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Intelligent Car Parking System Using WSN 6

Intelligent Car Parking System Using WSN

引用

6th Global Conference on Wireless and Optical Technologies, GCWOT 2023

作者： Ratti, Steve Azarsh Pirzada, Nasrullah Shah, Syed Mohammad Ali Naveed, Anjum Mehran University of Engineering & Technology Jamshro Department of Telecommunication Engineering Hyderabad Pakistan Mehran University of Engineering & Technology Hyderabad Pakistan Hazara University Mansehra Dept. of Computer Science & Technology Mansehra Pakistan

ISBN: (纸本)9798350333718

Numerous technologies are constantly being created due to the rapid rise in the standardization and automation of operational and management systems. It simplifies procedures and allows microelectronics technology to advance more often. It was managed by an Android app and controlled by sensor networks and parking systems. The wireless sensor network manages parking spot management, parking lot reservations, vehicle navigation, and other complex circumstances. This study suggests a wireless sensor network-based and android app-based intelligent parking system. Sensors installed beneath enable the cutting-edge parking system to function. The research suggests an intelligent parking system that uses wireless sensor networks and IR, magnetic, and light sensors. The ground-level sensors used by the advanced parking system function. The base station receives the node/slot information, and the base station delivers data to FTP. At the user's request, FTP transmits details about nodes and spaces to mobile devices. The idea came from using an IoT application to control the automobile parking system, which is crucial to our daily lives. For this purpose, a mobile application for Android called Smart Parker was developed. It provides information about a parking lot, such as available or reserved spaces. Sensors at the base station will send information about parking spaces to the FTP server. The user of the innovative payment system is required to pay to park the car. Cash is first collected using cash counters, but maintaining them is challenging. Later, different methods are employed to collect the payment. The payment is made by an Automated Vehicle Identification (AVI) tag using RFID technologies. Smart, debit, and credit cards are contact methods, whereas RFID technology and mobile devices are contactless alternatives. The smart parking system on FPGA (Finite State Machine Modeling) works on two main modules, i.e., the identification module and slot checking module. The

关键词： Internet of things

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Automated Road Extraction in Urban and Rural Regions from Remote Sensing Imagery with U-Net and ResNet-Based Architectures

Automated Road Extraction in Urban and Rural Regions from Re...

引用

Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Palvi Sharma Rakesh Kumar Meenu Gupta Ahmed J. Obaid Dept. of Computer Science & Engineering Chandigarh University Punjab India Dept. of Computer Science & Engineering Chandigarh University Punjab Faculty of Computer Science and Mathematics University of Kufa Najaf Iraq College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Accurate road extraction from Remote Sensing Imagery (RSI) is crucial for various applications, including urban planning, navigation, and disaster response. This study investigates automated road extraction in rural, semi-urban and urban regions using deep learning architectures, specifically UNet and Deep Residual U-Net (ResNet) models. We leverage High-Resolution Satellite Imagery (HRSI) to train and validate our models, focusing on the distinct characteristics and challenges presented by urban and rural environments. Our methodology involves comprehensive data preprocessing and augmentation techniques to enhance model robustness. We evaluate the performance of both architectures using metrics and achieved an accuracy of 90.29%, recall of 80.95%, precision of 74.89%, and F1- score of 77.80% for the Deep Residual U-Net. Experimental results demonstrates that while both models perform well, the Deep Residual U-Net exhibits superior accuracy and generalization, particularly in complex urban landscapes.

关键词： Training Accuracy Roads Computational modeling Urban planning computer architecture Feature extraction Satellite navigation systems Sensors Remote sensing

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Automated Traffic Light Control System for Ambulance Using Laser Signals and Raspberry Pi

Automated Traffic Light Control System for Ambulance Using L...

引用

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

作者： Arun Kumar S Anand Kumar M Robin Rohit Vincent Anusha M S Uttam Upendra Hegde N Nauman Pasha Dept. of Computer Studies Symbiosis Institute of Computer Studies and Research (SICSR) Pune India Dept. of Information Science and Engineering Networks & IoT Lab Presidency University Bangaluru India Dept. of Computer Science and Engineering Networks & IoT Lab Presidency University Bangaluru India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Urban traffic congestion poses a major challenge, particularly for emergency services like ambulances, where delays can have life-threatening consequences. This paper proposes a novel, cost-efficient Automated Traffic Light Control System aimed at prioritizing ambulances at busy intersections, thereby reducing delays and ensuring faster response times. The system utilizes Raspberry Pi Zero 2W, Laser Diodes (KY-008), and Light Dependent Resistors (LDRs) to detect ambulances and dynamically control traffic lights. Unlike traditional systems relying on GPS or RFID technologies, this laser-based approach offers greater precision and affordability through the use of modulated laser signals. Experimental results reveal that this system reduces ambulance response times by 35%. The tests were conducted under simulated urban conditions, highlighting the system's capability to outperform conventional solutions. The simplicity of integration and real-time responsiveness underscore its potential for widespread urban adoption.

关键词： Resistors Semiconductor lasers Traffic control Emergency services Road safety Real-time systems Delays Time factors Traffic congestion Radiofrequency identification

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Masked Ciphertext Comparison without Masking Conversions using Register Rotation and Chinese Remainder Theorem

Masked Ciphertext Comparison without Masking Conversions usi...

引用

International Conference on Information and Communication Technology Convergence (ICTC)

作者： Geumtae Kim Eunyoung Seo Yongwoo Lee Jong-Seon No Dept. Electrical and Computer Engineering Seoul National University Seoul Republic of Korea Dept. Electrical Engineering and Computer Science Daegu Gyeongbuk Institute of Science and Technology (DGIST) Daegu Republic of Korea Dept. Electrical and Computer Engineering Inha University Incheon Republic of Korea

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

In this paper, we propose a novel method for masked comparison using register rotation technique without masking conversions. In key encapsulation mechanisms (KEMs), ciphertext comparison is essential to ensure the security, but it is one of the most computationally expensive operations in masking environment. Previous works on masked ciphertext comparison have mainly focused on masking conversion and its post-processing. However, the proposed method introduces another register-based approach, which eliminates the need for time-consuming masking conversion and solely relies on register rotation using Chinese remainder theorem (CRT).

关键词： Encapsulation Memory management Registers Information and communication technology Cryptography Convergence

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Automated Leukemia Diagnosis from Microscopic Blood Smear Images Using Adapted Inception V3

Automated Leukemia Diagnosis from Microscopic Blood Smear Im...

引用

Big Data Analytics in Bioinformatics (DABCon), International Conference on

作者： M Vani Pujitha Srinija Srikantam Ch Sahithi Akhila Timmasarti Dept. of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350378047

ISBN: (纸本)9798350378054

Leukemia, a cancer characterized by the rapid overproduction of abnormal blood cells, presents notable diagnostic and treatment challenges. Identifying leukemia subtypes promptly and accurately is vital for devising effective treatments and improving patient outcomes. This study proposes an advanced Inception V3 model optimized for the automatic identification of leukemia from microscopic images of blood smears. By integrating cutting-edge deep learning methods and a customized model architecture, this system differentiates between Acute Lymphoblastic Leukemia (ALL), Acute Myeloid Leukemia (AML), Chronic Lymphocytic Leukemia (CLL), Chronic Myeloid Leukemia (CML), and healthy samples. The model benefits from preprocessing techniques like image resizing, normalization, and augmentation to enhance its performance. Through transfer learning, the Inception V3 model is fine-tuned on a rich dataset covering various leukemia subtypes. Experimental results demonstrate the model's high effectiveness, achieving 100% accuracy during training and 99.72% accuracy in testing, proving the system's reliability for automated leukemia detection.

关键词： Training Deep learning Adaptation models Accuracy Microscopy Transfer learning Leukemia Reliability Blood Testing

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Fake News Prediction in Hindi and English Robust Transformer Model

Fake News Prediction in Hindi and English Robust Transformer...

引用

2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Sanmugasundaram, R. Dileepan, D. Manikandan, M. Devi, M. Kiruthiga Ramya, N. Rajalakshmi Institute of Technology Dept. of Electronics and Communication Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Dept. of Electronics and Communication Chennai India Dr. M.G.R. Educational and Research Institute Dept. of Information Technology Chennai India Sri Sairam Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350319200

The ever-growing consumption of information from digital platforms such as social media has paved the way for information dissemination like never before, but not necessarily all the information or news delivered to us is authentic or holds true to reality. A huge amount of information is created and shared by social media users, some of which are manipulative and have no influence on reality, and can cause harm in a variety of ways. Implementation of an automated or AI-based system to classify text articles as misinformation is a challenging task as identifying the truth is complicated. An AI-based fake news prediction system is proposed to classify fake news from real ones by understanding the context of news and making decisions accordingly. To identify fake news by examining the context of the news, a Bi-directional Encoder Representations from Transformers (BERT) model, a pretrained model with a more potent feature extractor, has been implemented. BERT is well suited for tasks like predicting fake news because of its extensive contextualizing capabilities. The novelty of this paper is that the proposed system is trained, tested, and evaluated in 2 languages, English and Hindi to develop an overall reliable system to predict fake news in both languages. Two models of BERT namely DeBERTa and XLM-RoBERTa are chosen for the prediction of fake news. Further improvement in the model is achieved by using the additional news data to pre-train this model. Also, the model is fine-tuned to fit the requirements and to get the optimum result possible. The performance comparison of the chosen models is made and it is observed that the XLM-RoBERTa has improved accuracy over DeBERTa while testing for both languages. © 2023 IEEE.

关键词： API interface DeBERTa encoder fake news GPU XLM-RoBERTa

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ElHa Net: A Pattern Recognition Neural Network

ElHa Net: A Pattern Recognition Neural Network

引用

Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Elsadig Ahmed Mohamed Babiker Hanan Hassan Ali Adlan Dept. of Computer Science College of Computer Engineering and Science Prince Sattam Bin Abdulaziz University Kharj KSA Dept. of Computer Science Faculty of Mathematical Sciences Uof K Dept. of Computer Science College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh KSA

ISBN: (数字)9798331540104

ISBN: (纸本)9798331540111

Neural Networks are the state-of-the-art models that derive intelligent systems. Generalization of the neural network model is a judge of how well an architecture mimic human intelligence. Self-extraction neural networks become the dominant neural networks, especially in autonomous systems such as intelligent robotics, autonomous vehicles, ⃛ etc. This paper presents a neural network based on distance measure, ElHaNet Neural Network. ElHaNet network is a self-extraction neural network and is capable of pattern recognition. ElHaNet architecture composed of an extraction phase and a classification phase. Neurons in the extraction network capture patterns in the presented input. The classification network performs on the input patterns to be classified according to the problem in hand. The developed architecture is bench marked against well-known architectures for hand written digit recognition. The performance of the network is found to compete favorably with state-of-the-art models in the literature.

关键词： Hands Human intelligence Neurons Pattern recognition Biological neural networks Robots Autonomous vehicles

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Multimodal Representation Learning:Cross-modality and Shared Representation

Multimodal Representation Learning:Cross-modality and Shared...

引用

2022 International Conference on Industry 4.0 Technology, I4Tech 2022

作者： Mohan, Paul Brilley, Batley C Kumar A. A, Nippun Amrita School of Engineering Dept. of Computer Science & Engineering Bengaluru India Ai and Automation Litmus 7 Kochi India

ISBN: (数字)9781665471961

ISBN: (纸本)9781665471961

Various kinds of Information retrieval or processing task can be difficult basis on how the information is viewed or represented. Representation learning is a technique that allows a system to discover the representation of the data which used for detecting features and classifying things. As compared with learning from one representation, learning from different representations make the task easier. For example, learning something from images and their texts or learning from video and their audio produces good results than learning from one source (either image or text)or (video or audio). Learning from multiple modalities helps to achieve the target correctly within short time. This paper tries to design a Multimodal Autoencoder with minimum loss that can produce shared representation of images and its texts © 2022 IEEE.

关键词： Learning systems

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Surface traffic monitoring using OpenCV for various weather conditions through enhanced spatial correlation method

Surface traffic monitoring using OpenCV for various weather ...

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2022 International Mobile and Embedded Technology Conference, MECON 2022

作者： Ghosh, Dipak Bera, Soumokanti Datta, Pushan Kumar Amity University Dept. of Computer Science Engineering Kolkata India Amity University School of Engineering and Technology Kolkata India

ISBN: (纸本)9781665420204

It is a big challenge in the present urban traffic management system to develop a vehicle detection and traffic surveillance monitoring system through video capture using real time data capture model whereby machine learning may be used to recognise and categorise things. The idea is to analyse the motion and approach of cars acquired from CCTV cameras footage installed on the roads in different weather conditions and create an interactive software which should be able to gather and update important statistics like vehicle type classification and counts, lane usage details, for better regulation and control with ease. The core of this working model is to enhance vehicle detection and counting module which is implemented by analysing consecutive frames of video using frame differencing technique. The government has tried the best to ensure swift movement by implementing the broad usage of intelligent traffic controller system design. According to the WHO, around 2 million road traffic accidents result in deaths, with some nations devoting 2% of their GDP to road traffic accidents. Opportunities in the intelligent transport system market is growing at a global compound annual growth rate (CAGR) from 2019 to 2025 as an effective tool for road safety. © 2022 IEEE.

关键词： Information management

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Implementation of a Faster B+ Tree*

Implementation of a Faster B+ Tree*

引用

Information Technology (OCIT), OITS International Conference on

作者： Ankit Das Khushi Sahoo Bharati Mishra UG-Computer Science Engineering IIIT BBSR'24 Odisha India dept. of Computer Science Engineering IIIT BBSR Odisha India

ISBN: (数字)9798331510404

ISBN: (纸本)9798331510411

In our paper, we have tried to implement some enhancements to the B+ tree structure with the aim to improve its performance and throughput. Our focus is on improving the performance of B+ tree on SIMD architecture machines. By referencing prior research, we identified significant gaps between traditional B+ tree implementations and the capabilities of SIMD architectures, and we have tried to implement some solutions to overcome those gaps. These gaps primarily stem from the mismatch in how data is processed and accessed between the two paradigms. Traditional B+ trees are optimized for sequential data access patterns, whereas SIMD architectures excel in parallel data processing. We have tried to identify the disparities and implement some solutions to overcome them. Our approach aims to mitigate the adverse effects of these gaps, thereby enhancing the B+ tree's efficiency in modern computing environments. Through empirical evaluation and comparative analysis, we demonstrate the potential improvements in performance and throughput achievable with these modifications.

关键词： Single instruction multiple data Databases computer architecture Throughput Data processing Information technology

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