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Air quality and dust level monitoring systems in hospitals using IoT

Discover Internet of Things 2025年第1期5卷 1-18页

作者： Parkavi, A. Sowmya, B.J. Alex, Sini Anna Supreeth, S. Shruthi, G. Rohith, S. Chatterjee, Sudipta Lingaraj, K. Department of Computer Science and Engineering Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Electronics and Communication Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India Department of Electronics and Communication Engineering Brainware University Barasat West Bengal Kolkata700125 India Department of Computer Science and Engineering Rao Bahadur Y. Mahabaleswarappa Engineering College Bellary583104 India

This system provides a comprehensive overview of hospital environments by tracking air quality, dust, temperature, and humidity simultaneously, offering a more complete picture of indoor conditions than systems that focus on fewer parameters. The use of IoT technology enables real-time monitoring and alerts, allowing hospital staff to respond immediately to any issues. Additionally, predictive analytics anticipate future changes, giving staff time to take preventive measures. The integration with a mobile app and cloud-based data storage ensures easy access to data, convenient monitoring, and the ability to scale the system across multiple hospital facilities, catering specifically to the unique needs of different healthcare environments. © The Author(s) 2025.

关键词： Indoor air pollution

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Novel Design of EXOR Gate and its Application for Circuits using QCA Nanotechnology

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Micro and Nanosystems 2025年第2期17卷 151-163页

作者： Sharma, Vijay Kumar Khan, Gulista Kaushik, Neeraj Chauhan, Sudakar Singh School of Electronics & Communication Engineering Shri Mata Vaishno Devi University Katra India Department of Computer Science and Engineering Teerthanker Mahaveer University Moradabad India Department of Electronics & Communication Engineering Teerthanker Mahaveer University Moradabad India Department of Electronics & Communication Engineering Guru Ghasidas University Bilaspur India

Background: QCA nanotechnology is an emerging technology in the current scenario to develop the digital circuits for area-efficient, energy-efficient, low-power, and high-speed applications. The EXOR gate is broadly used in many digital applications. Therefore, an EXOR gate needs to be designed in an emerging technology to tackle the issues of the nanoscale regime of conventional metal oxide semiconductor (MOS) technology. Aim: This study aims to implement an efficient 3-input exclusive OR (EXOR) gate using quantum-dot cellular automata (QCA) nanotechnology and check its performance for subsequent circuits. Objective: The objective of this research work is to develop an efficient translation-based 3-input EXOR gate in QCA nanotechnology. The subsequent circuits are designed using the proposed EXOR gate to evaluate the efficacy of extension work. Methods: A unique feature of QCA nanotechnology is utilized for the design of the EXOR gate. The translation-based approach is applied for the implementation of the proposed EXOR gate. A QCA cell may be shifted from its initial position in order to form the logic function. The translation-based method saves the area requirement. Results: The proposed EXOR gate consists of only 10 QCA cells and 0.50 clocks. The energy dissipation and fault analyses are done for the proposed EXOR gate. A thorough comparative study is prepared for the performance evaluation. The various subsequent circuits, such as a full adder, a 4-bit parity checker, and a 4-bit binary to gray (BTG) code converter, are also designed using the proposed EXOR gate in order to check the extension work on the proposed EXOR gate. Conclusion: It is observed from the simulation work on the proposed EXOR gate that it saves layout area and is most cost-effective. It is highly optimized in terms of cell count and clock. The proposed EXOR gate saves 25% cell area and 23.08% design cost as compared to the best-reported design. The designed subsequent circuits also outperf

关键词： Adders

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Multiuser beamforming and transmission based on BAAS and signal prediction using channel allocation

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Multimedia Tools and Applications 2024年第39期83卷 86869-86882页

作者： Bansal, Dipali Gupta, Sindhu Hak Kaur, Harleen Department of Electronics and Communication Engineering Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering Jamia Hamdard New Delhi India

The performance for communication in a real-time mobile communication system is contingent upon the distribution of spectrum among the devices and the best antenna selection, which is determined by the Direction of Arrival and the power spectrum of the antenna characteristics. The prediction and directivity of signal spectrum level based on antenna parameters were the primary areas of research attention. Generally speaking, a number of techniques anticipate the antenna spectrum level by comparing the parameters of the channel capacity and the device communication signal strength. The best selection model for the communication antenna parameter's spectrum range was used to process this. In this case, improving mobile communication performance for the entire system is thought to be the primary goal of the study. To analyze the effectiveness of antenna optimization and its performance, this has been done by testing the beamforming method's parameter under a variety of scenarios. The Boundary Assisted Antenna Selection (BAAS) technique was employed in this to minimize the number of iterations required for feature learning and antenna selection. In order to create an improved beamforming model for parallel antenna parameter estimation, this was processed through the hybridization of multi-objective optimization. Using the combination of Beamforming with BAAS based antenna selection approach, which chooses the optimal feature attribute of the antenna power spectrum with Direction of Arrival (DOA), this can further accelerate performance. Metrics like the quantity of allocated spectrum, power factor, throughput, spectrum range of capacity, and other relevant factors can be used to validate the effectiveness of the suggested strategy. The results show that the proposed work achieved approximately 7.27% of Spectral efficiency (bps), Signal Power (dB) and Interference (dB). © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Natu

关键词： Beamforming

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Sarcasm Detection in X Data Using Node Embedding and Graph Convolutional Networks 4

Sarcasm Detection in X Data Using Node Embedding and Graph C...

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4th International Conference on Advancement in electronics and Communication engineering, AECE 2024

作者： Tyagi, Aditya Dayal Garg, Setu Sharma, Swati Tomar, Vivek Verma, Kimmi Computer Science & Application Noida India Its Engineering College Electronics & Communication Engineering Noida India Kiet Group of Institutions Computer Science & Engineering Ghaziabad India Computer Science & Engineering Dehradun India Abes Engineering College Computer Science & Engineering Ghaziabad India

ISBN: (纸本)9798350364729

An extensive amount of attention has been paid to the implications that the detection of sarcasm on online social networks such as Facebook, X etc. could have for the reason of sarcasm detection, sentiment analysis, content regulation, and public opinion. In light of the fact that traditional methods frequently struggle to comprehend the nuances of sarcasm, there is a growing interest in more advanced methodology that make uses of network architectures and deep learning. For the purpose of identifying instances of sarcasm in X data, this study presents a novel method that combines Graph Convolutional Networks (GCNs) and node embeddings. Our solution makes use of a generalized convolutional neural network (GCN), constructs a user-word interaction network, and incorporates word2vec for initial text embedding in order to capture both textual and contextual characteristics. Using our methodology, we demonstrate that it is superior to traditional methods in terms of both accuracy and robustness. © 2024 IEEE.

关键词： Graph embeddings

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A ROS-2 based Path Planning and Maze Solving of Autonomous Robots

A ROS-2 based Path Planning and Maze Solving of Autonomous R...

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2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems, iCACCESS 2024

作者： Nuhel, Ahsan Kabir Hossain, Sazzad Hassan, Md.Tonim Haider, Iftekhar Saha, Ankur Taha, Adnan Sabith Ony, Asma-UI Husna Electrical and Electronics Engineering Bangladesh University of Hertfordshire Computer Networks and Systems Security United Kingdom Electrical and Electronics Engineering Bangladesh Computer Science and Engineering Bangladesh University of Information Technology and Sciences Computer Science and Engineering Bangladesh

ISBN: (纸本)9798350350289

The goal of this study is to build a self-driving robot that can effectively navigate mazes by utilizing sophisticated computer vision algorithms with ROS2. Fusion 360 is used to create the robot model, and ROS2 launch files are used to initialize the sensor and control nodes. Attaining optimal path planning and accurate localization are the main objectives. The combination of novel motion planning based on the orientation of the robot with graph search algorithms like DFS, A∗, and Dijkstra's leads to effective maze discovery and traversal. Thorough testing shows that the system can successfully navigate unfamiliar maze configurations, demonstrating the dependability of ROS2 and modern robotics techniques. By completing this study, a foundation for future advancements in perception and planning to manage more complex settings is laid. © 2024 IEEE.

关键词： computer vision

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A novel end-to-end privacy preserving deep Aquila feed forward networks on healthcare 4.0 environment

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Discover Internet of Things 2025年第1期5卷 1-20页

作者： Kalpana, Ponugoti Tappari, Sunitha Smitha, L. Madhavi, Dasari Naresh, K. Vijayalakshmi, Maddala Department of Computer Science and Engineering AVN Institute of Engineering and Technology Telangana Hyderabad501510 India Department of Electronics and Telematics Engineering G Narayanamma Institute of Technology and Science Telangana Hyderabad India Department of Information Technology G Narayanamma Institute of Technology and Science Telangana Hyderabad India Sridevi Women’s Engineering College Telangana Hyderabad India Department of Computer Science and Engineering TKR College of Engineering & Technology Telangana Hyderabad500097 India

Healthcare 4.0 is considered to be the most resilient technology with the integration of Internet of Things (IoT), Artificial Intelligence (AI), and 5G wireless communication. These Internet-enabled devices compile human data and deliver it to remote clinical centers for efficient diagnosis and treatment processes. Though sensor-driven devices have largely eased everyday lives, these healthcare systems have been suffering from various security breaches and data privacy problems. This evokes a need for designing intelligent systems to eradicate data breaches and privacy problems. This research presents a groundbreaking framework that uniquely combines privacy-preserving optimized deep learning to effectively diagnose cardiac troubles utilizing edge and fog computing devices. The recommended framework is tested with real-time datasets and employs federated learning for training the network. To ensure high prediction performance, Gated Aquila Optimized Deep Feedforward Networks (GAODP) are used to predict heart diseases. In the primary instance, information is acquired by the IoT sensors and stored in fog gateways. Subsequently, the federated mode of GAODP is adopted for the prediction of heart diseases. The ablation tests are conducted utilizing IoT nodes connected to medical sensors, with fog gateways implemented using Embedded Jetson Nano gadgets. Extensive evaluations are carried out using the gathered datasets, and performance metrics are examined and analyzed. The experimentation involves various traditional deep learning frameworks to prove the performance of privacy-preserving models. Moreover, the duration required to construct the model is calculated to confirm the recommended model's intricacy. The outcome indicate that the recommended model has attained notable predictive accuracy, including precision (0.98), recall (0.975), specificity (0.98), F1-score (0.99) and accuracy (0.99) with less computational overhead. © The Author(s) 2025.

关键词： Diagnosis

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RSSI-Based 3D Wireless Sensor Node Localization Using Hybrid T Cell Immune and Lotus Optimization

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computers, Materials & Continua 2024年第12期81卷 4833-4851页

作者： Weiwei Hu Kiran Sree Pokkuluri Rajesh Arunachalam Bander A.Jabr Yasser A.Ali Preethi Palanisamy School of Information Engineering Technology&MediaUniversity of HenanKaifeng475004China Department of Computer Science and Engineering Shri Vishnu Engineering College forWomenBhimavaram534202Andhra PradheshIndia Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesThandalam602105ChennaiIndia

Wireless Sensor Network(WSNs)consists of a group of nodes that analyze the information from surrounding *** sensor nodes are responsible for accumulating and exchanging ***,node local-ization is the process of identifying the target node’s *** this research work,a Received Signal Strength Indicator(RSSI)-based optimal node localization approach is proposed to solve the complexities in the conventional node localization ***,the RSSI value is identified using the Deep Neural Network(DNN).The RSSI is conceded as the range-based method and it does not require special hardware for the node localization process,also it consumes a very minimal amount of cost for localizing the nodes in 3D *** position of the anchor nodes is fixed for detecting the location of the ***,the optimal position of the target node is identified using Hybrid T cell Immune with Lotus Effect Optimization algorithm(HTCI-LEO).During the node localization process,the average localization error is minimized,which is the objective of the optimal node *** the regular and irregular surfaces,this hybrid algorithm effectively performs the localization *** suggested hybrid algorithm converges very fast in the three-dimensional(3D)*** accuracy of the proposed node localization process is 94.25%.

关键词： Sensor node localization received signal strength indicator 3D wireless sensor network deep neural network average localization error and hybrid T cell immune with lotus effect optimization algorithm

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FOD-YOLO NET: Fasteners fault and object detection in railway tracks using deep yolo network1

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 8123-8137页

作者： Brintha, K. Jawhar, S. Joseph Department of Computer Science and Engineering Arunachala College of Engineering for Women Manavilai Tamil Nadu Nagercoil India Department of Electrical and Electronics Engineering Arunachala College of Engineering for Women Manavilai Tamil Nadu Nagercoil India

Automated railway security systems prevent train collisions with trackside obstructions that cause accidents in high-speed railways. Rail safety is being improved and accident rates reduced through continuous research. A rapid advancement in deep learning has promoted new possibilities for research in this field. In this work, a novel deep learning-based FOD-YOLO net is proposed for detecting the fasteners faults and objects in the railway tracks. There are two basic components in the deep learning-based YOLOv8: the backbone and the head. YOLOv8 utilizes an improved version of the CSPDarknet53 network for detecting objects on the railway track. The head of YOLOv8 consists of EfficientNet with various convolutional layers with squeeze and excitation blocks for detecting any defect in the track fasteners. These layers are liable for detecting the objectness scores, bounding boxes and class probabilities structured with fully connected layers for the objects and faults in tracks. Based on the results from the Yolo network, the alert message is sent to the loco pilot to avoid accidents using fuzzy logic. The experimental fallouts of proposed FOD-YOLO net achieve higher accuracy and yields better evaluation results with 98.14% accuracy, 98.84% precision and 95.94% recall. From the experimental results, the FOD-YOLO net improves the overall accuracy range by 5.44%, 4.72%, 0.73%, and 13.18% better than Fast RCNN, YOLOv5s-VF, YOLO-GD, and 2D-SSA + Deep network respectively. © 2024 – IOS Press. All rights reserved.

关键词： Object detection

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Duplicate image detection using deep learning modified SVM and k-NN classification method for multimedia application

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Soft Computing 2024年第13-14期28卷 7659-7670页

作者： Singh, Mahesh K. Kumar, Sanjeev Ranjan, Rajeev Nandan, Durgesh Department of Electronics and Communication Engineering Aditya Engineering College Surampalem India Department of Electronics and Communication Engineering Chandigarh University Punjab Gharuan India School of Computer Science and Artificial Intelligence SR University Telangana Warangal India

This paper discussed an efficient technique to detect near-duplicate images with higher accuracy. The proposed method improved the existing accuracy of near-duplicate image detection and classification using Haar wavelet feature extraction and classification algorithm. Due to advancements in digital image acquisition and image editing software, near-duplicate image counterfeiting has become predictable. This expanding problem needs the creation of sophisticated detection algorithms capable of effectively preventing the spread of near-duplicate image deception. Support vector machine (SVM) and K-near neighbor (KNN) classifiers are used in the classification stage of a modified deep learning model with a convolutional neural network (CNN). The classification model finds the better result on the Imperial College London dataset, with 99.12% using CNN with KNN classifier and 98.63% for CNN with SVM classifier. The classification model also gives better results on the Nanyang Technological University dataset, with 98.36% using CNN with the KNN classifier and 97.85% for CNN with the SVM classifier. The model is tested using publicly available images to find the results. According to the comparison results, this model performed significantly better result than the previous model. As a result, digital image forensics has become more critical, particularly in identifying image duplication. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Support vector machines

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High Voltage Gain DC-DC Converter With Wide Range of Soft Switching and Continues Input Current for Renewable Energy Applications

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IEEE Access 2025年 13卷 94878-94892页

作者： Moradmand Jazi, Hamed Mohammadzadeh, Behrouz Rahimzadeh Khorasani, Ramin Zanchetta, Pericle Adib, Ehsan Velasco-Quesada, Guillermo Martínez-García, Herminio Barcelona08019 Spain The University of Pavia Department of Electronics Computer Science and Electrical Engineering Pavia27100 Italy The Pennsylvania State University School of Electrical Engineering and Computer Science University ParkPA16802 United States Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan84156-83111 Iran

This paper presents a non-isolated high step-up DC-DC converter that achieves a high voltage gain through the application of coupled inductor and multiplier techniques. Also, in this topology continuous input current and a shared ground between input and output is obtained which enhances reliability of the proposed converter. Moreover, by utilizing ZVT technique, soft switching performance for both switches are achieved across the entire range of output power which reduces switching losses and improves efficiency significantly. Furthermore, the output diodes of this converter are turned off under ZCS condition, considerably reducing reverse-recovery losses. The paper includes an analysis of the proposed converter’s operation, a comparative study with existing high step-up converters and experimental results for 200 W laboratory prototype. © 2013 IEEE.

关键词： Solar fuels

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