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Drosophila Larvae-Inspired Soft Crawling Robot with Multimodal Locomotion and Versatile Applications

Research 2025年第1期7卷 35-46页

作者： Qin Fang Jingyu Zhang Yinhui He Nenggan Zheng Yue Wang Rong Xiong Zhefeng Gong Haojian Lu State Key Laboratory of Industrial Control and Technology Zhejiang UniversityHangzhou 310027China Institute of Cyber-Systems and Control Zhejiang UniversityHangzhou 310027China Medical School of Zhejiang University Hangzhou 310027China College of Computer Science and Technology Zhejiang UniversityHangzhou 310027China Stomatology Hospital School of StomatologyZhejiang University School of MedicineZhejiang Provincial Clinical Research Center for Oral DiseasesKey Laboratory of Oral Biomedical Research of Zhejiang ProvinceCancer Center of Zhejiang UniversityEngineering Research Center of Oral Biomaterials and Devices of Zhejiang ProvinceHangzhou 310000China

Soft crawling robots have been widely studied and applied because of their excellent environmental adaptability and flexible ***,most existing soft crawling robots typically exhibit a single-motion mode and lack diverse *** by Drosophila larvae,this paper proposes a compact soft crawling robot(weight,13 g;length,165 mm;diameter,35 mm)with multimodal locomotion(forward,turning,rolling,and twisting).Each robot module uses 4 sets of high-power-density shape memory alloy actuators,endowing it with 4 degrees of motion *** analyze the mechanical characteristics of the robot modules through experiments and simulation *** plug-and-play modules can be quickly assembled to meet different motion and task *** soft crawling robot can be remotely operated with an external controller,showcasing multimodal motion on various material *** a narrow maze,the robot demonstrates agile movement and effective maneuvering around *** addition,leveraging the inherent bistable characteristics of the robot modules,we used the robot modules as anchoring units and installed a microcamera on the robot's head for pipeline *** robot completed the inspection in horizontal,vertical,curved,and branched pipelines,adjusted the camera view,and twisted a valve in the pipeline for the first *** research highlights the robot's superior locomotion and application capabilities,providing an innovative strategy for the development of lightweight,compact,and multifunctional soft crawling robots.

关键词： multimodal locomotion versatile applications Drosophila larvae soft crawling robot weight soft crawling robots shape memory alloy actuators

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Efficient and Lightweight Framework for Confidential Medical Image Search using Edge Computing

Efficient and Lightweight Framework for Confidential Medical...

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Computational Intelligence and Network systems (CINS), International Conference on

作者： Arun Amaithi Rajan Vetriselvi V Aishwarya R Anitha Amaithi Rajan Department of Computer Science and Engineering Security Research Laboratory College of Engineering Guindy Anna University Chennai Tamil Nadu India Department of Computer Science and Business Systems Francis Xavier Engineering College Tirunelveli Tamil Nadu India

ISBN: (数字)9798331504106

ISBN: (纸本)9798331504113

In healthcare, medical images are playing a major role in accurate diagnosis. Cloud storage for medical images creates security risks and latency due to some critical vulnerabilities and their distance from data sources. By bringing computation closer to the source, edge computing improves efficiency, still edge servers cannot be trusted blindly, requiring robust security measures. In this article, an efficient and lightweight framework for secure medical image search and retrieval that leverages edge computing is proposed. Here, Medical images are encrypted and similarity-preserving hashcodes are generated locally at the source, which are sent to a trusted master edge server. Before being stored, searchable hashcodes undergo homomorphic encryption, allowing for secure computations on encrypted data. The master edge server generates $n$ shares of the encrypted images by applying a Learning With Errors (LWE) based image sharing scheme and distributing them across nearby edge devices. During retrieval, to ensure resilience against device compromise, $m$ out of $n$ shares are used to reconstruct the image. The master edge server retrieves and reconstructs the top $k$ relevant images in response to the query image. This framework enhances security and efficiency by reducing reliance on cloud storage and enabling secure processing on edge servers, making it ideal for modern healthcare environments. The efficiency and security of the proposed framework are proved theoretically and experimentally. Hence, it proved that the framework is performing better than existing state-of-the-art models.

关键词： Cloud computing Image edge detection Soft sensors Medical services Servers Medical diagnostic imaging Image reconstruction Secure storage Edge computing Resilience

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MMAL: Multi-Modal Analytic Learning for Exemplar-Free Audio-Visual Class Incremental Tasks 24

MMAL: Multi-Modal Analytic Learning for Exemplar-Free Audio-...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Yue, Xianghu Zhang, Xueyi Chen, Yiming Zhang, Chengwei Lao, Mingrui Zhuang, Huiping Qian, Xinyuan Li, Haizhou Department of Electrical and Computer and Engineering National University of Singapore Singapore Laboratory for Big Data and Decision National University of Defense Technology Hunan Changsha China School of Electronic Electrical and Communication Engineering University of the Chinese Academy of Sciences Beijing China National Key Laboratory of Information Systems Engineering National University of Defense Technology Hunan Changsha China Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangdong Guangzhou China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shenzhen China

ISBN: (纸本)9798400706868

Class-incremental learning poses a significant challenge under an exemplar-free constraint, leading to catastrophic forgetting and sub-par incremental accuracy. Previous attempts have focused primarily on single-modality tasks, such as image classification or audio event classification. However, in the context of Audio-Visual Class-Incremental Learning (AVCIL), the effective integration and utilization of heterogeneous modalities, with their complementary and enhancing characteristics, remains largely unexplored. To bridge this gap, we propose the Multi-Modal Analytic Learning (MMAL) framework, an exemplar-free solution for AVCIL that employs a closed-form, linear approach. To be specific, MMAL introduces a modality fusion module that re-formulates the AVCIL problem through a Recursive Least-Square (RLS) perspective. Complementing this, a Modality-Specific Knowledge Compensation (MSKC) module is designed to further alleviate the under-fitting limitation intrinsic to analytic learning by harnessing individual knowledge from audio and visual modality in tandem. Comprehensive experimental comparisons with existing methods show that our proposed MMAL demonstrates superior performance with the accuracy of 76.71%, 78.98%, and 76.19% on AVE, Kinetics-Sounds, and VGGSounds100 datasets, respectively, setting new state-of-the-art AVCIL performance. Notably, compared to those memory-based methods, our MMAL, being an exemplar-free approach, provides good data privacy and can better leverage multi-modal information for improved incremental accuracy. © 2024 ACM.

关键词： Contrastive Learning

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods 8

Detection of Detonators in Baggage Using Deep CNN Architectu...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Lynda, Oulhissane Mostefa, Merah Moldovanu, Simona Moraru, Luminita LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Laboratory of Signals and Systems Mostaganem Algeria Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Modelling & Simulation Laboratory MSlab Department of Computer Science and Information Technology Galati Romania Faculty of Sciences and Environment Modelling & Simulation Laboratory Dunarea de Jos University of Galati MSlab Department of Chemistry Physics & Environment Galati Romania

ISBN: (纸本)9798350309249

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL © 2024 IEEE.

关键词： Feature extraction

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CLGT: A Graph Transformer for Student Performance Prediction in Collaborative Learning 37

CLGT: A Graph Transformer for Student Performance Prediction...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Peng, Tianhao Liang, Yu Wu, Wenjun Ren, Jian Pengrui, Zhao Pu, Yanjun State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Engineering Beihang University China Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology China Institute of Artificial Intelligence Beihang University China

ISBN: (纸本)9781577358800

Modeling and predicting the performance of students in collaborative learning paradigms is an important task. Most of the research presented in literature regarding collaborative learning focuses on the discussion forums and social learning networks. There are only a few works that investigate how students interact with each other in team projects and how such interactions affect their academic performance. In order to bridge this gap, we choose a software engineering course as the study subject. The students who participate in a software engineering course are required to team up and complete a software project together. In this work, we construct an interaction graph based on the activities of students grouped in various teams. Based on this student interaction graph, we present an extended graph transformer framework for collaborative learning (CLGT) for evaluating and predicting the performance of students. Moreover, the proposed CLGT contains an interpretation module that explains the prediction results and visualizes the student interaction patterns. The experimental results confirm that the proposed CLGT outperforms the baseline models in terms of performing predictions based on the real-world datasets. Moreover, the proposed CLGT differentiates the students with poor performance in the collaborative learning paradigm and gives teachers early warnings, so that appropriate assistance can be provided. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Students

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An Innovative Evaluation of the Friction and Wear Behavior of Ferrous and Non-Ferrous Metals Under Lab and Lathe Wear Tests

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Journal of The Institution of Engineers (India): Series D 2024年 1-21页

作者： Bougoffa, M. Seyf Eddine Benchaa, Sayhia Benkrima, Yamina Hadibi, Abdelhak Benmesbah, Ouissem Benkhaled, Sihem Laboratory of Advanced Technologies in Mechanical Production-LRTAPM Department of Mechanical Engineering University of Badji Mokhtar P.o.Box 12 Annaba23000 Algeria Faculté Des Mathématiques Et Des Sciences de La Matière Université de Ouargla Ouargla30000 Algeria Ecole Normale Supérieure de Ouargla Ouargla30000 Algeria Laboratory Networks and Systems Laboratory-LRS Department of Computer Science University of Badji Mokhtar P.o.Box 12 Annaba23000 Algeria

Wear is a persistent challenge in industrial applications, stemming from the interaction of numerous complex and interrelated factors. CuZn37Pb2 and AISI 1060 steel are particularly susceptible to wear due to their extensive industrial applications. To address this problem and enhance the scientific literature, it is essential first to understand and analyze the impact of these interconnected factors and their interactions through experimental investigations that consider the maximum number of available factors. This study developed a new wear test tool, machined on a horizontal lathe, to conduct tests under both dry and lubricated conditions. A tribological comparison was made between the lathe test and a tribometer, considering the interplay of various factors. The study examined and compared initial surface roughness, load, sliding speed, wear track diameter, track width, contact temperature, wear loss, and wear rate in relation to the friction coefficient. Experiments were performed at torques ranging from 25 to 100 N, at speeds of 0.30, 0.40, and 0.50 m/s, and with wear track diameters of 4, 6, 8, and 10 mm. The worn surfaces and wear tracks of each sample were analyzed using optical microscopy, XRD patterns, and SEM–EDS. This work investigated the influence of temperature (ranging from 50 to 200 °C) on the friction properties of the two alloys. It was found that the morphological structure of the sample and the type of test significantly impact the tribological response of the surfaces, each interacting uniquely with varying contributions from the tribological parameters. Despite the wear rate calculation error being less than 45.14% in both tests, the findings indicated that the tribological response under ideal laboratory conditions differs from that in real-world environments. The study provided significant advancements in understanding and analyzing the wear phenomenon by addressing the largest number of accessible characteristics, many of which had not be

关键词： Lathes

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Robotic fish:Opening a new era of underwater detection

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The Innovation 2025年第3期6卷 13-14页

作者： Dongfang Li Suet To Zhicheng Xu Guanghong Liu Rob Law Edmond Q.Wu Limin Zhu State Key Laboratory of Ultraprecision Machining Technology Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHong Kong 999077China School of Electrical Engineering and Automation Fuzhou UniversityFujian 350108China China Electronics Technology Group Corporation Information Science Research InstituteBeijing 100086China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China Asia-Pacific Academy of Economics and Management Faculty of Business AdministrationUniversity of MacaoMacao 999078China Department of Computer Science and Engineering Shanghai JiaoTong UniversityShanghai 200240China

Escalating competition within aquatic environments,particularly in oceans,underscores the imperative for innovative solutions in underwater *** solutions necessitate the development of intelligent underwater equipment,with robotic fish emerging as a promising contender.1 Leveraging advancements in robotics and artificial intelligence,robotic fish offer a suite of advantages that position them as transformative assets in underwater exploration and *** fish are autonomous robots designed based on biomimetics principles that mimic the appearance of fish and can autonomously swim and perform specific tasks in water.

关键词： robotic fish intelligent underwater equipmentwith autonomous robots underwater detection oceans artificial intelligencerobotic fish aquatic environments innovative solutions

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Improving Prediction Quality of Face Image Preference Using Combinatorial Fusion Algorithm 16th

Improving Prediction Quality of Face Image Preference Using...

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16th International Conference on Brain Informatics, BI 2023

作者： Zhang, Zihan Schweikert, Christina Shimojo, Shinsuke Hsu, D. Frank Laboratory of Informatics and Data Mining Department of Computer and Information Science Fordham University YorkNY10023 United States Division of Computer Science Mathematics and Science St. John’s University QueensNY11439 United States Division of Biology and Biological Engineering/Computation and Neural Systems California Institute of Technology PasadenaCA91125 United States

ISBN: (纸本)9783031430749

When two face images are shown to a subject, the subject’s preference choice is influenced by many factors. Although it is a challenging task, one way to detect a subject’s preference is to analyze the eye movement gaze cascade in the recorded video sequence together with a set of extracted attributes (features). Combinatorial fusion algorithm (CFA) is a new information fusion and machine learning paradigm for combining multiple scoring systems using rank-score characteristic (RSC) function and cognitive diversity (CD). In this paper, we apply CFA in the analysis of the eye movement gaze cascade sequence. In particular, we characterize each of the attributes and measure the diversity between two attributes using RSC function and CD respectively. The novelty of the combinatorial fusion approach is the new paradigm of incorporating both score function and rank function as well as using both score combination and rank combination. In addition, our results demonstrate that weighted rank combination has some advantage over weighted score combination when the weight is measured using diversity strength. Since diversity strength depends only on cognitive diversity between two attributes but not on any specific sequence in the eye movement gaze cascade, our result facilitates learning and modeling in an unsupervised manner. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Information fusion

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PpNNT: Multiparty Privacy-Preserving Neural Network Training System

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 370-383页

作者： Feng, Qi He, Debiao Shen, Jian Luo, Min Choo, Kim-Kwang Raymond Wuhan University School of Cyber Science and Engineering Wuhan430072 China Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China School of Information Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Shanghai201204 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

By leveraging smart devices [e.g., industrial Internet of Things (IIoT)] and real-time data analytics, organizations, such as production plants can benefit from increased productivity, reduced costs, enhanced self-monitoring, and autonomous decision-making. In such a setting, machine learning plays an important role in data analytics, but the use of conventional centralized machine learning solutions may raise uncomfortable concerns about data privacy. Hence, one can explore the use of federated learning. In this article, we propose privacy-preserving deep neural network training (PpNNT), which is designed to support federated learning in the multiparty setting. To minimize the overall costs, we further design a hybrid architecture to fully maximize resource utilization. Our proposed design allows the PpNNT system to provide high security, efficiency, and scalability for IIoT data analytics, as evidenced by our theoretical security proof and experimental results on the CIFAR10 dataset. © 2020 IEEE.

关键词： Decision making

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Hybrid Signal Integrity Modeling and Analysis of Heterogeneous Integrated System With Neuromorphic Darwin Chip

IEEE Transactions on Signal and Power Integrity

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IEEE Transactions on Signal and Power Integrity 2024年 3卷 37-46页

作者： Chen, Quankun Ma, Hanzhi Li, Da Tao, Tuomin Tan, Shurun Liu, En-Xiao Schutt-Aine, Jose Li, Er-Ping Zhejiang University Key Laboratory of Advanced Micro/Nano Electronic Devices and Smart Systems and Applications College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang University University of Illinois at Urbana-Champaign Institute Haining314400 China A∗star Institute of High Performance Computing Singapore138632 Singapore University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering UrbanaIL61820 United States

This article introduces a comprehensive approach for designing and analyzing signal integrity in heterogeneous integrated systems that incorporate neuromorphic Darwin chips. The proposed integrated system architecture includes a neuromorphic Darwin chip, digital signal processing unit, microcontroller unit, field programmable gate arrays, and coding and decoding modules to encode and reconstruct analog spiking signals. The study evaluates the encoding module and the heterogeneous integration structure and conducts signal integrity analysis. To achieve optimal signal integrity performance, the article proposes a novel binocular eye diagram analysis technique. This innovative approach guides the encoding algorithm modification and improves the overall system performance. This research is the first to combine joint field-circuit simulation, heterogeneous integration modeling, and signal integrity analysis of the Darwin neuromorphic chip, and it is expected to serve as a valuable reference for future studies on similar systems. © 2022 IEEE.

关键词： Neural networks

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