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arXiv 2024年

作者： Mukherjee, Avideep Banerjee, Soumya Rai, Piyush Namboodiri, Vinay P. Department of Computer Science and Engineering Indian Institute of Technology Kanpur Uttar Pradesh India Department of Computer Science University of Bath Claverton Down BathBA2 7AY United Kingdom

Diffusion-based models demonstrate impressive generation capabilities. However, they also have a massive number of parameters, resulting in enormous model sizes, thus making them unsuitable for deployment on resource-constraint devices. Block-wise generation can be a promising alternative toward designing compact-sized (parameter-efficient) deep generative models since the model can generate one block at a time, instead of generating the whole image at once. However, block-wise generation is also considerably challenging because ensuring coherence across the generated blocks can be a non-trivial task. To this end, we design a retrieval-augmented generation (RAG) approach and leverage the corresponding blocks of the images retrieved by the RAG module to condition the training and generation stages of a block-wise denoising diffusion model. Our conditioning schemes ensure coherence across the different blocks during training and, consequently, during generation. While we showcase our approach using the latent diffusion model (LDM) as the base model, it can be used with other variants of denoising diffusion models. We validate the solution of the coherence problem through the proposed approach by reporting substantive experiments to demonstrate our approach’s effectiveness in compact model size and excellent generation quality. © 2024, CC BY.

关键词： Image denoising

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False Data Injection Attacks Against High Voltage Transmission Systems 19

False Data Injection Attacks Against High Voltage Transmissi...

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19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Radoglou-Grammatikis, Panagiotis Zafeiropoulou, Magda Atanasova, Maria Zlatev, Pencho Giannakidou, Sofia Lagkas, Thomas Argyriou, Vasileios Markakis, Evangelos K. Moscholios, Ioannis Sarigiannidis, Panagiotis University of Western Macedonia Department of Electrical and Computer Engineering Kozani50100 Greece Sofia1784 Bulgaria K3Y Ltd Sofia1612 Bulgaria International Hellenic University Department of Computer Science Kavala Campus 65404 Greece Kingston University London Department of Networks and Digital Media SurreyKT1 2EE United Kingdom Hellenic Mediterranean University Department of Electrical and Computer Engineering Crete71004 Greece University of Peloponnese Department of Informatics and Telecommunications Tripolis22100 Greece

ISBN: (纸本)9798350346497

The digitisation of the smart electrical grid provides several advantages and valuable services, such as self-monitoring, pervasive control and smart healing. However, despite the benefits of this progression, critical cybersecurity and privacy issues are raised due to the vulnerabilities of legacy Electrical Power and Energy Systems (EPES) and the evolution of stealthy cyberthreats and malware. In this paper, we give emphasis to False Data Injection Attacks (FDIAs) that can affect the EPES State Estimation (SE). In particular, we investigate two FDIA categories, namely: (a) Global Positioning System (GPS) Spoofing Attacks and (b) IEEE C37.118 FDIAs against an actual testbed emulating a high-voltage IEEE 9-Bus transmission grid. Finally, we provide a relevant Intrusion Detection System (IDS) capable of detecting the aforementioned FDIAs. The evaluation analysis demonstrates the impact of the above FDIAs and the efficiency of the proposed IDS. © 2023 IEEE.

关键词： Legacy systems

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Quantum-Inspired Superresolution for Multiple Incoherent Optical Point Sources

Quantum-Inspired Superresolution for Multiple Incoherent Opt...

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CLEO: science and Innovations, S and I 2022

作者： Qi, Luo Tan, Xiaojie Chen, Lianwei Ng, Kenneth Y.W. Danner, Aaron J. Tsang, Mankei Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 117583 Singapore Department of Physics National University of Singapore 2 Science Drive 3 117551 Singapore

We report a superresolution experiment based on SPLICE. We observe a worst-case error 41 times below the diffraction limit with two sources and > 24 dB signal-to-noise ratios for semiparametric estimation with mult... 详细信息

ISBN: (纸本)9781557528209

关键词： Signal to noise ratio

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Eight-direction least-square phase unwrapping algorithm for optical three-dimensional measurement 3

Eight-direction least-square phase unwrapping algorithm for ...

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3rd International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2023

作者： He, Yantao Liu, Sijia Pan, Minling Wang, Rongfu Li, Xueri Zhang, Rong Department of Computer Science Guangdong University of Science and Technology No. 2 Songshan Lake Section 523668 China School of Mechanical Engineering Hunan University of Science and Technology Taoyuan Road 411201 China School of Electronics and Information Engineering Beibu Gulf University No. 12 Binhai Avenue 535011 China

ISBN: (纸本)9781510668522

The least-square method is widely used in the fast phase unwrapping of optical three-dimensional measurement due to its high-speed and comprehensive characteristics. In order to solve the problem of measurement error propagation in the phase unwrapping algorithm based on least-square method, an improved eight-direction least-square phase unwrapping algorithm is proposed in this paper. This algorithm not only researches the gradient difference between the target phase and its eight-neighbor phases, improving measurement accuracy and reducing measurement errors. The superiority and robustness of this algorithm have been demonstrated through simulation data and measurement of industrial bearing roller end faces © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Measurement errors

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Synchronous speed control for industrial production line based on BP neural network

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International Journal of Manufacturing Technology and Management 2022年第2-4期36卷 127-140页

作者： Pan, Tianci Zhu, Changhong Department of Information Engineering Bowen College of Management Guilin University of Technology No. 317 Yanshan Street Guangxi Guilin541006 China School of Computer Science and Engineering Guilin University of Aerospace Technology No. 2 Jinji Road Guangxi Guilin541004 China

In order to overcome the problems of large speed control error and poor anti-interference effect existing in the traditional speed control methods, the paper proposes a speed synchronisation control method of industrial production line based on BP neural network. Firstly, the state of production equipment is adjusted through PLC operation instruction, and amplifier circuit is designed to reduce the influence of signal interference. Then the speed control parameters of the production line are adjusted by adaptive control method, and the parameters are fused by fuzzy control theory. Finally, the speed of the industrial production line is synchronously controlled by BP neural network. The experimental results show that the control error coefficient of this method is always lower than 9%, and the influence of step disturbance signal is low, indicating that this method has good application performance. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Speed control

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Measurement of Dissemination Characteristics of LTE-V2X and ITS-G5 for Safety-Critical Vehicle-to-Vehicle Communication 5

Measurement of Dissemination Characteristics of LTE-V2X and ...

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5th International Conference on Advanced Communication Technologies and Networking, CommNet 2022

作者： Stellwagen, Jochen Deegener, Matthias Kuhn, Michael Frankfurt University of Applied Sciences Facility 2: Computer Science and Engineering Frankfurt a.M. Germany Darmstadt University of Applied Sciences Department of Electrical Engineering Darmstadt Germany

ISBN: (数字)9781665450546

ISBN: (纸本)9781665450546

Vehicle-to-vehicle communication enables the exchange of information between road users with the aim of improving safety and efficiency in the traffic environment. The highly dynamic environment with fast moving participants presents major challenges for the transmission technologies that are used. Furthermore, safety-critical applications in particular, place high demands on the reliability of the systems. Currently, there are two competing technologies for Vehicle-to-Vehicle communication: ITS-G5 and LTE-V2X. These two technologies have the potential to be used together due to their specific characteristics. As a result, they can complement each other constructively. In preparation for a comprehensive study of such a hybrid utilization, measurements were carried out to determine the characteristics of each technology in real traffic environment. The results of these measurements with both systems show, on the one hand, the characteristic properties of the technologies. On the other hand, they provide the information needed for the subsequent development of situation- and environment-dependent transmission models, that can be used in simulation environments to investigate complex traffic scenarios. This investigation should provide a direct comparison between different transmission technologies for the V2X communication. The measurement is focused on the successful transmission of information. The goal is to characterize the technologies by parameters that can be directly compared with the requirements of V2X use cases. The measurement results should be later used to create dissemination models for a simulation environment to investigate the communication for several use cases in a larger scale, especially in hybrid V2X networks. © 2022 IEEE.

关键词： Vehicle to vehicle communications

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Quantized Tensor Neural Network

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ACM/IMS Transactions on Data science 2021年第4期2卷 1–18页

作者： Gao, Yuan Yang, Laurence T. Zheng, Dehua Yang, Jing Zhao, Yaliang School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technology Huazhong University of Science and Technology and Department of Computer Science St. Francis Xavier University AntigonishNSB2G2W5 Canada Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University and with Department of Computer Science St. Francis Xavier University Antigonish Canada

Tensor network as an effective computing framework for efficient processing and analysis of high-dimensional data has been successfully applied in many fields. However, the performance of traditional tensor networks still cannot match the strong fitting ability of neural networks, so some data processing algorithms based on tensor networks cannot achieve the same excellent performance as deep learning models. To further improve the learning ability of tensor network, we propose a quantized tensor neural network in this article (QTNN), which integrates the advantages of neural networks and tensor networks, namely, the powerful learning ability of neural networks and the simplicity of tensor networks. The QTNN model can be further regarded as a generalized multilayer nonlinear tensor network, which can efficiently extract low-dimensional features of the data while maintaining the original structure information. In addition, to more effectively represent the local information of data, we introduce multiple convolution layers in QTNN to extract the local features. We also develop a high-order back-propagation algorithm for training the parameters of QTNN. We conducted classification experiments on multiple representative datasets to further evaluate the performance of proposed models, and the experimental results show that QTNN is simpler and more efficient while compared to the classic deep learning models. © 2022 Association for Computing Machinery.

关键词： Tensors

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MIMO Virtual Array Design for mmWave 4D-Imaging Radar Sensors 31

MIMO Virtual Array Design for mmWave 4D-Imaging Radar Sensor...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Sichani, Nazila Karimian Alaee-Kerahroodi, Mohammad Raei, Ehsan Bhavani Shankar, M.R. Mehrshahi, Esfandiar Ghorashi, Seyyed Ali Department of Telecommunications Faculty of Electrical Engineering Shahid Beheshti University Tehran*** Iran University of Luxembourg Luxembourg Department of Computer Science and Digital Technologies School of Architecture Computing and Engineering University of East London LondonE16 2RD United Kingdom

ISBN: (纸本)9789464593600

The recently developed 4D-Imaging mmWave MIMO radars have significant advantages over conventional radar sensors because they offer a large virtual array in both azimuth and elevation. However, the physical placement of transmit and receive antennas to achieve the desired virtual array, while considering cost and efficiency is not intuitive. In this paper, we propose an optimization framework for optimally placing the transmit and receive array physical elements. The proposed framework obtains a sequence of optimal placements based on the coordinate descent by defining a desired virtual array and number of transmit and receive antenna elements as the objective. The objective function is then monotonically minimized in each iteration of the proposed algorithm, converging to a solution that guarantees the desired number of transmit and receive antenna elements, while the obtained virtual array is as close to the desired virtual array as possible. The simulation results are validated by simulating the virtual array of multiple commercially available 4D-Imaging radar products. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Radar imaging

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Advanced eHealth with Explainable AI: Secured by Blockchain with AI-Empowered Block Sensitivity for Adaptive Authentication 35

Advanced eHealth with Explainable AI: Secured by Blockchain ...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Dutta, Joy Eldeeb, Hossien B. Ho, Tu Dac Khalifa University C2PS Research Center Department of Computer Science Abu Dhabi United Arab Emirates University of Cambridge Electrical Engineering Division CambridgeCB3 0FA United Kingdom Faculty of Information Technology and Electrical Engineering Trondheim Norway

ISBN: (纸本)9798350362244

This paper presents an innovative, yet secure, eHealth framework that leverages Explainable Artificial Intelligence (XAI) and blockchain technology to enhance transparency and security in the IoT-edge-cloud continuum. The framework incorporates SHapley Additive exPlanations (SHAP) to provide real-time, model-agnostic explanations for AI predictions, enabling personalized health monitoring and informed decisionmaking in healthcare. To strengthen data security, a consortium blockchain is employed, and AI is utilized to identify block data sensitivity at the edge within blockchain-integrated IoT architectures using Random Forest (RF) algorithm. This approach achieves high accuracy in validating block sensitivity, enabling efficient selection of authentication mechanisms in the proof of authentication (PoAh) consensus within the consortium blockchain. This ensures heightened protection for sensitive data and contributes to improved overall blockchain performance. The proposed framework is evaluated in an edge computing environment and demonstrates significant potential for advancing security and authentication in eHealth, representing a substantial advancement in healthcare technology. © 2024 IEEE.

关键词： Electronic health record

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A novel hybrid CNN-KNN ensemble voting classifier for Parkinson’s disease prediction from hand sketching images

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Multimedia Tools and Applications 2024年 1-33页

作者： Saleh, Shawki Ouhmida, Asmae Cherradi, Bouchaib Al-Sarem, Mohammed Hamida, Soufiane Alblwi, Abdulaziz Mahyoob, Mohammad Bouattane, Omar EEIS Laboratory ENSET of Mohammedia Hassan II University of Casablanca Mohammedia Morocco STIE Team CRMEF Casablanca-Settat Provincial Section of El Jadida El Jadida24000 Morocco College of Computer Science and Engineering Taibah University Medina42353 Saudi Arabia 2IACS Laboratory ENSET of Mohammedia Hassan II University of Casablanca Mohammedia Morocco Department of Computer Science Applied College Taibah University Medina Saudi Arabia Dept. of Languages and Translation College of Arts and Humanities Taibah University Medina Saudi Arabia Technical Community College Taiz Yemen

Parkinson's disease is a progressive neurodegenerative disorder that causes significant physical disabilities and reduces the quality of life. This disease is caused by the loss of dopamine-producing cells in the brain. Its symptoms are Speech disorders, muscle rigidity, bradykinesia and tremors that cause involuntary shaking or trembling, typically starting in the hands, fingers, or limbs at rest. In this work, we focused on predicting this disease via the hand tremor, which appears in the speed and pen pressure that vary between healthy and affected people during sketching spiral and waves. To enhance the medical services, improve lifestyle and for early detection of people with Parkinson’s disease, we proposed an ensemble voting classifier that combines the Convolutional Neural Network and K-Nearest Neighbours (KNN) to decide whether or not a person has Parkinson's disease based on predicting spiral and wave sketching separately. Contrary to the traditional Convolutional Neural Network, the proposed architecture offers better flexibility in scenarios where data may be small and imbalanced to avoid overfitting or when capturing the nuanced relationships between data points (by considering their neighbours) can be beneficial. Moreover, the proposed system has been built to automate the extraction of features from images and perform classification. This work represents several approaches, such as image processing, developing Convolutional Neural Networks models, hyper-parameters tuning, transfer learning, feature extraction and developing a hybrid classifier that combines deep learning and machine learning to enhance the performance of the prediction. We have developed six models to predict Parkinson's disease using a Spiral-Wave dataset and provided a detailed explanation and comparison between their performances. Based on these models, we built the hybrid ensemble voting CNN-KNN classifier that reached 96.67% accuracy and 93.33% and 100% sensitivity and precision,

关键词： Feature extraction

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