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Heterogeneous Statistical QoS Provisioning for Scalable Software-Defined 6G Mobile Networks 57

Heterogeneous Statistical QoS Provisioning for Scalable Soft...

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57th Annual Conference on Information Sciences and systems, CISS 2023

作者： Zhang, Xi Zhu, Qixuan Poor, H. Vincent Texas AandM University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States

ISBN: (纸本)9781665451819

Due to the explosively increasing number of mobile users and the new types of data demands in the fifth generation (5G) mobile wireless network, research in wireless networks has shifted toward the development of the sixth-generation (6G) wireless network. Although the research for software-defined network (SDN) architectures in 5G mainly focuses on the dynamic programming for the internet backbone, these software programming techniques can be also applied at the network edge to support the exponentially increasing demands from mobile users under constrained wireless resources. In order to study the interference problem resulted by massive mobile users, the scaling law is a powerful tool to show how fast the levels of network imperfections can be tolerated as the number of mobile users increases. In this paper, we investigate the scaling behavior of software-defined architectures over 6G wireless networks. We consider the wireless channel in three scenarios: single-input-single-output (SISO), multiple-input-single-output (MISO), and multiple-input-multiple-output (MIMO), where we derive the corresponding scaling law for each scenario, respectively. Our derived scaling law shows how the network performance scales with the number of mobile users in a wireless network. Then, we propose a software-defined network slicing scheme to select the optimal mobile users and derive their optimal resource allocations, according to our derived scaling law, under SISO, MISO, and MIMO wireless channel, respectively. Finally, we validate and evaluate the derived scaling behavior of the software-defined architecture over 6G wireless networks through numerical analyses. © 2023 IEEE.

关键词： Wireless networks

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DNN Acceleration: A High-Accuracy Implementation for Base-2 Softmax Layer 3

DNN Acceleration: A High-Accuracy Implementation for Base-2 ...

引用

3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Thabit, Doha Alabassy, Bassma Watheq El-Kharashi, M. Safar, Mona Dessouky, Mohamed Ain Shams University Department of Electronics Engineering and Electrical Communications Cairo Egypt Ain Shams University Department of Computer and Systems Engineering Cairo Egypt

ISBN: (纸本)9798350306231

This paper introduces a high-accuracy implementation of the softmax layer in Deep Neural Networks (DNN) used in multi-category classification applications. Calculation of the exponentials or logarithms in the traditional base-e softmax is mathematically complex, which makes it hard to have a high-accuracy hardware implementation without being resource-consuming, so instead of using e as the exponential base, this paper presents a hardware implementation of the base-2 softmax function that makes use of 2 as the exponential base. Thus, the complex operations in base-e softmax will be replaced by simple shift and addition operations, with simpler LUTs and higher accuracy. The implemented hardware model relies on single-precision floating-point arithmetic cores, it achieves classification accuracy equal to 100% relative to a reference software model and has an area of 0.0802 mm2with a power consumption of 8.93 mW when synthesized under TSMC 28nm CMOS technology at the frequency of 1 GHz. © 2023 IEEE.

关键词： Deep neural networks

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A Hybrid Adversarial-TransUnet Architecture for Improving LV Segmentation Using Cardiac Cine Images 1st

A Hybrid Adversarial-TransUnet Architecture for Improving L...

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1st International Conference on Intelligent systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Abdeltawab, Hisham Jani, Md. Rafsan Hasan, Md Rakibul Yasser, Ibrahim Khalifa, Fahmi Department of Biomedical Engineering and Systems Cairo University Cairo12613 Egypt Department of Electrical and Computer Engineering Morgan State University BaltimoreMD21251 United States Department of Electronics and Communications Engineering Mansoura University Mansoura35516 Egypt

ISBN: (纸本)9783031823763

Cardiac image segmentation is crucial for the assessment of heart anatomy and function. Deep learning (DL) has been widely employed for cardiac image segmentation and the accuracy of the segmentation has improved due to the advances in DL research and the existence of large cardiac datasets. In this study, we introduce a DL framework based on adversarial training for the delineation of the left ventricle (LV) from cardiac magnetic resonance imagining (CMRI). Our framework consists of two networks: the segmenter which is a neural network that predicts segmentation maps for the myocardium, the discriminator which is a convolutional neural network that discriminates between the ground truth and the predicted segmentation maps. We investigate two different architectures for the segmenter (Unet and TransUnet). The adversarial training can identify and correct the differences between the predicted and the ground truth maps and results in better segmentation accuracy than stand-alone Unet or TransUnet. The proposed framework has been evaluated on cine CMRI, collected from 100 patients, and evaluated using different metrics (Dice coefficient, Jaccard Index, and Hausdorff distance). The results documented an improvement of the segmentation accuracy (0.91, 0.82, 4.69 mm) using the proposed framework with TransUnet compared to the segmentation obtained by stand-alone Unet (0.75, 0.68, 7.54 mm). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Enhanced DFT-Based Chaotic Image Block Cipher with Several Modes of Operation 12

Enhanced DFT-Based Chaotic Image Block Cipher with Several M...

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12th International Conference on Image Processing Theory, Tools and Applications, IPTA 2023

作者： Yassin, Mahmoud Y. M. Nasir, Ali A. Taha, Mostafa Iqbal, Naveed Department of Electrical Engineering Dhahran Saudi Arabia Center for Communication Systems and Sensing Kfupm Dhahran Saudi Arabia Carleton University Systems and Computer Engineering OttawaON Canada

ISBN: (纸本)9798350325416

Chaotic image encryption has been investigated extensively in the literature in different domains, including the frequency domain using Discrete Fourier Transform (DFT). Despite DFT-based chaotic image cryptosystems exhibiting good confusion properties, they suffer from poor diffusion characteristics and non-uniform histograms. Therefore, they are vulnerable to many attacks, such as differential attacks, and statistical cryptanalytic attacks. This paper presents an enhanced block cipher image cryptosystem with several modes of operation based on the concept of bit reversal using the 2D chaotic Baker map and DFT. The proposed enhancement develops an improved image encryption scheme that involves properly diffusion-confusion enhanced operations within modified versions of the known block cipher modes of operation, namely Cipher Block Chaining Mode (CBC), Cipher Feedback Mode (CFB), and Output Feedback Mode (OFB). These enhanced operations are based on a 2D chaotic baker map in the Fourier domain. The proposed enhancement is validated using a MATLAB simulation. Results show that the proposed work manages to overcome the abovementioned problems outstandingly and outperforms the existing results in the literature in many encryption quality metrics. © 2023 IEEE.

关键词： Discrete Fourier transforms

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Personalized Data-Driven State Models of the Circadian Dynamics in a Biometric Signal 46

Personalized Data-Driven State Models of the Circadian Dynam...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Ike, Chukwuemeka O. Wen, Yunshi Wen, John T. Oishi, Meeko M.K. Brown, Lee K. Julius, A. Agung Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering TroyNY United States Engineering Research Center TroyNY United States University of New Mexico Department of Electrical and Computer Engineering AlbuquerqueNM United States University of New Mexico Department of Internal Medicine AlbuquerqueNM United States

ISBN: (纸本)9798350371499

Circadian rhythms are endogenous 24-hour oscillations that are vital for maintaining our overall well-being. They are driven at a high level by a core circadian clock located in the brain, making their dynamics difficult to track. Various modeling approaches exist to predict the dynamics, but as the models are typically designed on population-level data, their performance is diminished on the individual level. This paper proposes a method for learning personalized latent state models, i.e., dynamical models that explicitly use latent state variables, that relate circadian input(s) to observable biometric signals. Our models combine an autoencoder with a recurrent neural network and use the pair to model the salient dynamics present in the data. We validate our method using experimental data, where the circadian input is light and the biometric data are actigraphy signals. We demonstrate that our method results in models with low-dimensional latent state that can accurately reconstruct and predict the observable biometric signals. Further, we show that the oscillation of the learned latent state agrees with the subjects' circadian clock oscillation as estimated with melatonin *** relevance - This proposes a technique for personal-ized modeling of the circadian system with potential applications in feedback control and individualized circadian studies. © 2024 IEEE.

关键词： Feedback control

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Massive-MIMO Based RSMA Under Nakagami-m Channel Over 6G mURLLC Wireless Networks 57

Massive-MIMO Based RSMA Under Nakagami-m Channel Over 6G mUR...

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57th Annual Conference on Information Sciences and systems, CISS 2023

ISBN: (纸本)9781665451819

The sixth generation (6G) mobile wireless networks are expected to provide massive ultra-reliable and low-latency communications (mURLLC) for data services, which require extremely stringent quality-of-services (QoS) with massive connectivity. Rate splitting (RS) multiple access schemes have been proposed recently for tackling the difficulty of the massive access yielded by a large number of mobile users (MUs) requesting to connect to a single base station. Massive multiple-input and multiple-output (massive-MIMO) communication is considered a promising solution to serve massive MUs in 6G wireless networks. However, how to apply RS schemes into massive-MIMO techniques to support the massive access has not been sufficiently studied. In this paper, we propose to integrate RS schemes with massive-MIMO techniques to achieve mURLLC transmissions under a Nakagamir $m$ fading wireless channel. First, we establish the system model for the integrated RS and massive-MIMO schemes. Then, we derive closed-form expressions for the signal-to-interference-plus-noise ratios (SINRs) under a Nakagami- $-m$ fading channel and obtain the channel capacity. Finally, we use numerical analyses to validate and evaluate our proposed RS and massive-MIMO integrated schemes. © 2023 IEEE.

关键词： Wireless networks

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Network-Agnostic Voltage-to-Power Sensitivities of a Real-Time Reduced Active Distribution Network

Network-Agnostic Voltage-to-Power Sensitivities of a Real-Ti...

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2023 IEEE PES Innovative Smart Grid Technologies - Asia, ISGT Asia 2023

作者： Prasad, Manorath Rather, Zakir Hussain Razzaghi, Reza Doolla, Suryanarayana Indian Institute of Technology Bombay Department of Energy Science and Engineering Mumbai India Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9798350327748

The advent of distribution-level phasor measurement units (D-PMUs) has enhanced the observability of active distribution networks (ADNs) and improved their energy management and control. Recently, various ADN control strategies that require detailed network parameter information have been proposed based on the voltage-to-power sensitivities of the ADN nodes. This paper proposes a new realistic approach to determining nodal voltage-to-power sensitivities of a synchrophasor measurement-based reduced ADN model. The real-time synchrophasor data dynamically updates the parameters of the reduced equivalent model to follow the time-varying operating conditions of the ADN accurately. The voltage-to-power sensitivities of the retained observable nodes are derived from the power flow Jacobian matrix using reduced equivalent model parameters. Thus, the proposed approach is agnostic about detailed line impedance data of the complex ADN. Further, the computational complexity is significantly decreased with the reduced number of nodes in the equivalent ADN model. The proposed approach has been tested on modified IEEE-33 bus benchmark system with high penetration of photovoltaic inverters using a DIgSILENT PowerFactory MATLAB co-simulation platform. © 2023 IEEE.

关键词： Electric power distribution

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Implementation of Multi-Agent Energy Management for Secondary Control of a Microgrid

Implementation of Multi-Agent Energy Management for Secondar...

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2023 IEEE International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD 2023

作者： Yoldas, Yeliz Onen, Ahmet Alawasa, Khaled El Haffar, Abdelsalam Ahshan, Razzaqul Islam, Md. Rabiul Muyeen, S.M. Noorfatima, Nadya Jung, Jaesung Kayseri University Department of Software Engineering Kayseri Turkey Sultan Qaboos University Department of Electrical and Computer Engineering Muscat Oman University of Wollongong School of Electrical Computer and Telecommunications Engineering NSW Australia Qatar University Electrical Engineering Department Doha Qatar Ajou University Department of Energy Systems Research Suwon Korea Republic of

ISBN: (纸本)9798350301571

The multi-agent energy management system (EMS) is needed to coordinate multiple agents of a microgrid to provide efficient operation of the system. In this study, multi-agent EMS is seen as the middle controller between primary and tertiary control levels. It is responsible for determining the relevant set points based on data gathered from the tertiary control level and sending them to the appropriate primary control level agents. The main objective of this paper is to propose a new optimization model for multi-agent EMS to minimize errors between the forecasted and real-time data at every predetermined time interval. Rather than using a traditional centralized microgrid controller to determine the dispatch of resources, a new framework-based multi-agent coordination is proposed to achieve the following objectives: minimizing load shedding, maximizing load dispatch and reliability, providing frequency, and maintaining voltage stability. © 2023 IEEE.

关键词： Dynamic energy management microgrid optimal control secondary control

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Model-based Loss Minimization Control of a Squirrel cage Induction Motor Drive with shorted Rotor under Indirect Field Orientation

Model-based Loss Minimization Control of a Squirrel cage Ind...

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2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Olajube, Ayobami Anubi, Olugbenga Moses Florida State University FAMU-FSU College of Engineering Center for Advanced Power Systems Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798350335446

This paper presents the development of an energy saving scheme via indirect field oriented control (IFOC) at the optimum operating region. A model-based steady state power loss analysis is carried out to determine the optimal speed and the optimal rotor flux that will serve as reference for the speed and torque tracking control. IFOC ensures the decoupled control of the torque and flux components of the stator current in a similar version as in DC drives through rotor flux orientation. The need for multiple sensors is eliminated by the analytical loss model approach, leading to an appreciable cost reduction. PI controller is used for the independent control of the flux and the torque producing components of the stator current. This is done at steady state, using the optimal rotor flux and speed computed in a loss minimization algorithm. The gains for optimum performance of the PI controller is selected via genetic algorithm (GA) optimization technique. The developed scheme is validated via a realistic numerical simulation and, the shorted rotor induction machine shows a very efficient speed and torque tracking performance at various load conditions and speed settings. © 2023 IEEE.

关键词： Induction machine

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Out-of-distribution Object Detection through Bayesian Uncertainty Estimation 26

Out-of-distribution Object Detection through Bayesian Uncert...

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26th International Conference on Information Fusion, FUSION 2023

作者： Zhang, Tianhao Wang, Shenglin Bouaynaya, Nidhal Calinescu, Radu Mihaylova, Lyudmila The University of Sheffield Automatic Control and Systems Engineering United Kingdom Rowan University Department of Electrical and Computer Engineering United States University of York Department of Computer Science United Kingdom

ISBN: (纸本)9798890344854

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset. © 2023 International Society of Information Fusion.

关键词： Object detection

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