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IEEE Transactions on Industrial Cyber-Physical systems 2024年 2卷 615-625页

作者： Lien, Shao-Yu Tseng, Chih-Cheng Hung, Wei-Cheng Tsai, Cheng-You Liu, Ting-Yu Deng, Der-Jiunn Lin, Yuan-Chun Lin, Shih-Cheng Chang, Chia-Chan Chang, Sheng-Fuh Institute of Intelligent Systems National Yang Ming Chiao Tung University Tainan711010 Taiwan Department of Electrical Engineering National Ilan University Ilan260007 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi621301 Taiwan Department of Computer Science and Information Engineering National Changhua University of Education Changhua50074 Taiwan Department of Electrical Engineering National Chung Cheng University Chiayi621301 Taiwan

The reconfigurable intelligent surface (RIS) steering reflective beam directions toward a target mobile user equipment (UE) has been a promising technology for coverage enhancement and physical-layer (PHY) security to avoid signal eavesdroppers and jammers. However, tracking and predicting the moving trajectory of UE demand significant computing burdens unaffordable to the RIS. Performing beam steering computations on the radio access network (RAN) intelligent controller in the open RAN (O-RAN) architecture therefore emerges as a revolutionary paradigm. In this paper, we propose a PHY-secured beam steering design based on a pre-processing-based Kalman filter algorithm. Through a pre-processing phase to derive the radiation feature, the proposed design accurately tracks the position of the mobile UE and quickly recovers the position search while position tracking fails. Through practical field-try experiments, we justify the capability of O-RAN RIC-empowered RIS for PHY security. © 2013 IEEE.

关键词： Radio access networks

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IoT Sensor Deployment in the Wildland Urban Interface: Leveraging Fire Risk Analysis 10

IoT Sensor Deployment in the Wildland Urban Interface: Lever...

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10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Purcell, Richard Naik, Kshirasagar Zaman, Marzia Lung, Chung-Horng Sampalli, Srinivas Mutakabbir, Abdul Dhindsa, Manavjit Singh Das, Ursula Dalhousie University Faculty of Computer Science HalifaxNS Canada University of Waterloo Electrical and Computer Engineering WaterlooON Canada Research and Development Cistel Technology OttawaON Canada Carleton University Systems and Computer Engineering OttawaON Canada

ISBN: (纸本)9798350373011

This paper investigates algorithms for distributing Internet of Things sensors within the Wildland-Urban Interface to enhance early wildland fire detection. Using geospatial data analysis and a validated wildland fire growth model, we generated burn maps to guide sensor placement strategies across a defined region. We evaluated even grid distribution, random distributions, and genetic algorithm-based methods, testing each against 50,000 burn maps with sensor counts ranging from 50 to 800. Results indicate that while even grid distribution achieved the highest detection rates, its practicality in real-world applications is limited. Genetic algorithms showed promise but require further exploration to simulate field deployment accurately. Notably, weighting sensor placement based on wildland fire growth risk did not significantly impact detection effectiveness, highlighting the need for further research into the representativeness of burn maps. © 2024 IEEE.

关键词： Genetic algorithms

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Application of TQWT based filter-bank for sleep apnea screening using ECG signals

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 893-904页

作者： Nishad, Anurag Pachori, Ram Bilas Acharya, U. Rajendra Discipline of Electrical Engineering Indian Institute of Technology Indore Indore453552 India Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore599489 Singapore Department of Biomedical Engineering School of Science and Technology SIM University Singapore599491 Singapore Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia

The sleep apnea is a disease in which there is the absence of airflow during respiration for at least 10 s. It may occur several times during the night sleep. This disease can lead to many types of cardiovascular diseases. To detect this disease, signals obtained from many channels of polysomnography are to be observed visually by physicians for the long duration. This procedure is expensive, time-consuming, and subjective. Hence, it is required to build an automated system to detect the sleep apnea with few channels. This paper uses single-lead electrocardiogram (ECG) signal to detect apneic and non-apneic events. The proposed method uses tunable-Q wavelet transform (TQWT) based filter-bank instead of TQWT to decompose the segment of ECG signal into several constant bandwidth sub-band signals. Then centered correntropies are computed from the various sub-band signals. The obtained features are then fed to the various classifiers to select the optimum performing classifier. In this work, we have obtained the highest classification accuracy, specificity, and sensitivity of 92.78%, 93.91%, and 90.95% respectively using random forest classifier. Hence, our developed prototype is ready for validation with the huge database and clinical usage. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Filter banks

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Removing/Minimising Gaussian noise from real time CCTV footages using GAN 1

Removing/Minimising Gaussian noise from real time CCTV foota...

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1st International Conference on Innovations in Communications, Electrical and computer engineering, ICICEC 2024

作者： Prashanth, B.S. Puneeth, B.H. Manoj Kumar, M.V. Sreenivasa, B.R. Suraj, Adyapadi Hegde, Akshay Prashant Pranava Aithal, K.S. Nitte Meenakshi Institute of Technology Department of Information Science & Engineering Karnataka Bangalore560064 India Bapuji Institute of Engineering and Technology Department of Computer Science and Business Systems Karnataka Davanagere577004 India Bapuji Institute of Engineering and Technology Department of Computer Science and Design Karnataka Davanagere577004 India

ISBN: (纸本)9798350376517

This paper describes a system that uses generative adversarial networks (GANs) to eliminate gaussian noise from CCTV of footages. Unwanted noise like gaussian noise frequently degrades the quality of images and makes them more difficult to interpret. In our approach, a discriminator network is used to direct the training of a generator network, whose job it is to produce denoised images from noisy inputs. This framework includes operations like picture enhancement, noise reduction, and evaluation with metrics like the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The resultant denoised images show enhanced visual quality and have potential uses in image analysis and computer vision. © 2024 IEEE.

关键词： Generative adversarial networks

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Machine learning-based multi-target regression to effectively predict turning movements at signalized intersections

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International Journal of Transportation science and Technology 2023年第1期12卷 245-257页

作者： Khaled Shaaban Ali Hamdi Mohammad Ghanim Khaled Bashir Shaban Department of Engineering Utah Valley UniversityOremUT 84058United States Faculty of Engineering and IT RMIT UniversityMelbourneVIC 3000Australia Ministry of Transport DohaQatar Computer Science and Engineering Department Qatar UniversityDohaQatar

Effective prediction of turning movement counts at intersections through efficient and accurate methods is essential and needed for various applications. Commonly predictive methods require extensive data collection, calibration, and modeling efforts to estimate turning movements. In this study, three models were proposed to estimate turning movements at signalized intersections using approach volumes. Two sets of data from the United States and Canada were obtained to develop and test the proposed models. Machine learning-based regression models, including random forest regressor (RFR) and multioutput regressor (MOR) in addition to an artificial neural network (ANN) model, were developed and trained to analyze the relationship between approach volumes and corresponding turning movements. Multiple evaluation measurements were utilized to compare the models. All models produced satisfactory results. The RFR regression model outperformed the MOR model. However, the ANN model had the best performance when compared to the other models. The proposed models provide traffic engineers and planners with reliable and fast methods to estimate turning movements.

关键词： Traffic analysis Traffic volume Traffic count Prediction Artificial neural network

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Cross-Modality Gait Recognition: Bridging LiDAR and Camera Modalities for Human Identification 18

Cross-Modality Gait Recognition: Bridging LiDAR and Camera M...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Wang, Rui Shen, Chuanfu Marin-Jimenez, Manuel J. Huang, George Q. Yu, Shiqi Southern University of Science and Technology Department of Computer Science and Engineering China The University of Hong Kong Department of Industrial and Manufacturing Systems Engineering Hong Kong University of Cordoba Department of Computing and Numerical Analysis Spain The Hong Kong Polytechnic University Department of Industrial and Systems Engineering Hong Kong

ISBN: (纸本)9798350364132

Current gait recognition research mainly focuses on identifying pedestrians captured by the same type of sensor, neglecting the fact that individuals may be captured by different sensors in order to adapt to various environments. A more practical approach should involve cross-modality matching across different sensors. Hence, this paper focuses on investigating the problem of cross-modality gait recognition, with the objective of accurately identifying pedestrians across diverse vision sensors. We present CrossGait inspired by the feature alignment strategy, capable of cross retrieving diverse data modalities. Specifically, we investigate the cross-modality recognition task by initially extracting features within each modality and subsequently aligning these features across modalities. To further enhance the cross-modality performance, we propose a Prototypical Modality-shared Attention Module that learns modality-shared features from two modality-specific features. Additionally, we design a Cross-modality Feature Adapter that transforms the learned modality-specific features into a unified feature space. Extensive experiments conducted on the SUSTech1K dataset demonstrate the effectiveness of CrossGait: (1) it exhibits promising cross-modality ability in retrieving pedestrians across various modalities from different sensors in diverse scenes, and (2) CrossGait not only learns modality-shared features for cross-modality gait recognition but also maintains modality-specific features for single-modality recognition. © 2024 IEEE.

关键词： Unattended sensors

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Stochastic Planning of Solar PV/Wind Turbine/Micro- Hydro/Biomass Microgrids Integrating Fuel Cell Electric Vehicles 11

Stochastic Planning of Solar PV/Wind Turbine/Micro- Hydro/Bi...

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11th International Conference on Smart Grid and Clean Energy Technologies, ICSGCE 2023

作者： Mohseni, Soheil Brent, Alan C School of Engineering and Computer Science Victoria University of Wellington Sustainable Energy Systems Wellington Faculty of Engineering Wellington6140 New Zealand

ISBN: (纸本)9798350305364

This paper presents a novel approach for stochastic planning of multi-dimensional microgrids (MGs) integrating solar photovoltaic panels, wind turbines, a micro-hydro power plant, a biomass power plant, battery storage, a super-capacitor bank, hydrogen storage, and fuel cell electric vehicles (FCEVs). More specifically, the paper introduces a stochastic planning framework that incorporates uncertainties in renewable energy generation, energy demand, and wholesale electricity price dynamics. An advanced metaheuristic optimization algorithm is applied to optimize the MG planning, considering the stochastic nature of renewable energy sources and dynamic component interactions. The proposed approach aims to improve the reliability and robustness of MGs under uncertain conditions. The performance of the proposed approach is evaluated through simulation studies and compared with standard deterministic methods to demonstrate its effectiveness in addressing uncertainties whilst optimizing multi-dimensional MGs. Importantly, the results indicate percentage changes of -22%, +4%, and +23% in the whole-life system cost in the best-case, most likely case, and worst-case stochastic scenarios compared to the deterministic methods. © 2023 IEEE.

关键词： Hydroelectric power plants

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A Review of Machine Learning Algorithms in Diabetes Management 4

A Review of Machine Learning Algorithms in Diabetes Manageme...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gayaki, Unnati Daronde, Subodh Tale, Abhay Barhate, Aditya Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha India Faculty of Engineering and Technology Department of Biomedical Engineering Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha India

ISBN: (纸本)9798331523923

Diabetes is a prevalent and chronic disease affecting millions worldwide, posing significant challenges in its management and treatment. This review article aims to explore the current and potential future roles of machine learning (ML) in diabetes management, focusing on progress, applications, and challenges. It discusses various ML techniques, including support vector machines (SVM), logistic regression, decision trees, ensemble methods, and advanced deep learning approaches such as long short-term memory (LSTM) networks and convolutional neural networks (CNN). These methods have demonstrated benefits in analytics, patient monitoring, decision support systems, personalised treatment, and patient engagement. However, significant challenges persist, such as computational complexity, data quality, limited interoperability of systems, and ethical concerns regarding AI use in healthcare. The article addresses these challenges by fostering collaboration between AI implementers and health care professionals. This collaboration is essential to develop long-term, legally compliant, and solution-oriented frameworks that enhance patient care and diabetes management and pave the way for more efficient and ethical AI applications in healthcare. © 2025 IEEE.

关键词： Support vector machines

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Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis

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computers, Materials & Continua 2023年第1期74卷 897-914页

作者： Sandeep Kumar Muhammad Badruddin Khan Mozaherul Hoque Abul Hasanat Abdul Khader Jilani Saudagar Abdullah AlTameem Mohammed AlKhathami Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Social media,like Twitter,is a data repository,and people exchange views on global issues like the COVID-19 *** media has been shown to influence the low acceptance of *** work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individual’s sensitivities and feelings that lead to *** work proposes a method to analyze the opinion of an individual’s tweet about the COVID-19 *** paper introduces a sigmoidal particle swarm optimization(SPSO)***,the performance of SPSO is measured on a set of 12 benchmark problems,and later it is deployed for selecting optimal text features and categorizing *** proposed method uses TextBlob and VADER for sentiment analysis,CountVectorizer,and term frequency-inverse document frequency(TF-IDF)vectorizer for feature extraction,followed by SPSO-based feature *** Covid-19 vaccination tweets dataset was created and used for training,validating,and *** proposed approach outperformed considered algorithms in terms of ***,we augmented the newly created dataset to make it balanced to increase performance.A classical support vector machine(SVM)gives better accuracy for the augmented dataset without a feature selection *** shows that augmentation improves the overall accuracy of tweet *** the augmentation performance of PSO and SPSO is improved by almost 7%and 5%,respectively,it is observed that simple SVMwith 10-fold cross-validation significantly improved compared to the primary dataset.

关键词： Twitter data analysis sentiment analysis social media analytics swarm intelligence COVID-19 vaccine

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STDP-Driven Automated Retinal Circuit with 7nm FinFET for Motion and Looming Detection: A Hybrid Model with Image Analysis

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IEEE Access 2025年 13卷 95594-95608页

作者： Rabeya, Rubina Akter Islam, Md Turiqul Uddin, Shah Muhammad Imtiyaj Al-Shidaifat, Alaaddin Song, Hanjung Choi, Heung-Kook Kim, Hee-Cheol Department of Digital Anti-Aging Healthcare Inje university Gimhae50834 Korea Republic of Department of Nanoscience and Engineering Centre for Nano Manufacturing Inje university Gimhae50834 Korea Republic of Middle East University Faculty of Information Technology Computer Science Department Amman11831 Jordan Department of Computer Engineering Inje university Gimhae50834 Korea Republic of

This paper presents a spike-timing-dependent plasticity (STDP) driven silicon retina designed using 7nm FinFET technology for detecting object motion and looming threats inspired by the biological visual system. The proposed neuromorphic architecture integrates an automated motion spiking circuit with STDP and advanced image analysis, mimicking natural retinal processing to identify potential hazards. Two specialized circuits are introduced to generate bipolar spiking signals for object motion and looming detection. These spikes are processed by an STDP-based spiking neural network, qualifying real-time distance estimation of approaching objects. Model Results demonstrate accurate collision time prognosis based on object dynamics. YOLOv5 and MiDaS enhance object recognition and depth perception, improving situational awareness. The proposed model uses a hybrid strategy combining object detection and depth estimation to identify objects with different spatial distances and confidence levels accurately. Experimental results show a motion detection accuracy of 93.2% and an average latency of around 15 ms, 66 frames per second, which proves this is suitable for time-sensitive applications. Because the 7nm FinFET technology improves system efficiency and responsiveness, it can be used in real-time applications where high-precision motion detection is necessary in dynamic conditions. However, potential challenges include scalability for large-scale deployment and hardware constraints associated with real-time processing on FinFET-based circuits. The proposed system can be used for robotic vision systems, autonomous vehicles, and other contexts where real-time visual processing and threat detection are needed. © 2013 IEEE.

关键词： Motion estimation

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