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检索条件"主题词=Software Algorithms"

共 12566 条记录，以下是4261-4270 订阅

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A Novel Unmanned Aerial Vehicle Recognition Method Based on Bayesian Coherent Point Drift with Full Polarization Information

A Novel Unmanned Aerial Vehicle Recognition Method Based on ...

引用

Progress in Electromagnetic Research Symposium (PIERS)

作者： Hao Wu Dahai Dai Bo Pang Penghui Ji Xuesong Wang State Key Laboratory of Complex Electromagnetic Environment Effects and Electronics and Information System National University of Defense Technology Changsha China College of Electronic Science National University of Defense Technology Changsha China

ISBN: (纸本)9781665409889

The utilization of polarimetric information can expand the understanding of UAVs which effectively interprets shape and structure. In this paper, based on the theory of Bayesian Formulation of Coherent Point Drift (BCPD), a novel radar target recognition algorithm has been proposed. Firstly, the canonical scattering centers are extracted based on the radar imaging results of UAVs which helps to understand the structures and scattering properties. Then we build a novel similarity measurement between the model UAV sample and the test samples which not only reflects the prominent scattering centers but also preserve the overall structure features. UAV electromagnetic data can be acquired which is measured in the anechoic chamber or simulated by electromagnetic software. Finally, we fuse three polarization channels with Bayesian criteria to improve the accuracy of recognition results. The experiment proves that the recognition rate of proposed algorithm with full polarization is better than that with single polarization channel.

关键词： Target recognition software algorithms Scattering Radar imaging Autonomous aerial vehicles Time measurement software

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A Deep Dive Into Understanding The Random Walk-Based Temporal Graph Learning

A Deep Dive Into Understanding The Random Walk-Based Tempora...

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IEEE International Workshop/Symposium on Workload Characterization

作者： Nishil Talati Di Jin Haojie Yet Ajay Brahmakshatriya Ganesh Dasika Saman Amarasinghe Trevor Mudge Danai Koutra Ronald Dreslinski University of Michigan Massachusetts Institute of Technology Advanced Micro Devices Inc

ISBN: (纸本)9781665441742

Machine learning on graph data has gained significant interest because of its applicability to various domains ranging from product recommendations to drug discovery. While there is a rapid growth in the algorithmic community, the computer architecture community has so far focused on a subset of graph learning algorithms including Graph Convolution Network (GCN), and a few others. In this paper, we study another, more scalable, graph learning algorithm based on random walks, which operates on dynamic input graphs and has attracted less attention in the architecture community compared to GCN. We propose high-performance CPU and GPU implementations of two important graph learning tasks, that cover a broad class of applications, using random walks on continuous-time dynamic graphs: link prediction and node classification. We show that the resulting workload exhibits distinct characteristics, measured in terms of irregularity, core and memory utilization, and cache hit rates, compared to graph traversals, deep learning, and GCN. We further conduct an in-depth performance analysis focused on both algorithm and hardware to guide future software optimization and architecture exploration. The algorithm-focused study presents a rich trade-off space between algorithmic performance and runtime complexity to identify optimization opportunities. We find an optimal hyperparameter setting that strikes balance in this trade-off space. Using this setting, we also perform a detailed microarchitectural characterization to analyze hardware behavior of these applications and uncover execution bottlenecks, which include high cache misses and dependency-related stalls. The outcome of our study includes recommendations for further performance optimization, and open-source implementations for future investigation.

关键词： Runtime Machine learning algorithms Heuristic algorithms software algorithms Computer architecture Prediction algorithms Hardware

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Hardware Accelerator for Object Detection using Tiny YOLO-v3

Hardware Accelerator for Object Detection using Tiny YOLO-v3

引用

IEEE India Conference (INDICON)

作者： Manan Sharma R Rahul S Madhusudan S.P. Deepu David S Sumam NITK Surathkal Mangalore Karnataka India

ISBN: (纸本)9781665441766

For applications that require object detection to be performed in real-time, this paper presents a custom hardware accelerator, implementing state of the art Tiny YOLO-v3 algorithm. The proposed architecture achieves a reasonable tradeoff between the speed of computation (measured in frames per second or FPS) and the hardware resources required. Each CNN layer is pipelined and parameterized to make the complete design re-configurable. The proposed hardware accelerator was synthesized using the SCL(Semi-Conductor Laboratory, India) 180 nm CMOS process and also using Vivado Xilinx software with Virtex Ultrascale+ FPGA as the target device. The pipelined architecture, along with other architectural novelties, provided a higher frame-rate of 32.1 FPS and a performance of 166.4 GOPS at 200 MHz clock frequency.

关键词： software algorithms Object detection Computer architecture Throughput software Real-time systems Convolutional neural networks

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Crack Detector Automation for Roads by Deep Learning

Crack Detector Automation for Roads by Deep Learning

引用

International Conference on Computing for Sustainable Global Development (INDIACom)

作者： R Bharathi N Ashwini R Bhagya G Bhagya Rameez Pathan Sambedh Kandel BMS Institute of Technology and Management mBangalore India BMS Institute of Technology and Management Bangalore India RV College of Engineering Bangalore India

A thorough analysis is required for assessment of structural health of road infrastructures. A Computer Vision Technology based analysis of images will help us for better management of roads/ pavements in long run. A Convolutional Neural Networks (CNN) based Deep Learning algorithm can be constructed, which can take in an input image used for pavement classification. In this paper, a python based algorithm is proposed and it is simulated for experimental results using automated system software which detects the cracks on the road. The approach used in this paper is mainly based on full surface reconstruction in 3D pavement. The automated method achieves same accuracy with a reduced cost when compared to manual computation.

关键词： Deep learning Surface reconstruction Three-dimensional displays Roads software algorithms Classification algorithms System software

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Testing of SLAM methods in the Matlab environment

Testing of SLAM methods in the Matlab environment

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Michal Mihálik Marián Hruboš Aleš Janota Faculty of Electrical Engineering and Information Technology University of Zilina Zilina Slovak Republic University of Faculty of Electrical Engineering and Information Technology Zilina Slovak Republic

ISBN: (数字)9781728180533

ISBN: (纸本)9781728180540

This work is focused on establishing TCP / IP connection between SICK LD-OEM 1000 laser scanner and PC. Space measurements will be made using this connection. Then we analyze the data and process it. The data is processed by the Monte Carlo Simultaneous localization and mapping algorithm, which will be used to compile a map of the scanned space. This work is based on MATLAB software environment.

关键词： Simultaneous localization and mapping Monte Carlo methods software algorithms Measurement by laser beam software Matlab Testing

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A Speech Enhancement Algorithm combining Spectral Subtraction and Wavelet Transform

A Speech Enhancement Algorithm combining Spectral Subtractio...

引用

IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Yi Yang Peipei Liu Huili Zhou Ye Tian School of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu China

ISBN: (纸本)9781665428408

Spectral subtraction is widely used in the research of speech enhancement because of its small calculation amount and fast processing speed. However, traditional spectral subtraction has a large amount of residual music noise in the process of speech noise reduction. In order to solve this problem, a speech enhancement algorithm based on improved spectral subtraction and improved threshold wavelet transform is proposed. The method first uses spectral subtraction to denoise the noisy speech signal, and then the processed signal is subjected to wavelet transform perform voice enhancement. By using MATLAB software to simulate the system, the experimental results show that compared with the traditional spectrum subtraction method, the improved method can effectively suppress the music noise generated by the single spectrum subtraction method, improve the output signal-to-noise ratio and recognizability of the speech signal, and enhance the speech The effect is remarkable.

关键词： Noise reduction software algorithms Speech recognition Speech enhancement Wavelet analysis Feature extraction software

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Scalable Fault Detection Based on Precise Access Path

Scalable Fault Detection Based on Precise Access Path

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Asia-Pacific Conference on software Engineering

作者： Chi Li Yuexing Wang Min Zhou Ming Gu School of Software Engineering Tsinghua University Beijing China

ISBN: (纸本)9781665437851

Precise static analysis is necessary for an industrial environment to ensure reliability and security, which is usually field-sensitive and inter-procedural. However, it faces the problem of insufficient scale capability when being applied to various industrial environments: (1) Field-sensitive analysis can not assure termination if field accesses are modeled by unbounded access paths; (2) Inter-procedural analysis may lead to path explosion problems because of the unbounded length of call chains. While using longer access paths or call chains can improve precision, the analysis may have poor performance in terms of efficiency. Specifically, an industry-strength method should be scalable enough to face different applications. This paper presents a scalable fault detection method based on the precise access path. Precise access path models a memory location with accurate operations and offsets from a source. Points-to relations of variables are used to refine it. It can differentiate elements of aggregate structures and is more precise than the ordinary access path. Based on the precise access path, we perform an inter-procedural analysis with the help of an intra-procedural analysis and combined function summary. Furthermore, our method is designed backward to detect error handling bugs. Compared with the state-of-the-art tools, our method is more scalable, with higher precision and efficiency on both benchmarks and 11 widely-used applications.

关键词： Analytical models Fault detection Computer bugs software algorithms Static analysis Benchmark testing Security

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Bug Reproduction Based on EFSM Model

Bug Reproduction Based on EFSM Model

引用

Information Communication and software Engineering (ICICSE), IEEE International Conference on

作者： ChengQian Ren Ying Shang Beijing University of Chemical Technology Beijing China

ISBN: (纸本)9781665430302

Bug reproduction is an important task for uncovering the causes of the bug and providing appropriate fixes. However, this task is usually labor-intensive and time-taking. Several solutions have been proposed to automate this task. The proposed solutions typically either use program runtime data, or bug stack traces to generate a test case that triggers the represented bug. In this paper, we propose a novel bug reproduction approach that combines the bug information and extended finite state machine model to generate effective test cases needed to reproduce a bug. The experimental results of 15 bugs in four common systems show that the proposed method can quickly and accurately reproduce bugs.

关键词： Runtime Conferences Computer bugs software algorithms Automata Task analysis

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Towards a Mobile Robot Localization Benchmark with Challenging Sensordata in an Industrial Environment

Towards a Mobile Robot Localization Benchmark with Challengi...

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International Conference on Advanced Robotics (ICAR)

作者： Florian Spieß Jonas Friesslich Daniel Bluemm Fabio Mast Dmitrij Vinokour Samuel Kounev Tobias Kaupp Norbert Strobel University of Applied Sciences Wuerzburg-Schweinfurt Friedrich-Alexander-Universit&#x00E4 t Erlangen-N&#x00FC rnberg Faculty of Mathematics and Computer Science Julius-Maximilians-University of Wuerzburg Institute Digital Engineering (IDEE) University of Applied Sciences Wuerzburg-Schweinfurt Institute of Medical Engineering University of Applied Sciences Wuerzburg-Schweinfurt

ISBN: (数字)9781665436847

ISBN: (纸本)9781665436854

To arrive at a realistic assessment of localization methods in terms of their performance in an industrial environment under various challenging conditions, we provide a benchmark to evaluate algorithms both for individual components as well as multi-sensor systems. For several sensor types, including wheel odometry, RGB cameras, RGB-D cameras, and LIDAR, potential issues were identified. The accuracy of wheel odometry, for example, when there are bumps on the track. For each sensor type, we explicitly chose a track for the benchmark dataset containing situations where the sensor fails to provide adequate measurements. Based on the acquired sensor data, localization can be achieved either using a single sensor information or sensor fusion. To help evaluate the output of associated localization algorithms, we provide a software to evaluate a set of metrics as part of the paper. An example application of the benchmark with state-of-the-art algorithms for each sensor is also provided.

关键词： Location awareness Service robots software algorithms Wheels Benchmark testing Sensor fusion Robot sensing systems

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Design of a marine hydrogen fuel cell management system based on multi-sensor fusion

Design of a marine hydrogen fuel cell management system base...

引用

Image Processing, Electronics and Computers (IPEC), Asia-Pacific Conference on

作者： Qingyuan Hu School of Information Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9781728190198

As a new generation of clean energy, hydrogen fuel cells have been used in the marine industry, and their safety monitoring is an important measure to ensure the safe operation of ships. The system uses multiple sensors to determine the current operating status through real-time information fusion, and uses pulse width to regulate the flow rate of the inlet valve to achieve safe control of the hydrogen fuel and maximise its output power. The article analyses the composition of the control system, the sensor system and its working principle, and uses an STM32-based microprocessor to design the safety control hardware and give the implementation flow of its software algorithm. The test results show that the system is capable of monitoring the safety of the marine hydrogen fuel cell under operating conditions and can cut off the inlet valve in case of emergency to achieve the safety control function.

关键词： Hydrogen software algorithms Fuel cells Sensor fusion Valves Sensor systems Safety

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