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2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023

作者： Naser, Hussein Mohammed Ahmed Al-Ani, Oras Muttair, Karrar Shakir Farhan Mosleh, Mahmood Taher, Hazeem B. Middle Technical University Department of Computer Engineering Techniques Electrical Engineering Technical College Baghdad Iraq The Islamic University College of Technical Engineering Department of Computer Techniques Engineering Najaf Iraq Thi-Qar University College of Education for Pure Sciences Department of Computer Science Thi-Qar Iraq

ISBN: (纸本)9798350302523

The primary design hurdles in creating an antenna for contemporary wireless communication were mutual coupling and size reduction. This paper suggests an ultra-wideband (UWB) antenna with four elements, Multiple Input Multiple Output (MIMO). Each element contains two ports with dual-polarized patches to boost capacity, lessen the effects of reciprocal coupling, and maintain the right size. A semi-oval geometric design with slots has been placed above the patch of the intended antenna to decrease the effects of mutual coupling. With the help of four integrated elements, eight integrated ports, and a Rogers insulating layer, the CST STUDIO 2019 program simulates a single antenna element and a MIMO antenna in a space of (60.48 × 60.48) mm2. The MIMO antenna bandwidth is 2 GHz, the operational frequency is 8.837 GHz while the return loss is -37 dB in the resonance (7.69-9.71 GHz). The MIMO antennas' diversity gains (DG) were around 10, their gain was appropriate (about 8 dB), and their envelope correlation coefficient (ECC) was under 0.006. © 2023 IEEE.

关键词： Slot antennas

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Smart Processing of Exceptional Values in electrical Networks Using Neuro-Fuzzy Algorithms

Smart Processing of Exceptional Values in Electrical Network...

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2024 IEEE International Students' Conference on electrical, Electronics and computer science, SCEECS 2024

作者： Anwar, Mahammad Kothiyal, Arnav Nandankar, Praful V. Barve, Amit Patil, Harshal Maranan, Ramya Ballari Institute of Technology and Management Ballari Visvesvaraya Technological University Department of Electrical and Electronics Engineering Karnataka India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Government College of Nagpur Department of Electrical Engineering Maharashtra 441108 India Parul Institute of Technology Faculty of Engineering and Technology Parul University Department of Computer Science and Engineering P.O. Limda Gujarat 391760 India Department of Computer Science and Engineering Maharashtra Pune412115 India Saveetha School of Engineering Simats Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350348460

This research introduces a novel smart meter topology within the context of an integrated PV-Wind hybrid system for DC electric micro-networks. The proposed topology incorporates advanced techniques such as Linear Interpolation, Artificial Neural Network (ANN), and Adaptive Neuro-Fuzzy Inference System (ANFIS) for identifying and correcting outliers in the collected data. The integration of the smart meter with outlier detection and correction modules enhances the accuracy and reliability of the measurements, contributing to the overall efficiency of the micro-network. Linear Interpolation is employed to estimate and fill in missing data points, ensuring a continuous and consistent dataset. The ANN module is designed to identify outliers by learning patterns from historical data, allowing for real-time anomaly detection. Furthermore, the ANFIS module enhances the system's adaptability by providing a fuzzy logicbased approach to correct outliers, taking into account the dynamic nature of renewable energy sources in the hybrid system. The proposed smart meter topology offers a comprehensive solution for ensuring the integrity of data collected in DC electric micronetworks. By leveraging the synergies of Linear Interpolation, ANN, and ANFIS, the system demonstrates improved robustness against data anomalies, thereby enabling more accurate monitoring, control, and optimization of the PV-Wind hybrid system. This research contributes to the advancement of smart grid technologies, particularly in the realm of renewable energy micro-networks, by addressing the challenges associated with data quality and reliability. © 2024 IEEE.

关键词： Integrated Control Mean Squared Error Micro-Networks Modeling outliers smart meter

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Efficient Multi-purpose Video Annotation for Fast Labeling

Efficient Multi-purpose Video Annotation for Fast Labeling

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2023 IEEE International Conference on Visual Communications and Image Processing, VCIP 2023

作者： Mobaraki, Mobina Ahani, Soodeh Yi, Kwang Moo Asadi, Mahyar Van Heusden, Klaske Dumont, Guy A. University of British Columbia Electrical and Computer Engineering Vancouver Canada University of British Columbia Computer Science Vancouver Canada Novarc Technologies Co. Vancouver Canada School of Mechanical Engineering Kelowna Canada

ISBN: (纸本)9798350359855

We propose an efficient method to label the frames of a video consistently and quickly. Our method is motivated by welding videos, where molten metal moves continuously, and some frames may be noisy due to the high intensity of arc and spatters that can obscure the desired point to be annotated. The traditional annotation methods which pause the videos and annotate each frame individually are time-consuming and may result in inconsistent labeling between different annotators for noisy frames of a video. Our new proposed method benefits from the fact that video sequences are contiguous. We track the location of the cursor while the video is being played, with the user controlling the playback speed, including the playback direction. We process the recorded cursor locations to convert them to the final annotation. Compared to frame-to-frame annotation methods, we show that our proposed interface speeds up the annotating process by 21 times while maintaining consistency of the labels. © 2023 IEEE.

关键词： Liquid metals

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Enhancing Cloud Data Processing with Amortized Analysis and Splay Trees for Cyber Threat Intelligence 2023

Enhancing Cloud Data Processing with Amortized Analysis and ...

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15th International Conference on Contemporary Computing, IC3 2023

作者： Westmoreland, Deven Hossain, Gahangir Electrical Engineering and Computer Science Texas A&M University-Kingsville United States Information Science University of North Texas United States

ISBN: (纸本)9798400700224

This paper focuses on using amortized analysis with splay trees to calculate the time complexity in cyber threats on the cloud network. The paper explains little bit detail on how each of these analysis and modeling methods work, and then shows how to use the amortized algorithm for cyber intelligence - calculating attack vectors efficiently so that it reaches a time complexity of O(log n) for the splay operations. Amortized analysis is good for determining a better time complexity than when worst-case is used in a way that is too pessimistic and may not even be possible to happen, hence proposed an effective method in robust cyber incidence identification. The distributed network on the clouds forms a tree, hence the splay trees are proposed as a good alternative to other binary search trees, in improving amortized calculation. Finally, the combined approach helps speed up computation efficiency. © 2023 ACM.

关键词： Binary trees

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A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

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computers, Materials & Continua 2023年第4期75卷 1157-1177页

作者： Guo-Wen Sun Gang Xiang Wei He Kai Tang Zi-Yi Wang Hai-Long Zhu School of Computer Science and Information Engineering Harbin Normal UniversitySongbei DistrictHarbin150025China Beihang University School of Automation Science and Electrical Engineering Beijing100191China Beijing Aerospace Automatic Control Institute Beijing100074China Rocket Force University of Engineering Xi’anShaanxi Province710025China

Wireless sensor networks (WSNs) operate in complex and harshenvironments;thus, node faults are inevitable. Therefore, fault diagnosis ofthe WSNs node is essential. Affected by the harsh working environment ofWSNs and wireless data transmission, the data collected by WSNs containnoisy data, leading to unreliable data among the data features extracted duringfault diagnosis. To reduce the influence of unreliable data features on faultdiagnosis accuracy, this paper proposes a belief rule base (BRB) with a selfadaptivequality factor (BRB-SAQF) fault diagnosis model. First, the datafeatures required for WSN node fault diagnosis are extracted. Second, thequality factors of input attributes are introduced and calculated. Third, themodel inference process with an attribute quality factor is designed. Fourth,the projection covariance matrix adaptation evolution strategy (P-CMA-ES)algorithm is used to optimize the model’s initial parameters. Finally, the effectivenessof the proposed model is verified by comparing the commonly usedfault diagnosis methods for WSN nodes with the BRB method consideringstatic attribute reliability (BRB-Sr). The experimental results show that BRBSAQFcan reduce the influence of unreliable data features. The self-adaptivequality factor calculation method is more reasonable and accurate than thestatic attribute reliability method.

关键词： Self-adaptive quality factor belief rule base wireless sensor networks fault diagnosis

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Second Order Rate Regions of Gaussian Broadcast Channels under Heterogeneous Blocklength Constraints

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IEEE Transactions on Communications 2024年第2期72卷 801-814页

作者： Lin, Pin-Hsun Lin, Shih-Chun Chen, Peng-Wei Mross, Marcel A. Jorswieck, Eduard A. Technische Universitaät Braunschweig Institute for Communications Technology Braunschweig38106 Germany National Taiwan University Department of Electrical Engineering Taipei10617 Taiwan National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan Hsinchu300031 Taiwan

Future wireless access networks aim to simultaneously support a large number of devices with heterogeneous service requirements, including data rates, error rates, and latencies. While achievable rate and capacity results exist for Gaussian broadcast channels in the asymptotic blocklength regime, the characterization of second-order achievable rate regions for heterogeneous blocklength constraints is not available. Therefore, we investigate a two-user Gaussian broadcast channel (GBC) with heterogeneous blocklength constraints, specified according to users' channel output signal-To-noise ratios (SNRs). We assume that the user with higher output SNR has a shorter blocklength constraint. We show that with sufficiently large output SNR, the stronger user can perform the early decoding (ED) technique to decode and subtract the interference via successive interference cancellation (SIC). To achieve this goal, we derive an explicit lower bound on the necessary number of received symbols for a successful ED, using an independent and identically distributed Gaussian input. The second-order rate region is also derived. Numerical results show that ED can outperform the hybrid non-orthogonal multiple access scheme when the stronger channel is sufficiently better than the weaker one. Under the considered setting, about 7-dB SNR gain can be achieved compared to treating interference as noise. These results show that ED with SIC is a promising technique for future wireless networks. © 1972-2012 IEEE.

关键词： Signal to noise ratio

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ViT-Reg: Regression-Focused Hardware-Aware Fine-Tuning for ViT on tinyML Platforms

IEEE Design and Test

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IEEE Design and Test 2024年

作者： Shaharear, Md Ragib Mazumder, Arnab Neelim Mohsenin, Tinoosh Johns Hopkins University Department of Electrical & Computer Engineering United States University of Maryland Department of Computer Science & Electrical Engineering Baltimore County United States

Vision Transformers (ViTs) have demonstrated significant improvements in image classification tasks. However, deploying them on resource-constrained tinyML platforms presents considerable challenges due to their high computational demands and dynamic power consumption. Current methods rely heavily on computationally intensive architecture search techniques to identify optimal configurations, which are not well-suited for tinyML devices. This paper introduces ViT-Reg, a regression-based hardware-aware fine-tuning approach that identifies suitable ViT architectures for tinyML platforms. The proposed method enables efficient exploration of the configuration space, drastically reducing the computational overhead typically associated with architecture searches. ViT-Reg is hardware-aware, utilizing polynomial regression to narrow the search space while treating accuracy as a constraint. In experiments conducted on the CIFAR-10 and Tiny-ImageNet datasets, ViT-Reg deployed on Nvidia Jetson Nano achieved a 55.6% and 37.4% reduction in dynamic power consumption, along with a 65% and 60% improvement in energy efficiency compared to baseline ViT models. Finally, ViT-Reg provides an 8× improvement in energy efficiency relative to recent hardware implementations of the VGG model. © 2024 IEEE.

关键词： real-time and energy efficient deployment Regression tinyML Hardware Vision Transformer

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Evaluating collaborative rationality-based decisions: A literature review

Evaluating collaborative rationality-based decisions: A lite...

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2022 International Conference on ENTERprise Information Systems, CENTERIS 2022 - International Conference on Project MANagement, ProjMAN 2022 and International Conference on Health and Social Care Information Systems and Technologies, HCist 2022

作者： Elgendy, Nada Elragal, Ahmed Päivärinta, Tero M3S Faculty of Information Technology and Electrical Engineering University of Oulu Finland Department of Computer Science Electrical and Space Engineering Lulea University of Technology Sweden

Decision making has evolved throughout the years, nowadays harnessing massive amounts and types of data through the unprecedented capabilities of data science, analytics, machine learning, and artificial intelligence. This has potentially led to higher quality and more informed decisions based on the collaborative rationality between humans and machines, no longer bounded by the cognitive capacity and limited rationality of each on their own. However, the multiplicity of modes of collaboration and interaction between humans and machines has also increased the complexity of decision making, consequentially complicating ex-Ante and ex-post decision evaluation. Nevertheless, evaluation remains crucial to enable human and machine learning, rationalization, and sensemaking. This paper addresses the need for more research on why and how to evaluate collaborative rationality-based decisions, setting the stage for future studies in developing holistic evaluation solutions. By analyzing four relevant streams of literature: 1) classical decision theory and organizational management, 2) cognitive and neuroscience, 3) AI and ML, and 4) data-driven decision making, we highlight the limitations of current literature in considering a holistic evaluation perspective. Finally, we elaborate the theoretical underpinnings from the knowledge base on how humans and machines evaluate decisions, and the considerations for evaluating collaborative rationality-based decisions. © 2023 Elsevier B.V.. All rights reserved.

关键词： Decision making

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Regional Style and Color Transfer 5

Regional Style and Color Transfer

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5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Ding, Zhicheng Li, Panfeng Yang, Qikai Li, Siyang Gong, Qingtian Columbia University Fu Foundation School of Engineering and Applied Science New York United States University of Michigan Electrical Engineering and Computer Science Ann Arbor United States University of Illinois Urbana-Champaign Department of Computer Science Urbana United States Pace University Lubin School of Business New York United States

ISBN: (纸本)9798350373820

This paper presents a novel contribution to the field of regional style transfer. Existing methods often suffer from the drawback of applying style homogeneously across the entire image, leading to stylistic inconsistencies or foreground object twisted when applied to images with foreground elements such as person figures. To address this limitation, we propose a new approach that leverages a segmentation network to precisely isolate foreground objects within the input image. Subsequently, style transfer is applied exclusively to the background region. The isolated foreground objects are then carefully reintegrated into the style-transferred background. To enhance the visual coherence between foreground and background, a color transfer step is employed on the foreground elements prior to their reincorporation. Finally, we utilize feathering techniques to achieve a seamless amalgamation of foreground and background, resulting in a visually unified and aesthetically pleasing final composition. Extensive evaluations demonstrate that our proposed approach yields significantly more natural stylistic transformations compared to conventional methods. © 2024 IEEE.

关键词： Color

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An Efficient Method for Quantifying the Aggregate Flexibility of Plug-In Electric Vehicle Populations

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IEEE Transactions on Smart Grid 2024年 1-1页

作者： Taha, Feras Al Vincent, Tyrone Bitar, Eilyan School of Electrical and Computer Engineering Cornell University Ithaca NY USA Department of Electrical Engineering and Computer Science Colorado School of Mines Golden CO USA

Plug-in electric vehicles (EVs) are widely recognized as being highly flexible electric loads that can be pooled and controlled via aggregators to provide low-cost energy and ancillary services to wholesale electricity markets. To participate in these markets, an aggregator must encode the aggregate flexibility of the population of EVs under their command as a single polytope that is compliant with existing market rules. To this end, we investigate the problem of characterizing the aggregate flexibility set of a heterogeneous population of EVs whose individual flexibility sets are given as convex polytopes in half-space representation. As the exact computation of the aggregate flexibility set—the Minkowski sum of the individual flexibility sets—is known to be intractable, we study the problem of computing maximum-volume inner approximations to the aggregate flexibility set by optimizing over affine transformations of a given convex polytope in half-space representation. We show how to conservatively approximate these set containment problems as linear programs that scale polynomially with the number and dimension of the individual flexibility sets. The inner approximation methods provided in this paper generalize and improve upon existing methods from the literature. We illustrate the improvement in approximation accuracy and performance achievable by our methods with numerical experiments. IEEE

关键词： Aggregates

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