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Real-Time Object Detection on Edge Devices Using Mobile Neural Networks 2

Real-Time Object Detection on Edge Devices Using Mobile Neur...

2nd IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2024

作者： Jose Reena, K. Nigam, Charul Kirubasri, G. Jayachitra, S. Aeron, Anurag Suganthi, D. Vistas Department of Computer Science Tamilnadu Chennai India Institute of Information Technology and Management Department of It New Delhi India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India Psna College of Engineering and Technology Department of Ece Tamilnadu Dindigul India Dit University Dehradun School of Computing Department of Computer Science and Engineering Dehradun India Saveetha College of Liberal Arts and Sciences Simats Department of Computer Science and Engineering Thandalam Chennai India

ISBN: (纸本)9798350306415

In an era when edge computing rapidly evolves, this study addresses the significant difficulty of real-time object identification on resource-constrained edge devices. We provide a unique neural network model for edge scenarios in this paper. Our model covers object recognition system accuracy, computational efficiency, and speed. Object detection methods that need processing are impractical for edge computing. This paper proposes a Mobile Neural Network tailored to the limitations. Pruning, which decreases model size by 30%, and quantization make the model efficient. On several edge devices, the model had an average accuracy of 92% on Dataset 1 and 89% on Dataset 2. The model's 40-millisecond inference time and 2.3-watt power consumption were impressive. It outperformed standard CNN models and edge-optimized algorithms like YOLOv3 and SSD MobileNet under identical conditions. This study shows that the proposed paradigm can revolutionise edge computing real-time object detection. The model is suitable for high-responsiveness, energy-efficient applications. © 2024 IEEE.

关键词： Edge Computing Mobile Neural Networks Optimization Techniques Real-Time Object Detection

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Exploring the Potential of Quantum Computing for Revolutionary Problem-Solving and Computational Advancements

Exploring the Potential of Quantum Computing for Revolutiona...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Guru Vimal Kumar, M. Paramesh, J. Sriram, K.P. Anbalagan, E. Sasikumar, S. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India Information Technology St. Joseph's Institute of Technology OMR Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331542344

Quantum computing is a model of computing a new approach to perform and solve computational problems and comes up with amazing advantages such as exponential speed-up of specific tasks that lead to a revolution of industries. In this study, Quantum Computing potential is discussed in multiple fields - drug discovery and optimization, and quantum machine learning contrasted with traditional methods based on the running time, accuracy, and scalability of quantum and classical algorithms. Applying several quantum algorithms to molecular simulation, optimization problem, and image classification data sets, quantum algorithms solved problems with at least 70% less number of iterations and at worse the same accuracy as classical methods. For instance, researchers noted that quantum neural networks performed better than classical networks in image recognition. The quantum algorithms in this study were implemented and simulated using IBM Qiskit, D-Wave's Ocean SDK and TensorFlow Quantum. Based on the literature reviewed;it was established that quantum computing has potential to revolutionalise endeavours in drug design, cryptography and artificial intelligent applications. While there are issues in qubit stability quantum decoherence, errors and other related complications, it is predicted that all these problems will be solved by qubit stability and quantum error correction shortly and therefore, realistic, scalable quantum systems are in the next few years. Further research is required on the quantum technology one which may bring a revolution in the way different industries solve their problems. © 2024 IEEE.

关键词： Qubits

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Multi-View Human Activity Recognition in Ambient Assisted Living Using Lightweight Deep Learning Models 5

Multi-View Human Activity Recognition in Ambient Assisted Li...

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5th International Conference on Electrical, Communication and computer engineering, ICECCE 2024

作者： Bari, Ahsanul Karim, Hezerul Abdul Farid, Fahmid Al Asaduzzaman, Mina Amirabdollahian, Farshid Mansor, Sarina Multimedia University Faculty of Engineering Cyberjaya63100 Malaysia Multimedia University Faculty of Information Science and Technology Melaka63100 Malaysia University of Hertfordshire Faculty of Engineering and Computer Science Hatfield United Kingdom

ISBN: (纸本)9798331529437

Human Activity Recognition (HAR) is crucial for the development of intelligent assistive technologies in Ambient Assisted Living (AAL) environments. This paper proposes an innovative method for Multi-View Human Activity Recognition (MV-HAR) using lightweight deep learning models, specifically MobileNet and Cyclone-CNN (CCNet), to achieve quick and precise activity detection. Utilizing the Robot House Multi-View Human Activity Recognition (RHM-HAR) dataset, which contains four different views-front, back, ceiling (omni), and mobile robot-our models effectively address challenges related to viewpoint variation and motion dynamics. The dataset includes 14 multi-view daily living action classes, providing a balanced set of synchronized human actions suitable for multi-domain neural network learning. MobileNet and CCNet are employed for their high recognition accuracy, computational efficiency, and real-time application capabilities in AAL scenarios. We propose a Mutual information (MI)-based method to assess the redundancy and relevance of each viewpoint, ensuring the fusion of multi-view data with minimum redundancy and maximum relevance. Benchmarking results demonstrate that multi-view combinations significantly enhance recognition performance compared to single-view models, particularly in complex activities involving high levels of movement. © 2024 IEEE.

关键词： Assistive technology

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Retrieval Augmented Generation Model for Paper Recommendation System

SN Computer Science

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SN computer science 2025年第6期6卷 1-13页

作者： Yadav, Neha Gopinathan, Dhanalekshmi Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India

Academic paper recommendation systems are vital in streamlining research by helping scholars identify relevant literature efficiently. However, traditional approaches often fail to capture profound contextual relevance, especially in multidisciplinary domains. This study proposes a novel Retrieval-Augmented Generation (RAG) model that synergistically combines retrieval and generative mechanisms to address these limitations. The RAG architecture, including data preparation, model training, and integration of neural retrieval and generation components, is described in detail. Experimental results demonstrate that the RAG model significantly outperforms standard content-based filtering, achieving precision of 0.78, recall of 0.72, F1-score of 0.75, and MRR of 0.83. These results validate the model’s ability to generate contextually relevant and accurate academic recommendations. This paper identifies future directions to further enhance the model’s applicability across disciplines.

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Unsupervised Color Segmentation with Reconstructed Spatial Weighted Gaussian Mixture Model and Random Color Histogram

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computers, Materials & Continua 2024年第3期78卷 3323-3348页

作者： Umer Sadiq Khan Zhen Liu Fang Xu Muhib Ullah Khan Lerui Chen Touseef Ahmed Khan Muhammad Kashif Khattak Yuquan Zhang School of Computer and Information Science Hubei Engineering UniversityXiaogan432000China Institute for AI Industrial Technology Research Hubei Engineering UniversityXiaogan432000China Key Lab of Mountain Hazards and Surface Processes Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China University of Chinese Academy of Sciences Beijing100049China College of Aviation Zhongyuan University of TechnologyZhengzhou451191China Research Center of Digital Mountain and Remote Sensing Application Institute of Mountain Hazards and EnvironmentChinese Academy of SciencesChengdu610041China School of Computer Science University of PoonchAzad Jammu Kashmir12350Pakistan School of Computer Science and Information Engineering Hubei UniversityWuhan430062China

Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture *** the Gaussian mixture model enhances the flexibility of image segmentation,it does not reflect spatial information and is sensitive to the segmentation *** this study,we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model(GMM)without parameter *** proposed model highlights the residual region with considerable information and constructs color ***,we incorporate the content-based color saliency as spatial information in the Gaussian mixture *** segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum ***,the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation.A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art,facilitating both analytical and aesthetic *** experiments,we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge *** the study,the proposed model showcases notable advancements in unsupervised image segmentation,with probabilistic rand index(PRI)values reaching 0.80,BDE scores as low as 12.25 and 12.02,compactness variations at 0.59 and 0.7,and variation of information(VI)reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets,respectively,outperforming current leading-edge methods and yielding more precise segmentations.

关键词： Unsupervised segmentation color saliency spatial weighted GMM random color histogram

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KAN-HyperpointNet for Point Cloud Sequence-Based 3D Human Action Recognition

KAN-HyperpointNet for Point Cloud Sequence-Based 3D Human Ac...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Chen, Zhaoyu Li, Xing Huang, Qian Geng, Qiang Yang, Tianjin Han, Shihao College of Information Science and Technology & Artificial Intelligence Nanjing Forestry University China College of Computer Science and Software Engineering Hohai University Nanjing China

ISBN: (纸本)9798350368741

Point cloud sequence-based 3D action recognition has achieved impressive performance and efficiency. However, existing point cloud sequence modeling methods cannot adequately balance the precision of limb micro-movements with the integrity of posture macro-structure, leading to the loss of crucial information cues in action inference. To overcome this limitation, we introduce D-Hyperpoint, a novel data type generated through a D-Hyperpoint Embedding module. D-Hyperpoint encapsulates both regional-momentary motion and global-static posture, effectively summarizing the unit human action at each moment. In addition, we present a D-Hyperpoint KANsMixer module, which is recursively applied to nested groupings of D-Hyperpoints to learn the action discrimination information and creatively integrates Kolmogorov-Arnold Networks (KAN) to enhance spatio-temporal interaction within D-Hyperpoints. Finally, we propose KAN-HyperpointNet, a spatio-temporal decoupled network architecture for 3D action recognition. Extensive experiments on two public datasets: MSR Action3D and NTU-RGB+D 60, demonstrate the state-of-the-art performance of our method. © 2025 IEEE.

关键词： 3D action recognition D-Hyperpoint KAN-HyperpointNet point cloud sequence

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CampusSight—College Management Automation Using Full Stack and RPA 1

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Manohar, Eashaan Duraimurugan, N. Vijay, K. Narayana, K.E. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Information Technology Rajalakshmi Engineering College Chennai India

ISBN: (数字)9789819789467

ISBN: (纸本)9789819789450

The college management diaspora encapsulates various stakeholders such as students, faculty members, administrative personnel, and other staff. Unfortunately, most of the management workflows are undertaken manually, leading to silos, long paper work and admin overhauls. The pivotal aim of this research project is to digitalize the college management by bringing the entire setup into an intelligent application, leveraging key technologies like Automation, data analytics, full stack development. This application ultimately streamlines cumbersome tasks and enhance working across the college environment, promoting transparency and facilitating seamless interfaces for the entire user base. Swapping the monotonous manual workings with state-of-art digital platform will elevate the overall student experience by bringing down the high latency issue with the manual undertakings. Prime features of the application features management in terms of attendance, absences, on-duty requests, result disbursement, course registration, and facilitation of placement processes. Also, the system will promote enhanced organizing of events related to campus, industrial visits, increasing the net student engagement within the college and opening new opportunities. The system prioritizes core functionalities such as maintaining clean design and nimble experience, aligning with the increasing trend of digital transformation, granting greater college management autonomy. The application not only brings down the complexities prevailing in the existing college administration but also helps the higher education and placement processes alike, by leveraging key concepts like Data Analysis, Machine Learning and Artificial Intelligence from the data and insights stored, thereby adopting agile college management practices and emphasizing rapid digital growth, contributing to the entire education community. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Students

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A radius-incorporated localized multiple kernel learning algorithm for detecting depression in speech

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International Journal of Speech Technology 2023年第2期26卷 371-378页

作者： Jiang, Haihua Hu, Bin Liu, Zhenyu Wang, Gang Zhang, Lan School of Computer and Information Engineering Zhejiang Gongshang University Hangzhou China Faculty of Information Technology Beijing University of Technology Beijing China School of Information Science and Engineering Lanzhou University Lanzhou China Beijing Anding Hospital of Capital Medical University Beijing China Lanzhou University Second Hospital Lanzhou China

Early intervention for depression could provide a means to reducing the disease burden, but there is a lack of objective diagnostic methods. This study investigated automatic depression classification on a speech dataset of 85 healthy controls (51 females and 34 males) and 85 depressed patients (53 females and 32 males). Considering that there are obvious differences in the performance of different types of speech features, we propose a radius-incorporated localized multiple kernel learning (trLMKL) algorithm for detecting depression in speech to make the best use of speech features. To improve the classification accuracy, we combine the information of both the margin and the radius of the MEB to learn the gating model parameters in our algorithm. Furthermore, we do not directly incorporate the radius of the MEB, but incorporate the trace of the total scattering matrix of training data. This method can avoid the time cost of calculating the radius at each iteration and decrease the computational complexity. Comprehensive experiments were carried out on our depressed speech dataset and 10 UCI datasets. Our algorithm achieved better classification performance overall than SimpleMKL and LMKL, and it was efficient at detecting depression, indicating its potential for use as a diagnostic method for depression. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Iterative methods

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25th International Conference on Digital Audio Effects, DAFx 2022

作者： Silva, Nishal Turchet, Luca Department of Information Engineering and Computer Science University of Trento Italy

ISBN: (纸本)9783200085992

Automatic detection of musical patterns is an important task in the field of Music information Retrieval due to its usage in multiple applications such as automatic music transcription, genre or instrument identification, music classification, and music recommendation. A significant sub-task in pattern detection is the real-time pattern detection in music due to its relevance in application domains such as the Internet of Musical Things. In this study, we present a method to identify the occurrence of known patterns in symbolic monophonic music streams in real-time. We introduce a matrix-based representation to denote musical notes using its pitch, pitch-bend, amplitude, and duration. We propose an algorithm based on an independent similarity index for each note attribute. We also introduce the Match Measure, which is a numerical value signifying the degree of the match between a pattern and a sequence of notes. We have tested the proposed algorithm against three datasets: a human recorded dataset, a synthetically designed dataset, and the JKUPDD dataset. Overall, a detection rate of 95% was achieved. The low computational load and minimal running time demonstrate the suitability of the method for real-world, real-time implementations on embedded systems. Copyright: © 2022 Nishal Silva et al.

关键词： Music

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Rethinking Transferable Adversarial Attacks With Double Adversarial Neuron Attribution

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 354-364页

作者： Zhu, Zhiyu Jin, Zhibo Wang, Xinyi Zhang, Jiayu Chen, Huaming Choo, Kim-Kwang Raymond University of Sydney School of Electrical and Computer Engineering DarlingtonNSW2008 Australia University of Malaya Faculty of Computer Science and Information Technology KualaLumpur50603 Malaysia SuZhou Yierqi Jiangsu Suzhou215000 China The University of Texas at San Antonio San AntonioTX78249 United States

Transferable adversarial attacks are a threat to deep neural networks, in particular, for black-box scenarios where access to model information is limited. One can, for example, exploit the intermediate layer neurons to generate transferable adversarial samples. However, current works show limitations in providing feature-level attack mechanisms across multiple victim models. In light of the attribution methods, in this article, we investigate the attribution similarity across different models. We leverage the similarity to incorporate different attribution properties to enhance the sample transferability, for the first time, formulating a novel neuron attribution-based transferable attack termed DANAA++. Specifically, we utilize a range of adversarial attack methods to generate different baseline points through adversarial training. The attribution results are thus obtained along both linear and nonlinear integration paths. In our experiments, the baseline points and integration paths significantly help improve the transferability of adversarial samples. Our approach provides novel insights for building effective attribution-based feature-level adversarial attacks. https://***/LMBTough/DANAAPP. © 2020 IEEE.

关键词： Iterative methods

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