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1st International Conference on Electronics, Communication and Signal Processing, ICECSP 2024

作者： Vanamoju, Sri Vaibhav Mohan Dev Vineetha, Moturi Veda Tekchandani, Hitesh Joshi, Pallavi Shukla, Praveen Kumar Khanna, Anita Electronics and Communication Koneru Lakshmaiah Education Foundation Hyderabad India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Hyderabad India Amrita Vishwa Vidyapeetham Mysuru Department of Computer Science Mysuru India School of Computing and Intelligent Systems Manipal University Jaipur Jaipur India Electrical Engineering Guru Ghasidas Vishwavidyalaya Bilaspur Bilaspur India

ISBN: (纸本)9798350364590

Understanding facial expressions is key to successful communication. It's like decoding a secret language that helps us know if someone is happy, sad, angry, surprised, or something else altogether. Harnessing facial emotion recognition (FER) in machines holds the potential to revolutionize human-computer interaction, improve customer service experiences and provide invaluable support in medical diagnosis. Machines can comprehend human's needs more effectively by reading the emotions from their facial expression and therefore this results in a more natural conversation between them and the machine. Various machine and deep learning methodologies have been implemented for FER. In this research work, the authors propose the state-of-the-art YOLO (You Only Look Once) based deep learning classifier for FER. The YOLO algorithm is a single-shot approach that efficiently classifies objects in a single pass. It achieves this by using a single neural network to predict bounding boxes and target class probabilities by utilizing the entire image as input. YOLOv8 is a recent iteration of the YOLO object detection algorithm is popular for its speed and accuracy which offers improvements over prior versions, making it a suitable choice for real-time intended task of FER. With the proposed classifier, the authors achieved test accuracy of 72.56% on the FER-2013 dataset in a seven-class FER task. © 2024 IEEE.

关键词： Adversarial machine learning

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Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces 38

Taming "data-hungry" reinforcement learning? Stability in co...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Duan, Yaqi Wainwright, Martin J. Department of Technology Operations and Statistics Stern School of Business New York University New YorkNY10012 United States Laboratory for Information and Decision Systems Statistics and Data Science Center Department of Electrical Engineering and Computer Science Department of Mathematics Massachusetts Institute of Technology CambridgeMA02139 United States

We introduce a novel framework for analyzing reinforcement learning (RL) in continuous state-action spaces, and use it to prove fast rates of convergence in both off-line and on-line settings. Our analysis highlights two key stability properties, relating to how changes in value functions and/or policies affect the Bellman operator and occupation measures. We argue that these properties are satisfied in many continuous state-action Markov decision processes. Our analysis also offers fresh perspectives on the roles of pessimism and optimism in off-line and on-line RL. © 2024 Neural information processing systems foundation. All rights reserved.

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Short-Term Traffic Flow Prediction Method Based on Spatio-Temporal Characteristics of Complex Road Network

Short-Term Traffic Flow Prediction Method Based on Spatio-Te...

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2021 International Conference on Applied Mathematics, Modeling and computer Simulation, AMMCS 2021

作者： Zhu, Huawei Mao, Yihao Zhao, Yiming Shi, Liyuan Shao, Yanli School of Information and Electrical Engineering Zhejiang University City College Hangzhou310015 China Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9781643682549

With the development of urbanization, the number of residents' motor vehicles has increased sharply, and traffic congestion problem has become increasingly serious. The construction of Intelligent Traffic System (ITS) has become the main means to alleviate traffic congestion. Short-term traffic flow prediction has guiding significance for residents' travel planning and intelligent management of transportation, and has become one of the research hotspots in intelligent transportation ***, A short-term traffic flow prediction method based on the spatio-temporal characteristics of complex road networks is proposed to further improve the prediction accuracy and reduce the prediction cost. Firstly, a graph convolutional network (GCN) capable of processing non-Euclidean data structures is used to extract the spatial characteristics of traffic flow data. Then, the long and short-term memory (LSTM) neural network is used to process the time characteristics. Finally, the two are combined to realize the effective processing of the spatio-temporal characteristics of traffic flow data. Experimental results on the real traffic flow dataset prove the feasibility and effectiveness of the proposed method, and can provide a basis for intelligent traffic control and smart city construction. © 2022 The authors and IOS Press.

关键词： Traffic congestion

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Zero Forcing Beamforming With Sidelobe Suppression Using Neural Networks 28

Zero Forcing Beamforming With Sidelobe Suppression Using Neu...

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28th European Wireless Conference, EW 2023

作者： Mallioras, Ioannis Yioultsis, Traianos V. Kantartzis, Nikolaos V. Lazaridis, Pavlos I. Vlahov, Atanas Poulkov, Vladimir Zaharis, Zaharias D. School of Electrical and Computer Eng. Aristotle University of Thessaloniki Thessaloniki Greece Maggioli SpA Santarcangelo Di Romagna Italy School of Computing and Engineering University of Huddersfield Huddersfield United Kingdom Intelligent Communication Infrastructures Laboratory Sofia Tech Park JSC Sofia Bulgaria Faculty of Telecommunications Technical University of Sofia Sofia Bulgaria

ISBN: (纸本)9783800762262

The use of deep learning in the field of wireless communications has already shown great potential. In this work we present a deep feedforward and a deep recurrent neural network trained as null steering beamformers that target high sidelobes in order to establish low sidelobe level for any desired incoming signal. Using of a zero-forcing algorithm, we apply a sidelobe-damping algorithm where iteratively, a constant number of sidelobes is nullified until a desired sidelobe level is reached. In this way, we can create a dataset which we later use to train our NN models. Using Bayesian optimization, we perform hyperparameter tuning to configure structure and training related parameters for the NNs under examination. The NN models are later fine-tuned using a small dataset containing more demanding cases. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Recurrent neural networks

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Adaptive Frequency Hopping for 5G New Radio mMTC Security

Adaptive Frequency Hopping for 5G New Radio mMTC Security

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2023 IEEE International Conference on Industrial Technology, ICIT 2023

作者： Chan, Wai Ming Kwon, Hyuck M. Chou, Remi A. Love, David J. Fahmy, Sonia Hussain, Syed Rafiul Kim, Sang Wu Valk, Chris Vander Brinton, Christopher G. Marojevic, Vuk Pham, Khanh D. Kim, Taejoon The University of Kansas Department of Electrical Engineering and Computer Science LawrenceKS66045 United States Wichita State University Department of Electrical and Computer Engineering WichitaKS67260 United States School of Computing Wichita State University WichitaKS67260 United States Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Purdue University Department of Computer Science West LafayetteIN47907 United States The Pennsylvania State University Department of Computer Science and Engineering State CollegePA16802 United States Iowa State University Department of Electrical and Computer Engineering AmesIA50011 United States Raytheon Bbn CambridgeMA02138 United States Mississippi State University Department of Ece StarkvilleMS39762 United States Air Force Research Laboratory AlbuquerqueNM87123 United States

ISBN: (纸本)9798350336504

3GPP standards (e.g., LTE and 5G New Radio (NR)) have revolutionized commercial broadband access. Unfortunately, the openness of 3GPP specifications and high-level of adaptability make fifth-generation (5G) networks increasingly sensitive to unsophisticated attacks in the network. While 5G NR has introduced frequency hopping (FH) for massive machine-type communications (mMTC), there has not been a unified approach to designing the FH patterns in practical 5G systems. In this paper, we investigate the multi-user FH design problem at the physical resource grid of the 5G communication networks. We present various criteria for FH pattern design and propose two algorithms that are implementable with low complexity. The first scheme utilizes known interference patterns to design a set of user equipment (UE) FH patterns that are immune to interfering attacks. The second scheme leverages interference statistics and minimizes the expected number of hits by the interferer. Through numerical simulations and analytical modeling, we demonstrate the efficacy of the proposed schemes, which outperform the uniform FH pattern and achieve near-optimal performance. © 2023 IEEE.

关键词： Frequency hopping

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RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection

arXiv

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arXiv 2025年

作者： Shi, Jianrui Zhao, Yong Cui, Zeyang Shen, Xiaoming Zeng, Minhang Liu, Xiaojie Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computing and Data Science The University of Hong Kong Hong Kong Pengcheng Laboratory Shenzhen China

Object detection plays a crucial role in smart video analysis, with applications ranging from autonomous driving and security to smart cities. However, achieving real-time object detection on edge devices presents significant challenges due to their limited computational resources and the high demands of deep neural network (DNN)-based detection models, particularly when processing high-resolution video. Conventional strategies, such as input down-sampling and network up-scaling, often compromise detection accuracy for faster performance or lead to higher inference latency. To address these issues, this paper introduces RE-POSE, a Reinforcement Learning (RL)Driven Partitioning and Edge Offloading framework designed to optimize the accuracy-latency trade-off in resource-constrained edge environments. Our approach features an RL-Based Dynamic Clustering Algorithm (RL-DCA) that partitions video frames into non-uniform blocks based on object distribution and the computational characteristics of DNNs. Furthermore, a parallel edge offloading scheme is implemented to distribute these blocks across multiple edge servers for concurrent processing. Experimental evaluations show that RE-POSE significantly enhances detection accuracy and reduces inference latency, surpassing existing methods. © 2025, CC BY.

关键词： Computation offloading

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Editorial: Advances in flexibility exploration and utilization for low carbon power systems

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Frontiers in Energy Research 2023年 11卷

作者： Liu, Yu Zang, Haixiang Guo, Yi Hong, Tianqi School of Electrical Engineering Southeast University Nanjing China College of Energy and Electrical Engineering Hohai University Nanjing China Power Systems Laboratory ETH Zürich Zürich Switzerland School of Electrical and Computer Engineering University of Georgia AthensGA United States

Aiming at the carbon neutrality mission worldwide, renewable energy becomes a critical focus in the 12 low-carbon future. Integration of large-scale renewable energy leads to the profound changes of 13 power systems, while intermittency and uncertainty become the new features and challenges of 14 system operation. Therefore, a more flexible and intelligent system is expected, for the consumption 15 of highly penetrated renewables. 16In this context, flexible resources, no matter from the source side, the grid side, or the demand side, 17 are significantly desired. and proposed a consensus control-based distributed power tracking system for power grid simulation. 37Furtherly assisted by the communication network, the computational efficiency was accelerated, in 38 addition to the privacy protection of the regulation resources. 39 to system-level, whose achievements have the potential to inspire the realization of a low carbon 49 network. However, there are still many challenges and opportunities related to the flexible regulation 50 in the green future, and more theories and technologies need to be further investigated.

关键词： renewable energy integration low carbon networks flexibility resources coordination optimal operation modelling and aggregation

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Improved Data Encoding for Emerging computing Paradigms: From Stochastic to Hyperdimensional computing

arXiv

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arXiv 2025年

作者： Moghadam, Mehran Shoushtari Aygun, Sercan Najafi, M. Hassan Electrical Computer and Systems Engineering Department Case Western Reserve University ClevelandOH44106 United States School of Computing and Informatics University of Louisiana at Lafayette LafayetteLA70503 United States

Data encoding is a fundamental step in emerging computing paradigms, particularly in stochastic computing (SC) and hyperdimensional computing (HDC), where it plays a crucial role in determining the overall system performance and hardware cost efficiency. This study presents an advanced encoding strategy that leverages a hardware-friendly class of low-discrepancy (LD) sequences, specifically powers-of-2 bases of Van der Corput (VDC) sequences (VDC-2n), as sources for random number generation. Our approach significantly enhances the accuracy and efficiency of SC and HDC systems by addressing challenges associated with randomness. By employing LD sequences, we improve correlation properties and reduce hardware complexity. Experimental results demonstrate significant improvements in accuracy and energy savings for SC and HDC systems. Our solution provides a robust framework for integrating SC and HDC in resource-constrained environments, paving the way for efficient and scalable AI implementations. © 2025, CC BY.

关键词： Encoding (symbols)

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A discussion regarding the application of the hertz contact theory on biological samples in afm nanoindentation experiments

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Micro and Nanosystems 2021年第1期13卷 42-48页

作者： Kontomaris, Stylianos Vasileios Stylianou, A. Nikita, K.S. Malamou, A. Mobile Radio Communications Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Faculty of Architecture Engineering & Built Environment Athens Metropolitan College Athens Greece Cancer Biophysics Laboratory Department of Mechanical and Manufacturing Engineering Faculty of Engineering University of Cyprus Nicosia Cyprus Radar Systems and Remote Sensing Lab of School Electrical & Computer Engineering National Technical University of Athens Athens Greece

Atomic Force Microscopy (AFM) Nanoindentation procedure regarding biological samples poses significant challenges with respect to the accuracy of the provided results. These challenges are related to the inhomogeneity of biological samples, various uncertainties in experimental methods and certain approximations regarding the theoretical analysis. The most commonly used theoretical model for data processing at the linear elastic regime regarding biological samples is the Hertz model. This paper focuses on the investigation of the resulting errors of the basic equation of the Hertz theory that depends on the ratio, indentation depth/indenter’s radius regarding the Young’s modulus calculation. Several examples in the literature that do not take into account the value of the ratio indentation depth/indenter’s radius are reported and the related errors are presented and discussed. In addition, an extended new equation is derived which takes into account the influence of the aforementioned ratio on the calculation of the Young’s modulus and can be easily used for calculations. Moreover, a rational explanation, regarding the extended differences of the Young’s modulus calculations using the same experimental results when these are processed using the Hertz model and the Oliver & Pharr analysis (which is the general model that applies for any axisymmetric indenter) is provided. In conclusion, the derived equation is further combined with equations which take into account the shape of the sample in order to provide a complete and reliable theoretical tool which can be generally applied in order to reduce the errors produced by the current methodology. © 2021 Bentham Science Publishers.

关键词： Nanoindentation

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Enhancing Breast Cancer Diagnosis with Multi-Resolution Vision Transformers and Robust Decision-Making

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IEEE Access 2025年 13卷 89704-89722页

作者： Sabry, Margo Balaha, Hossam Magdy Ali, Khadiga M. Soliman, Tayseer Hassan A. Gondim, Dibson Ghazal, Mohammed Alghamdi, Norah Saleh El-Baz, Ayman Assiut University Information Systems Department Assiut Egypt University of Louisville J.B. Speed School of Engineering Bioengineering Department LouisvilleKY40292 United States Mansoura University Faculty of Medicine Pathology Department Mansoura Egypt University of Louisville Department of Pathology and Laboratory Medicine LouisvilleKY United States Abu Dhabi University Electrical Computer and Biomedical Engineering Department Abu Dhabi United Arab Emirates Princess Nourah bint Abdulrahman University College of Computer and Information Sciences Department of Computer Sciences Riyadh Saudi Arabia

This study aims to improve breast cancer (BC) diagnosis through a novel multi-resolution Vision Transformer (ViT)-based framework with ensemble decision-making, addressing limitations in traditional single-magnification models. The proposed framework uses multiscale feature extraction at three magnification levels (L0, L1, L2 or 16x, 4x, 2x) to capture both fine-grained and high-level tumor features. A stacking ensemble method combines predictions from ViT models trained at these levels, improving classification robustness. Postprocessing techniques, including region-growing and fast-marching level set algorithms, refine whole-slide image (WSI) prediction and postprocessing quality. Performance was evaluated via metrics such as precision, recall, the F1 score, accuracy, and specificity across 50 trials with perturbed conditions. The framework achieved a top accuracy of 97.08%, with precision and recall above 94%. The suggested stack configuration outperformed individual models and other stacking configurations, demonstrating balanced performance with minimal variability. Statistical analysis highlighted the reliability and consistency of the framework under perturbed conditions. The multi-resolution ViT-based framework significantly improves BC classification by integrating multiscale analysis and ensemble decision-making. Its high accuracy and robustness make it a valuable tool for reducing interobserver variability in digital pathology workflows. © 2013 IEEE.

关键词： Image segmentation

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