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An Effective Statistical Test Suite for Pseudorandom number Generators in Digital Signatures and Security Robustness Evaluation including a wavelet Test for Randomness

An Effective Statistical Test Suite for Pseudorandom number ...

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

作者： Dimitrios A. Karras National & Kapodistrian University of Athens (NKUA) Greece

ISBN: (数字)9781728170862

ISBN: (纸本)9781728170879

The need for random and pseudorandom numbers arises in many cryptographic applications including digital signatures management. For instance, common cryptosystems employ keys that must be generated in a random fashion. Many cryptographic protocols also require random or pseudorandom inputs at various points, e.g., for auxiliary quantities used in generating digital signatures, or for generating challenges in authentication protocols. A suitable set of statistical tests for randomness of message digests (MD5, SHA) and digital signatures DSA algorithms is outlined and evaluated in this paper based mainly on the revisions of National Institute of Standards and Technology (NIST) procedures in detecting deviations of a binary sequence from randomness. Although such an analysis exists for AES standard there is no similar study for message digests or digital signatures. The contribution of this paper lies in the attempt to fill in this gap from an educational point of view, providing relevant plots of the derived sequences, as well as to introduce a novel Discrete wavelet Transform Test for Randomness.

关键词： NIST Robustness Generators Cryptography Informatics Digital signatures Standards

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Multi-level Stress Assessment Using Multi-domain Fusion of ECG signal

Multi-level Stress Assessment Using Multi-domain Fusion of E...

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Annual International conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Zeeshan Ahmad Naimul Mefraz Khan Department of Electrical and Computer Engineering Ryerson University Toronto ON Canada

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Stress analysis and assessment of affective states of mind using ECG as a physiological signal is a burning research topic in biomedical signal processing. However, existing literature provides only binary assessment of stress, while multiple levels of assessment may be more beneficial for healthcare applications. Furthermore, in present research, ECG signal for stress analysis is examined independently in spatial domain or in transform domains but the advantage of fusing these domains has not been fully utilized. To get the maximum advantage of fusing different domains, we introduce a dataset with multiple stress levels and then classify these levels using a novel deep learning approach by converting ECG signal into signal images based on R-R peaks without any feature extraction. Moreover, We made signal images multimodal and multi-domain by converting them into time-frequency and frequency domain using Gabor wavelet transform (GWT) and Discrete Fourier Transform (DFT) respectively. Convolutional Neural networks (CNNs) are used to extract features from different modalities and then decision level fusion is performed for improving the classification accuracy. The experimental results on an in-house dataset collected with 15 users show that with proposed fusion framework and using ECG signal to image conversion, we reach an average accuracy of 85.45%.

关键词： Stress Electrocardiography Feature extraction Machine learning Discrete Fourier transforms Time-frequency analysis

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image compression and enlargement algorithms

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International Journal of Circuits, Systems and signal processing 2021年 15卷 836-846页

作者： Burova, I.G. Dem’yanovich, Yu.K. Terekhov, A.N. Altynova, A.Yu. Satanovskiy, A.D. Babushkin, A.A. St. Petersburg State University 7/9 Universitetskaya nab St.Petersburg199034 Russia

In some cases, there are problems associated with the compression and enlargement of images. The use of splines is quite effective in some cases. In this paper, a new image compression algorithm is presented. The features of increasing the size of an image when using local polynomial or non-polynomial splines are considered. The main method for enlarging an image is based on the use of splines of the second and third order of approximation. Polynomial and trigonometric splines are considered. To speed up the process of enlarging the image, we used the parallelization techniques. © 2021, North Atlantic University Union NAUN. All rights reserved.

关键词： image compression

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A Review of Motion Segmentation: Approaches and Major Challenges

A Review of Motion Segmentation: Approaches and Major Challe...

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International Multidisciplinary Information Technology and Engineering conference (IMITEC)

作者： Jana Mattheus Hans Grobler Adnan M. Abu-Mahfouz University of Pretoria South Africa Council for Scientific and Industrial Research South Africa

ISBN: (数字)9781728195209

ISBN: (纸本)9781728195216

Motion segmentation has applications in, amongst others, robotics, traffic monitoring, sports analysis, inspection, video surveillance, compression, and video indexing. However, the performance of most methods is limited compared to human capabilities. Based on extensive literature the following challenges remain: occlusions, temporary stopping, missing data, and segmenting multiple objects. In this paper, several popular and state-of-the-art methods were reviewed, with the focus on the most important attributes. These methods were classified according to the main approach taken, namely image Difference, Optical Flow, wavelet, Statistical, Layers, Manifold Clustering, Template Matching, and Deep Learning. The investigated methods are compared and major research challenges are highlighted. Based on the review, improvements are identified as a basis for future research.

关键词： Manifolds Computer vision Motion segmentation Video surveillance Trajectory Video signal processing Sports

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Feature Level Medical image Fusion with Deep Learning

Feature Level Medical Image Fusion with Deep Learning

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International conference on

作者： R. Prathipa R. Ramadevi Sathyabama Institute of Science and Technology Chennai India Department of Biomedical Engineering Saveetha School of Engineering Chennai India

In recent years, medical image fusion has emerged as a comprehensive analytical method that frequently employs various modification techniques. The objective of multi-modal medical image fusion is to enhance image quality while preserving specific characteristics by combining images from different imag- ing modalities. This field encompasses cutting-edge domains such as image processing, computer vision, pattern recognition, ma- chine learning, and artificial intelligence. Furthermore, medical image fusion has found extensive clinical applications, enabling healthcare professionals to gain a deeper understanding of lesions by integrating medical images from multiple modalities. In this study, we propose the utilization of a Grey Wolf algorithm, which comprises various learning models, to minimize squared error and optimize the process through weighted entropy during image fusion. The successful results are attributed to a hybridization of the Discrete wavelet Transform (DWT) and the Discrete Curvelet Transform (DCT). By employing Principal Component Analysis, Grey Wolf Optimization, and a Recurrent Neural Network, we have developed a novel fusion model for unsupervised categorization known as GWO-RNN. This model is designed to work with multi-modality images that incorporate structural and sparse constraints. The flexibility and precision of image fusion are further enhanced through correlation and covariance analysis using eigenvalues and eigenvectors. Matlab simulations of the proposed workbench are conducted, comparing it against state-of-the-art methods to assess performance metrics, including peak signal-to-noise ratio, mean square error, correlation, and accuracy. The proposed workbench not only meets but surpasses all performance benchmarks while also reducing computational overhead.

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TCCT-Net: Two-Stream Network Architecture for Fast and Efficient Engagement Estimation via Behavioral Feature signals

TCCT-Net: Two-Stream Network Architecture for Fast and Effic...

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IEEE Computer Society conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Alexander Vedernikov Puneet Kumar Haoyu Chen Tapio Seppänen Xiaobai Li Center for Machine Vision and Signal Analysis University of Oulu Finland State Key Laboratory of Blockchain and Data Security Zhejiang University China

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Engagement analysis finds various applications in healthcare, education, advertisement, services. Deep Neural Networks, used for analysis, possess complex architecture and need large amounts of input data, computational power, inference time. These constraints challenge embedding systems into devices for real-time use. To address these limitations, we present a novel two-stream feature fusion "Tensor-Convolution and Convolution-Transformer Network" (TCCT-Net) architecture. To better learn the meaningful patterns in the temporal-spatial domain, we design a "CT" stream that integrates a hybrid convolutional-transformer. In parallel, to efficiently extract rich patterns from the temporal-frequency domain and boost processing speed, we introduce a "TC" stream that uses Continuous wavelet Transform (CWT) to represent information in a 2D tensor form. Evaluated on the EngageNet dataset, the proposed method outperforms existing baselines, utilizing only two behavioral features (head pose rotations) compared to the 98 used in baseline models. Furthermore, comparative analysis shows TCCT-Net’s architecture offers an order-of-magnitude improvement in inference speed compared to state-of-the-art image-based Recurrent Neural Network (RNN) methods. The code will be released at https://***/vedernikovphoto/TCCT_Net.

关键词： Convolutional codes Accuracy Recurrent neural networks Continuous wavelet transforms wavelet domain Convolution Computational modeling

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A Framework For Single image Dehazing Using DWT Based Cross Bilateral Filter Fusion of Generative and ASM Models

A Framework For Single Image Dehazing Using DWT Based Cross ...

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International conference on Innovative Sustainable Computational Technologies (CISCT)

作者： Banala Revanth Sanjay K. Dwivedi Manoj Kumar Department of Computer Science Babasaheb Bhimrao Ambedkar University Lucknow India

ISBN: (纸本)9781665474177

Due to the latest advancements in technologies like artificial intelligence, machine learning, and deep learning in image processing, computer vision, and their uses in various applications, there has been a lot of interest in the restoration of murky images. This paper gives a novel single-image dehazing (SID) framework for the restoration of single hazy image (SHI). The framework has three parts. The first part of the framework comprises three networks. A white-balanced auto-encoder network in the front, followed by a pair of sequential auto-encoder networks at the end for image dehazing (ID). A white balance auto-encoder network is used for correcting the white balance error in the input hazy image (HI). Pair of sequential auto- encoder networks gives haze-reduced output by taking corrected white balanced error image as input. The second part comprises an ASM-based model Dark Channel Prior (DCP) for ID. DCP produce haze-free images (HFI) by taking HI as input. The third part presents the fusion model for integrating the first and second parts of the framework. DWT-based cross bilateral filter model is the fusion model (FM) to get the final HFI. The first part of the framework is trained by using a hybrid loss function (perceptual loss function combined with MSE loss function) on O- HAZE, I-HAZE, and MRFID data sets. The proposed framework got considerably good result in terms of Structural Similarity Index (SSIM), Peak signal-to-noise ratio (PSNR), compared with cutting-edge methods.

关键词： Training Measurement Learning systems PSNR Benchmark testing Discrete wavelet transforms image restoration

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Cotton Jassid Infestation Monitoring Using Sentinel-2 MSI, Landsat 9 TIRS, and PlanetScope imagery with Machine Learning and Data Fusion

Cotton Jassid Infestation Monitoring Using Sentinel-2 MSI, L...

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International conference on Computer and applications (ICCA)

作者： Souleymane Bah M. D. Tanguy Mesreil Houegbe Emmanuel Wilfried G. Megnigbeto Vinasetan Ratheil Houndji Bignon Nicanor Kouton N. Gérard Gouwakinnou C. Eugène Ezin Institut de Mathématiques et de Sciences Physiques Université d'Abomey-Calavi Dangbo Bénin BiPLAC Laboratoire d'Ecologie de Botanique et de Biologie végétate Parakou Benin Division protection phytosanitaire Institut de Recherche sur le Coton Parakou Bénin Institut de Formation et de Recherche en Informatique Université d'Abomey-Calavi Abomey-Calavi Benin

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The excessive use of synthetic pesticides in the fields is the cause of numerous problems to human health and biodiversity. Knowing the location and severity of the emergence of pests enables targeted treatment, helping to reduce the use of pesticides. Given that traditional field inspection requires considerable effort with limited effectiveness, remote sensing combined with machine learning is an essential complement for large-scale, efficient monitoring. This study therefore aims to develop a machine learning-based solution for detecting cotton jassids from free satellite imagery data in Alibori, northern Benin. Multispectral and thermal infrared images respectively from the Sentinel-2 and Landsat 9 satellites were fused in the most correlated bands using signal processing algorithms Pseudo Wigner Distribution (PWD), Nonsubsampled Contourlet Transform (NSCT), and Discrete wavelet transform (DWT). Two configurations, in terms of data augmentation, were used. Areas infested by jassids were identified in merged and unmerged satellite images (Sentinel-2, Landsat 9, and PlanetScope, a commercial satellite) using Support Vector Machine (SVM), Random Forest (RF), and CatBoost supervised machine learning algorithms. The green and Thermal Infrared Sensor 1 (TIRS1) bands are the most correlated in configuration 1 and the blue and TIRSI bands in configuration 2 for respectively Sentinel-2 and Landsat 9. The DWT and NSCT signal processing algorithms produced the least Root Mean Square Deviation (RMSE) for image fusion (0.46 for DWT and 0.33 for NSCT). The best result in terms of detecting performance was obtained with the composite treatment of Landsat 9 images and the RF algorithm, with an overall accuracy of 88.23%, an Fl-score of 82%, and a Kappa index of 79.92% in configuration 1 and in configuration 2 for composite treatment of Sentinel-2 an overall accuracy of 92.36%, an Fl-score of 90.75 % and a Kappa index of 86.36% with CatBoost algorithm.

关键词： Radio frequency Support vector machines Satellites Machine learning algorithms signal processing algorithms Landsat Discrete wavelet transforms Satellite images Cotton Monitoring

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signal Restoration with Fractional Transform Sparse Representation and Autoencoder

Signal Restoration with Fractional Transform Sparse Represen...

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International conference on Computer and Communication Engineering, ICCCE

作者： Shivani Saxena Surbhi Mistry Nilesh Patel Mohendra Roy Ahsan Z. Rizvi Computer Sciences and Engineering Institute of Advanced Research Gandhinagar India Biotechnology and Bioengineering Institute of Advanced Research Gandhinagar India Information and Communication Technology Pandit Deendayal Energy University Gandhinagar India

Signa1 denoising is a crucial task in image processing, audio analysis, and communications. Traditional methods often struggle to strike a balance between reducing noise and preserving important signal details. Existing methods exploit the potential of fractional transforms combined with sparse representation techniques. However, these approaches face challenges in achieving the desired balance due to limited sensitivity to signal variations, high computational complexity, restricted adaptability to diverse noise types, and the need for careful selection of transform parameters. To overcome these limitations, we propose a novel method called Fractional Transform Sparse Representation Denoising (FTSRD), which integrates fractional wavelet transform (FWT), fractional DCT (FDCT), sparse representation, and Deep Denoising Autoencoder (DDAE) techniques. The integration of DDAE in FTSRD provides several advantages. DDAE, being a deep learning-based model, has the capability to learn complex noise patterns and extract meaningful signal features. Experimental evaluations were conducted on synthetic signals and ECG signals. The results demonstrate the superior performance of FTSRD in reducing noise while preserving essential structural details. It also surpasses traditional methods in subjective and objective quality measures. In conclusion, FTSRD addresses the challenges associated with noise reduction and signal detail preservation, making it a valuable tool in various denoising applications.

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Novel multi-bit and bitwise minimum error replacement technique for watermarking and data hiding

Novel multi-bit and bitwise minimum error replacement techni...

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SICE Annual conference

作者： Rong-Jian Chen Su-Min Hung Yu-Cheng Peng Jui-Lin Lai Shi-Jinn Horng Department of Electronic Engineering National United University Miaoli Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781424476428

This paper presents an novel bitwise minimum error replacement (MER) technique for data hiding. Such a bitwise MER can embed k-bit (k ≥ 1) secret data into cover data only introduce minimum embedding error smaller than (2 k-1 - 1)2 i-k according to the embedding location i. To achieve the goal of MER, we develop novel multi-bit bitwise 3-step MER algorithms to adaptively evaluate the most similar value to replace the original one, which consist of three steps: (1) embed logo data into cover data, (2) adjust the least-significant bits (LSBs) of cover data, and (3) adjust the maximum-significant bits (MSBs) of cover data. Reason that we called these multi-bit MER algorithms are "bitwise" is that data replacements are based on the logic and algebraic bit operations to achieve minimum error. Many simulations show that the proposed MER performs good embedding quality for watermarking and steganography applications.

关键词： Watermarking PSNR Discrete wavelet transforms image color analysis Brightness Robustness signal processing algorithms

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