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检索条件"主题词=Signal Processing Algorithms"

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Steganography Authenticated Quadrant Stacked Multiple Image Visual Cryptography Scheme with Perfect Reconstruction

Steganography Authenticated Quadrant Stacked Multiple Image ...

引用

Innovations in High Speed Communication and signal processing (IHCSP), International Conference on

作者： Sivasidambaram G Sathishkumar R C Rama Krishna Computer Science & Engineering National Institute of Technical Teachers Training & Research Chandigarh India Electronics & Communication Engineering Perunthalaivar Kamarajar Institute of Engineering & Technology Karaikal India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Visual Cryptography Scheme (VCS) is an image based encryption procedure which encodes the secret image into multiple binary images called shares. The shares are random meaningless patterns of identical number of 0’s and 1’s, and hence the shares do not expose the secret. In conventional VCS, shares are printed over transparencies or transmitted digitally to the receiver. During decryption, shares are overlaid to recover the secret which can be recognizable by Human Visual System (HVS) devoid of any processing. However the deciphered secret image will not be a perfect reconstruction in conventional VCS and it encounters contrast loss. "Two-in-One Image Secret Sharing Scheme" (TiOISSS) combines VCS that can decipher the ambiguous secret recognizable through HVS during the first phase and “Polynomial Image Secret Sharing Scheme” (PISSS) to achieve perfect reconstruction in the second phase. TiOISSS is implemented for single secret in most works. In the proposed scheme, TiOISSS is extended to share multiple secrets, in terms of four secrets per share, by stacking the shares over the four quadrants, termed as "Two-in-One Multiple Image Secret Sharing Scheme (TiOMISSS)". The number of secrets can be extrapolated as one share for every four secrets. Steganography data is encoded into the shares for authentication. The efficiency of the scheme is assessed using metrics PSNR and SSIM. The scheme results in better quality with infinite PSNR and SSIM value of 1 due to perfect reconstruction.

关键词： Steganography Visualization Technological innovation Stacking signal processing algorithms Authentication Visual systems signal processing Encryption Image reconstruction

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Augmenting the Crayfish Optimization with Gaussian Distribution Parameter for Improved Optimization Efficiency

Augmenting the Crayfish Optimization with Gaussian Distribut...

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Cognitive Robotics and Intelligent Systems (ICC - ROBINS), International Conference on

作者： Himani Daulat Tarun Varma Krishna Chauhan Department of Electronics and Communication Engineering Malviya National Institute of Technology Jaipur Rajasthan India Department of Electronics and Communication Engineering VIT Bhopal University Bhopal Madhya Pradesh India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

In the realm of optimization algorithms, the Crayfish Optimization Algorithm (COA) has emerged as a promising metaheuristic approach influenced by the swarming behavior of crayfish. Many adjustments have been made to successfully improve the search efficiency of meta-heuristic algorithms. Since the beginning, these adjustments have mainly been made to elevate the caliber of the solutions by using various clustering techniques and learning theories, frequently ignoring the population and fitness *** designing an optimization algorithm, diversity is more important. Although COA has strong optimization capabilities, it frequently experiences premature convergence in complex optimization environments. Additionally, two exploitation stages make its exploration capabilities less robust. To overcome these limitations, this paper suggests a control approach that assesses COA’s population and fitness diversity by incorporating the Gaussian Distribution (GD) parameter. The new algorithm is called GD-COA, and its performance is verified by optimizing 27 uni-model and multi-model benchmark functions. The comparison results for the relatively few iterations show that GD-COA is more effective and efficient than traditional COA.

关键词： Simulation Sociology Metaheuristics signal processing algorithms Clustering algorithms Benchmark testing Gaussian distribution

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A Novel CNN Architecture for Alzheimer's Disease Classification Using MRI Images: Comparison with Traditional Machine Learning Models

A Novel CNN Architecture for Alzheimer's Disease Classificat...

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IEEE International Conference on signal processing, Information, Communication & Systems (SPICSCON)

作者： Mohammad Sakib Tohura Nur M. Mofazzal Hossain Department of EEE University of Liberal Arts Bangladesh (ULAB) Dhaka Bangladesh Department of EEE Southeast University Dhaka Bangladesh

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

Automated medical image processing has significantly improved with recent advances in deep learning and imaging technologies, particularly in the area of neuroimaging-based Alzheimer's disease (AD) diagnosis. Although various imaging modalities are available for individual patients, there is still a lack of research on the creation of multi-modal models that incorporate different modalities. AD is a neurological condition that worsens with time and affects daily activities. It affects memory, cognition, and behavior. AD cannot be cured, but it can be slowed down in its progression and quality of life enhanced with early care. Medical research is using machine learning (ML) more and more to diagnose and track diseases. Magnetic Resonance Imaging (MRI) can be used to detect AD, although it is still challenging to spot subtle changes in the brain in the early stages of the illness. In order to categorize four classes associated with AD symptoms Mild Demented (L-1), Very Mild Demented (L-2), Non-Demented (L-3), and Moderate Demented (L-4) we present a novel CNN architecture in this study. In order to identify the best accurate model, we also evaluate the performance of six ML algorithms. We trained and tested our proposed technique on the DATASET, which consists of 6400 MRI images and is publicly available on Kaggle database. Our results demonstrate that the proposed CNN model outperforms existing state-of-the-art methods with an accuracy of 98.7% and an F1-score of 98.9%. The most effective machine learning model that was looked at was Linear Regression (LR), which had an accuracy of 84.1% and an F1-score of 83.7%.

关键词： Deep learning Accuracy Machine learning algorithms Magnetic resonance imaging signal processing algorithms Medical services signal processing Brain modeling Alzheimer's disease Medical diagnostic imaging

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Audio Feature Extraction algorithms and Implementation Technologies Analysis

Audio Feature Extraction Algorithms and Implementation Techn...

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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Shaoxuan Li Computer College Xi’an Shiyou University Xian China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

LIBROSA is a powerful Python audio data processing library introduced in recent years. Based on LIBROSA provided source codes, two types of feature data extraction algorithms are analyzed in detail, including MEL power spectrum and CHROMA syllable spectrums. By each algorithm, 128, 12 floating point numbers are extracted as the feature data of their corresponding spectrums. In addition, from multi perspectives of basic music theory, mathematical analysis, signal processing, computer programming and so on, deeply discusses LIBROSA library functions involved Discrete Fourier Transform (DFT), Triangular filter, Gaussian filter, and other programming technologies. The DFT algorithm, time domain window function, syllable feature data extraction, band-pass filter code analysis and other contents discussed have important reference value in data processing, deep learning and other fields of underlying model creation, source codes development and so on

关键词： Band-pass filters Codes Source coding Discrete Fourier transforms signal processing algorithms Programming Feature extraction

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Phonation Attributes and Artificial Intelligence (AI) to Delineate the Progression of Amyotrophic Lateral Sclerosis (ALS): A Neurodegenerative Disorder

Phonation Attributes and Artificial Intelligence (AI) to Del...

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International Conference on Computer Engineering, Network and Intelligent Multimedia (CENIM)

作者： Rumana Islam Mohammed Tarique Dept. of Elect. and Comp. Engg Univ. of Sci. and Tech. of Fujairah (USTF) Fujairah UAE

ISBN: (数字)9798350368802

ISBN: (纸本)9798350368819

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative illness affecting the motor neurons. The etiology of motor neuron degeneration is challenging to unveil. It is a complex disorder. The onset of this disease is so elusive that the symptoms are overlooked. The delayed prognosis results in the gradual progression of paralysis. This research explores several vocal features to suspect this disease from the voice signals unleashing artificial intelligence (AI) techniques. The significant signal processing based features are computed from the sustained phonation of vowel ‘/a/’ samples from healthy and ALS patients to detect this fatal disease. The successful formulation of feature matrix from the cepstral and acoustic domains is researched by designing several machine learning-based models. The designed system could achieve an excellent performance (100% accuracy) to recognize the ALS patients with an artificial neural network (ANN) based model.

关键词： Cepstral analysis Neurons signal processing algorithms Artificial neural networks signal processing Motors Vectors Timbre Artificial intelligence Diseases

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Mortality Prediction through Machine Learning: Insights from XGBoost and Random Forest Model

Mortality Prediction through Machine Learning: Insights from...

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signal processing and Advance Research in Computing (SPARC), International Conference on

作者： Praveen Kumar Misra Varnika Tiwari Rahul Gupta Barkha Gupta Department of Mathematics and Statistics Dr. Shakuntala Misra National Rehabilitation University Lucknow Uttar Pradesh India

ISBN: (数字)9798350385205

ISBN: (纸本)9798350385212

This study explores the use of machine learning algorithms to predict deaths caused by different risk factors in various countries. Using a large dataset that covers many years and regions, we applied models like XGBoost and Random Forest to see how well they could predict death rates based on health and environmental factors. The XGBoost model was very accurate with an $\text{R}^{2}$ of $\mathbf{0. 9 9 5 6}$, showing it could explain most of the variability in death rates. The Random Forest model also performed well with an $\text{R}^{2}$ of 0.9926. These findings show that machine learning can be very useful in health analysis, helping to understand the risk factors that lead to higher death rates. The study discusses how these results can inform public health policies and guide future research.

关键词： Analytical models Machine learning algorithms Accuracy Computational modeling Mortality signal processing algorithms Predictive models signal processing Reliability Random forests

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Improved Particle Swarm Algorithm for Sparse Array with Superior Global Sidelobe Levels

Improved Particle Swarm Algorithm for Sparse Array with Supe...

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IEEE International Conference on Electronic Information and Communication Technology (ICEICT)

作者： Jinyang Luo Yuyan Duan Kun Li Lixia Yang Anhui University Hefei China

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

In order to solve the challenge of sparse array optimization to achieve the global optimal peak sidelobe level, an improved particle swarm optimization algorithm is introduced. This method is used to optimize a sparse array consisting of 60 antennas and 86 half-wavelength apertures. The simulation results show that compared with the traditional particle swarm optimization and simulated annealing algorithms, the improved particle swarm optimization algorithm has excellent performance in coping with the challenge of sparse array optimization.

关键词： Array signal processing Simulation signal processing algorithms Simulated annealing Aperture antennas Information and communication technology Particle swarm optimization

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Image Enhancement Algorithm for Cold Cathode Weld Defect Based on Improved Retinex Theory

Image Enhancement Algorithm for Cold Cathode Weld Defect Bas...

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Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Zhefan Wang Bing Zhu Siyu Wang Weixin Gao College of Electronic Engineering Xi’an Shiyou University Xi’an China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

To address the issues of low contrast and poor visual quality in cold cathode weld defect images, an image enhancement algorithm based on an improved Retinex theory is proposed. First, bilateral filtering is used to remove stripe noise in the image. Next, the guided filter in the improved Retinex algorithm is used to estimate the illumination component, thus obtaining the reflection component. The illumination component is processed using contrast-limited adaptive histogram equalization (CLAHE), while the local details of the reflection component are enhanced using an improved Butterworth high-pass filter. The illumination and reflection components are then fused in a proportional manner. Finally, the image is enhanced using an improved homomorphic filter, resulting in the enhanced image. The algorithm's effectiveness is evaluated using subjective and objective assessment methods, with results indicating good image enhancement performance and more visible details.

关键词： Welding signal processing algorithms Lighting Filtering algorithms Information filters Filtering theory Reflection Image enhancement X-ray imaging Cathodes

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Designing a Graphics processing Unit for 2D Rendering on FPGA for Educational Purpose

Designing a Graphics Processing Unit for 2D Rendering on FPG...

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International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE)

作者： Rupak Saha Nahin Ul Sadad Md. Nazrul Islam Mondal Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350388282

ISBN: (纸本)9798350388299

In this paper, design and implementation of a 2D graphics processing hardware unit using FPGA for educational purpose is presented. We propose a more simple & minimalist GPU architecture, which is flexible and easy to understand than existing industrial GPUs. We present a platform for understanding GPU architecture by implementing of graphics algorithms on FPGA for students to use this in laboratories. Students can be benefited from this work as it can be used as a base understanding tool in terms of GPU architecture. It can also be extended to be used in cross disciplinary applications like 3D rendering, computer vision and signal processing visualisation. This research includes descriptions of hardware realization of 2D rasterization rendering algorithms. Hardware Description Language (HDL) called verilog is utilized in designing the architecture. Xilinx Vivado Design Suite software is used for FPGA implementation, targeting Artix 7 FPGA board, which allows users to comprehend the fundamental ideas of hardware design and their applications in graphics rendering.

关键词： Visualization Three-dimensional displays Heuristic algorithms Graphics processing units signal processing algorithms Computer architecture Rendering (computer graphics)

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Enhancing Precision in Facial Micro- Expression Recognition with Support Vector Machine in Comparison to ELM Algorithm

Enhancing Precision in Facial Micro- Expression Recognition ...

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IEEE International Conference on Intelligent Techniques in Control, Optimization and signal processing (INCOS)

作者： T.J. Nagalakshmi Anji Reddy Department of Electronics and Commnication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

The primary objective of this study is to develop a new method for recognising facial micro-expressions by comparing the accuracy of the SVM and ELM algorithms. The extreme learning machine (N=10) and support vector machine (N=10) used a sample size of 10 and 20 iterations respectively to identify micro expressions with g power of 80%, limit of 0.608, and confidence stretch of 95%. In comparison to extreme learning machines, which have an accuracy of 91.0%, support vector machines will deliver superior exactness (94.8 % ). The difference in statistical significance, p = 0.608 (p<0.05), According to the free example T -test value, the study's findings are not statistically significant. When it comes to detecting facial micro-expressions, support vector machine algorithms outperform extreme learning machine methods in terms of accuracy.

关键词： Support vector machines Training Machine learning algorithms Extreme learning machines Face recognition signal processing algorithms signal processing

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