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Fingerprint Image Enhancement: Multi-Filter Approaches and Stage Timing Evaluation

Fingerprint Image Enhancement: Multi-Filter Approaches and S...

引用

Sustainable Expert Systems (ICSES), International Conference on

作者： Prof Neeraj Bhargava Pramod Singh Rathore Ankur Goswami Kapil Chauhan Suresh Choudhary School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India Department of Computer Applications Govt. Engineering College Bikaner India Aryabhattt College of Engieering and research Centre India MDS University Ajmer India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The performance of fingerprint recognition and verification systems heavily depends on the quality of the input fingerprint images. This paper introduces a multi-filter image enhancement strategy tailored for enhancing low-quality fingerprint images. The objective is to enhance the visibility and definition of ridge patterns, tackling problems stemming from irregular skin contacts and contaminated sensor surfaces. Author incorporates a new normalization technique that adjusts for finger flexibility, improving the correction of anomalies and boosting overall image quality, especially in detecting fine details. Experimental evidence shows that technique surpasses previous methods in effectiveness.

关键词： Image quality Image matching Surface contamination Refining Fingerprint recognition Boosting Skin Timing Security Reliability

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Performance Evaluation of Bit-String Normalisation of Fingerprint Database

Performance Evaluation of Bit-String Normalisation of Finger...

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Intelligent Technologies (ACOIT), Asian Conference on

作者： Neeraj Bhargava Pramod Singh Rathore Ankur Goswami Kapil Chauhan Sankarsan Panda School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India Department of Computer Applications Govt. Engineering College Bikaner India Aryabhattt College of Engieering and research Centre India MDS University Ajmer India

ISBN: (数字)9798350374957

ISBN: (纸本)9798350374964

This study presents a comprehensive methodology for fingerprint analysis, employing a sequence of advanced image processing techniques including preprocessing, skeletonization, and minutiae extraction, leading to the generation of a digital bit-string representation. By enhancing image quality through histogram equalization and adaptive thresholding, and by refining fingerprint ridge patterns to single pixel widths via skeletonization, the proposed techniques facilitate precise minutiae extraction. These steps significantly improve the accuracy and reliability of biometric identification systems, as demonstrated by the dense and accurate distribution of minutiae points. The resultant digital encoding supports efficient data storage and rapid matching processes, essential for robust biometric verification. This research underscores the potential of these methodologies to enhance the performance of fingerprint-based security systems and lays the groundwork for future integrations with machine learning for automated and scalable biometric solutions.

关键词： Accuracy Image coding Thresholding (Imaging) Scalability Refining Machine learning Fingerprint recognition Biometric identification Robustness Security

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Peak Pattern Classification in Mass Spectrometer Data using Deep Learning Techniques

Peak Pattern Classification in Mass Spectrometer Data using ...

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Electronics, Computing, Communication and Control Technology (ICECCC), International Conference on

作者： Ushas A K Dhanya M B Jerrin Thomas Panachakel Deepak S Viji H Smitha V Thampi R Satheesh Thampi Department of CSE College of Engineering Trivandrum Thiruvananthapuram India Space Physics Laboratory VSSC ISRO Thiruvananthapuram India Department of ECE Govt. Engineering College Thrissur India Computer & IT Group VSSC ISRO Thiruvananthapuram India

ISBN: (数字)9798350371802

ISBN: (纸本)9798350371819

Mass spectrometry is a powerful analytical tool that measures the mass-to-charge ratio (m/q) of ions. Its applications span diverse fields, from environmental analysis on Earth to deciphering planetary atmospheres in space missions. CHandra's Atmospheric Composition Explorer-2 (CHACE-2) is a neutral gas mass spectrometer deployed on the Chandrayaan-2 orbiter, identifies the lunar exosphere's composition by analyzing the m/q ratios of collected gas samples. However, manually interpreting these mass spectra to pinpoint specific elements is time-consuming. This study presents deep learning techniques, particularly ID Convolutional Neural Networks (ID CNNs) and a ID CNN-Long Short-Term Memory (LSTM) hybrid model, to automate peak pattern identification. CNNs demonstrate particular proficiency in extracting local features within the data, while LSTMs capture temporal dependencies. By leveraging deep learning, this approach offers significant potential for rapid and accurate identification of specific peak patterns in mass spectrometer data.

关键词： Atmospheric measurements Space missions Supervised learning Terrestrial atmosphere Training data Pattern classification Mass spectroscopy

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Performance Analysis of Channel Engineered GAA-TFETs for Optimized Device Design

Performance Analysis of Channel Engineered GAA-TFETs for Opt...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Dandu, Obulesu Sathish, Anchula Jntua Department of Ece Andhra Pradesh Anantapuramu515002 India Govt. Polytechnic Department of Ece Andhra Pradesh Tadipatri515411 India Rajeev Gandhi Memorial college of Engineering and Technology Department of Ece Andhra Pradesh Nandyal518501 India

ISBN: (纸本)9798331527495

This paper provides an extensive evaluation of the characteristics of the proposed Gate-All-Around Transistor (GAATFET) made of silicon (Si), silicon-germanium (SiGe), and germanium (Ge) as channel material considering most relevant device metrics in the form of ON current (Jon), OFF current (JoJ J), subthreshold swing (SS), threshold voltage. Based on such analysis, the channel material Ge shows to achieve maximum, which places Ge among the best for such applications, whereas SiGe and Si channel devices offer minimum, providing efficient suppression of leakage currents. The lowest SS was determined for SiGe (23.1 mV/dec), thereby increasing the switching effi-ciency of this compound, whereas the highest value of SS is recorded by Ge, with 40.3 m V /dec due to the relatively narrow bandgap of the latter. The threshold voltages varied between 0.66 V for Si and 0.84 V for Ge and lie at 0.712 V for SiGe. The present findings stress the importance of material selection in channel materials to optimize the performance of GAATFETs for specific applications. Si excels in energy-efficient designs with minimal leakage, Ge dominates in high-performance scenarios requiring high drive current, and SiGe emerges as a versatile choice, balancing power efficiency and performance. © 2025 IEEE.

关键词： Leakage currents

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Improving Facial Recognition Accuracy Using Local Binary Patterns

Improving Facial Recognition Accuracy Using Local Binary Pat...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Prof Neeraj Bhargava Pramod Singh Rathore Deepshikha Saxena Ankur Goswami School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India MDS University Ajmer India Department of Computer Applications Govt. Engineering College Bikaner India

The proposed method involves pre-processing of the dataset, which includes normalization and resizing of the images to a standard size. Local binary pattern is then used to extract features from the pre-processed images. These features are used to train a machine learning model, specifically a support vector machine, to recognize faces in the images. The effectiveness of the proposed approach was evaluated using a publicly available dataset, and the results indicate a high level of accuracy in face detection, with a recognition rate of over 90%. The approach was also tested on images with varying contrast and rotation angles, and the results demonstrate its ability to accurately identify faces under such conditions. The proposed method has practical applications in various areas such as surveillance, access control, and forensics. It has the potential to be integrated into security systems for real-time face recognition and identification, improving the accuracy and efficiency of such systems. Furthermore, the method can be extended to include more complex features for more accurate and robust face detection. Overall, this research contributes to the growing body of knowledge on face detection techniques and their practical applications.

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A Machine-Learning Approach to Time Series Forecasting of Temperature 6

A Machine-Learning Approach to Time Series Forecasting of Te...

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6th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2022

作者： Pant, Janmejay Sharma, Rakesh Kumar Juyal, Amit Singh, Devendra Pant, Himanshu Pant, Puspha Graphic Era Hill University Department of Computer Science and Engineering Bhimtal Campus India Pal College of Technology and Management Department of Computer Application Haldwani India Graphic Era Hill University Department of Computer Application Dehradun India M.B. Govt. P.G College Kumaun University Department of Geography Haldwani India

ISBN: (纸本)9781665482714

In the modern world, weather forecasting is an essential application. The forecasts can help us reduce weather- related losses. The need for a massive data and highly computationally intensive parameterization procedure can be eliminated or reduced by the use of machine learning and deep learning algorithms for forecasting. This research work intends to forecast the temperature of three cities (Dehradun, Mukteshwar and Pantnagar) of Uttarakhand using emerging time series model AR/MA (Auto Regressive Integrated Moving Average). The results of this study prove that using the auto ARIMA produces very less MAPE (Mean Absolute Percentage Error) score for all testing data of all three regions. Our used model produces 8.45%,9.65% and 5.64% mean absolute percentage error in temperature data for Dehradun, Mukteshwar and Pantnagar respectively. Hence this paper explains briefly how different parameters can be used to formulate the AR/MA model to predict temperature. MAPE (Mean Absolute Percentage Error) indicates that auto AR/MA model yields excellent results. © 2022 IEEE.

关键词： Time series

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A Deep Learning Aided Intelligent Framework for Condition Monitoring of Electrical Machinery

A Deep Learning Aided Intelligent Framework for Condition Mo...

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Devices for Integrated Circuit (DevIC)

作者： Biswarup Ganguly Radha Kumari Ray Arunava Chatterjee Subho Paul Electrical Engineering Department Meghnad Saha Institute of Technology Kolkata India Application Development Associate Accenture Kolkata India Electrical Engineering Department Raghunathpur Govt. Polytechnic Purulia India School of Electrical Engineering and Computer Science Washington State University Pullman WA USA

Single-phase induction motors (SPIM) are fairly efficiently employed in household devices and possess wide applications in the industrial system. Typically, current signature-analysis (CSA) based methods have been used for fault diagnosis, but not always been an efficient solution, especially for fractional kW SPIMs that may be replaced entirely iffaulty. This paper presents a deep learning (DL) based fault detection scheme of a SPIM which is in-service in household devices. The fault detection is done employing a wavelet based deep dense convolutional neural network (W-D2CNN), where several wavelets are used as convolutional filters for convolution operation. The convoluted feature maps are further passed through a proposed nonlinearity, i.e., SLReLU, for richer feature extraction. The uniqueness of the proposed method lies in the feature learning ability, where upper-and lower-layer features of the dense block fuse via concatenation operation. Also, the SL-ReLU has been designed to combine logarithmic function, softsign, and ReLU operation. The designed SL-ReLU minimizes the probability of convoluted output and gradient explosion, due to which training has been improved along with feature learning ability. Comprehensive results reveal the efficacy of the proposed work in terms of performance metrics with state-of-the-art DL frameworks using the same dataset. The proposed work can also be extendable to multiphase-induction machines or any other electrical device working in any environment for fault identification.

关键词： Representation learning Fault diagnosis Training Deep learning Fault detection Feature extraction Explosions

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Enhancing Facial Recognition and Tracking for Security Applications using Haar Cascade Classifier

Enhancing Facial Recognition and Tracking for Security Appli...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： Neeraj Bhargava Pramod Singh Rathore Meenakshi Jha Ankur Goswami School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur Jaipur India MDS University Ajmer Department of Computer Applications Govt. Engineering College Bikaner India

Facial recognition is used in biometric technology. Facial recognition is the process of using the face to recognize or verify a person's identity. It captures, analyses, and compares patterns based on a person's facial details. The task of authenticating or identifying human faces from multimedia photos is accomplished using facial recognition technology. Viola and Jones present a method capable of accurately and quickly detecting faces in images. This technology can be used to accurately detect the facial features. Face tracking technology provides better opportunities for security and surveillance. This increases the level of protection. For example, facial tracking software helps improve surveillance strategies and serves as a basis for identifying terrorists and criminals. National borders, casinos, museums, banks or prisons are other examples where this system can be used for security. This paper proposes a face detection and eye and smile recognition system with real time images, using the Haar Cascade classifier for face, eye and smile recognition. This trace is based on the jpeg/jpg file format and can be acquired from any type of camera. It works with almost all types of image formats. Its classifier will detect facial features. In this research paper a comparison is also analyzed between face and facial detection by a camera captured image and saved images. Analysis of both types of code is also being made in this research work. The document also gives the percentage of accuracy by comparing the results. Therefore, this work aims at face processing with eye and smile detection. In this article it first detects the face using a web camera and then the captured image is processed and, on the image, it detects the face, eyes and smile.

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An intelligent weather prediction system based on IOT 4

An intelligent weather prediction system based on IOT

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4th International Conference on Devices for Integrated Circuit, DevIC 2021

作者： Sadhukhan, Mrinmoy Dasgupta, Sudakshina Bhattacharya, Indrajit Indira Gandhi National Open University School of Computer and Information Sciences WB Kolkata India Govt. College of Engg. and Textile Technology Department of Information Technology WB Serampore India Kalyani Govt. Engineering College Department of Computer Applications WB Kalyani India

ISBN: (纸本)9781728199559

Drastically changes in climate conditions reflect an unpredictable behavior of weather which becomes a real problem in daily life. The conventional forecasting model reports some inaccurate forecasts due to noncompliance with the climatic dynamicity. This paper proposed an improved solution for weather monitoring using low-cost GPS-enabled IOT devices, connected with different sensors. The sensed information is stored in the server to predict the weather parameters such as temperature, humidity, air pressure, etc. of any defined application region of interest. Here, some soft computing tools like SVM, KNN, DNN, Ridge, Linear Regression, and ANN have been explored on these weather parameters and establishes a correlation between conditions in a prescribed geographical location for upcoming days. © 2021 IEEE.

关键词： Weather forecasting

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Adaptive Clustering Algorithm for Big Health Data Classification

Adaptive Clustering Algorithm for Big Health Data Classifica...

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Innovation in Technology (ASIANCON), Asian Conference on

作者： Neeraj Bhargava Pramod Singh Rathore Meenakshi Jha Ankur Goswami School of Engineering & System Sciences MDS University Ajmer India Department of Computer and Communication Engineering Manipal University Jaipur India MDS University Ajmer India Department of Computer Applications Govt. Engineering College Bikaner India

This paper presents an analysis of tobacco and alcohol consumption among different age groups, using data collected by the World Health Organization (WHO). The analysis quantifies alcohol and tobacco consumption in grams per day, aggregating the total amount consumed by each age group. Consumption of alcohol is considered not only through wine, but also through other mediums such as medicine, inhalers, etc. while tobacco is consumed through smoking, dry tobacco, etc. Our analysis also takes into account whether the cases of consumption were controlled or uncontrolled. The majority of cases analyzed in our study were found to be controlled. Finally, we present graphical representations of the analysis results, highlighting the dependencies between different variables.

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