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Unified Dictionary Training Approach for High-Resolution Image Enhancement

Unified Dictionary Training Approach for High-Resolution Ima...

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Artificial Intelligence and signal processing (AISP), INDIA, International conference on

作者： Kannuru Srinadh Valaparla David Goruputi Lakshmi Karlapudi Ramakrishna Department of ECE NIT Rourkela Odisha India Department of ECE Anurag Engineering College(A) Kodada Telangana India Department of Mathematics NIT Rourkela Odisha India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

This research presents a new unified dictionary training super-resolution (UDTSR) approach to single and multi-image super-resolutions that uses a bilevel optimization frame-work and patchwise sparse recovery. By employing interconnected dictionaries to bridge the two spaces of image patches, we ensure that a sparse representation of a low-resolution (LR) image patch can accurately reconstruct its corresponding high-resolution (HR) patch. To facilitate efficient stochastic gradient descent, implicit differentiation calculates the gradient. Further-more, by using a neural network model for rapid sparse inference and selective processing of visually essential areas, we can almost tenfold improve the performance of real-world applications. Also, by discovering the fundamental relationships between different data modalities, our approach overcomes the difficulty of dealing with panchromatic and multispectral images. For example, using shared and individual sparse representations, we describe a data model that can detect similarities and differences in multimodal signals. Single image super-resolution (SISR) and multi-frame super-resolution (MFSR) are advanced separately, with minimal research on their ideal combination. We propose a novel UDTSR analysis using an iterative shrinkage and thresholding algorithm. Our simulations of many combinations of SISR and MFSR, such as x2, x3, and x4, confirm our theory quantitatively and qualitatively.

关键词： Training Dictionaries Sparse approximation Superresolution Stochastic processes signal processing algorithms Noise measurement signal resolution Optimization Noise level

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Optimizing Job Matching Through Automated Resume Screening with NLP and Machine Learning

Optimizing Job Matching Through Automated Resume Screening w...

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Intelligent signal processing and Effective Communication Technologies (INSPECT), IEEE International conference on

作者： CH Sai Surya Glvs Kiriti K Satya Saketh P Sri Charan A. Anjali 2021IMT-024 Information Technology ABV-IIITM Gwalior 2021IMT-035 Information Technology ABV-IIITM Gwalior 2021IMT-050 Information Technology ABV-IIITM Gwalior 2021IMT-072 Information Technology ABV-IIITM Gwalior Information Technology ABV-IIITM Gwalior

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

Resume screening determines a candidate's qual-ification for a position based on their education, experience, and other resume information. Candidates are only called for interviews if their resume matches the job description. Manual resume examination is time-consuming and burdensome, prompting companies to use applicant tracking systems to shortlist candidates based on their skills. The primary cause of rejection is often a mismatch between the job role and the applicant's skill set. Job seekers must understand which job categories align with their skill sets. This study employs natural language processing (NLP) and machine learning (ML) techniques to evaluate applicants in real time. Methods such as tokenization, stemming, lemmatization, TF-IDF, and string matching are used to identify keywords. Subsequently, ML models are applied to determine the most suitable role for each applicant. Four models-SVM, logistic regression, random forest, and k-nearest neighbors-are compared for accurate classification, with SVM outperforming the other benchmarks. This project aims to revolutionize the resume screening process by integrating advanced technologies and refining our methodologies.

关键词： Support vector machines Training Logistic regression Accuracy Resumes Refining signal processing algorithms Training data Nearest neighbor methods Random forests

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NeuroSonic: An Innovative Neuroacoustic Therapy to Enhance Mental Wellness with EEG Brain signal

NeuroSonic: An Innovative Neuroacoustic Therapy to Enhance M...

引用

IEEE Calcutta conference (CALCON)

作者： Trisha Paul Susmita Das Swati Chowdhuri Ankur Chanda Biswarup Neogi Sheli Sinha Chaudhuri Electronics and Telecommunication Engineering Department Jadavpur University Kolkata India Department of Electronics and Computer Science Narula Institute of Technology Kolkata India Department Institute of Engineering and Management Electronics and Electrical Engineering Kolkata India Department of Computer Science Engineering (AIML) Institute of Engineering and Management Kolkata India R&DJIS College of Engineering Kolkata India

ISBN: (数字)9798331516666

ISBN: (纸本)9798331516673

NeuroSonic” presents an innovative EEGbased music treatment system painstakingly designed to improve mental wellness. NeuroSonic is a program that uses state-of-the-art algorithms to evaluate brainwave data from the frontal and temporal lobes within a particular frequency band. Leveraging electroencephalography (EEG) data obtained from participants engaged in to solve mathematical problem statements. The research aims to elucidate the distinct patterns of neural activation across different brain lobes. Through meticulous statistical analysis of EEG signals, the underlying mechanisms governing brain activity before and after listening the healing sounds and these conditions are explored using EMOTIVE. The study employs advanced signal processing techniques and statistical analysis to discern temporal and spatial dynamics in neural responses. We have collected EEG data from 20 no. of subjects both male and female. ANOVA analysis is used for statistical representation and SVM algorithm is used for data modelling process. We got 85 percent accuracy in data modelling. The challenging job is the mapping of EEG data and integrating it into the proposed device. This method utilizes EEG data and frequency analysis to create customized sonic settings for Autism patients, enhancing their mental health and endurance through therapeutic auditory experiences.

关键词： Support vector machines Temporal lobe Cognitive neuroscience signal processing algorithms Mental health Brain modeling Electroencephalography Real-time systems Data models Analysis of variance

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Robust Adaptive OS-CFAR Detector for Maneuvering Group Target Based on Formation Prior Knowledge

Robust Adaptive OS-CFAR Detector for Maneuvering Group Targe...

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signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Mengxin Shi Qi Jiang Rui Wang Radar Technology Research Institute School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Real-Time Information Processing Technology of Embedded Beijing China The Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Detecting the individuals such like the bird flocks or a UAV swarm is a great challenge for radar systems. The lack of prior knowledge about the characteristics of dynamic group targets leads to the performance degradation of conventional detectors. In this study, we present a robust adaptive OS-CFAR detector for maneuvering group targets, leveraging prior knowledge of their formation. The group target’s state is updated and predicted using the random matrix approach. The projection length of the group target in the radar beam direction is computed using the predicted information during the group target tracking process. Using the projection length, the number of reference units occupied by the group target within the reference window is determined, and this prior knowledge is applied to update the detection parameters of the OS-CFAR detector. The simulation result shows that the algorithm can update the detection parameters timely and avoid target missed detection.

关键词： Target tracking Simulation Radar detection Detectors Radar Radar tracking Prediction algorithms Time measurement Real-time systems Motion measurement

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Research on Machine Learning and signal processing of IQ signals

Research on Machine Learning and Signal Processing of IQ Sig...

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International conference on Electronic Measurement and Instruments, ICEMI

作者： Xiaoyu Yu Hao Zeng Yu Tian Lianping Guo Peng Ye Meng Wang Chunming Liu School of Automation Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study UESTC Shenzhen China School of Automation Engineering UESTC Chengdu China Department of Research and Development Uni-Trend Technology (China) CO. Ltd Dongguan China University of Electronic Science and Technology of China Chengdu China

IQ signals are a core technology of the zero-IF architecture in modern communication technology, but they are often unstable due to factors such as amplitude and phase imbalances. Utilizing the characteristics of IQ two-way signals, common IQ signal calibration algorithms use compensation, frequency domain calibration, etc. This article uses the characteristics of Machine Learning and signal processing to train the existing IQ signals and predict the signals based on the training set. Through simulation experiments and actual data measurement, it can be known that different prediction models will have different correction effects. Among them, the Neural Networks model, which is mainly adopted in this article, has a relatively ideal effect on signal processing and prediction, and can also significantly make the signal smoother.

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A fast ray space transform for wave field processing using acoustic arrays 28

A fast ray space transform for wave field processing using a...

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28th European signal processing conference, EUSIPCO 2020

作者： Borra, Federico Pezzoli, Mirco Comanducci, Luca Bernardini, Alberto Antonacci, Fabio Tubaro, Stefano Sarti, Augusto Politecnico di Milano Piazza Leonardo Da Vinci 32 Milano20122 Italy

ISBN: (纸本)9789082797053

The importance of soundfield imaging techniques is expected to further increase in the next few years thanks to the ever-increasing availability of low-cost sensors such as MEMS microphones. When it comes to processing a relevant number of sensor signals, however, the computational load of space-time processing algorithms easily grows to unmanageable levels. The Ray Space Transform (RST) was recently introduced as a promising tool for soundfield analysis. Given the collection of signals captured by a uniform linear array of microphones, the RST allows us to collect and map the directional components of the acoustic field onto a domain called "ray space", where relevant acoustic objects are represented as linear patterns for advanced acoustic analysis and synthesis applications. So far the computational complexity of the RST linearly increases with the number of microphones. In order to alleviate this problem, in this paper we propose an alternative efficient implementation of the RST based on the Non Uniform Fast Fourier Transform. © 2021 European signal processing conference, EUSIPCO. All rights reserved.

关键词： Array processing

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From advanced Digital signal processing to Machine Learning

From Advanced Digital Signal Processing to Machine Learning

引用

IEEE Frontiers in Education conference (FIE)

作者： Zhou, Jun Yeh, Hen-Geul

ISBN: (纸本)9781665438513

Contribution: A modular three-credit senior undergraduate or graduate course that includes both advanced digital signal processing (DSP) and machine learning (ML) is introduced. This project-based course is featured at exploiting the connections between the two popular areas. It presents the advantages and limitations of each area to the students with the help of various specially designed course projects. Background: At most schools, DSP and ML are taught in two separate courses although the two have many similarities in both theorems and applications. It's partially due to the fact that the two areas are largely supported by two research communities in the literature. Intended Outcomes: Students understand the fundamentals of the DSP and the ML from both theorems and applications perspectives with a clear picture of the advantages and limitations in the state-of-the-art. Set up initial motivations and inspire the students to the future research activities. Application Design: An approach that concentrated content and project work on the modular domain knowledge was taken. The authors started with the probability and statistics, followed by a series of carefully selected topics from each area that can be combined in a systematic way, from theorems to applications, from algorithms to implementations. An oral presentation session was included to emphasize independent analysis and professional skills development in the student selected project. Findings: Based on the course survey, the students reported positive feedbacks on the newly structured course. It showed that this course can benefit from the strategic introduction of desirable technology in the curriculum efficiently. From the theorems perspective, students reported the domain knowledge of the two areas. From the applications perspective, students were exposed to the state-of-the-art tools in two disciplines, career preparation, and in-class engagement. A direct assessment showed a promising result.

关键词： advanced digital signal processing machine learning professional skills project-based learning

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An Analysis on the Energy Consumption of Coverage Path Planning algorithms on Multiple UAVs

An Analysis on the Energy Consumption of Coverage Path Plann...

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Artificial Intelligence Robotics, signal and Image processing (AIRoSIP), International conference on

作者： Anees Ul Husnain Takao Ito Norrima Binti Mokhtar Syed Ahmad Asyraaf Din Syed Amran Tsutomu Ito Dahari Mahidzal Department of Electrical Engineering Universiti Malaya Kuala Lumpur Malaysia Graduate School of Advanced Science and Engineering Hiroshima University Higashi- Hiroshima Japan Ube National College of Technology Ube Yamaguchi Japan

ISBN: (数字)9798350327472

ISBN: (纸本)9798350327489

One of the greatest limitations that aerial vehicles face nowadays is the limitation in flight durations. This issue becomes more critical in search applications where a specific area is required to be covered through drone using Coverage Path Planning (CPP) algorithms. Searching in bigger regions multiple drones are often deployed to cover an area. This work utilized multiple combinations of UAVs using different CPP algorithms to find the most optimal choice to cover a region. There were four simulation scenarios with the above-mentioned combinations which were evaluated for multiple parameters. These parameters include coverage completeness, coverage time, battery consumption and the number of maneuvers required to cover the region using ROS Gazebo Simulation and Qground Control. The data retrieved from the simulation results were then compared to determine the best CPP method. It was observed from the results that the Expanding Spiral method proved to be the fastest CPP method with the least coverage time, lowest battery consumption and least number of turns.

关键词： Energy consumption Spirals Simulation Image processing Path planning Batteries Robots Optimization Faces Drones

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Efficient Recognition of Volatile Organic Compounds Using Low-Cost E-Nose Device and Random Forest Classification

Efficient Recognition of Volatile Organic Compounds Using Lo...

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International conference on Control, Instrumentation, and Automation

作者： Ricardo Macías-Quijas Ramiro Velázquez Amir Aminzadeh Ghavifekr Roberto de Fazio Paolo Visconti Facultad de Ingeniería Universidad Panamericana Aguascalientes Mexico Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran Dept. of Innovation Engineering University of Salento Lecce Italy

ISBN: (数字)9798331509972

ISBN: (纸本)9798331509989

This paper introduces a novel method for the efficient recognition of volatile organic compounds (VOCs) using an electronic nose (e-nose) device coupled with advanced signal processing and classification techniques. The proposed e-nose is a cost-effective sensor array designed to detect a broad spectrum of VOCs with applications in several domains including industrial safety. Data collected from the sensors are processed using the Filter Diagonalization Method (FDM), a novel non-Fourier spectral estimation algorithm that provides reliable footprints from the VOCs. The classification stage employs a Random Forest (RF) model to analyze the FDM footprints and predict the VOC present in the e-nose’s surroundings. Experimental results show that the FDM-RF approach achieves a 96.4% classification accuracy.

关键词： Radio frequency Volatile organic compounds Accuracy Estimation signal processing algorithms Electronic noses Software Safety Software reliability Frequency division multiplexing

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A New Semantic Segmentation Technique for Interference Mitigation in Automotive Radar

A New Semantic Segmentation Technique for Interference Mitig...

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IEEE conference on Wireless Communications and Networking

作者： Ahmed A. ElSharkawy Abdallah S. Abdallah Mohamed W. Fakhr Department of Computer Engineering Arab Academy for Science Technology and Maritime Transport (AASTMT) School of Engineering Penn State Erie - The Behrend College

Recent autonomous vehicles and advanced Driver Assistance Systems (ADAS) are equipped with several sensing technologies, including cameras, LiDAR, radar, and ultrasonic. Due to its exceptional features, radar is increasingly utilized in a range of ADAS applications. Unfortunately, this increases the likelihood of radar-to-radar interference, which hinders radar functionality. Numerous research studies have investigated interference mitigation using various traditional signal processing or deep learning techniques. This paper presents a new technique utilizing the U-Net deep neural network (DNN) model for interference mitigation via semantic segmentation in such ADAS scenarios. By comparing the performance of the proposed model to previously published deep-learning-based approaches, our new model has demonstrated promising improvements based on standard evaluation criteria.

关键词： Deep learning Analytical models Semantic segmentation signal processing algorithms Interference Object detection signal processing

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