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Assessment of Water Conservation Function Based on Deep Autoencoder

Assessment of Water Conservation Function Based on Deep Auto...

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IEEE Annual Ubiquitous computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Xiaoqing Fan Xianghong Lin Hao Liu College of Computer Science and Engineering Northwest Normal University Lanzhou China College of Agriculture Ningxia University Yinchuan China

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

the accuracy of water conservation assessments is crucial for formulating water resource management and ecological protection policies. However, existing methods overly rely on expert judgment and struggle with precision when handling high-dimensional data. To address this, we propose a deep autoencoder-based method for evaluating water conservation functions. For the Gannan water conservation area, we developed a comprehensive evaluation index system and integrated multisource data to create an ecological dataset. By employing a deep autoencoder model combined with convolutional neural networks and a joint training strategy, we achieved feature extraction and dimensionality reduction, mapping high-dimensional data to a low-dimensional latent space. Subsequently, we used the K-means clustering algorithm to validate the model’s classification performance. the clustering accuracy and FMI based on AE-CNN extracted latent variables were significantly higher than those obtained by clustering the raw data directly. this demonstrates that the model effectively extracts data features and significantly enhances classification accuracy, providing robust support for ecological protection and water management.

关键词： Training Accuracy Biological system modeling Ecosystems Water conservation Feature extraction Indexes Data mining Convolutional neural networks Protection

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Energy-Efficient Scheduling for Best-Effort Interactive Services to Achieve high Response Quality

Energy-Efficient Scheduling for Best-Effort Interactive Serv...

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International symposium on Parallel and Distributed Processing (IPDPS)

作者： Zhihui Du Hongyang Sun Yuxiong He Yu He David A. Bader Huazhe Zhang Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Engineering Nanyang Technological University Singapore Microsoft Research Redmonds WA USA College of Computing Georgia Institute of Technology Atlanta GA USA School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781467360661

high response quality is critical for many best-effort interactive services, and at the same time, reducing energy consumption can directly reduce the operational cost of service providers. In this paper, we study the quality-energy tradeoff for such services by using a composite performance metric that captures their relative importance in practice: Service providers usually grant top priority to quality guarantee and explore energy saving secondly. We consider scheduling on multicore systems with core-level DVFS support and a power budget. Our solution consists of two steps. First, we employ an equal sharing principle for both job and power distribution. Specifically, we present a “Cumulative Round-Robin” policy to distribute the jobs onto the cores, and a “Water-Filling” policy to distribute the power dynamically among the cores. Second, we exploit the concave quality function of many best-effort applications, and develop Online-QE, a myopic optimal online algorithm for scheduling jobs on a single-core system. Combining the two steps together, we present a heuristic online algorithm, called DES (Dynamic Equal Sharing), for scheduling best-effort interactive services on multicore systems. the simulation results based on a web search engine application show that DES takes advantage of the core-level DVFS architecture and exploits the concave quality function of best-effort applications to achieve high service quality with low energy consumption.

关键词： Multicore processing Schedules Heuristic algorithms Optimal scheduling Energy consumption Dynamic scheduling

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Data Compression Techniques for high-Speed NetworkCommunications

Data Compression Techniques for High-Speed NetworkCommunicat...

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International Conference on computing and Networking Technology (ICCNT)

作者： Manish Nagpal G Rajalakshmi Nitish Vashisht N. Beemkumar Jayashree V. Bagade K. Yuvaraj Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Engineering and Technology Mechanical Engineering JAIN (Deemed to be University) Bangalore Karnataka India Department of Information Technology Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Data compression is an important factor of excessive-pace network communications, as it may lessen the number of facts that need to be transmitted over a given network. Facts compression techniques search to lessen the scale of data via encoding it in a more green format. Common compression techniques utilized in networks consist of static Huffman encoding, variable period encoding, arithmetic encoding, differential coding, run-period encoding, and compression with a chance distribution. Every approach has its personal deserves and boundaries, so the selection of a compression technique is dependent on the type of information and the network situations. Additionally, some of these techniques carry out better while implemented in aggregate with one another, offering a usual development in network throughput.

关键词： performance evaluation high-speed networks Aggregates Data compression throughput Encoding Safety Arithmetic

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FPGA-QHAR: throughput-Optimized for Quantized Two-Stream Human Action Recognition on the Edge

FPGA-QHAR: Throughput-Optimized for Quantized Two-Stream Hum...

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International symposium on high Capacity Optical Networks and Enabling Technologies, HONET

作者： Azzam Alhussain Mingjie Lin College of Engineering and Computer Science University of Central Florida Orlando Florida USA

Accelerating Human Action Recognition (HAR) efficiently for real-time surveillance and robotic systems on edge chips remains a challenging research field, given its high computational and memory requirements. this paper proposed an integrated end-to-end HAR scalable HW/SW accelerator co-design based on an enhanced 8-bit quantized Two-Stream SimpleNet-PyTorch CNN architecture. Our network accelerator was trained on UCF101 and UCF24 datasets and implemented on edge SoC-FPGA. Our development uses partially streaming dataflow architecture to achieve higher throughput versus network design and resource utilization trade-off. We also fused all convolutional, batch-norm, and ReLU operations into a single homogeneous layer and utilized the Lucas-Kanade motion flow method to enable a high parallelism accelerator design and optimized on-chip engine computing. Furthermore, our proposed methodology achieved nearly 81 % prediction accuracy with an approximately 24 FPS real-time inference throughput at 187MHz on ZCU104, which is 1.7x - 1.9x higher than the prior research. Lastly, the designed framework was benchmarked against several hardware chips for higher throughput and performance measurements and is now available as an open-source project on Github for training and implementation on edge platforms.

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Improved Denoising for Multi-frame Image Restoration with Convolutional Neural Networks

Improved Denoising for Multi-frame Image Restoration with Co...

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International Conference on computing and Networking Technology (ICCNT)

作者： Hitesh Kalra Kiran Lokesh Maney Omkaresh S. Kulkarni K. Ranjith Singh Gunveen Ahluwalia T. Surulivel Rajan Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Mangement - UG Mangement JAIN (Deemed to be University) Bangalore Karnataka India Department of Computer Science and Engineering (AI & ML) Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Mechanical Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Multi-body photograph restoration is a key application of state-of-the-art image processing. With the improvement of cutting-edge convolutional neural networks (CNNs), it has come to be modern-day viable to repair photos with improved denoising performance. CNNs allow the present-day modern-day complicated non-linear features with high accuracy and fast inference. A CNN-based totally multi-body image healing (MFIR) technique is proposed that is able to seize the spatial correlations between noise and texture in multi-body snapshots. Experiments on benchmark datasets show that our technique achieves overall performance, demonstrating the effectiveness of modern-day CNNs in this challenge. Our approach is powerful to extraordinary multi-frame noise levels and works higher than CNNs with the best unmarried-body entry. Additionally, our proposed technique will be extended to different picture healing responsibilities with diverse today’s corpora.

关键词： Correlation Accuracy Noise reduction Noise Maintenance engineering Benchmark testing Image restoration Convolutional neural networks Noise measurement Noise level

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Parallel Multi-hop Routing Protocol for 5G Backhauling Network Using HPC Platform

Parallel Multi-hop Routing Protocol for 5G Backhauling Netwo...

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International Conference on computer Engineering and Systems

作者： Somaya A. Aboulrous Ali A. El-Moursy Mohamed Saad Amany Abdelsamea Salwa M. Nassar Hazem Abbas Computer and Systems Engineering Ain Shams University Cairo Egypt Computer Engineering University of Sharjah Sharjah UAE Electronics Research Institute Giza Egypt

ISBN: (数字)9780738105598

ISBN: (纸本)9781665405232

Fifth generation (5G) systems are considered as the future of telecommunications and data processing. 5G systems are envisaged to increase current network capacity by 1000-fold. In addition to increasing spectral efficiency and utilizing wider bandwidths, ultra network densification with wireless small cells is considered a significant capacity-enhancing method for meeting the ever-increasing demand of 5G data traffic requirements. the enormous amounts of data traffic generated in inter-small-cell 5G backhaul networks require efficient routing protocols to speed up the routing decisions, while ensuring high data rates, low latency, and low power consumption requirements. therefore, a parallel routing protocol is proposed to speed up routing decisions in 5G backhaul networks, using high performance computing (HPC). We explore the efficiency of utilizing HPC to manage and speed up the parallel routing protocol using different communication network sizes. Our numerical results indicate that our HPC implementation achieves a routing speed-up of 37× for large network size (2048).

关键词： Wireless communication 5G mobile communication Spectral efficiency Spread spectrum communication Routing Routing protocols Telecommunications

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Towards a Lightweight Self-Sovereign Identity Framework for IoT Network in a zero trust Environment

Towards a Lightweight Self-Sovereign Identity Framework for ...

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IEEE Annual Ubiquitous computing, Electronics & Mobile Communication Conference (UEMCON)

作者： Efat Fathalla Youssef Azab Electrical and Computer Engineering Virginia Military Institute Lexington VA USA Rockbridge County High School Lexington VA USA Mountain Gateway Community Collage Clifton Forge VA USA

ISBN: (数字)9798331540906

ISBN: (纸本)9798331540913

IoT technology is one of the most prevalent technologies today, but modern IoT security solutions are increasingly becoming obsolete. As the demand for advanced security measures grows, there is a significant emphasis on enhancing IoT security to address contemporary challenges. In order to meet these modern IoT needs, this paper introduces SSIoT, a new approach to lightweight and secure self-sovereign identity-enabled decentralized authentication for IoT devices. SSIoT integrates secret sharing schemes with blockchain technology to deliver transparent and reliable SSI-based digital identity management while minimizing storage and computational overhead. By storing IoT identity credentials externally, SSIoT protects the device’s identity credentials against tampering or misuse. the effectiveness of this model is validated through evaluations and discussions, which simulate costs and estimate verification times on actual IoT devices.

关键词： performance evaluation Computational modeling Image edge detection Authentication Termination of employment Blockchains Zero Trust Internet of things Reliability Object recognition

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Scalable performance Awareness for In Situ Scientific Applications

Scalable Performance Awareness for In Situ Scientific Applic...

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IEEE International Conference on e-Science and Grid computing

作者： Matthew Wolf Jong Choi Greg Eisenhauer Stéphane Ethier Kevin Huck Scott Klasky Jeremy Logan Allen Malony Chad Wood Julien Dominski Gabriele Merlo Oak Ridge National Laboratory Oak Ridge TN USA School of Computer Science Georgia Institute of Technology Atlanta GA USA Princeton Plasma Physics Laboratory Princeton NJ USA University of Oregon Eugene OR USA The University of Tennessee Knoxville TN USA University of Texas Austin TX USA

ISBN: (数字)9781728124513

ISBN: (纸本)9781728124520

Part of the promise of exascale computing and the next generation of scientific simulation codes is the ability to bring together time and spatial scales that have traditionally been treated separately. this enables creating complex coupled simulations and in situ analysis pipelines, encompassing such things as "whole device" fusion models or the simulation of cities from sewers to rooftops. Unfortunately, the HPC analysis tools that have been built up over the preceding decades are ill suited to the debugging and performance analysis of such computational ensembles. In this paper, we present a new vision for performance measurement and understanding of HPC codes, MonitoringAnalytics (MONA). MONA is designed to be a flexible, high performance monitoring infrastructure that can perform monitoring analysis in place or in transit by embedding analytics and characterization directly into the data stream, without relying upon delivering all monitoring information to a central database for post-processing. It addresses the trade-offs between the prohibitively expensive capture of all performance characteristics and not capturing enough to detect the features of interest. We demonstrate several uses of MONA; capturing and indexing multi-executable performance profiles to enable later processing, extraction of performance primitives to enable the generation of customizable benchmarks and performance skeletons, and extracting communication and application behaviors to enable better control and placement for the current and future runs of the science ensemble. Relevant performance information based on a system for MONA built from ADIOS and SOSflow technologies is provided for DOE science applications and leadership machines.

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DeepVM: Integrating Spot and On-Demand VMs for Cost-Efficient Deep Learning Clusters in the Cloud

DeepVM: Integrating Spot and On-Demand VMs for Cost-Efficien...

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IEEE/ACM International symposium on Cluster computing and the Grid (CCGRID)

作者： Yoochan Kim Kihyun Kim Yonghyeon Cho Jinwoo Kim Awais Khan Ki-Dong Kang Baik-Song An Myung-Hoon Cha Hong-Yeon Kim Youngjae Kim Dept. of Computer Science and Engineering Sogang University Seoul Republic of Korea LG Electronics Oak Ridge National Laboratory TN USA ETRI Daejeon Republic of Korea

ISBN: (数字)9798350395662

ISBN: (纸本)9798350395679

Distributed Deep Learning (DDL), as a paradigm, dictates the use of GPU-based clusters as the optimal infrastructure for training large-scale Deep Neural Networks (DNNs). However, the high cost of such resources makes them inaccessible to many users. Public cloud services, particularly Spot Virtual Machines (VMs), offer a cost-effective alternative, but their unpredictable availability poses a significant challenge to the crucial checkpointing process in DDL. To address this, we introduce DeepVM, a novel solution that recommends cost-effective cluster configurations by intelligently balancing the use of Spot and On-Demand VMs. DeepVM leverages a four-stage process that analyzes instance performance using the FLOPP (FLoating-point Operations Per Price) metric, performs architecture-level analysis with linear programming, and identifies the optimal configuration for the user-specific needs. Extensive simulations and real-world deployments in the AWS environment demonstrate that DeepVM consistently outperforms other policies, reducing training costs and overall makespan. By enabling cost-effective checkpointing with Spot VMs, DeepVM opens up DDL to a wider range of users and facilitates a more efficient training of complex DNNs.

关键词： Training Deep learning Checkpointing Measurement Cloud computing Costs Artificial neural networks Linear programming

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ASNet: Introducing Approximate Hardware to high-Level Synthesis of Neural Networks

ASNet: Introducing Approximate Hardware to High-Level Synthe...

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International symposium on Multiple-Valued Logic

作者： Saman Froehlich Lucas Klemmer Daniel Große Rolf Drechsler DFKI GmbH and Group of Computer Architecture University of Bremen Germany

ISBN: (数字)9781728154060

ISBN: (纸本)9781728154077

Approximate computing is a design paradigm which makes use of error tolerance inherent to many applications in order to trade off accuracy for performance. One classic example for such an application is machine learning with Neural Networks (NNs). Recently, LeFlow, a high-Level Synthesis (HLS) flow for mapping Tensorflow NNs into hardware has been proposed. the main steps of LeFlow are to compile the Tensorflow models into the LLVM Intermediate Representation (IR), perform several transformations and feed the result into a HLS tool. In this work we take HLS-based NN synthesis one step further by integrating hardware approximation. To achieve this goal, we upgrade LeFlow such that (a) the user can specify hardware approximations, and (b) the user can analyze the impact of hardware approximation already at the SW level. Based on the exploration results which satisfy the NN quality expectations, we import the chosen approx. HW components into an extended version of the HLS tool to finally synthesize the NN to Verilog. the experimental evaluation demonstrates the advantages of our proposed ASNet for several NNs. Significant area reductions as well as improvements in operation frequency are achieved.

关键词： Artificial neural networks Tools Hardware design languages Generators Libraries Hardware Complexity theory

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