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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Loganathan, P. Priyeshkumar, A.T. Kodeeswari, K. Reena, K Jose Poonguzhali, I. Balaji, M. Department of Electronics and Communication Engineering Namakkal India Mahendra College of Engineering Department of Electronics and Communication Engineering Salem India Department of Electronics and Communication Engineering Erode India B. S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Applications Vandalur India Panimalar Engineering College Department of Electronics and Communication Engineering Chennai India Vivekananda College of Technology for Women Department of Electronics and Communication Engineering Tiruchengode India

ISBN: (纸本)9798331521394

The Adaptive Beamforming Algorithm (ABA) represents a promising approach to suppress interference in dense wireless sensor networks (WSNs), enhancing signal fidelity and network throughput. In dense WSNs, the proximity of sensor nodes leads to high interference levels, which degrade energy efficiency and communication reliability. This is exacerbated by static beamforming methods that oversimplify time and frequency utilization. This work proposes a set of adaptive beamforming solutions, including digital, machine learning (ML)-empowered, and hybrid beamforming approaches. The Adaptive Array Signal Processing (AASP) method introduces real-time interference analysis with null steering to suppress unwanted signals and maximize the signal-to-interference-plus-noise ratio (SINR). Hybrid beamforming, combining analog and digital domains, balances computational cost with performance. Additionally, a deep learning-based beam steering module dynamically adjusts beam patterns based on interference forecasts. Extensive simulations demonstrate the proposed methods' superiority over conventional beamforming, achieving significant SINR improvement, enhanced network throughput, and improved energy efficiency. In ultra-dense environments, the results indicate a 10-fold reduction in interference power alongside better scalability. Proactive interference mitigation using ML models further ensures adaptability to varying network conditions. These techniques establish a baseline for deploying adaptive beamformers in dense WSNs, addressing scalability and real-time computational challenges. They also pave the way for implementing advanced 5G/6G-capable sensor systems, enabling resilient communication in high-interference environments. Key-words: signal to interference plus noise ratio(SINR), adaptive beamforming, interference mitigation, hybrid beamforming, null steering, beam steering, computational efficiency. © 2025 IEEE.

关键词： Beamforming

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An Energy Efficient IoT Based Smart Street Lighting Using Low Cost SOC 1

An Energy Efficient IoT Based Smart Street Lighting Using Lo...

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1st IEEE International Conference on electronics, Computing, Communication and Control technology, ICECCC 2024

作者： Velu, Anitha Ramamoorthy, Raghu Manasa, S.M. Navulkumar, Devakirubai Sri Sairam College of Engineering Department of Electronics and Communication Engineering Bengaluru India The Oxford College of Engineering Department of Computer Science and Engineering Bengaluru India Jayam College of Engineering and Technology Department of Computer Science and Engineering Dharmapuri India

ISBN: (纸本)9798350371802

Internet of Things (IoT) has radically improved and modernized in every aspect of human existence. The term IoT is a recent trend that states the development a self-configurable network by connecting a variety of hardware objects, to internet without requiring human input to improve service delivery and automate processes by means of data transmission to the cloud. Streetlights are an essential component of any city, although they provide safer roadways, improved night vision, and increased visibility of public spaces it uses a significant amount of electricity and operates at maximum intensity from dusk till dawn. The wastage of electricity can be secured by having lights turn OFF on their own if it is not in use. This work presents an energy efficient IoT-based smart street lighting by using low-cost System On Chip (SOC) circuit. This work deploys an LDR sensor which acts as a decision device, to choose whether to switch ON or OFF the street light based on the level of ambient light. This simple switch has a relay at its output and is activated by either light or dark. This switching may be done using a cost-effective Wi-Fi module namely, ESP8266 for which the LDR sensed value is given as input. Any user can check the status of a street light whether it is ON/OFF from anywhere using internet in real time. It promises great dependability and remarkable stability for long-term. The proposed work is implemented using a NodeMCU board that is designed to supply the required light intensity at different times. © 2024 IEEE.

关键词： Internet of things

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Collective Diagnostic Prototypical in Internet of Medical Things for Depression Identification using Deep Learning Algorithm

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Recent Patents on engineering 2024年第9期18卷 24-33页

作者： Selvarajan, Shitharth Thirumalaisamy, Manikandan Manoharan, Hariprasath Devendiran, Ramkumar Lakshmi Narayanan, S. Kumar, Ashwani Department of Computer Science Kebri Dehar University Kebri Dehar250 Ethiopia School of Computer Science Engineering and technology Bennett University U.P India Department of Artificial Intelligence and Machine Learning Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Poonamallee Tamil Nadu Chennai600 123 India School of Computer Science and Engineering VIT-AP UNIVERSITY Andhra Pradesh Amaravathi522237 India Gojan School of Business and Technology Chennai India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India

Background: The majority of wearable technology that is present in various patents for Internet of Medical Things (IoMT) health monitoring systems is introduced to recognize various bodily indicators. The enumerated patents indicate that monitored values are sent to a central server, where they are all treated by experts at the appropriate moment. Therefore, a new patent technique by expanding the use of wireless devices, has been discovered that such communication technologies can recognize specific depression traits and mood swings. Objectives: The major objective of the proposed method is to analyze the disputes that arise in the characteristics of an individual by observing the leveling periods that are identified from the processed image. In addition, the rate of data transfer in case of any dispute is maximized therefore recognition problem is solved at a minimized distance. Further, the steady state probability values are achieved at low delay thus minimizing the dropout packets in the monitored system using IoMT and LSTM. Methods: A balanced record with four distinct parameters—such as livelihood, self-reliance, correlation, and precision—is employed with the projected model on IoMT for depression identification. As a result, high data transfer rates and low distance separation are used to process the identification framework. Additionally, by combining an original matrix representation with the input feature set using LSTM, a novel framework with great efficiency is created. Results: In order to assess the results of IoMT using LSTM, four situations are split apart and their probability ratios are calculated. The results of each situation are then contrasted with the current methodology, and it is found that when there is a low dropout ratio, depression in a person is quickly diagnosed. Conclusion: The comparison analysis demonstrates that the proposed method, when compared to the current method, offers the best-compromised outcomes at roughly 64%. © 2024 B

关键词： Data transfer

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Modelling of 7-Segment Hex Decoder using Silicon Microring Resonator

Modelling of 7-Segment Hex Decoder using Silicon Microring R...

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2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Raju, Kommisetti Murthy Nirmala Bai, K.S. Mondal, Bikas Roy, Bhaskar Banerjee, Sudipta Kundu, Sumanta Electronics and Communication Engineering Shri Vishnu Engineering College for Women Bhimavaram India Electronics and Communication Engineering Jntua College of Engineering Kalikiri Kalikiri India Electronics and Computer Science Narula Institute of Technology Kolkata India Asansol Engineering College Asansol India Pune India Electrical Engineering Narula Institute of Technology Kolkata India

ISBN: (纸本)9798331540821

A creative design for a 7-segment hex decoder that makes use of an optical microring Resonator (OMRR). Digital devices called decoders are used to transform binary input into the required output format. The hexadecimal is widely used in programming languages, memory addressing, error codes, etc. The main advantage of 'Hexadecimal' numerals is that they require less memory to store more numbers. After processing the binary code, the decoder accepts it and converts it back into a format that output devices can understand. The entire design has been simulated step-by-step in MATLAB. An all-optical decoder with a quick response time can significantly enhance various aspects of digital data communication and processing. The microring resonator is modulated utilizing an optical pump signal, which is the powerful optical laser beam. This microring resonator-based optical decoder can be applied to optical communication and networking, as well as optical secure coding circuits, as a basic building block. © 2024 IEEE.

关键词： Laser beams

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Artificial gannet optimization enabled deep convolutional neural network for autism spectrum disorders classification using MRI image

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Multimedia Tools and Applications 2024年第30期83卷 74757-74783页

作者： Prasad, Vadamodula Ganeshan, R. Rajeswari, R. Department of Computer Science & amp Engineering Lendi Institute of Engineering & ampTechnology Jonnada Village Andhra Pradesh Vizianagaram District 535005 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh Sehore466114 India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Chennai India

Autism Spectrum Disorder (ASD) is a neurodevelopment-based disability caused by variations in the brain. This may cause impact on social skills and communication of an individual. Autism is a highly challenging issue to diagnose at the early stages. ASD is one of the important problems to diagnose because it starts manifesting at low ages. This paper aims at ASD classification using MRI Images by Artificial Gannet Optimization enabled Deep Convolutional Neural Network (AGO_DCNN). Here, an MRI image is assumed that be subjected to pre-processing of the image and feature extraction phases as an input. The bilateral filter is employed to remove the noises from the input MRI image and in the pre-processing of an image stage Region of Interest (ROI) extraction is conducted. The pivotal region extraction phase receives a filtered image after which AGO is used to extract the pivotal region. AGO is a newly designed approach by merging two optimizations namely Artificial Ecosystem-based Optimization (AEO) and Gannet Optimization Algorithm (GOA). At the same time, features are extracted from the input image during the feature extraction stage. In a feature extraction stage, some features like texture and statistical features are extracted. At last, the classification of ASD is conducted using DCNN, wherein by AGO the classifier is tuned. Furthermore, AGO_DCNN attained better outcomes in terms of maximal True Positive Rate (TPR), True Negative Rate (TNR), and accuracy values of about 93.7%, 90.8%, 96.8% and False Positive Rate (FPR) and minimal False Negative Rate (FNR) and values of 78.7% and 74.7%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Classification of Breast Cancer Using Computational Machine Learning Algorithms 8th

Classification of Breast Cancer Using Computational Machine ...

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8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Gagana, P. Aladakatti, Darshan Kodipalli, Ashwini Rao, Trupthi Kamal, Shoaib Electronics and Communication Engineering Global Academy of Technology Bangalore India Computer Science and Engineering Global Academy of Technology Bangalore India Artificial Intelligence and Data Science Engineering Global Academy of Technology Bangalore India

ISBN: (纸本)9789819976324

Cancer is a disease that brings fear in everyone’s heart. In the top 5 cancers listed by Stephanie Watson and reviewed by Medically Reviewed by Jennifer Robinson, says that breast cancer stands in the top third position among other dreadful cancers like Prostate Cancer, Pancreatic Cancer, Colorectal Cancer, and Lung Cancer. Numerous kinds of research are made on this terrifying disease yet millions and millions of women have lost their precious life due to breast cancer. A huge number of patients have been victimized by this curse on women. In severe cases, doctors fail to give hope and treatment as the patient’s conditions reach beyond medical help. We all have heard the quote "Prevention is better than cure", well in our case earlier detection is better than late realization. As earlier as modern-day treatments are based on the concept of molecular heterogeneity, which is why they can be influenced by various features. Early breast cancer, usually confined to the breast, is considered a curable disease. This stage is commonly known as benign, which in our dataset is represented as however when the symptoms are not detected and treated at the early stage the treatment becomes more complicated and cannot to treated using the present therapeutic options and might have an associated risk of spreading to other parts of the body. This condition is called malignant and this is represented as M in the dataset. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Diseases

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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An Artificial Magnetic Conductor backed Cylindrical Dielectric Resonator Antenna for portable Wireless Communications

An Artificial Magnetic Conductor backed Cylindrical Dielectr...

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2024 IEEE Microwaves, Antennas, and Propagation Conference, MAPCON 2024

作者： Upadhyay, Nidhi Kaur, Amanpreet Pattanayak, Arnab Singh, Ashima JRF- CEEMS-TIET VT Thapar Institute of Engineering and Technology Punjab India Electronics and Communications Thapar Institute of Engineering and Technology Punjab India Computer Sciences Thapar Institute of Engineering and Technology Punjab India

ISBN: (纸本)9798350379693

A compact Cylindrical Dielectric Resonator Antenna (CDRA) is proposed for the portable wireless communication in this paper. CDRA is in high demand by various smart wireless devices, due the low less, moderate gain and compact size. The proposed antenna structure is realized using simulation followed by fabrication using a high dielectric material (Ετ = 9. 9) alumina on a Rogers RT Duroid substrate (Ετ = 2. 2). A microstrip feed line is used to excite antenna for radiation. The CDRA resonates at a frequency of ~2.9 GHz with a 10-dB bandwidth and gain of 250 MHz and 3.3 dBi, respectively. To enhance the gain of the antenna and reduce the back-lobe radiation, an artificial magnetic conductor (AMC) is proposed which can be placed at the back of the antenna. The simulation results show that integration of AMC helps to achieve a gain up to 6.6 dBi and improve the impedance matching, as well. The proposed CDRA can be used in smart portable wireless devices for applications like body-area-network, wireless sensing, and low power automative RADAR. © 2024 IEEE.

关键词： Antenna lobes

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Indian Sign Language Recognition using Skin Segmentation and Vision Transformer 20

Indian Sign Language Recognition using Skin Segmentation and...

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20th IEEE India Council International Conference, INDICON 2023

作者： Agarwal, Agrima Sreemathy, R. Turuk, Mousami Jagdale, Jayashree Kumar, Vishal Pune Institute of Computer Technology Department of Electronics & Telecommunication Engineering Pune India Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9798350305593

Sign Language is the common mode of communication among the speech and hearing-impaired people, but interpreting this language becomes a challenge for others who don't practise it. To bridge this communication gap, many Artificial Intelligence based models have been designed worldwide. In the Indian context, the field is still relatively new. A 72-word self-created Indian Sign Language dataset has been used in this study. A Vision transformer model, consisting of just 2 transformer layers has been proposed. The pre-processing used on the images are YCbCr conversion and morphological operation based skin segmentation. The model achieves a test accuracy of 99.56% and experimentation with different publicly-available datasets confirms its superiority over previous methods. © 2023 IEEE.

关键词： Indian Sign Language Sign Language Recognition Skin segmentation Vision Transformer YCbCr Mapping

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Real-time Indian Sign Language Recognition using Skeletal Feature Maps 14

Real-time Indian Sign Language Recognition using Skeletal Fe...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kumar, Vishal Sreemathy, R. Turuk, Mousami Jagdale, Jayashree Agarwal, Agrima Pune Institute of Computer Technology Department of Electronics & Telecommunication Engineering Pune India Pune Institute of Computer Technology Department of Information Technology Pune India

ISBN: (纸本)9798350335095

Sign language recognition is a widely researched problem that has gained momentum since the advent of deep learning. Many researchers have designed models using different deep learning architectures claiming high validation accuracy but low real-time performance. The proposed model achieves consistent prediction in real time through Skeletal Feature Maps using keypoints extracted by MediaPipe. These images are passed to a novel 15-layer CNN architecture for classification. The proposed model can achieve a high validation accuracy of 99.84% over a self-created 106-word Indian Sign Language dataset, that is higher than other machine learning models such as SVM, Gaussian Naive Bias, Decision Tree, Random Forest and some pre-trained standard CNN architectures. The salient feature of the proposed model is it is consistent and accurate independent of the background, subject and lighting conditions. Further, the model is implemented as a desktop application and tested by different signers. © 2023 IEEE.

关键词： Decision trees

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