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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： Chempavathy B K. David Raju P. K. Mani S. Vijayalakshmi N. Janaki D. Karthikeyan Department of Computer Science and Engineering New Horizon College of Engineering Bangalore India Department of Electrical and Electronics Engineering Malla Reddy Engineering College For Women Hyderabad India Department of Electrical and Electronics Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai India Department of Electrical and Electronics Engineering K. Ramakrishnan College of Technology Samayapuram Tiruchirappalli Tamil Nadu Department of Electrical and Electronics Engineering Vels Institute of Science Technology and Advanced Studies Chennai Tamilnadu India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350372816

ISBN: (纸本)9798350372823

Asynchronous motors are widely used in a variety of sectors. Even though induction motors are strong and dependable, they are susceptible to a variety of problems. Faults in induction motors can result in horrible events such as operator injuries, manufacturing disruption, and raw material loss. As a result, defect identification has become more crucial in induction motor management. Among the different problems that can occur in the motor, bearing failure is a severe issue that can cause catastrophic damage to the machine if not detected at an early stage of the fault. As consequently, the functioning of bushings in induction machinery must be checked on a regular basis. This research proposes a novel method for analyzing speed using discrete wavelet transform (DWT) and identifying bearing difficulties using Probabilistic Neural Network (PNN) optimized by improved salp swarm algorithm. The stator electricity is examined and classified when an induction motor is operating under varied loading conditions with good and troublesome bearings. Scale-Invariant Feature Transform (SIFT) is a feature extraction method that condenses visual content into a set of points that may be used to find similar patterns in other images. The proposed PNN classifier can classify different types of bearing faults, and the empirical findings validate the created approach. PNN-based motor bearing recognizing defects and diagnosis outperforms SVM and ANN classifiers in this regard. The Shortest path accuracy of the PNN Classifier is 98.5% respectively.

关键词： Support vector machines Visualization Induction motors Accuracy Neural networks Transforms Feature extraction

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Improving Adversarial Transferability Through Hybrid Augmentation

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SSRN 2023年

作者： Zhu, Peican Fan, Zepeng Guo, Sensen Tang, Keke Li, Xingyu Xi’an China School of Computer Science NWPU Xi’an China School of Cybersecurity NWPU Xi’an China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada

Many works have shown that the adversarial examples being generated on a known substitute model have the ability to mislead other unknown black-box models, which has attracted widespread attention. Recently, many model augmentation methods have been presented to boost the corresponding transferability of adversarial examples, which usually attack a set of models simulated with augmented images. However, existing model augmentation methods focus on augmentations within a single domain to simulate models, thereby restricting the diversity of the simulated models. In this manuscript, we present a novel model augmentation method named Hybrid Augmentation Method (HAM), which combines our proposed channel-wise scaling (CS) and spectrum masking (SM) to enhance the diversity of simulated models. Specifically, we first scale the image by channel with CS, which augments them in spatial domain. Then we randomly remove some frequency information of the image with our SM, further augmenting the image in frequency domain. Instead of confining the augmentation to a single domain, we augment the image both in spatial and frequency domain simultaneously. This enables us to fully exploit the properties of image in different domains and largely increases the diversity of simulated models to create more powerful adversarial examples. Later, we conduct extensive experiments to demonstrate the superiority of our method on both undefended and defense models, which outperforms the considered attacks by a large extent. Moreover, our HAM can be integrated with other attacks to further enhance adversarial transferability. © 2023, The Authors. All rights reserved.

关键词： Artificial intelligence

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A disposable tartrazine sensor fabricated with a synchronously activated nanocomposite comprising gadolinium molybdate nanoflowers anchored on functionalized carbon nanofibers

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New Journal of Chemistry 2023年第6期48卷 2660-2672页

作者： Jayapaul, Abishek Sakthivel, Rajalakshmi Lin, Yu-Chien Dhawan, Udesh Liu, Xinke Wen, Hsiao-Wei Chung, Ren-Jei No. 1 Section 3 Zhongxiao E. Rd. Taipei10608 Taiwan Centre for the Cellular Microenvironment Division of Biomedical Engineering James Watt School of Engineering Mazumdar-Shaw Advanced Research Centre University of Glasgow GlasgowG116EW United Kingdom College of Materials Science and Engineering Chinese Engineering and Research Institute of Microelectronics Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore Department of Food Science and Biotechnology National Chung Hsing University Taichung402202 Taiwan Food and Animal Product Safety Inspection Center National Chung Hsing University Taichung402202 Taiwan Taipei10608 Taiwan

Tartrazine (TRZ) is a synthetic food dye considered noxious to human health and a toxic environmental contaminant owing to the presence of a harmful azo group (-N 00000000 00000000 00000000 00000000 11111111 00000000 11111111 00000000 00000000 00000000 N-) in its chemical structure. The global necessity to prevent food contamination has led scientific researchers to develop disposable sensing platforms. This study aims to integrate nanotechnology with electrochemistry to fabricate a disposable screen-printed carbon electrode (SPCE) with a nanocomposite composed of gadolinium molybdate (Gd2MoO6) nanoflowers decorated on an enmeshed matrix of acid-functionalized carbon nanofibers (f-CNF). The hierarchal arrangement of distinct components in the nanocomposite is promoted by synergism through the formation of a hybrid heterojunction. This heterojunction in the electrode modifier activates the electrode to sense TRZ with a remarkable limit of detection (0.8 nmol L−1) and quantification (2 nmol L−1) over a broad linear range of 0.025 μM to 216.45 μM. Furthermore, the functional sensing ability of the proposed sensor was analyzed in various commercial food products. © 2024 The Royal Society of Chemistry.

关键词： Nanocomposites

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STARGATE: Spatial Transcriptomic Analysis with Recurrent and Graph Attention Techniques using Ensemble Learning

STARGATE: Spatial Transcriptomic Analysis with Recurrent and...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ning Li Jiayidaer Badai Dengjie Chen Ming Xiao Le Zhang College of Computer Science Sichuan University Chengdu China College of Mechanical and Electrical Engineering Fujian A&F University Fuzhou China College of Computer Science Sichuan University Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Chengdu China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Previous research has identified hematoma as the primary cause of mortality and disability in intraventricular hemorrhage (IVH), yet neurologic outcomes post-IVH have not significantly improved following hematoma evacuation. Therefore, utilizing spatial transcriptomics sequencing technology to acquire and analyze IVH data is of significant importance for the early diagnosis and treatment of acute cerebral hemorrhage. However, existing spatial transcriptomics clustering algorithms often overlook local information, fail to integrate multimodal data, and suffer from performance instability. To address these issues, we developed a multimodal spatial transcriptomics clustering algorithm named STARGATE, which initially captures the global spatial topology and local long-sequence gene set features of cells by integrating Graph Convolutional Networks (GCNs) based on self-attention mechanisms and Long Short-Term Memory networks (LSTMs), subsequently fuses gene expression, tissue imagery, morphological, and spatial coordinate features using Gated Recurrent Units (GRUs), and finally enhances the accuracy and robustness of predictions through ensemble learning methods. Our key experimental results include: (1) In clustering experiments on our proprietary mouse IVH spatial transcriptomics dataset, the STARGATE model achieved Adjusted Rand Index (ARI), Normalized Mutual Information (NMI), and Fowlkes-Mallows Index (FMI) scores of 0.80, 0.84, and 0.82, respectively, all superior to classic algorithms such as stLearn and CCST. (2) In clustering experiments on the mouse IVH dataset, the STARGATE model reached an accuracy of 89.63%, with an excellent confusion matrix, demonstrating outstanding accuracy and stability. (3) In clustering experiments on the public dataset DLFPC, which is known for its high data complexity leading to generally lower clustering metrics, the STARGATE model's ARI, NMI, and FMI indicators still outperformed classic algorithms such as SpaGCN and Seurat.

关键词： Accuracy Transcriptomics Stability criteria Clustering algorithms Data models Spatial databases Topology Indexes Ensemble learning Hemorrhaging

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Efficient Compute Resource Sharing of RISC-V Packed-SIMD Using Simultaneous Multi-threading 30th

Efficient Compute Resource Sharing of RISC-V Packed-SIMD Us...

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30th International Conference on Parallel and Distributed Processing Techniques, PDPTA 2024, held as part of the World Congress in computer Science, computer engineering and Applied Computing, CSCE 2024

作者： Takata, Shogo Nakajo, Hironori Department of Electrical Engineering and Computer Science Graduate School of Engineering Tokyo University of Agriculture and Technology 2–24–16 Nakacho Tokyo Koganei-shi184–8588 Japan Division of Advanced Information Technology Computer Science Institute of Engineering Tokyo University of Agriculture and Technology 2–24–16 Nakacho Tokyo Koganei-shi184–8588 Japan

ISBN: (纸本)9783031856372

AI tasks are gaining popularity in the area of IoT and edge devices. To run such tasks on devices, QNNs are used because of their reduced size and ability to be computed with simple integer arithmetic. There have been many implementations to support such a network format. However, when considering thread-level parallelism to speedup the program, many often implement a multi-core architecture or clusters which needs to copy all resources for each core. In this paper, we introduce a new RISC-V Out-of-Order Simultaneous Multi-Threading core "B4SMT" with RISC-V Packed-SIMD extension for evaluation. We also show that even a single executor could increase the performance of a 1D median filter by over 100×, and a matrix multiplication by over 30 on more than 16 threads efficiently. Furthermore, we suggest that other infrequently used executors may be placed as a shared resource efficiently in an SMT core. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Median filters

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A Little Chit-Chat Goes a Long Way: Design and Evaluation of Task-and Person-Oriented Styles for Social Robots

A Little Chit-Chat Goes a Long Way: Design and Evaluation of...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Elie Saad Joost Broekens Mark A. Neerincx Department of Intelligent Systems Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands School of Engineering Holy Spirit University of Kaslik Jounieh Lebanon The Leiden Institute of Advanced Computer Science Leiden University Leiden The Netherlands The Netherlands Organization for Applied Scientific Research (TNO) Soesterberg The Netherlands

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Whereas the reception task is a promising application domain for social robots, knowledge is lacking about how to design the appropriate re-usable communication styles for a reception robot. This paper presents the use and evaluation of an iterative interaction-design (ID) method with which task- and person-oriented multi-modal communication styles have been designed for such a robot. First, we report on an evaluation study of the ID-method with Industrial Design students (N =13) who designed these two communication styles for a Pepper robot. This provided a set of distinct designs of the two styles, for which the differences in design parameters were in line with social science theory. The task-oriented style showed a more formal, shorter and less chatty communication. Second, we present findings from a Mechanical Turk study conducted to evaluate the perception of these style designs. Participants (N =301) were presented with videos showing the robot acting as a receptionist and were asked to rate their perception of the robot, the service experience and the orientation of the designs. Overall, the interaction with the robot was appreciated well. The robot with a person-oriented style was perceived to be more animate and likeable. Analysis showed that chit-chat was the main contributor to the perceived difference between the person-oriented and task-oriented styles. This is an important finding as it gives interaction designers a validated best-practice approach to make interaction style more or less personal.

关键词： Service robots Social robots Design tools Behavioral sciences Iterative methods Robots Videos

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Synthetic frequency dimension state coupling in modulated LNOI ring cavity devices

Synthetic frequency dimension state coupling in modulated LN...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Balčytis, Armandas Dinh, Xuan Hiep Ozawa, Tomoki Ota, Yasutomo Baba, Toshihiko Iwamoto, Satoshi Mitchell, Arnan Nguyen, Thach G. Integrated Photonics and Applications Centre RMIT University MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku University Sendai9808577 Japan Department of Applied Physics and PhysicoInformatics Keio University Yokohama2238522 Japan Department of Electrical and Computer Engineering Yokohama National University 795 Tokiwadai Hodogayaku Yokohama2408501 Japan Research Center for Advanced Science and Technology The University of Tokyo 461 Komaba Meguroku Tokyo1538904 Japan

ISBN: (纸本)9781957171258

We created a modulatorequipped thinfilm lithium niobate integrated ring resonator simulating a synthetic frequency dimension tightbinding model. It produces frequency states spaced over a 600 GHz bandwidth. Reconfigurable coupling enables simulation of disparate twodimensional interactions. © Optica Publishing Group 2023 © 2023 The Author(s)

关键词： Niobium compounds

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Intelligent Breast Cancer Prediction Empowered with Fusion and Deep Learning

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computers, Materials & Continua 2021年第4期67卷 1033-1049页

作者： Shahan Yamin Siddiqui Iftikhar Naseer Muhammad Adnan Khan Muhammad Faheem Mushtaq Rizwan Ali Naqvi Dildar Hussain Amir Haider School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan School of Computer Science Minhaj University LahoreLahore54000Pakistan Department of Computer Science&Information Technology Superior UniversityLahore54000Pakistan Department of Computer Science Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Information Technology Khwaja Fareed University of Engineering and Information TechnologyRahim Yar Khan64200Pakistan Department of Unmanned Vehicle Engineering Sejong UniversitySeoul05006Korea School of Computational Sciences Korea Institute for Advanced Study(KIAS)85 HoegiroDongdaemunguSeoul02455Korea Department of Intelligent Mechatronics Engineering Sejong UniversitySeoul05006Korea

Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality *** to clinical statistics,one woman out of eight is under the threat of breast *** and inheritance patterns may be a reason behind its spread among ***,some preventive measures,such as tests and periodic clinical checks can mitigate its risk thereby,improving its survival chances *** diagnosis and initial stage treatment can help increase the survival *** that purpose,pathologists can gather support from nondestructive and efficient computer-aided diagnosis(CAD)*** study explores the breast cancer CAD method relying on multimodal medical imaging and decision-based *** multimodal medical imaging fusion,a deep learning approach is applied,obtaining 97.5%accuracy with a 2.5%miss rate for breast cancer prediction.A deep extreme learning machine technique applied on feature-based data provided a 97.41%***,decisionbased fusion applied to both breast cancer prediction models to diagnose its stages,resulted in an overall accuracy of 97.97%.The proposed system model provides more accurate results compared with other state-of-the-art approaches,rapidly diagnosing breast cancer to decrease its mortality rate.

关键词： Fusion feature breast cancer prediction deep learning convolutional neural network multi-modal medical image fusion decision-based fusion

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention 26

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Bao, Chengao Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Tokyo Institute of Technology Yokohama226-8502 Japan Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798331521950

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method. © 2025 IEEE.

关键词： Modal analysis

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Joint and Simultaneous Optimization of Artificial Noise-aided Precoding for Secure Communications

Joint and Simultaneous Optimization of Artificial Noise-aide...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Eunsung Choi Mintaek Oh Jinseok Choi Jeonghun Park Namyoon Lee Naofal Al-Dhahir Electrical Engineering Ulsan National Institute of Science and Technology Ulsan Republic of Korea Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Republic of Korea Electrical and Electronics Engineering Yonsei University Seoul Republic of Korea Electrical Engineering Korea University Seoul Republic of Korea Electrical and Computer Engineering Department The University of Texas Dallas TX USA

The joint design of secure precoding and artificial noise (AN) transmission scheme is promising to improve secrecy performance. However, in downlink multi-user multiple-input multiple-output (MU-MIMO) systems with multiple eavesdroppers, joint design of secure precoding and AN structure involves several challenges: an objective function is non-convex and non-smooth, and a precoding matrix and AN matrix have different design principles. Classically, to jointly design precoding and AN covariance matrix, an alternating optimization approach is used which has limitations in terms of the secrecy rate performance since it does not offer joint and simultaneous optimization of the precoding and AN covariance matrices. In this paper, we propose a novel optimization framework that optimizes the precoder and the AN covariance matrix jointly and simultaneously to maximize the secrecy rate. First, we approximate the objective function to a tractable non-convex form. Next, we derive the first-order optimality condition by leveraging the nonlinear eigenvalue problem (NEP) form. Finally, we utilize an efficient technique with low computational complexity for identifying the major eigenvector of the NEP which corresponds to the best stationary point. Simulations illustrate that the proposed methods enhance the secrecy rate performance compared to the existing secure precoding methods.

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