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检索条件"任意字段=16th Conference on Signal and Data Processing of Small Targets"

共 487 条记录，以下是121-130 订阅

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theoretical Model of the signal Reflected from Rotating Blades of Aerial targets

Theoretical Model of the Signal Reflected from Rotating Blad...

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Digital signal processing and its Applications (DSPA), International conference on

作者： Ekaterina Plotnitskaya Vladimir Veremyev Evgenii Vorobev Research Institute “Prognoz” Saint Petersburg Electrotechnical University “LETI” Saint Petersburg Russia

ISBN: (数字)9781728175287

ISBN: (纸本)9781728175294

the theoretical model representing the signal reflected from rotating blades of aerial objects with different rotor configurations is described. the proposed model is used concerning the radar operating in UHF-band for simulation of the reflected signals from the propeller-driven airplane, helicopter, and small multi-rotor drone. the simulation results are compared with the real data recorded by a passive radar utilizing signals of second-generation terrestrial digital video broadcasting standard for target illumination.

关键词： Blades Rotors Atmospheric modeling Helicopters Mathematical model Radar Airplanes

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An Improvement of Radar-Based Method Used in Safety Protection for Trackside Staff 10

An Improvement of Radar-Based Method Used in Safety Protecti...

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10th International conference on Power and Energy Systems, ICPES 2020

作者： Jing, Jin Zhu, Jian Electrification Signal and Communication Institute Lanzhou China

ISBN: (纸本)9781665404945

Despite the strict rules of safety management and safety protection established by the railway administration department, workers are still at risk of collisions with the running trains because of the carelessness of safety guards and inclement weather as a train approaches the working area. A radar-based safety protection method proposed in this paper can detect the approaching train 1.5 km away from the workplace and alert the people in advance so that they have enough time to withdraw from the possible collision place. the proposed system can resolve the defects of guard failure, which can cause workers to fall into danger. the system can be easily set up depending on the location. However, the radar pre-warning distance may be decreased in the railway lines with small curve radii. therefore, an improvement method using wireless communication are supposed to improve the detection distance in such a way that it can meet the requirements of special lines and high-speed railways. the hardware diagrammatic drawing of the radar-based system and its' improvement method and the flow chart of radar data processing were also introduced. the test result of the system in a marshalling yard showed that the radar-based system can positively safeguard railway workers from possible dangers. Related hardware structure and software process method can provide technical reference with regards the development of other safety measures for passengers and railroad staff. © 2020 IEEE.

关键词： Railroads

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Position Monitoring of Multiple Moving targets Based on ZigBee and GPS

Position Monitoring of Multiple Moving Targets Based on ZigB...

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International conference on data Science and Business Analytics (ICDSBA)

作者： Dai Yu Wang Xuan Long Wenjia Yu Peng Ma Junyu Zhixing College of Hubei University Wuhan China

Moving target monitoring is widely used in many fields such as industrial control, military, and security. Traditional moving target monitoring generally uses infrared, ultra-sonic and other technologies, which have the disadvantages of complex implementation technology, high hardware cost and high environmental requirements. this paper proposes a low-cost, easy-to-deploy, and small-scale environment with little environmental impact by collecting GPS monitoring signals of multi-target speed and location and signal transmission and processing methods based on ZigBee wireless ad hoc network. signal acquisition and real-time data storage, transmission and processing methods have high application value and promotion significance.

关键词： Industrial control Zigbee Memory Real-time systems Hardware Trajectory Security

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Iterative signal Detection Based on MSD-CG Method for Uplink Massive MIMO Systems 16

Iterative Signal Detection Based on MSD-CG Method for Uplink...

引用

16th International Multi-conference on Systems, signals and Devices (SSD)

作者： Albataineh, Zaid Yarmouk Univ Dept Elect Engn Irbid Jordan

ISBN: (纸本)9781728118208

the multiple-input multiple-output (MIMO) wireless system can either be massive or conventional "small-scale" ones. It has been verified that the massive MIMO technology has a great potential to improve transmission range, spectral efficiency, and coverage compared to conventional MIMO. For realistic antenna configurations systems, there is an increase in the computational complexity associated with signal detection in massive MIMO systems. Several theoretical iterative techniques have been proposed to balance the computational complexity and performance in massive MIMO systems. However, most of these methods have very slow convergence rate. therefore, we employ the multiple search direction conjugate gradient (MSD-CG) method in massive MIMO uplink systems to reduce the data detection complexity and enhance the performance. the efficiency of the proposed method has been improved by a projection technique that requires a search direction in each sub-domain rather than forming all search directions conjugate to each other. In this paper, we compare the proposed approach to the approximate and exact methods. Our results indicate that the proposed algorithm with realistic antenna configurations is superior to the existing methods for large-scale MIMO systems.

关键词： Multiple-Input Multiple-Output (MIMO) Massive MIMO multiple search direction conjugate gradient (MSD-CG) conjugate gradient (CG)

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Fast and Light-weight Binarized. Neural Network Implemented in an FPGA using LUT-based signal processing and its Time-domain Extension for Multi-bit processing 9

Fast and Light-weight Binarized. Neural Network Implemented ...

引用

9th IEEE International conference on Consumer Electronics (ICCE-Berlin)

作者： Fuchikami, Ryuji Issiki, Fumio 5-8-16-203 Muromi Sawara Ku Fukuoka 8140015 Japan 5-21-17 Kikuna Kouhoku Ku Yokohama Kanagawa 2220011 Japan

ISBN: (纸本)9781728127453

Fast and small-resourced implementation of convolutional neural network (CNN) into a field-programmable gate array (FPGA) was realized using a binarized neural network (NN). We propose a set of neuron and network models optimized for fully binarized implementation of general NNs using the look-up-tables (LUTs) in modern FPGAs, which is herein referred to as sparse-LUT model. Arrayed MNIST data images of more than 40 characters input from a camera were recognized with 92.8% accuracy and classified with the colored marks on the characters on organic light-emitting diode (OLED) display images with 1-ms cycle time, <1.0-ms delay in the LUT-based CNN recognition, and <2-ms total time delay. In combination with stochastic time-divided signal processing, binarized signals in this model can be extended for processing multi-bit (analogue-like) signals in an oversampling manner with increased recognition accuracies up to 98.6% in MNIST and 58.3% in CIFAR-10 image data sets using CNN. the source codes for the binarized NN core were released and open-sourced.

关键词： Stochastic systems

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Identifying Disease Modules Based on Connectivity and Semantic Similarities 14th

Identifying Disease Modules Based on Connectivity and Semant...

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14th International conference on Bio-inspired Computing: theories and Applications, BIC-TA 2019

作者： Su, Yansen Zhu, Huole Zhang, Lei Zhang, Xingyi Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei China

ISBN: (纸本)9789811534140

the identification of disease modules has attracted increasing attention due to the importance in comprehending pathogenesis of complex diseases. Most of the existing methods were based on the protein-protein interaction (PPI) networks with the incompleteness and incorrectness of the interactome, which results in many disease-related proteins and pathways not within their disease modules. In this paper, we propose a method named IDMCSS to effectively identify disease modules by adding some potential interactions and removing some incorrect interactions in the existing human PPI network. Firstly, the connective similarity and semantic similarity are calculated between the known disease proteins and each of their neighbors. then, the network is adjusted by adding the interactions with large connective and semantic similarity, and removing those with small connective and semantic similarity. Further, the proteins are sorted in the descending order according to the similarity to the known disease proteins, and the sorted proteins are added into a candidate node set one by one until a certain biological information is not enriched in the candidate node set. Finally, the connected subnetwork with the largest number of nodes in the candidate node set is selected as the disease module. the disease modules identified by IDMCSS involve crucial biological processes of related diseases and can predict 12 targets for drug intervention. the experimental results on asthma demonstrate the effectiveness of the proposed method in comparison to existing algorithms for disease module identification. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Proteins

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CROSS-CONNECTED NETWORKS FOR MULTI-TASK LEARNING OF DETECTION AND SEGMENTATION 26

CROSS-CONNECTED NETWORKS FOR MULTI-TASK LEARNING OF DETECTIO...

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26th IEEE International conference on Image processing (ICIP)

作者： Kawakami, Rei Yoshihashi, Ryota Fukuda, Seiichiro You, Shaodi Iida, Makoto Naemura, Takeshi Univ Tokyo Tokyo Japan CSIRO Data61 Tokyo Japan

ISBN: (纸本)9781538662496

Multi-task learning improves generalization performance in neural networks by sharing knowledge among related tasks. Existing models are for task combinations annotated on the same dataset;research on how to utilize the knowledge of successful single-task convolutional neural networks (CNNs) that are trained on individual datasets is limited. We propose a cross-connected CNN, an architecture that connects single-task CNNs through convolutional layers that transfer useful information to their counterparts. We evaluated the architecture with a combination of detection and segmentation using datasets of two targets: pedestrians and wild birds. Experiments demonstrate how well our CNN learns general representations from multi-task learning.

关键词： Multi-task Learning Pedestrian Detection Bird Detection Semantic Segmentation

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Deep Learning for Direct Automatic Target Recognition from SAR data

Deep Learning for Direct Automatic Target Recognition from S...

引用

IEEE Radar conference (RadarConf)

作者： Kazemi, Samia Yonel, Bariscan Yazici, Birsen Rensselaer Polytech Inst Dept Elect Comp & Syst Engn 110 8th St Troy NY 12180 USA

ISBN: (纸本)9781728116792

Automatic target recognition for synthetic aperture radar involves three processing steps: imaging, identifying regions of interests and processing of the identified regions for target classification. Motivated by the successes of data driven approach in various fields, we establish a framework for synthetic aperture radar target recognition that does not require image formation. We present a deep neural network architecture that classifies targets directly from the slow-time and fast-time sampled received signal. Classification decisions are made based on how closely optimized sets of vectors corresponding to each class represent a new sample, and these vectors from all classes under consideration collectively forms a dictionary.

关键词： Dictionaries Synthetic aperture radar Training Radar imaging Neural networks Target recognition Optimization

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Classification of pickable and unpickable strawberries under farm conditions

Classification of pickable and unpickable strawberries under...

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IEEE International conference on Automation Science and Engineering (CASE)

作者： Yuanyue Ge Ya Xiong Pål Johan From Faculty of Science and Technology Norwegian University of Life Sciences Norway

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

In this paper, we address the problem of deformed point clouds of small targets under farm condition. Strawberry point cloud data were extracted through image detection and corresponding coordinate transformation. the deformed point cloud caused by adhesion of adjacent objects make the localization inaccurate. therefore, multiple features in three spatial dimensions were constructed to represent the shape and distribution of the strawberry point data. Some fundamental features, such as eccentricity and least moment of inertia, were utilized, while some new features, such as 3D shape matrix and ratio of data amount in certain space, were proposed. Furthermore, classifiers that can identify unpickable strawberry data and other defined classes were trained and evaluated on constructed features and different label sets. the evaluation results show that the classifier could effectively identify unpickable cases. this work proposes a new problem and provides possible solutions that could optimize the performance of machine vision system of the strawberry harvesting robot.

关键词： three-dimensional displays Shape Feature extraction Adhesives Two dimensional displays Cameras data mining

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A Resonance-Type Terahertz-Frequency signal Detector Based on an Antiferromagnetic Tunnel Junction 8

A Resonance-Type Terahertz-Frequency Signal Detector Based o...

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8th IEEE Int Conf on Advanced Optoelectron and Lasers (CAOL) / Sci Workshop on data Sci in Modern Optoelectronics and Laser Engineering (DSMOLE) / 16th Sci Workshop on Measurement Uncertainty - Scientific, Normative, Appl and Methodical Aspects (UM)

作者： Artemchuk, P. Yu Sulymenko, O. R. Prokopenko, O., V Taras Shevchenko Natl Univ Kyiv Fac Radio Phys Elect & Comp Syst Kiev Ukraine

ISBN: (纸本)9781728118147

Detection of terahertz-frequency (TF) electromagnetic signals is an important task required for many applications of TF electromagnetic radiation in medicine, security, communications, technology and science. Here we present a theoretical proposal of a novel resonance-type terahertz-frequency signal detector (TFSD) based on an antiferromagnetic tunnel junction (ATJ). the junction is a four-layer structure Pt/AFM/MgO/Pt consisting of two platinum (Pt) layers, a layer of an antiferromagnet (AFM) and the MgO tunneling barrier. When an external bias dc current is flowing in the bottom Pt layer (adjacent to the AFM), it produces TF oscillations of junction's tunneling magnetoresistance that being mixed with an input TF ac current, in turn, leads to the generation of an output dc voltage across the junction. Using a simple electrical model of the TFSD we show that TFSD's output voltage reduces with an increase of the signal frequency, but it can be sufficient for some applications in the frequency range 0.1 -1 thz (similar to 0.4 mV for input power of 1 mu W), if the ATJ having thin MgO tunneling barrier (thickness similar to 1 nm) and small cross-sectional area (similar to 100x100 nm(2)) is used.

关键词： terahertz-frequency signal resonance-type detector antiferromagnetic tunneljunction

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