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IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Bin Li Jun Li Qinghua Guo National Key Laboratory of Radar Signal Processing Xidian University Xian China School of Electrical Computer and Telecommunications Engineering University of Wollongong Wollonggong Australia

This work deals with the issue of target detection in passive multiple-input and multiple-output (MIMO) radar networks. Compared to distributed detection, centralized detection offers better performance but at the cost of high communication and computational costs. To address this issue, we propose a novel moments space based centralized detection method, where the moments of received signal amplitudes are computed as their features, and a low complexity moments space log-likelihood ratio test is proposed. As only the moments need to be sent to the fusion center, the proposed method enjoys low communication and computational costs. Numerical results demonstrate that the proposed method significantly outperforms existing methods in various scenarios.

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Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge Computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

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Rule-Based Controller Design for Frequency Control in Islanded Microgrids

Rule-Based Controller Design for Frequency Control in Island...

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International Conference on Power Electronic Drives

作者： Sugandh Pratap Singh V. P. Singh Francesco Benedetto Electrical Engineering Department Indian Institute of Technology Kanpur UP India Electrical Engineering Department Malaviya National Institute of Technology Jaipur Rajasthan India Economic Department Signal Processing for Telecommunications and Economics Laboratory University of ROMA TRE Italy

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

This paper presents a Cohen-Coon rule-based controller designed for frequency regulation in islanded microgrids, crucial for maintaining stability during grid disconnections. The microgrid integrates diverse renewable and conventional energy sources, including PV panels, wind turbines, and biodiesel generators, alongside energy storage systems. The study focuses on transforming the microgrid's higher-order model into a simplified first-order plus dead time model and optimizing controller tuning using the Cohen-Coon method. Real-time simulations on Opal-RT validate the controller's performance under varying load conditions and intermittent renewable energy inputs. Results demonstrate the controller's effectiveness in minimizing frequency deviations, affirming its robustness for ensuring stable power supply in dynamic microgrid environments.

关键词： Renewable energy sources Biological system modeling Microgrids Power system stability Real-time systems Stability analysis Robustness Wind turbines Tuning Load modeling

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Fuzzy Intelligence Learning Based on Bounded Rationality in IoMT Systems: A Case Study in Parkinson's Disease

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IEEE Transactions on Computational Social Systems 2023年第4期10卷 1607-1621页

作者： Zhang, Chao Ding, Juanjuan Zhan, Jianming Sangaiah, Arun Kumar Li, Deyu Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education Shanxi University School of Computer and Information Technology Taiyuan030006 China Hubei Minzu University School of Mathematics and Statistics Enshi445000 China International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As a cause of interfering with routine activities, freezing of gait (FOG) is a severe syndrome of Parkinson's disease (PD) and usually performs as an abrupt and momentary inability to effective stepping forward. Advanced wearable acceleration sensors based on socially implemented Internet of medical things (IoMT) devices can remotely provide a platform for recognizing FOG. However, due to the diverse data acquisition modes that appear in classic IoMT devices, the obtained data may contain imprecise, hesitant, and incomplete ones. Meanwhile, the bounded rationality owned by neurologists usually has a big impact on using wearable acceleration sensors to predict illnesses. Therefore, the objective of this article lies in exploring a fuzzy intelligence learning approach based on bounded rationality in IoMT systems and providing a valid scheme for biomedical data analysis. Specifically, a brand-new three-way group decision-making approach by means of TODIM (an acronym in Portuguese for interactive multicriteria decision-making) with incomplete dual hesitant fuzzy (DHF) information and its applications in detecting FOG in PD using IoMT devices are systematically explored. First, taking advantage of DHF sets (DHFSs) when depicting realistic group decision information, the concept of multigranulation (MG) incomplete DHF information systems is built. Second, adjustable MG DHF probabilistic rough sets (PRSs) are further put forward via DHF similarity relations. Third, a three-way group decision-making approach is constructed by virtue of adjustable MG DHF PRSs and TODIM. Finally, the validity, effectiveness, and practicality of the constructed three-way group decision-making approach are investigated by a University of California, Irvine (UCI) dataset with several experimental analyses in the background of FOG detection in PD using IoMT devices. The experimental result indicates that the developed fuzzy intelligence learning approach achieves reasonable diagnostic conclusions

关键词： Diagnosis

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Constrained Energy Minimization (CEM) for Hyperspectral Target Detection: Theory and Generalizations

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IEEE Transactions on Geoscience and Remote Sensing 2024年 62卷

作者： Chang, Chein-I. Information and Technology College Dalian116026 China University of Maryland Baltimore County Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering BaltimoreMD21250 United States National Cheng Kung University Department of Electrical Engineering Tainan70101 Taiwan

Target detection is a fundamental task of hyperspectral imaging where constrained energy minimization (CEM) has been widely used for subpixel target detection techniques. Due to its effectiveness, CEM has been generalized to various versions, such as kernel CEM (KCEM), kernel target-constrained interference-minimized filter (KTCIMF), ensemble cascaded CEM (ECEM), and hierarchical CEM (HCEM). Unfortunately, these generalizations overlooked the key design rationale behind CEM. This article revisits CEM for hyperspectral target detection (HTD) and proves how and why it works mathematically. Specifically, several new CEM generalizations are derived and particularly noteworthy. By including spatial information in an iterative process, KCEM, ECEM, and HCEM can be generalized to iterative KCEM (IKCEM), iterative KTCIMF (IKTCIMF), iterative ECEM (IECEM), and iterative HCEM (IHCEM). Also, by utilizing an iterative random training sampling (IRTS) to generate the desired target signature to be detected, these algorithms are further generalized to IRTS KCEM (IRTS-KCEM), IRTS ECEM (IRTS-ECEM), and IRTS HCEM (IRTS-HCEM). A comprehensive analysis along with comparative study on these generalizations is conducted through extensive experiments to demonstrate the effectiveness of IKCEM, IHCEM, and IECEM. © 1980-2012 IEEE.

关键词： Iterative methods

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Entangled Photons Based Particle-Level Temporal-Spatial Matching processing in Quantum Radar

Entangled Photons Based Particle-Level Temporal-Spatial Matc...

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2021 CIE International Conference on Radar, Radar 2021

作者： Wang, Weihao Li, Jun Wei, Rongyu Guo, Qinghua Xidian University National Laboratory of Radar Signal Processing Xian China School of Electrical Computer and Telecommunications Engineering University of Wollongong Wollongong Australia

ISBN: (纸本)9781665498142

With the continuous development of quantum technology, entangled signal has been used in more and more fields. Due to the unique temporal-spatial correlation characteristics of entangled signal, it provides promising solutions to problems such as super-resolution and anti-interference. Especially in the context of weak signal, the entangled signal-based sensing is more prominent. In this paper, we present a quantum imaging framework based on particle level matching processing under the correlation characteristics of entangled photons. The temporal-spatial correlation property of the entangled photon is used to realize the matched filtering to accumulate the weak signals in time domain and reconstructed the target image in spatial domain. © 2021 IEEE.

关键词： Entangled-photons imaging particle-level processing quantum radar

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Investigation on the Advantages of Quantum Illumination Radar by Using Radar Equation

Investigation on the Advantages of Quantum Illumination Rada...

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2021 CIE International Conference on Radar, Radar 2021

作者： Wei, Rongyu Li, Jun Wang, Weihao Guo, Qinghua Xidian University National Laboratory of Radar Signal Processing Xian China School of Electrical Computer and Telecommunications Engineering University of Wollongong Wollongong Australia

ISBN: (纸本)9781665498142

Quantum radar uses the strong correlations of entangled photons and has a great potential to improve the detection sensitivity. Recently, many research results have been achieved in theoretical analysis and experiments on the performance advantages of quantum illumination radar. However, there are limited researches on its detection range. Based on the analysis of the noise tolerance of entangled state photons, we study the advantages of quantum illumination radar in detection range based on radar equation. It is shown that the detection range of quantum illumination radar is larger than that of classical radar in some regimes due to the strong noise suppression ability of quantum radar. © 2021 IEEE.

关键词： detection range quantum illumination radar radar equation

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Power system of DC/DC applications: Case of cooking

Power system of DC/DC applications: Case of cooking

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作者： El Moussaoui, Noureddine Lamkaddem, Ali Rhiat, Mohammed Kassmi, Khalil Malek, Rachid Deblecker, Olivier Bachiri, Najib Mohamed First University Faculty of Sciences Department of Physics Laboratory of Electromagnetic Signal Processing & Renewable Energy LESPRE Team Electronic Materials & Renewable Energy EMRE Oujda Morocco University of Mons Polytech. Mons - Electrical Power Engineering Unit Mons Belgium Berkane Morocco

In this paper, we present the structure, the sizing and the experimentation of a power system that allows to feed, through solar batteries, the DC (direct current) equipment with photovoltaic renewable energies (Cookers, distillers, refrigerators, drying, …). The proposed system is based on the use of a multi-branch Boost type DC/DC converter, controlled by a Microcontroller, which generates PWM signals of frequency 20 kHz and variable α duty cycle. The electrical energy, produced by the photovoltaic panels (600 W), is stored in the solar batteries (24 V, 520 Ah) and then transferred to the application through the proposed DC/DC converter. The experimentation of the proposed system to supply a solar cooker (Box Oven) of 500–600 W, heated by thermal resistances, shows efficiencies of the DC/DC converter of 86 %, heating temperature of the thermal resistance and water that reach 640 °C and 99 °C after 20 s and 25 min, energy supplied by the batteries and that of heating are respectively of the order of 491 Wh and 421 Wh (That is to say 3.93 % and 3.37 % of the total energy supplied by the battery). The comparison of all the results obtained with those simulated and the economic analysis of the use of renewable energies stored in the batteries, show the good functioning and the validity of the power system, proposed in this work. © 2022

关键词： Solar power generation

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Effects of the Flatness Network Parameter Threshold on the Performance of the Rectified Linear Unit Memristor-Like Activation Function in Deep Learning

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SN computer Science 2024年第8期5卷 1-10页

作者： Tchepgoua Mbakop, Marcelle Mboupda Pone, Justin Roger Chassem Kamdem, Priva Kengne, Romanic Research Unit of Condensed Matter Electronics and Signal Processing (RU-MACETS) Department of Physics Faculty of Science University of Dschang P. O. Box 67 Dschang Cameroon Research Unit of Automation and Applied Computer (RU-AIA) Electrical Engineering Department of IUT-FV University of Dschang P.O. Box 134 Bandjoun Cameroon Research Unit of Fundamental Computer Sciences and Artificial Intelligence (RU-FCSAI) Department of Computer Sciences Faculty of Science University of Dschang P. O. Box 67 Dschang Cameroon

In this contribution, we improve of the performance of the Rectified Linear Unit Memristor Like Activation Function with the implication to help training process of CNN without a lot of epochs by computing the best value of the flatness network parameter (p). In this regards the flatness network parameter threshold (p) has been investigated and a good performance of the activation function has been discovered at p = 4.5. We firstly used the MNIST and the CIFAR-10 datasets to trained and test the Alex-Net architecture model of convolutional neural network (CNN) and we showed better performances of Rectified Linear Unit Memristor-like Activation Function compared to those of the literature. We noticed that the performance of Alex-Net also improved and the better performance was recorded when p = 4.5 with 99.50%, 99.25%, 98.81% for training, validation and testing accuracy respectively when using the MNIST. These results open the outcome to reduce the training time of neural networks when this activation function is used. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Activation function Artificial intelligence Deep learning Flatness network parameter threshold Memristive windows function Neural network

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Image Segmentation for Improved Lossless Screen Content Compression

Image Segmentation for Improved Lossless Screen Content Comp...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Shabhrish Reddy Uddehal Tilo Strutz Hannah Och André Kaup Department of Electrical Engineering and Computer Science Coburg University Coburg Germany Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen Germany

In recent years, it has been found that screen content images (SCI) can be effectively compressed based on appropriate probability modelling and suitable entropy coding methods such as arithmetic coding. The key objective is determining the best probability distribution for each pixel position. This strategy works particularly well for images with synthetic (textual) content. However, usually screen content images not only consist of synthetic but also pictorial (natural) regions. These images require diverse models of probability distributions to be optimally compressed. One way to achieve this goal is to separate synthetic and natural regions. This paper proposes a segmentation method that identifies natural regions enabling better adaptive treatment. It supplements a compression method known as Soft Context Formation (SCF) and operates as a pre-processing step. If at least one natural segment is found within the SCI, it is split into two subimages (natural and synthetic parts) and the process of modelling and coding is performed separately for both. For SCIs with natural regions, the proposed method achieves a bit-rate reduction of up to 11.6% and 1.52% with respect to HEVC and the previous version of the SCF.

关键词： Image segmentation Adaptation models Image coding signal processing Probability distribution Entropy coding Acoustics

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