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IEEE Conference on Wireless Communications and Networking

作者： Caijuan Chen Chaofeng Zhang Gaolei Li Yuchen Liu The Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan Advanced Institute of Industrial Technology Tokyo Japan School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science NC State University North Carolina US

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The escalating frequency of geological disasters, local conflicts, and public health crises necessitates robust emergency communication systems within Internet of Things (IoT) frameworks to ensure public safety. Employing Power-Domain NOMA (PD-NOMA) and Successive Interference Cancellation (SIC) enables effective multi-device communication that is crucial for emergency responses. However, the practical implementation of NOMA technologies in emergency scenarios is hindered by existing resource constraints and the requirement of prioritizing critical emergency information. To address these limitations, we propose a multi-objective optimization-based NOMA framework to maximize network throughput while ensuring quality of service (QoS) and energy efficiency. This framework incorporates a novel communication grouping algorithm based on queue penalty optimization and an Actor-Critic-based dynamic updating algorithm for multiple resource allocation. Our study proposes a practical approach to enhance emergency communication in disaster areas, ensuring efficient and reliable network performance under various constraints.

关键词： NOMA Interference cancellation Heuristic algorithms Disasters Quality of service Throughput Dynamic scheduling Internet of Things Resource management Optimization

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Emergence of Natural and Robust Bipedal Walking by Learning to Control Up to Ninety Muscles from Biologically Plausible Objectives

SSRN

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SSRN 2023年

作者： Schumacher, Pierre Geijtenbeek, Thomas Caggiano, Vittorio Kumar, Vikash Schmitt, Syn Martius, Georg Haeufle, Daniel F.B. Max-Planck Institute for Intelligent Systems Tübingen Germany Hertie Institute for Clinical Brain Research Center for Integrative Neuroscience Tübingen Germany Goatstream Netherlands Meta AI New York United States Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Germany Department of Computer Science University of Tübingen Germany Institute of Computer Engineering Heidelberg University Germany

Humans show unparalleled ability when maneuvering diverse terrains. While motor control researchers have studied these skills in simulation for a long time, most control approaches are only able to generate single motions. Recently, reinforcement learning~(RL) has shown great promise in the development of robust controllers capable of generating complex behaviors. The application to highly complex biomechanical models was, however, only possible under extensive use of motion capture data, limiting the abilities of the resulting agents. In this paper, we demonstrate that the combination of a recent RL algorithm with a biologically plausible reward function is capable of learning controllers for 4 different musculoskeletal human models and achieves bipedal motion with up to 90 muscles without demonstrations. Our controllers are robust enough to generalize to diverse and unseen terrains, while only a single objective function is needed for training. We validate our findings on 4 models in 2 different simulators and 2 different tasks, walking and running. We see this as a first step for the motor control, biomechanics, and rehabilitation communities to generate highly complex human movements with RL, without needing to collect large amounts of experimental data or limiting themselves to simple models that are not representative of the diverse skills that human beings exhibit every day. © 2023, The Authors. All rights reserved.

关键词： Muscle

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Digital Over-the-Air Federated Learning in Multi-Antenna Systems

arXiv

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arXiv 2023年

作者： Wang, Sihua Chen, Mingzhe Shen, Cong Yin, Changchuan Brinton, Christopher G. The Beijing Laboratory of Advanced Information Network The Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications Beijing100876 China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States The Charles L. Brown Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA United States The School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

In this paper, the performance optimization of federated learning (FL), when deployed over a realistic wireless multiple-input multiple-output (MIMO) communication system with digital modulation and over-the-air computation (AirComp) is studied. In particular, a MIMO system is considered in which edge devices transmit their local FL models (trained using their locally collected data) to a parameter server (PS) using beamforming to maximize the number of devices scheduled for transmission. The PS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all devices. Due to the limited bandwidth in a wireless network, AirComp is adopted to enable efficient wireless data aggregation. However, fading of wireless channels can produce aggregate distortions in an AirComp-based FL scheme. To tackle this challenge, we propose a modified federated averaging (FedAvg) algorithm that combines digital modulation with AirComp to mitigate wireless fading while ensuring the communication efficiency. This is achieved by a joint transmit and receive beamforming design, which is formulated as an optimization problem to dynamically adjust the beamforming matrices based on current FL model parameters so as to minimize the transmitting error and ensure the FL performance. To achieve this goal, we first analytically characterize how the beamforming matrices affect the performance of the FedAvg in different iterations. Based on this relationship, an artificial neural network (ANN) is used to estimate the local FL models of all devices and adjust the beamforming matrices at the PS for future model transmission. The algorithmic advantages and improved performance of the proposed methodologies are demonstrated through extensive numerical experiments. Copyright © 2023, The Authors. All rights reserved.

关键词： Beamforming

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Facial Recognition Impact in Smartphone Sector 4

Facial Recognition Impact in Smartphone Sector

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4th International Conference on Inventive Research in Computing Applications, ICIRCA 2022

作者： Jha, Amaresh Sudhakar, P. Lokhande, Sharayu Ashish Kumar Ishodzhanova, Galina Venkatrao, K. Zhakupova, Aliya School of Digital Media Journalism & Mass Communication Gd Goenka University India Vignan's Nirula Institute of Technology and Science for Women Department of Ece India Computer Engineering Army Institute of Technology India International Education Corporation Department of Architecture Kazakhstan Srkr Engineering College Department of Ece India International Education Corporation Department of Linguistics Kazakhstan

ISBN: (数字)9781665497077

ISBN: (纸本)9781665497077

In recent times, technological advancement brings many benefits for people and facial recognition technology is one of them. Nowadays, S martphone users increasingly use facial recognition features that help in verifying identity of users for different purposes, for example, accessing their device before making any payment from their phones. This study paper has analysed impact of facial recognition systems in S martphone sectors by discussing its advantages and challenges to understand both its negative and positive impact. Literature review section has provided relevant information on selected topic as it has elaborated that different large organisation have their own face recognition software for Apple, Facebook, and Google. Additionally, it has mentioned that there are two different types of facial recognition software such as camera-based and infrared;however, regarding effectiveness, infrared works more appropriate rather than camera-based recognition technologies. The paper has also elaborated that this technology offers automating time-tracking systems and it has touch less sign options, which help them in limiting physical contact with others. Result of this study showed that facial recognition helps to protect any type of unauthorised access in a Smartphone as well as it could prevent cyber threats by recognising face of S martphone users. Additionally, due to this software's fast identification feature, it could successfully verify faces of people within a few seconds. However, as this software is based on artificial intelligence, unlike other applications, it is highly vulnerable as hackers can hack Smartphones if strong algorithms are more used. Lastly, to prevent this issue a strong AI infrastructure and model security is required in future. © 2022 IEEE.

关键词： Smartphones

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Natural and Robust Walking using Reinforcement Learning without Demonstrations in High-Dimensional Musculoskeletal Models

arXiv

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arXiv 2023年

Humans excel at robust bipedal walking in complex natural environments. In each step, they adequately tune the interaction of biomechanical muscle dynamics and neuronal signals to be robust against uncertainties in ground conditions. However, it is still not fully understood how the nervous system resolves the musculoskeletal redundancy to solve the multiobjective control problem considering stability, robustness, and energy efficiency. In computer simulations, energy minimization has been shown to be a successful optimization target, reproducing natural walking with trajectory optimization or reflex-based control methods. However, these methods focus on particular motions at a time and the resulting controllers are limited when compensating for perturbations. In robotics, reinforcement learning (RL) methods recently achieved highly stable (and efficient) locomotion on quadruped systems, but the generation of human-like walking with bipedal biomechanical models has required extensive use of expert data sets. This strong reliance on demonstrations often results in brittle policies and limits the application to new behaviors, especially considering the potential variety of movements for high-dimensional musculoskeletal models in 3D. Achieving natural locomotion with RL without sacrificing its incredible robustness might pave the way for a novel approach to studying human walking in complex natural environments. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Mortar: Morphing the Bit Level Sparsity for General Purpose Deep Learning Acceleration

Mortar: Morphing the Bit Level Sparsity for General Purpose ...

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Asia and South Pacific Design Automation Conference

作者： Yunhung Gao Hongyan Li Kevin Zhang Xueru Yu Hang Lu School of Electronics Engineering and Computer Science Peking University Beijing China State Key Lab of Computer Architecture Institute of Computing Technology CAS University of Chinese Academy of Sciences Beijing China Shanghai Integrated Circuits R&D Center Co. Ltd Shanghai China

Vanilla Deep Neural Networks (DNN) after training are represented with native floating-point 32 (fp32) weights. We observe that the bit-level sparsity of these weights is very abundant in the mantissa and can be directly exploited to speed up model inference. In this paper, we propose Mortar, an off-line/on-line collaborated approach for fp32 DNN acceleration, which includes two parts: first, an off-line bit sparsification algorithm to construct the target formulation by “mantissa morphing”, which maintains higher model accuracy while increasing bit-level sparsity; second, the associating hardware accelerator architecture to speed up the on-line fp32 inference through manipulating the enlarged bit sparsity. We highlight the following results by evaluating various deep learning tasks, including image classification, object detection, video understanding, video & image super-resolution, etc.: We (1) increase bit-level sparsity up to 1.28~2.51x with only a negligible -0.09~0.23% accuracy loss, (2) maintain on average 3.55% higher model accuracy while increasing more bit-level sparsity than the baseline, (3)and our hardware accelerator outperforms up to 4.8x over the baseline, with an area of 0.031 mm 2 and power of 68.58mW.

关键词： Deep learning Training Mortar Superresolution Software algorithms Inference algorithms Software

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Simulative performance analysis of arq schemes for free-space optical inter-HAP channel model 16

Simulative performance analysis of arq schemes for free-spac...

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16. ITG-Fachtagung Photonische Netze - 16th ITG Conference on Photonic Networks

作者： Parthasarathy, Swaminathan Kirstädter, Andreas Giggenbach, Dirk WesslingD-82234 Germany Institute of Communication Networks and Computer Engineering University of Stuttgart StuttgartD-70569 Germany

ISBN: (纸本)9783800739387

Automatic Repeat reQuest (ARQ) data link layer protocols improve the reliability of communication in Free Space Optical (FSO) fading channels using feedback information. The objective of this paper is to analyse the simulative performance of different ARQ mechanisms for a FSO inter-High Altitude Platform (HAP) scenario. A bi-directional symmetrical inter-HAP channel was modelled considering the combined influence of atmospheric turbulence-induced scintillation and miss-pointing induced fading at the receiver. Then selected ARQ schemes were investigated via event-based simulations in terms of transmission efficiency. © 2020 *** right reserved.

关键词： Data Link Layer

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Innovative Approaches in Network architecture: Enhancing Connectivity and Performance in the Era of 5G and Beyond

Innovative Approaches in Network Architecture: Enhancing Con...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Sunita Pachar Satti Sudha Mohan Reddy Lakshmi Narasimhan Srinivasagopalan Balla Mounica B Srinivasa Rao Sivakoteswararao Katta IBM Department GLA University Department of ECE SRKR Engineering College India Software Development Enterprise Architecture Plano TX USA Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science Hyderabad Ace Engineering College Hyderabad Department of Mechanical KIETW Kakinada

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

This research study examines the evolving ecosystem of network applications for enhancing connectivity and performance in 5G and beyond (B5G) networks. The objective is to streamline large-scale deployment of vertical applications through a middleware layer, facilitating interactions among network operators and third parties based on varying trust levels. The proposed model addresses the complexity and computational demands of enabling adaptable, secure, and scalable applications for diverse 5G platforms. In particular, this study highlights the 5G-EPICENTRE project, which enables traffic management and dynamic control, especially for missioncritical Public Protection and Disaster Relief (PPDR) applications. By optimizing resource allocation, the model reduces computational costs while meeting the unique demands of PPDR services, such as high-quality video and data for emergency operations. The model's effectiveness will be evaluated through experiments leveraging 5G Core (5GC) control-plane capabilities, with a focus on quality of service (QoS) and latency. Practical limitations, including the integration challenges of multi-network APIs, are discussed in the conclusion.

关键词： Adaptation models 5G mobile communication Computational modeling Scalability Ecosystems Quality of service Telecommunications Complexity theory Resource management Protection

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Towards Efficient Elastic Parallelism for Deep Learning Processor

Towards Efficient Elastic Parallelism for Deep Learning Proc...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Jinyu Cheng Ruyi Qian Qinwen Shi Gaomei Hu Mengjuan Gao Qirun Huo Yuanchao Xu College of Information Engineering Capital Normal University Beijing China SKL of Computer Architecture CAS Institute of Computing Technology Beijing China

Resources underutilization is the most troublesome problem faced by almost all deep learning processors. To address this challenge, researchers have proposed various methods, especially in exploiting the parallelism of deep neural network models. However, some parallelisms have mutual effects, for example, intra-operator parallelism has adverse effects on inter-operator parallelism, resulting in a waste of resources. In order to make full use of chip resources, this paper applies the idea of elastic scheduling to the driver level of deep learning processor to achieve elastic parallelism. That is, if a task consists of multiple independent subtasks, it can be executed as long as the minimum resource requirements of the sub tasks are met. In addition, based on greedy scheduling algorithm, some improved methods are proposed, including subtask awareness, NUMA awareness, and cluster alignment awareness. Our analysis and preliminary evaluation demonstrate that, by reducing intra-operator parallelism, elastic parallelism can help improve chip utilization, and our proposed scheduling optimizations can also further improve the overall system performance.

关键词： Deep learning Program processors Scheduling algorithms System performance Pipelines Neural networks Optimization methods

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Adaptable Invisibility Management Using Kirigami-Inspired Transformable Metamaterials

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Research 2021年第1期2021卷 1034-1044页

作者： He-Xiu Xu Mingzhao Wang Guangwei Hu Shaojie Wang Yanzhao Wang Chaohui Wang Yixuan Zeng Jiafang Li Shuang Zhang Wei Huang Air and Missile Defense College Air Force Engineering UniversityXi’an 710051China Institute of Flexible Electronics Northwestern Polytechnical UniversityXi’an 710072China Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore 117583 Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement(MOE)Beijing 100081China School of Physics Beijing Institute of TechnologyBeijing 100081China Department of Physics University of Hong KongHong KongChina Department of Electronic&Electrical Engineering University of Hong KongHong KongChina

Many real-world applications,including adaptive radar scanning and smart stealth,require reconfigurable multifunctional devices to simultaneously manipulate multiple degrees of freedom of electromagnetic(EM)waves in an on-demand ***,kirigami technique,affording versatile and unconventional structural transformation,has been introduced to endow metamaterials with the capability of controlling EM waves in a reconfigurable ***,we report for a kirigami-inspired sparse meta-architecture,with structural density of 1.5%in terms of the occupation space,for adaptive invisibility based on independent operations of frequency,bandwidth,and *** on the general principle of dipolar management via structural reconstruction of kirigami-inspired meta-architectures,we demonstrate reconfigurable invisibility management with abundant EM functions and a wide tuning range using three enantiomers(A,B,and C)of different geometries characterized by the folding angleβ.Our strategy circumvents issues of limited abilities,narrow tuning range,extreme condition,and high cost raised by available reconfigurable metamaterials,providing a new avenue toward multifunctional smart devices.

关键词： tuning visibility smart

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