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Super-Resolution Image using Enhanced Deep Residual Networks and the DIV2K Dataset 2

Super-Resolution Image using Enhanced Deep Residual Networks...

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2nd international conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Senthilkumar, S. Vetriselvi, S. Kalaivani, K. Arunkumar, P. Malathi, M. Kumar, S. Praveen E.G.S. Pillay Engineering College Department of Electronics and Communication Engineering Tamil Nadu Nagapattinam611002 India Vellore Institute of Technology School of Computer Science and Engineering Vellore632014 India M Kumarasamy college of engineering Department of Electronics and Communication Engineering Tamil Nadu Thalavapalayam639113 India E.G.S. Pillay Engineering College Department of Computer Science and Engineering Tamil Nadu Nagapattinam611002 India

ISBN: (纸本)9798350368413

Recent advances in deep learning have significantly improved the performance of super-resolution (SR) techniques, yet challenges persist in balancing image quality and computational efficiency. This paper presents a novel approach utilizing Enhanced Deep Residual Networks (EDRN) to effectively address these challenges. Utilizing the high-quality and diverse DIV2K dataset, this method employs a combination of Mean Squared Error (MSE) and perceptual loss functions to achieve an optimal balance between pixel accuracy and perceptual quality. Comprehensive evaluation using metrics like Peak signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) demonstrates that the proposed EDRN consistently outperforms baseline models, such as SRCNN, VDSR, and EDSR, by delivering superior detail restoration and minimizing patterns. Visual comparisons further indicate the enhanced quality of the super-resolved images, making this approach a valuable asset for high-resolution imaging applications. © 2024 IEEE.

关键词： Deep residual networks DIV2K Dataset Image processing Super resolution image

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Underwater Thruster Stripe Detection based on Principal Component Analysis and Deep learning 2

Underwater Thruster Stripe Detection based on Principal Comp...

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2nd international conference on Image processing, Computer Vision and machine learning, ICICML 2023

作者： Shi, Lei Zhou, Zhonghai Cui, Xiao Wang, Shiqiwei Qilu University of Technology Shandong Academy of Sciences Institute of Oceanographic Instrumentation Qingdao266061 China

ISBN: (纸本)9798350331417

This article proposed a stripe detection method of underwater thruster image based on principal component analysis (PCA) and deep learning. Firstly, the principal component analysis method is used to denoise underwater thruster image, and the denoised image is taken as the input of the trimmed convolutional neural network similar to VGGNet. Then, the output value is obtained after network training by using the depth learning method, and the stripe are obtained. The experimental results have shown that this method can effectively extract underwater thruster stripe features, and the stripe detection results are clear and *** method can be used to detect faults in underwater thruster. © 2023 IEEE.

关键词： deep learning principal component analysis (PCA) stripe detection underwater thruster

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A comprehensive study on machine learning approaches for emotion recognition 2

A comprehensive study on machine learning approaches for emo...

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2nd international conference on Artificial Intelligence and signal processing, AISP 2022

作者： Kumar, Nitin Gupta, Nidhi Department of Mathematics and Scientific Computing National Institute of Technology Himachal Pradesh Hamirpur India

ISBN: (纸本)9781665442909

Emotion recognition is the process to study about the emotions in a human being. This is a research field where one can understand and recognize the feelings of human and ability of expression which varies from each other at great extent. Several methods have been developed to study emotions such as facial expression, speech method, textual method and EEG signal. In this study work, we have reviewed several methods to find an efficiency of emotions up to accurate observations. Several papers on emotion recognition from the year 2007 to 2021 are been explored in this paper to observe the accuracy 95.20% using electroencephalogram (EEG) signal and 95% using EEG signals with statistical features and neural network. The average accuracy ranges in between 63% to 73% using EEG signal and facial expressions, both. © 2022 IEEE.

关键词： Emotion Recognition

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Newton-Based Trainable learning Rate 48

Newton-Based Trainable Learning Rate

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Retsinas, George Sfikas, Giorgos Filntisis, Panagiotis Paraskevas Maragos, Petros National Technical University of Athens School of E.C.E. Athens15773 Greece University of West Attica Dept. of Surveying & Geoinformatics Engineering Athens12243 Greece

ISBN: (纸本)9781728163277

Selecting an appropriate learning rate for efficiently training deep neural networks is a difficult process that can be affected by numerous parameters, such as the dataset, the model architecture or even the batch size. In this work, we propose an algorithm for automatically adjusting the learning rate during the training process, assuming a gradient descent formulation. The rationale behind our approach is to train the learning rate along with the model weights. Specifically, we formulate first and second-order gradients w.r.t. the learning rate as functions of consecutive weight gradients, leading to a cost-effective implementation. Our extensive experimental evaluation validates the effectiveness of the proposed method for a plethora of different settings. The proposed method has proven to be robust to both the initial learning rate and the batch size, making it ideal for an off-the-shelf optimizing scheme. © 2023 IEEE.

关键词： adaptive learning rate gradient descent

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2nd international conference on Advances in Information and Communication Technology, ICTA 2023

2nd International Conference on Advances in Information and ...

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2nd international conference on Advances in Information and Communication Technology, ICTA 2023

ISBN: (纸本)9783031508172

The proceedings contain 35 papers. The special focus in this conference is on Advances in Information and Communication Technology. The topics include: A Real-time Crowd Density Level Detection System;an Architecture for More Fine-Grained Hidden Representation in Named Entity Recognition for Biomedical Texts;application of Deep learning Methods for Forest Fire Intelligent Image processing;choosing Data Points to Label for Semi-supervised learning Based on Neighborhood;classification of Fermentation Levels of Cacao Beans (Theobroma cacao L.) Using Sensing Technology and Neural Networks;DCDynUnet: Deep Supervision Attention Context for Brain Segmentation;enhancing Wildfire Detection Using Semi-supervised Fuzzy Clustering on Satellite Imagery;Evaluating the Performance of Some Deep learning Model for the Problem of Emotion Recognition Based on EEG signal;a Novel Arm Bone Fracture Detection Using Deep learning;feature Reduction for Interpretability of Neuro-Fuzzy Classifier;improved Accuracy of Path System on Creating Intelligence Base;the Vehicle Routing Problem with Drones for Fresh Agricultural Products;ViT-SigNet: Combining Deep CNN and Vision Transformer for Enhanced Signature Verification;combining Local Search and Multi-objective Optimization Algorithm in signalized Intersection Optimization;Comparative Evaluation of PRR and PODA Methods for Model Order Reduction in Electrical Circuits;data-Driven Narratives: Unleashing the Potential of R for Journalistic Storytelling;dynamic Analysis of Capsubot Model in Liquid Environment by Numerical Method;genetic Programming–A Preliminary Study of Knowledge Transfer in Mutation;improvement of Spectral Clustering Method in Social Network Community Detection;a Systematic Review of Artificial Intelligence in Geographic Information Systems;probable Characteristics of Multi-channel Queuing Systems with "Impatient" and "Patient" Claims.

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Power Consumption Prediction of Digital Circuits using machine learning 2

Power Consumption Prediction of Digital Circuits using Machi...

引用

2nd international conference on Artificial Intelligence and signal processing, AISP 2022

作者： Bhavesh, Modi Divy Anilkumar, Nair Anoopkumar Patel, Manish I. Gajjar, Ruchi Panchal, Dipesh Electronics & Communication Engineering Department Institute of Technology Nirma University Gujarat India

ISBN: (纸本)9781665442909

The demand for Integrated Circuits (ICs) is increasing exponentially, leading to the challenges of a more reliable and effective Electronic Design Tool (EDA) for the circuit design flow. To overcome such limitations machine learning (ML) is used, which can learn from the previous design data and can apply it to the unknown design given to it. In this context, the paper proposes the use of the regression technique of ML to estimate the power consumption of the MOSFET-based digital circuits. For this purpose, to train the ML-based regressor model, a dataset is created from the PMOS based Resistive Load Inverter (RLI), NMOS based RI.I, and CMOS-based NAnd gate layout. For the formation of the dataset, a 90nm MOS technology node is used and it inculcates the features like capacitance, resistance, number of MOSFET, their respective width and length, and the average power consumption of the respective layout. The regressor model used to predict the power consumption in this work is linear regressor, polynomial regressor, random forest regressor, decision tree regressor, and the extra tree regressor. At last, from the experimental results, it is observed that the extra tree regressor performs better for the RLI circuits with the MSE value of 4.02X10'10and R2value of 0.61, and for the NAnd gate, the polynomial linear regressor excels with the MSE value of 7.27X10"10and R2value of 0.65. © 2022 IEEE.

关键词： machine learning

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2022 international conference on signal and Information processing, IConSIP 2022

2022 International Conference on Signal and Information Proc...

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2nd international conference on signal and Information processing, IConSIP 2022

ISBN: (纸本)9781728168852

The proceedings contain 85 papers. The topics discussed include: audio processing for tones validation using deep learning;effective generation of visual questions;elicitation of intracerebral hemorrhage using deep learning;underwater image enhancement using color constancy via homomorphic filtering and depth estimation;fusion based underwater image enhancement and detail preserving;FPGA based feature extraction in real time computer vision - a comprehensive survey;enhancement of liver ultrasound images by guided image filtering technique;exploration of horizontal and vertical components in polarimetric decomposition based on volume scattering;audio source count estimation using deep learning;detection of pneumonia from x-ray images using eigen decomposition and machine learning techniques;audio based detection of saw blade sharpness using machine learning;performance analysis and evaluation of estimator for RF cavity detuning measurement;speaker identification and verification using deep learning;and motor imagery based EEG signal classification using multi-scale CNN architecture.

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Enhancing Blind Source Separation in the Cocktail Party Problem Using Independent Component Analysis 2

Enhancing Blind Source Separation in the Cocktail Party Prob...

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2nd international conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Singh, Kulvinder Piyush Singh, Manakpreet Department of Computer Science and Engineering Chandigarh University Punjab Mohali India

ISBN: (纸本)9798350340235

Independent Component Analysis makes services as diverse as possible by reducing the second-order system and any higher-order dependence on the data provided. It is based on the assumptions of statistically independent substrates from many unknown sources. The ICA is based on the black box method which means it does not need to know internal features. Therefore, in this paper, the basic principles of how to Divide the Blind Source (Mode of Unchecked Reading) will be used to solve a real-world problem such as the problem where a cocktail party is going on. In addition, a pointer-based approach is illustrated to explain the ICA's previous processing steps in terms of the audio issues caused while recording in a part of Cocktail party and the non-mix processes in the ICA. In addition, a variety of advanced methods of evaluating independent sources in the ICA model will be compared. © 2023 IEEE.

关键词： BSS-Blind Source Separation Cocktail Party Algorithm ICA-Independent Component Analysis signal Separation Unsupervised learning

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Fast 3D Object Segmentation Using DBSCAN Clustering Based on Supervoxel 2

Fast 3D Object Segmentation Using DBSCAN Clustering Based on...

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2nd international conference on signal Image processing and Communication, ICSIPC 2022

作者： Yao, Tuo Yang, Qinghua Zhang, Rui School of Mechanical Engineering and Automation Shanghai University Shanghai200444 China

ISBN: (纸本)9781510657694

For visual perception of indoor mobile robots, real-time segmentation of 3D objects is a very challenging problem. Due to the complexity and disorder of indoor point cloud data, many methods have been proposed to improve the segmentation accuracy, which most of them cannot meet the requirement of rapidity. Therefore, this paper proposes a method for fast segmentation of 3D point clouds based on supervoxel density clustering. First, the ground under the indoor scene is removed by using plane fitting algorithm and the point cloud data after ground removal is denoised at the same time. Then, the target object is over-segmented to obtain the supervoxels with local geometric features. The core points of the nearest neighbors at the center of the supervoxels are searched out using the kdtree indexing acceleration. Finally, the density clustering of the supervoxels is performed based on the core point density up to. Experimental results on publicly available datasets show that our proposed method can effectively improve the speed of the algorithm while ensuring accuracy. © 2022 SPIE.

关键词： Clouds Image segmentation Detection and tracking algorithms 3D acquisition 3D vision Data modeling Data processing machine learning Visualization Voltage controlled current source

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Design and Performance Analysis of a Broadband Rectengular Microstrip Patch Antenna for 5G Application 2

Design and Performance Analysis of a Broadband Rectengular M...

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2nd international conference on Paradigm Shifts in Communications Embedded Systems, machine learning and signal processing, PCEMS 2023

作者： Qureshi, Md. Jasim Uddin Sayeed, Md. Abu Hossain, Md. Azad Islam, Thohidul Chittagong University of Engineering & Technology Electronics & Telecommunication Engineering Chattogram Bangladesh Port City International University Electrical & Electronic Engineering Chattogram Bangladesh

ISBN: (纸本)9798350310719

A microstrip line-fed broadband rectangular microstrip patch antenna is proposed to work in 5G applications in an endeavor to play a significant role in the communication system, which operates at 28 GHz. An initial rectangular microstrip patch antenna has been designed and analysis its performance. The intended operating frequency range has been tuned in using a cutting the slot and edge with a partial ground plane approach (28.00 GHz-29.96 GHz). The inset-fed microstrip patch antenna presented in this paper has a defected ground structure (DGS) and is constructed on a Teflon substrate with a 2.1 dielectric constant. The use of a defected ground slot increases the antenna's bandwidth and efficiency compare to full ground plane of antenna. With a wide -10dB bandwidth of 1.96 GHz, the developed antenna is projected to function at 28.90 GHz for the 5G application. Its return loss is -39.42dB. Additionally, the antennas have an overall size of 35 x39x1.57 mm3, a wide bandwidth of 1.96 GHz, and strong gain and directivity across the whole operating band. The VSWR value is 1. This proposed antenna produces better outcomes than some of the existing antennas described in a recent scholarly study. First, an antenna is designed, followed by simulation and performance analysis done using Computer Simulation Technology (CST) software. The improvement of the antenna's parameters and operational bandwidth for 5G applications significantly improved during analysis. It follows that this antenna will probably be adequate for 5G wireless communication systems. © 2023 IEEE.

关键词： Defected ground structures

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