检索结果-内蒙古大学图书馆

13th ieee International conference on Communication Systems and network Technologies, CSNT 2024

作者： Hasija, Taniya Ramkumar, K.R. Kaur, Amanpreet Mittal, Sudesh Kumar Singh, Bhupendra Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Centre for Artificial Intelligence & Robotics Defence Research and Development Organization Bangalore India

ISBN: (纸本)9798350305463

Today system and application programming is moving toward concurrent and parallel programming with the development of multicore and multiprogramming architectures. In an effort to improve study performance, researchers are looking for more efficient methods to include multiprocessing and multicore programming into their simulation systems. This article provides an overview of multicore programming and illustrates how it can be implemented. The paper also focusing the limitations of primitive data types for diverse applications, especially in the context of computer systems. The article delves into the necessity of big numbers and arithmetic on a significant scale. Focusing on C programming, the article showcases the implementation of big numbers, providing scholars with a comprehensive understanding of the concept and its practical realization. © 2024 ieee.

关键词： Multicore programming

来源：评论

学校读者我要写书评

暂无评论

Stability Verification of Neural network Controllers Using Mixed-Integer programming

引用

ieee TRANSACTIONS ON AUTOMATIC CONTROL 2023年第12期68卷 7514-7529页

作者： Schwan, Roland Jones, Colin N. Kuhn, Daniel Ecole Polytech Fed Lausanne Automatic Control Lab CH-1015 Lausanne Switzerland Ecole Polytech Fed Lausanne Risk Analyt & Optimizat Chair CH-1015 Lausanne Switzerland

In this article, we propose a framework for the stability verification of mixed-integer linear programming (MILP) representable control policies. This framework compares a fixed candidate policy, which admits an efficient parameterization and can be evaluated at a low computational cost, against a fixed baseline policy, which is known to be stable but expensive to evaluate. We provide sufficient conditions for the closed-loop stability of the candidate policy in terms of the worst-case approximation error with respect to the baseline policy, and we show that these conditions can be checked by solving a mixed-integer quadratic program. In addition, we demonstrate that an outer and inner approximation of the stability region of the candidate policy can be computed by solving an MILP. The proposed framework is sufficiently general to accommodate a broad range of candidate policies including ReLU neural networks (NNs), optimal solution maps of parametric quadratic programs, and model predictive control (MPC) policies. We also present an open-source toolbox in Python based on the proposed framework, which allows for the easy verification of custom NN architectures and MPC formulations. We showcase the flexibility and reliability of our framework in the context of a dc-dc power converter case study and investigate its computational complexity.

关键词： Mixed-integer programming model predictive control neural networks stability guarantees verification

来源：评论

学校读者我要写书评

暂无评论

ILP-based Multi-Branch CNNs Mapping on Processing-in-Memory Architecture 6

ILP-based Multi-Branch CNNs Mapping on Processing-in-Memory ...

引用

6th International conference on AI Circuits and Systems (AICAS)

作者： Han, Haodong Wang, Junpeng Ding, Bo Chen, Song Univ Sci & Technol China Hefei Anhui Peoples R China

ISBN: (纸本)9798350383638;9798350383645

3D-stacked-DRAM-based processing-in-memory (DRAM-PIM) architectures demonstrate benefits in memory access bandwidth and energy efficiency and effectively mitigate the storage wall challenge posed by CNNs. However, DRAM-PIM architectures have a huge mapping space for multi-branch CNNs and inadequate mapping increases the latency of CNNs and the memory requirement of nodes. In this work, we propose an integer linear programming (ILP) method that integrates layer scheduling and resource quantity allocation to minimize overall latency. An ILP-based binding method is introduced to bind layers onto a node array of DRAM-PIM architectures with the maximum memory requirement of nodes reduced. Experimental results demonstrate that our method reduces the latency of branching structures in CNNs and achieves better memory balancing between nodes compared to the baseline method.

关键词： convolutional neural network mapping 3D-stacked DRAM processing in memory

来源：评论

学校读者我要写书评

暂无评论

An open Source Simulation Framework for Moving-network-Convoy Based Cellular V2X Communication in Intelligent Traffic Systems 98

An Open Source Simulation Framework for Moving-Network-Convo...

引用

98th ieee Vehicular Technology conference (VTC-Fall)

作者： Lakshminarasimhan, Venkatnarayanan Knoll, Alois Tech Univ Munich Sch Comp Informat & Technol Dept Comp Engn Chair Robot Artificial Intelligence & Realtime Sy Munich Germany

ISBN: (纸本)9798350329285

open source system level simulators play a crucial role in the early stage research and optimization of cellular V2X communication networks and cooperative intelligent transportation systems by providing for accurate virtual modelling, rapid evaluation of system level design and algorithm development, and by offering the possibility to build-up on previous research. Amidst the various architectures under consideration for future cellular V2X, small moving cells or moving network convoys are promising candidates, considering their inherent mobility. The orchestration of such architectures in combination with the availability of precise information about the vehicles from road side infrastructure open up new possibilities for a cost-optimal deployment and a resource-optimal operation of the cellular V2X networks. Towards this goal, we propose and present an open source simulation framework for moving-network-convoy based cellular V2X applications, and make it available for wider use at https://***/tum-air/ConvoyV2X

关键词： C-V2X C-ITS System Simulation

来源：评论

学校读者我要写书评

暂无评论

P4-based In-network Telemetry for FPGAs in the open Cloud Testbed and FABRIC

P4-based In-Network Telemetry for FPGAs in the Open Cloud Te...

引用

ieee conference on Computer Communications (ieee INFOCOM)

作者： Bal, Sandeep Han, Zhaoyang Handagala, Suranga CeviK, Mert Zink, Michael Leeser, Miriam Northeastern Univ Boston MA 02115 USA Univ Massachusetts Amherst MA 01003 USA

ISBN: (纸本)9798350384482;9798350384475

In recent years, Field Programmable Gate Arrays (FPGAs) have gained prominence in cloud computing data centers, driven by their capacity to offload compute-intensive tasks and contribute to the ongoing trend of data center disaggregation, as well as their ability to be directly connected to the network. While FPGAs offer numerous advantages, they also pose challenges in terms of configuration, programmability, and monitoring, particularly in the absence of an operating system with essential features like the TCP/IP networking stack. This paper introduces an In-band network Telemetry (INT) approach based on the P4 language for FPGA data plane programming. The goal is to facilitate monitoring and network performance analysis by providing one-way packet delay information. The approach is demonstrated in the open Cloud Testbed (OCT) and FABRIC testbeds, both offering open access to the research community with greater FPGA availability than commercial clouds. The workflow enables researchers to create custom P4 programs and bitstreams for installation on FPGAs. The paper presents a multi-step approach allowing experimentation within the New England Research Cloud (NERC), testing in OCT, and final deployment in FABRIC, well-suited for one-way delay measurements due to synchronized clocks via GPS time signals. Contributions include the provision of a P4 workflow for FPGAs in a research cloud, a novel FPGA clock-based INT approach, and a comprehensive evaluation through simulation and experiments in the open Cloud and FABRIC testbeds.

关键词： FPGA Testbeds P4 INT

来源：评论

学校读者我要写书评

暂无评论

Efficient Content Distribution in Fog-Based CDN: A Joint Optimization Algorithm for Fog-Node Placement and Content Delivery

引用

ieee INTERNET OF THINGS JOURNAL 2024年第9期11卷 16578-16590页

作者： Yadav, Prateek Kar, Subrat Indian Inst Technol Delhi Dept Elect Engn New Delhi 110016 India

Efficient content distribution to end-users poses a significant challenge in content distribution networks (CDNs). Traditional CDNs rely on cloud-based architectures, which may not be optimal for delivering content in densely populated areas due to increased network latency and bandwidth limitations. These problems can be mitigated by adopting fog/edge computing, which deploys computing nodes near end-users to reduce latency and improve content delivery. Although factors such as deployment and distribution are often considered separately, there are relatively few studies on how node deployment affects content distribution. Moreover, current research on fog-based content distribution networks (fog-based CDNs) does not often address formal methods for key challenges, such as (R1) optimal fog node placement;(R2) providing efficient content distribution to end-users;and (R3) minimizing the overall fog-based CDN cost. Therefore, we propose an algorithm to jointly optimize the placement of fog nodes and the content distribution, called the joint optimization algorithm for the fog-node placement and content distribution (JFnP-CDA). The algorithm uses a two-step procedure including clustering and Voronoi method, and nonlinear programming to optimize R1, R2, and R3. We consider four parameters for this: 1) a given geographical region;2) locations of open public Wi-Fi access points (OPWAPs) in that region;3) Quality of Service (QoS, with delivery in delay as the measure);and 4) cost to generate optimal service subregions (and, thereby, distribute content to edge-network hot spots). We evaluated the effectiveness of the proposal by implementing real-world OPWAP data, and the results show that the algorithm JFnP-CDA outperforms the baseline methods.

关键词： Costs Quality of service Servers Internet of Things Edge computing Content distribution networks Delays Content distribution network (CDN) fog-based content delivery network (fog-CDN) fog computing Internet of Things (IoT) model joint optimization

来源：评论

学校读者我要写书评

暂无评论

Decentralized Waveform Co-design for Integrated Sensing and Communications systems via Approximate Dynamic programming 21

Decentralized Waveform Co-design for Integrated Sensing and ...

引用

ieee 21st Consumer Communications and networking conference (CCNC)

作者： Doly, Shammi A. Chiriyath, Alex R. Herschfelt, Andrew Azam, Md Ali Ragi, Shankarachary Bliss, Daniel W. Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85287 USA Arizona State Univ Ctr Wireless Informat Syst & Computat Architectur Tempe AZ 85281 USA

ISBN: (纸本)9798350304572

The increasing demand for cost-efficient yet reliable swarms of multi-function unmanned aerial systems (UASs) has tremendous potential in both military and civilian applications. Advancements in ISACs research, such as RF Convergence exclusively provide a promising path toward implementing multi-function UASs, often limited by insufficient front-end capabilities as the network scales up. Because the interconnection bandwidth and computational complexity required to process the data among the ISACs nodes using centralized architectures are high. In this paper, we propose a decentralized waveform co-design method to reduce computational complexity while maximizing the mutual benefits of users in a multi-function UASs network based on the theory of decentralized, partially-observable Markov decision processes (Dec-POMDPs). To address the computational intractability of solving Dec-POMDPs (as with any decision-theoretic framework), we extend an approximate dynamic programming approach we recently developed-nominal belief-state optimization (NBO) in the context of radar-communications waveform co-design. We conduct a numerical study to benchmark the performance of the DecPOMDP-based waveform design approach against a centralized decision optimization approach, which demonstrates a 50% reduction in computation time at the cost of moderate loss in ranging precision.

关键词： RF convergence ISACs Dec-POMDP waveform co-design UASs target tracking approximate dynamic programming

来源：评论

学校读者我要写书评

暂无评论

Neural network Approach to Channel estimation in Wireless Communication 10

Neural Network Approach to Channel estimation in Wireless Co...

引用

10th ieee International conference on Electronics, Computing and Communication Technologies (ieee CONECCT)

作者： Tangudu, Narendranath Durga Kumar, Swaraj Vankayala, Satya Kumar Yoon, Seungil Samsung R&D Inst Stand Res Grp Bengaluru India Samsung R&D Inst Call SW Grp Network Call & OAM Team Bengaluru India Samsung Elect OAM SW R&D Grp Suwon South Korea

ISBN: (纸本)9798350385939;9798350385922

To meet the high throughput and stringent Quality of Service (QoS) requirements of the fifth and the sixth generation (5G and 6G) users, operators have explored technologies such as Multi-Input Multi-Output (MIMO), massive MIMO (mMIMO), Base Station (BS) densification, Non-Orthogonal Multiplex Access (NOMA) etc. The Central Unit (CU) and Distributed Unit (DU) split architectures defined in open Radio Access network (ORAN) and 3rd Generation Partnership Project (3GPP), gives the possibilities to explore the software-based methods, which were restricted earlier due to hardware-centric systems. With software defined Radio Access network (RAN) at cloud and artificial intelligence being a promising domain for solving various problems, these two can be leveraged in exploring to solve or optimize various existing challenges like channel estimation in the wireless receivers, which is very computationally expensive. In this work, we attempted to improve the performance of the compute intensive module channel estimation using machine learning architecture. Convolutional Neural network (CNN) based image processing architectures, Super Resolution CNN (SRCNN) and Denoising CNN (DnCNN) were implemented for channel estimation module, along with various combinations of spatial data arrangements of the input signal. The spatial data arrangement of complex and real components of input signal outperformed and yielded better accuracy compared to traditional methods such as estimated Minimum Mean Square Error (MMSE) and Approximate Linear Minimum Mean Square Error (ALMMSE).

关键词： Channel estimation convolutional neural network image super resolution image denoising machine learning

来源：评论

学校读者我要写书评

暂无评论

Tunable Hybrid Proposal networks for the open World

Tunable Hybrid Proposal Networks for the Open World

引用

ieee/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Inkawhich, Matthew Inkawhich, Nathan Li, Hai Chen, Yiran Duke Univ Durham NC 27706 USA Air Force Res Lab Wright Patterson AFB OH USA

ISBN: (纸本)9798350318920;9798350318937

Current state-of-the-art object proposal networks are trained with a closed-world assumption, meaning they learn to only detect objects of the training classes. These models fail to provide high recall in open-world environments where important novel objects may be encountered. While a handful of recent works attempt to tackle this problem, they fail to consider that the optimal behavior of a proposal network can vary significantly depending on the data and application. Our goal is to provide a flexible proposal solution that can be easily tuned to suit a variety of open-world settings. To this end, we design a Tunable Hybrid Proposal network (THPN) that leverages an adjustable hybrid architecture, a novel self-training procedure, and dynamic loss components to optimize the tradeoff between known and unknown object detection performance. To thoroughly evaluate our method, we devise several new challenges which invoke varying degrees of label bias by altering known class diversity and label count. We find that in every task, THPN easily outperforms existing baselines (e.g., RPN, OLN). Our method is also highly data efficient, surpassing baseline recall with a fraction of the labeled data.

关键词： Algorithms Algorithms and algorithms formulations Image recognition and understanding Machine learning architectures

来源：评论

学校读者我要写书评

暂无评论

Analysis of open Set Deep Neural network Variants towards Classification of Known and Unknown Signals 20

Analysis of Open Set Deep Neural Network Variants towards Cl...

引用

ieee 20th Consumer Communications and networking conference (CCNC)

作者： Kompella, Srihari K. Kompella, Sastry Montgomery Blair High Sch Montgomery AL 20901 USA SPARLab Inc Gaithersburg MD USA

ISBN: (纸本)9781665497343

In environments crowded with electromagnetic activity, cognitive radios (CR) must have the ability to differentiate between different signals, so as to understand their origins if needed, and decide whether they are friendly or unfriendly signals. This is extremely important in the case of military and intelligence applications, but is beginning to become important in the commercial world as well, given the recent discussion around 5G and ORAN. In this paper, we develop and compare three open and three closed-set signal classification models based on various Machine Learning (ML) architectures. Results show that openset networks are able to perform very close to their closed-set counterparts while also being able to classify unknown signals that they have not been trained on.

关键词： open Set Closed Set Convolutional Neural network Residual network Long-Short Term Memory LSTM network

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：