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检索条件"任意字段=IEEE Symposium on Computational Intelligence in Image and Signal Processing"

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Path Planning for an Omnidirectional Mobile Robot Based on Modified A * Algorithm with Energy Model

Path Planning for an Omnidirectional Mobile Robot Based on M...

引用

symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Cong Liu Xiaobin Xu Xinhong Li Zhijie Pan Kai Hu You Shu College of Mechanical & Electrical Engineering Hohai University Changzhou China College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Changzhou Changgong Electronic Technology Co. Ltd Changzhou China Jiangsu Lanling Chemical Group Co. Ltd Changzhou China

ISBN: (纸本)9781665426565

A path planning framework for an omnidirectional mobile robot based on modified A * algorithm with energy model is proposed. The kinematic model of the omnidirectional mobile robot is established, and the energy consumption model of the omnidirectional mobile robot during motion is established based on the detailed derivation of formulas and used to improve the A* algorithm. The derived results show that the extra power consumed in steering movement is only related to the steering angle.

关键词： Energy consumption Friction Conferences computational modeling Wheels Kinematics Path planning

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Real-time volumetric imaging system for CMUT arrays

Real-time volumetric imaging system for CMUT arrays

引用

ieee symposium (IUS) Ultrasonics

作者： Jung Woo Choe Ömer Oralkan Amin Nikoozadeh Anshuman Bhuyan Byung Chul Lee Mustafa Gence Butrus T. Khuri-Yakub Edward L. Ginzton Laboratory University of Stanford Stanford CA USA Stanford University Stanford CA US

We designed and implemented a flexible real-time volumetric ultrasound imaging system for capacitive micromachined ultrasonic transducer (CMUT) arrays, consisting of an ultrasound data acquisition system, an FPGA board, and a host PC. The system works with arbitrary-shaped CMUT arrays and non-standard beamforming methods, as well as with regular-shaped CMUT arrays and conventional beamforming methods. In this paper, we present the system design and real-time imaging results obtained using this system with a ring array, a rectangular array, and a linear array. In synthetic phased array (SPA) imaging with a 64-element ring array, we could display 3 image planes with a total of about 70,000 pixels in real time, at a frame rate of 9 frames per second (fps) which was limited by the computational load on the CPU required for synthetic beamforming. On the other hand, the frame rate in classic phased array (CPA) imaging is limited by the data transfer time. In CPA imaging with a 16×16-element rectangular array, a frame rate of 5.4 fps was achieved for 1,250 acquisitions per frame and a 2.5-cm imaging depth. The frame rate can be increased by reducing the number of pixels processed in SPA, or by reducing the number of beams received in CPA, at the expense of degraded image quality or reduced field of view.

关键词： Imaging Real time systems Array signal processing Ultrasonic imaging Apertures Geometry Ultrasonic transducer arrays

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An Efficient Method for Ground Maneuvering Target Refocusing Based on DPT-MFP

An Efficient Method for Ground Maneuvering Target Refocusing...

引用

ieee International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Mingming Tian Guisheng Liao Shengqi Zhu XiongPeng He Yongjun Liu Yunpeng Li National Key Lab of Radar Signal Processing Xidian University Xi'an China School of Artificial Intelligence Xidian University Xi'an China

ISBN: (数字)9781728190457

ISBN: (纸本)9781728190464

Ground maneuvering targets may be smeared in a synthetic aperture radar (SAR) image due to the Doppler ambiguity, high-order range migration (RM) and Doppler frequency migration (DFM) caused by unknown motion parameters. To address these problems, an efficient method based on discrete polynomial-phase transform (DPT) and matched filtering process (MFP) is developed to refocus the ground maneuvering targets in this paper. Firstly, the DPT is applied to eliminate the quadratic RM, cubic RM and quadratic DFM simultaneously. Then, the matched filtering functions are constructed to remove the residual linear RM and linear DFM. At last, the target energy is well focused by performing the azimuth fast Fourier transform (FFT). The proposed algorithm has high computational efficiency and superior performance of target detection and motion parameters estimation under high signal to noise (SNR) environment. The numerical results prove the effectiveness of the proposed method.

关键词： Parameter estimation Filtering Transforms Radar polarimetry Doppler effect Synthetic aperture radar signal to noise ratio

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Fast Grouped High-order SVD Method for Poisson Noise Removal in Video Sequential images

Fast Grouped High-order SVD Method for Poisson Noise Removal...

引用

symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Jiajun Fu Jun Zhang School of Science Nanjing University of Science and Technology Nanjing P.R. China

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

High-order singular value decomposition (HOSVD) based low-rank tensor approximation has been successfully applied to high-dimensional data with multi-linear structures such as video sequential images and hyperspectral images. In some special imaging environments such as night vision surveillance, video images are usually seriously polluted by Poisson noise, but most current low-rank tensor approximation models only consider Gaussian noise. Moreover, due to the computational limitation of traditional SVD, HOSVD usually has low computational efficiency. In this paper, a low-rank and sparse regularization based tensor approximation model is proposed for Poisson noise removal in video sequential images. Considering diversity of image components in standard tensor unfoldings, we introduce a new multi-group nuclear norm based low-rank tensor prior in this new model which can be computed by grouped SVD. Using the technique of alternating direction method of multipliers(ADMM), we develop a fast parallel algorithm to solve the new model. Numerical results demonstrate our method is 9~10 times faster than traditional HOSVD method and can achieve similar or better results.

关键词： Tensors computational modeling Surveillance Numerical models computational efficiency Standards Singular value decomposition

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Compressed sensing subtracted rotational angiography with multiple sparse penalty

Compressed sensing subtracted rotational angiography with mu...

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ieee International symposium on Biomedical Imaging

作者： H. Langet C. Riddell Y. Trousset A. Tenenhaus E. Lahalle G. Fleury N. Paragios Department of Signal Processing and Electronic Systems Supélec Gif-sur-Yvette France Center for Visual Computing Ecole Centrale Paris Chatenay-Malabry France GE Healthcare Systems Buc France Center for Visual Computing Ecole des Ponts ParisTech Champs-sur-Marne France GALEN team INRIA Saclay île-de-France Orsay France

Digital Subtraction Rotational Angiography (DSRA) is a clinical protocol that allows three-dimensional (3D) visualization of vasculature during minimally invasive procedures. C-arm systems that are used to generate 3D reconstructions in interventional radiology have limited sampling rate and thus, contrast resolution. To address this particular subsampling problem, we propose a novel iterative reconstruction algorithm based on compressed sensing. To this purpose, we exploit both spatial and temporal sparsity of DSRA. For computational efficiency, we use a proximal implementation that accommodates multiple ℓ 1 -penalties. Experiments on both simulated and clinical data confirm the relevance of our strategy for reducing subsampling streak artifacts.

关键词： image reconstruction Standards Compressed sensing Angiography Protocols Bones Minimization

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4.5 BioAIP: A Reconfigurable Biomedical AI Processor with Adaptive Learning for Versatile Intelligent Health Monitoring

4.5 BioAIP: A Reconfigurable Biomedical AI Processor with Ad...

引用

ieee International Conference on Solid-State Circuits (ISSCC)

作者： Jiahao Liu Zhen Zhu Yong Zhou Ning Wang Guanghai Dai Qingsong Liu Jianbiao Xiao Yuxiang Xie Zirui Zhong Hongduo Liu Liang Chang Jun Zhou University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9781728195490

ISBN: (纸本)9781728195506

Intelligent health monitoring devices automatically detect abnormalities in users' biomedical signals (e.g. arrhythmia from an ECG signal or a seizure from an EEG signal) through signal classification. Compared to conventional machine learning methods, neural-network-based AI classification methods are promising in achieving higher classification accuracy, but with significantly increased computational complexity, posing challenges to real-time performance and low power consumption. AI processors have been designed to accelerate neural networks for general AI applications such as image and voice recognition. They are not suitable for biomedical AI processing, which requires a combination of biomedical and AI processing hardware. In addition, the design redundancy for general AI applications results in large power consumption making it unsuitable for ultra-low-power health monitoring devices. There are also some biomedical AI processors such as ECG/EEG/EMG AI processors. However, they are customized for specific algorithms and tasks, prohibiting algorithm upgrades, limiting their applicability. In addition, prior designs lack adaptive learning to address the patient-to-patient variation issue. In this work, the BioAlP is proposed - a reconfigurable biomedical Al processor with adaptive learning. It has the following key features: 1) A reconfigurable biomedical Al processing architecture with reconfigurable neural network and biomedical processing engines to support versatile biomedical Al processing. 2) An event-driven biomedical Al processing architecture and approximate data compression technique to reduce power consumption. 3) An Al-based adaptive-learning architecture to address patient-to-patient variation. 4) A reconfigurable FIR engine reusing the neural-network engine to reduce the hardware overhead.

关键词： Adaptive learning Program processors Power demand Speech recognition Artificial intelligence Task analysis Monitoring

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On-Demand Earth System Data Cubes

On-Demand Earth System Data Cubes

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ieee International symposium on Geoscience and Remote Sensing (IGARSS)

作者： David Montero César Aybar Chaonan Ji Guido Kraemer Maximilian Söchting Khalil Teber Miguel D. Mahecha Remote Sensing Centre for Earth System Research (RSC4Earth) Leipzig University Institute of Earth System Science & Remote Sensing Leipzig University German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Image Processing Laboratory Universitat de València Image and Signal Processing Group Leipzig University Department of Remote Sensing Helmholtz Centre for Environmental Research (UFZ)

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Advancements in Earth system science have seen a surge in diverse datasets. Earth System Data Cubes (ESDCs) have been introduced to efficiently handle this influx of high-dimensional data. ESDCs offer a structured, intuitive framework for data analysis, organising information within spatio-temporal grids. The structured nature of ESDCs unlocks significant opportunities for Artificial intelligence (AI) applications. By providing well-organised data, ESDCs are ideally suited for a wide range of sophisticated AI-driven tasks. An automated framework for creating AI-focused ESDCs with minimal user input could significantly accelerate the generation of task-specific training data. Here we introduce cubo, an open-source Python tool designed for easy generation of AI-focused ESDCs. Utilising collections in SpatioTemporal Asset Catalogs (STAC) that are stored as Cloud Optimised GeoTIFFs (COGs), cubo efficiently creates ESDCs, requiring only central coordinates, spatial resolution, edge size, and time range.

关键词： Earth Instruments Training data Geoscience and remote sensing Spatiotemporal phenomena Sensors Task analysis

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Application-specific compression of large MD data preserving physical characteristics

Application-specific compression of large MD data preserving...

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ieee symposium on Large Data Analysis and Visualization (LDAV)

作者： Patrick Gralka Sebastian Grottel Guido Reina Thomas Ertl Department of Mathematics Instituto Politecnico Nacional Mexico Department of Physics Universidad Autonoma Metropolitana Iztapalapa Mexico Department of Physics Instituto Politecnico Nacional Mexico

ISBN: (纸本)9781479924363

Application areas like physics or thermodynamics often require simulations of very large data sets, up to the order of 10 12 particles or even larger, to obtain results relevant for realistic industrial processes. Persisting such data is too costly, prohibiting interactive visual analysis in a classical post-processing fashion. Thus, analysis is restricted to statistical aggregation or visual in-situ exploration, both requiring an inkling of the results beforehand. We alleviate this issue by applying an application-optimized lossy compression. Reducing the size while at the same time preserving relevant physical characteristics of the data allows for accessibility on workstations and practical long-term storage. The compression is achieved by generating a density volume that is processed using wavelet decomposition, quantization and run-length encoding. Our reconstruction of particle data ensures the restoration of physically relevant properties. It employs a model based on stochastic distributions complemented by further adjustments. We evaluate the precision of the reconstruction for several data sets and a wide range of compression variants to show the effectiveness and user-adjustable trade-offs of the presented method.

关键词： Data models Quantization (signal) Wavelet transforms image reconstruction Springs Encoding computational modeling

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Research on Inertial Navigation Simulation Based on AirSim

Research on Inertial Navigation Simulation Based on AirSim

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symposium of image, signal processing, and Artificial Vision (STSIVA)

作者： Shanchuan Huang Xiaofeng He Jun Mao Lilian Zhang Yunxiao Lv College of Intelligence Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350363203

ISBN: (纸本)9798350363210

Simulating unmanned systems in a virtual environment has become a crucial approach for the development of navigation technologies. This paper presents a drone simulation platform based on AirSim. It analyzes the deficiencies of the system's built-in inertial navigation simulation sensors and designs a method to generate inertial navigation simulation data based on the true state values of the drone, through the inverse process of inertial navigation calculations. Finally, the algorithm is tested through simulation flight experiments, confirming the feasibility of the approach proposed in this paper.

关键词： Laser radar Simulation Noise Virtual environments Inertial navigation Sensor systems Sensors Gyroscopes Informatics Drones

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Part-level Action Parsing via a Pose-guided Coarse-to-Fine Framework

Part-level Action Parsing via a Pose-guided Coarse-to-Fine F...

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ieee International symposium on Circuits and Systems

作者： Xiaodong Chen Xinchen Liu Wu Liu Kun Liu Dong Wu Yongdong Zhang Tao Mei Universiy of Science nd Technoogy of Chin Hefei ChinJD.co Beijing ChinJD.co Beijing ChinJD.co Beijing ChinJD.co Beijing ChinUniversiy of Science nd Technoogy of Chin Hefei ChinJD.co Beijing Chin

ISBN: (数字)9781665484855

ISBN: (纸本)9781665484862

Action recognition from videos, i.e., classifying a video into one of the pre-defined action types, has been a popular topic in the communities of artificial intelligence, multimedia, and signal processing. However, existing methods usually consider an input video as a whole and learn models, e.g., Convolutional Neural Networks (CNNs), with coarse video-level class labels. These methods can only output an action class for the video, but cannot provide fine-grained and explainable cues to answer why the video shows a specific action. Therefore, researchers start to focus on a new task, Part-level Action Parsing (PAP), which aims to not only predict the video-level action but also recognize the frame-level fine-grained actions or interactions of body parts for each person in the video. To this end, we propose a coarse-to-fine framework for this challenging task. In particular, our framework first predicts the video-level class of the input video, then localizes the body parts and predicts the part-level action. Moreover, to balance the accuracy and computation in part-level action parsing, we propose to recognize the part-level actions by segment-level features. Furthermore, to overcome the ambiguity of body parts, we propose a pose-guided positional embedding method to accurately localize body parts. Through comprehensive experiments on a large-scale dataset, i.e., Kinetics-TPS, our framework achieves state-of-the-art performance and outperforms existing methods over 31.10% ROC score.

关键词： Circuits and systems Detectors Streaming media signal processing computational efficiency Convolutional neural networks Task analysis

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