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visual SLAM for Dynamic Environments Based on Static Key-Points Detection

Visual SLAM for Dynamic Environments Based on Static Key-Poi...

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virtual Reality (ICVR), IEEE International conference on

作者： Liangtao Zhong Xiaoqiang Zhang Lingyan Ran Yamin Han Hongyu Chu School of Information Engineering Southwest University of Science and Technology Mianyang China School of Computer Science Northwestern Polytechnical University Xi’an China College of Information Engineering Northwest A&F University Xianyang China

Recently, the technique of virtual reality has drawn great attention from both academics and industry fields. In many VR applications, the time-varying orientations and the positions of the VR headset are needed, where the technique of simultaneous localization and mapping (SLAM) can be applied. While great progress has been made in vision-based SLAM algorithms and systems in the recent two decades, the problem of visual SLAM in dynamic environments with moving objects is still unaddressed. In this paper, a static key-point detection approach is proposed based on optical flow feature clustering and potential moving region detection and is utilized for RGB-D SLAM in a dynamic environment. Firstly, considering the fact that there could be multiple moving objects in the scene, an optical flow feature is designed to cluster the multiple motion patterns in the scene. Secondly, an ultra-lightweight object detection network is utilized to detect potential moving regions in the scene, without using GPU resources. Finally, by utilizing the key-point clustering and potential moving regions detection, the static key-points are detected and inputted into the keypoint-based visual SLAM methods. The proposed approach is easy to insert as a pre-processing module in various key-point based visual SLAM systems. Extensive experimental results with qualitative and quantitative evaluations have demonstrated the performance with satisfactory accuracy of the proposed approach in various challenging dynamic scene scenarios.

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Circular Synthetic Aperture Radar for Near-field Terahertz Imaging

Circular Synthetic Aperture Radar for Near-field Terahertz I...

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International conference on Multimedia, Signal processing and Communication Technologies, (IMPACT)

作者： Mavis Gezimati Ghanshyam Singh Department of Electrical and Electronic Engineering Science Centre for Smart Information and Communication Systems University of Johannesburg Johannesburg South Africa

ISBN: (纸本)9781665476485

The potential of synthetic aperture radar (SAR) systems to yield high resolution images has led to its exploration in new applications. The conventional microwave radar imaging suffer limited range resolution in the sub-millimeter range and optical imaging methods are constrained by the diffraction limit. In the Terahertz (THz) spectrum, SAR systems are capable of operating in all weather, all time and penetrating through clouds, smoke, dust etc. to achieve better resolution images beyond the diffraction limit. In this paper, the different aspects of Multimedia computing are investigated in THz circular SAR as a new mode of radar imaging for near-field applications for example indoor environment. We have explored a new toolbox that enables the rapid development of near-field THz imaging SAR systems and generation of large near-field THz imaging scenarios that could be used for data driven applications. A circular SAR imaging system is presented for 2D (two-dimensional) imaging of targets in THz-SAR and the radar images are reconstructed using the Back Propagation and Polar Formatting algorithms. The SAR performance evaluation metrics are reported and scope for future work is discussed.

关键词： Terahertz wave imaging Image resolution Imaging Signal processing algorithms Multimedia computing Radar imaging Reconstruction algorithms

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Autostereoscopic Measurement System for Rapid 3D Inspection of Wire Bonding

Autostereoscopic Measurement System for Rapid 3D Inspection ...

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2021 International conference of optical Imaging and Measurement, ICOIM 2021

作者： Gao, Sanshan Cheung, Chi Fai Li, Da The Hong Kong Polytechnic University State Key Laboratory of Ultra-Precision Machining Technology Department of Industrial and Systems Engineering Hong Kong

ISBN: (纸本)9780738112121

The quality of wire bonding is an important factor in the manufacturing of various precision electronic components in semiconductor industry. The wire bonding defects, including high loop and low loop, determined by the bonding height information, inevitably affect the performance of the product. It is vital to measure height information quickly and accurately to reduce the failure rate in the manufacturing process. However, traditional measurement system often needs to move the target samples for a number of times. To address the key measurement problems, a three-dimensional (3D) optical measurement system is developed based on autostereoscopy technologies. A micro-lens array is used to divide the light emitted by the sample into different directions. The elemental images from different view angles contain original 3D information and is recorded by a high-resolution image sensor. On the basis of the multi-view stereo information contained in the image, the elemental images can be refocused into multi-focus images. The 3D information can be directly extracted from the multi-focus images through self-developed algorithms. Experiment results show that the system can complete the measurement in 2 seconds and reach a high measurement accuracy at a sub-micrometer level. This verifies the feasibility of the proposed measurement system which is efficient and accurate for 3D inspection of wire bonding. © 2021 IEEE.

关键词： Stereo image processing

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A Substation Safety Measure Ticket Generation Algorithm based on Neural Network and Inference Engine

A Substation Safety Measure Ticket Generation Algorithm base...

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Intelligent systems and Computational Networks (ICISCN), International conference on

作者： Yin Wu Huicai Zhao Xingfu Huang Liqiong Huang Yuyang Hu Guangxi Power Grid Power Dispatching and Control Center Nanning China Guangxi Power Grid Limited Liability Company Nanning Power Supply Bureau Nanning China Guangxi Power Grid Limited Liability Company Guilin Power Supply Bureau Guilin China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Intelligent substations use fiber optic as the information transmission medium, transforming the interconnection relationship between secondary devices into fiber optic connections. The corresponding secondary circuits and hardware platforms are replaced with virtual circuits and software templates, resulting in a significant change in maintenance work methods and higher requirements for operators' maintenance work. This article proposes an intelligent substation safety measure ticket generation algorithm based on multi-scale feature convolutional neural network (MSF-CNN) and inference machine. Firstly, a neural network model suitable for safety ticket generation was designed and trained. Combining the structural characteristics of DenseNet, multi-scale feature extraction and logical relationship modeling were achieved through multi-layer convolution, concatenation, and pooling processing. The model uses hyperbolic tangent function as the activation function to improve the accuracy of regression prediction. Secondly, a custom word vector and equipment association modeling method based on SCD file parsing are proposed for the electricity proprietary terms and multi item logical relationships involved in the safety measures ticket text, in order to improve data expression accuracy and model understanding ability. In addition, based on the functional correlation and input-output interaction characteristics of substation equipment, a three-layer neural network-based inference engine was constructed to generate maintenance safety measures for primary and secondary equipment. The experimental results showed that the MSF-CNN model performed the best in accuracy, recall, and F1 score, with an accuracy of 92.5%, recall of 90.8%, and F1 score of 91.6%.

关键词： optical fibers Substations Accuracy Neural networks Training data Data models Inference algorithms Safety Maintenance Engines

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TTC4MCP: Monocular Collision Prediction Based on Self-Supervised TTC Estimation

TTC4MCP: Monocular Collision Prediction Based on Self-Superv...

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IEEE/RSJ International conference on Intelligent Robots and systems (IROS)

作者： Changlin Li Yeqiang Qian Cong Sun Weihao Yan Chunxiang Wang Ming Yang Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Global Institute of Future Technology Shanghai Jiao Tong University Shanghai China University of Michigan-Shanghai Jiao Tong University Joint Institute ShanghaiJiao Tong University Shanghai China

vision-based collision prediction for autonomous driving is a challenging task due to the dynamic movement of vehicles and diverse types of obstacles. Most existing methods rely on object detection algorithms, which only predict predefined collision targets, such as vehicles and pedestrians, and cannot anticipate emergencies caused by unknown obstacles. To address this limitation, we propose a novel approach using pixel-wise time-to-collision (TTC) estimation for monocular collision prediction (TTC4MCP). Our approach predicts TTC and optical flow from monocular images and identifies potential collision areas using feature clustering and motion analysis. To overcome the challenge of training TTC estimation models without ground truth data in new scenes, we propose a self-supervised TTC training method, enabling collision prediction in a wider range of scenarios. TTC4MCP is evaluated on multiple road conditions and demonstrates promising results in terms of accuracy and robustness.

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Deep CNN with batch renormalization based channel estimation algorithms

Deep CNN with batch renormalization based channel estimation...

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2021 International conference on Signal Image processing and Communication, ICSIPC 2021

作者： An, Xu Dong Liu, Guang Hui Chen, Lei Yang, Meng Wu, Han China Telecommunication Technology Labs China Academy of Information and Communications Technology Beijing China

ISBN: (数字)9781510645417

ISBN: (纸本)9781510645400

In recent years, mobile communication technology is developing rapidly, and people's demand for wireless high-speed data communication is increasing day by day. In order to meet the needs of people, some key techniques to enhance the spectrum and energy efficiency are proposed, among which orthogonal frequency division multiplexing (OFDM) technology is a modulation method widely used in wireless broadband systems to combat frequency selective fading in wireless channels. However, the use of higher-order modulation makes the system complex, and more advanced channel estimation techniques are needed to recover the original signal more efficiently at the receiver side. In this paper, the channel transmission matrix is treated as a natural image processing and a deep CNN based denoising network is trained. The network used in this paper has the following advantages: (1) Improving the learning ability of the denoising network by increasing the width instead of the depth. (2) Using the null convolution to expand the perceptual field enables the network to extract more contextual information and reduce the computational cost. (3) Solving the mini-batch problem under hardware resource constrained conditions by Batch renormalization. Also it can accelerate the convergence of the network training. We simulate the OFDM based communication system, and the results prove that the method has excellent performance. © 2021 SPIE.

关键词： Frequency selective fading

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CROMA: remote sensing representations with contrastive radar-optical masked autoencoders 23

CROMA: remote sensing representations with contrastive radar...

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Proceedings of the 37th International conference on Neural information processing systems

作者： Anthony Fuller Koreen Millard James R. Green Department of Systems and Computer Engineering Carleton University Ottawa Canada Department of Geography and Environmental Studies Carleton University Ottawa Canada

A vital and rapidly growing application, remote sensing offers vast yet sparsely labeled, spatially aligned multimodal data; this makes self-supervised learning algorithms invaluable. We present CROMA: a framework that combines contrastive and reconstruction self-supervised objectives to learn rich unimodal and multimodal representations. Our method separately encodes masked-out multispectral optical and synthetic aperture radar samples—aligned in space and time—and performs cross-modal contrastive learning. Another encoder fuses these sensors, producing joint multimodal encodings that are used to predict the masked patches via a lightweight decoder. We show that these objectives are complementary when leveraged on spatially aligned multimodal data. We also introduce X- and 2D-ALiBi, which spatially biases our cross- and self-attention matrices. These strategies improve representations and allow our models to effectively extrapolate to images up to 17.6 × larger at test-time. CROMA outperforms the current SoTA multispectral model, evaluated on: four classification benchmarks— finetuning (avg.↑ 1.8%), linear (avg.↑ 2.4%) and nonlinear (avg.↑ 1.4%) probing, kNN classification (avg.↑ 3.5%), and K-means clustering (avg.↑ 8.4%); and three segmentation benchmarks (avg.↑ 6.4%). CROMA's rich, optionally multimodal representations can be widely leveraged across remote sensing applications.

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75th anniversary of Academician of the RAS victor Alexandrovich Soifer 6

75th anniversary of Academician of the RAS Victor Alexandrov...

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6th International conference on information Technology and Nanotechnology (IEEE ITNT)

作者： Krasnikov, Gennadiy Russian Acad Sci Dept Nanotechnol & Informat Technol Moscow Russia

ISBN: (纸本)9781728170411

As the Academician-Secretary of the Department for Nanotechnology and information Technology of the RAS, I talk on milestones of administrative and scientific work of President of S. P. Korolyov Samara National Research University, Research Director of the Image processing systems Institute of the Russian Academy of Sciences, and Academician of the RAS, victor Alexandrovich Soifer, an outstanding scientist in the field of diffractive computer optics and image processing. I am looking into his contribution into the development of diffractive optics & diffractive nanophotonics and systems for image analysis & understanding.

关键词： diffractive computer optics diffractive nanophotonics image analysis & understanding optical computations

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Locally-Adaptive Nonparametric Online Learning 34

Locally-Adaptive Nonparametric Online Learning

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34th conference on Neural information processing systems (NeurIPS)

作者： Kuzborskij, Ilja Cesa-Bianchi, Nicolo DeepMind London England Univ Milan Dept Comp Sci Milan Italy Univ Milan DSRC Milan Italy

ISBN: (纸本)9781713829546

One of the main strengths of online algorithms is their ability to adapt to arbitrary data sequences. This is especially important in nonparametric settings, where performance is measured against rich classes of comparator functions that are able to fit complex environments. Although such hard comparators and complex environments may exhibit local regularities, efficient algorithms, which can provably take advantage of these local patterns, are hardly known. We fill this gap by introducing efficient online algorithms (based on a single versatile master algorithm) each adapting to one of the following regularities: (i) local Lipschitzness of the competitor function, (ii) local metric dimension of the instance sequence, (iii) local performance of the predictor across different regions of the instance space. Extending previous approaches, we design algorithms that dynamically grow hierarchical "-nets on the instance space whose prunings correspond to different "locality profiles" for the problem at hand. Using a technique based on tree experts, we simultaneously and efficiently compete against all such prunings, and prove regret bounds each scaling with a quantity associated with a different type of local regularity. When competing against "simple" locality profiles, our technique delivers regret bounds that are significantly better than those proven using the previous approach. On the other hand, the time dependence of our bounds is not worse than that obtained by ignoring any local regularities.

关键词： Comparators (optical)

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7th International conference on Signal and information processing, Networking and Computers, ICSINC 2020

7th International Conference on Signal and Information Proce...

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7th International conference on Signal and information processing, Networking and Computers, ICSINC 2020

ISBN: (纸本)9789813341012

The proceedings contain 129 papers. The special focus in this conference is on Signal and information processing, Networking and Computers. The topics include: The introduction and prospect of extended range forecasting in 11~30 days at the national meteorological center in china;cross-screen tracking method based on user behavior data;a virtual optical network mapping algorithm based on machine learning;big data ecological platform architecture design and construction practice in telecommunication operators;an evaluation model of user lifetime value based on improved rfm and ahp method;sharing c-band frequency between satellite and terrestrial 5g communication systems;architecture and technology of multi-source heterogeneous data system for telecom operator;intelligent detection of network coverage problems based on the clustering algorithm;exploration and practice of China unicom multi-scene capability construction and open platform for large-scale heterogeneous data;poc testing analysis of big data products;the next generation identity platform for digital era based on blockchain;research on rulebase metamodel construction and management;kubernetes realizes the cloud & network cooperation based on hardware sdn controller;data visualization of 5g speed test platform using tableau;intelligent mobile communication network plan for 5g based on insight big data;research and application of precision marketing algorithms for etc credit card based on telecom big data;lunar observation research on low-orbit remote sensing satellites;soc design and implementation of can 2.0 protocol toward satellite applications;remote sensing satellite rapid response technology based on orbit maneuver;angular momentum management design and on orbit verification of geo navigation satellite;preface.

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