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Light People:*** May Lau,newly elected US NAE member in Hong Kong,talks about future of photonics and women in science

Light(science & Applications) 2024年第4期13卷 602-608页

作者： Yating Wan Chenzi Guo Electrical and Computer Engineering the ComputerElectrical and Mathematical Sciences and Engineering DivisionKing Abdullah University of Science and Technology23955-6900 ThuwalSaudi Arabia Changchun Institute of Optics Fine Mechanics and PhysicsChinese Academy of SciencesChangchunChina

Editorial Photonics technology remains a driving force in today’s scientific landscape,marked by continuous innovation and crossdisciplinary *** an enlighting conversation with Light:science&Applications,*** May Lau,a pioneer in photonics research,shares her deep insights on the evolution of technologies of LEDs,lasers,challenges of hetero-epitaxy,and the future of micro-LEDs and quantum dot *** honored as a member of the US National Academy of engineering(NAE)for her significant contributions to photonics and electronics using III-V semiconductors on silicon,*** stands out as the sole Hong Kong scholar inducted into the NAE this year,joining 114 new and 21 international *** this exclusive Light People interview,*** shares her journey as a pioneering woman in engineering,her commitment to mentorship and academia,and her perspective on advancing female representation in *** summary provided is distilled from ***’s thoughtful responses during the *** a deeper exploration of ***’s experiences and advice,the full interview is available in the Supplementary material.

关键词： interview lighting driving

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Enhancing Human Activity Detection and Classification Using Fine Tuned Attention-Based Transformer Models

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SN computer science 2024年第8期5卷 1142页

作者： Yadav, Ram Kumar Daniel, A. Semwal, Vijay Bhaskar Department of Computer Science and Engineering Amity University Gwalior India Department of Computer Science and Engineering MANIT Bhopal India

Recognition of human activity is an active research area. It uses the Internet of Things, Sensory methods, Machine Learning, and Deep Learning techniques to assist various application fields like home monitoring, robotics, surveillance, and healthcare. However, researchers face problems such as time complexity, more execution time of the model, and classification accuracy. This paper introduces a novel approach to overcome the issue as mentioned earlier by using the deep learning transformer model such as ViT(Vision Transformer), DieT(Data-efficient image Transformers), and SwinV2 transformer, which are used for image-based datasets (i.e., Standard40, MPII human pose) and VideoMAE transformer is used for video-based UCF101 and HMDB51 datasets. The approaches achieved remarkable accuracy in classifying human activities. Evaluations using the ViT, DeiT, and Swin transformer V2 with Stanford40 are 90.8%, 90.7%, and 88%;similarly, MPII Human Pose datasets show 87%, 85.6%, and 87.1%. In addition, this paper's method has achieved remarkable accuracies of 94.15% and 78.44%, respectively, when applying the VideoMAE transformer to video-based activity recognition on the UCF101 and HMDB51 datasets. These findings emphasize the efficacy of the attention-based transformer (i.e., ViT, DeiT, SwinV2, and VideoMAE) model and the novelty of earlier no-result evaluation on these various datasets with attention-based transformers. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Attention-based transformers Data augmentation Deep learning HAR Machine learning

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Energy-Efficient and Robust Communication in Multi-Connectivity Enabled mmWave Ultra-Dense Networks

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年第2期9卷 621-634页

作者： Kim, Jungjun Lee, Jihwan Bahk, Saewoong Seoul National University Department of Electrical and Computer Engineering INMC Seoul08826 Korea Republic of

The ultra-dense network (UDN) concept with multi-connectivity (MC) has emerged as a promising scenario for millimeter-wave (mmWave) communications due to its synergistic effect. However, mmWave UDNs with MC face challenges such as escalated energy consumption and computational complexity for traffic splitting decisions. To save energy and reduce the need for frequent traffic splitting decisions in the considered scenario, we formulate the problem as a mixed integer programming (MIP). Our objective is to determine which base stations (BSs) to enable for energy saving and how to split user traffic to maintain robust communication in MC enabled UDNs. To this end, this paper proposes energy-efficient and robust communication (EERC), an ensemble of heuristic methods and convex optimization, to achieve our goal without directly solving the computationally expensive MIP. In addition, we consider greedy algorithm that potentially offers enhanced optimality. We analyze the complexity of each scheme and demonstrate that EERC is the most viable option considering both performance and complexity. Through extensive simulations, we demonstrate that EERC achieves over 98% optimality for small-sized networks. More importantly, we evaluate EERC in realistic scenarios using a 3-D ray-tracing simulator and confirm that EERC performs very well even in urban terrain environments with buildings. © 2017 IEEE.

关键词： Convex optimization

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Learning From Mistakes: A Multilevel Optimization Framework

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第6期6卷 1651-1663页

作者： Zhang, Li Garg, Bhanu Sridhara, Pradyumna Hosseini, Ramtin Xie, Pengtao University of California Department of Electrical and Computer Engineering San DiegoCA92093 United States

Bi-level optimization methods in machine learning are popularly effective in subdomains of neural architecture search, data reweighting, etc. However, most of these methods do not factor in variations in learning difficulty, which limits their performance in real-world applications. To address the above problems, we propose a framework that imitates the learning process of humans. In human learning, learners usually focus more on the topics where mistakes have been made in the past to deepen their understanding and master the knowledge. Inspired by this effective human learning technique, we propose a multilevel optimization framework, learning from mistakes (LFM), for machine learning. We formulate LFM as a threestage optimization problem: 1) the learner learns, 2) the learner relearns based on the mistakes made before, and 3) the learner validates his learning. We develop an efficient algorithm to solve the optimization problem. We further apply our method to differentiable neural architecture search and data reweighting. Extensive experiments on CIFAR-10, CIFAR-100, ImageNet, and other related datasets powerfully demonstrate the effectiveness of our approach. © 2020 IEEE.

关键词： Adversarial machine learning

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Illumination-Free Clustering Using Improved Slime Mould Algorithm for Acute Lymphoblastic Leukemia Image Segmentation

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Journal of Bionic engineering 2023年第6期20卷 2916-2934页

作者： Krishna Gopal Dhal Swarnajit Ray Sudip Barik Arunita Das Department of Computer Science and Application Midnapore College(Autonomous)Paschim MedinipurMidnaporeWest BengalIndia Department of Computer Science and Engineering Maulana Abul Kalam Azad University of TechnologyKolkataWest BengalIndia Department of Computer Science and Engineering Kalyani Government Engineering CollegeKalyaniWest BengalIndia

Partitional clustering techniques such as K-Means(KM),Fuzzy C-Means(FCM),and Rough K-Means(RKM)are very simple and effective techniques for image ***,because their initial cluster centers are randomly determined,it is often seen that certain clusters converge to local *** addition to that,pathology image segmentation is also problematic due to uneven lighting,stain,and camera settings during the microscopic image capturing ***,this study proposes an Improved Slime Mould Algorithm(ISMA)based on opposition based learning and differential evolution’s mutation strategy to perform illumination-free White Blood Cell(WBC)*** ISMA helps to overcome the local optima trapping problem of the partitional clustering techniques to some *** paper also performs a depth analysis by considering only color components of many well-known color spaces for clustering to find the effect of illumination over color pathology image *** and visual results encourage the utilization of illumination-free or color component-based clustering approaches for image ***-KM and“ab”color channels of CIELab color space provide best results with above-99%accuracy for only nucleus ***,for entire WBC segmentation,ISMA-KM and the“CbCr”color component of YCbCr color space provide the best results with an accuracy of above 99%.Furthermore,ISMA-KM and ISMA-RKM have the lowest and highest execution times,*** the other hand,ISMA provides competitive outcomes over CEC2019 benchmark test functions compared to recent well-established and efficient Nature-Inspired Optimization Algorithms(NIOAs).

关键词： Pathology image Image segmentation Clustering Color space White blood cell Optimization Swarm intelligence Fuzzy clustering Rough clustering

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Deep learning for object recognition: A comprehensive review of models and algorithms

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International Journal of Cognitive Computing in engineering 2025年 6卷 298-312页

作者： Tsirtsakis, Paschalis Zacharis, Georgios Maraslidis, George S. Fragulis, George F. Department of Electrical and Computer Engineering University of Western Macedonia ZEP Campus Kozani50100 Greece

The rapid advancements in artificial intelligence (AI) and machine learning (ML) have significantly enhanced progress in computer vision, opening doors to innovative technological possibilities and enabling a range of real-world applications. Despite these developments, object recognition remains a complex domain with persistent challenges and limitations. This work seeks to address these challenges by investigating the effectiveness of deep learning (DL) methods in object detection tasks. Leveraging DL allows for the direct learning of feature representations from image data, resulting in advanced performance. This review provides a comprehensive examination of fundamental models and algorithms, with a particular focus on neural network (NN) frameworks utilized for feature extraction. Additionally, it evaluates the benchmark datasets commonly employed to assess their performance in object recognition tasks. © 2025 The Authors

关键词： Contrastive Learning

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IC Backside Tamper Detection using Impedance Sensing

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IEEE Access 2025年 13卷 90706-90725页

作者： Mosavirik, Tahoura Tajik, Shahin Worcester Polytechnic Institute Electrical and Computer Engineering Department WorcesterMA01609 United States

The expansion of flip-chip technologies and lack of backside protection made integrated circuits (ICs) vulnerable to certain classes of physical attacks mounted from the IC’s backside, including laser-assisted probing, electromagnetic side-channel analysis, body biasing injection (BBI), and focused ion beam (FIB) editing. Existing countermeasures are expensive, incompatible with standard IC fabrication, and unsuitable for legacy systems such as field-programmable gate arrays (FPGAs), which are widely used in industrial and defense systems. In this paper, we demonstrate how impedance monitoring of the printed circuit board (PCB) and the IC package’s power distribution network (PDN) using on-chip circuit-based network analyzers can detect IC backside tampering. Our method is based on the fact that any attempt to expose the backside silicon substrate, such as the removal of the fan and heatsinks, or polishing it, leads to changes in the equivalent impedance of the package’s PDN, and hence scanning the package impedance will reveal if the package integrity has been violated. To validate our claims, we deployed an on-FPGA network analyzer on an AMD Zynq UltraScale+ MPSoC (16 nm), which is part of a multi-PCB *** performed a series of backside tamper attacks, including the removal of cooling components and IC backside silicon milling/polishing, across different temperatures, and evaluated detection effectiveness leveraging the difference of means as the statistical metric. © 2013 IEEE.

关键词： Focused ion beams

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Composite spectral spatial pixel CNN for land-use hyperspectral image classification with hybrid activation function

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Multimedia Tools and Applications 2025年第12期84卷 10527-10550页

作者： Banerjee, Anasua Swain, Satyajit Rout, Minakhi Bandyopadhyay, Mainak School of Computer Engineering Kalinga Institute of Industrial Technology-Deemed-to-be-University Bhubaneswar India Department of Computer Science and Engineering National Institute of Technology Silchar India

Deep learning methods have played a prominent role in the development of computer visualization in recent years. Hyperspectral imaging (HSI) is a popular analytical technique based on spectroscopy and visible imaging which examines the pattern of light in a target and recognizes objects based on varying spectral properties. However, in remote sensing, detecting surface material via HSI analysis is a critical and difficult task. The performance of spectral-spatial data exploitation is well established to outperform typical spectral pixel-wise techniques. Because of its great feature extraction ability, convolutional neural networks (CNN) have emerged as a potent deep learning approach. CNN translates the input features of an image into an equivalent CNN feature map, in addition to naturally combining spectral and spatial information. However, spectral-spatial properties when combined with pixel-wise extraction can learn more minute details of objects present on the earth’s terrain. In this paper, a noble Composite Spectral Spatial Pixel CNN model for the classification of hyperspectral data is presented which is an amalgamation of 3D-2D-1D CNN. While the 3D and 2D CNN exploit the spectral-spatial features effectively, 1D CNN works on pixel-wise feature extraction. Further, to optimize the classification performance of the proposed model, a new hybrid activation function Flatten-T Swish is also used which is the combination of ReLU and Swish function. The proposed model is compared with other state-of-the-art models based on three popular HSI datasets, and it is found that the proposed model performs better among others in terms of classification and computation time, giving 98.87% accuracy for Indian Pines, 99.92% accuracy for Pavia University, and 99.99% accuracy for Salinas Valley dataset. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Extraction

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Consensus in Heterogeneous Higher-Order Agents Driven by Geometric Mean under Weight-Unbalanced Digraph 10

Consensus in Heterogeneous Higher-Order Agents Driven by Geo...

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10th Indian Control Conference, ICC 2024

作者： Sanskar, Prajjwal Tejal, R. Dunna, Vijaya Kumar Sahoo, Soumya Ranjan Sen, Arijit Department of Electrical Engineering and Computer Science IISER Bhopal India Department of Electrical Engineering IIT Kanpur India

ISBN: (纸本)9798331517212

This paper proposes a distributed algorithm to drive all agents in a heterogeneous multi-agent system (MAS) to the geometric mean of their initial conditions. It is defined as the GM-consensus problem. The proposed GM-consensus algorithm has a distributed observer for estimating the geometric mean of agents’ initial states. Its later part is a local controller for tracking these observed states. This structure facilitate the scaling of the algorithm for the agents having higher-order model. The numerical example exemplifies the efficiency of the proposed distributed GM-consensus algorithm. © 2024 IEEE.

关键词： Consensus algorithm

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Addressing Scalability Issues of Blockchains With Hypergraph Payment Networks

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2427-2440页

作者： Kotzer, Arad Ladóczki, Bence Tapolcai, János Rottenstreich, Ori Technion—Israel Institute of Technology Department of Computer Science Haifa3200003 Israel Budapest University of Technology and Economics Department of Telecommunications and Artificial Intelligence Budapest1111 Hungary Technion—Israel Institute of Technology Department of Electrical and Computer Engineering Department of Computer Science Haifa3200003 Israel

Payment channels are auspicious candidates in layer-2 solutions to reduce the number of on-chain transactions on traditional blockchains and increase transaction throughput. To construct payment channels, peers lock funds on 2-of-2 multisig addresses and open channels between one another to transact via instant peer-to-peer transactions. Transactions between peers without a direct channel are made possible by routing the payment over a series of adjacent channels. In certain cases, this can lead to relatively low transaction success rates and high transaction fees. In this work, we introduce pliability to constructing payment channels and graft edges with more than two endpoints into the payment graph. We refer to these constructions as hyperedges. We present hyperedge-based topologies to form hypergraphs and compare them to Bitcoin’s Lightning network and other state-of-the-art solutions. The results demonstrate that hyperedge-based implementations can both increase transaction success rate, in addition to decreasing the network cost by more than 50% compared to that of the Lightning Network. © 2004-2012 IEEE.

关键词： Blockchain

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