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International Conference on Advanced Technologies for Communications, ATC

作者： Senura Hansaja Wanasekara Han Huy Dung Ngoc Hung Nguyen Van-Dinh Nguyen College of Engineering and Computer Science & Center for Environmental Intelligence VinUniversity Hanoi Vietnam School of Electrical and Electronic Engineering Hanoi University of Science and Technology

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

The integration of electroencephalography (EEG) and photoplethysmography (PPG) in consumer-grade wearable devices is revolutionizing healthcare by enabling continuous health monitoring. However, these devices face significant challenges in terms of power consumption, computational complexity, and data storage, necessitating efficient data compression techniques. This paper proposes a modified Golomb-Rice coding method tailored for lossy compression of multi-channel EEG and PPG signals. By leveraging the statistical properties of filtered data and employing an optimized parameter estimation strategy, the proposed method achieves high compression ratios while minimizing computational overhead and preserving critical signal information. The proposed algorithm shifts data to handle negative values, scales and converts it into an appropriate format for encoding, and uses a novel parameter estimation technique to optimize the Golomb-Rice parameter. Extensive simulations on EEG and PPG datasets demonstrate that this parameter estimation method yields significantly better performance, improving both efficiency and processing speed. Results underscore the importance of preprocessing in data compression and provide a robust framework for future advancements in biomedical signal processing.

关键词： Parameter estimation Signal processing algorithms Data compression Filtering algorithms Signal processing Brain modeling Electroencephalography Encoding Wearable devices Monitoring

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Dynamics of synthetic yeast chromosome evolution shaped by hierarchical chromatin organization

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National science Review 2023年第5期10卷 240-250页

作者： Sijie Zhou Yi Wu Yu Zhao Zhen Zhang Limin Jiang Lin Liu Yan Zhang Jijun Tang Ying-Jin Yuan Frontiers Science Center for Synthetic Biology (Ministry of Education) Tianjin University Key Laboratory of Systems Bioengineering(Ministry of Education) School of Chemical Engineering and TechnologyTianjin University Institute for Systems Genetics NYU Langone Health School of Computer Science and Technology College of Intelligence and ComputingTianjin University Epigenetic Group Frasergen Bioinformatics Co.Ltd. Department of Computer Science University of South Carolina

Synthetic genome evolution provides a dynamic approach for systematically and straightforwardly exploring evolutionary *** Chromosome Rearrangement and Modification by LoxP-mediated Evolution(SCRaMbLE) is an evolutionary system intrinsic to the synthetic yeast genome that can rapidly drive structural ***,we detect over 260 000 rearrangement events after the SCRaMbLEing of a yeast strain harboring 5.5 synthetic yeast chromosomes(synⅡ,synⅢ,synⅤ,circular synⅥ,synⅨR and synⅩ).Remarkably,we find that the rearrangement events exhibit a specific landscape of *** further reveal that the landscape is shaped by the combined effects of chromatin accessibili and spatial contact *** rearrangements tend to occur in 3D spatially proximal and chromatin-accessible *** enormous numbers of rearrangements mediated by SCRaMbLE provide a driving force to potentiate directed genome evolution,and the investigation of the rearrangement landscape offers mechanistic insights into the dynamics of genome evolution.

关键词： synthetic biology genome rearrangement hierarchical chromatin organization synthetic yeast genome SCRaMbLE Saccharomyces cerevisiae

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1D CNN Model: BERT-Based Approach for Depression and Suicide Identification 2nd

1D CNN Model: BERT-Based Approach for Depression and Suicide...

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Emergent Converging Technologies and Biomedical Systems Select Proceedings of the 2nd International Conference, ETBS 2022

作者： Devika, S.P. Pooja, M.R. Ravi, Vinayakumar Department of Computer Science and Engineering Vidyavardhaka College of Engineering Karnataka Mysuru India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia

ISBN: (纸本)9789819922703

Depression was worldwide psychological well-being issue, with severe belongings leading toward self-harm and suicide. An automated mechanism for detecting depression can greatly aid in clinical diagnosis and early management for depression. We propose novel text detection for depression diagnosis approach based on linguistic information from social media like Reddit posts. The suggested technique was composed of components: an attention layer to handle linguistic content, a One-Dimensional Convolutional Neural Network (1D CNN) to handle text, and a fully linked network that integrates the model output to assess depressed state. The study begins with a thorough examination of the process of creating a big labeled dataset derived from a spare dataset using an semi-supervised learning strategy to defeat data abundance. Dataset was reviewed rigorously in order to create a text classifier with many classes and comprehend the elements and attitudes associated with the various target groups. The study creates text analysis models and deep learning neural networks in order to add an evaluation tool in online suicide risk monitoring website trends. The findings can be put into a mental health report professionals to better grasp the essential trend. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Risk assessment

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Data collection of wireless sensor network based on trajectory optimization of laser-charged UAV

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High-Confidence Computing 2024年第2期4卷 116-128页

作者： Chuanwen Luo Jian Zhang Jin Qian Yi Hong Zhibo Chen Yunan Hou Xiujuan Zhang Yuqing Zhu School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing 100083China College of Information Engineering Taizhou UniversityTaizhou 225300China School of Computer Science Qufu Normal UniversityRizhao 276826China Research Center of Applied Mathematics and Machine Intelligence Zhejiang LabHangzhou 311121China Department of Computer Science California State UniversityCA 90032USA

Unmanned Aerial Vehicle(UAV)can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks(WSNs),which is crucial for providing seamless services and improving the performance in the next generation wireless ***,since the UAV are powered by batteries with limited energy capacity,the UAV cannot complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring *** overcome this problem,we study the Real-time Data Collection with Lasercharging UAV(RDCL)problem,where the UAV is utilized to collect data from a specified WSN and is recharged using Laser Beam Directors(LBDs).This problem aims to collect all sensory data from the WSN and transport it to the base station by optimizing the flight trajectory of UAV such that realtime data performance is ensured It has been proven that the RDCL problem is *** address this,we initially focus on studying two sub-problems,the Trajectory Optimization of UAV for Data Collection(TODC)problem and the Charging Trajectory Optimization of UAV(CTO)problem,whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas,*** we propose an approximation algorithm to solve each of them with the constant ***,we present an approximation algorithm that utilizes the solutions obtained from TODC and CTO problems to address the RDCL ***,the proposed algorithm is verified by extensive simulations.

关键词： Wireless sensor networks Laser-charged UAV Trajectory optimization Data collection

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Learning discriminative foreground-and-background features for few-shot segmentation

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Multimedia Tools and Applications 2024年第18期83卷 55999-56019页

作者： Jiang, Cong Zhou, Yange Liu, Zhaoshuo Feng, Chaolu Li, Wei Yang, Jinzhu School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China College of Science Northeastern University Liaoning Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Liaoning Shenyang110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Liaoning Shenyang110819 China

Few-shot Semantic Segmentation (FSS) endeavors to segment novel categories in a query image by referring to a support set comprising only a few annotated examples. Presently, many existing FSS methodologies primarily embrace the prototype learning paradigm and concentrate on optimizing the matching mechanism. However, these approaches tend to overlook the discrimination between the features of foreground background. Consequently, the segmentation results are often imprecise when it comes to capturing intricate structures, such as boundaries and small objects. In this study, we introduce the Discriminative Foreground-and-Background feature learning Network (DFBNet) to enhance the distinguishability of bilateral features. DFBNet comprises three major modules: a multi-level self-matching module (MSM), a feature separation module (FSM), and a semantic alignment module (SAM). The MSM generates prior masks separately for the foreground and background, employing a self-matching strategy across different feature levels. These prior masks are subsequently used as scaling factors within the FSM, where the features of the query’s foreground and background are independently scaled up and then concatenated along the channel dimension. Furthermore, we incorporate a two-layer Transformer encoder-based semantic alignment module (SAM) in DFBNet to refine the features, thereby creating a greater distinction between the foreground and background features. The performance of DFBNet is evaluated on the PASCAL-5i and COCO-20i benchmarks, demonstrating its superiority over existing solutions and establishing new state-of-the-art results in the field of few-shot semantic segmentation. The codes will be released if this paper is accepted. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2023.

关键词： Semantic Segmentation

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Application of Deep Reinforcement Learning in Training Autonomous Vehicles: A bibliometric analysis 16

Application of Deep Reinforcement Learning in Training Auton...

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16th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Elnahas, Fatma Elshenhabi, Omar Muley, Deepti Ghanim, Mohammad Computer Science and Engineering Department College of Engineering Qatar University Qatar Qatar Transportation and Traffic Safety Center Department of Civil and Environmental Engineering Qatar University Qatar Ministry of Transport Qatar

Deep Reinforcement Learning (DRL), a subset of machine learning, combines reinforcement learning with deep learning by using deep neural networks as function. This study assesses the research undertaken for advancement of autonomous vehicles (AVs) using DRL techniques. A bibliometric analysis was conducted using journal papers from Scopus database. A systematic pre-defined screening methodology was applied for selecting the articles. Initial search provided 5964 articles for AVs and DRL. After screening only 401 articles were retained for further investigation indicating only 6.7% relevant articles. The work in this area showed transformative trajectory from 2017 to 2024, characterized by distinct phases of growth. The keywords analysis showed that "reinforcement learning"and "autonomous vehicles"are central with stronger connectivity. The classification of articles in four main categories indicated that most of the articles were related to safety (44%) and traffic efficiency (30%) improvement indicating gap in the literature in other areas. The outcomes of this analysis can lead future research directions. © 2025 The Author(s).

关键词： Deep reinforcement learning

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Electromyography (EMG) Signal Acquisition for Exoskeleton Control: Enhancing Precision in Human-Machine Interaction

Electromyography (EMG) Signal Acquisition for Exoskeleton Co...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Rajendra Thilahar, C. Manoharan, Madhanagopal Sudalai, Perumal Kumaravelu, R. Sadayan, Rajendra Boopathy Kaliaraj, Gobi Saravanan Saveetha Engineering college Department of Computer Science and Engineering Chennai India Makerspace Center of Excellence for Additive Manufacturing Sathyabama Institute of Science and Technology Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Mechanical Engineering Chennai India Saveetha Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Chennai India College of Engineering Guindy Anna University Department of Mechanical Engineering Chennai India Center of Excellence for Additive Manufacturing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331528126

Electromyography (EMG) signals, which measure the electrical activity of muscles during contraction, deal with the hope of controlling the actions of the exoskeleton. This paper exposes the acquisition and application of EMG signals for precise and responsive control of exoskeletons. Moreover, the natural neuromuscular signals generated by the user aim to create a seamless interface between the human body and the assistive device, enhancing functionality and user experience. The EMG signals are obtained by placing the electrodes in the respective area of our body, filtering the data, signal amplification, and using noise reduction techniques. Furthermore, EMG data is processed for noise removal, where artefacts ensure the clarity and reliability of the acquired signals. Those signals were used to develop a control algorithm to translate muscle activity into corresponding exoskeleton movements. The challenges and limitations associated with EMG-based controls are signal variability, cross-talk, and calibration. Finally, this paper presents results, which demonstrate that EMG-based control offers a high degree of accuracy and responsiveness, especially requiring to control the fine motor rotation and varying force levels. This approach not only improves the usability of exoskeletons for individuals with mobility impairments but also has certain potential applications like EMG-controlled exoskeletons in various domains, including rehabilitation, assistive technology, various military settings and human-machine interfaces. EMG-driven systems to revolutionize the field of wearable robotics, paving the way for more intuitive and effective human-machine interfaces. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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MinimapPool: An improved flexible and efficient parallel algorithm based on minimap2

MinimapPool: An improved flexible and efficient parallel alg...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wang, Zhenang Cui, Yingbo Shang, Jiandong Ji, Boya Li, Jianan Peng, Shaoliang Zhengzhou University School of Computer Science and Artificial Intelligence Zhengzhou China National University of Defense Technology School of Computer Changsha China Zhengzhou University National Supercomputing Center in Zhengzhou Zhengzhou China Hunan University College of Computer Science and Electronic Engineering Changsha China

ISBN: (纸本)9798350386226

Third-generation sequencing techniques have achieved major breakthroughs in sequencing long reads and speed. Continuous improvements in sequencing techniques have reduced sequencing costs, and the number of sequencing data files has shown explosive growth. In terms of sequence alignment, to deal with these high numbers and large-scale data, the conventional serial alignment method can no longer effectively meet the research requirements, therefore, it is of great importance to develop a faster, low-load, and compatible parallel alignment program. In this paper, we propose a parallel task pool algorithm based on the minimap2, a sequence alignment tool, and develop the task pool parallel alignment program based on this algorithm. We compare the program's work with the average segmentation parallel alignment program. The results show that the task pool parallel alignment program has significant improvement in speedup, memory load, segmentation flexibility, and computational efficiency, it also has good scalability and computational stability. MinimapPool is available at https://***/krkrcc/MinimapPool. © 2024 IEEE.

关键词： Parallel processing systems

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An upgraded variant of backtracking search algorithm and its application to COVID-19 CT images segmentation

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Multimedia Tools and Applications 2025年 1-57页

作者： Debnath, Sanjib Nama, Sukanta Chakraborty, Sanjoy Saha, Apu Kumar Mirjalili, Seyedali Department of Computer Science & Engineering National Institute of Technology Agartala Tripura Agartala799046 India Department of Science & Humanities Gomati District Polytechnic Fulkumari Tripura Udaipur799013 India Department of Information Technology Bir Bikram Memorial College Tripura Agartala India Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India Center for Artificial Intelligence Research and Optimization Torrens University Australia Brisbane Australia Faculty of Electrical Engineering and Computer Science VŠB-TU Ostrava Ostrava Czech Republic University Research and Innovation Center Obuda University Budapest Hungary Department of Computer Science & Engineering The ICFAI University Tripura Tripura 799210 India

To improve the performance of the original backtracking search algorithm (BSA), this work combines BSA with a centralized population initialization and an elitism-based local escape operator and proposes a new, improved variant of BSA called CiBSA. BSA's performance is improved by centralized population initialization regarding search capability, convergence speed, and capacity to jump out of the local optimum. The suggested algorithm's elitism-based local escape operator method prevents BSA searching slowness and enhances convergence rates and local search efficiency. The performance of the proposed algorithm has been tested on a set of 54 test suites. An extensive comparison with other recent meta-heuristics algorithms (MA) demonstrates that both methodologies substantially assist BSA in boosting the quality of its solutions and speeding up the convergence rate. The proposed CiBSA is then applied to a real-world problem: image segmentation using Kapur's entropy function. The proposed CiBSA is successfully applied to the segmentation of COVID-19 CT images using Kapur's entropy for grey-scale images. Considering the performance matrices Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Intersection over Union (IoU), the suggested CiBSA outperformed conventional MA algorithms. In COVID-19 CT image segmentation trials, CiBSA has superior segmentation capability and adaptability at varied threshold levels than other approaches. As a result, CiBSA has a lot of potential for enhancing COVID-19 diagnosis accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Signal to noise ratio

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A Barrier-Based Machine Learning Approach for Intrusion Detection in Wireless Sensor Networks

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computers, Materials & Continua 2025年第3期82卷 4181-4218页

作者： Haydar Abdulameer Marhoon Rafid Sagban Atheer Y.Oudah Saadaldeen Rashid Ahmed Information and Communication Technology Research Group Scientific Research CenterAl-Ayen UniversityThi-Qar64011Iraq College of Computer Sciences and Information Technology University of KerbalaKarbala56001Iraq Enginerring Technical College Al-Ayen UniversityThi-Qar64011Iraq Information Technology College University of BabylonHilla51002Iraq Department of Computer Science College of Education for Pure ScienceUniversity ofThi-QarNasiriyah64001Iraq Artificial Intelligence Engineering Department College of EngineeringAl-Ayen UniversityThi-Qar64001Iraq Department of Computer Science Bayan UniversityErbilKurdistan44001Iraq

In order to address the critical security challenges inherent to Wireless Sensor Networks(WSNs),this paper presents a groundbreaking barrier-based machine learning *** applications like military operations,healthcare monitoring,and environmental surveillance increasingly deploy WSNs,recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational *** proposed method innovatively partitions the network into logical segments or virtual barriers,allowing for targeted monitoring and data collection that aligns with specific traffic *** approach not only improves the *** are more types of data in the training set,and this method uses more advanced machine learning models,like Convolutional Neural Networks(CNNs)and Long Short-Term Memory(LSTM)networks together,to see coIn our work,we used five different types of machine learning *** are the forward artificial neural network(ANN),the CNN-LSTM hybrid models,the LR meta-model for linear regression,the Extreme Gradient Boosting(XGB)regression,and the ensemble *** implemented Random Forest(RF),Gradient Boosting,and XGBoost as baseline *** train and evaluate the five models,we used four possible features:the size of the circular area,the sensing range,the communication range,and the number of sensors for both Gaussian and uniform sensor *** used Monte Carlo simulations to extract these *** on the comparison,the CNN-LSTM model with Gaussian distribution performs best,with an R-squared value of 99%and Root mean square error(RMSE)of 6.36%,outperforming all the other models.

关键词： Intrusion detection system(IDS) hybrid models of CNN-LSTM WSN extreme gradient boosting(XGBoost)regressor ensemble model

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