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Power Allocation in Multi-Agent Networks via Dueling DQN Approach

Power Allocation in Multi-Agent Networks via Dueling DQN App...

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IEEE international conference on signal and image processing (ICSIP)

作者： Zhihao Xuan Guiyi Wei Zhengwei Ni School of Computer and Information Engineering Zhejiang Gongshang University Hangzhou China School of Information & Electronic Engineering Zhejiang Gongshang University Hangzhou China

ISBN: (纸本)9781665446006

As an emerging computing technology, edge computing transfers computing power from the cloud to the edge of the network, which greatly improves the quality of service, but different edge devices have signal interference during the communication process, which will affect the downlink rate. However, the power allocation of the edge server can effectively reduce the influence of interference. In this paper, we take channel changes in the actual scenario into account, intending to maximize the downlink rate, propose a power allocation algorithm based on Dueling DQN under the multi-cell edge computing model. We have carefully designed Dueling DQN to maximize the downlink rate, the algorithm uses centralized training and distributed execution, the executor does not need to get complete network information, so it has better robustness. Finally, we compare with five baseline algorithms including the " DQN " , "FP" and "WMMSE" algorithms in various situations. The results of the experiment show that the proposed algorithm is better than the other five baseline algorithms.

关键词： Training Simulation image processing Interference Quality of service Downlink Robustness

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An Improved Reinforcement Learning for Security-Constrained Economic Dispatch of Battery Energy Storage in Microgrids 1

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2nd international conference on Neural Computing for advanced Applications, NCAA 2021

作者： Zha, Zhongyi Wang, Bo Fan, Huijin Liu, Lei Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Artificial Intelligence and Automation School Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9789811651885

ISBN: (纸本)9789811651878

Battery energy storage systems are widely used in microgrids integrated with volatile energy resources for their ability in peak load shifting. Security constrained economic dispatch over the system’s lifecycle is a constrained multi-period stochastic optimization problem, which is intractable. We propose an improved actor-critic-based reinforcement learning combined with a protection layer security control method for this issue, where the distributional critic net is applied to estimate the expected total reward value of a period more accurately, and the policy net with a mask action layer is used to make secure and real-time decision. Additionally, we propose a protection layer to assist the policy net as a secondary control to prevent the unsafe state of microgrids due to the trial-and-error learning of reinforcement learning. Numerical test results show the proposed algorithm can perform better than the conventional economic dispatch and other reinforcement learning algorithms while guaranteeing safe operation. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Renewable energy resources

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Biomedical image processing with Improved SPIHT Algorithm and optimized Curvelet Transform Technique

Biomedical Image Processing with Improved SPIHT Algorithm an...

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international conference on advanced Computing and Communication Systems (ICACCS)

作者： L Jubair Ahmed B Anish Fathima M Mahaboob B Gokulavasan Sri Eshwar College of Engineering Coimbatore India Sri Krishna College of Engineering and Technology Coimbatore Sri Eshwar College of Engineering Coimbatore

ISBN: (纸本)9781665405218;9781665446341

The prime idea is to propose an effective compression technique obtaining improved PSNR and better compression ratio (CR) with Curvelet transformation technique and encoding the multi-wavelet parameters using Set Partitioning In Hierarchical Trees (SPIHT) algorithm. Also a comparative study is presented which highlights a dynamic image encoding algorithm against the conventional algorithms such as SVM, EZW & SPECK. Both quantitative and qualitative parameters of algorithm are analyzed for bio-medical images. Analysis inferences represents that the proposed technique treats structures and patterns considerably superior than conventional techniques.

关键词： Support vector machines image coding Communication systems Heuristic algorithms image edge detection Transforms Inference algorithms

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Route Planning Based on Street Criteria for Autonomous Driving Vehicles

Route Planning Based on Street Criteria for Autonomous Drivi...

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international conference on Vision, image and signal processing (ICVISP)

作者： Eric Neidhardt David Suske German Aerospace Center Institute of Transportation Systems Berlin Germany

ISBN: (纸本)9781665408356

Modern day, smart vehicles have many comfort functions and can support the driver in a variety of traffic situations. This leads to increased safety and comfort. In road traffic, however, there are many different situations. Some of them cannot completely be handled by an automated vehicle. It depends on the level of automation (SAE level) and the used technology of the vehicle if a certain situation or a specific street topology is problematic. The fastest or shortest route is therefore no longer necessarily the best route. Criteria like fuel or energy consumption are already addressed by some routing algorithms. This paper proposes that criteria such as comfort and safety should be considered in route selection of autonomous driving vehicles. For safety and to make optimum use of the automation of his vehicle, a user is willing to accept a longer journey. In this situation we are not looking for the best route, but several pareto-optimal routes. In this paper an Approach is proposed to handle different infrastructure criteria for routing autonomous driving vehicles. Because many possible criteria exist and their impact on the vehicle is related to the technology and SAE level of the vehicle, the resulting concept is designed as flexible as possible.

关键词： Automation image edge detection Roads signal processing algorithms signal processing Routing Safety

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Fontnet: On-Device Font Understanding and Prediction Pipeline

Fontnet: On-Device Font Understanding and Prediction Pipelin...

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IEEE international conference on Acoustics, Speech and signal processing

作者： Rakshith S Rishabh Khurana Vibhav Agarwal Jayesh Rajkumar Vachhani Guggilla Bhanodai Samsung R&D Institute India Bangalore India

Fonts are one of the most basic and core design concepts. Numerous use cases can benefit from an in depth understanding of Fonts such as Text Customization which can change text in an image while maintaining the Font attributes like style, color, size. Currently, Text recognition solutions can group recognized text based on line breaks or paragraph breaks, if the Font attributes are known multiple text blocks can be combined based on context in a meaningful manner. In this paper, we propose two engines: Font Detection Engine, which identifies the font style, color and size attributes of text in an image and a Font Prediction Engine, which predicts similar fonts for a query font. Major contributions of this paper are three-fold: First, we developed a novel CNN architecture for identifying font style of text in images. Second, we designed a novel algorithm for predicting similar fonts for a given query font. Third, we have optimized and deployed the entire engine On-Device which ensures privacy and improves latency in real time applications such as instant messaging. We achieve a worst case On-Device inference time of 30ms and a model size of 4.5MB for both the engines.

关键词： Privacy Text recognition image color analysis Pipelines signal processing algorithms signal processing Prediction algorithms

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Research on Evaluation Method of image Blending Based image Simulation

Research on Evaluation Method of Image Blending Based Image ...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Yu Zhang Yan Zhang Zhiguang Shi Xiaoran Shi Di Liu Yuchang Suo National Key Laboratory of Science and Technology on Automatic Target Recognition College of Electronic Science and Technology National University of Defense Technology Changsha Hunan Province China

ISBN: (纸本)9781665404136;9781665447300

Object detection and tracking algorithms for specific targets are important technologies in the fields of autonomous driving and video surveillance. The verification of related algorithms and the training of data-driven machine learning methods often rely on high-quality and large-scale data support. Therefore, data simulation methods are usually used to supplement the quantity and richness of data. This paper proposes an image simulation performance evaluation method based on the Probably Approximately Correct (PAC) learnable theory: experience consistency loss, which is used to quantitatively evaluate simulated images. Compared with the traditional performance evaluation method based on expert score and Turing test, this evaluation method can objectively and quantitatively reflect the characteristic consistency between the simulated image and the real image. The experimental results show that the evaluation results of this method are consistent with the qualitative analysis results, which can directly reflect the performance improvement of the simulation image for the machine learning model.

关键词： Performance evaluation Training Analytical models Machine learning algorithms Target tracking Computational modeling Machine learning

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Fake Currency Detection with Machine Learning Algorithm and image processing

Fake Currency Detection with Machine Learning Algorithm and ...

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international conference on ent Computing and Control Systems (ICICCS)

作者： Aman Bhatia Vansh Kedia Anshul Shroff Mayand Kumar Bickey Kumar Shah Aryan Delhi Technological University Delhi India The University of Sheffield Sheffield South Yorkshire UK

This paper deals with the matter of identifying the currency that if the given sample of currency is fake. Different traditional strategies and methods are available for fake currency identification based on the colors, width, and serial numbers mentioned. In the advanced age of Computer science and high computational methods, various machine learning algorithms are proposed by image processing that gives 99.9% accuracy for the fake identity of the currency. Detection and recognition methods over the algorithms include entities like color, shape, paper width, image filtering on the note. This paper proposes a method for fake currency recognition using K-Nearest Neighbours followed by image processing. KNN has a high accuracy for small data sets making it desirable to be used for the computer vision task. In this, the banknote authentication dataset has been created with the high computational and mathematical strategies, which give the correct data and information regarding the entities and features related to the currency. Data processing and data Extraction is performed by implementing machine learning algorithms and image processing to acquire the final result and accuracy.

关键词： Machine learning algorithms image recognition image color analysis Shape Process control Feature extraction Data processing

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Perceptual Quality Assessment for Recognizing True and Pseudo 4k Content

Perceptual Quality Assessment for Recognizing True and Pseud...

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IEEE international conference on Acoustics, Speech and signal processing

作者： Wenhan Zhu Guangtao Zhai Xiongkuo Min Xiaokang Yang Xiao-Ping Zhang Shanghai Jiao Tong University China Ryerson University Canada

To meet the imperative demand for monitoring the quality of Ultra High-Definition (UHD) content in multimedia industries, we propose an efficient no-reference (NR) image quality assessment (IQA) metric to distinguish original and pseudo 4K contents and measure the quality of their quality in this paper. First, we establish a database including more than 3000 4K images composed of natural 4K images together with upscaled versions interpolated from 1080p and 720p images by fourteen algorithms. To improve computing efficiency, our model segments the input image and selects three representative patches by local variances. Then, we extract the histogram features and cut-off frequency features in the frequency domain as well as the natural scenes statistic (NSS) based features from the representative patches. Finally, we employ support vector regressor (SVR) to aggregate these extracted features as an overall quality metric to predict the quality score of the target image. Extensive experimental comparisons using seven common evaluation indicators demonstrate that the proposed model outperforms the competitive NR IQA methods and has a great ability to distinguish true and pseudo 4K images.

关键词： Measurement image quality Support vector machines Frequency-domain analysis Aggregates signal processing algorithms signal processing

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Matching as Color images: Thermal image Local Feature Detection and Description

Matching as Color Images: Thermal Image Local Feature Detect...

引用

IEEE international conference on Acoustics, Speech and signal processing

作者： Bhavesh Deshpande Sourabh Hanamsheth Yawen Lu Guoyu Lu Intelligent Vision and Sensing Lab Rochester Institute of Technology

ISBN: (纸本)9781728176055;9781728176062

Feature detection and extraction is considered to be one of the most important aspects when it comes to any computer vision application, especially the autonomous driving field that is highly dependent on it. Thermal imaging is less explored in the field of autonomous driving mainly due to the high cost of the cameras and inferior techniques available for detection. Due to advances in technology the former does not hold true anymore and there lies tremendous scope for improvement in the latter. Autonomous driving relies heavily on multiple and sometimes redundant sensors, for which thermal sensors are a preferred addition. Thermal sensors being completely dependent on the infrared radiation emitted are able to frame and recognize objects even in the complete absence of light. However detecting features persistently through subsequent frames is difficult due to the lack of textures in thermal images. Motivated by this challenge, we propose a triplet based Siamese CNN for feature detection and extraction for any given thermal image. Our architecture is able to detect larger number of good feature points on thermal images than other best performed feature detection algorithms with superb matching performance based on our extracted descriptors.

关键词： Feature detection Design methodology conferences signal processing algorithms Computer architecture Thermal sensors signal processing

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international conference on Communications, signal processing and VLSI, IC2SV 2019

International Conference on Communications, Signal Processin...

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international conference on Communications, signal processing and VLSI, IC2SV 2019

ISBN: (纸本)9789813340572

The proceedings contain 36 papers. The special focus in this conference is on Communications, signal processing and VLSI. The topics include: Design and Analysis of Wearable Step-Shaped Sierpinski Fractal Antenna for WBAN Applications;Evaluation of Real-Time Embedded Systems in HILS and Delay Issues;acoustic Feedback Cancellation Using Optimal Step-Size Control of the Partition Block Frequency-Domain Adaptive Filter;A Compact IPD Based on-Chip Bandpass Filter for 5G Radio Applications;An 18-Bit Incremental Zoom ADC with Reduced Delay Overhead Data Weighted Averaging for Battery Measurement Systems;a Non-Uniform Digital Filter Bank Hearing Aid for Aged People with Hearing Disability;optimal Resource Allocation Based on Particle Swarm Optimization;Design and Simulation of a W-Band FMCW Radar for Cloud Profiling Application;Single Layer Asymmetric Slit Circularly Polarized Patch Antenna for WLAN Application;recognition of Natural and Computer-Generated images Using Convolutional Neural Network;contour and Texture-Based Approaches for Dental Radiographic and Photographic images in Forensic Identification;an Efficient Low Latency Router Architecture for Mesh-Based NoC;high-Performance image Visual Feature Detection and Matching;systolic-Architecture-Based Matrix Multiplications and Its Realization for Multi-Sensor Bias Estimation algorithms;fast Encoding Using X-Search Pattern and Coded Block Flag Fast Method;Energy Efficient and Accurate Hybrid CSA-CIA Adders;machine Learning Oriented Dynamic Cost Factors-Based Routing in Communication Networks;Design of Look-Up Table with Low Leakage Power Enhanced Stability 10 T SRAM Cell;Conformal Omni Directional Antenna for GPS Applications;total Internal Reflection Quasi-Phase-Matching-Based Broadband Second Harmonic Generation in Isotropic Semiconductor: A Comparative Analysis;automated Bayesian Drowsiness Detection System Using Recurrent Convolutional Neural Networks.

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