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IEEE BITS the Information Theory Magazine 2023年第3期3卷 64-77页

作者： Dupraz, Elsa Leduc-Primeau, Francois Cai, Kui Dolecek, Lara Lab-STICC UMR CNRS IMT Atlantique Brest29238 France Polytechnique Montreal Department of Electrical Engineering MontrealQCH3T 1J4 Canada Singapore University of Technology and Design Science Mathematics and Technology Cluster Singapore487372 Singapore UCLA Department of Electrical and Computer Engineering Los AngelesCA90095 United States

This article explores the emerging field of in-memory computing, which has the potential to significantly improve energy efficiency in many signal processing and machine learning applications. In-memory computing systems are impacted by various sources of noise and perturbations that can affect their computation accuracy. Therefore, this article aims to identify the key challenges to be addressed from an information-theoretic point of view in this area. It first identifies relevant computation structures and noise models from the literature of hardware implementation, and then reviews existing works in information theory and error-correction for in-memory computing. Finally, it identifies key open avenues for establishing information-theoretic foundations of in-memory computing systems, and for providing insightful design rules leading to highly energy-efficient computing systems. © 2021 IEEE.

关键词： Error correction

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Testing and Development of a Multi-Object Tracker Based on Deep Learning Techniques 1st

Testing and Development of a Multi-Object Tracker Based on D...

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1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Rajput, Shaurya Verma, Sourabh Verma, Om Prakash Gupta, Himanshu Sharma, Tarun Ateya, Abdelhamied A. Centre for Artificial Intelligence Dr. B.R. Ambedkar National Institute of Technology Jalandhar Punjab Jalandhar India Department of Instrumentation and Control Engineering Dr. B.R. Ambedkar National Institute of Technology Jalandhar Punjab Jalandhar India Department of Computer Science-Internet of Things ABES Institute of Technology Ghaziabad201009 India University Meerut India Department of Electronics and Communications Engineering Zagazig University Sharqia Zagazig44519 Egypt EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia

ISBN: (纸本)9789819981342

In today’s scenario, computer vision is one of the fundamental research areas of artificial intelligence including object detection and object tracking which are the upcoming trends. In the present work, the TransTrack model has been reimplemented which includes ResNet50 as a backbone network. Further, to improve the overall performance of the TransTrack model, some recently developed backbones such as ResNet152 and SwinT have been incorporated. Moreover, to test the optimum training of the existing model, it has been retrained for higher epochs (such as 160 epochs). However, the accuracy of the existing tracker has improved significantly as the number of epochs increases but, at the same time, it increases the computational complexity. On the contrary, when the existing tracker (yields 68.4% accuracy with ResNet50) is incorporated with ResNet152 and SwinT, the performance has significantly improved (71% for ResNet152 and 71.6% for SwinT) by 1.7% and 2.1%, respectively. The mentioned analysis is enough to prove that the incorporation of a more efficient backbone has the potential to improve the performance of trackers. In the future, the newly introduced backbone may open the window to improve the performance of multi-object tracking (MOT). © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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D-MRFTE: A Decentralized Relay-Based Approach for Multi-Robot Unknown Area Exploration

D-MRFTE: A Decentralized Relay-Based Approach for Multi-Robo...

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IEEE International Conference on Robotics and Biomimetics

作者： Ankit Soni Chirag Dasannacharya Avinash Gautam Virendra Singh Shekhawat Sudeept Mohan Department of Computer Science and Information Systems INSPIRE Lab Birla Institute of Technology and Science Pilani Rajasthan India

In this paper, a decentralized relay-based approach (D-MRFTE) for unknown area exploration using a team of autonomous mobile robots is proposed under communication constraints. Using the relay robots, the multi-robot system forms a high-latency decentralized network with distributed copies of exploration information for which eventual consistency and completeness are ensured through meetups. The meetups act as a safety net and set a bound on latency by ensuring data transfer at periodic intervals whenever the multi-robot network gets fragmented. The information exchange related to the robot’s state and the ongoing exploration is facilitated by the relay robots. The robots use timestamps to assimilate the latest available information by using version vectors. To achieve a consistent state of explorer robots, the relays schedule meetups with other relays they come in contact with, creating a tightly-knit group. Our approach, under two communication models, i.e., Disk-based and Line-of-Sight-based, exhibits superior performance compared with two state-of-the-art algorithms in terms of completion time and distance traveled by the robot team. The simulations are conducted in a Player/Stage simulator with different robot team sizes.

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Modeling and Simulation of Electric Vehicle using MATlab

Modeling and Simulation of Electric Vehicle using MATLAB

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2024 OPJU International technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Bahuguna, Neeraj Kumar Sati, Govind Gusain, Raj Vidyarthi, Anurag Prakash, Rishi Gowri, R. Gb Pant University of Agriculture and Technology Department of Electrical Engineering Pantnagar India Vaaman Engineers India Ltd. Hindustan Zinc Pmp Department of Instrumentation Engineering Pantnagar India Propagation Research Lab Department of Electronics & Communication Engineering Dehradun India Graphic Era Hill University Department of Computer Science Engineering Dehradun India

ISBN: (纸本)9798350373783

With the progression of technology, the automobile industry has experienced a significant transformation due to the emergence of Electric Vehicles (EVs). EVs offer a sustainable means of transportation that doesn't harm the environment. The decline of global oil reserves and the detrimental effects of air pollution caused by conventional motor vehicles have motivated numerous researchers to explore alternative energy sources for vehicle propulsion. This study aims to investigate power flow calculations to ensure that the amount of electrical energy aligns with the requirements of the electric vehicle. Using MATlab/Simulink software, a model of a small electric vehicle is designed to achieve optimal power flow response within the EV energy system and this article outlines the process of modeling an Electric Vehicle using MATlab and Simulink, elucidating different components such as the Motor Model, Battery Model, Power Model, and Regenerative System. By employing Simulink, the designed Electric Vehicle is simulated, and important metrics such as Motor Torque, Vehicle Speed, and State of Charge of the Battery are calculated using a given Driving Cycle. The collected data is then analyzed to enhance the model's performance. © 2024 IEEE.

关键词： MATlab

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Optimized efficient attention-based network for facial expressions analysis in neurological health care

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computers in Biology and Medicine 2024年 179卷 108822-108822页

作者： Munsif, Muhammad Sajjad, Muhammad Ullah, Mohib Tarekegn, Adane Nega Cheikh, Faouzi Alaya Tsakanikas, Panagiotis Muhammad, Khan Sejong University Seoul143-747 Korea Republic of Digital Image Processing Lab Department of Computer Science Islamia College Peshawar25000 Pakistan Department of Computer Science Norwegian University for Science and Technology Gjøvik2815 Norway Department of Computer Science Norwegian University for Science and Technology Gjøvik2815 Norway Institute of Communication and Computer Systems National Technical University of Athens Athens15773 Greece Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Facial Expression Analysis (FEA) plays a vital role in diagnosing and treating early-stage neurological disorders (NDs) like Alzheimer's and Parkinson's. Manual FEA is hindered by expertise, time, and training requirements, while automatic methods confront difficulties with real patient data unavailability, high computations, and irrelevant feature extraction. To address these challenges, this paper proposes a novel approach: an efficient, lightweight convolutional block attention module (CBAM) based deep learning network (DLN) to aid doctors in diagnosing ND patients. The method comprises two stages: data collection of real ND patients, and pre-processing, involving face detection and an attention-enhanced DLN for feature extraction and refinement. Extensive experiments with validation on real patient data showcase compelling performance, achieving an accuracy of up to 73.2%. Despite its efficacy, the proposed model is lightweight, occupying only 3MB, making it suitable for deployment on resource-constrained mobile healthcare devices. Moreover, the method exhibits significant advancements over existing FEA approaches, holding tremendous promise in effectively diagnosing and treating ND patients. By accurately recognizing emotions and extracting relevant features, this approach empowers medical professionals in early ND detection and management, overcoming the challenges of manual analysis and heavy models. In conclusion, this research presents a significant leap in FEA, promising to enhance ND diagnosis and *** code and data used in this work are available at: https://***/munsif200/Neurological-Health-Care. © 2024 Elsevier Ltd

关键词： Feature extraction

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Performance Analysis and Optimization Techniques in Unity 3D 3

Performance Analysis and Optimization Techniques in Unity 3D

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Singh, Narinder Pal Sharma, Bhanu Sharma, Avinash Chitkara University Institute of Engineering and Technology Chitkara University Immersive and Interactive Technology Lab Punjab India Department of Computer Science and Engineering Mm Haryana Mullana-Ambala133207 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Unity3d is a powerful toolset that can be used to build 3D interactive applications and technology like games, simulations, augmented reality, immersive reality, etc. This game engine can execute the graphics, simulate physics, play audio, provide interactions, facilitate photorealistic rendering, and is excellent for cross-platform development. Although everything that users perceive is super-optimized, still an application suffers from poor performance, because of inefficient or poorly organized code, disorganized memory management, unnecessary use of overly complex physics simulations, etc. Poor performance of the application can break the user experience. Another side of an application properly optimized provides a smooth and responsive experience. To provide a good experience to the users, advanced knowledge of engines is required. Even if each application required a unique approach for optimization, Still there are key techniques we can adopt in most situations. We are aiming at filling this gap, by evaluating the best techniques while performing tests and analyses with Unity3d tools. We started by explaining game engine architecture. Then we performed tests while comparing different techniques and came up with observations and findings. As Unity3d, aggressively contribute to developing innovative applications related with 3D computer graphics to promote and support industry and Manufacturing. This work is contribution to the Unity3D community to develop good performance applications to provide a good user experience. © 2022 IEEE.

关键词： Augmented reality

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GBSVR: Granular Ball Support Vector Regression

arXiv

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arXiv 2025年

作者： Rastogi, Reshma Bisht, Ankush Kumar, Sanjay Chandra, Suresh MLSI Lab Department of Computer Science South Asian University New Delhi India Department of Computer Science Deshbandhu College University of Delhi New Delhi India Department of Mathematics Indian Institute of Technology Delhi New Delhi India

Support Vector Regression (SVR) and its variants are widely used to handle regression tasks, however, since their solution involves solving an expensive quadratic programming problem, it limits its application, especially when dealing with large datasets. Additionally, SVR uses an ϵ-insensitive loss function which is sensitive to outliers and therefore can adversely affect its performance. We propose Granular Ball Support Vector Regression (GBSVR) to tackle problem of regression by using granular ball concept. These balls are useful in simplifying complex data spaces for machine learning tasks, however, to the best of our knowledge, they have not been sufficiently explored for regression problems. Granular balls group the data points into balls based on their proximity and reduce the computational cost in SVR by replacing the large number of data points with far fewer granular balls. This work also suggests a discretization method for continuous-valued attributes to facilitate the construction of granular balls. The effectiveness of the proposed approach is evaluated on several benchmark datasets and it outperforms existing state-of-the-art approaches. © 2025, CC BY.

关键词： Wind forecasting

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EEG VMAC Toolbox: A User-Friendly Open-Source Toolbox for EEG Signals Visualization Manipulation Analysis and Classification

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Procedia computer science 2025年 258卷 1062-1080页

作者： Gopal Chandra Jana Abhishek Karmakar Anupam Agrawal Interactive Technologies & Multimedia Research (ITMR) Lab Department of Information Technology Indian Institute of Information Technology Allahabad Prayagraj – 211015 Uttar Pradesh India Indian Institute of Information Technology Kalyani Department of Computer Science and Engineering Kalyani – 741235 West Bengal India

Electroencephalography (EEG) is a well-known, non-invasive method for monitoring and recording electrical activities of the human brain. Problem: EEG signal visualization, manipulation, analysis, and classification are essential for clinical experts, doctors, and researchers to make certain decisions. So, there is a need for a toolbox that can provide such functionalities with a user-friendly graphical user interface (GUI); availability may be commercial or open-source. Aim: This paper describes the proposed and developed open-source EEG VMAC Toolbox, which provides an interface with features including a series of state-of-the-art methods for EEG signal analysis. Method: The main menu options of EEG VMAC Toolbox are - 1) File, 2) Signal Visualization, 3) Filtering, 4) Signal Decomposition, 5) Feature Reduction, 6) Feature Extraction, 7) label, 8) Classification Models, and 9) Help. Each menu of the toolbox contains several functionalities. In addition to these nine menus, a file conversion option is available at the bottom of the toolbox. EEG VMAC Toolbox integrates all major state-of-the-art functionalities for EEG signal visualization, manipulation, analysis, and classification, which would be a valuable addition to the current literature. Results and Findings: The EEG VMAC Toolbox has been developed using Python programming language and tested over the CHB-MIT EEG Scalp EEG dataset, a benchmark dataset for seizure detection. So, this toolbox has a provision to bring psychologists, neuroscientists, clinical experts, and EEG researchers on the same platform to pursue extensive investigation and research for better reach.

关键词： Electroencephalogram (EEG) EEG toolbox EEG signal visualization toolbox EEG signal analysis tool Open-source toolbox Python EEG toolbox

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An Image Mosaicing-Based Method for Bird Identification on Edge Computing Devices 1

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Proceedings of the 18th International Conference on Soft Computing Models in Industrial and Environmental Applications, SOCO 2023

作者： Teterja, Dmitrij Garcia-Rodriguez, Jose Azorin-Lopez, Jorge Sebastian-Gonzalez, Esther van der Walt, Rita Elise Booysen, M.J. Department of Computer Science and Technology University of Alicante Alicante Spain Department of Ecology University of Alicante Alicante Spain Department of Electrical and Electronic Engineering/MIH Media Lab Stellenbosch University Stellenbosch South Africa Department of Electrical and Electronic Engineering Stellenbosch University Stellenbosch South Africa

ISBN: (数字)9783031425363

ISBN: (纸本)9783031425356

Bird identification is an important task in wildlife monitoring and conservation. However, traditional methods for bird identification often require significant computational resources, making them impractical for use on edge computing devices. In this paper, we propose an image mosaicing-based method for bird identification on edge computing devices. Our method combines image mosaicing and deep learning techniques to accurately identify bird species from a series of individual images. It achieves excellent accuracy of 99.51% for ResNet50 and 97.37% for MobileNetV2, depending on the specific use case. These results demonstrate the effectiveness and potential of our approach for real-time bird recognition and identification on edge computing devices, making it suitable for a wide range of applications including monitoring and conservation of bird populations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep neural networks

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Generative Adversarial Networks for Dynamic Malware Behavior: A Comprehensive Review, Categorization, and Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Gebrehans, Ghebrebrhan Ilyas, Naveed Eledlebi, Khouloud Lunardi, Willian T. Andreoni, Martin Yeun, Chan Yeob Damiani, Ernesto Lab Khalifa University of Science and Technology Abu Dhabi United Arab Emirates University of California Riverside United States Khalifa University of Science and Technology Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Abu Dhabi United Arab Emirates Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Khalifa University of Science and Technology Center for Secure Cyber-physical Systems Abu Dhabi United Arab Emirates University of Milan Dept. of Computer Science Italy

This paper highlights the critical role of Machine Learning (ML) in combating the dynamic nature of cybersecurity threats. Unlike previous studies focusing mainly on static analysis, this work surveys the literature on dynamic analysis-based malware generation and detection. The study addresses the complexities of applying GANs to tabular data with heavy-tailed and multimodal distributions. It also examines the challenges of generating sequential malware behavior data and categorizes GAN-based models and their primary use cases. Furthermore, the paper evaluates adversarial losses and their limitations in generating dynamic malware behavior. Finally, it identifies existing metrics to assess GAN generalization in malware research and suggests future research directions based on identified limitations. © 2020 IEEE.

关键词： Generative adversarial networks

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