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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...

引用

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

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

引用

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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Supervised map ICA: applications to brain functional MRI

Supervised map ICA: applications to brain functional MRI

引用

International conference on Neural Information processing

作者： Y. Matsuyama R. Kawamura Department of Electrical Electronics and Computer Engineering Waseda University Tokyo Japan

This paper gives a method to control or organize itself an activation pattern of fMRI maps obtained by ICA (independent component analysis). The presented method uses an additional term to the convex divergence's gradient. The following merits are observed: (i) Prior knowledge can be effectively used so that obtained activation patterns properly reflect the task on the subject. (ii) Difficulty of finding the appropriate activation pattern due to the permutation can be avoided. Experiments on brain fMRI maps for visual cortices are tried and reported.

关键词： Independent component analysis Magnetic resonance imaging Application software Cost function Continuous wavelet transforms signal analysis Source separation Iterative algorithms Convergence Optimization methods

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Mixed DSP/FPGA implementation of an error-resilient image transmission system based on JPEG2000

Mixed DSP/FPGA implementation of an error-resilient image tr...

引用

Asilomar conference on signals, Systems & Computers

作者： M. Grangetto E. Magli M. Martina F. Vacca Dipartimento di Elettronica Politechnico di Torino Center for Wireless Multimedia Communications Torino Italy

ISBN: (纸本)078037147X

This paper describes a demonstrator of an error-resilient image communication system over wireless packet networks, based on the novel JPEG2000 standard. In particular, the decoder implementation is addressed, which is the most critical task in terms of complexity and power consumption, in view of use on a wireless portable terminal for cellular applications. The system implementation is based on a mixed DSP/FPGA architecture, which allows us to parallelize some computational tasks, thus leading to efficient system operation.

关键词： Digital signal processing Field programmable gate arrays image communication Transform coding Tiles Decoding image coding Discrete wavelet transforms Multimedia systems Wireless communication

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Nonsubsampled Contourlet Transform for Texture Classifications using Support Vector Machines

Nonsubsampled Contourlet Transform for Texture Classificatio...

引用

IEEE International conference on Networking, Sensing and Control

作者： Shutao Li Xinmin Fu Bin Yang College of Electrical and information Engineering Hunan University Changsha China

In this paper, a new texture classification method using the nonsubsampled contourlet transform (NSCT) and support vector machines (SVMs) is proposed. The NSCT provides a shift-invariant, multiscale, and multidirectional image representation that has proven to be very efficient in image analysis applications. Firstly, features are extracted from NSCT coefficients of source images. In addition, SVMs, which have been demonstrated excellent performance as classifiers in a variety of pattern recognition problems, are used as classifiers for texture classification. The algorithm is tested on texture images from Brodatz album. Experimental results demonstrate that the proposed method produces more accurate classification results than other methods.

关键词： Support vector machines Support vector machine classification Filter bank image texture analysis image representation Feature extraction Biomedical signal processing wavelet transforms Pattern recognition Classification algorithms

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Morphological road segmentation in urban areas from high resolution satellite images

Morphological road segmentation in urban areas from high res...

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International conference on Digital signal processing (DSP)

作者： R. Gaetano J. Zerubia G. Scarpa G. Poggi Ariana Project INRIA Sophia-Antipolis France University Federico II Naples Italy

High resolution satellite images provided by the last generation sensors significantly increased the potential of almost all the image information mining (iiM) applications related to earth observation. This is especially true for the extraction of road information, task of primary interest for many remote sensing applications, which scope is more and more extended to complex urban scenarios thanks to the availability of highly detailed images. This context is particularly challenging due to such factors as the variability of road visual appearence and the occlusions from entities like trees, cars and shadows. On the other hand, the peculiar geometry and morphology of man-made structures, particularly relevant in urban areas, is enhanced in high resolution images, making this kind of information especially useful for road detection. In this work, we provide a new insight on the use of morphological image analysis for road extraction in complex urban scenarios, and propose a technique for road segmentation that only relies on this domain. The keypoint of the technique is the use of skeletons as powerful descriptors for road objects: the proposed method is based on an ad-hoc skeletonization procedure that enhances the linear structure of road segments, and extracts road objects by first detecting their skeletons and then associating each of them with a region of the image. Experimental results are presented on two different high resolution satellite images of urban areas.

关键词： Roads Skeleton image segmentation image edge detection Transforms image resolution Urban areas

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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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Data compression by recurrent neural network dynamics

Data compression by recurrent neural network dynamics

引用

IEEE Region 10 International conference TENCON

作者： Leong Kwan Li Applied mathematics department Hong Kong Polytechnic University Hong Kong China

ISBN: (纸本)0780336798

Data compression is dominated by the Fourier or wavelet transforms which approximate the given function or sequence as a linear sum of the basis functions. In this paper, we discuss the use of dynamical systems for compression. Since leaky-integrator model neural nets can approximate arbitrary finite sequences, we propose to compress a 'not too wild' signal by a recurrent neural network. As an initial valued problem, the information to be stored are the parameters of the system and the initial states. Elementary analysis on error and compression ratio are also given.

关键词： Data compression Function approximation Neural networks image coding Discrete transforms Recurrent neural networks Feedforward neural networks Nonlinear dynamical systems Mathematical analysis Software packages

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Classifier-Based Feature Fusion for Texture Discrimination

Classifier-Based Feature Fusion for Texture Discrimination

引用

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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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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