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Enterprise crowdsourcing platform for geospatial image analysis

Enterprise crowdsourcing platform for geospatial image analy...

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IEEE signal processing and Communications applications (SIU)

作者： Fatih Kahraman Cengiz Hüroğlu Mümin İmamoğlu Büşra Y. Özcan Muhammed İ. Kalkan Muhammet A. Hocaoğlu Ergin Öztürk Binnur Kurt TÜBİTAK BİLGEM BTE Kocaeli Türkiye Omega Eğitim ve Danışmanlık İstanbul Türkiye

Although today's computing systems present powerful solutions to process big data with the help of recent advances in cloud computing technologies, many problems remain unsolved due to lack of acceptable algorithms. For instance, searching for a lost plane or damage assessment after earthquake on a high-resolution remote sensing satellite image are unsolved problems. In recent years, in order to solve such unsolved problems, a group of expert and nonexpert people called crowd is utilized. An expert in the group is not expected to solve whole problem. Instead, geospatial image is partitioned in space and each expert in the pool studies the partition assigned to him/her. The solution to the original problem is obtained by merging the partial solutions. There are two open issues: i) How to partition the space and how to distribute the partitions to the crowd ii) How to merge the partial solutions. In this study, we devise several algorithms to address these issues, introduce our web-based platform, and crowd-sourcing implementation.

关键词： Crowdsourcing Geospatial analysis Remote sensing Java image analysis Big data

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A Multiplier-Less Level-3 Haar wavelet Transform Approximation Requiring Five Additions Only

A Multiplier-Less Level-3 Haar Wavelet Transform Approximati...

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Proceedings of the IEEE Dallas Circuits and Systems Workshop (DCAS)

作者： Morgana M. A. Da Rosa Guilherme Paim Henrique B. Seidel Sé rgio Almeida Eduardo A. C. Da Costa Sergio Bampi Graduate Program on Electronic Engineering and Computing Universidade Cat&#x00F3 lica de Pelotas (UCPel) Brazil Graduate Program in Microelectronics (PGMICRO) Federal University of Rio Grande do Sul (UFRGS) Brazil

ISBN: (数字)9781665498852

ISBN: (纸本)9781665498869

Approximate transform kernels have emerged as an alternative design to reduce the hardware complexity of digital signal processors by exploiting the intrinsic error resilience of many embedded consumer electronics applications such as signal, image and video processing, and computer vision. The approximate level-4 Haar wavelet transform (AxHWT-4) is the state-of-the-art approximate transform for ECG (electrocardiogram) signal processing and reduces more than nine times the energy consumption compared to the exact transform. In this work, we propose an approximate level-3 Haar wavelet transform (AxHWT-3) for increasing the hardware design efficiency with related to the AxHWT-4 while obtaining higher R-peak detection accuracy in the electrocardiogram signal. Our AxHWT-3 approximation is performed in the Haar matrix kernel to eliminate its multiplications which in the hardware design results in savings of energy consumption and VLSI hardware area. Our AxHWT3 proposal achieves an R-peak detection accuracy of more than 99% and reduces the energy consumption by 15.98 times and the VLSI area by 12.64 times compared to the exact solution (i.e., the original level-3 Haar). In addition, the proposed AxHWT3 outperforms the state-of-the-art AxHWT-4 with a savings of about 32% in power dissipation and 19.5% in VLSI area.

关键词： wavelet transforms Energy consumption Computer architecture Very large scale integration Electrocardiography signal processing Hardware

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An efficient VLSI architecture for 2-D dual-mode SMDWT

An efficient VLSI architecture for 2-D dual-mode SMDWT

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IEEE International conference on Networking, Sensing and Control

作者： Chih-Hsien Hsia Jen-Shiun Chiang Shih-Hao Chang National Taiwan University of Science and Technology Taipei TW Tamkang University Taipei TW National Taiwan University Taipei TW

ISBN: (纸本)9781467351980

In this paper, we propose a highly efficient VLSI architecture for 2-D dual-mode (supporting 5/3 and 9/7 lifting-based) Symmetric Mask-based Discrete wavelet Transform (SMDWT) to improve the critical issue of the 2-D Lifting-based Discrete wavelet Transform (LDWT), and then obtains the benefit of low-latency reduced complexity, and low transpose memory. The SMDWT also has the advantages of reduced complexity, regular signal coding, short critical path, reduced latency time, and independent subband coding processing. The transpose memory requirement of the N×N is 9N. The architecture is based on the parallel and folding scheme processing to achieve higher hardware utilization ratio and reduce the silicon area. It is suitable for Very Large Scale Integration (VLSI) implementation and can be applied to real-time operating of computer vision applications.

关键词： Discrete wavelet transforms CMOS integrated circuits CMOS technology Very large scale integration Irrigation Biomedical imaging image recognition

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Improving in Spectral Resolution for Closely-Spaced Targets Based on MUSIC Algorithm

Improving in Spectral Resolution for Closely-Spaced Targets ...

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International conference on Artificial Intelligence and image processing(ICAiiP’2012);International conference on Innovations in Electrical and Electronics Engineering(ICIEE’2012);International conference on Mechanical,Automobile and Biodiesel Engineering(ICMABE’2012);International conference on History,Literature and Management(ICHLM’2012);International conference on Agriculture,Chemical and Environmental Sciences(ICACES’2012);International conference on Biological and Medical applications(ICBMA’2012)

作者： M.Pourahmadi Department of Electrical Engineering Yazd branchIslamic Azad University

The multiple signal classification (MUSIC) method is severely based on the number of singular values of a multistatic response matrix (or signal subspace rank).In this paper we show that in the presence of multiple scattering between the targets,some singular values will greatly decrease and combine with the noise ones,so the performance of the most popular MUSIC methods degrades and even fails to estimate closely spaced target locations because of a reduction in signal *** such a case,we introduce a method to eliminate ambiguity in determining the number of scatterers employing a pre-processing step by two methods:discrete stationary wavelet transform (DSWT) and empirical mode decomposition (EMD).The DSWT is developed on the basis of features of the reflection coefficient between the *** application of this algorithm to measurement data,shows its superiority over the *** results indicate that MUSIC+DSWT yields accurate estimation of the target locations,even when there exist considerable noise and multiple scattering in the received signal.

关键词： MUSIC algorithm,wavelet transform,spectral resolution,microwave imaging.

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wavelet coding of polygonal mesh data based on triangular lattice structure with expanded nodes

Wavelet coding of polygonal mesh data based on triangular la...

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IEEE International conference on image processing

作者： M. Tsunoda T. Terasaki K. Fukuda A. Kawanaka Department of Electrical and Electronics Engineering Faculty of Science and Technology Sophia University Japan

Efficient representations of a 3D object shape and its textural data have attracted wide attention for the transmission of computer graphics data and for interactive design in manufacturing. Polygonal mesh data, which consist of connectivity information, geometric data and textural data are often used for representing a 3D object in many applications. We propose a compression method for polygonal mesh data by structuring its geometric and textural data on a triangular lattice with expanded nodes. The structuralized geometric and textural data are compressed using a 2D signal coding method. Experimental results following from the application of the new method to polygonal mesh data show that the proposed scheme performs efficiently.

关键词： Lattices Shape Computer graphics Surface texture Geometry Tree graphs Data engineering Design engineering Computer aided manufacturing Application software

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A Decentralized GPS Positioning Consensus Based on Secure image Watermarking for UAV Networks

A Decentralized GPS Positioning Consensus Based on Secure Im...

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Innovative and Intelligent Information Technologies (IC3IT), International conference on

作者： Brahim Ferik Lakhdar Laimeche Abdallah Meraoumia Abdelkader Laouid Ahcene Bounceur Mostefa Kara Laboratory of Mathematics Informatics and Systems (LAMIS) University of Larbi Tebessi Tebessa Algeria Artificial Intelligence and its Applications Laboratory (LIAP) University of El Oued El Oued Algeria Information Systems Department University of Sharjah Sharjah United Arab Emirates Information & Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (数字)9798331541064

ISBN: (纸本)9798331541071

The popularity of Unmanned Aerial Vehicles (UAVs) in recent years has accelerated the demand for robust image security to secure communication and authenticate images or videos from usage by unauthorized parties who may seek access to sensitive information. Having unauthorized and deceptive communication, from interference in signals to data manipulation, will be a challenge. This paper presents a novel approach to integrating GPS positioning with secure image capture and decentralized coordination for UAVs. Using EI-Gamal encryption and Magic Number Fragmentation, GPS coordinates are securely encrypted, and GPS data into captured images through a hybrid Discrete wavelet Transform-Discrete Cosine Transform (DWT-DCT) watermarking technique. A consensus mechanism ensures decentralized and privacy-preserving computation of secure meeting points for UAV s. Moreover, the system maintains high image quality, with superior PSNR and SSIM values, while demonstrating resilience against signal processing and geometric attacks. These findings affirm the method's ability to extract encrypted GPS coordinates reliably, enhancing UAV network security.

关键词： Watermarking Transforms signal processing Autonomous aerial vehicles Discrete wavelet transforms Encryption Consensus protocol Discrete cosine transforms Global Positioning System Videos

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Regularized Shearlet Network for face recognition using single sample per person

Regularized Shearlet Network for face recognition using sing...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Mohamed Anouar Borgi Demetrio Labate Maher El'Arbi Chokri Ben Amar Research Groups on Intelligent Machines University of Sfax Sfax Tunisia Department of Mathematics University of Houston Houston TX USA

This paper presents an improved approach to face recognition, called Regularized Shearlet Network (RSN), which takes advantage of the sparse representation properties of shearlets in biometric applications. One of the novelties of our approach is that directional and anisotropic geometric features are efficiently extracted and used for the recognition step. In addition, our approach includes a module based on regularization theory (RSN) to control the trade-off between the fidelity to the data (gallery) and the smoothness of the solution (probe). In this work, we address the challenging problem of the single training sample per subject (STSS). We compare our new algorithm against different state-of-the-arts method using several facial databases, such as AR, FERET, FRGC, FEI, CK. Our tests show that the RSN approach is very competitive and outperforms several standard face recognition methods.

关键词： Face Face recognition Feature extraction Databases image coding wavelet transforms

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Classifier-Based Feature Fusion for Texture Discrimination

Classifier-Based Feature Fusion for Texture Discrimination

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International conference on Information Engineering and Computer Science (ICIECS)

作者： Jianglin Ma Zhouwei Zhang Chengyi Wang Zhong Chen Remote Sensing Image Processing Laboratory Institute of Remote Sensing Applications Chinese Academy and Sciences Beijing China Institute for Pattern Recognition & Artificial Intelligence Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781424449934

A classifier-based method to select and fuse grey level co-occurrence matrix (GLCM), Gaussian Markov random field (GMRF) and discrete wavelet transform (DWT) features to improve texture discrimination is presented. Feature selection via wrapper approaches is applied to find the optimal combination of texture features. The fused features have obtained higher discrimination accuracy compared with individual features. The curse of dimensionality is shown to affect discrimination accuracy, and feature selection and reduction helps obtain higher accuracy. Overall our proposed classifier-based method obtains the highest discrimination accuracy compared to other feature reduction methods such as principal component analysis (PCA) and linear discriminant analysis (LDA). Meanwhile GLCM features are found to produce higher discrimination accuracy than GMRF and DWT, and LDA is demonstrated to obtain higher discrimination accuracy than PCA.

关键词： Discrete wavelet transforms Linear discriminant analysis signal processing algorithms Feature extraction Remote sensing Markov random fields Principal component analysis Classification algorithms Fuses wavelet analysis

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Duality properties for the variants of the Discrete W Transform (DWT)

Duality properties for the variants of the Discrete W Transf...

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IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)

作者： B. N. Madhukar S. H. Bharathi School of Electronics and Communication Engineering Reva University Bengaluru India

ISBN: (数字)9781728137353

ISBN: (纸本)9781728137360

This paper gives the derivations of new duality properties for the four variants of the DWT. DWT and its variants are finite durational discrete transforms that are used in a host of applications in the fields of Digital signal processing, Digital image processing, Communication Systems, among a host of others. The Duality properties can be applied for the computation of the temporal signal from its frequency-domain counterpart or contra if and only if existing pair of DWT, including their counterparts, are known. The utility of the Duality properties lie in the abatement of computational complexity and disbursement intrinsically.

关键词： Discrete wavelet transforms Indexes Discrete transforms Discrete Fourier transforms Kernel

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Hand Movement Recognition Using Cross Spectrum image Analysis of EMG signals-A Deep Learning Approach

Hand Movement Recognition Using Cross Spectrum Image Analysi...

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Sustainable Technology and Engineering applications (NCETSTEA), 2020 National conference on Emerging Trends on

作者： Sayanjit Singha Roy Kaniska Samanta Soumya Chatterjee Sayantan Dey Arnab Nandi Ronjoy Bhowmik Shoumosree Mondal Electrical Engineering Department Techno India University Kolkata India Electrical Engineering Department St. Thomas’ College of Engineering and Technology Kolkata India

ISBN: (数字)9781728143620

ISBN: (纸本)9781728143637

Accurate recognition of hand movements using electromyography (EMG) signals is important to develop robust human-machine interface system for upper limb prosthetic control. For accurate detection and classification of different movements, proper feature selection from EMG signals is necessary, failure of which may often lead to incorrect results. To this end, this article presents an image processing aided deep learning approach for detection and classification of hand movement EMG signals. In this study, EMG signals for both left hand and right hand movements were procured from an existing database. A reference signal for each category was chosen as reference and cross spectrum of the rest of the EMG signals was done with the reference signal. The resultant cross-wavelet spectrum images of both classes of EMG signals were fed to a pre-trained convolution neural network (CNN) for the purpose of detection and classification of hand movements. It has been observed that the proposed method returned an average classification accuracy of 97.6% in segregating different categories of EMG signals. Besides, the performance of the proposed method analyzed using different CNN architectures was also found to be robust. The proposed method can be implemented for real-time detection of hand movements.

关键词： Convolution neural network classification cross-wavelet transform electromyography and hand movement hand movement Taxonomy Electromyogram cross spectrum wavelet transforms signals Reference signals Movements

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