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检索条件"主题词=approximation algorithms"

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Frequency Estimation in Data Streams: Learning the Optimal Hashing Scheme (Extended Abstract)

Frequency Estimation in Data Streams: Learning the Optimal H...

引用

38th IEEE International Conference on Data Engineering (ICDE)

作者： Bertsimas, Dimitris Digalakis, Vassilis, Jr. MIT Cambridge MA 02139 USA

ISBN: (数字)9781665408837

ISBN: (纸本)9781665408837

We present a novel approach for the problem of frequency estimation in data streams that is based on optimization and machine learning. Contrary to state-of-the-art streaming frequency estimation algorithms, which heavily rely on random hashing to maintain the frequency distribution of the data steam using limited storage, the proposed approach exploits an observed stream prefix to near-optimally hash elements and compress the target frequency distribution. We develop and solve (exactly and approximately) an optimization formulation, which enables us to compute optimal or near-optimal hashing schemes for elements seen in the observed stream prefix;then, we use machine learning to hash unseen elements. We empirically evaluate the proposed approach both on synthetic datasets and on real-world search query data. We show that the proposed approach outperforms existing approaches by one to two orders of magnitude in terms of its average (per element) estimation error and by 45-90% in terms of its expected magnitude of estimation error.

关键词： Estimation error Machine learning algorithms Conferences Machine learning Data engineering approximation algorithms Frequency estimation

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Enhancing Spectral Efficiency in IoT Networks using Deep Deterministic Policy Gradient and Opportunistic NOMA

Enhancing Spectral Efficiency in IoT Networks using Deep Det...

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IEEE Conference on Vehicular Technology (VTC)

作者： Neha Mazhar Syed Asad Ullah Haejoon Jung Qurrat-Ul-Ain Nadeem Syed Ali Hassan School of Electrical Engineering & Computer Science (SEECS) National University of Sciences & Technology (NUST) Islamabad Pakistan Department of Electronic Engineering Kyung Hee University (KHU) Yongin South Korea Engineering Division New York University (NYU) Abu Dhabi Abu Dhabi United Arab Emirates NYU Tandon School of Engineering Brooklyn United States of America

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Amidst the ongoing debate about limited spectral availability, there remains a persistent demand for the development of spectrally efficient self-sustainable network (SSN) models. This paper addresses this challenge by optimizing spectral efficiency (SE) in uplink transmissions for an energy harvesting (EH)-enabled secondary user (SU) that operates opportunistically among multiple primary users (PUs) in an Internet-of-things (IoT) network. The PUs are assumed to employ a rotational time division multiple access (TDMA) scheme for transmissions, where the signals are divided into time slots for each PU to transmit data in a cyclic manner, while the SU uses an opportunistic non-orthogonal multiple access (NOMA) technique to transmit data without interfering with the PU transmissions, such that, at any given time slot, a PU and a SU share the same frequency band simultaneously. The SE of the system is maximized jointly by employing convex optimization and a deep reinforcement learning (DRL) model, specifically the deep deterministic policy gradient (DDPG) algorithm. Simulations demonstrate that the proposed approach significantly improves the SE of the considered IoT network, highlighting its potential for efficient spectrum management in IoT networks. We present a comprehensive SE analysis of the system, which further underscores the robustness and adaptability of our approach in optimizing SE under diverse operational conditions.

关键词： NOMA Time division multiple access Adaptation models Vehicular and wireless technologies Time-frequency analysis Spectral efficiency Benchmark testing approximation algorithms Convex functions Uplink

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Using Quantum Resistant Variants of NIST IR 8454 Lightweight Encryption algorithms in Image Encryption

Using Quantum Resistant Variants of NIST IR 8454 Lightweight...

引用

International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Muhammed Saadetdin Kaya Kenan İInce Cyber Security Vocational School Ankara University Ankara Türkiye Software Engineering Inonu University Malatya Türkiye

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

This work examines the potential of quantum resistant variants of NIST IR 8454 lightweight encryption algorithms in the field of image encryption. Selected algorithms from ASCON, TinyJAMBU and SPARKLE families were analyzed using various performance metrics. The research includes entropy analysis, correlation analysis, histogram analysis, NPCR and UACI tests, PSNR and SSIM tests, and calculation speed analysis. The results show that all tested algorithms meet the basic security requirements for image encryption. In particular, an increase in entropy of approximately $13 \%$ in encrypted images, significant reduction in pixel correlations, and high sensitivity to small changes in the input image are observed. While ASCON80pq shows the fastest performance, all algorithms provide basic resistance to quantum attacks. This work reveals that quantumresistant lightweight encryption algorithms are promising for image security, especially in resource-constrained environments such as IoT. However, it is emphasized that these algorithms need to be further investigated in different image types, specific attack scenarios and real-world applications.

关键词： Resistance Sensitivity Correlation NIST approximation algorithms Entropy Robustness Encryption Standards Testing

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Evaluation of Erasure Coding and Opportunistic Offloading algorithms Using DPU in Distributed Storage Systems

Evaluation of Erasure Coding and Opportunistic Offloading Al...

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IEEE Non-Volatile Memory Systems and Applications Symposium (NVMSA)

作者： Junghyun Ryu Hongsu Byun Myungcheol Lee Jinchun Choi Youngjae Kim Dept. of Computer Science and Engineering Sogang University Seoul South Korea Electronics and Telecommunications Research Institute Daejeon South Korea

ISBN: (数字)9798350388558

ISBN: (纸本)9798350388565

In this paper, we explore accelerating Erasure Coding by applying the BlueField-3 DPU to distributed storage systems. First, we evaluated the performance of Erasure Coding when executed on the CPU and DPU, respectively. The evaluation results showed that the DPU had a execution time approximately 10.5 times slower than the CPU. This finding indicates that unconditional offloading is not beneficial; instead, offloading should be considered opportunistically when the CPU is busy and not be able to handle Erasure Coding tasks effectively. Based on this, we propose an opportunistic algorithm for offloading Erasure Coding to the DPU. Our future work is to refine the proposed offloading algorithm and apply it to distributed storage systems using DPUs to improve the performance of Erasure Coding.

关键词： Nonvolatile memory approximation algorithms Encoding Computational efficiency Servers

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A Subspace Gradient Descent Method for High-Dimensional Simulation-Based Optimization

A Subspace Gradient Descent Method for High-Dimensional Simu...

引用

Chinese Automation Congress (CAC)

作者： Yuhang Zhu Xiaoliang Lv Qing-shan Jia Xiaohong Guan Department of Automation Tsinghua University Beijing China Faculty of Electronic and Information Engineering Xi'an Jiaotong University Xi'an China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

High-dimensional simulation-based optimization problems are common and important in practice. Most of these problems have two key characteristics: (1) the objective function is unimodal, and (2) the factors influencing the objective function are far fewer than the number of dimensions. Gradient descent (GD) methods are well-suited for unimodal problems, but in high-dimensional cases, gradient estimation requires a large number of simulations, making it difficult to apply. This paper proposes a Subspace gradient descent (SubGD) method. First, a subspace is determined using historical data, ensuring that the gradient projection in this subspace closely aligns with the actual gradient. Then, the gradient projection in this subspace is estimated using the finite difference method (FDM). Finally, update the parameters by the estimated gradient projection in this subspace. Compared to the traditional GD methods directly using FDM for gradient estimation, SubGD significantly reduces the number of simulations. In our experiments on a 200-dimensional optimization problem and a 124-dimensional processor software parameter optimization problem, our method outperformed genetic algorithms, traditional GD methods, and Bayesian optimization methods.

关键词： Software algorithms Estimation Optimization methods Linear programming approximation algorithms Software Frequency division multiplexing Optimization Genetic algorithms Finite difference methods

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Energy-Aware Adaptive QoS Algorithm for IoT

Energy-Aware Adaptive QoS Algorithm for IoT

引用

MECHATRONIKA International Symposium

作者： Akeel Othman Dušan Maga Jaromír Hrad Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic

ISBN: (数字)9798350394900

ISBN: (纸本)9798350394917

Today, energy management systems represent a cornerstone of every embedded system, especially in IoT devices, which have become a fundamental part of our lives in this digitalized world. Many researchers have focused on using EMS to conserve energy and extend the lifetime of IoT devices. We chose a different approach to provide more energy, thereby prolonging the operational lifetime of IoT devices and enhancing their QoS parameters. This approach is based on providing an energy-aware management system that adapts the QoS based on a machine learning model predicting the available solar energy hourly, which led to extending the operational lifetime of the IoT device by approximately 300 %, the QoS in terms of active duty cycle by 400 %, and transmission power strength by 200 %.

关键词： Accuracy Machine learning algorithms Quality of service Solar energy Machine learning Predictive models Prediction algorithms approximation algorithms Internet of Things Energy management systems

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The Defect Detection Method for Automotive Parts Based on Improved Single Shot MultiBox Detector Using Feature Enhancement

The Defect Detection Method for Automotive Parts Based on Im...

引用

Signal Processing and Intelligent Computing (SPIC), International Conference on

作者： Yingzi Wei Zhongwen Zhao Kanfeng Gu College of Information Science and Engineering Shenyang Ligong University Shenyang China Shenyang Institute of Automation of the Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

The application of machine vision systems holds significant potential for detecting the processing quality of automotive parts in industrial intelligent manufacturing. To overcome the limitations of traditional Single Shot Detector (SSD) algorithms in identifying defects in automotive parts, we propose an improved SSD method. This enhanced approach integrates a Convolutional Block Attention Module (CBAM) into the feature extraction network. Within the CBAM, the spatial attention module increases the model’s sensitivity to features of the machined part of the workpiece, while the channel attention module enhances the model’s generalization in detection tasks. By up-sampling and fusing shallow and deep feature layers, we construct multi-scale feature information, significantly improving the detection capability for small targets such as holes and welded joints on workpieces. Additionally, we introduce Focal Loss to address sample category imbalance and reduce the model’s false alarm rate. Compared to the traditional SSD algorithm, our experimental results demonstrate that the mean average precision (mAP) of our model in workpiece defect detection is improved by approximately 10.3%, and the detection accuracy is increased by 16.1%. This advancement offers a robust solution for quality detection in the automotive parts processing industry.

关键词： Accuracy Target recognition Welding Signal processing algorithms Machining Detectors Feature extraction approximation algorithms Automotive engineering Defect detection

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Research on Genetic Algorithm of Network Search under Computer Artificial Intelligence Big Data Technology

Research on Genetic Algorithm of Network Search under Comput...

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Communications Technology and Computer Science (ACCTCS), Asia-Pacific Conference on

作者： Yuan Li Xin Yu Modern Finance Industry School Shandong Institute of Commerce and Technology Jinan Shandong China

ISBN: (数字)9798350359985

ISBN: (纸本)9798350359992

In this paper, an improved genetic simulated annealing algorithm (SAGA) for wireless sensor networks is studied and designed. The aim of this project is to better study the optimal transmission path in wireless sensor networks (WSN s) based on inter-node communication and reduce node energy consumption. Firstly, the model is built based on the optimal control index. Then the fitness function, cross operation and variation operation in the evolutionary process are studied. The routing calculation and genetic evolution calculation are carried out at the same time until the approximate optimal path is found. This method can solve the traditional local optimization problem well. Then the Metropolis criterion is given to make the jump variable in the simulated annealing algorithm have some regularity. Simulation results show that the proposed algorithm can effectively solve the problem of wireless sensor path optimization. Compared with the existing methods, the proposed method not only saves a lot of energy, but also saves a lot of calculation time.

关键词： Wireless communication Wireless sensor networks Optimal control Simulated annealing Genetics approximation algorithms Search problems

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A McEliece-type Cryptosystem using a Random Inverse Matrix and an Error Vector with Large Hamming Weight

A McEliece-type Cryptosystem using a Random Inverse Matrix a...

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International Conference on Advanced Computer Information Technologies (ACIT)

作者： Farshid Haidary Makoui T. Aaron Gulliver Mohammad Dakhilalian Department of Electrical & Computer Engineering University of Victoria Victoria BC Canada Department of Electrical & Computer Engineering Isfahan University of Technology Isfahan Iran

ISBN: (数字)9798350350043

ISBN: (纸本)9798350350050

The McEliece cryptosystem has emerged as a finalist in Round 4 of the NIST Post-Quantum Cryptography (PQC) competition. The Shor algorithm underscores the potential vulnerability of cryptographic primitives to quantum attacks. The McEliece cryptosystem has been shown to be resistent to these attacks. Currently, no known attack is capable of breaking this cryptosystem in polynomial time. Despite this, the McEliece cryptosystem has received little attention in practical applications primarily due to the large public key size. Recent progress to address this issue has reduced the size of the public key by approximately $\mathbf{3 8 \%}$. This paper introduces a McEliece-type cryptosystem which incorporates a large weight error vector and a random inverse matrix to improve security. A key generation algorithm is presented that employs a random matrix to construct the public and private keys. This increases the difficulty of attacks and allows for smaller key sizes than with the McEliece cryptosystem.

关键词： Public key NIST approximation algorithms Vectors Polynomials Information technology Hamming weight Computer security

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An Simplified Algorithm for PAPR Reduction in HPLC Dual Mode Communication System and Its FPGA Implementation

An Simplified Algorithm for PAPR Reduction in HPLC Dual Mode...

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International Conference on Electronics Technology (ICET)

作者： Hongjian Gao Jianding Fu Jingyu Ren Yang Lu Fei Huang State Grid Smart Grid Research Institute Co. Ltd Beijing China State Grid Chongqing Electric Power Company Electric Power Science Research Institute Chongqing China

ISBN: (数字)9798350363951

ISBN: (纸本)9798350363968

The integration of power line carriers and wireless communication using Orthogonal Frequency Division Multiplexing (OFDM) has become a new development direction for grid information collection systems. However, it faces the challenge of high peak-to-average power ratio (PAPR) of the transmitted signals. To address this issue, this paper proposes an simplified Partial Transmit Sequence (PTS) technique. In the calculation of rotation coefficients, V times L/2-point IFFT transforms are used instead of V times L-point IFFT transforms. This not only reduces the resource consumption caused by IFFT transform but also effectively controls the performance loss of OFDM symbol PAPR suppression within a very small range (less than 1.5%). Finally, the simplified PTS scheme is implemented on an Altera FPGA chip, resulting in a reduction of approximately 15% in resource consumption compared to the original simplified PTS technique.

关键词： Wireless communication Costs Partial transmit sequences Symbols Peak to average power ratio Transforms approximation algorithms

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