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Novel Design of UWB Jeans Based Textile Antenna for Body-Centric communications

computer Systems Science & engineering 2022年第9期42卷 1079-1093页

作者： Mohammad Monirujjaman Khan Bright Yeboah-Akowuah Kaisarul Islam Eric Tutu Tchao Sumanta Bhattacharyya Rajesh Dey Mehedi Masud Fahad Alraddady Department of Electrical and Computer Engineering North South UniversityDhaka1229Bangladesh Department of Computer Engineering Kwame Nkrumah University of Science and TechnologyKumasiGhana Electronics and Communication Engineering Cambridge Institute of TechnologyRanchiIndia Department of Electronics and Communication Engineering Brainware Group of Institutions-SDETKolkataIndia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

This research presents an ultra-wideband (UWB) textile antenna designfor body-centric applications. The antenna is printed on a 1 mm thick denim substrate with a 1.7 relative permittivity. The jeans substrate is sandwiched between apartial ground plane and a radiating patch with a Q-shaped slot. The slotted radiating patch is placed above the substrate and measures 27.8 mm × 23.8 mm. In freespace, the antenna covers the ultra-wideband spectrum designated by the Federalcommunication Commission (FCC). Various parameters of the antenna designwere changed for further performance evaluation. Depending on the operating frequency, the antenna's realized gain varied from 2.7 to 5 dB. The antenna achievedhigh radiation efficiency with an omnidirectional radiation pattern. A parametricstudy was performed in research on varying antenna substrates and other components of the antenna. The three outermost layers of the human body are used tomodel a human phantom for on-body simulation. After that, the antenna wasplaced at five different distances from the phantom. The findings demonstrate thatat close distances to the phantom, the antenna's gain and efficiency at lower frequencies are reduced. The antenna's radiation efficiency and gain were muchhigher at higher frequencies for distances greater than 6 mm. Compared to freespace, the antenna's radiation pattern was more omnidirectional, especially athigher frequencies. This antenna is novel, compact and has an ultra wide bandwidth, a maximum of 94.60% radiation efficiency and a 5 dBi gain that will makeit a good candidate for body-centric communications.

关键词： Ultra-wideband body-centric communication on-body textile jeans slotted-patch WBAN

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Seamless V2I Connectivity: How THz Waves and Base Station Density Contribute 18

Seamless V2I Connectivity: How THz Waves and Base Station De...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Singh, Sameer Kumar Gupta, Nishant Singh, Rohit Kumar, Shiv Kumbhani, Brijesh Indian Institute of Technology Electrical Engineering Department Ropar India LNM Institute of Information Technology Communication and Computer Engineering Department Jaipur India Dr B R Ambedkar National Institute of Technology Electronics and Communication Department Jalandhar India

ISBN: (纸本)9798350391725

Higher frequencies, such as Terahertz waves (THz waves), are gaining widespread attention and emerging as the foundational framework for next-generation communication systems (e.g., 6G). These frequencies offer a larger bandwidth, unlocking new opportunities, including applications like vehicular communication. Due to significant traffic variations, vehicles frequently experience outages. Therefore, this scenario requires ultra-reliability and improved coverage. To meet these requirements, we introduced an architecture that uses joint transmission from two distinct Terahertz Stations (TSs), which offers several benefits. Primarily, this collaborative approach is expected to enhance network connectivity by reducing outage probability. Additionally, the use of THz waves can support higher data rates and lower latency, which are crucial for real time communication. © 2024 IEEE.

关键词： Gravity waves

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Automated Land Cover Classification in Urban Environments with Deep Learning-Based Semantic Segmentation

Automated Land Cover Classification in Urban Environments wi...

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

作者： Sheela, S. Jyothi, S. Latha, A.P. Ganesh, H.J. Komala, M. Naveen Kumar, C. Mallikarjunaswamy, S. Sharmila, N. Global Academy of Technology Department of Computer Science and Engineering Bengaluru India Dayananda Sagar College of Engineering Department of Information Science and Engineering Bengaluru India Global Academy of Technology Department of Information Science and Engineering Bengaluru India 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 Jss Science and Technology Universit Dept of Electronics and Electrical Engineering Mysuru India

ISBN: (纸本)9798350388602

Partisan symbolism has become a vital asset for various applications, including land cover classification, urban planning, disaster management, and environmental monitoring. In this study, a deep learning approach is proposed for the semantic segmentation of satellite images. The proposed model leverages a multi-level U-Net architecture, which has shown effectiveness in various image segmentation tasks. The dataset used in this study comprises satellite images with corresponding ground truth masks labeling different land cover classes, such as buildings, roads, vegetation, water bodies, and unlabeled regions. The data is preprocessed, and the ground truth masks are encoded into categorical labels to facilitate model training. The model is trained using a combination of pixel-wise crossentropy loss and the Jaccard coefficient as evaluation metrics to optimize performance. Test results demonstrate the effectiveness of the proposed approach in accurately segmenting satellite images, achieving high accuracy and Jaccard coefficient values on both training and validation datasets. Additionally, a comprehensive analysis of the model's performance is provided, discussing potential applications and future directions for satellite image segmentation tasks. The trained model is saved for further deployment and integration into satellite image processing pipelines, offering valuable insights for various remote sensing applications. © 2024 IEEE.

关键词： Satellite imagery

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Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model

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computer Systems Science & engineering 2023年第2期44卷 1793-1806页

作者： G.Ignisha Rajathi R.Ramesh Kumar D.Ravikumar T.Joel Seifedine Kadry Chang-Won Jeong Yunyoung Nam Department of Computer Science and Business Systems Sri Krishna College of Engineering and TechnologyCoimbatore641008India Department of Information Technology Sri Krishna College of TechnologyCoimbatore641008India Department of Electronics and Communication Engineering Kings Engineering CollegeChennai602117India Department of Electronics and Communication Engineering R.M.K.Engineering CollegeChennai601206India Deparmtent of Applied Data Science Noroff University CollegeKristiansandNorway Medical Convergence Research Center Wonkwang UniversityIksanKorea Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

Recently,Internet of Medical Things(IoMT)has gained considerable attention to provide improved healthcare services to *** earlier diag-nosis of brain tumor(BT)using medical imaging becomes an essential task,auto-mated IoMT and cloud enabled BT diagnosis model can be devised using recent deep learning *** this motivation,this paper introduces a novel IoMT and cloud enabled BT diagnosis model,named *** IoMTC-HDBT model comprises the data acquisition process by the use of IoMT devices which captures the magnetic resonance imaging(MRI)brain images and transmit them to the cloud ***,adaptive windowﬁltering(AWF)based image preprocessing is used to remove *** addition,the cloud server executes the disease diagnosis model which includes the sparrow search algorithm(SSA)with GoogleNet(SSA-GN)*** IoMTC-HDBT model applies functional link neural network(FLNN),which has the ability to detect and classify the MRI brain images as normal or ***ﬁnds useful to generate the reports instantly for patients located in remote *** validation of the IoMTC-HDBT model takes place against BRATS2015 Challenge dataset and the experimental analysis is car-ried out interms of sensitivity,accuracy,and speciﬁ*** experimentation out-come pointed out the betterment of the proposed model with the accuracy of 0.984.

关键词： Internet of medical things healthcare brain tumor disease classiﬁcation deep learning metaheuristics

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An implementation of personalized therapy in Clinical Decision Support System using adaptive transformer and hybrid deep learning network

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Australian Journal of Electrical and electronics engineering 2025年第1期22卷 105-120页

作者： Praveena Rachel Kamala, S. T, Saraswathi. Kaliraj, V. Nithya, A. Hema, R. Ramyadevi, R. Department of Information Technology Easwari Engineering college Tamil Nadu Chennai India Department of CSE S.A Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering St.Joseph’s College of Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering Easwari Engineering College Tamil Nadu Chennai India Department of Computer Science and Applications SRM Institute of Science and Technology Tamil Nadu Chennai India

This paper suggests a new mechanism from deep learning concept for personalised therapy in Clinical Decision Support Systems (CDSS). Basically, the texts used for the observation are acquired from the standard data sources and then forwarded to the text preprocessing task. In the pre-processing phase, the punctuation and special character removal, stop word removal, and stemming process are applied to remove noise and help to eliminate the redundant information in order to improve the data quality. Further, the pre-processed text is applied to the Adaptive Transformer Net (ATN) for the feature extraction purpose, where the attributes in this task are optimally determined with the aid of the Adaptive Walrus Optimization Algorithm (AWOA). Finally, the resultant text is subjected to the Hybrid Deep Learning Network (HDLNet). The HDLNet model is implemented by integrating the ‘Residual Long Short-Term Memory (Residual LSTM) with Dilated Recurrent Neural Network (Dilated RNN)’. From the results, the sensitivity analysis performed in the implemented technique secured 3.7% more efficient than LSTM, 7.76% improved than MobileNet, 6.7% superior to residual LSTM, and 0.90% effective than dilated RNN in dataset 1. Throughout the validation, the conventional techniques are evaluated with the suggested personalised therapy in CDSS to prove its efficacy. ©, Engineers Australia.

关键词： Long short-term memory

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Reinforcement Learning with an Ensemble of Binary Action Deep Q-Networks

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computer Systems Science & engineering 2023年第9期46卷 2651-2666页

作者： A.M.Hafiz M.Hassaballah Abdullah Alqahtani Shtwai Alsubai Mohamed Abdel Hameed Department of Electronics&Communication Engineering Institute of TechnologyUniversity of KashmirSrinagarJ&K190006India Department of Computer Science Faculty of Computers and InformationSouth Valley UniversityQenaEgypt College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkhrjKSA Department of Computer Science Faculty of Computers and InformationLuxor UniversityLuxorEgypt

With the advent of Reinforcement Learning(RL)and its continuous progress,state-of-the-art RL systems have come up for many challenging and real-world *** the scope of this area,various techniques are found in the *** such notable technique,Multiple Deep Q-Network(DQN)based RL systems use multiple DQN-based-entities,which learn together and communicate with each *** learning has to be distributed wisely among all entities in such a scheme and the inter-entity communication protocol has to be carefully *** more complex DQNs come to the fore,the overall complexity of these multi-entity systems has increased many folds leading to issues like difficulty in training,need for high resources,more training time,and difficulty in fine-tuning leading to performance *** a cue from the parallel processing found in the nature and its efficacy,we propose a lightweight ensemble based approach for solving the core RL *** uses multiple binary action DQNs having shared state and *** benefits of the proposed approach are overall simplicity,faster convergence and better performance compared to conventional DQN based *** approach can potentially be extended to any type of DQN by forming its *** extensive experimentation,promising results are obtained using the proposed ensemble approach on OpenAI Gym tasks,and Atari 2600 games as compared to recent *** proposed approach gives a stateof-the-art score of 500 on the Cartpole-v1 task,259.2 on the LunarLander-v2 task,and state-of-the-art results on four out of five Atari 2600 games.

关键词： Deep Q-networks ensemble learning reinforcement learning OpenAI Gym environments

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A Novel Hybrid Tunicate Swarm Naked Mole-Rat Algorithm for Image Segmentation and Numerical Optimization

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computers, Materials & Continua 2022年第5期71卷 3445-3462页

作者： Supreet Singh Nitin Mittal Urvinder Singh Rohit Salgotra Atef Zaguia Dilbag Singh Department of Electronics&Communication Engineering Chandigarh UniversityMohali140413India Department of Electronics&Communication Engineering TIETPatiala147004India Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia School of Electrical Engineering and Computer Science Gwangju Institute of Science and TechnologyGwangju61005Korea

This paper provides a new optimization algorithm named as tunicate swarm naked mole-rat algorithm(TSNMRA)which uses hybridization concept of tunicate swarm algorithm(TSA)and naked mole-rat algorithm(NMRA).This newly developed algorithm uses the characteristics of both algorithms(TSA and NMRA)and enhance the exploration abilities of *** from the hybridization concept,important parameter of NMRA such as mating factor is made to be self-adaptive with the help of simulated annealing(sa)mutation operator and there is no need to define its value *** evaluating the working capabilities of proposed TSNMRA,it is tested for 100-digit challenge(CEC 2019)test problems and real multi-level image segmentation *** the results obtained for CEC 2019 test problems,it can be seen that proposed TSNMRA performs well as compared to original TSA and *** case of image segmentation problem,comparison of TSNMRA is performed with multi-threshold electro magnetism-like optimization(MTEMO),particle swarm optimization(PSO),genetic algorithm(GA),bacterial foraging(BF)and found superior results for TSNMRA.

关键词： Optimization NMRA TSA image segmentation thresholding

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Android Dashboard App Development for Autonomous Vehicles Using FireBase 7

Android Dashboard App Development for Autonomous Vehicles Us...

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7th IEEE International Conference on Computational Systems and information Technology for Sustainable Solutions, CSITSS 2023

作者： Adarsh, U. Prasad, Sangeeta Jayanth, C. Madhumitha, P. Manonmani, S. Kumari, M.Uttara Department of Computer Science and Engineering Karnataka Bengaluru India Department of Information Science and Engineering Karnataka Bengaluru India Department of Electronics and Communication Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350343144

This paper presents the design, implementation, and testing of a user-friendly mobile application utilizing Android Studio and Firebase for real-time data monitoring from self-driving vehicles. The application offers an intuitive interface to display crucial vehicle parameters, including speedometer, fuel level, battery percentage, RPM, temperature, distance traveled, steering angle, motor and battery temperature, and tire pressures. Additionally, the app incorporates a notification system to promptly alert users to any parameter deviations from safe ranges. The development process encompassed UI design, Firebase integration for data storage and retrieval, and the application of logical algorithms to present data effectively. This paper elaborates on the testing phase, highlighting encountered challenges and the corresponding solutions that were implemented. The outcome is a scalable, easily maintainable application poised to enhance the safety and reliability of self-driving vehicles, thus contributing to their global acceptance as a dependable mode of transportation. © 2023 IEEE.

关键词： Digital storage

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Open-Source Gesture-Powered Augmented Reality-Based Remote Assistance Tool for Industrial Application: Challenges and Improvisation 3rd

Open-Source Gesture-Powered Augmented Reality-Based Remote A...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Sharma, Chitra Sharma, Kanika Kumar, Manni Garg, Pardeep Goyal, Nitin Department of Electronics and Communication Engineering National Institute of Technical Teachers Training and Research Chandigarh India Department of Computer Science and Engineering Chandigarh University Punjab Mohali India Department of Electronics and Communication Engineering Jaypee University of Information Technology Himachal Pradesh Waknaghat Solan India Department of Computer Science and Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendragarh123031 India

ISBN: (纸本)9789819986453

In this paper, development theory and procedure are to be presented to inspect and enhance the pre-existing gesture interaction with mobile Augmented Reality applications, as well as the application of gesture-enabled, augmented reality systems in the industry. The testing of the prototype has been done by utilizing the standard manipulation tasks that are typical in the Augmented Reality context. Also, a comparative analysis is performed with the previously existing gesture-based and touch-based input techniques used in various Augmented Reality applications. The task performance data has been evaluated on the basis of in-person user feedback. When tested, the gesture input methods introduced via the current research were found to be better than the existing interaction techniques. Most of the participants preferred to use the gesture interaction methodology explained within the current research paper for further Augmented Reality based research and development work. Quick response time and a straightforward approach to implementation were some of the most common feedback obtained by the user. The research paper mainly explains the thorough notion behind current and upcoming gesture-based interaction technologies. Also, a brief discussion targeted toward the future implications of this research work has been attached herewith for later reference and usage. The limitations and the obstacles in the pathway of the implementation of augmented reality in industries have been discussed within this research paper and attempts have been made to minimize or eliminate the drawbacks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Augmented reality

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Brain Tumor Identification Using Data Augmentation and Transfer Learning Approach

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computer Systems Science & engineering 2023年第8期46卷 1845-1861页

作者： K.Kavin Kumar P.M.Dinesh P.Rayavel L.Vijayaraja R.Dhanasekar Rupa Kesavan Kannadasan Raju Arfat Ahmad Khan Chitapong Wechtaisong Mohd Anul Haq Zamil S.Alzamil Ahmed Alhussen Department of Electronics and Communication Engineering Kongu Engineering College638060Tamil NaduIndia Department of Electronics and Communication Engineering Sona College of Technology636005Tamil NaduIndia Department of Computer Science and Engineering Sri Sairam Institute of TechnologyChennai600044Tamil NaduIndia Department of Electrical and Electronics Engineering Sri Sairam Institute of TechnologyChennai600044Tamil NaduIndia Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudur602117Tamil NaduIndia Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117Tamil NaduIndia Department of Computer Science College of ComputingKhon Kaen UniversityKhonKaen40002Thailand School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia

A brain tumor is a lethal neurological disease that affects the average performance of the brain and can be *** India,around 15 million cases are diagnosed *** mitigate the seriousness of the tumor it is essential to diagnose at the ***,the manual evaluation process utilizing Magnetic Resonance Imaging(MRI)causes a few worries,remarkably inefficient and inaccurate brain tumor ***,the examination process of brain tumors is intricate as they display high unbalance in nature like shape,size,appearance,and ***,a precise and expeditious prognosis of brain tumors is essential for implementing the of an implicit *** computer models adapted to diagnose the tumor,but the accuracy of the model needs to be *** all the above mentioned things,this work aims to identify the best classification system by considering the prediction accuracy out of Alex-Net,ResNet 50,and Inception *** augmentation is performed on the database and fed into the three convolutions neural network(CNN)models.A comparison line is drawn between the three models based on accuracy and *** accuracy of 96.2%is obtained for AlexNet with augmentation and performed better than ResNet 50 and Inception V3 for the 120th *** the suggested model with higher accuracy,it is highly reliable if brain tumors are diagnosed with available datasets.

关键词： AlexNet brain tumor data augmentation inception V3 ResNet 50

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