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AI based Automatic Vehicle Detection from Unmanned Aerial Vehicles (UAV) using YOLOv5 Model 5

AI based Automatic Vehicle Detection from Unmanned Aerial Ve...

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5th IEEE International conference on Image Processing Applications and Systems (IPAS)

作者： Panthakkan, Alavikunhu Valappil, Najiya Al-Mansoori, Saeed Al-Ahmad, Hussain Univ Dubai UD Coll Engn & IT Dubai U Arab Emirates Banasthali Vidyapith Dept Elect Aliyabad Rajasthan India Mohammed Bin Rashid Space Ctr MBRSC Dubai U Arab Emirates

ISBN: (纸本)9781665462198

Unmanned aerial vehicle (UAV) detection of moving vehicles is becoming into a significant study area in traffic control, surveillance, and military applications. the challenge arises in keeping minimal computational complexity allowing the system to be real-time as well. Applications of vehicle detection from UAVs include traffic parameter estimation, violation detection, number plate reading, and parking lot monitoring. the one stage detection model, YOLOv5 is used in this research work to develop a deep neural model-based vehicle detection system on highways from UAVs. In our system, several improvised strategies are put forth that are appropriate for small vehicle recognition under an aerial view angle which can accomplish real-time detection and high accuracy by incorporating an optimal pooling approach and dense topology method. Tilting the orientation of aerial photographs can improve the system's effectiveness. Metrics like hit rate, accuracy, and precision values are used to assess the performance of the proposed hybrid model, and performance is compared to that of other state-of-the-art algorithms.

关键词： Unmanned Aerial Vehicles Vehicle Detection YOLO-V5 Aerial Images Dense Topology Optimal pooling

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An improved variable step size LMS adaptive filtering algorithm based on hyperbolic sine function and its application

An improved variable step size LMS adaptive filtering algori...

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Big Data & Artificial Intelligence & Software Engineering (ICBASE), International conference on

作者： Zhengang Wan Yonghao Zhou Jiangsu University of Science and Technology Zhenjiang China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

this paper presents an enhanced variable step-size LMS (VSS-LMS) algorithm using a hyperbolic tangent function to address the poor performance of traditional VSS-LMS algorithms in non-Gaussian environments. the conventional VSS-LMS struggles with adaptability and convergence speed under non-Gaussian noise. To overcome this, a parameter-free VSS-LMS algorithm based on the hyperbolic tangent function is proposed. this new algorithm introduces a nonlinear step-size error model with five adaptive coefficients ($\beta,\text k,\text b,\text a, \text m$). It analyzes convergence and algorithmic complexity and compares performance with traditional LMS, VSS-LMS, and an improved VSS-LMS under various non-Gaussian noise conditions. Simulation results demonstrate that the proposed algorithm significantly improves convergence speed and steady-state characteristics, achieving near-theoretical SNR performance.

关键词： Adaptation models Simulation Software algorithms Filtering algorithms Steady-state complexity theory Application software Convergence Signal to noise ratio

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Community Deception Based on Genetic Algorithm Using Graph Local Structure

Community Deception Based on Genetic Algorithm Using Graph L...

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International conference on Advanced Information Technologies (ICAIT)

作者： Cing Gel Vung Yi Yi Hlaing Faculty of Computer Science University of Computer Studies (Kalay) Kalay Myanmar Faculty of Computer Science University of Computer Studies Mandalay Mandalay Myanmar

ISBN: (数字)9798331510046

ISBN: (纸本)9798331510053

Many researchers have studied community detection attacks in different ways but there is no best method to fool all community detection algorithms. In this study, to concern privacy protection, we aim to generate the perturbation network using genetic algorithm based on heuristic approach from community disclosure by community detection algorithms. Clustering coefficient and randomization of initialization population are applied to ensure the diversity of the solution spaces and to remove the repetition of same chromosomes in population. this system proposes a local features-based mutation approach for generating the adversarial network effective in community deception. the experimental exhibits the optimal strategy with normalizing mutual information (NMI) and Modularity (Q) by running on difference datasets with variety community detection algorithms.

关键词： Privacy Social networking (online) Network topology Perturbation methods Topology Reliability Detection algorithms Time complexity Protection Genetic algorithms

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Uncertainty and the Social Planner's Problem: Why Sample complexity Matters 22

Uncertainty and the Social Planner's Problem: Why Sample Com...

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5th ACM conference on Fairness, Accountability, and Transparency, FAccT 2022

作者： Cousins, Cyrus Department of Computer Science Brown University ProvidenceRI United States

ISBN: (纸本)9781450393522

Welfare measures overall utility across a population, whereas malfare measures overall disutility, and the social planner's problem can be cast either as maximizing the former or minimizing the latter. We show novel bounds on the expectations and tail probabilities of estimators of welfare, malfare, and regret of per-group (dis)utility values, where estimates are made from a finite sample drawn from each group. In particular, we consider estimating these quantities for individual functions (e.g., allocations or classifiers) with standard probabilistic bounds, and optimizing and bounding generalization error over hypothesis classes (i.e., we quantify overfitting) using Rademacher averages. We then study algorithmic fairness through the lens of sample complexity, finding that because marginalized or minority groups are often understudied, and fewer data are therefore available, the social planner is more likely to overfit to these groups, thus even models that seem fair in training can be systematically biased against such groups. We argue that this effect can be mitigated by ensuring sufficient sample sizes for each group, and our sample complexity analysis characterizes these sample sizes. Motivated by these conclusions, we present progressive sampling algorithms to efficiently optimize various fairness objectives. © 2022 ACM.

关键词： Parallel processing systems

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Dimensionality Reduction Algorithm With Ordered Pair Of Normalized Real Numbers

Dimensionality Reduction Algorithm With Ordered Pair Of Norm...

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Computer Engineering and Intelligent Control (ICCEIC) International conference on

作者： Xuncheng Zhu Yining Zhao Ning Li Dianyi Huang Wenan Li Haoyang Cui Hui Zhang Yunnan Institute of Standardization 1305 Dianchi Road Kunming China School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

Before classifying high-dimensional data, dimension-ality reduction algorithms are often used to process the data to achieve the purpose of reducing computational complexity and interference of redundant information, and improving the correct rate of classification. However, various dimensionality reduction algorithms have their advantages and disadvantages, and their performance varies greatly in different datasets, and it is difficult to choose the optimal dimensionality reduction algorithm. To address this problem, this paper introduces a new mathematical concept of ordered pair of normalized real numbers into the field of dimensionality reduction algorithm for the first time, and puts forward the new method of combining different dimensionality reduction algorithms through ordered pair of normalized real numbers, to realize the complementarity of different dimensionality reduction algorithms, improve the accuracy of subsequent classification. In addition, this paper also proposes the nearest neighbor classification algorithm of ordered pair of normalized real numbers, which realizes the classification of ordered normalized real number pairs. through experiments on multiple real data sets and comparing a variety of improved k-nearest neighbor algorithms, it is verified that the proposed method has better performance.

关键词： Dimensionality reduction Accuracy Interference Nearest neighbor methods Classification algorithms Computational complexity Intelligent control

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Experimental Analysis of the Effects of Simulator complexity on Human Performance 5

Experimental Analysis of the Effects of Simulator Complexity...

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5th International conference on System Reliability and Safety (ICSRS)

作者： Yang, Taewon Park, Bumjun Lee, Sungheon Choi, Jeong Hun Park, Jooyoung Boring, Ronald L. Kim, Jonghyun Chosun Univ Dept Nucl Engn Gwangju South Korea Korea Hydro & Nucl Power Dept Radiat Safety Hongnong Yeonggwang South Korea Idaho Natl Lab Dept Human Factors & Reliabil Idaho Falls ID USA

ISBN: (纸本)9781665400497

Human reliability analysis (HRA), which is used to predict accidents resulting from human errors, is an important factor in probabilistic safety assessments that comprehensively evaluate the safety of nuclear power plants (NPPs). this study analyzes how simulator complexity affects the performance of NPP operators and falls under Idaho National Laboratory's project to collect additional HRA data through the use of simulations. Experiments were conducted featuring two types of simulators and scenarios as independent variables. the data collected via these experiments were further evaluated using an analysis of variance (ANOVA) test and correlation analysis, resulting in the derivation of four human performance charts.

关键词： HRA human error human performance

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Low complexity Adaptive ML Approaches for End-to-End Latency Prediction 1

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5th International conference on Machine Learning for Networking, MLN 2022

作者： Larrenie, Pierre Bercher, Jean-François Lahsen-Cherif, Iyad Venard, Olivier Thales SIX Gennevilliers France LIGM Univ. Gustave Eiffel CNRS Marne-la-Vallée France Rabat Morocco ESYCOM Univ. Gustave Eiffel CNRS Marne-la-Vallée France

ISBN: (数字)9783031361838

ISBN: (纸本)9783031361821

Software Defined Networks have opened the door to statistical and AI-based techniques to improve efficiency of networking. Especially to ensure a certain Quality of Service (QoS) for specific applications by routing packets with awareness on content nature (VoIP, video, files, etc.) and its needs (latency, bandwidth, etc.) to use efficiently resources of a network. Monitoring and predicting various Key Performance Indicators (KPIs) at any level may handle such problems while preserving network bandwidth. the question addressed in this work is the design of efficient, low-cost adaptive algorithms for KPI estimation, monitoring and prediction. We focus on end-to-end latency prediction, for which we illustrate our approaches and results on data obtained from a public generator provided after the recent international challenge on GNN [12]. In this paper, we improve our previously proposed low-cost estimators [6] by adding the adaptive dimension, and show that the performances are minimally modified while gaining the ability to track varying networks. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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Fast VVC Inter Coding Algorithm Based-On Frequency Domain and Deep Learning

Fast VVC Inter Coding Algorithm Based-On Frequency Domain an...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International conference on

作者： Jie Xu Lei Chen Jian Mao School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Versatile Video Coding (VVC) has achieved significant improvements in coding efficiency over its predecessor, High Efficiency Video Coding (HEVC), but at the cost of a substantial increase in complexity. In VVC, inter coding is a crucial part of the encoder. Owing to the fact that the Rate-Distortion Optimization (RDO) entails an exhaustive search, the encoding complexity is relatively high. To accelerate this process, this paper proposes a method that combines frequency-domain analysis and deep learning. By constructing a large-scale dataset and designing a region-aware residual network, the proposed method is capable of predicting the coding mode of the Coding Unit (CU). the experimental results indicate that this approach can reduce the encoding time of VVC by 15.17%, while the Bjøntegaard Delta Bit-Rate (BD-BR) only increases by 1.71%.

关键词： Deep learning Frequency-domain analysis Computational modeling Rate-distortion Predictive models Prediction algorithms Encoding complexity theory Residual neural networks High efficiency video coding

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Path Optimization in Quantum Networks Using Surface Codes

Path Optimization in Quantum Networks Using Surface Codes

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International conference on

作者： Yuanbo Zhang Lin Chen School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Surface codes have emerged as a promising approach for protecting quantum information against decoherence in quantum computation and communication. In this paper, we investigate the fundamental problem of path computation in quantum networks where surface codes are systematically employed in quantum information transmission. Specifically, we formulate the problem of finding a min-cost path subject to end-to-end fidelity constraint. the problem is non-trivial as only a subset of nodes in the network can perform coding and decoding operations due to its high cost, adding a combinatorial flavor to it. Our problem being NP-hard, we design an approximation path optimization algorithm by technically employing a discretization scheme allowing to bound the time and space complexity of the algorithm to be polynomial. We also demonstrate the effectiveness of our algorithm via extensive numerical analysis.

关键词： Codes Costs Numerical analysis Approximation algorithms Polynomials Encoding Decoding complexity theory Quantum networks Optimization

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A comparison of grid-connected photovoltaic systems using P&O and Fuzzy Logic MPPT algorithms

A comparison of grid-connected photovoltaic systems using P&...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Benkirane Yassine Benhala Bachir Raihani Abdelhadi LESSI Laboratory Faculty of Science Dhar El Mahraz Sidi Mohammed Ben Abdellah University Fes Morocco EEIS Laboratory ENSET Mohammedia Hassan II University Mohammedia Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

Photovoltaic solar energy, derived from solar radiation, has become one of the most widely used renewable energy sources in recent years. In this paper, we compare two MPPT control algorithms, P&O and fuzzy logic, in a grid-connected photovoltaic solar system. A DC/DC Boost converter is used to optimize power by providing a higher continuous voltage, while an inverter converts this energy into alternating current, which is injected into the grid. the goal is to generate clean energy to supply electrical loads or inject any surplus into the grid.

关键词： Fuzzy logic Photovoltaic systems Maximum power point trackers Radiation effects Solar energy Inverters Stability analysis Solar system complexity theory Solar radiation

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