检索结果-内蒙古大学图书馆

10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Sengupta, Anirban Chaurasia, Rahul Indian Institute of Technology Department of Computer Science and Engineering Indore India Indian Institute of Information Technology Department of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798331533229

This paper presents a novel Swarm Intelligence driven Electronic System-Level (ESL) synthesis-based security methodology for functional Trojan fortification (called 'SWIFT'). Backdoor functional Trojan covertly inserted in the reusable hardware designs is a major concern for ensuring reliability functionality of consumer electronics (CE) systems and also for ensuring consumer trust. Erroneous computed output due to malicious functional Trojan can cause malfunctioning of such reusable IP cores. Therefore, the proposed methodology introduces a framework that integrates triple modular redundancy (TMR) along with a unique multi-vendor allocation that enables the generation of Trojan fortified (resistant) reusable hardware intellectual property (IP) designs capable of countering functional Trojan, if present and producing the correct output. The proposed approach integrates the swarm intelligence-based design space exploration (DSE) process with Trojan fortification design process to optimize any possible design overhead incurred due to TMR design. Results of the experimental evaluation prove that the proposed approach can generate low-cost Trojan fortified IP designs at meagre exploration time and at the expense of nominal overhead compared to prior work (that handle Trojan detection only). Further, the presented approach can handle a several potential vulnerabilities in the DSP design, which shows its robust security strength. © 2024 IEEE.

关键词： Swarm intelligence

来源：评论

学校读者我要写书评

暂无评论

A dynamic and resource sharing virtual network mapping algorithm

引用

Digital Communications and Networks 2023年第5期9卷 1101-1112页

作者： Xiancui Xiao Xiangwei Zheng Ji Bian Cun Ji Xinchun Cui School of Information Science and Engineering Shandong Normal UniversityJinan250014China School of Information Engineering Shandong Management UniversityJinan250358China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250014China School of Computer Science and Technology Qufu Normal UniversityRizhao276800China

Network virtualization can effectively establish dedicated virtual networks to implement various network ***,the existing research works have some shortcomings,for example,although computing resource properties of individual nodes are considered,node storage properties and the network topology properties are usually ignored in Virtual Network(VN)modelling,which leads to the inaccurate measurement of node availability and *** addition,most static virtual network mapping methods allocate fixed resources to users during the entire life cycle,and the users’actual resource requirements vary with the workload,which results in resource allocation *** on the above analysis,in this paper,we propose a dynamic resource sharing virtual network mapping algorithm named NMA-PRS-VNE,first,we construct a new,more realistic network framework in which the properties of nodes include computing resources,storage resources and topology *** the node mapping process,three properties of the node are used to measure its mapping ***,we consider the resources of adjacent nodes and links instead of the traditional method of measuring the availability and priority of nodes by considering only the resource properties,so as to more accurately select the physical mapping nodes that meet the constraints and conditions and improve the success rate of subsequent link ***,we divide the resource requirements of Virtual Network Requests(VNRs)into basic subrequirements and variable sub-variable requirements to complete dynamic resource *** former represents monopolizing resource requirements by the VNRs,while the latter represents shared resources by many VNRs with the probability of occupying resources,where we keep a balance between resource sharing and collision among users by calculating the collision *** results show that the proposed NMAPRS-VNE can increase the average acceptance rate and network revenu

关键词： Network virtualization VNRs Network frameworks Dynamic resource allocation Resource sharing

来源：评论

学校读者我要写书评

暂无评论

Parallel Implementation of the CCSDS Turbo Decoder on GPU

引用

China Communications 2024年第10期21卷 70-77页

作者： Liu Zhanxian Liu Rongke Zhang Haijun Wang Ning Sun Lei Wang Jianquan School of Computer&Communication Engineering University of Science and Technology BeijingBeijing 100083China School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Institute of Artificial IntelligenceUniversity of Science and Technology BeijingBeijing 100083China School of Information Engineering Zhengzhou UniversityZhengzhou 450001China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China

This paper presents a software turbo decoder on graphics processing units(GPU).Unlike previous works,the proposed decoding architecture for turbo codes mainly focuses on the Consultative Committee for Space Data Systems(CCSDS)***,the information frame lengths of the CCSDS turbo codes are not suitable for flexible sub-frame parallelism *** mitigate this issue,we propose a padding method that inserts several bits before the information frame *** obtain low-latency performance and high resource utilization,two-level intra-frame parallelisms and an efficient data structure are *** presented Max-Log-Map decoder can be adopted to decode the Long Term Evolution(LTE)turbo codes with only small *** proposed CCSDS turbo decoder at 10 iterations on NVIDIA RTX3070 achieves about 150 Mbps and 50Mbps throughputs for the code rates 1/6 and 1/2,respectively.

关键词： CCSDS CUDA GPU parallel decoding turbo codes

来源：评论

学校读者我要写书评

暂无评论

Secure Accelerated Computing: High-Level Synthesis Based Hardware Accelerator Design for CNN Applications 10

Secure Accelerated Computing: High-Level Synthesis Based Har...

引用

10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Chaurasia, Rahul Sengupta, Anirban Indian Institute of Information Technology Department of Computer Science and Engineering Bhopal India Indian Institute of Technology Department of Computer Science and Engineering Indore India

ISBN: (纸本)9798331533229

This paper introduces a novel methodology for designing secure hardware accelerator tailored for convolutional neural network (CNN) applications, leveraging security-aware high-level synthesis (HLS). The methodology outlined offers comprehensive security measures, including hindrance against reverse engineering (RE) and intellectual property (IP) piracy concerns through architectural transformations and embedded biometric palmprint constraints, respectively. Key contributions of this methodology include: i) robust hindrance against RE attack, ii) robust piracy detection with no additional design overhead, iii) definitive proof of IP ownership by achieving significantly lower probability of coincidence (P), indicating lower probability of false positive, and iv) enhanced tamper tolerance (TT) and resilience against brute force attacks compared to recent methodologies. © 2024 IEEE.

关键词： High level synthesis

来源：评论

学校读者我要写书评

暂无评论

Collaborative Service Provisioning for UAV-Assisted Mobile Edge Computing

引用

Chinese Journal of Electronics 2024年第6期33卷 1504-1514页

作者： Yuben QU Zhenhua WEI Zhen QIN Tao WU Jinghao MA Haipeng DAI Chao DONG The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology Colllege of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Xi'an Research Institute of High Technology Department of Information and Communication Noncommissioned Officer Academy of PAP National University of Defense Technology Department of Computer Science and Technology Nanjing University

Unmanned aerial vehicle(UAV)-assisted mobile edge computing(MEC), as a way of coping with delaysensitive and computing-intensive tasks, is considered to be a key technology to solving the challenges of terrestrial MEC networks. In this work, we study the problem of collaborative service provisioning(CSP) for UAV-assisted MEC. Specifically, taking into account the task latency and other resource constraints, this paper investigates how to minimize the total energy consumption of all terrestrial user equipments, by jointly optimizing computing resource allocation, task offloading, UAV trajectory, and service placement. The CSP problem is a non-convex mixed integer nonlinear programming problem, owing to the complex coupling of mixed integral variables and non-convexity of CSP. To address the CSP problem, this paper proposes an alternating optimization-based solution with the convergence guarantee as follows. We iteratively deal with the joint service placement and task offloading subproblem, and UAV movement trajectory subproblem, by branch and bound and successive convex approximation, respectively,while the closed form of the optimal computation resource allocation can be efficiently obtained. Extensive simulations validate the effectiveness of the proposed algorithm compared to three baselines.

关键词： Energy consumption Multi-access edge computing Collaboration Programming Autonomous aerial vehicles Minimization Trajectory Resource management Optimization Convergence

来源：评论

学校读者我要写书评

暂无评论

Automatic emotion recognition using deep neural network

引用

Multimedia Tools and Applications 2025年 1-30页

作者： Sujatha, R. Chatterjee, Jyotir Moy Pathy, Baibhav Hu, Yu-Chen School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of CSE Graphic Era University Dehradun India School of Electrical Engineering Vellore Institute of Technology Vellore India Department of Computer Science Tunghai University Taichung City Taiwan

Emotions are a vital semantic part of human correspondence. Emotions are significant for human correspondence as well as basic for human–computer cooperation. Viable correspondence between people is possibly achieved when both the importance and the emotion of the correspondence are perceived by all groups included. Understanding the significance of language has generally been concentrated on in natural language processing (NLP) as a semantic examination. In NLP, the text can be handled appropriately for classification. Emotion detection from facial emotion is the subfield of social signal processing applied in a wide assortment of regions, explicitly for human and PC collaboration. Many researchers have proposed various approaches, generally utilizing machine learning concepts. Automatic emotion recognition (AER) is significant for working with consistent intuitiveness between a person and a smart device toward fully acknowledging an intelligent society. Many researchers examined cross-lingual and multilingual speech emotion as a stage toward language-free emotion acknowledgment in natural speech. In the present work, we are proposing a deep learning-based AER system using four openly accessible datasets, namely Basic Arabic Vocal Emotions Dataset (BAVED), Acted Emotional Speech Dynamic Database (AESDD), Urdu written in Latin/Roman Script (URDU), and Toronto Emotional Speech Set (TESS), by utilizing the Jupyter notebook and a Python library for music and audio synthesis named Librosa. The experimental results exhibited that the proposed approach achieves better than the existing approaches, i.e., the accuracy of the proposed system with the URDU dataset is 96.24%, the TESS dataset is 99.10%, the AESDD dataset is 65.97%, and the BAVED dataset is 73.12%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Integration of Blockchain and IPFS: healthcare data management & sharing for IoT Environment

引用

Multimedia Tools and Applications 2024年 1-22页

作者： Mishra, Rajiv Kumar Yadav, Rajesh Kumar Nath, Prem Department of Computer Science & amp Engineering Jaypee Institute of Information Technology Noida India Department of Computer Science & amp Engineering Delhi Technological University Delhi India Department of Computer Science HNB Garhwal University Srinagar India

The immense volume of data generated and collected by smart devices has significantly enhanced various aspects of our daily lives. However, safeguarding the sensitive information shared among these devices is crucial. Ensuring the security of the Internet of Things (IoT) ecosystem from unauthorized access is imperative. Blockchain technology emerges as a promising solution to address these security concerns. Nevertheless, the effectiveness of Blockchain in handling the extensive data generated by smart devices is challenged by the rapid pace of IoT data generation and the slower transaction validation speed within Blockchain networks. This research aims to resolve these issues by integrating Blockchain with the Inter-Planetary File System (IPFS), creating a robust framework for secure data recording on a distributed storage network while enabling authorized access to the stored data. The proposed mechanism involves defining and recording access policies and cryptographic hash content on the Blockchain network, while storing the actual IoT-generated data on IPFS to enhance the confidentiality, integrity, and availability (CIA) triad. Performance assessments of the proposed scheme demonstrate its security and practicality, validating its potential for real-world application. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Electronic health record

来源：评论

学校读者我要写书评

暂无评论

Geometry-Aware Self-Supervised Indoor 360° Depth Estimation via Asymmetric Dual-Domain Collaborative Learning

引用

IEEE Transactions on Multimedia 2025年 27卷 3224-3237页

作者： Wang, Xu He, Ziyan Zhang, Qiudan Yang, You Zhao, Tiesong Jiang, Jianmin Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Huangzhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China Fuzhou University College of Physics and Information Engineering Fuzhou350108 China

Being able to estimate monocular depth for spherical panoramas is of fundamental importance in 3D scene perception. However, spherical distortion severely limits the effectiveness of vanilla convolutions. To push the envelope of accuracy, recent approaches attempt to utilize Tangent projection (TP) to estimate the depth of 360° images. Yet, these methods still suffer from discrepancies and inconsistencies among patch-wise tangent images, as well as the lack of accurate ground truth depth maps under a supervised fashion. In this paper, we propose a geometry-aware self-supervised 360° image depth estimation methodology that explores the complementary advantages of TP and Equirectangular projection (ERP) by an asymmetric dual-domain collaborative learning strategy. Especially, we first develop a lightweight asymmetric dual-domain depth estimation network, which enables to aggregate depth-related features from a single TP domain, and then produce depth distributions of the TP and ERP domains via collaborative learning. This effectively mitigates stitching artifacts and preserves fine details in depth inference without overspending model parameters. In addition, a frequent-spatial feature concentration module is devised to simultaneously capture non-local Fourier features and local spatial features, such that facilitating the efficient exploration of monocular depth cues. Moreover, we introduce a geometric structural alignment module to further improve geometric structural consistency among tangent images. Extensive experiments illustrate that our designed approach outperforms existing self-supervised 360° depth estimation methods on three publicly available benchmark datasets. © 1999-2012 IEEE.

关键词： Self-supervised learning

来源：评论

学校读者我要写书评

暂无评论

OFDM with Generalized Cascade Index Modulation for Secure Communications

引用

China Communications 2024年第10期21卷 210-225页

作者： Hu Zeng Jin Xiaopin Yao Yudong Chen Fangjiong Wan Dehuan Liu Yun College of Information Science and Technology Zhongkai University of Agriculture and EngineeringGuangzhou 510225China College of Information Engineering China Jiliang UniversityHangzhou 310018China Department Electrical and Computer Engineering Stevens Institute of Technology HobokenNJ 07030USA School of Electronic and Information Engineering South China University of TechnologyGuangzhou 510641China Center for Data Science and Artificial Intelligence Guangdong University of FinanceGuangzhou 510521China

Cascade index modulation(CIM) is a recently proposed improvement of orthogonal frequency division multiplexing with index modulation(OFDM-IM) and achieves better error *** CIM, at least two different IM operations construct a super IM operation or achieve new functionality. First, we propose a OFDM with generalized CIM(OFDM-GCIM) scheme to achieve a joint IM of subcarrier selection and multiple-mode(MM)permutations by using a multilevel digital ***, two schemes, called double CIM(D-CIM) and multiple-layer CIM(M-CIM), are proposed for secure communication, which combine new IM operation for disrupting the original order of bits and symbols with conventional OFDM-IM, to protect the legitimate users from eavesdropping in the wireless communications. A subcarrier-wise maximum likelihood(ML) detector and a low complexity log-likelihood ratio(LLR) detector are proposed for the legitimate users. A tight upper bound on the bit error rate(BER) of the proposed OFDM-GCIM, D-CIM and MCIM at the legitimate users are derived in closed form by employing the ML criteria detection. computer simulations and numerical results show that the proposed OFDM-GCIM achieves superior error performance than OFDM-IM, and the error performance at the eavesdroppers demonstrates the security of D-CIM and M-CIM.

关键词： cascade index modulation low-complexity detector maximum likelihood OFDM physical layer security

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Models Based on Weakly Supervised Learning and Clustering Visualization for Disease Diagnosis

引用

computers, Materials & Continua 2023年第9期76卷 2649-2665页

作者： Jingyao Liu Qinghe Feng Jiashi Zhao Yu Miao Wei He Weili Shi Zhengang Jiang School of Computer and Information Engineering Chuzhou UniversityChuzhou239000China School of Computer Science and Technology Changchun University of Science and TechnologyChangchun130022China Zhongshan Institute of Changchun University of Science and Technology ZhongshanChina School of Intelligent Engineering Henan Institute of TechnologyXinxiang453003China

The coronavirus disease 2019(COVID-19)has severely disrupted both human life and the health care *** diagnosis and treatment have become increasingly important;however,the distribution and size of lesions vary widely among individuals,making it challenging to accurately diagnose the *** study proposed a deep-learning disease diagnosismodel based onweakly supervised learning and clustering visualization(W_CVNet)that fused classification with ***,the data were *** optimizable weakly supervised segmentation preprocessing method(O-WSSPM)was used to remove redundant data and solve the category imbalance ***,a deep-learning fusion method was used for feature extraction and classification recognition.A dual asymmetric complementary bilinear feature extraction method(D-CBM)was used to fully extract complementary features,which solved the problem of insufficient feature extraction by a single deep learning ***,an unsupervised learning method based on Fuzzy C-Means(FCM)clustering was used to segment and visualize COVID-19 lesions enabling physicians to accurately assess lesion distribution and disease *** this study,5-fold cross-validation methods were used,and the results showed that the network had an average classification accuracy of 85.8%,outperforming six recent advanced classification models.W_CVNet can effectively help physicians with automated aid in diagnosis to determine if the disease is present and,in the case of COVID-19 patients,to further predict the area of the lesion.

关键词： Classification COVID-19 deep learning segmentation unsupervised learning weakly supervised

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：