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检索条件"任意字段=18th Conference on Signal and Data Processing of Small Targets"

共 390 条记录，以下是91-100 订阅

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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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A Fuzzy Based Gaussian Weighted Moving Windowing for Denoising Electrocardiogram (ECG) signals 13th

A Fuzzy Based Gaussian Weighted Moving Windowing for Denoisi...

引用

13th International conference on Application of Fuzzy Systems and Soft Computing (ICAFS)

作者： Kavalcioglu, Cemal Bilgehan, Bulent Near East Univ Dept Elect & Elect Engn Near East BlvdPOB 99138Mersin 10 Nicosia Trnc Turkey

ISBN: (纸本)9783030041649

the electrocardiogram is a graphical record of the biological signal that is thought to be susceptible to electrical activity of the heart and utilized in order to clinical diagnosis. Electrocardiogram signal is very responsive in nature, and even if there is small noise mixed with the original signal, assorted characteristics of signal change. ECG signal voltage level is as low as 0.5 to 5 mV and is sensitive to artifacts larger than this. Human electrocardiogram signal range frequency ingredients from 0.05 Hz to 100 Hz and are related to noise, muscle movements, network current, and ambient electromagnetic interference. Electrocardiogram is a very significant sign detects abnormal heart rhythms and examines cause of chest pain and widely utilized in cardiology. Most digital signals are infinitely large or too large to be manipulated as a whole. Because statistical calculations require that all points be present for analysis, it is difficult to statistically analyze sufficiently large signals. To avoid these problems, engineers characteristically analyze small subsets of the aggregate data with an operation named windowing. Fuzzy logic is a mathematical logic that attempts to solve problems with a clear, uncertain data spectrum that makes it possible to obtain a series of correct results. this manuscript suggests denoising method Gaussian Weighted Moving Windowing for denoising Electrocardiogram signals to remove random noise. this study is interpreted with the actual data set and confirmed according to Peak signal to noise ratio.

关键词： Electrocardiogram Windowing Gaussian weighted moving windowing Denoising ECG signal processing Fast fourier transform Fuzzy logic

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New Doppler processing for the Detection of small and Slowly-Moving targets in Highly Ambiguous Radar Context 26

New Doppler Processing for the Detection of Small and Slowly...

引用

European signal processing conference (EUSIPCO)

作者： Aouchiche, Linda Ferro-Famil, Laurent Adnet, Claude Univ Rennes 1 Rennes France Thales Air Syst Limours France

ISBN: (纸本)9789082797015

this paper presents a novel Doppler processing scheme for pulse Doppler radars operating in at intermediate Pulse Repetition Frequency (PRF) and suffering from range and Doppler ambiguities. One of the main drawbacks of the classical Doppler processing approach concerns the suppression of small and slowly-moving targets when rejecting ground clutter returns. In order to address this problem a two-step Doppler method is proposed in this paper. the first step uses a new iterative algorithm that resolves ambiguities and detects fast (exo-clutter) targets. the detection of slow (endo-clutter) targets is then performed by an adaptive detection scheme that uses a new covariance matrix estimation technique. Pulse trains with different characteristics are then associated for enhanced detection performance.

关键词： Doppler effect Clutter Covariance matrices Training data signal resolution Estimation Doppler radar

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Block-Division Based Ultra-wideband Fusion of Multiple Radar Bands for Resolution Enhanced Imagery 12

Block-Division Based Ultra-wideband Fusion of Multiple Radar...

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12th International conference on signal processing and Communication Systems (ICSPCS)

作者： Yuan, Saisai Chen, Chengzeng Xu, Xiaojian Beihang Univ Sch Elect & Informat Engn Beijing Peoples R China Beijing Inst Radio Measurement Beijing Peoples R China

ISBN: (纸本)9781538656020

Parametric model based ultra-wideband fusion of multiple radar band data is usually not directly applicable to radar image processing for large sized complex targets, due to the fact that the required model orders are extremely high. In this paper, a block-division based data fusion technique is proposed, which can be used for multiple radar band fusion over ultra-wide bandwidth and image resolution enhancement for complex targets. In image domain, a large sized complex target is first divided into small blocks for each radar band. Each block is then equivalent to a small sized simple target whose missing phase history data between two different radar bands can be interpolated based on parametric models derived from the measurement data of the corresponding bands. Resolution enhanced image can then be reconstructed by integrating all the image blocks generated from the multi-band fused data. Numerical examples are presented to demonstrate the usefulness of the proposed technique.

关键词： radar imaging ultra-wideband data fusion complex target block-division

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Exploring Quantization in Few-Shot Learning

Exploring Quantization in Few-Shot Learning

引用

Annual IEEE Northeast Workshop on Circuits and Systems (NEWCAS)

作者： Meiqi Wang Ruixin Xue Jun Lin Zhongfeng Wang School of Electronic Science and Engineering Nanjing University Nanjing China

ISBN: (数字)9781728170442

ISBN: (纸本)9781728170459

Training the neural networks on chip, which enables the local privacy data to be stored and processed at edge platforms, is earning vital importance with the explosive growth of Internet of things (IoT). Although the on-chip training has been widely investigated in previous arts, there are few works related to the on-chip learning of Few-Shot Learning (FSL), an emerging topic which explores effective learning with only a small number of samples. In this paper, we explore the effectiveness of quantization, a mainstream compression method that helps reduce the memory footprint and computational resource requirements of a full-precision neural network to enable the on-chip deployment of FSL. We first perform extensive experiments on quantization of three mainstream meta-learning-based FSL networks, MAML, Meta-SGD, and Reptile, for both training and testing stages. Experimental results show that the 16-bit quantized training and testing models can be achieved with negligible losses on MAML and Meta-SGD. then a comprehensive analysis is presented which demonstrates that a most favorable trade-off between accuracy, computational complexity, and model size can be achieved using the Meta-SGD model. this paves the way for the deployment of FSL system on the resource-constrained platforms.

关键词： Computational modeling Quantization (signal) Training Task analysis Analytical models Computational complexity Adaptation models

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Recognition and Classification of Rotorcraft by Micro-Doppler Signatures Using Deep Learning 18th

Recognition and Classification of Rotorcraft by Micro-Dopple...

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18th International conference on Computational Science (ICCS)

作者： Liu, Ying Liu, Jinyi Univ Chinese Acad Sci Sch Comp & Control Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

ISBN: (纸本)9783319936987;9783319936970

Detection and classification of rotorcraft targets are of great significance not only in civil fields but also in defense. However, up to now, it is still difficult for the traditional radar signal processing methods to detect and distinguish rotorcraft targets from various types of moving objects. Moreover, it is even more challenging to classify different types of helicopters. As the development of high-precision radar, classification of moving targets by micro-Doppler features has become a promising research topic in the modern signal processing field. In this paper, we propose to use the deep convolutional neural networks (DCNNs) in rotorcraft detection and helicopter classification based on Doppler radar signals. We apply DCNN directly to raw micro-Doppler spectrograms for rotorcraft detection and classification. the proposed DCNNs can learn the features automatically from the micro-Doppler signals without introducing any domain background knowledge. Simulated data are used in the experiments. the experimental results show that the proposed DCNNs achieve superior accuracy in rotorcraft detection and superior accuracy in helicopter classification, outperforming the traditional radar signal processing methods.

关键词： Convolutional neural network Deep learning Target detection Classification Micro-Doppler

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An Infrared small Target Detection Method Based on Block Compressed Sensing 13th

An Infrared Small Target Detection Method Based on Block Com...

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13th International conference on Intelligent Information Hiding and Multimedia signal processing (IIH-MSP)

作者： Yang, Jingli Cui, Zheng Jiang, Shouda Harbin Inst Technol Dept Automat Test & Control Xidazhistr 92 Harbin 150080 Heilongjiang Peoples R China

ISBN: (纸本)9783319638560;9783319638553

Aiming at improving the real-time performance of infrared weapon systems, a method based on block compressed sensing is proposed to detect infrared small target, which is also easy to be implemented with hardware. the proposed method can detect and locate small infrared targets by classifying compressed results of blocks. In addition, in order to solve the low detection accuracy caused by using uniform block, the proposed method uses overlapping blocks to reduce the maximum distance between the center of the test sample block and that of the target. Experiments show that the proposed method can effectively improve the detection accuracy of infrared small targets.

关键词： Compressed sensing Infrared target detection Overlapping blocks

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Long-term testing of acoustic system for tracking low-flying aircraft 10

Long-term testing of acoustic system for tracking low-flying...

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10th International conference on Knowledge and Systems Engineering (KSE)

作者： Sedunov, Alexander Salloum, Hady Sutin, Alexander Sedunov, Nikolay Stevens Inst Technol STAR Ctr Hoboken NJ 07030 USA

ISBN: (纸本)9781538661130

Stevens Institute of Technology conducted a long-term test of an acoustic system designed to track low-flying small aircraft in remote locations. the system consists of 4 nodes located between 1 and 4 km apart in a mountainous terrain. Each node is comprised of a pyramid-shaped volumetric cluster of 5 microphones, an embedded computer, and a pan-tilt-zoom camera steered to detected targets in real time. A communication device was used to transfer data to a centralized location. Each node estimates the direction of arrival toward the sound sources and sends it along to a central processing computer. the central computer combines the data from all nodes to generate tracks and classify targets. the duration and the scale of the deployment allowed to identify and solve many problems, including the effects of propagation delays between nodes on cooperative localization and tracking, the seasonal changes in environmental noise, persistent and transient noise sources, and the diversity of targets of opportunity and their signatures. the propagation delay effects led to the development of separate trackers for review of target trajectories and for immediate action such as automatically steering the camera.

关键词： signal processing passive acoustics low-flying aircraft

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Predicting Group Performance in Task-Based Interaction 18

Predicting Group Performance in Task-Based Interaction

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20th ACM International conference on Multimodal Interaction (ICMI)

作者： Murray, Gabriel Oertel, Catharine Univ Fraser Valley Abbotsford BC Canada Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9781450356923

We address the problem of automatically predicting group performance on a task, using multimodal features derived from the group conversation. these include acoustic features extracted from the speech signal, and linguistic features derived from the conversation transcripts. Because much work on social signal processing has focused on nonverbal features such as voice prosody and gestures, we explicitly investigate whether features of linguistic content are useful for predicting group performance. the conclusion is that the best-performing models utilize both linguistic and acoustic features, and that linguistic features alone can also yield good performance on this task. Because there is a relatively small amount of task data available, we present experimental approaches using domain adaptation and a simple data augmentation method, both of which yield drastic improvements in predictive performance, compared with a target-only model.

关键词： Group interaction task performance multimodal interaction meetings social signal processing data augmentation domain adaptation semi-supervised learning

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Borehole Radar data processing Based on Empirical Mode Decomposition 17

Borehole Radar Data Processing Based on Empirical Mode Decom...

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17th International conference on Ground Penetrating Radar, GPR 2018

作者： Ma, Chunguang Zhao, Qing Huo, Jianjian Xie, Longhao University of Electronic Science and Technology of China School of Resource and Environment Chengdu611731 China

ISBN: (纸本)9781538657775

Borehole radar (BHR) is a highly efficient geophysical exploration tool, however, the application is limited due to relatively small signal to noise ratio (SNR) of the testing data. Empirical Mode Decomposition (EMD) is a data processing method, which takes the particular advantage in analyzing the nonlinear and non-stationary data. this paper introduces the fundamentals of EMD method and the complex signal analysis theory, and focuses on the sufficient conditions and specific decomposition steps for signal processing based on the EMD. then an actually radar profile, which was obtained by a BHR system in a limestone fracture zone, is used as an example to be analyzed in the following processes. Firstly, the original radar profile is preprocessed to avoid the mode confusion and noise interference to the radar echo. Secondly, the EMD method is used to process a single-channel radar data, and analyze the high-low frequency components of the radar signal. thirdly, the various intrinsic mode of the preprocessing radar profile is also obtained by the EMD. Finally, we reconstruct the intrinsic mode profile, which contains the effective formation information, calculate the complex signals of the reconstruction radar profile using the Hilbert transform (HT), extract the three characteristic parameters such as instantaneous amplitude, instantaneous phase and instantaneous frequency, and draw the separate instantaneous parameters profiles. the results demonstrate that the EMD method has the strong adaptability in processing the BHR signal under a low SNR and the capability to separate the high-low components of the radar echo effectively. the three-instantaneous information produced by the EMD method and complex signal analysis technique, can highlight the abnormal geological feature, provide the theoretical basis and mutual authentication information for evaluating the original radar profile, and avoid the deviation relying solely on a time-distance profile. © 2018 IEEE.

关键词： Empirical mode decomposition

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