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A Novel Access Point Deployment Framework for mmWave Cell-Free Massive MIMO Networks

IEEE Transactions on Wireless communications 2025年第6期24卷 4581-4597页

作者： Topal, Ozan Alp Demir, Ozlem Tugfe Bjornson, Emil Cavdar, Cicek School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical and Electronics Engineering TOBB University of Economics and Technology Ankara Turkey

Millimeter-wave network deployment is an essential and ongoing problem due to the limited coverage and expensive network infrastructure. In this work, we solve a joint network deployment and resource allocation optimization problem for a mmWave cell-free massive MIMO network considering indoor environments. The objective is to minimize the number of deployed access points (APs) for a given environment, bandwidth, AP cooperation, and precoding scheme while guaranteeing the rate requirements of the user equipments (UEs). Considering coherent joint transmission (C-JT) and non-coherent joint transmission (NC-JT), we solve the problem of AP placement, UE-AP association, and power allocation among the UEs and resource blocks jointly. For numerical analysis, we model a mid-sized airplane cabin in ray-tracing as an exemplary case for IDS. Results demonstrate that a minimum data rate of 1 Gbps can be guaranteed with less than 10 APs with C-JT. From a holistic network design perspective, we analyze the trade-off between the required fronthaul capacity and the processing capacity per AP, under different network functional split options. We observe an above 600 Gbps fronthaul rate requirement, once all network operations are centralized, which can be reduced to 200 Gbps under physical layer functional splits. 2002-2012 IEEE.

关键词： Integer programming

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Alzheimer's Disease Classification using Convolutional Neural Networks

Alzheimer's Disease Classification using Convolutional Neura...

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2023 IEEE International Conference on Innovative Data communication Technologies and Application, ICIDCA 2023

作者： Archana, B. Kalirajan, K. KGiSL Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Kpr College of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350397208

Deep learning has got tremendous popularity in recent years for addressing problems in wide range of sectors, including medical image research. Alzheimer's Disease (AD) is a broad brain degeneration-related neurodegenerative disease that causes progressive mental decline in both middle-aged and elderly people. Imaging and laboratory testing can rule out additional potential causes and helps in the diagnosis the symptoms of dementia. Adults who have Alzheimer's disease have varied degrees of memory loss and knowledge forgetting. This the statement implies that how much its important in for the early disease diagnosis. In this article, deep learning techniques are implemented to classify brain neuro images such as MRI dataset ADNI into groups for Mild cognitive impairment (MCI), Alzheimer's Disease (AD), Cognitively Normal (CN) and Healthy Person. According to the results, CNN's classification rate was outperformed by the images being classified with accuracy of 95.82%. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Exciton-polaritons in a 2D hybrid organic-inorganic perovskite microcavity with the presence of optical Stark effect

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Chinese Physics B 2024年第3期33卷 551-555页

作者： Kenneth Coker 郑楚媛 Joseph Roger Arhin Kwame Opuni-Boachie Obour Agyekum 张伟利 School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Department of Electrical and Electronic Engineering Ho Technical UniversityHo 00233Ghana Telecommunication Engineering Department Kwame Nkrumah University of Science and TechnologyKumasi 00233Ghana

This study investigates the properties of exciton-polaritons in a two-dimensional(2D)hybrid organic-inorganic perovskite microcavity in the presence of optical Stark *** both steady and dynamic state analyses,strong coupling between excitons of perovskite and cavity photons is revealed,indicating the formation of polaritons in the perovskite ***,it is found that an external optical Stark pulse can induce energy shifts of excitons proportional to the pulse intensity,which modifies the dispersion characteristics of the polaritons.

关键词： exciton-polaritons perovskite microcavity optical Stark effect

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Advancing Event Recognition in Videos Using Hybrid Deep Learning Model

Advancing Event Recognition in Videos Using Hybrid Deep Lear...

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2024 IEEE International Conference on Recent Advances in Science and engineering technology, ICRASET 2024

作者： Jyothi, H. Komala, M. Mallikarjunaswamy, S. Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350388602

Event recognition in videos plays a critical role in various applications such as surveillance, security, and human-computer interaction. Conventional methods like Hidden Markov Models (HMM) and Support Vector Machines (SVM) have been extensively used, but they often struggle with handling complex data patterns and large-scale video datasets, leading to limited accuracy and inefficiency in real-time scenarios. These approaches fail to capture intricate temporal dependencies and suffer from high computational costs, making them less effective for dynamic event detection. To address these limitations, we propose a Hybrid Deep Learning Event Recognition Model (HDL-ERM), which combines the strengths of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. CNN is used for feature extraction, while LSTM effectively captures temporal sequences in video data. The HDL-ERM model significantly improves event recognition accuracy by 0.25%, while reducing computational load by 0.18% compared to conventional methods. By leveraging deep learning techniques, our proposed model enhances performance across various parameters such as processing speed, detection accuracy, and adaptability to different video environments. © 2024 IEEE.

关键词： Convolutional neural networks

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Facial Recognition through Enhanced Access Control System with OTP Verification 5

Facial Recognition through Enhanced Access Control System wi...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Kumari, Gidla Vijaya Raju, K.C.N. Prasanna, Yadla Sai Kaivalya, M. Kumar, Mallula Praveen Gorji, Kanakarao Bvc Institute of Technology and Science Department of Electronics and Communication Engineering Batlapalem India Godavari Institute of Engineering and Technology Department of Ece Rajamandury India Sri Vasavi Engineering College Department of Electronics and Communication Engineering Tadepalligudem India Aditya College of Engineering and Technology Department of Ece Surampalem India Srkr Engineering College Department of Electronics and Communication Engineering Bhimavaram India Sasi Institute of Technology and Engineering Department of Ece Tadepalligudem India

ISBN: (纸本)9798350389609

Ensuring safety has traditionally relied on locks, which can be either electrical or mechanical, typically operated with a limited number of keys and securing larger areas necessitates numerous locks. However, the prevailing systems often lack robust security, primarily due to reliance on mechanical locks susceptible to tampering. The primary aim of this project is to enhance security in areas such as homes and private spaces. Facial recognition technology has gained significant attention in enhancing access control systems due to its accuracy and convenience. This paper proposes an innovative approach to access control utilizing facial recognition coupled with one-time password verification for heightened security. Through the fusion of these technologies, the proposed system aims to mitigate security risks associated with traditional access control methods while providing a seamless and user-friendly authentication experience. This integration of biometric technology and OTP authentication aims to bolster security measures, providing a more reliable and efficient access control solution for diverse environments. © 2024 IEEE.

关键词： Authentication

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Development of IoT-Based Biometric Attendance System Using Fingerprint Recognition

Development of IoT-Based Biometric Attendance System Using F...

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International Conference on Network Security and Blockchain technology, ICNSBT 2023

作者： Chowdhury, Prasun Bhattacharyya, Debnandan Das, Ritaban Burnwal, Sourav Kr. Prasad, Asis Department of Electronics and Communication Engineering St. Thomas’ College of Engineering and Technology Kolkata India

ISBN: (纸本)9789819944323

This paper presents a system of IoT-based online attendance monitoring using fingerprint as biometric feature. It can eliminate fraudulent attendance problem faced by other attendance monitoring systems like RFID. Individual have to be present physically to register his or her attendance by giving own fingerprint impression. The system will also reduce problems such as keeping records in papers manually which can be lost or damaged easily. In the proposed method, firstly, fingerprints of the candidates have to be stored in a remote database through user interface using IoT. During verification, fingerprint sensor will check the impression of particular candidate with the pre-registered template, and the result will be shown in OLED display. Continuous assessment of attendance will be recorded in Web database which can be accessed by both candidate and admin. Leave management system works on request-approve method where the users can apply for pre-leave/post-leave of various types like EL, CL, ML, etc. Also, the system allows the admin to publish public notices that can be accessed even without login. Implementing this system in corporate will help the authority to keep track of the attendance of employees and to increase transparency to promote proficiency in work-culture. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Biometrics

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Design a Focused Radome Applicator for Small Tumors Treatment 2

Design a Focused Radome Applicator for Small Tumors Treatmen...

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2nd International Conference on Microwave, Antenna and communication, MAC 2024

作者： Sharma, Nitika Kumari, Kiran Khanna, Rajesh Kumar Rai, Mayank Shankar Singh, Hari Kaur, Amanpreet Thapar Institute of Engineering and Technology Department of Electronics and Communication Engineering Punjab Patiala147004 India

ISBN: (纸本)9798350350104

This paper uses a focused radome applicator based on metasurface structure for superficial/small tumor treatment. The designed applicator comprises a stacked antenna integrated with a frequency selective surface (FSS), covered with a 5 mm Teflon layer to shield the applicator from external influences. The antenna size is 32 × 32 mm2, and the FSS unit cell size is 10 × 10 mm2, respectively. A heterogeneous phantom augmented with a 10 mm water bolus is used to evaluate the performance of the proposed antenna. The evaluation metrics include the specific absorption rate (SAR) and its related parameters, such as depth of electric field penetration in terms of PD and EFS. Additionally, both steady-state and transient temperature distributions within the tumor-embedded phantom are analyzed. © 2024 IEEE.

关键词： Spiral antennas

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CrackSpot: deep learning for automated detection of structural cracks in concrete infrastructure

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Asian Journal of Civil engineering 2024年第1期25卷 1079-1090页

作者： Shashidhar, R. Manjunath, D. Shanmukha, S.M. Department of Electronics and Communication Engineering JSS Science and Technology University Mysore 570006 India

To maintain structural integrity and avoid structural failures that could harm neighboring infrastructure, pollute the environment, and even result in fatalities, routine inspection and repair of concrete infrastructure are required. Throughout the structure’s operational life, routine visual inspections are typically undertaken to detect various problems caused by environmental exposure (such as cracks, loss of material, rusting of metal bindings, etc.). Visual examination can yield a variety of data that may enable the cause of distress to be positively identified. Its effectiveness is subject to human error and depends on the investigator’s skill and experience and because of their size and difficult-to-reach features, huge structures like dams, bridges, and tall skyscrapers can be prohibitively dangerous. The approach presented here uses deep learning techniques to identify the structural cracks on concrete surfaces to achieve easy detection of the cracks and high accuracy. Here, we propose an integrated Tensrflow CNN and image processing-based crack-finding method to detect cracks with high precision. Thousands of photos of cracked and non-cracked structure surface datasets are considered while developing the model. Image features are extracted during pre-processing to increase training effectiveness. The developed model has a 97.11% accuracy rate and an F1-score of 97%. The results show that the designed model is highly precise and effective in identifying cracks in structures and more accurate than many implemented techniques. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Convolutional neural network Crack detection Feature extraction Image processing Structural damage Structural load

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Developments in Microfluidic Integrated Lab-on-Chip Devices for DNA Biosensing Towards Unifying the Emergence of Nanobiosensor

Biomedical Materials and Devices

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Biomedical Materials and Devices 2024年第2期3卷 1104-1124页

作者： Parthasarathy, P. Venkatesh, M. Department of Electronics and Communication Engineering CMR Institute of Technology Karnataka Bengaluru 560037 India

In recent decades, the demand for microfluidic and nanofluidic lab-on-a-chip (LOC) devices has significantly increased, particularly for medical diagnostics and environmental monitoring of pathogenic agents at the point of care (POC). By integrating microfluidics with biosensing technologies, these systems offer precise control over nano-litre volumes, enabling the execution of multiple bioassays on a compact, portable platform. This innovative approach is transforming high-throughput diagnostics through the development of portable LOC devices that allow rapid, sensitive, and accurate detection of a wide range of diseases. These devices combine multiple bioassay processes and provide optimal fluid control at the nano-litre scale, facilitating the swift detection of pathogens for environmental surveillance. This paper aims to offer a comprehensive review of the critical need for LOC devices with user-friendly interfaces, high sensitivity, quick response times, portability, and reliability, highlighting their potential as valuable tools in the ongoing efforts to detect and manage infectious diseases. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Bioassays DNA (Deoxyribonucleic Acid) Lab-on-chip Microfluidic Nanobiosensor Point-of-care Device

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Real-Time Fire Surveillance and SMS Alert System with Ruggedized GSM Module 5

Real-Time Fire Surveillance and SMS Alert System with Rugged...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Anju, M.I. Ajay Sharma, S. Pranathi, Polisetty SRM Institute of Science and Technology Faculty of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518097

This paper presents the design and implementation of an innovative real-time fire surveillance system that integrates a ruggedized GSM module for immediate SMS alerts. The proposed system addresses critical challenges in early fire detection and notification, particularly in harsh industrial environments, data centers, and manufacturing facilities where conventional fire alarm systems may be compromised by extreme conditions. Our proposed model can detect fires in real-time, significantly reducing the response time compared to traditional smoke detectors and heat sensors. To achieve cost-effectiveness and ease of deployment, we have integrated the ESP32-CAM, a low-cost Wi-Fi-enabled microcontroller with built-in camera functionality, into the system. The ESP32-CAM provides real-time video feeds and facilitates fire detection through image processing while significantly reducing hardware costs. We have also integrated a ruggedized GSM module to ensure the reliability and functionality of the fire detection system in adverse conditions. Our novel approach leverages YOLO(You Only Look Once) algorithm which has enhanced detection capabilities to identify fire incidents through visual analysis, complementing conventional sensors to create a robust, multi-modal detection system. The YOLOv10 implementation demonstrates unprecedented speed and accuracy, processing video feeds at 120 frames per second with a fire detection accuracy above 94 percent from the tests we conducted. © 2024 IEEE.

关键词： Fire alarm systems

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