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58th Asilomar Conference on Signals, Systems and computers, ACSSC 2024

作者： Sadeghi, Alireza Polyzos, Konstantinos D. Giannakis, Georgios B. University of Minnesota Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350354058

Self-supervised (SeSu) learning is a powerful subclass of unsupervised methods that aims to alleviate the need for large-scale annotated datasets to successfully train data-hungry machine learning models. To this end, SeSu methods learn contextualized embeddings from unlabeled data to efficiently tackle downstream tasks. Despite their success, most existing SeSu approaches are heuristic, and typically fail to exploit multiple views of data available for the problem at hand. This becomes particularly challenging when non-linear dependencies among multiple views or data samples exist, often emerging in applications such as learning over large-scale graphs. In this context, the present paper builds upon kernel-based learning framework to introduce principled SeSu approaches. Specifically, in lieu of the well-celebrated Representer theorem, this work posits that the optimal function for addressing the downstream problem resides in a Reproducing Kernel Hilbert space. The proposed SeSu approach then learns 'low-dimensional' embeddings to approximate the feature map associated with the optimal underlying kernel. By judiciously combining the learned embeddings from multiple views of data, this paper demonstrates that a wide range of downstream problems over graphs can be efficiently solved. Numerical tests using synthetic and real graph datasets showcase the merits of the proposed approach relative to competing alternatives. © 2024 IEEE.

关键词： Self-supervised learning

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AI-Assisted Model to Classify Prostate Cancer Diagnosis for Early Intervention 15

AI-Assisted Model to Classify Prostate Cancer Diagnosis for ...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Alabri, Yahya Khalid Dhanalakshmi, V. Bino, Dhivya Middle East College Muscat Department of Computing and Electrical Engineering Oman Sultan Qaboos University Muscat College of Engineering Department of Electrical and Computer Engineering Oman

ISBN: (纸本)9798350370249

Prostate cancer is one of the most common types of cancer that men suffer from. It can usually be without any symptoms, which puts men's lives in danger. There are different tests such as Screening, Prostate Specific Antigen (PAS), and MRI which the doctor can do to make sure the person has prostate cancer. Our aim for this project is to develop a model that helps doctors discover cancer in the early stages which helps the doctors to discover cancer without waiting for the long process which currently done. We use a dataset that contains 10 values related to the prostate. We implement the dataset with the different algorithms after we preprocess the dataset. The Naïve Bayes algorithm gives the highest accuracy which reaches 88%. In addition, we notice that some factors such as perimeter, area, and compactness are key factors in determining malignant cells. © 2024 IEEE.

关键词： Urology

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A Decentralized Communication Framework Based on Dual-Level Recurrence for Multiagent Reinforcement Learning

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IEEE Transactions on Cognitive and Developmental Systems 2024年第2期16卷 640-649页

作者： Li, Xuesi Li, Jingchen Shi, Haobin Hwang, Kao-Shing Northwestern Polytechnical University School of Computer Science and Engineering Xi'an710129 China National Sun Yat-sen University Department of Electrical Engineering Kaohsiung804 Taiwan

Designing communication channels for multiagent is a feasible method to conduct decentralized learning, especially in partially observable environments or large-scale multiagent systems. In this work, a communication model with dual-level recurrence is developed to provide a more efficient communication mechanism for the multiagent reinforcement learning field. The communications are conducted by a gated-attention-based recurrent network, in which the historical states are taken into account and regarded as the second-level recurrence. We separate communication messages from memories in the recurrent model so that the proposed communication flow can adapt changeable communication objects in the case of limited communication, and the communication results are fair to every agent. We provide a sufficient discussion about our method in both partially observable and fully observable environments. The results of several experiments suggest our method outperforms the existing decentralized communication frameworks and the corresponding centralized training method. © 2016 IEEE.

关键词： Decision making

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Distributed Event-triggered Secondary Control for Average Bus Voltage Regulation and Proportional Load Sharing of DC Microgrid

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Journal of Modern Power Systems and Clean Energy 2022年第3期10卷 678-688页

作者： Zhongwen Li Zhiping Cheng Jikai Si Shuhui Li School of Electrical Engineering Zhengzhou UniversityZhengzhou 450001China Department of Electrical and Computer Engineering The University of AlabamaTuscaloosaAL 35487USA

This paper proposes a novel distributed event-triggered secondary control method to overcome the drawbacks of primary control for direct current(DC)*** eventtriggered distributed communication,the proposed control method can achieve system-wide control of parallel distrubted generators(DGs)with two main control objectives:①estimate the average bus voltage and regulate it at the nominal value;②achieve accurate current sharing among the DGs in proportion to their power output ***,the proposed control strategy can be implemented in a distributed way that shares the required tasks among the ***,it shows the advantages of being flexible and ***,this paper proposes a simple event-triggered condition that does not need extra state ***,limited communication among neighbors is required only when the event-triggered condition is satisfied,which significantly reduces the communication burden at the cyber layer.

关键词： Direct current(DC)microgrid droop control event-triggered secondary control distributed consensus load sharing

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Improving the Performance of Firewalls through Network Traffics Logs' Classification of Firat Dataset Using Decision Tree Algorithm 10

Improving the Performance of Firewalls through Network Traff...

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10th International Conference on Web Research, ICWR 2024

作者： Talaeepoor, Maryam Soleimani Neysiani, Behzad Jezi, Mohammad Davarpanah Naghshe Jahan Institute of Higher Education Faculty of Computer Engineering Department of Computer Engineering Isfahan Iran Department of Software Engineering Isfahan Iran Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan Iran

ISBN: (纸本)9798350394986

As long as a computer system is connected to the Internet, it is susceptible to attack as a victim. In computer networks, it becomes important to manage the network based on parameters such as network size and network data. Firewalls are devices that help network administrators in this case to establish security in the network, and can be based on the rules that the firewall is based on. It is configured to control incoming and outgoing network traffic. Firewalls can be considered the most vital components of the network in establishing security. Firat University introduced a dataset containing firewall logs with multiple classes for firewall decisions. This study uses data mining techniques to improve the validation performance of classification using various machine learning algorithms like neural networks, deep learning, and kNN. The experimental results show more than 10% improvement according to precision and recall rates among various folding scenarios used in related works with minor improvement in accuracy, too. The decision tree algorithm is fast and explainable versus other algorithms. © 2024 IEEE.

关键词： Classification (of information)

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Computationally efficient reductions between some statistical models 36

Computationally efficient reductions between some statistica...

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36th International Conference on Algorithmic Learning Theory, ALT 2025

作者： Lou, Mengqi Bresler, Guy Pananjady, Ashwin School of Industrial and Systems Engineering Georgia Tech United States Department of Electrical Engineering and Computer Science MIT United States Schools of Industrial and Systems Engineering and Electrical and Computer Engineering Georgia Tech United States

We study the problem of approximately transforming a sample from a source statistical model to a sample from a target statistical model without knowing the parameters of the source model, and construct several computationally efficient such reductions between canonical statistical experiments. In particular, we provide computationally efficient procedures that approximately reduce uniform, Erlang, and Laplace location models to general target families. We illustrate our methodology by establishing nonasymptotic reductions between some canonical high-dimensional problems, spanning mixtures of experts, phase retrieval, and signal denoising. Notably, the reductions are structure-preserving and can accommodate missing data. We also point to a possible application in transforming one differentially private mechanism to another. © 2025 M. Lou, G. Bresler & A. Pananjady.

关键词： Differential privacy

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Bayesian Multidimensional Scaling for Location Awareness in Hybrid-Internet of Underwater Things

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 496-509页

作者： Ruhul Amin Khalil Nasir Saeed Mohammad Inayatullah Babar Tariqullah Jan Sadia Din IEEE Department of Electrical Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar 25120Pakistan the Department of Electrical Engineering Northern Border UniversityArar 73222Saudi Arabia College of Science and Engineering Hamad Bin Khalifa UniversityDoha 34110Qatar

Localization of sensor nodes in the internet of underwater things(IoUT)is of considerable significance due to its various applications,such as navigation,data tagging,and detection of underwater ***,in this paper,we propose a hybrid Bayesian multidimensional scaling(BMDS)based localization technique that can work on a fully hybrid IoUT network where the nodes can communicate using either optical,magnetic induction,and acoustic *** communication technologies are already used for communication in the underwater environment;however,lacking localization *** and magnetic induction communication achieves higher data rates for short *** the contrary,acoustic waves provide a low data rate for long-range underwater *** proposed method collectively uses optical,magnetic induction,and acoustic communication-based ranging to estimate the underwater sensor nodes’final ***,we also analyze the proposed scheme by deriving the hybrid Cramer-Rao lower bound(H-CRLB).Simulation results provide a complete comparative analysis of the proposed method with the literature.

关键词： Bayesian multidimensional scaling(BMDS) hybrid Cramer-Rao lower bound(H-CRLB) internet of underwater things(IoUT) signals of opportunity(SOA)approach

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Stochastic Framework for Smart Home Energy Management: Integrating Demand Response and Renewable Resources Using Improved ABC Algorithm 12

Stochastic Framework for Smart Home Energy Management: Integ...

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12th Iranian Conference on Renewable Energies and Distributed Generation, ICREDG 2025

作者： Haghighi, Mehran Masoudi, Mohammad Reza Karimi, Hamid Sezavar, Hamid Reza Department of Electrical Engineering Tehran Iran Shiraz Electrical Power Distribution Company Shiraz Iran Qom University of Technology Department of Electrical and Computer Engineering Qom Iran

ISBN: (纸本)9798331525132

This paper presents a home energy management system that optimizes the electricity demand of home appliances according to demand response programs. This home is equipped with renewable energy, including wind turbines and solar panels. The uncertainty of renewable energy is modeled using stochastic programming. Different types of loads have been used in this home, and the mentioned loads have been properly adjusted and distributed to minimize the cost of energy and deal with fluctuations in renewable energy. An improved Artificial Bee Colony algorithm has been used to solve this problem. The results confirm that the introduced model can efficiently harvest all possible energy of the wind-solar system, manage uncertainty, minimize cost, and operate independently of the grid. These goals are achieved through the complex coordination of load management strategies in the proposed load response framework, which leads to an efficient and flexible energy management system. The numerical results reported in this paper show the effectiveness of the proposed method. The proposed plan is generally usable and valuable for any other type of smart home. © 2025 IEEE.

关键词： Stochastic programming

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Early Detection of Heart Rate Conditions in Autistic Children Using IoT-Based Systems 4

Early Detection of Heart Rate Conditions in Autistic Childre...

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4th International Conference on electrical engineering and computer Science, ICECOS 2024

作者： Albar, Nizam Melinda, Melinda Away, Yuwaldi Universitas Syiah Kuala Dept. Electrical Engineering Master of Electrical Engineering Study Program Banda Aceh Indonesia Universitas Syiah Kuala Engineering Faculty Dept. Electrical Engineering and Computer Banda Aceh Indonesia

ISBN: (纸本)9798350368253

Early detection of heart rate conditions in autistic children is crucial for anticipating tantrum reactions and providing prompt medical intervention. Previous research designed a prototype for acquiring Electrocardiogram (ECG) signals using the AD8232 sensor, but it required a long time to identify heart rate conditions. This study aims to develop the previous prototype using Internet of Things (IoT) implementation to acquire and identify the heart rate conditions of autistic children in real time. The ECG signal data is analyzed using Butterworth Filter and Artificial Neural Network (ANN) integrated into Raspberry Pi and results are transmitted to the Blynk App. Testing over 20 minutes showed that in the first 10 minutes, the system identified a normal heart rate at 105.19 BPM with 67% accuracy, while in the second 10 minutes, the heart rate rose to 117.19 BPM with tantrum symptoms and 75% accuracy. This system helps medical personnel provide immediate intervention during tantrums. © 2024 IEEE.

关键词： Butterworth filters

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Video and image quality improvement using an enhanced optimized dehazing technique

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Multimedia Tools and Applications 2024年 1-19页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The detrimental effects of atmospheric haze frequently plague outdoor imagery. This phenomenon arises from the scattering of light by minute particles within the ambient environment surrounding the scene to be imaged. Haze engenders an overall whitening of the image, leading to diminished contrast. To address these issues and enhance the quality of hazy images and videos, an enhanced dehazing technique is proposed. The proposed technique includes image enhancement before the optimal dehazing process. The enhancement stage entails the implementation of both homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE), serving to control the image dynamic range, while concurrently heightening the image contrast. The culminating stage encompasses an optimized dehazing technique, adept at expunging haze-induced artifacts from images. The homomorphic processing and CLAHE, applied in the pre-processing step, establish a foundation for the subsequent dehazing procedure. This proposed methodology is systematically applied to a gamut of visual outputs, including visible videos, Near-Infrared (NIR) frames, and authentic hazy images. Comparative evaluations of the proposed technique, homomorphic-processing-based enhanced dehazing, and standalone dehazing techniques is undertaken on different video types encompassing five frames. It is evident that the proposed technique, synergizing the homomorphic processing, CLAHE, and dehazing, outperforms alternative strategies. Furthermore, the proposed technique is subjected to a comparative study with various existing dehazing techniques over real hazy images. The assessment depends on Peak Signal-to-Noise Ratio (PSNR), correlation, and entropy metrics. The results underscore the efficacy of the proposed technique, particularly in terms of spectral entropy enhancement of dehazed frames. For both visible and NIR frames, the percentages of enhancement by the proposed technique are 17.66% and 118.48%, respectively. ©

关键词： Image enhancement

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