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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Jiseung Youn Sunghyun Cho Dept. of Computer Science & Engineering Major in Bio-Artificial Intelligence Hanyang University Seoul Republic of Korea Dept. of Computer Science & Engineering Hanyang University ERICA Ansan Republic of Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

As Internet of Things (IoT) technologies are inves-tigated to support key 5G services, significant research efforts are being made to enable machine-type communication (MTC). Cell-free IoT (CF-IoT) systems, characterized by favorable prop-agation and macro diversity, are being studied in conjunction with grant-free random access (GFRA) as a potential enabler to achieve massive connectivity for MTC. However, existing GFRA schemes in CF-IoT transmit preambles at a fixed power level resulting in severe preamble collisions at nearby access points (APs). In this paper, we analyzes the analysis of preamble collisions in massive connectivity scenario and proposes a sim-ple power control scheme to address preamble collision. The proposed scheme mitigates preamble collisions by transmitting preambles only to the nearest AP in high-density access scenes. Simulation results show that the proposed method improves the random access success rate compare to existing GFRA method by 10%.

关键词： 5G mobile communication Simulation Power control Information and communication technology Internet of Things Convergence

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Sentiment Analysis on Twitter Data of Omicron (B.l.l.529) using Natural Language Processing

Sentiment Analysis on Twitter Data of Omicron (B.l.l.529) us...

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2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2022

作者： Datta, Suseta Mitra, Poojarini Kundu, Pritam Ministry of Electronics and Information Technology National Institute of Electronics and Information Technology Kolkata India University of Engineering & Management Dept. of Computer Applications Kolkata India University of Engineering & Management Dept. of Computer Science and Engineering Kolkata India

ISBN: (纸本)9781665477192

The COVID pandemic is causing outrageous interference in everyday life and financial activity. Close to two years after the presence of COVID, WHO allotted the variety B.l.l.529 a variety of concern, named 'Omicron'. Online diversion data assessment is created and transformed into a more renowned subject of investigation. In this paper, a sizably voluminous heap of appraisals and assessments are culminated with online redirection information. The evaluations and appearances of Twitter electronic diversion stage clients are summarised and researched by considering sentiment analysis by utilising various natural language processing techniques based on positive, negative, and neutral tweets. All potential outcomes are considered for investigating the feelings of Twitter clients. For the most part, tweets are assessed clearly, and this assessment ensures the headway of this investigation study. Different kinds of analyzers are utilised and measured. The 'TextBlob Sentiment Analyzer' has given the highest polarity score based on positivity, negativity, and neutrality rates in terms of inspiration, pessimism, and impartiality. A total dataset is fully determined and classified with all the analyzers, and a comparative result is also measured to find the ideal analyzer. It is intended to apply boosting machine learning methods to increase the accuracy of the proposed architecture before further implementation. © 2022 IEEE.

关键词： Sentiment analysis

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Bayesian-Driven Deep Learning and Machine Learning Approaches for Handwritten Word Recognition

Bayesian-Driven Deep Learning and Machine Learning Approache...

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Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Medisetty Dinesh Lingamaneni Sagar Pallapu Moksha Pappula Baladhitya M. Srinivas Computer Science Engineering Amrita Vishwa Vidyapeetham Amaravati India Dept. of Computer Science Amrita Vishwa Vidyapeetham Amaravati India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Identifying handwritten words poses a complicated problem owing to the variances in handwriting styles and the possible noise and distortions present in the data. The performance of used deep learning and traditional machine learning handwriting recognition methods for recognizing handwritten words is studied in this paper and the advantages of using deep learning are discussed. In particular, neural network models such as CNN and 2DLSTM networks had a great work in this area. In contrast, we detect whole handwritten words without the need of segments into standalone characters. The CNNs are used for feature extraction, the Bidirectional LSTMs for sequence prediction and output is decoded via a CTC layer. The model was evaluated over the IAM words dataset from the IAM handwriting database, achieving 87% accuracy and a character error rate of 7.77%. In addition, we experimented with ML techniques, including SVM, RF has got an accuracy of 72% and 80% accuracies, and a Bayesian network of 61% accuracy, to provide a comparative analysis.

关键词： Deep learning Support vector machines Radio frequency Handwriting recognition Accuracy Error analysis Neural networks Feature extraction Bayes methods Convolutional neural networks

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On Detecting Low-Pass Graph Signals Under Partial Observations

On Detecting Low-Pass Graph Signals Under Partial Observatio...

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Sensor Array and Multichannel Signal Processing Workshop

作者： Hoang-Son Nguyen Hoi-To Wai Dept. of Computer Science & Engineering The Chinese University of Hong Kong Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong

ISBN: (数字)9798350344813

ISBN: (纸本)9798350344820

The application of graph signal processing (GSP) on partially observed graph signals with missing nodes has gained attention recently. This is because processing data from large graphs are difficult, if not impossible due to the lack of availability of full observations. Many prior works have been developed using the assumption that the generated graph signals are smooth or low pass filtered. This paper treats a blind graph filter detection problem under this context. We propose a detector that certifies whether the partially observed graph signals are low pass filtered, without requiring the graph topology knowledge. As an example application, our detector leads to a pre-screening method to filter out non low pass signals and thus robustify the prior GSP algorithms. We also bound the sample complexity of our detector in terms of the class of filters, number of observed nodes, etc. Numerical experiments verify the efficacy of our method.

关键词： Array signal processing Conferences Low-pass filters Signal processing algorithms Detectors Filtering algorithms Complexity theory

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Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems for Next-Generation Wireless Networks

Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems f...

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Electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Satya Prakash Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This study explores the optimisation of Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems through the use of phased arrays and advanced beamforming methods. With a focus on overcoming the challenges that come with 5G wireless communication, like high data rate needs, interference resistance, and efficient spectrum usage, this work presents a novel approach that combines dynamic beam steering and adaptive precoding to significantly enhance system performance. Through thorough modelling, we demonstrate considerable improvements in bit error rates (BER), throughput, and spectrum efficiency compared to traditional MIMO-OFDM configurations. The results demonstrate a 25% improvement in throughput and a 40% reduction in BER in densely populated metropolitan environments, underscoring the potential of phased arrays and beamforming to further improve 5G network performance. This work not only validates the efficacy of the proposed enhancements but also establishes a foundation for future advancements in wireless communication systems by highlighting the critical roles that phased arrays and beamforming will play in achieving the maximum potential of 5G technology.

关键词： Phased arrays Beam steering Adaptive systems Array signal processing 5G mobile communication Precoding Wireless networks Bit error rate Interference Throughput

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Dynamic Road Management in the Era of CAV

arXiv

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arXiv 2024年

作者： Younis, Mohamed Lee, Sookyoung Lalouani, Wassila Tan, Dayuan Gupte, Sanket Dept. of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD United States Dept. of Computer Science and Engineering Ewha Womans University Seoul Korea Republic of

Traffic management and on-road safety have been a concern for the transportation authorities and the engineering communities for many years. Most of the implemented technologies for intelligent highways focus on safety measures and increased driver awareness, and expect a centralized management for the vehicular traffic flow. Leveraging recent advances in wireless communication, researchers have proposed solutions based on vehicle-to-vehicle (V2V) and vehicle-to-Infrastructure (V2I) communication in order to detect traffic jams and better disseminate data from on-road and on-vehicle sensors. Moreover, the development of connected autonomous vehicles (CAV) have motivated a paradigm shift in how traffic will be managed. Overall, these major technological advances have motivated the notion of dynamic traffic management (DTM), where smart road reconfiguration capabilities, e.g., dynamic lane reversal, adaptive traffic light timing, etc. will be exploited in real-time to improve traffic flow and adapt to unexpected incidents. This chapter discusses what the challenges in realizing DTM are and covers how CAV has revolutionized traffic management. Moreover, we highlight the issues for handling human-driven vehicles while roads are transitioning to CAV only traffic. Particularly, we articulate a new vision for inter-vehicle communication and assessment of road conditions, and promote a novel system for traffic management. Vehicle to on-road sensors as well as inter-vehicle connectivity will be enabled through the use of hand-held devices such as smartphones. This not only enables real-time data sharing but also expedites the adoption of DTM without awaiting the dominant presence of autonomous vehicle on the road. The proposed traffic management system incorporates computation and communication capabilities in traffic lights and road-side units, and accounts for the human-factor in controlling the traffic flow. The goal is to allow autonomous and driver-centric routing decis

关键词： Traffic congestion

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Car Defect Detection Using Image Processing

Car Defect Detection Using Image Processing

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Durai, S. Sujithra, T. Charan, Athmakuri Sai Pulyala, Sai Kiran Satyam, Battula Vishnuwardhan Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Avadi Dept. of Cse Chennai India Manipal Institute of Technology Computer Science and Engineering Manipal India

ISBN: (纸本)9798350334357

Defect Detection became one of the hardest problems in this world nowadays. As of now humans detected the defects with the help of their naked eye. This process is time consuming and this can be done by only experts and these decisions may be differ from expert to expert. To avoid this problem we are training the collected data set. This will consume very less time and provides the best solution. The detection of defects is essential in quality control in manufacturing to get high quality products in less time. Humans can identify large defects but in case of micro defects humans can't found those so, this implementation will helpful in finding large as well as micro and milli defects as well. In this project we are trying to detect the defects such as dents and ripples present on the surface of the car which are macro in size at the time of manufacturing the cars and in the case of accidents happened. We'll use a high-quality camera to simultaneously capture photographs of the vehicle's five sides. We will use Convolution Neural Network [CNN] for training (which highlights the location of the defects). Once trained, that model we can use it for different end user applications. © 2023 IEEE.

关键词： Feature extraction

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Fortifying IoT Devices: AI-Driven Intrusion Detection via Memory-Encoded Audio Signals

Fortifying IoT Devices: AI-Driven Intrusion Detection via Me...

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IEEE Cybersecurity Development (SecDev)

作者： Ramyapandian Vijayakanthan Karley M Waguespack Irfan Ahmed Aisha Ali-Gombe Dept. of Computer & Information Sciences Towson University Dept. of Computer Science & Engineering Louisiana State University Dept. of Computer Science Virginia Commonwealth University

IoT devices have become an increasingly accessible target for evasive attacks, such as botnets, due to insecure network services, deprecated software components, unencrypted data communication, and other vulnerabilities. To address these security concerns, our work makes several significant contributions toward curating datasets and designing and developing a robust and effective Host-Based Intrusion Detection algorithm (HIDS) for IoT devices. The proposed algorithm leverages memory-based fingerprints to train a convolutional neural network (CNN) model. Our approach is based on the premise that despite the heterogeneity of IoT devices, the functionality of each IoT device is often unique and remains relatively constant throughout its lifespan. Thus, to develop an effective IDS algorithm based on anomaly detection, we encode the dynamic IoT device memory into sound wave signals, extract discriminable features such as MFCCs and Chroma features, and pass these deterministic fingerprints as feature vectors to a device-specific CNN model. When trained using three different testbed datasets, our model achieved 100% accuracy for known and anomaly memory instances. Additionally, the evaluation of our features and feature engineering process showed that our model, which is trained with the features from memory-encoded signals, is more reliable and robust than those algorithms that leverage raw memory bytes as feature vectors.

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Role of IoT in Smart Cities: A Review, Applications, Open Challenges and Solutions

Role of IoT in Smart Cities: A Review, Applications, Open Ch...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Kolla Vivek Gurrala Swetha V T Ram Pavan Kumar M Anusuri Krishna Veni Neti Praveen Masthan Rao Kale Dept. of CSE QIS College of Engineering & Technology (A) Ongole Andhra Pradesh India Dept. of CSE Malla Reddy College of Engineering and Technology (A) Telangana India Dept. of Computer Science Kakaraparti Bhavanarayana College Vijayawada Andhra Pradesh India Dept. of Computer Science & Engineering – Data Science Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Dept. of Computer Science and Design SRKR Engineering College Bhimavaram Andhra Pradesh India Dept. of CSE Pace Institute of Technology and Sciences (A) Ongole Andhra Pradesh India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

Smart cities have emerged as a response to the fast expansion of metropolitan areas; these places strive to improve the lives of their residents while simultaneously promoting environmentally responsible growth. To maximize urban services, the Internet of Things (IoT) is an essential technology that allows for the seamless integration of sensors, devices, and systems. This allows smart cities to become a reality. With an emphasis on smart transportation, energy management, healthcare, environmental monitoring, and public safety, this paper explores the critical role of IoT in smart cities. Data security, privacy, scalability, and interoperability are some of the problems that arise when smart cities use IoT, despite the revolutionary potential it has. These obstacles are thoroughly examined in this work, along with possible solutions to these problems. This study gives a thorough overview of the IoT and its impact on smart city development, innovation, and urban resilience by focusing on the present situation, applications, and unanswered questions.

关键词： Waste management Data privacy Technological innovation Smart cities Scalability Data security Medical services Public security Internet of Things Interoperability

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TSPvis: A Temperature Sensitive Paint Formulation Visual Design System

TSPvis: A Temperature Sensitive Paint Formulation Visual Des...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Jing Liao Yadong Wu Guijuan Wang Fupan Wang Huarong Chen Li Long Dept. Computer Science and Technology Southwest University of Science and Technology Mianyang China Dept. Computer Science and Engineering Sichuan University of Science and Engineering Yibin China

The composition and proportion parameters of temperature sensitive paint formulation are complex. The current methods have a long development cycle and low efficiency. We design a visual formulation design system for temperature sensitive paint, helps desingers use historical data to improve the efficiency of formulation design. TSPvis provides a visual formulation design function, implements the proportion decision of formulation based on decision tree, and recommends reference for ingredients proportion to the formulation designer. We use the DPC clustering algorithm to quickly obtain the radial basis center, and use the Adam algorithm to improve the weight calculation from the hidden layer to the output layer, to improve RBF neural network. TSPvis use this RBF as a model to predict performance of the formulation which designed before, and feeds back to the designer to improve the formulation designing of temperature sensitive paint.

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