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Asia-Pacific Microwave conference (APMC)

作者： Whitworth, Avon Ishmael, Khaldoon Yavari, Ehsan Boric-Lubecke, Olga Univ Hawaii Dept Elect Engn Holmes Hall 4832540 Dole St Honolulu HI 96822 USA Adnov LLC 2800 Woodlawn DrSuite 297 Honolulu HI 96822 USA

ISBN: (纸本)9784902339451

Doppler radar can potentially be used to detect respiration and heart rates of human subjects without the inconvenience of attaching transducers to the subjects. We simulated breathing rates of two subjects with two oscillating targets, and recorded the resulting data received when the targets are subject to a continuous wave signal transmitted by a 2.4GHz quadrature channel receiver. Using small changes in the initial center position of one target which passed through the null and optimum positions in the I and Q channels, and then applying a Fast Fourier Transform, we demonstrated that it is possible to remotely distinguish the oscillation frequency of each target.

关键词： Biomedical signal detection biomedical signal processing Doppler radar frequency measurement Fourier transforms

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On the use of deep neural networks for the detection of small vehicles in ortho-images 24

On the use of deep neural networks for the detection of smal...

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24th IEEE International conference on Image processing, ICIP 2017

作者： Terrail, Jean Ogier Du Jurie, Frederic Safran Electronics and Defense Normandie Univ UNICAEN ENSICAEN CNRS France Normandie Univ UNICAEN ENSICAEN CNRS France

ISBN: (纸本)9781509021758

this paper addresses the question of the detection of small targets (vehicles) in ortho-images. this question differs from the general task of detecting objects in images by several aspects. First, the vehicles to be detected are small, typically smaller than 20×20 pixels. Second, due to the multifarious-ness of the landscapes of the earth, several pixel structures similar to that of a vehicle might emerge (roof tops, shadow patterns, rocks, buildings), whereas within the vehicle class the inter-class variability is limited as they all look alike from afar. Finally, the imbalance between the vehicles and the rest of the picture is enormous in most cases. Specifically, this paper is focused on the detection tasks introduced by the VEDAI dataset [1]. this work supports an extensive study of the problems one might face when applying deep neural networks with low resolution and scarce data and proposes some solutions. One of the contributions of this paper is a network severely outperforming the state-of-the-art while being much simpler to implement and a lot faster than competitive approaches. We also list the limitations of this approach and provide several new ideas to further improve our results. © 2017 IEEE.

关键词： Deep neural networks

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Performance Analysis on 3D Beamforming for Downlink In-Band Wireless Backhaul for small Cells 86

Performance Analysis on 3D Beamforming for Downlink In-Band ...

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86th IEEE Vehicular Technology conference (VTC-Fall)

作者： Niu, Jinping Li, Geoffrey Y. Li, Yanyan Fang, Dingyi Zheng, Jie Li, Xun Northwest Univ Sch Informat & Technol Xian 710127 Shaanxi Peoples R China Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Huawei Shanghai Res Inst Shanghai 200040 Peoples R China

ISBN: (纸本)9781509059355

three-dimensional (3D) beamforming and small cells are two effective techniques to meet the demand of explosive data rate in 5G wireless networks nowadays. the cooperation of these two schemes can help small cell involved heterogeneous networks (HetNets) to achieve high performance. In this paper, we investigate the performance of small cell in-band wireless backhaul in a downlink HetNet considering 3D beamforming. We first analyze the received signals of small cells and users for in-band small cell backhauling and then derive the closed-form achievable data rates for users and the overall system based on gamma distribution. Finally, we formulate the problem based on the derived achievable data rate, followed by analysis of the problem. Simulation results demonstrate that combining 3D beamforming with HetNets can significantly improve the system performance.

关键词： three-dimensional displays Array signal processing Antennas Wireless communication Interference Downlink Macrocell networks

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Supervised Semi-Autoencoder Learning for Multi-Layered Neural Networks 17

Supervised Semi-Autoencoder Learning for Multi-Layered Neura...

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Joint 17th World Congress of International-Fuzzy-Systems-Association / 9th International conference on Soft Computing and Intelligent Systems (IFSA-SCIS)

作者： Kamimura, Ryotaro Takeuchi, Haruhiko Tokai Univ IT Educ Ctr 4-1-1 Kitakaname Hiratsuka Kanagawa 2591292 Japan Natl Inst Adv Ind Sci & Technol Human Informat Res Inst 1-1-1 Higashi Tsukuba Ibaraki 3058566 Japan

ISBN: (纸本)9781509049172

the present paper proposes a new type of layer-wise learning for multi-layered neural networks. Multi-layered neural networks have the serious problem of vanishing information, where information contained in input patterns is gradually lost, and preventing neural networks from learning input patterns. In particular, the autoencoders used in the greedy layer-wise pre-training tend to lose the original information by going through multiple layers. For this problem, we propose a new approach called "supervised semi-autoencoder" to solve the problem of vanishing information. the new method is close to the ordinary autoencoder, but the outputs are the original inputs and the corresponding targets for amplifying information in input patterns. In addition, the computational procedures are simplified by using potential learning, in which the potentiality of neurons is determined before learning, and is given as initial weights. thus, the complicated adjustment of parameters is not necessary. the method was applied to the well-known crab data set as well as the real data set of eye-tracking records. In both cases, with relatively small-sized data, neural networks could find internal representations close to those obtained by larger ones, and better generalization performance could be observed.

关键词： Estimation Field programmable gate arrays OFDM Modulation Filtration Clocks signal processing algorithms

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Vibration data Acquisition and Fault Diagnosis System of Hydro Turbine Based on CompactRIO 5

Vibration Data Acquisition and Fault Diagnosis System of Hyd...

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5th International conference on Mechanical, Automotive and Materials Engineering, CMAME 2017

作者： Weiming, Gong Ming, Zhao Xinfeng, Ge Yongsheng, Ding Jinqi, Du Yizhong, Zhang Jiangdu Water Conservancy Project Administrative Office of Jiangsu Province Yangzhou225200 China Research Institute of Yunnan Power Grid Co. Ltd. Kunming650217 China College of Energy and Electric Engineering Hohai University Nanjing211100 China Yunnan Electric TestResearch Institute Group CO. LTD Kunming650217 China Hunan Electric Power Research Institute of State Grid Changsha410007 China

ISBN: (纸本)9781538604311

this paper uses NI CompactRIO hardware platform, the CRIO has a small, convenient and suitable for harsh environments. Based on Labview software platform, developed vibration condition monitor data acquisition and processing system, using the FPGA module and real-Time processing module, realized high accuracy real-Time and high sampling rate, and through wavelet analysis, processing the collected the signal. Due to the time-frequency characteristics of the wavelet analysis, the analysis of singularity of the fault signal has more advantages than the traditional spectrum analysis. through the wavelet analysis, vibration CompactRIO system can accurate extraction of fault feature of vibration unit. © 2017 IEEE.

关键词： Failure analysis

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Vibration data Acquisition and Fault Diagnosis System of Hydro Turbine Based on CompactRIO

Vibration Data Acquisition and Fault Diagnosis System of Hyd...

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5th International conference on Mechanical, Automotive and Materials Engineering (CMAME)

作者： Gong Weiming Zhao Ming Ge Xinfeng Ding Yongsheng Du Jinqi Zhang Yizhong Jiangdu Water Conservancy Project Adm Off Jiangsu Yangzhou 225200 Jiangsu Peoples R China Yunnan Power Grid Co Ltd Res Inst Kunming 650217 Yunnan Peoples R China Hohai Univ Coll Energy & Elect Engn Nanjing 211100 Jiangsu Peoples R China Yunnan Elect Test & Res Inst Grp CO LTD Kunming 650217 Yunnan Peoples R China Hunan Elect Power Res Inst State Grid Changsha 410007 Hunan Peoples R China

ISBN: (纸本)9781538604328

this paper uses NI CompactRIO hardware platform, the CRIO has a small, convenient and suitable for harsh environments. Based on Labview software platform, developed vibration condition monitor data acquisition and processing system, using the FPGA module and real-time processing module, realized high accuracy real-time and high sampling rate, and through wavelet analysis, processing the collected the signal. Due to the time-frequency characteristics of the wavelet analysis, the analysis of singularity of the fault signal has more advantages than the traditional spectrum analysis. through the wavelet analysis, vibration CompactRIO system can accurate extraction of fault feature of vibration unit..

关键词： CompactRIO data Acquisition Hydro Turbine Condition monitoring Fault diagnosis

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small Base Station Management - Improving Energy Efficiency in Heterogeneous Networks

Small Base Station Management - Improving Energy Efficiency ...

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International Wireless Communications and Mobile Computing conference

作者： Dongfeng Fang Feng Ye Yi Qian Hamid Sharif Department of Electrical and Computer Engineering University of Nebraska-Lincoln NE USA Department of Electrical and Computer Engineering University of Dayton Ohio USA

In this paper, we propose a heterogeneous network (HetNet) system with a cloud control center to dynamically manage small base stations (SBSs) based on traffic load. the cloud can provide a user equipment (UE) association mechanism to balance both traffic load and spectrum allocation of SBSs and the macro base station (MBS) with throughput requirements of uplink and downlink. Our proposed association mechanism and SBS management mechanism can optimize the energy efficiency (EE) of the network and UE by considering EE of both uplink and downlink. Device-to-device communications are adopted under service request probability of UE and distance limitation. the EE optimization problem is solved in two steps in this paper. First, a decoupled association for UE over uplink and downlink is adopted. Least path loss criterion is used for uplink association. And priority SBS under signal-to-interference-plus-noise rate threshold and data rate requirement is applied in downlink association. After association, the SBSs management is implemented iteratively for adjusting the operation of SBSs to maximize the EE of both the network and UE. Simulation results show that our proposed method can improve the EE of the system with better performance on offloading traffic from the MBS to SBSs.

关键词： Heterogeneous network Association mechanism Energy efficiency Device-to-device communications SBS management mechanism

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data-Driven crowd simulation

Data-Driven crowd simulation

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IEEE conference on Advanced Video and signal Based Surveillance (AVSS)

作者： Niccoló Bisagno Nicola Conci Bo Zhang University of Trento Trento Italy Dalian Maritime University Dalian City China

In this work we propose a framework for data-driven crowd simulation starting from a small set of trajectories. To model the macroscopic behavior, our method extracts pedestrian trajectories from real videos, clusters all trajectories of pedestrians who intend to reach the same goal, and computes the velocity field associated with each exit region in the scene to guide virtual agents toward their destinations, namely, the goal-dependent path selection. While at the microscopic level, the simulation is performed using on the one hand the Social Force Model to handle the collision-avoidance among agents and on the other hand the computed velocity fields to model the macroscopic behavior. the experimental results demonstrate that the velocity field can be exploited to effectively reproduce crowd behaviors.

关键词： Computational modeling Trajectory Force Mathematical model Videos Heating systems Atmospheric modeling

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Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields

Weakly supervised training of deep convolutional neural netw...

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IEEE conference on Advanced Video and signal Based Surveillance (AVSS)

作者： Alessandro Corbetta Vlado Menkovski Federico Toschi Eindhoven University of Technology The Netherlands

Overhead depth map measurements capture sufficient amount of information to enable human experts to track pedestrians accurately. However, fully automating this process using image analysis algorithms can be challenging. Even though hand-crafted image analysis algorithms are successful in many common cases, they fail frequently when there are complex interactions of multiple objects in the image. Many of the assumptions underpinning the hand-crafted solutions do not hold in these cases and the multitude of exceptions are hard to model precisely. Deep Learning (DL) algorithms, on the other hand, do not require hand crafted solutions and are the current state-of-the-art in object localization in images. However, they require exceeding amount of annotations to produce successful models. In the case of object localization, these annotations are difficult and time consuming to produce. In this work we present an approach for developing pedestrian localization models using DL algorithms with efficient weak supervision from an expert. We circumvent the need for annotation of large corpus of data by annotating only small amount of patches and relying on synthetic data augmentation as a vehicle for injecting expert knowledge in the model training. this approach of weak supervision through expert selection of representative patches, suitable transformations and synthetic data augmentations enables us to successfully develop DL models for pedestrian localization efficiently.

关键词： Sensors Training data Training Neural networks Image analysis data models Cameras

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2C-SVM based radar detectors in gaussian and K-distributed real interference

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2017年 10305 LNCS卷 257-268页

作者： Mata-Moya, David Jarabo-Amores, Maria-Pilar Rosa-Zurera, Manuel Rosado-Sanz, Javier Del-Rey-Maestre, Nerea Signal Theory and Communications Department Superior Polytechnic School University of Alcalá Alcalá de Henares Madrid28805 Spain

ISBN: (纸本)9783319591520

this paper tackles the design and evaluation of cost sensitive Support Vector Machine (2C-SVM) based radar detectors in presence of Gaussian and K-Distributed clutter. 2C-SVM based solutions are able to approximate the Neyman-Pearson detector for a specific false alarm rate (PFA). Real data acquired in different wind conditions by a coherent, pulsed and X-Band radar were considered. A statistical analysis is carried out to design the 2C-SVM for detecting targets with unknown parameters in Gaussian and non-Gaussian interference. A grid search of the best training parameters to approximate the pair detection probability (PD) and PFA of the NP detector is required. Results prove the capability of the 2C-SVM based detectors to maximize the PD for a desired PFA independently of the detection problem likelihood functions. © Springer International Publishing AG 2017.

关键词： Clutter (information theory)

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