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Information Science, parallel and distributed Systems (ISPDS), International Conference on

作者： Ya Zhou Ning Zhao Guimin Huang Nanxiao Deng Qingkai Guo Guangxi Key Laboratory of Image and Graphic Intelligent Processing School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

In sentence similarity research methods, sentence similarity is often calculated from semantic aspects, however, the influence of other features is ignored. For example, the influence of sentence syntactic structure and word order on sentence similarity is not considered. Therefore, This paper proposes a method to measure the degree of similarity between sentences based on a multi-feature fusion of semantics, syntactic similarity and word order similarity, respectively. For the aspect of semantic similarity, in this paper, we first preprocess the sentence and obtain the word vectors, and then obtains sentence vector features through neural networks, so as to calculate the sentence semantic similarity; Finally, for the aspect of word order similarity, word order is considered as the auxiliary feature information to calculate sentence similarity since word order has a smaller effect on sentence similarity compared to the above information. The experimental results show that the proposed model has an accuracy of 77.4 % and an F1 value of 84.3% on the MRPC dataset.

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Satellite image Classification using Convolutional Neural Network with Huber Loss Function for Land Cover Mapping in Forest Ecosystem

Satellite Image Classification using Convolutional Neural Ne...

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distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Ramy Riad Al-Fatlawy P. Raviprakash Gummagatta Yajaman Vybhavi Sripelli Jagadish S Meenakshi Sundaram Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Malla Reddy Engineering College for Women (UGC-Autonomous) Hyderabad India Department of CSE Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India School of Computer Science & Artificial Intelligence SR University Warangal India Department of CSE Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Satellite image classification for land cover involves determining high-resolution imagery to recognize and classify various types of land covers. This process assists in monitoring forest health, managing resources, and assessing biodiversity effectively. However, accurately classifying images for land cover is difficult because of diverse and complex nature of forest environments and varying image qualities. In this research, the Convolutional Neural Network with Huber Loss Function (CNN-HLF) is proposed to classify the satellite images accurately for landcover mapping in forest ecosystem. The CNN-HLF effectively manage the variability and complexity of forest environments which improves the performance of land cover. Initially, the Eurosat dataset is used to determine the CNN-HLF performance. Resizing and image sharpening is applied to adjust the size and enhance the clarity of features. DenseNet201 is employed for extracting features effectively from pre-processed images. The proposed CNN-HLF achieves a high accuracy of 99.87% using Eurosat dataset compared to existing methods like GoogleNet with pre-processing and Lightweight parallel CNN (LPCNN).

关键词： Training Accuracy Ecosystems Land surface Forestry Feature extraction Stability analysis Robustness Satellite images Convolutional neural networks

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1st International Conference on Advances in Signal processing and Communication Engineering, ICASPACE 2021

1st International Conference on Advances in Signal Processin...

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1st International Conference on Advances in Signal processing and Communication Engineering, ICASPACE 2021

ISBN: (纸本)9789811955495

The proceedings contain 44 papers. The special focus in this conference is on Advances in Signal processing and Communication Engineering. The topics include: Single-Precision Floating-Point Multiplier Design Using Quantum-Dot Cellular Automata with Power Dissipation Analysis;compression Techniques for Low Power Hardware Accelerator Design: Case Studies;sequence Set Design for Radar System;design of an All Digital Phase-Locked Loop Using Cordic Algorithm;analysis of Deep Learning Algorithms for image Denoising;an Extensive Survey on Assessment of Multicore Processors for Embedded Systems;image Segmentation Techniques and Optimization Algorithms for Lung Cancer Detection;handwritten to Text Document Converter;An Efficient Energy Aware for Reliable Route Discovery Using Energy with Movement Detection Technique in MANET;Lumped Circuit Modeling at Nanoscale (Part-ii: Coupling Between Two Nanospheres);design and Implementation of Imprecise Adders for Low-Power Applications;speech Processed Public Addressing System;an Approach Towards Data Privacy Issues in distributed Cyber Physical System;Classification of LPI Radar Signals Using Multilayer Perceptron (MLP) Neural Networks;a Systematic Review on Screening of Diabetic Retinopathy and Maculopathy Using Artificial Intelligence;Area Efficient and High-Throughput Radix-4 1024-Point FFT Processor for DSP Applications;air Quality Monitoring System Based on Artificial Intelligence;an Improved Technique for Identification of Forgery image Detection Using Clustering Method;EEG Signal Analysis During Stroop Task for Checking the Effect of Sleep Deprivation;UWB Localization Procedures with Range Control methods—A Review;deep Learning Model for Multiclass Classification of Diabetic Retinal Fundus images Using Gradient Descent Optimization;multilevel Authentication to Wireless Sensor Networks Against Malicious Attacks Using Butterfly Method.

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Intelligent image processing based on VPX architecture 8

Intelligent image processing based on VPX architecture

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8th Symposium on Novel Photoelectronic Detection Technology and Applications

作者： Tao, Xin Ou, Weilin He, Wenbo Huazhong Institute of Electro-Optics Wuhan National Laboratory for Optoelectronics Wuhan430074 China

ISBN: (数字)9781510653122

ISBN: (纸本)9781510653115

Multiple bands cameras with high pixel resolution and fast frame rate bring challenges for real-time signal process which is important in monitoring and alerting application. Especially, the deep neural network algorithm which has been applied in practical EO system, requires high parallel computation ability. Thus, the traditional signal processing method in imaging sensor cannot satisfy the novel demands for balancing the capability and consumption. The system based on VPX architecture and all optical signal transmission can provide a promising improvement in computation capability, transmission bandwidth and data transfer capability. In this paper, a novel signal processing system for intelligent computing based on VPX architecture is indicated. Based on the system structure and components indication, the main configuration design is also explained. Especially the thermal deisgn of the full system is explained in details, because the thermal effect and related methods are important in high speed conputation system. The main intelligent signal processing circuit is based on FPGA SOC processor. After introducing the hardware design and scheme, an optimized SSD algorithm is introduced for multi-scales target detection, and the method to applying these algorithms on FPGA chip for real-time compuation is explained. Finally, this method is verified in both visible and IR images, especially for the small target in IR images. The whole design is verified that it can provide a promising solution for high performance and distributed EO imaging system. © 2022 SPIE

关键词： Data transfer

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Detecting and analysing geomorphological structures in images of comet 67P/Churyumov-Gerasimenko using Fourier transform

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MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 2021年第3期503卷 3449-3459页

作者： Ruzicka, Birko-Katarina Schroeter, Matthias Pack, Andreas Boehnhardt, Hermann Max Planck Inst Sonnensyst Forsch Justus von Liebig Weg 3 D-37077 Gottingen Germany Georg August Univ Gottingen Geowissensch Zentrum Abt Isotopengeol Goldschmidtstr 1 D-37073 Gottingen Germany Max Planck Inst Dynam & Self Org D-37077 Gottingen Germany

We present a method for automatized detection and analysis of quasi-periodic lineament structures from images at pixel precision. The method exploits properties of the images' frequency domain found by using the Fourier transform. We developed this method with the goal of detecting lineament structures in an image of the Hathor cliff of comet 67P/Churyumov-Gerasimenko, which are caused by layerings and furrows in the nucleus material. Using our method, we determined the orientation and wavelength range of these structures. The detected layering edges have similar orientations and spatial separations of 9-20 m, and are ubiquitous throughout the image. We suggest that the layerings are a global feature of the comet nucleus that provide information about formation and evolution of comet 67P. The furrows are non-uniformly distributed throughout the image. Their orientation is broadly parallel to the direction of the local gravity vector at the Hathor cliff, with spacings similar to those of the layering structures. The furrows are interpreted as signatures of local down-slope movement of cliff material. We demonstrate that the developed method is broadly applicable to the detection and analysis of various kinds of quasi-periodic structures like geological layering, folding and faulting, and texture analysis in general. In order to facilitate the application of our method, this paper is accompanied by a demo program written in MATLAB.

关键词： methods: data analysis techniques: image processing comets: general comets: individual: 67P/Churyumov Gerasimenko

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Adorym: a multi-platform generic X-ray image reconstruction framework based on automatic differentiation

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OPTICS EXPRESS 2021年第7期29卷 10000-10035页

作者： Du, Ming Kandel, Saugat Deng, Junjing Huang, Xiaojing Demortiere, Arnaud Tuan Tu Nguyen Tucoulou, Remi De Andrade, Vincent Jin, Qiaoling Jacobsen, Chris Argonne Natl Lab Adv Photon Source Lemont IL 60439 USA Northwestern Univ Appl Phys Program Evanston IL 60208 USA Brookhaven Natl Lab Natl Synchrotron Light Source II Upton NY 11973 USA Univ Picardie Jules Verne Lab Reactivite & Chim Solides LRCS CNRS UMR 7314 Hub Energie 15 Rue Baudelocque F-80039 Amiens France European Synchrotron Radiat Facil 71 Ave Martyrs F-38000 Grenoble France Northwestern Univ Dept Phys & Astron Evanston IL 60208 USA Northwestern Univ Chem Life Proc Inst Evanston IL 60208 USA

We describe and demonstrate an optimization-based X-ray image reconstruction framework called Adorym. Our framework provides a generic forward model, allowing one code framework to be used for a wide range of imaging methods ranging from near-field holography to fly-scan ptychographic tomography. By using automatic differentiation for optimization, Adorym has the flexibility to refine experimental parameters including probe positions, multiple hologram alignment, and object tilts. It is written with strong support for parallel processing, allowing large datasets to be processed on high-performance computing systems. We demonstrate its use on several experimental datasets to show improved image quality through parameter refinement. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

关键词： Astronomical interferometry image quality image reconstruction Imaging techniques Three dimensional imaging X ray imaging

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image Inpainting using Automatic Structure Propagation with Auxiliary Line Construction

Image Inpainting using Automatic Structure Propagation with ...

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Summer Virtual Conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD-Summer), ACIS International

作者： Yuto Urano Irawati Nurmala Sari Weiwei Du Department of Information Science Kyoto Institute of Technology Kyoto Japan Information and Human Science Kyoto Institute of Technology Kyoto Japan

ISBN: (纸本)9798350396386

Existing image inpainting methods used traditional and deep learning methods to restore a large missing region in the damaged image. This often leads to color discrepancy and blurriness. Pre-processing of prior line detection by user assistance is usually employed to reduce the blurry of center region by segmenting the large region into more minor. However, it operates manually, which is time-consuming. This paper introduces a technique to generate two-line types: penetrator and interactor in constructing auxiliary lines as guidance. These lines assist structure propagation established automatically, while the remaining small regions are filled by texture propagation. Experiments on large regular masks demonstrate that our proposed approach generates higher-quality results than other methods.

关键词： Deep learning image segmentation image color analysis image restoration Artificial intelligence Software engineering

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Accelerating the performance of data analytics using network-centric processing 21

Accelerating the performance of data analytics using network...

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15th ACM International Conference on distributed and Event-Based Systems, DEBS 2021

作者： Boughzala, Bochra Koldehofe, Boris University of Groningen Bernoulli Institute Groningen Netherlands

ISBN: (纸本)9781450385558

distributed execution of real-time data analytics such as event stream processing is the key to scalability, performance and reliable detection of situation changes. Although real-time analytics is highly I/O centric, existing methods supporting the efficient execution of data analytics functions mostly rely on traditional compute models that are available in data centers, e.g., CPU or GPU based processing models, but treat the network mainly as a blackbox. However, with recent advance in software-defined networking (SDN) and the standardization of packet processing pipeline, data analytics functions can be offloaded to programmable switches and benefit from hardware acceleration in an easier and more flexible way than a decade ago. In this paper we focus on the potential of in-network processing to enhance the performance of the overall real-time data analytics application. We aim to contribute to an (i) understanding on how in-network processing can accelerate real-time data analytics and (ii) assess what models of in-network computing can accelerate which event processing functions considering the limitations of network models compared to traditional compute models. We motivate the potential and illustrate the research problems in the context of load balancing which is an important concept in the data-parallel execution of event processing systems. © 2021 ACM.

关键词： Data Analytics

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Instant water body variation detection via analysis on remote sensing imagery

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JOURNAL OF REAL-TIME image processing 2021年第5期18卷 1577-1590页

作者： Wu, Yirui Han, Pengfei Zheng, Zhan Hohai Univ Coll Comp & Informat Nanjing Peoples R China

Water resource is one of the most valuable natural resources for human beings, which requires to be monitored for careful protection. Inspired by a significant power of machine learning methods, researchers have successfully developed many applications to automatically perform identification on the water body via analyzing remote sensing images. Since a similar category of ground objects could show a large difference in spectral representation, researchers try to propose distinctive and effective features to offer redundant information for category classification. Moreover, large amount of high-resolution remote sensing images require analyzing algorithms to be parallel processed for instant feedback. Based on these requirements, we propose a novel water body variation detection via analysis on remote sensing images. Specifically, the proposed method firstly perform pixel-level classification to locate abnormal changes with thoughts of visual word patterns. Afterwards, the proposed method proposes block division method to construct parallel running version with Mapreduce structure. With high representational and parallel running abilities, the proposed method is capable to accurately detect variation areas on remote sensing images with instant feedback. Experiments on several self-collected datasets show the proposed method has achieved better efficiencies than comparative studies.

关键词： Real-time RS applications of detection and estimation theory Remote sensing images Water body variation detection method distributed processing

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Cuckoo Search Optimized Histogram Equalization for Low Contrast image Enhancement

Cuckoo Search Optimized Histogram Equalization for Low Contr...

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International Conference on parallel distributed and Grid Computing (PDGC)

作者： Niveditta Thakur Nafis Uddin Khan Sunil Datt Sharma Jaypee University of Information Technology Solan India

ISBN: (纸本)9781665454025

image enhancement is a process to improve the visual standard of image so as to extract spatial features of image. Histogram Equalization is method by which image can be improved for better perception and interpretation by different automated image processing system and human beings. Although, for low-contrast images (with lower dynamic range) traditional histogram equalization deteriorate quality of the output image by introducing washed-out form caused by too much brightness change. This paper presents histogram equalization based image enhancement method which is optimized using cuckoo search algorithm. Foremost purpose of the proposed method is to control over and under enhancement by calculating clip limits of the image histogram before performing histogram equalization. This method uses cuckoo search algorithm to automatically control the clip limit according to the image nature using improved fitness function. Effectiveness of the proposed method in comparison to other methods is analyzed using well known image quality metrics.

关键词： image quality Histograms Visualization Heuristic algorithms Feature extraction Noise measurement Indexes

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