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Adaptive Noise Cancellation Technique in signal Estimation using Particle Swarm Optimization 5

Adaptive Noise Cancellation Technique in Signal Estimation u...

引用

5th international conference on Energy, Power, and Environment, ICEPE 2023

作者： Mawrie, Renialson Swer, Lambha Chauhan, Abhishek Singh, Ksh Milan Nit Meghalaya Shillong India

ISBN: (纸本)9798350313123

Noise is one of the most hindering factors in signal processing as they are random in nature, and it may obliterate the signal of interest. Noise may arise due to errors in the sensor as well as erroneous transmission channels. These errors are inherent in real-world applications and may cause errors in measuring quantity. In our proposed model, Particle Swarm Optimization PSO is used to optimize the adaptive filter coefficients of an Adaptive line enhancer (ALE) to remove the noise from the received signal. The proposed PSO based ALE is designed in MATLAB, and its performance is investigated for different scenarios. © 2023 ieee.

关键词： Adaptive filters

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Machine Learning, Deep Learning Models for Agro-Meteorology applications 4

Machine Learning, Deep Learning Models for Agro-Meteorology ...

引用

4th international conference on signal processing and Communication, ICSPC 2023

作者： Jala, Prakash Kumar Meenal, R. Nagabushanam, P. Selvakumar, A. Immanuel Jude Hemanth, D. Rajasekaran, E. Electrical and Electronics Engineering Karunya Institute of Technology and Sciences Coimbatore India V.S.B. College of Engineering Department of Science and Humanities Karur India

ISBN: (纸本)9798350300772

Increasing water, energy, and food demand from the rising population of India needs organized water management using clean and sustainable energy with improved crop yield. Agricultural Meteorology is the application of meteorology to the measurement and analysis of the physical environment in the agricultural field. It is the performance of the weather and climate factors that has direct relevance to agriculture and their outcome in crop production. Over the present period, there have been massive improvements in weather prediction systems;yet, their direct applications have not been completely explored in effective crop water management. The aim of this paper is to review the Machine Learning (ML) and Deep Learning (DL) models for agro-meteorological applications. ML and DL-based Climate-smart agriculture approaches are discussed in this paper to maximize crop yield and minimize water use. This review study can be used as an effective platform for irrigation planning, design, and management in smart farming and to address the current challenges in agro-meteorological applications. © 2023 ieee.

关键词： Meteorology

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Cost-Effective Solutions for Underwater Ultrasonic Flow Rate Sensors in Renewable Energy applications - an Appraisal 2

Cost-Effective Solutions for Underwater Ultrasonic Flow Rate...

引用

2nd international conference on Smart Technologies for Power and Renewable Energy, SPECon 2024

作者： Pillai, Abhilash N Abhiram, B. Arun, R. Harin, Bs Nisha, K.L. Sathayadevan, Shiju Vyshak, K. Amrita Vishwa Vidyapeetham Amritapuri Department of Electronics and Communication Engineering Kollam India Amrita Vishwa Vidyapeetham Amritapuri Center for Cybersecurity Systems and Networks Kollam India Amrita Vishwa Vidyapeetham Amritapuri Tranquility IoT & Big Data Solutions Kollam India

ISBN: (纸本)9798350383041

Precise measurement of underwater fluid flow rates plays a critical role in diverse applications within the realm of smart technologies for power and renewable energy (PRE), including monitoring tidal energy infrastructure, optimizing hydropower generation, and safeguarding sensitive marine ecosystems. However, the high cost of traditional ultrasonic flow rate sensors hinders their widespread adoption in these applications. This paper critically analyzes recent advancements in underwater ultrasonic flow rate sensors, emphasizing novel cost-effective strategies and approaches specifically tailored for PRE applications. The paper classifies sensors based on their operating principles (transit-time, Doppler, and correlation) and delves into fundamental concepts, practical considerations, and recent developments. Notably, it identifies key challenges like material costs, transducer design complexity, and signal processing inefficiencies, ultimately limiting cost-effectiveness. The paper then explores potential solutions such as utilizing cheaper materials, employing innovative transducer designs, and implementing efficient signal processing techniques to address these challenges. Furthermore, the paper delves into ongoing research efforts and future directions aimed at further optimizing sensor performance and cost-efficiency for broader adoption within the dynamic field of smart power and renewable energy. © 2024 ieee.

关键词： Cost effectiveness

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Food Safety Control Using CNN Model in image processing Technique

Food Safety Control Using CNN Model in Image Processing Tech...

引用

2023 international conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Saranya, P. Durga, R. Vistas Department of Computer Science Chennai India

ISBN: (纸本)9798350317060

Adulteration is one of the major problems in food products. It makes the food products impure and alters their original form. Food adulteration lowers the food's quality by introducing adulterants or deleting necessary ingredients. Adding contaminants to the pure form of food is becoming a common practice to boost market growth. Some of the important food products that we consume daily such as fruits and vegetables have been adulterated with chemicals which cause harmful to health and cause various diseases. This research paper focuses on the overview of image processing techniques with CNN Models to detect adulteration from sample images of food products that we consume in our daily life. The images of food products such as cashew nuts, Rice grains, Corn seeds, green coffee beans, virgin olive oil are taken in some of the paper and various image processing techniques with convolution neural network algorithms are used to detect the adulterant mixed in the products from this the research paper gives an overall view of food adulteration with image processing and CNN models and its applications. © 2023 ieee.

关键词： Hyperspectral imaging

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An Improved Depth image Clustering Algorithm Based on Spatial Information 2

An Improved Depth Image Clustering Algorithm Based on Spatia...

引用

2nd international conference on image processing, Computer Vision and Machine Learning, ICICML 2023

作者： Yan, Li Su, Shan Hu, Xiao Zhou, Yuquan Chen, Changjun Wuhan University School of Geodesy and Geomatics Wuhan China Hubei Luojia Laboratory Wuhan China

ISBN: (纸本)9798350331417

Depth image spatial clustering is an important task in the fields of computer vision and machine learning, aiming to group pixels or point cloud data of depth images into clusters with similar features. This is crucial for tasks such as object recognition, scene segmentation, 3D reconstruction, and other applications. The K-means algorithm is a commonly used clustering method that divides data points into K clusters, ensuring that each data point belongs to the cluster whose center is closest to it. However, traditional K-means algorithms face several challenges in depth image spatial clustering, such as sensitivity to the choice of K and the influence of cluster center initialization on the results. In this study, we propose an improved depth image clustering method based on spatial information. The method includes depth image edge extraction, intra-edge region growing, unclassified point re-clustering, and connected region merging. Through experiments conducted on datasets from TUM, Bonn, and our collected data, our algorithm demonstrates superior performance in depth image clustering tasks, higher clustering accuracy, fewer unclassified points, and faster convergence speed. © 2023 ieee.

关键词： Depth image clustering K-Means RGB-D datasets Spatial information

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Unpaired image-to-image Translation for High-Accuracy CTA Generation from CT images Using Cycle GAN

Unpaired Image-to-Image Translation for High-Accuracy CTA Ge...

引用

2024 international conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2024

作者： Soliman, Nancy Mohamed Elhelw, Mohamed Elattar, Mustafa Medical Imaging and Image Processing Research Group Center for Informatics Science Nile University Giza Egypt School of Information Technology and Computer Science Nile University Giza Egypt

ISBN: (纸本)9798331533137

This study explores the application of CycleGAN, a variant of Generative Adversarial Networks (GANs), for generating Computed Tomography Angiography (CTA) images directly from CT scans. Traditional CTA methods involve risks associated with contrast agents and radiation, prompting the need for noninvasive alternatives. CycleGAN facilitates unpaired image-to-image translation, bypassing the requirements for paired datasets typically used in medical imaging. Using a dataset comprising CT scans from 87 patients and CTA scans from 56 healthy aortas, our study employed advanced preprocessing techniques to optimize the CycleGAN performance. Quantitative evaluations demonstrate the model’s efficacy with a Structural Similarity Index (SSIM) of 0.73 ± 0.09 and a Filtered Peak signal-to-Noise Ratio (Filtered PSNR) of 18.43 ± 0.96, indicating robust image synthesis capabilities. Our Study advances medical imaging by automating CTA image synthesis, potentially improving diagnostic accuracy and patient care while mitigating procedural risks. Future research directions include refining model architectures, expanding datasets, and validating synthesized images across broader clinical contexts. ©2024 ieee.

关键词： Computerized tomography

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Segmentation Based Fine Registration of Very High Resolution Multi-Temporal images

Segmentation Based Fine Registration of Very High Resolution...

引用

2023 ieee international conference on Paradigm Shift in Information Technologies with Innovative applications in Global Scenario, ICPSITIAGS 2023

作者： Tamboli, Asif Yadav, Pratik Chauhan, Deepak Kumar Parthiban, P. Department of Radiodiagnosis Karad India Jcep Km Gad E&tc India School of Computing Graphic Era Hill University Uttarakhand Dehradun India National Institute of Technology Tiruchirappalli Department of Production Engineering Trichy620015 India

ISBN: (纸本)9798350329773

This procedure seeks to provide an overview of both traditional and contemporary picture registration techniques. The practise of overlaying photographs from the same scene that were acquired at various times, from various angles, and/or by various sensors is known as image registration. Two photos are geometrically aligned via the registration (the reference and sensed images). The examined techniques are divided into two categories-area-based and feature-based-and four fundamental image registration phases. After colour correction, colour blending is performed to further smooth the colour transition between neighbouring photos. The mean values of all the overlapping areas between each pair of adjacent images must be extracted in order to create the global error function. To put it another way, this results in unneeded delays because the colour correction and panorama stitching processes cannot begin until the entire image sequence has been captured. The color/luminance inconsistency issue that existed in panoramic imaging is addressed in this process with an effective image stitching approach. The suggested method is intended for panorama stitching on mobile devices, and it enhances the colour blending and correction processes that were previously proposed in order to speed up processing. © 2023 ieee.

关键词： image registration

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Improved Object Detection in Forward-Looking Sonar images with Attention-YOLOv8 and Denoising Algorithms 6

Improved Object Detection in Forward-Looking Sonar Images wi...

引用

6th international conference on Information Communication and signal processing, ICICSP 2023

作者： Wang, Zitao Cao, Tianyu Shi, Bo Guo, Wenbo Ge, Qiqi Shanghai Jiao Tong University Department of Automation Shanghai China

ISBN: (纸本)9798350339994

The utilization of forward-looking sonar (FLS) has become increasingly significant for detecting underwater objects. However, despite its widespread use, target recognition tasks for underwater unmanned vehicles (UUVs) are still predominantly carried out through postprocessing techniques. This can be attributed to the limitations of computing devices deployed on UUVs, which have restricted computational capabilities due to power constraints. Furthermore, conventional object detection approaches designed for natural images are inadequate when applied to sonar images with high levels of noise. To address these challenges and enable UUVs to autonomously detect underwater objects, we propose an innovative solution that involves the integration of an improved version of YOLOv8 with attention mechanism, allowing the model to obtain more feature information while reducing the number of parameters required. Additionally, we developed a comprehensive preprocessing module consisting of removal, contrast enhancement, and noise filtering techniques. Our experimental results demonstrate that the proposed method achieves high accuracy, robustness, and can be efficiently implemented on hardware processors. Our code is available on https:/***/wangzitao7777/underwater-detection. © 2023 ieee.

关键词： image processing lightweight network object detection sonar image

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A Review on Digital Video Watermarking Security: Significance and Persistent Challenges 2

A Review on Digital Video Watermarking Security: Significanc...

引用

2nd ieee international conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Saini, Anuradha Bhardwaj, Sushil Rimt University Department of Computer Applications Punjab Mandi Gobindgarh India

ISBN: (纸本)9798350384277

Given the rapid strides in technology, the need for authentication and security in long-distance communications has taken center stage. Particularly in the film industry, the pirated distribution of digital videos poses a significant threat due to the ease of global dissemination via the widespread accessibility of the internet. Video watermarking has obtained notable prominence across many applications including fingerprinting, copyright protection, annotation, security, and copy prevention/control. Watermarking, irrespective of its form i.e. image, audio, or video, involves further data embedded within a digital video signal, primarily for the detection of copyright owners. Consequently, several approaches to digital video watermarking have surfaced to tackle this issue. As a pivotal method, watermarking has turned up for confirming ownership, safeguarding copyrights and assuring authenticity. Researchers utilize techniques of watermarking to conceal sensitive information, this information can be in two modes, either in visible mode or invisible mode on the basis of user preferences, thereby playing a crucial role in establishing information ownership. To ensure robust watermarking, optimization processes are frequently employed. An overview encompassing various trends and new techniques widely observed in the domain of digital watermarking is aimed to be presented by this paper. It is intended as a comprehensive guide for researchers interested in this field. © 2024 ieee.

关键词： Authentication

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A MULTI-RESOLUTION LOW-RANK TENSOR DECOMPOSITION 47

A MULTI-RESOLUTION LOW-RANK TENSOR DECOMPOSITION

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47th ieee international conference on Acoustics, Speech and signal processing (ICASSP)

作者： Rozada, Sergio Marques, Antonio G. King Juan Carlos Univ Dept Signal Theory & Commun Madrid Spain

ISBN: (纸本)9781665405409

The (efficient and parsimonious) decomposition of higher-order tensors is a fundamental problem with numerous applications in a variety of fields. Several methods have been proposed in the literature to that end, with the Tucker and PARAFAC decompositions being the most prominent ones. Inspired by the latter, in this work we propose a multi-resolution low-rank tensor decomposition to describe (approximate) a tensor in a hierarchical fashion. The central idea of the decomposition is to recast the tensor into multiple lower-dimensional tensors to exploit the structure at different levels of resolution. The method is first explained, an alternating least squares algorithm is discussed, and preliminary simulations illustrating the potential practical relevance are provided.

关键词： Tensor decomposition Low-rank approximation Kronecker decomposition multi-resolution approximation

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