis-Menten decay kinetics operate under sub-saturation conditions. This characterization of timing driven by decreasing copy numbers has implications for improving the precision of biological clocks and understanding the timing of cell-cycle events. © 2024 IEEE.

关键词： Stochastic systems

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Optimal control of electric automated buses in intercity lines: scenario-based analysis of the Pareto-optimal solutions

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IFAC-PapersOnLine 2024年第10期58卷 37-42页

作者： C. Pasquale S. Sacone S. Siri A. Ferrara Department of Informatics Bioengineering Robotics and Systems Engineering University of Genova Italy Department of Electrical Computer and Biomedical Engineering University of Pavia Italy

This paper deals with the optimal control of electric and automated buses that have to follow an intercity line, where some stops are equipped with a flash-charging infrastructure to charge the batteries, while others are not. In order to control these buses, it is necessary to account for the traffic conditions along the roads of the bus line, and to minimize two objectives related to the deviations from the bus timetable and the lack of energy, at the end of the bus trip, with respect to a desired final energy level. The resulting optimal control problem is a multi-objective linear quadratic problem with both continuous and integer variables. In this paper, we propose and apply two methods based on lexicographic ordering aimed at determining the Pareto-optimal solutions of the control problem in twelve scenarios inspired by a real case.

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Exploring Biological Features of Electroencephalography Signals for Explanation-Guided Learning

Exploring Biological Features of Electroencephalography Sign...

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2024 International Conference on Electronics, Information, and Communication, ICEIC 2024

作者： Joo, Hyosung Kim, Dongseok Trang, Le Thi Quan, Luong Do Anh Woo, Jihwan University of Ulsan Department of Electrical Electronic and Computer Engineering Ulsan Korea Republic of University of Ulsan Department of Biomedical Engineering Ulsan Korea Republic of

ISBN: (纸本)9798350371888

Explanation-guided learning (EGL) has gained prominence for improving both the explainability and performance of deep neural networks by integrating additional supervision signals based on the elucidation of the model. However, its applicability in analyzing electroencephalography (EEG) signals remains underexplored. This study attempted to identify and evaluate biological features for EGL-based EEG analysis using compact convolutional neural networks. The identified features included anatomical location, dipolarity, relative peak power, and event-related desynchronization of the cortical source. The feasibility of the features was evaluated with performance prediction tests, which predict model performance based on the interpreted features. All features except dipolarity possessed a significant relationship with model performance. These findings indicate the possibility of utilizing these features as biological supervision signals in future EGL-based EEG analysis. © 2024 IEEE.

关键词： Electroencephalography

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Role of Deep Learning Models in Intelligent Classification of Various Date Fruit Bunches 17

Role of Deep Learning Models in Intelligent Classification o...

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17th International Conference on Developments in eSystems engineering, DeSE 2024

作者： Abuowda, Zainab Salam, Nour Ramadan, Shorouk Hassan, Taimur Ghazal, Mohammed Almajali, Eqab Hussain, Abir Jafar Yousaf, Jawad Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350368697

This study presents a performance comparison of different deep machine learning models for the intelligent segregation of date fruit bunches of numerous varieties. The shape, size, and color of the various types of dates with different maturity conditions require experienced farmers to estimate the accrual class. This study has used a transfer learning approach on various machine learning models (VGG-19, MobileNetV2, DenseNet, and NASNet) to classify five different kinds of date bunches (Barhi, Khalas, Menifi, Nabout saif, and Sullaj). Each model is trained on a dataset of 11,944 images of five different date classes. The comparison deduces that DenseNet produces the best accuracy, precision, and recall when compared with the performance of other models. The maximum achieved accuracy of the trained DenseNet model is 100% for the validation dataset and 98.4% for the test dataset. © 2024 IEEE.

关键词： Contrastive Learning

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Gait Optimization for Legged Systems Through Mixed Distribution Cross-Entropy Optimization 23

Gait Optimization for Legged Systems Through Mixed Distribut...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Tsikelis, Ioannis Chatzilygeroudis, Konstantinos University of Patras Computer Engineering and Informatics Department PatrasGR-26504 Greece Department of Electrical & Computer Engineering PatrasGR-26504 Greece Department of Mathematics PatrasGR-26110 Greece

ISBN: (纸本)9798350373578

Legged robotic systems can play an important role in real-world applications due to their superior load-bearing capabilities, enhanced autonomy, and effective navigation on uneven terrain. They offer an optimal trade-off between mobility and payload capacity, excelling in diverse environments while maintaining efficiency in transporting heavy loads. However, planning and optimizing gaits and gait sequences for these robots presents significant challenges due to the complexity of their dynamic motion and the numerous optimization variables involved. Traditional trajectory optimization methods address these challenges by formulating the problem as an optimization task, aiming to minimize cost functions, and to automatically discover contact sequences. Despite their structured approach, optimization-based methods face substantial difficulties, particularly because such formulations result in highly nonlinear and difficult to solve problems. To address these limitations, we propose CrEGOpt, a bi-level optimization method that combines traditional trajectory optimization with a black-box optimization scheme. CrEGOpt at the higher level employs the Mixed Distribution Cross-Entropy Method to optimize both the gait sequence and the phase durations, thus simplifying the lower level trajectory optimization problem. This approach allows for fast solutions of complex gait optimization problems. Extensive evaluation in simulated environments demonstrates that CrEGOpt can find solutions for biped, quadruped, and hexapod robots in under 10 seconds. This novel bi-level optimization scheme offers a promising direction for future research in automatic contact scheduling. © 2024 IEEE.

关键词： Cost functions

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Enhancing Wildlife Protection: Poacher Detection Using Machine Learning Models 17

Enhancing Wildlife Protection: Poacher Detection Using Machi...

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17th International Conference on Developments in eSystems engineering, DeSE 2024

作者： Gad, Omar Mohamed Elbatanouny, Hagar Almajali, Eqab Yousaf, Jawad Hussain, Abir Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Electrical Computer and Biomedical Engineering Department Abu Dhabi University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350368697

Wildlife conservation is a pressing global concern, with illegal poaching posing a severe threat to many endangered species. In recent years, advanced technologies like machine learning and digital signal processing have shown significant potential in supporting conservation efforts by detecting and preventing illegal poaching. This work explores the integration of these technologies into wildlife conservation strategies, focusing on their role in identifying and combating poachers. Two datasets are utilized:"Poacher Detection 3 Classes" and"Illegal Poacher Detection". Several machine learning models including Support Vector Machines (SVM), Random Forest (RF), Decision Trees (DT), and Convolutional Neural Networks (CNN) are applied to detect illegal activities in these datasets. For the first dataset, the SVM model achieved the best accuracy of 93%, while the CNN model performed best on the second dataset, achieving accuracy of 75%. In the final phase, both datasets were combined, and augmented images were introduced to increase data diversity. On the combined dataset, the Random Forest model achieved the highest accuracy of 83%. These results demonstrate the effectiveness of machine learning in improving wildlife conservation efforts. © 2024 IEEE.

关键词： Adversarial machine learning

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Lecture Hall Monitoring System using Access Control Mechanism at the University of Ghana 9

Lecture Hall Monitoring System using Access Control Mechanis...

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9th IEEE International Conference on Adaptive Science and Technology, ICAST 2024

作者： Adjaye, Aboagye Isaac Abdul-Kadir, Napari Kelly, Norman Michael, Amui Longdon, Sowah Nii Percy, Okae Alidu, Abubakari Kan-Dapaah, Kwabena University of Ghana Department of Computer Engineering Ghana Academic City University College Department of Electrical and Electronic Engineering Ghana University of Ghana Department of Biomedical Engineering Ghana

ISBN: (纸本)9798350385403

In an era where technology is evolving rapidly, there is a need to efficiently secure access control systems within educational institutions and office environments. Access control systems are crucial in ensuring the security and legal access to physical and digital resources. In this research, a system was designed and implemented to monitor the lecture hall and office environment within the School of engineering Sciences, University of Ghana. The system integrated multiple layers of access controls, including biometric fingerprint recognition, Radio Frequency Identification (RFID) card authentication, and personal identification number (PIN). A database was generated to identify people who use the facilities. This provided a robust and user-friendly hybrid system. The system leverages modern database technologies to record and manage access, enabling real-time monitoring of lecture halls and office spaces. Moreover, it offers a user-friendly interface for administrators, lecturers, lab attendants, and other users. The database generated could also be used to check class attendance. The results demonstrate significant improvements in security, attendance tracking, and overall access management efficiency in the School of engineering Sciences. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Flexible Sensors for IoT-Based Health Monitoring

IEEE Journal on Flexible Electronics

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IEEE Journal on Flexible Electronics 2025年第2期4卷 63-88页

作者： Majumder, Sumit Roy, Arup Kumer Mondal, Tapas Deen, M. Jamal Chittagong University of Engineering and Technology Department of Biomedical Engineering Chattogram4349 Bangladesh Chittagong University of Engineering and Technology Department of Chemistry Chattogram4349 Bangladesh McMaster University Department of Pediatrics HamiltonONL8S 4K1 Canada McMaster University School of Biomedical Engineering Department of Electrical and Computer Engineering HamiltonONL8S 4K1 Canada

The global population is aging due to increased life expectancy and declining birth rates. As a result, there is a growing prevalence of chronic diseases such as heart disease, hypertension, and diabetes, among the older population. These conditions not only diminish the quality of life but also significantly drive up healthcare costs. Consequently, the demand for efficient and cost-effective healthcare solutions is rising. Traditional healthcare systems are often challenged by issues of accessibility and equity, particularly in regions with inadequate medical infrastructure and geographic barriers. In response to these challenges, this article explores the potential of advanced flexible sensor technologies, integrated with cutting-edge communication and computing tools such as the Internet of Things (IoT), artificial intelligence (AI), and big data analytics. These sensors enable continuous, unobtrusive monitoring of vital signs, and health parameters, facilitating personalized and preventive care in the comfort of an individual’s home. However, the widespread adoption of these technologies faces several obstacles, including challenges related to manufacturing scalability, cost, mechanical stability, and data security. This article reviews the current state of research and development in flexible sensors and their integration with modern technologies for IoT-based health monitoring. It also examines key challenges and concerns associated with their use and outlines the future potential for these sensors to revolutionize healthcare monitoring and management. © 2022 IEEE.

关键词： Costs

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EventLFM:event camera integrated Fourier light field microscopy for ultrafast 3D imaging

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Light(Science & Applications) 2024年第7期13卷 1401-1415页

作者： Ruipeng Guo Qianwan Yang Andrew S.Chang Guorong Hu Joseph Greene Christopher V.Gabel Sixian You Lei Tian Department of Electrical and Computer Engineering Boston UniversityBostonMA 02215USA Department of Physiology and Biophysics Boston UniversityBostonMA 02215USA. Neurophotonics Center Boston UniversityBostonMA 02215USA. Research Laboratory of Electronics(RLE)in the Department of Electrical Science and Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA. Department of Biomedical Engineering Boston UniversityBostonMA 02215USA

Ultrafast 3D imaging is indispensable for visualizing complex and dynamic biological *** scanning-based techniques necessitate an inherent trade-off between acquisition speed and space-bandwidth product(SBP).Emerging single-shot 3D wide-field techniques offer a promising alternative but are bottlenecked by the synchronous readout constraints of conventional CMOS systems,thus restricting data throughput to maintain high SBP at limited frame *** address this,we introduce EventLFM,a straightforward and cost-effective system that overcomes these challenges by integrating an event camera with Fourier light field microscopy(LFM),a state-of-theart single-shot 3D wide-field imaging *** event camera operates on a novel asynchronous readout architecture,thereby bypassing the frame rate limitations inherent to conventional CMOS *** further develop a simple and robust event-driven LFM reconstruction algorithm that can reliably reconstruct 3D dynamics from the unique spatiotemporal measurements captured by *** results demonstrate that EventLFM can robustly reconstruct fast-moving and rapidly blinking 3D fluorescent samples at kHz frame ***,we highlight EventLFM’s capability for imaging of blinking neuronal signals in scattering mouse brain tissues and 3D tracking of GFP-labeled neurons in freely moving *** believe that the combined ultrafast speed and large 3D SBP offered by EventLFM may open up new possibilities across many biomedical applications.

关键词： field linking overcome

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Enhanced Sound Recognition and Classification Through Spectrogram Analysis, MEMS Sensors, and PyTorch: A Comprehensive Approach 19th

Enhanced Sound Recognition and Classification Through Spectr...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Spournias, Alexandros Nanos, Nikolaos Faliagka, Evanthia Antonopoulos, Christos Voros, Nikolaos Keramidas, Giorgos Electrical and Computer Engineering Department University of the Peloponnese Patra Greece School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9783031545207

The importance of sound recognition and classification systems in various fields has led researchers to seek innovative methods to address these challenges. In this paper, the authors propose a concise yet effective approach for sound recognition and classification by combining spectrogram analysis, Micro-Electro-Mechanical Systems (MEMS) sensors, and the Pytorch deep learning framework. This method utilizes the rich information in audio signals to develop a robust and accurate sound recognition and classification system. The authors outline a three-stage process: data acquisition, feature extraction, and classification. MEMS sensors are employed for data acquisition, offering advantages such as reduced noise, low power consumption, and enhanced sensitivity compared to traditional microphones. The acquired audio signals are then preprocessed and converted into spectrograms, visually representing the audio data’s frequency, amplitude, and temporal attributes. During feature extraction, the spectrograms are analyzed to extract significant features conducive to sound recognition and classification. The classification task is performed using a custom deep learning model in Pytorch, leveraging modern neural networks’ pattern recognition capabilities. The model is trained and validated on a diverse dataset of audio samples, ensuring its proficiency in recognizing and classifying various sound types. The experimental results demonstrate the effectiveness of the proposed method, surpassing existing techniques in sound recognition and classification performance. By integrating spectrogram analysis, MEMS sensors, and Pytorch, the authors present a compact yet powerful sound recognition system with potential applications in numerous domains, such as predictive maintenance, environmental monitoring, and personalized voice-controlled devices. © ICST Institute for computer Sciences, Social informatics and Telecommunications engineering 2024.

关键词： Data acquisition

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