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检索条件"机构=Department Computer and Communication System Engineering"

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A 180-Degree Phase Difference Output of Wilkinson Power Divider Using Microstrip Parallel Coupled Lines 6

A 180-Degree Phase Difference Output of Wilkinson Power Divi...

引用

6th International Conference on Power, Energy and Innovations, ICPEI 2024

作者： Phanomupatum, Pratya Phromloungsri, Ravee Tama, Rachata Pongthavornkamol, Tiwat Wongpatsa, Decha Sonasang, Somchat Nakhon Phanom University Faculty Industrial Technology Department of Electronics Nakhon Phanom Thailand Udon Thani Rajabhat University Computer and Communication Engineering Faculty of Technology Udon Thani Thailand National Electronic and Computer Technology Center National Science and Technology Development Agency Location and Automatic Identification System Research Team Communication and Network Research Group Pathumthani Thailand Faculty Industrial Technology Nakhon Phanom Thailand

ISBN: (纸本)9798350356779

The design of a 180° output phase difference Wilkinson power divider for L-band applications is detailed in this article. The Wilkinson power is generated through the utilization of the parallel coupler's phase shifter. To showcase the potential of the suggested power divider, an FR4 microwave substrate is fabricated with a narrow band of 1.6 GHz to accommodate radio astronomy systems. The insertion loss, return loss, and isolation loss for the power divider's difference phase are accurately simulated at the operational frequency. © 2024 IEEE.

关键词： Radio astronomy

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TRACING THE MOTION IN HIGH-SPEED VIDEO FRAMES USING DEEP LEARNING

TRACING THE MOTION IN HIGH-SPEED VIDEO FRAMES USING DEEP LEA...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Gayathri, R. Harsha, B.K. Sharmiladevi, D. Sajitha, L.P. Rao, Vuda Sreenivasa Nirmala, G. Department of ECE Rajalakshmi Engineering College Tamilnadu Chennai India Department of ECE CMR Institute of Technology Karnataka Bengaluru India Department of Electronics and Communication Engineering Sona College of Technology Tamilnadu Salem India Department of Computer Science and Business System R. M. K Engineering College Tamilnadu Kavaraipettai India Department of CSE Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of Computer Science and Engineering R.M.D Engineering College Chennai India

ISBN: (纸本)9798350305708

One of the most important jobs of a self-driving vehicle's visual system is to identify and categorize obstacles along the route. Cars need to be detected, categorized, localized, and linked across frames if they are to be tracked. With the low cost and high utility of visual information, vision-based technologies have gained popularity for this purpose. In this paper, we develop a deep learning-based motion tracking from high-speed video frames. The model is developed using deep convolutional neural network that makes it to be applicable for classification of objects in the video frames. The deep convolutional neural network classifies the obstacles present in the pathway and offers a better classification pattern than the conventional models. © 2023 IEEE.

关键词： Application Convolutional Neural Network Deep Learning High-Speed Video Motion Vehicle Video Frames

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The Spreading Prediction and Severity Analysis of Blood Cancer Using Scale-Invariant Feature Transform

The Spreading Prediction and Severity Analysis of Blood Canc...

引用

2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Mohammed, Vazeer Ali Mohammed, Mehmood Ali Mohammed, Murtuza Ali Ramakrishnan, Rakesh Logeshwaran, J. Lewis University Department of Computer & Information System IL United States University of the Cumberlands Department of Information Technology WilliamsburgKY United States University of the Cumberlands Department of Information Technology Union CityCA United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641202 India

ISBN: (纸本)9798350300826

Blood cancer is an ever-growing global health concern. Early detection and accurate diagnosis are key to successful treatment and long-term prognosis of the disease. Despite various advances in medical technologies, current methods for blood cancer detection are far from perfect and often suffer from a lack of accuracy. This paper proposes the use of Scale-Invariant Feature Transform (SIFT) based approaches for predicting the spreading of blood cancer and assessing its severity. SIFT is a computer vision technique that extracts meaningful features from an image. Through SIFT, meaningful patterns in medical imaging datasets, such as those used for blood cancer detection, can be identified. The output from SIFT can then be used to build predictive models which can accurately predict the spread of the disease and its severity. Additionally, this paper explains how machine-learning algorithms and artificial neural networks can be used to further enhance the accuracy of these predictions. Finally, the proposed approach is validated using benchmark datasets and its accuracy is compared with existing methods for blood cancer prediction. The proposed model reached 89.71% accuracy, 89.25% precision, 90.71% recall and 89.40% f1-score results. The results show that prediction accuracy increases significantly when SIFT is used. It is concluded that SIFT can be a powerful tool for predicting blood cancer and estimating its severity. © 2023 IEEE.

关键词： Diseases

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An In-Depth Study of Cybersecurity Threats and Intelligent Broadcasting Network Topology 1

An In-Depth Study of Cybersecurity Threats and Intelligent B...

引用

1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Chauhan, Usha Gupta, Manali Galgotias University Department of Electrical Electronics and Communication Engineering Uttar Pradesh Greater Noida India Noida Institute of Engineering and Technology Department of Computer Science and Business System Uttar Pradesh Greater Noida India

ISBN: (纸本)9781665456876

The smart grid has stacked ICT power equipment. Smart grids use modern electrical sensors. Future adoption of smart devices and their interaction with networks may offer cyberthreats. Several smart grid subsystems may be exposed if a component doesn't respond in real time, reducing efficiency and reliability. Superannuated networks create cyber security risks. This article explores a secure cyber infrastructure, related breakthroughs, and cyber security concerns in a complex system. Cybersecurity goals, objectives, and risk assessments are complete. These papers are summarized. Prioritize security. Then, examine smart grid hacks to uncover weaknesses and their repercussions. This research designed a cyber-security plan for breaches, counterattacks, and actions. Future research is discussed. © 2022 IEEE.

关键词： Cybersecurity

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Adapting Spatial Transformer Networks Across Diverse Hardware Platforms: A Comprehensive Implementation Study

Adapting Spatial Transformer Networks Across Diverse Hardwar...

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IEEE International Conference on Artificial Intelligence Circuits and systems (AICAS)

作者： Meriem Bettayeb Eman Hassan Muhammad Umair Khan Yasmin Halawani Hani Saleh Baker Mohammad Computer and Communication Engineering Department System-on-Chip (SoC) Lab Khalifa University Abu Dhabi UAE College of Engineering and IT University of Dubai Dubai UAE

ISBN: (数字)9798350383638

ISBN: (纸本)9798350383645

The field of artificial intelligence (AI) holds a variety of algorithms designed with the goal of achieving high accuracy at low computational cost and latency. One popular algorithm is the vision transformer (ViT), which excels at various computer vision tasks for its ability to capture long-range dependencies effectively. This paper analyzes a computing paradigm, namely, spatial transformer networks (STN), in terms of accuracy and hardware complexity for image classification tasks. The paper reveals that for 2D applications, such as image recognition and classification, STN is a great backbone for AI algorithms for its efficiency and fast inference time. This framework offers a promising solution for efficient and accurate AI for resource-constrained Internet of Things (IoT) and edge devices. The comparative analysis of STN implementations on the central processing unit (CPU), Raspberry Pi (RPi), and Resistive Random Access Memory (RRAM) architectures reveals nuanced performance variations, providing valuable insights into their respective computational efficiency and energy utilization.

关键词： Accuracy Resistive RAM Transformers Hardware Computational efficiency Classification algorithms Central Processing Unit

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A Novel Predictive Analysis to Identify the Weather Impacts for Congenital Heart Disease Using Reinforcement Learning

A Novel Predictive Analysis to Identify the Weather Impacts ...

引用

2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Mohammed, Mehmood Ali Ramakrishnan, Rakesh Mohammed, Murtuza Ali Mohammed, Vazeer Ali Logeshwaran, J. University of the Cumberlands Department of Information Technology WilliamsburgKY United States University of the Cumberlands Department of Information Technology Union CityCA United States Lewis University Department of Computer & Information System IL United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641202 India

ISBN: (纸本)9798350300826

Reinforcement learning is a powerful approach for predictive analysis to identify the weather impacts for congenital heart disease. The key advantages of this method include the utilization of sensor data to predict the weather conditions on a daily basis and the ability to learn from feedback and adapt the predictions over time. In this paper, an innovation model has proposed by using reinforcement learning algorithm. It can gain important insights regarding the impact of weather on congenital heart disease. The predictive models developed using this approach have the potential to help medical professionals as well as the general public in better predicting and managing congenital heart disease. This could help reduce costs associated with care and ultimately improve health outcomes. © 2023 IEEE.

关键词： Predictive analytics

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Scheme of Quantum Controlled Teleportation with Authentication Step

Scheme of Quantum Controlled Teleportation with Authenticati...

引用

International Conference on Smart communications and Networking (SmartNets)

作者： Waleed Abed Abohana Yousif Samer Mudhafar Akram Talib Kareem Haider Salih Mahdi Ali Hamzah Abbas Communication System Engineering Department College of Electrical Engineering Imam Reza International University Mashhad Iran Computer Techniques Engineering Department College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

The most important branch of quantum telecommunications is quantum teleportation because in this branch we transmit what we do not know and all about its content without sending any qubits and using only quantum entanglement. In recent years, many designs have been proposed in this field, which are usually not in the hands of users from the beginning, such as the first design of channel qubits, these qubits are transmitted using quantum channels, so establishing security or authentication of users is important. In this paper, we will present a design with an authentication step that is transmitted from the sender simultaneously to 2 unknown qubit receivers, two different qubits. This plan also has 2 controllers for licensing and security review of protocol and destruction of channel qubits in case of error.

关键词： Protocols Quantum entanglement Reviews Qubit Quantum channels Authentication Receivers

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Bidirectional Quantum Teleportation By Simultaneously Sending A Secret Classic Bit From Both Sides

Bidirectional Quantum Teleportation By Simultaneously Sendin...

引用

International Conference on Smart communications and Networking (SmartNets)

作者： Akram Talib Kareem Yousif Samer Mudhafar Waleed Abed Abohana Haider Salih Mahdi Ali Hamzah Abbas Communication System Engineering Department College of Electrical Engineering Imam Reza International University Mahshad Iran Computer Techniques Engineering Department College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

Branches of quantum telecommunications are the branches that transmit information using quantum technologies. In some of these branches, quantum information is transmitted, and in others, classical information. The quantum teleportation branch is a branch that transmits an unknown qubit without sending qubits and using entanglement. The branches of quantum direct communication, quantum secret sharing, quantum dense coding, quantum dialogue, etc. are branches that transfer classical bits. In this paper, we intend to present a design that transmits both unknown qubits and 2 bits of classical messages in two directions and simultaneously by using the entanglement and technology of quantum teleportation.

关键词： Quantum entanglement Qubit Teleportation Encoding Telecommunications Cryptography

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Quantum Broadcast By Entanglement Swapping

Quantum Broadcast By Entanglement Swapping

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International Conference on Smart communications and Networking (SmartNets)

作者： Ali Hamzah Abbas Yousif Samer Mudhafar Akram Talib Kareem Waleed Abed Abohana Communication System Engineering Department College of Electrical Engineering Imam Reza International University Mashhad Iran Computer Techniques Engineering Department College of Technical Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350385328

ISBN: (纸本)9798350385335

Quantum Broadcast is an important branch with the ability to send known qubits to more than one quantum user. This feature states that a qubit can be transferred using entanglement between qubits without sending even a single qubit of information. This is a miracle today. This paper introduces a new branch in the field of quantum broadcast, the efficiency of which does not change from previous designs, but has two unique new features. First, it is the first quantum broadcast scheme based on entanglement displacement. Second is the first quantum broadcast scheme, which transmits a state similar to Bell entangled states instead of a simple single qubit state.

关键词： Quantum entanglement Qubit

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Traffic Light Control Using Reinforcement Learning 2

Traffic Light Control Using Reinforcement Learning

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2nd IEEE International Conference on Integrated Circuits and communication systems, ICICACS 2024

作者： Masfequier Rahman Swapno, S.M. Nuruzzaman Nobel, S.M. Ramachandra, A.C. Islam, Md Babul Haque, Rezaul Rahman, Mohammad Mominur Bangladesh University of Business and Technology Dept of CSE Dhaka Bangladesh Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India UNICAL Dept. of Computer Modeling Electronic and System Engineering Rende Italy East West University Dept of CSE Dhaka1212 Bangladesh Hamad Bin Khalifa University College of Science and Engineering Qatar

ISBN: (纸本)9798350317558

Nowadays, one of the biggest issues in urban areas is traffic. This problem wastes important time and contributes to air and sound pollution. This affects people's general quality of life in addition to posing health dangers. Our study attempts to mitigate these problems, effectively cutting down on wait times and delays. Our Reinforcement Learning method creates intelligent agents that can adjust traffic lights at crossings instantly. Our objective is to minimize delays, reduce congestion, reduce travel times, improve safety, and improve traffic flow. We implemented the Deep Q Learning algorithm which activities yield the greatest benefits under various traffic scenarios. Our model can the sequence time since the Green signal (GS) lasts 10 seconds and the Red signal (RS) lasts 5 seconds. The waiting period is shortened by 50% as a result. This study suggests reinforcement learning may improve traffic signal controller synchronization and urban traffic congestion. This novel method may improve transport efficiency and sustainability. © 2024 IEEE.

关键词： Action Deep Q Learning GS Model Q Value Rewards RS State Traffic signal vehicles Waiting Time

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