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检索条件"任意字段=3rd International Symposium on Information Processing in Sensor Networks"

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Deep learning semantic statistical features' labeling of SAR image based on K-MEANS clustering and wavelet transform 3

Deep learning semantic statistical features' labeling of SAR...

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2020 3rd international symposium on Big Data and Applied Statistics, ISBDAS 2020

作者： Peng, Jinxi Su, Yuanqi Xue, Xiaorong Song, Donghong Xue, Xiaoyong Wu, Aihua Department of Computer Science and Technology Xi'an JiaoTong University Xi'an Shaanxi710049 China Jinzhou Liaoning121001 China School of Computer and Information Engineering Anyang Normal University Anyang Henan455000 China Middle School of Luonan County Shaanxi726100 China Department of Ai and Software Engineering South China Institute of Software Engineering Guangzhou University Guangzhou Guangdong510990 China

[Purpose] Because Synthetic Aperture Radar (SAR) image contains much speckle, at present, it's hard to obtain good results for SAR image with the traditional image classification methods. However, SAR image has rich texture information which facilitates SAR image segmentation. There is a Deep Learning method of K-means data clustering that is used to calculate the optimal k value to avoid more-segmentation or less-segmentation, the advantages of fuzzy theory and neural network are used to improve the classification accuracy of image processing.[Methods] According to the statistical characteristics of SAR images and the semantics of fuzzy neural networks analysis, an efficient image segmentation method based on Deep Learning Semantic analysis and wavelet transform is proposed to achieve precision of classification;Firstly, the texture features of SAR image are extracted by Deep Learning Semantic clustering;Secondly, the SAR image are characterized according to SAR semantics. Based on one of the Deep Learning Semantic k-means algorithms, the best k-value iterations of SSE(Sum of the Squared Errors)and SC(Silhouette Coefficient) are performed, and the suitable value k is selected;Finally, by Deep Learning segmentation based on the texture feature of the SAR image and the filtered gray component vector, and the SAR image is classified to facilitate the change detection of images.[Results] The experimental results show that a good k value detection is performed on the CPU/GPU platform. The SAR images is compared with two different results between the wavelet filtering and Deep Learning semantic method images which are single or multiple standard classified through detection-change SAR is better than the Empirical Approach, then the labels are classified for the accuracy and computational efficiency that are calculated. The classification results were accurately ameliorated. [Conclusions] The similarity calculation of k-value cluster is an important precondition, so it's

关键词： Semantics

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2D and 3D-integrated image sensor

2D and 3D-integrated image sensor

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Midwest symposium on Circuits and Systems (MWSCAS)

作者： Zhe Ming Liu Ming-Chih Lin Chieh Ning Chan Oscal T.-C. Chen Department of Electrical Engineering National Chung Cheng University Chiayi Taiwan

This work develops a 2 Dimension (2D)/ 3 Dimension (3D)-integrated image sensor that adopts the same photodiode under different biased voltages and integrated sensing circuitry. By using the TSMC CMOS technology, four different photodiodes, N-well_P-substrate, P-diffusion_N-well, Pdiffusion_ N-well_P-substrate and N-diffusion_P-substrate, are available. Since P-substrate is used for grounding and P-diffusion_N-well has poor quantum efficiency, only the photodiode of P-diffusion_N-well_P-substrate can be employed by biasing two different voltages to P-diffusion. If the photodiode is driven by little smaller than its breakdown voltage, it functions at the Geiger mode to work for 3D depth sensing. Otherwise, this photodiode performs 2-D gray-level sensing. In the sensing circuitry, a feed-back mechanism is employed to yield a charge supply for a high dynamic range of 2-D sensing, and a feed-back inverter is adopted to boost the processing speed of 3-D sensing. These two feedbacks are effectively integrated in an active pixel to establish 2D/3D image sensor. Based on the TSMC 0.35μm 2P4M technology, a 352×288-pixel 2D/3D image sensor was implemented where its dynamic range can reach 134dB under photocurrents from 10-13A to 5×10-7A and sensing time can be accelerated to 16.3ns. The proposed image sensor can effectively capture 2D and 3D information of an object at the same location without additional alignment and post-processing.

关键词： Image sensors Photodiodes Voltage CMOS technology Dynamic range CMOS image sensors Integrated circuit technology Grounding Integrated circuit yield Inverters

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Deep long short-term memory networks for speech recognition

Deep long short-term memory networks for speech recognition

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international symposium on Chinese Spoken Language processing

作者： Jen-Tzung Chien Alim Misbullah Department of Electrical and Computer Engineering National Chiao Tung University Hsinchu

ISBN: (纸本)9781509042951

Speech recognition has been significantly improved by applying acoustic models based on deep neural network which could be realized as the feedforward NN (FNN) or the recurrent NN (RNN). In general, FNN is feasible to project the observations onto a deep invariant feature space while RNN is beneficial to capture the temporal information in a sequential data for speech recognition. RNN based on long short-term memory (LSTM) is capable of storing inputs over a long time period and thus exploiting a self-learned mechanism for long-range temporal context. Considering the complimentary FNN and RNN in their modeling capabilities, this paper presents a deep model which is constructed by stacking LSTM and FNN. Through the cascade of LSTM cells and fully-connected feedforward units, we explore the temporal patterns and summarize the long history of previous inputs in a deep learning machine. The experiments on 3 rd CHiME challenge and Aurora-4 show that the stacks of hybrid model with FNN post-processor outperform stand-alone FNN and LSTM and the other hybrid models for robust speech recognition.

关键词： Hidden Markov models Speech recognition Logic gates Feedforward neural networks Stacking Acoustics Topology

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Fault-tolerant Data Aggregation Scheme for Monitoring of Critical Events in Grid based Healthcare sensor networks 11

Fault-tolerant Data Aggregation Scheme for Monitoring of Cri...

引用

Simulation Multiconference

作者： Ather Saeed Andrew Stranieri Richard Dazeley Center for Informatics and Applied Optimization University of Ballarat and Melbourne Institute of Technology

ISBN: (纸本)9781617828409

Wireless sensor devices are used for monitoring patients with serious medical conditions. Communication of content-sensitive and context sensitive datasets is crucial for the survival of patients so that informed decisions can be made. The main limitation of sensor devices is that they work on a fixed threshold to notify the relevant Healthcare Professional (HP) about the seriousness of a patient's current state. Further, these sensor devices have limited processor, memory capabilities and battery. A new grid-based information monitoring architecture is proposed to address the issues of data loss and timely dissemination of critical information to the relevant HP. The proposed approach provides an opportunity to efficiently aggregate datasets of interest by reducing network overhead and minimizing data latency. To narrow down the problem domain, in-network processing of datasets with Grid monitoring capabilities is proposed for the efficient execution of the computational, resource and data intensive tasks. Interactive wireless sensor networks do not guarantee that data gathered from the heterogeneous sources will always arrive at the sink (base) node, but the proposed aggregation technique will provide a fault tolerant solution to the timely notification of a patient's critical state. Experimental results received are encouraging and clearly show a reduction in the network latency rate.

关键词： Data aggregation Grid computing In-network processing sensor networks Fault-tolerance unified retrieval models Fault tolerance sensor networks Grid computing grids Grid Dataset Device sensor data delay data loss Patient Monitoring

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Real-Time Hand Postures Recognition using Low Computational Complexity Artificial Neural networks and Support Vector Machines

Real-Time Hand Postures Recognition using Low Computational ...

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3rd international symposium on Communications, Control, and Signal processing (ISCCSP 2008), vol.3

作者： Ticiano A. C. Bragatto Gabriel S. I. Ruas Marcus V. Lamar Department of Electrical Engineering Brasilia University Brazil Department of Computer Science Brasilia University Brazil

This paper proposes two main techniques for reduce computational complexity on Artificial Neural networks, using piecewise linear activation function, and Support Vector Machines built on a probability based binary tree. These methods are compared with well-known classifiers based on the computational complexity, correct rate and time taken to process the required information. The results show that probability based binary tree SVM has an equivalent recognition rate and is faster than ANNs.

关键词： Hand Posture Recognition Artificial Neural networks Support Vector Machines

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An Optimal Pole Placement state feedback with Feedforward digital control applied to a three-level NPC inverter implemented in FPGA

An Optimal Pole Placement state feedback with Feedforward di...

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IEEE international symposium on Industrial Electronics (ISIE)

作者： Marcelo D. Pedroso Claudinor B. Nascimento Mauricio dos S. Kaster Angelo M. Tusset Department of Electronic Federal University of Technology of Parana Ponta Grossa Brazil

This paper presents an Optimal-Pole-Placement Control (OPPC) combined with Feedforward (FF) scheme to control a 1φ-3L-NPC-Inverter. The OPPC is an optimal linear state feedback control that improves the transient response time with controlled input/output current and voltage overshoot. The inclusion of a feedforward branch between the reference signal and duty-cycle strategy ensures null steady state error and it reduces the output voltage distortion as well as the response time of the DC bus capacitor voltage balancing. To verify the performance of the NPC-Inverter, the proposed control is compared to a dual PI voltage and current control loops strategy. For both control schemes, the system uses just a voltage sensor, with the current calculated from the voltage. Simulations results using the Altera DSP Builder software in a Matlab/Simulink environment are presented to demonstrate the NPC-Inverter performance implemented in FPGA.

关键词： Voltage control Feedforward neural networks Pi control Capacitors Steady-state Field programmable gate arrays Digital signal processing

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A vision-based human action recognition system for moving cameras through deep learning 2

A vision-based human action recognition system for moving ca...

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2nd international Conference on Signal processing and Machine Learning, SPML 2019

作者： Chang, Ming-Jen Hsieh, Jih-Tang Fang, Chiung-Yao Chen, Sei-Wang National Taiwan Normal University No. 88 Sec. 4 Tingzhou Rd. Wenshan Dist. Taipei City116 Taiwan

ISBN: (纸本)9781450372213

This study presents a vision-based human action recognition system using a deep learning technique. The system can recognize human actions successfully when the camera of a robot is moving toward the target person from various directions. Therefore, the proposed method is useful for the vision system of indoor mobile robots. The system uses three types of information to recognize human actions, namely, information from color videos, optical flow videos, and depth videos. First, Kinect 2.0 captures color videos and depth videos simultaneously using its RGB camera and depth sensor. Second, the histogram of oriented gradient features is extracted from the color videos, and a support vector machine is used to detect the human region. Based on the detected human region, the frames of the color video are cropped and the corresponding frames of the optical flow video are obtained using the Farnebäck method (https://***=.org/3.4/d4/dee/***). The number of frames of these videos is then unified using a frame sampling technique. Subsequently, these three types of videos are input into three modified 3D convolutional neural networks (3D CNNs) separately. The modified 3D CNNs can extract the spatiotemporal features of human actions and recognize them. Finally, these recognition results are integrated to output the final recognition result of human actions. The proposed system can recognize 13 types of human actions, namely, drink (sit), drink (stand), eat (sit), eat (stand), read, sit down, stand up, use a computer, walk (horizontal), walk (straight), play with a phone/tablet, walk away from each other, and walk toward each other. The average human action recognition rate of 369 test human action videos was 96.4%, indicating that the proposed system is robust and efficient. © 2019 Association for Computing Machinery.

关键词： Optical flows

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STUDY ON SOIL SAMPLING information SYSTEM

STUDY ON SOIL SAMPLING INFORMATION SYSTEM

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The 3rd international symposium on Intelligent information Technology in Agriculture(ISIITA)

作者： Yunping Chen Xiu Wang National Engineering Research Center for information Technology in Agriculture Mailbox 2449-26,Beijing,100089,P.R.China

The quality of soil sampling has a significant effect on the accuracy of site specific fertilizer application in precision *** numbers of soil sampling site and the distribution within a field have great importance for achieving a high quality *** high quality soil samples are the basic decision material for site specific fertilizer *** fast and low cost sampling methods can promote this technique to be applied *** this paper,a general framework of an information processing system for soil sampling has been *** modules have been conceived to response to some main function about soil sampling.A simple system,which is developed using Component-based technology based on RS,GIS and GPS,has been designed and implemented the general *** this system,we can design soil sampling sites and optimize sampling path easily.A navigation function has been designed,with which a vehicle can navigate to each soil sample location fast and *** the system,the geographic coordinates and the soil laboratory analysis results of samples can be integrated to ***, connecting with a sensor,the system can detect and record soil sample properties *** system has been applied successfully on the automatic soil sampling machine which is designed by National Engineering Research Center for information Technology in Agricultural.

关键词： component technology precision agriculture soil sampling

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Performance of an Interference Canceller for P-MP Satellite networks with Nonlinear TWTA

Performance of an Interference Canceller for P-MP Satellite ...

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3rd international symposium on Communications, Control, and Signal processing (ISCCSP 2008), vol.3

作者： Takeshi Uratani Ryusuke Miyamoto Takao Hara Minoru Okada Department of Information Science Nara Institute of Science and Technology Ikoma Nara Japan

This paper addresses the design and performance evaluation of an interference canceller for frequency reuse satellite communications in a channel containing a nonlinear device. In this study, a reference simulator has been developed for the evaluation of an interference canceller for P-MP (Point to Multi-Point) satellite networks. In the interference canceller, a precise replica of an unwanted signal has to be generated in order to cancel it. However, nonlinear device causes the difference between the unwanted signal and the replica in the transmission path. Another potential degradation due to nonlinearity is that the superposed signal is affected by the intermodulation of both wanted and unwanted carriers. By the evaluation of this study, it has been proved that carrier super-positioning is feasible even in the nonlinear channel by properly taking the back off of the satellite transponder to minimize BER performance.

关键词： Interference cancellation Signal generators Satellite communication Frequency Degradation Nonlinear distortion Artificial satellites information science Transponders Bit error rate

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Base Station Selection and Per-cell Codebook Optimization for CoMP with Joint processing

Base Station Selection and Per-cell Codebook Optimization fo...

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IEEE international symposium on Personal, Indoor and Mobile Radio Communications

作者： P. Baracca F. Boccardi V. Braun A. Tulino Department of Information Engineering University of Padova Italy Bell Laboratories Alcatel-Lucent

ISBN: (纸本)9781467325660

In cellular networks coordination among base stations (BSs) has been recognized as an important solution to handle inter-cell interference and increase spectral efficiency. In frequency division duplex systems one of the main issue that sensibly degrades the performance of coordinated multipoint (CoMP) transmission techniques is the imperfect channel state information (CSI) due to the limited bandwidth available for the feedback transmission. In this paper we focus on a CoMP scenario with data and CSI sharing among the BSs and we consider a feedback transmission scheme where each user equipment (UE) quantizes the different channels by using codebooks designed for a single-cell scenario. Due to the different propagation characteristics of the channels between a UE and each BS and by considering a constraint on the number of available feedback bits, we propose two practical algorithms depending on the large-scale fading to a) select the subset of BSs from whom the UE prefers to be served and b) optimize the number of feedback bits allocated to each channel. The developed techniques allow a UE to send more feedback bits to the BSs with a stronger signal and numerical results show the merits of the proposed approach.

关键词： Codebook Optimization CoMP Joint processing

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