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9th international conference on Science, technology, Engineering and Mathematics, ICONSTEM 2024

作者： Johari, Lalit Ramya, P. Suganya, M. Praveena, Hirald Dwaraka Lathamageshwari, P.S. Rajendiran, M. Iftm University Department of Computer Applications Uttar Pradesh Moradabad244102 India Bannari Amman Institute of Technology Department of Electronics And Communication Engineering Tamil Nadu Sathyamangalam638401 India Annapoorana Engineering College Department of Electronics And Communication Engineering Tamil Nadu Salem636308 India Department of Electronics And Communication Engineering Andhra Pradesh Tirupati517102 India Tamil Nadu Kavaraipettai601206 India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350365092

This paper explores the operation of deep literacy- grounded noise reduction ways in electronic signal processing. Noise hindrance poses a significant challenge in colorful signal processing operations, impacting the delicacy and trustability of data analysis and interpretation. Traditional noise reduction styles frequently calculate on heuristic approaches or signal processing algorithms, which may have limited effectiveness, especially in complex and dynamic surroundings. In discrepancy, deep literacy algorithms, similar as convolutional neural networks(CNNs) and intermittent neural networks(RNNs), offer promising results for noise reduction by learning complex patterns and connections directly from data. This paper determines the state- of- the- art deep literacy- grounded noise reduction ways, including denoising autoencoders, generative inimical networks(GANs), and intermittent neural network- grounded models. Case studies and experimental results demonstrate the effectiveness and performance of these ways in colorful signal processing tasks, similar as audio denoising, image restoration, and biomedical signal processing. The integration of deep literacy- grounded noise reduction styles into electronic signal processing systems shows significant eventuality for perfecting signal quality and enhancing the delicacy of data analysis in different operations. © 2024 IEEE.

关键词： Recurrent neural networks

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Indoor Positioning based on the Integration of IMU and UWB 8

Indoor Positioning Based on the Integration of IMU and UWB

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8th international conference on Communication, image and signal Processing, CCISP 2023

作者： Yanming, Liang Jiale, Du Haiyang, Zhao Kui, Xu School of Automation Information and Engineering Xi'an University of Technology Xi'an China

ISBN: (纸本)9798350305838

Accurate indoor location information is of utmost importance for the navigation of modern indoor mobile robots. However, achieving accurate positioning solely with ultra-wideband (UWB) technology in indoor environments is challenging due to the susceptibility of UWB measurements to non-line-of-sight (NLOS) effects. To address this issue, this paper proposes an indoor positioning system that integrates an inertial measurement unit (IMU) with UWB. Firstly, an analysis of the errors present in UWB measurement data leads to the development of a two-step calibration method based on least squares (LS) to estimate error parameters. Subsequently, an Extended Kalman Filter (EKF)-based integration localization algorithm for IMU and UWB is proposed to mitigate the impact of NLOS. Through comprehensive simulations, the suggested algorithm's performance is assessed and contrasted with the weighted least squares (WLS) approach. Experimental findings show that the two-step LS-based calibration method estimates error parameters with accuracy. Moreover, the EKF-based IMU and UWB integration algorithm achieves approximately half improvement in localization accuracy compared to WLS, it also demonstrates excellent robustness. Consequently, the suggested indoor positioning system, which integrates IMU and UWB, satisfies the requirements for accuracy for indoor mobile robot navigation. © 2023 IEEE.

关键词： Indoor positioning systems

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Multi-Objectives based Reliable Routing Algorithm for IoT Health Care

Multi-Objectives Based Reliable Routing Algorithm for IoT He...

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2023 international conference on Smart systems for Applications in Electrical Sciences, ICSSES 2023

作者： Raghavendra, M. Sunitha, N.R. Siddaganga Institute of Technology Department of Cse Tumkur India

ISBN: (纸本)9798350347296

Efficient routing techniques play an important role in IoT healthcare. Existing Multi-objectives based routing is done based on optimal route delay, energy, distance, overhead, QOS, trust and security. Since the speed and Received signal Strength (RSSI) are very important parameters in the critical IoT health care, routes will have major impact. The proposed approach considers these two additional parameters, which uses clustering technique to organize the IoT network. The nodes are grouped together to from a cluster and cluster head is selected. The data will be sent to the cluster head. The next forwarding cluster head is selected based on above said parameters. and data will finally reach the Gateway. The simulation results shows that proposed algorithm - Multi-Objectives based Reliable Routing Algorithm (MOBRRA) for IoT Health Care, performs better compared to the existing algorithm w.r.t. the energy, delivery probability, latency, network load, network life time and throughput. © 2023 IEEE.

关键词： internet of things

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Pseudo Pairs based Unsupervised Deep Hashing for image Retrieval 7

Pseudo Pairs Based Unsupervised Deep Hashing for Image Retri...

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7th international conference on Vision, image and signal Processing, ICVISP 2023

作者： Zhang, Hao School of Management Tianjin University of Technology Tianjin China

ISBN: (纸本)9781839539848

The research presents a new deep learning framework, the pseudo-pair-based unsupervised deep hashing (PPUDH), designed to enhance image retrieval systems. PPUDH employs a soft clustering approach that iteratively trains clusters with strong discriminative capabilities and creates binary codes (BCs) with heightened correlation sensitivity. These clusters are then amalgamated to form an additional distribution for deriving hash codes (HCs). The model undergoes optimization via standard stochastic gradient descent (SGD). This optimization process marries the reconstruction loss from the encoder tasked with auto-reconstruction with the loss incurred from meeting binary code requirements. The efficacy of PPUDH has been validated through comprehensive evaluations of three renowned datasets. The outcomes of these tests demonstrate that PPUDH offers a considerable advancement over existing top-tier methods in the field. © The Institution of Engineering & technology 2023.

关键词： Binary codes

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Machine Learning based Pattern Recognition with HOG Features for Detection of Islanding 3

Machine Learning Based Pattern Recognition with HOG Features...

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3rd IEEE international conference on Electrical Power and Energy systems, ICEPES 2024

作者： Pal, Kumaresh Goswami, Subhadip Bhuyan, Anshuman Koley, Santanu Bera, Bidisha Basu, Sarmistha National Institute of Technology Department of Electrical Engineering Jamshedpur India National Institute of Technology Department of Electrical Engineering Arunachal Pradesh India Gandhi Institute for Education & Technology Department of Electrical Engineering Bhubaneswar India Haldia Institute of Technology Department of Computer Science Engineering Haldia India Global Institute of Science and Technology Department of Computer Science and Technology Haldia India Calcutta Institute of Engineering and Management Department of Physics Kolkata India

ISBN: (纸本)9798350390728

Islanding is a significant difficulty that arises from the high integration of distributed generation sources. Islanding could potentially damage the clients and their equipment. According to the IEEE 1547 DG interconnection requirements, islanding must be detected within two seconds, and the DG must be shut down. This research introduces a new method for identifying islanding using image pattern recognition utilising a Machine Learning classification algorithm. The suggested technique utilises the Random Forest (RF) classification algorithm. images are utilised to extract the histogram of oriented gradient (HOG) features in order to detect both non-islanding and islanding occurrences. The spectrogram images are formed from the time-series signal collected from the point of common coupling. The images are utilised to extract features from histogram of oriented gradient, which are subsequently used as feature vector inputs for training and testing the RF classifiers. The spectrogram image is derived using the rate of change of the voltage parameter. The efficacy of the RF classifier is evaluated using 5, 10, 20, and 25-fold cross-validations. The classification findings indicates that islanding detection utilising a Random Forest classifier and Histogram of Oriented Gradients (HOG) feature for image pattern recognition obtained outstanding performance with 98.75% accuracy and a detection time of 192 milliseconds. © 2024 IEEE.

关键词： Spectrographs

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Density based Traffic signal Light Control with Machine to Machine Communication System

Density Based Traffic Signal Light Control with Machine to M...

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2024 international conference on Advances in Computing, Communication, Electrical, and Smart systems, iCACCESS 2024

作者： Sakib, Nazim Uddin Faisal, Md Mostafa Amir Sakib, Muhammad Sadman Islam, Md. Sajjad-Ul Chowdhury, Mohammad Jawad Ahmed, Alif International Islamic University Chittagong Dept. of Electronic and Telecommunication Engineering Chattogram4318 Bangladesh International Islamic University Chittagong Dept. of Electrical and Electronic Engineering Chattogram4318 Bangladesh

ISBN: (纸本)9798350350289

Automation has significantly simplified our daily lives, but the escalating number of vehicles on the roads, particularly in Bangladesh, has led to persistent traffic congestion and increased human effort. The existing manual traffic light system proves inadequate for addressing this issue promptly. To address this, we propose an experimental solution, an automated traffic signal system operating 24/7 with Machine-to-Machine (M2M) communication. This system aims to minimize human effort, save time, and effectively alleviate traffic congestion. Our approach involves a vehicle density measurement device utilizing a Light-Dependent Resistor (LDR) and laser technology to gauge the density of passing vehicles. This device identifies the maximum number of cars within a predetermined time frame. If the LDR detects a high volume of traffic, signaling excessive congestion, this information is relayed to the master unit (ESP32). Conversely, if the LDR registers a normal flow with fewer vehicles, this data is also transmitted to the master unit. The master unit analyzes the data to determine the traffic conditions. In typical situations, when the LDR and laser detect a vehicle, the corresponding green LED is illuminated. This dynamic adjustment of LED status based on vehicle density helps mitigate undesirable traffic congestion, ultimately saving valuable time for the public. Notably, our solution operates without the need for internet connectivity for M2M communication. © 2024 IEEE.

关键词： Traffic congestion

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Chaos Theory based Gravitational Search Algorithm For Medical image Segmentation 27th

Chaos Theory Based Gravitational Search Algorithm For Medica...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Rather, Sajad Ahmad Roy, Partha Pratim Das, Sujit Department of Computer Science and Engineering Indian Institute of Technology Roorkee247667 India Department of Computer Science and Engineering National Institute of Technology Telangana506004 India

ISBN: (纸本)9783031781032

Multilevel thresholding plays a crucial role in image processing, with extensive applications in object detection, machine vision, medical imaging, and traffic control systems. It entails the partitioning of an image into distinct regions based on optimal pixel values. However, as the number of threshold levels increases, so does the computational cost for segmentation. To address this challenge, a novel method is proposed namely Chaos theory based Gravitational Search Algorithm (CGSA) for multilevel thresholding. CGSA combines the standard Gravitational Search Algorithm (GSA) for exploration with chaotic maps for exploitation of the complex pixel problem space. In this study, Kapur’s entropy method is utilized to segment sample images into various partitions based on optimal pixel values. The effectiveness of CGSA in real-world scenarios is evaluated using COVID-19 chest CT scan imaging datasets from Kaggle database. The quality, symmetry, and consistency of the segmented output are assessed using metrics like Peak signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Feature Similarity Index Measure (FSIM). Qualitative analysis includes convergence curves, segmented graphs, colormap images, and box plots. Statistical validation is conducted using the signed Wilcoxon rank sum test. Additionally, a comparison is made between CGSA’s performance and that of eight state-of-the-art heuristic algorithms. The findings demonstrate the superior performance of CGSA, evidenced by its reduced computational time and enhanced image quality metrics values. Specifically, CGSA achieved SSIM of 0.81, FSIM of 0.82, and PSNR of 24.27, surpassing the performance of other competitive algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： signal to noise ratio

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GNSS Anti-spoofing Detection based on Gaussian Mixture Model Machine Learning 25

GNSS Anti-spoofing Detection based on Gaussian Mixture Model...

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IEEE 25th international conference on Intelligent Transportation systems (ITSC)

作者： Feng, Zejian Seow, Chee Kiat Cao, Qi Univ Glasgow Sch Comp Sci Glasgow Lanark Scotland

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

Nowadays, the security of Global Navigation Satellite System (GNSS) has raised much more concerns due to the reliance on its position, velocity, and timing (PVT) information, which is of vital importance to various internet of Things (IoT) systems, robotics, 5G technology and many applications of Intelligent Transportation systems (ITSC). It has been shown that GNSS system can be easily spoofed and masqueraded to provide ill intent payload damages. This paper proposes a novel algorithm based on unsupervised machine learning Gaussian Mixture Models (GMM) to provide antispoofing capability of GNSS signal such as GPS signal. It segregates GPS signals that are not under spoofing, from spoofed GPS signals that will result in malicious changes of pseudo-range measurements. It has been found out that the proposed GMM clustering algorithm is able to cluster the positions generated by the un-spoofed GPS signals properly and return the PRN (pseudo-range noise) codes of the satellites without spoofing effectively. The proposed GMM clustering algorithm could cluster the position points generated by nonspoofed signals properly by more than 90% and 77% accuracy for one and three spoofed satellites respectively.

关键词： Global navigation satellite system Machine learning algorithms Satellites Clustering algorithms Machine learning Timing internet of Things

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Improved Pulse Coupled Neural Network based on Maximize SNR for Vessel Extraction

Improved Pulse Coupled Neural Network based on Maximize SNR ...

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2024 international conference on Advancements in Power, Communication and Intelligent systems, APCI 2024

作者： Singh, Davinder Paul Murat Singh, Ram Gopi, T. Deepa, D. Gandhi, K. Parimala Kumar, Divesh Pandit Deen Dayal Energy University Department of Computer Science and Engineering Gandhinagar India Woxsen University School of Technology Telangana India Annamacharya Institute of Technology and Sciences Department of Ece Hyderabad India St Joseph's College of Engineering Department of Ads Chennai India Nehru Institute of Technology Department of Chemistry Coimbatore India Graphic Era Hill University Department of Electronics and Communication Engineering Uttarakhand India

ISBN: (纸本)9798350363289

Aiming at the problems of branch loss and fracture in the fundus retinal vessel segmentation algorithm, with improved pulsed coupled neural network (PCNN) is proposed. As the matched filter can maximize the signal to noise ratio even though using the gaussian filter, therefore, it is used for reducing the noise. First, matching filters are used to enhance the retinal image, and then a segmentation algorithm is suggested using a combination of matching filter and PCNN. Finally, the evaluation on DRIVE database demonstrates that the sensitivity, specificity, and accuracy of the algorithm are 0.7310, 0.9640, and 0.9711 accordingly compared with other algorithms. Simulation experiments illustrate that the Matched-PCNN algorithm resolves the effect of low light and optic disc highlight on vessel segmentation, and demonstrates good connectivity between blood vessels. © 2024 IEEE.

关键词： Matched filters

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Research on surface defect detection of glass wafer based on visual inspection

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ENERGY REPORTS 2022年 8卷 381-389页

作者： Huang, Zhangyu Ling, Long Univ Birmingham Dept Elect Elect & Syst Engn Birmingham B152TT W Midlands England Zhuhai Coll Sci & Technol Guangdong Intelligent Vis Precis Detect Engn Tech Zhuhai 519041 Peoples R China

Glass wafer (GW) is used in a variety of integrated circuit (IC) packaging applications and as substrates to provide better performance and cost-effectiveness. Glass wafer (GW) protects the IC from impact and corrosion while maintaining the contract pins and leads that connect it to the external circuit. In the process of technology or production, this kind of structure is continuously working for a long time. Due to the inherent defects such as bubbles generation, starvation and the structure is often suffered from acid, alkali, moisture, vibration and other factors, which makes its internal structure gradually form corrosion stains. These defects have posed a serious threat to the quality and performance of equipment. based on these disadvantages, this paper analyzes the defect detection principle of Glass wafer, then designed a method of determine the defect region. The edge signal processing method of visual image defects is studied, the edge detection and defect feature extraction model is established, and the principle of defining strong and weak edges is clarified. A defect feature classifier based on multi-layer perceptron (MLP) is created, and a segmentation algorithm of the classifier is implemented. Finally, a multi-channel image detection experimental platform is built to verify the typical unit structure. The experimental results show that the rate of defective features recognition is high, the detection rate is fast, and it has practical application value in engineering. The research of this recognition method has positive theoretical significance for accurately evaluating the overall reliability of GW structure and ensuring the safe operation of equipment. (c) 2022 The Author(s). Published by Elsevier Ltd. Peer-review under responsibility of the scientific committee of the international conference on Energy Storage technology and Power systems, ESPS, 2022.

关键词： Glass wafer (GW) Integrated circuit (IC) Surface defect Defective features recognition Multi-layer perceptron (MLP)

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