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Strain modified nano-scale Ge/Ge0.98Sn0.02 exotic pin photo-sensor array for IR sensing: theoretical reliability and experimental feasibility studies

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Optical and Quantum electronics 2024年第12期56卷 1-20页

作者： Bhadra, Bias Chinara, Suchismita Kundu, Abhijit Department of Computer Science and Engineering Chaibasa Engineering College Jharkhand Chaibasa India Department of Computer Science and Engineering NIT Rourkela Odisha Rourkela India Department of Electronics and Communication Engineering Chaibasa Engineering College Jharkhand Chaibasa India

In this paper, the superiority of Ge/Ge0.98Sn0.02asymmetrical supper lattice structure based vertically doped nano-scale pin photo-sensor under operating wavelength of 1200 nm to 2200 nm is reported. The authors have developed non-linear Strain Modified Quantum-Corrected Drift–Diffusion (SMQCDD) model for analyzing the electrical and optical characteristics of the photo-sensor. The inclusion of a small amount of Sn (2%) into the pure Ge material creates in-plane bi-axial strain in the intrinsic region (i-region) of the device. This results in increases in the value of the out-plane mobility of the charge particles. As a result, the overall performance of the photo-sensor enhances significantly. The authors have used in-plane induced bi-axial strain to accelerate the out-plane mobility of the charge particles by incorporation of the exotic asymmetrical supper lattice structure in the i-region of the photo-sensor. The validation of the non-linear SMQCDD model is performed through comparison of the simulated data obtained from SMQCDD model with the experimental results under a-like thermal/structural/electrical conditions. Additionally, the authors have designed 3X2 array of photo-sensors and studied the photo-electrical characteristics at the said operating wavelength. The proposed device offers better performance in terms of quantum efficiency (0.619: single-type photo-sensor;0.708: array-type photo) and photo-responsivity (0.056 A/W: single-type photo-sensor;0.808 A/W:for array-type photo-sensor)at 1600 nm wavelength compared to its conventional flat Si counterpart. The developed exotic pin photo-sensor can be used as a sensing device for applications in optical communication and bio-medical systems. As far as the authors are aware, this is the first report on nano-scale Ge/Ge0.98Sn0.02exotic pin photo-sensor. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Nanocrystals

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Design of Clustering Techniques in Cognitive Radio Sensor Networks

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computer Systems science & engineering 2023年第1期44卷 441-456页

作者： R.Ganesh Babu D.Hemanand V.Amudha S.Sugumaran Department of Electronics and Communication Engineering SRM TRP Engineering College621105Tamil NaduIndia Department of Computer Science and Engineering Sriram Engineering College602024Tamil NaduIndia Department of Electronics and Communication Engineering Saveetha School of Engineering602105Tamil NaduIndia Department of Electronics and Communication Engineering Vishnu Institute of TechnologyBhimavaram534202Andhra PradeshIndia

In recent decades,several optimization algorithms have been developed for selecting the most energy efficient clusters in order to save power during trans-mission to a shorter distance while restricting the Primary Users(PUs)*** Cognitive Radio(CR)system is based on the Adaptive Swarm Distributed Intelligent based Clustering algorithm(ASDIC)that shows better spectrum sensing among group of multiusers in terms of sensing error,power sav-ing,and convergence *** this research paper,the proposed ASDIC algorithm develops better energy efficient distributed cluster based sensing with the optimal number of clusters on their *** this research,multiple random Sec-ondary Users(SUs),and PUs are considered for ***,the pro-posed ASDIC algorithm improved the convergence speed by combining the multi-users clustered communication compared to the existing optimization *** results showed that the proposed ASDIC algorithm reduced the node power of 9.646%compared to the existing ***,ASDIC algorithm reduced 24.23%of SUs average node power compared to the existing *** of detection is higher by reducing the Signal-to-Noise Ratio(SNR)to 2 dB *** proposed ASDIC delivers low false alarm rate compared to other existing optimization algorithms in the primary *** results showed that the proposed ASDIC algorithm effectively solves the multimodal optimization problems and maximizes the performance of net-work capacity.

关键词： Adaptive swarm distributed clustering cognitive radio clustering algorithm distributed swarm intelligent energy efficient distributed cluster-based sensing multi modal optimization

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Falcon Optimization Algorithm-Based Energy Efficient communication Protocol for Cluster-Based Vehicular Networks

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computers, Materials & Continua 2024年第3期78卷 4243-4262页

作者： Youseef Alotaibi B.Rajasekar R.Jayalakshmi Surendran Rajendran Department of Software Engineering College of ComputingUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Electronics and Communication Engineering Sathyabama Institute of Science and TechnologyChennai600119India Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai602105India

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)*** networks give a safe and more effective driving experience by presenting time-sensitive and location-aware *** communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with ***,the scheme of an effectual routing protocol for reliable and stable communications is *** research demonstrates that clustering is an intelligent method for effectual routing in a mobile ***,this article presents a Falcon Optimization Algorithm-based Energy Efficient communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in *** FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the *** accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust *** the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR *** experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

关键词： Vehicular networks communication protocol clustering falcon optimization algorithm routing

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An FPGA Prototype for Parkinson’s Disease Detection using Machine Learning on Voice Signal

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IEEE Access 2025年 13卷 91113-91128页

作者： Khan, Mujeev Moiz, Abdul Khan, Gani Nawaz Wajid, Mohd Usman, Mohammed Ali, Jabir Z.H.C.E.T. Aligarh Muslim University Department of Electronics Engineering India Bennett University Department of Electronics and Communication Engineering India Bennett University School of Computer Science Engineering and Technology India

Parkinson’s disease (PD) is a chronic neurological disorder caused by a reduction in dopamine levels in the brain. Early diagnosis is crucial for effective treatment. This paper proposes an efficient machine learning model for PD detection using voice-based features, which offer a non-invasive, cost-effective, and accessible alternative to complex imaging methods. To enhance classification performance and reduce computational complexity, we evaluate three feature selection algorithms—Chi-squared (χ2), Minimum Redundancy Maximum Relevance (mRMR), and Analysis of Variance (ANOVA) — and adopt an incremental feature selection approach, where each feature set increment is assessed across five classifiers: K-Nearest Neighbors (KNN), Decision Tree (DT), Artificial Neural Network (ANN), Logistic Regression (LR), and Support Vector Machine (SVM). To address dataset imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) is applied. Among the evaluated approaches, the ANOVA algorithm yields the most optimal feature set, with the top 10 features enabling the Quadratic SVM to achieve a classification accuracy of 98.86%. The Quadratic SVM, optimized for minimal power consumption, is implemented on a Nexys A7 FPGA. This setup achieves a dynamic power consumption of just 23 mW and delivers a performance acceleration of 155× compared to equivalent computations on a 6th-generation Intel i5 processor. By combining a high-accuracy machine learning model with an energy-efficient FPGA implementation, our approach offers a powerful and portable solution for real-time PD detection. © 2013 IEEE.

关键词： Neurodegenerative diseases

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A robust framework for Alzheimer’s disease detection and staging: incorporating multi-feature integration, MRMR feature selection, and Random Forest classification

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Multimedia Tools and Applications 2024年 1-29页

作者： HK, Ravikiran R, Deepak H A, Deepak M S, Prapulla Kumar S, Sharath G H, Yogeesh Department of Electronics and Communication Engineering Navkis College of Engineering Karnataka Hassan573217 India Department of Electronics and Communication Engineering BGS Institute of Technology Adichunchanagiri University B.G Nagar Karnataka Mandya571448 India Department of Computer Science Malnad College of Engineering Karnataka Hassan573202 India Department of Electronics and Communication Engineering Government Engineering College KarnatakaChamarajanagar 571313 India

In advancing Alzheimer’s disease detection, this study presents a potent hybrid approach that leverages sophisticated features. Commencing with the TVL1 norm, the method reduces noise and enhances relevant information within neuroimaging data. Further cartoon features from the TVL1 norm are extracted as texture features. GLSZM features unveil critical texture pattern variations, an early hallmark of Alzheimer’s, by meticulously analyzing the size and distribution of homogeneous zones in the brain. Simultaneously, level-set segmented image features provide invaluable structural insights into the brain’s morphology. Integrating a Convolutional Neural Network (CNN) empowers the system to decipher intricate spatial patterns. At the same time, minimum redundancy and maximum relevance (MRMR) feature selection meticulously curates the most salient and non-redundant features. Finally, a Random Forest classifier seamlessly harmonizes these diverse feature sources, culminating in a robust model that can discern subtle signatures of Alzheimer’s disease. The novel approach not merely presents a rapid and precise module for Alzheimer’s disease (AD) diagnosis. Still, it also provides the capacity for both comprehensive global classification ("Normal vs. Mild Cognitive Impairment (MCI) vs. AD") and precise local classification. The latter facet addresses the intricate undertaking of differentiating MCI into three categories: Very Mild Dementia (VMD), mild dementia (MD), and Moderate Dementia (MoD) as early AD stages. Moreover, the study undertakes a comparative analysis of the proposed methodology against previous techniques in the field. The reported results demonstrate exceptional accuracy and robustness, with an overall testing accuracy of 93.11%, showcasing the method’s effectiveness in AD diagnosis and staging. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Fake and propaganda images detection using automated adaptive gaining sharing knowledge algorithm with DenseNet121

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3519-3531页

作者： Muthukumar, A. Raj, M. Thanga Ramalakshmi, R. Meena, A. Kaleeswari, P. Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil India Department of Electronics and Communication Engineering KLN College of Engineering Tamil Nadu Pottapalayam India

An additional tool for swaying public opinion on social media is to present recent developments in the creation of natural language. The term "Deep fake" originates from deep learning technology, which effortlessly and seamlessly steers someone toward digital media. Artificial Intelligence (AI) techniques are a crucial component of deep fakes. The generative powers of generative capabilities greatly reinforce the advancements in language modeling for content generation. Due to low-cost computing infrastructure, sophisticated tools, and readily available content on social media, deep fakes propagate misinformation and hoaxes. These technologies make it simple to produce misinformation that spreads fear and confusion to everyone. As such, distinguishing between authentic and fraudulent content can be challenging. This study presents a novel automated approach for the identification of deep fakes, based on Adaptive Gaining Sharing Knowledge (AGSK) and using DenseNet121 architecture. During pre-processing, the image’s sensitive data variance or noise is eliminated. Following that, CapsuleNet is used to extract the feature vectors. The deep fake is identified from the resulting feature vectors by an AGSK with DenseNet121 architecture, together with the hyper-parameter that has been optimized using the AGSK model. Propaganda and defamation pose less of a concern, and the results of the suggested deepfake image recognition model show how reliable and successful the model is. The accuracy of detection is 98% higher than other cutting-edge models. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Digital storage

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ANovel Light Weight CNN Framework Integrated with Marine Predator Optimization for the Assessment of Tear Film-Lipid Layer Patterns

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computer Modeling in engineering & sciences 2023年第7期136卷 87-106页

作者： Bejoy Abraham Jesna Mohan Linu Shine Sivakumar Ramachandran Department of Computer Science and Engineering College of Engineering MuttatharaThiruvananthapuramKerala695008India Department of Computer Science and Engineering Mar Baselios College of Engineering and TechnologyThiruvananthapuramKerala695015India Department of Electronics and Communication Engineering College of Engineering TrivandrumKerala695016India

Tear film,the outermost layer of the eye,is a complex and dynamic structure responsible for tear *** tear film lipid layer is a vital component of the tear film that provides a smooth optical surface for the cornea and wetting the ocular *** eye syndrome(DES)is a symptomatic disease caused by reduced tear production,poor tear quality,or excessive *** diagnosis is a difficult task due to its multifactorial *** of several clinical tests available,the evaluation of the interference patterns of the tear film lipid layer forms a potential tool for DES *** instrument known as Tearscope Plus allows the rapid assessment of the lipid layer.A grading scale composed of five categories is used to classify lipid layer *** reported work proposes the design of an automatic system employing light weight convolutional neural networks(CNN)and nature inspired optimization techniques to assess the tear film lipid layer patterns by interpreting the images acquired with the Tearscope *** designed framework achieves promising results compared with the existing state-of-the-art techniques.

关键词： Dry-eye syndrome tearscope plus tear film deep neural networks

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Secure approach to sharing digitized medical data in a cloud environment

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Data science and Management 2024年第2期7卷 108-118页

作者： Kukatlapalli Pradeep Kumar Boppuru Rudra Prathap Michael Moses Thiruthuvanathan Hari Murthy Vinay Jha Pillai Department of Computer Science and Engineering CHRIST(Deemed to be University)Bengaluru560074India Department of Electronics and Communication Engineering CHRIST(Deemed to be University)Bengaluru560074India

Without proper security mechanisms, medical records stored electronically can be accessed more easily than physical files. Patient health information is scattered throughout the hospital environment, including laboratories, pharmacies, and daily medical status reports. The electronic format of medical reports ensures that all information is available in a single place. However, it is difficult to store and manage large amounts of data. Dedicated servers and a data center are needed to store and manage patient data. However, self-managed data centers are expensive for hospitals. Storing data in a cloud is a cheaper alternative. The advantage of storing data in a cloud is that it can be retrieved anywhere and anytime using any device connected to the Internet. Therefore, doctors can easily access the medical history of a patient and diagnose diseases according to the context. It also helps prescribe the correct medicine to a patient in an appropriate way. The systematic storage of medical records could help reduce medical errors in hospitals. The challenge is to store medical records on a third-party cloud server while addressing privacy and security concerns. These servers are often semi-trusted. Thus, sensitive medical information must be protected. Open access to records and modifications performed on the information in those records may even cause patient fatalities. Patient-centric health-record security is a major concern. End-to-end file encryption before outsourcing data to a third-party cloud server ensures security. This paper presents a method that is a combination of the advanced encryption standard and the elliptical curve Diffie-Hellman method designed to increase the efficiency of medical record security for users. Comparisons of existing and proposed techniques are presented at the end of the article, with a focus on the analyzing the security approaches between the elliptic curve and secret-sharing methods. This study aims to provide a high level of s

关键词： Electronic medical records Cloud computing Data privacy Attribute-based encryption Authentication

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Utilization of Two Cross Two Switch for Bitonic Sorting Circuit

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Micro and Nanosystems 2025年第1期17卷 45-57页

作者： Bhattacharjee, Subhasree Sarkar, Soumyadip Das, Kunal Sadhu, Arindam Sarkar, Bikramjit Department of Computer Application Narula Institute of Technology Kolkata India Department of Computer Science Acharya Prafulla Chandra College Kolkata India Department of Electronics & Communication Engineering Greater Kolkata College of Engineering and Management West Bengal India Department of Computer Science & Engineering JIS College of Engineering West Bengal India

Background: The synthesis of reversible logic has gained prominence as a crucial research area, particularly in the context of post-CMOS computing devices, notably quantum computing. Objective: To implement the bitonic sorting circuit, 2x2 switch, is used. The overarching objective of implementing a reversible sorting circuit is to mitigate power consumption while promising secure communication. The successful implementation of the bitonic sorting circuit utilizing the proposed 2x2 switch is validated through simulations conducted in IBM Qiskit. Methods: This study begins by employing the control swap gate to implement a 2x2 switch, aimed at enabling concurrent computing operations within multiprocessor systems to enhance throughput. Subsequently, following the successful implementation of the 2x2 switch, the study proceeds to design a bitonic sorting circuit. Results: Bitonic sort using a 2x2 switch on the IBM Quantum Experience (IBMQ) platform via the QISKit SDK in Python 3.6. Specifically, Qiskit v0.29.0 is utilized, with OpenQASM. OpenQASM serves as the quantum assembly and instruction language. Conclusion: This work presents a methodology for constructing a 2x2 switch, utilizing a reversible control swap gate as its core component. Additionally, it demonstrates the intricate development and implementation of a bitonic sorting circuit, capitalizing on this innovative switch architecture. © 2025 Bentham science Publishers.

关键词： Quantum computers

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Numerical simulation and performance optimization of non-toxic Cs2TiBr6 single-halide perovskite solar cell by introducing interfacial defect layers

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Optical and Quantum electronics 2025年第2期57卷 1-25页

作者： Kaur, Jaspinder Sharma, Ajay Kumar Basu, Rikmantra Singh, Harjeevan Department of Electronics and Communication Engineering NIT Delhi New Delhi110040 India Department of Computer Science and Engineering NIT Delhi New Delhi110040 India Department of Electronics and Communication Engineering Chandigarh University Punjab Mohali140413 India

Recently, lead-based perovskite solar cells (PSCs) grabbed the worldwide popularity in photovoltaic industry owing to its remarkable properties. Few challenges such as toxic elements, instability, low shelf life etc. restricts the use of inorganic–organic lead based PSCs. To overcome these issues, eco-friendly, lead-free and stable cesium titanium (Cs2TiBr6) single-halide based absorber material have been introduced. In this work, FTO/SnO2/ Cs2TiBr6/MoOx/Au structure has been simulated by SCAPS-1D. The factors such as light absorbing layer thickness including charge transport layer thickness, doping, defect density, operating temperature, quantum efficiency etc. have thoroughly been examined in relation to different characteristics of PSCs. After calculation of all these parameters then a comparison is made between the proposed (Cs2TiBr6) structure with the previously reported experimental and simulation based Cs2TiBr6 perovskite structures. As a result, maximum power conversion efficiency (PCE) reached upto 20.11% along a FF = 82.17, Voc = 1.488 V, and Jsc = 16.34 mA/cm2. Hence, this work motivates the researchers to fabricate the highly efficient, low-toxic and stable PSCs in future for solar cell industry. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Perovskite

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