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Sparse Channel Estimation in Massive MIMO Systems Using Compressed Sensing and Neural Networks

Sparse Channel Estimation in Massive MIMO Systems Using Comp...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

In this paper, based on compressed sensing and neural networks, we present an intelligent sparse channel estimation technique for Massive MIMO systems. We show that our approach improves considerably the estimation accuracy in terms of Mean Squared Error (MSE) and Normalized Mean Squared Error (NMSE) with respect to classical methods. We demonstrate [Figure: ] that our approach is computationally more efficient, requires less time and resources (memory/disk) compared to the existing literature but for a robustness of performance across distal or low Signal-to-Noise Ratios (SNRs). We also confirm our method exhibits fast convergence and efficiently accommodates a variety of sparsity levels. By visualizing it using heatmaps and ROC curves we show that this method is able to effectively differentiate between signal and noise, asserting our approach as a powerful tool for the contemporary set of problems present in wireless communications. © 2024 IEEE.

关键词： Signal to noise ratio

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Comprehensive Learning Particle Swarm Optimization(CLPSO) based hybrid cloud task scheduling with predefined completion time constraints 1

Comprehensive Learning Particle Swarm Optimization(CLPSO) ba...

引用

1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Nallola, Sandhya Rani Ayyasam, Vadivel Gitam Deemed to Be University Gitam School of Technology Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798350348057

This paper proposed comprehensive learning PSO based on deadline constraint task scheduling for a hybrid IaaS cloud environment. One of the significant challenges in managing cloud services is scheduling tasks to VMs in such a way as to optimize the objective (that may be a reduction in make span, idle time, and waiting time or increasing the profit, availability, and serviceability). In this study, profit maximization is taken as an objective. This study concentrates on the workload on a batch, i.e., a bag of independent tasks which can be a kind of image rendering process, simulation, data processing, or video rendering process. We have used CLPSO for task scheduling and fixed the fitness function as profit maximization. The particle is designed so that we can easily obtain the allocation sequence by ranking the particle's coordinates. We found that for the given set of VMs (private and external cloud), the optimal or best number of tasks that can be taken is 3 to 5, as the average profit for those tasks is higher than the other set of tasks. Similarly, for a different set of cases, we can run the same setup and find the best number of tasks that can be accommodated to increase the profit. This study can be extended by changing the objective and can be repeated to get the inference on the new objective. © 2023 IEEE.

关键词： Profitability

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EaseIt: An Android Based Health Monitoring Mobile Application 3

EaseIt: An Android Based Health Monitoring Mobile Applicatio...

引用

3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Shirley, C.P. Francis, Sharon Natasha Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Tamil Nadu Coimbatore India

ISBN: (纸本)9798350322842

Diseases such as diabetes, high blood pressure, high cholesterol, etc. have grown significantly in importance in recent years. In order to preserve optimum health, the blood pressure measurements, medications, and diet of the patients diagnosed with these diseases must be effectively monitored and controlled. However, frequent monitoring requires a person to go to a healthcare center, which is not feasible due to various real-time interfaces. To address this issue, a mobile application that monitors the factors that influence a patient's blood work, and assists patients in making decisions about their diet, treatment, and medication adjustments based on the data gathered is presented. It also gives alerts and suggestions based on a graph-generated report. In addition, the app features a communication channel that connects the patient and doctor, facilitating easy and efficient monitoring and management of diseases. © 2023 IEEE.

关键词： Telemedicine

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BioPoint: Enhancing Human-computer Interaction through Single-Site, Multi-Sensor Gesture Recognition 46

BioPoint: Enhancing Human-Computer Interaction through Singl...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Isabel, Xavier Campbell, Evan Olivier, Loïc Gagné, Gabriel Scheme, Erik Côté-Allard, Ulysse Gagnon-Turcotte, Gabriel Laval University Dept. of Electrical and Computer Engineering QC Canada University of New Brunswick Department of Electrical and Computer Engineering FrederictonNBE3B 5A3 Canada University of Oslo Dept. of Technology Systems Oslo Norway

ISBN: (纸本)9798350371499

Advancements in human-computer interaction (HCI) and machine learning are seen as key avenues to help individuals living with upper limb disabilities in accomplishing their activities of daily living. Multi-channel myoelectric systems are a promising approach for HCI due to their intuitive and accurate capture of user intent through muscle activity. However, such systems are still bulky compared to widely accepted smartwatches-like devices and as such pose a challenge for seamless integration into daily life. Consequently, this work presents the first single-site EMG-IMU-PPG system for multi-gesture recognition from the wrist. Overall, when evaluated on eight able-bodied participant, the system achieves an average accuracy of 65.28%±10.45% on 17 finger and wrist gestures and 90.98%±5.65% on eight hand gestures. This study also introduces "Myo Hero", an online environment inspired by "Guitar Hero."Myo Hero provides a dynamic method for testing multi-gesture recognition systems, improving the evaluation process's appeal. Online evaluation of the multimodal system substantially outperformed random play, signifying EMG-IMU-PPG's potential for human-computer interaction. © 2024 IEEE.

关键词： Electromyography

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Text Generation for Hindi 6

Text Generation for Hindi

引用

6th IEEE International Conference on Advances in Science and technology, ICAST 2023

作者： Ghude, Tejashree Chauhan, Roshni Dahake, Krushna Bhosale, Atharv Ghorpade, Tushar Ramrao Adik Institute of Technology Dept. of Computer Engineering Navi Mumbai400 706 India

ISBN: (纸本)9798350359817

There is a rapid increase of multimodal data such as texts, images, etc. all over the internet and it is a strenuous task to obtain relevant and vital information from it. Hence, it became a challenging task to process this multimodal data for information retrieval. Thus, language generation model would be helpful for generating precise summary. In the present systems, language generation of textual data is majorly focused, but it's been observed that images can also contribute to the language generation process. Hence, the proposed model is built on an integrated system to process texts and images of both English and Hindi language data and generates concise summary. Text rank algorithm and Term Frequency-Inverse Document Frequency algorithm is used for text generation for both Hindi and English Language. Additionally, a Neural Image Captioning Model is used to generate captions of images which are then correlated with the textual data for Language Generation. The approach of combining TF-IDF algorithm and Text rank algorithm with Image Captioning model aggregately was effective to generate precise summary while considering the information captured by the images. The text generation model built in this research is language independent and paved a way as a use case in generating text for smart composition of sentences, Question-Answering system on various websites, Search Engine Optimization, etc. © 2023 IEEE.

关键词： Machine learning

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Efficient Development of Gesture Language Translation System using CNN 15

Efficient Development of Gesture Language Translation System...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mukhedkar, Moresh Deshmukh, Dilip P. Chaudhari, Om Shinde, Harshala Shinde, Amruta Kadam, Abhijeet D.Y.Patil University School of Engineering & Technology Dept. of Computer Engineering Ambi Pune India D.Y.Patil University School of Engineering & Technology Dept. of Mechanical Engineering Ambi Pune India D.Y.Patil University School of Engineering & Technology Dept. of Information Technology Ambi Pune India

ISBN: (纸本)9798350370249

Gesture Language Translation System (GLTS) is an interdisciplinary research field that intersects computer vision, machine learning, also human-machine interaction. This study offers a extensive exploration regarding current technologies, methodologies, and challenges in GLTS. The paper explores the evolution of sign communication recognition systems and their applications across diverse domains, such as human-computer interaction, virtual reality, and healthcare. Particular emphasis is given to sign communication translation, an emerging concept facilitating the translation of gestures into meaningful language, thereby enabling more intuitive communication. Challenges associated with translating gestures into language are discussed, and potential solutions are explored to enhance the accuracy and efficiency of GLTS. The research underscores the real-world impact of GLTS, showcasing its potential to revolutionize human-machine interactions across various contexts. The inclusion of case studies and examples of successful GLTS implementations provides practical insights and lessons learned from these instances. The methodology section details the research approach, encompassing the dataset, tools, and algorithms utilized for evaluating GLTS system effectiveness. The presentation and analysis of experimental results, along with relevant metrics and performance evaluations, form a critical component of this study. The discussion part analyses the results in light of the body of previous research, considers field implications, and suggests possible directions for further study and advancement. © 2024 IEEE.

关键词： Machine translation

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A Case Study of Detecting HULK-based DDoS Traffic with Random Forest Classifier

A Case Study of Detecting HULK-based DDoS Traffic with Rando...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Cai, Yi-Xian Chang, Tsu-Chen Wang, Chih-Chiang National Kaohsiung University of Science and Technology Dept. of Computer Science and Information Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350324174

Modern network administration desires to have early detection of DDoS traffic before damages occur. Nevertheless, as Internet traffic grows over years, it becomes more challenging to detect DDoS traffic in an efficient and effective manner. The survey of existing literature shows that random forest classifier, when applied to DDoS detection, yields great performance at low learning costs. This work is devoted to a case study of implementing and using random forest classifier to detect DDoS traffic generated by a well-known DDoS tool named HULK. Our result indicates that when used with a good feature selection mechanism, random forest classifier can achieve a high detection accuracy with fast training time. © 2023 IEEE.

关键词： HTTP

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A Comparative Study of the Performance of Real time databases and Big data Analytics Frameworks 7

A Comparative Study of the Performance of Real time database...

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7th International Multi-Topic ICT Conference: IMTIC 2023

作者： Mustafa, Syed Muhammd Nabeel Farooq, Muhammad Umer Zehra, Syeda Sundus Noronha, Jospeh Peter Terenece Ned University of Engineering and Technology Dept. of Computer Science & I.T Karachi Pakistan Sir Syed University of Engineering and Technology Dept. of Software Engineering Karachi Pakistan

ISBN: (纸本)9798350338461

The amelioration of information technology and the infiltration of it almost all the sectors of world have resulted in massive efflux of data. The rampant increase in the generation of data has introduced to a new term called, Big data. Big data is letting industry set the pace of future research and development. The need rose to further refine the data and get most out of it. Scientists and researchers performed analytic on the Big data to analyze it for patterns and behaviors hidden inside the data. Big data analytics aid in understanding the data through various tools, algorithms and frameworks. It allows stakeholders to make business plans and strategies according to the revelations from data. There are various tools and technologies available to perform analytics on the data. The most popular among scientists and researchers are real-time database and Big data analytics frameworks. In the paper, we evaluate the performance of the tools and technologies of Big data analytics. Mongodb and Firebase are real-time databases implemented in this study and Impala and Hive are the BDA frameworks. The parameter to perform the comparison is the query execution time. It is found out that Impala and Hive gave lesser execution time than Mongodb and Firebase. The query execution time of Impala on 50 mb dataset and 100 mb dataset was recorded to be 4 ms and 14 ms respectively. Hence, Big data Analytics Frameworks are better performing analytics tools for Big data Analytics. © 2023 IEEE.

关键词： Big data

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Deep Learning-Based Image Analysis for Detecting Nutrient Deficiencies of Tomato Plants 5

Deep Learning-Based Image Analysis for Detecting Nutrient De...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Nirasha Jayasiri, K.C. Chandrasiri, Sanjeevi Rupasinghe, Shalini Karunanayake, K.O. Rivindu Uthayachandran, Abineshh Zihara, M.I. Fathima Sri Lanka Institute of Information Technology Dept. of Computer Science and Software Engineering Sri Lanka

ISBN: (纸本)9798350358131

Tomato is one of the most popular crops worldwide. The success of a tomato crop is highly dependent on the health of the plants. Nutrient deficiency surveillance is typically conducted through visual inspections, which can be challenging for inexperienced farmers and home gardeners to work accurately. There is a growing need for a quick, reliable, and accurate nutrient deficiency identification system to address this issue. The proposed method is based on image processing and deep learning technologies that are highly effective for image classification tasks. Two models were trained using a dataset collected from tomato plants in Sri Lanka and evaluated using Mask Region-based Convolutional Neural Network (Mask R-CNN) and, You Only Look Once (YOLO) for deficiency classification, and results obtained 92% and 98% accuracy, respectively. Deficiency dispersion level expressed as a percentage using Mask R-CNN and followed by image processing techniques. Overall, this proposed system offers a convenient and accessible tool for farmers and home gardeners to monitor and maintain the health of their tomato plants, enabling them to achieve optimal yields and ensure profitable returns. © 2023 IEEE.

关键词： Object detection

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An Implementation of Feature Selection for Detecting LOIC-based DDoS Attack

An Implementation of Feature Selection for Detecting LOIC-ba...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Cai, Yi-Xian Chen, Shih-Chieh Wang, Chih-Chiang National Kaohsiung University of Science and Technology Dept. of Computer Science and Information Engineering Kaohsiung Taiwan

ISBN: (纸本)9798350324174

Machine learning method is efficient and effective in detecting DDoS attacks, but it all begins from identifying and selecting their important features. This paper presents an implementation of feature selection for DDoS detection based on Random Forest method. In our implementation, we use a LOIC software flood DDoS requests to a target computer, then control the target to extract the features from the captured IP packets, and finally calculate their Gini feature importance and ranking for subsequent feature selection. © 2023 IEEE.

关键词： Feature Selection

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