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International Conference on Advanced Technologies for communications, ATC

作者： Masaki Kitagawa Toshiharu Kojima Department of Computer and Network Engineering Graduate School of Informatics and Engineering The University of Electro-Communications Chofu Tokyo Japan

ISBN: (数字)9798350353983

ISBN: (纸本)9798350353990

In helicopter satellite communications, the periodic blockage of the transmission channel arises due to rotor blades. Modified Walsh-Hadamard code division multiplexing (MWHCDM) overcomes the periodic blockage. In MWHCDM, estimating the symbol timing offset (STO) is necessary to perform demultiplexing. The carrier frequency offset (CFO) significantly degrades the accuracy of STO estimation. Thus, STO estimation requires estimating and removing the CFO, although CFO estimation is only possible without STO. This paper proposes a joint CFO and STO estimation method to resolve this dilemma, combining the CFO and STO estimation to complement each other. computer simulation shows that the proposed method accurately estimates both CFO and STO.

关键词： Code division multiplexing computer simulation Helicopters Estimation Symbols Rotors Frequency estimation Timing Frequency division multiplexing Satellite communications

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Design and Implementation of Advanced Driver Assistance Systems

Design and Implementation of Advanced Driver Assistance Syst...

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Telecommunications Conference (ITC-Egypt), International

作者： Waleed Abd-Elshafi Ali Hatem Abdelhamid Saleh Haneen Salah Eid Ahmed Mohamed Fathi Abd-El Rahman Amr Mohamed Department of Communications and Computer Engineering Higher Institute of Engineering EL- Shorouk Academy Cairo Egypt Department of Electronics and Communications Engineering Misr University for Science and Technology Giza Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

Advanced Driver Assistance Systems (ADAS) are a pivotal advancement in automotive technology, enhancing vehicle safety and driving experience. In our project, we have successfully designed and built a robotic prototype car. We have implemented a comprehensive control system for the car's movement, and the following features of ADAS systems: Bump Detection, Precise Lane Tracking, Adaptive Cruise Control, Traffic Sign Recognition, and Blind Spot Detection. Bump detection systems are equipped to detect and alert drivers of speed bumps and other road irregularities, in which we use NVIDIA Jetson Nano, Astra Pro Plus Depth camera as RGB and Depth sensor and use Custom AI Model based on YOLOv5 to detect different Bumps on the road and we use Python, TensorRT, and OpenCV. Lane Departure Warning (LDW) alerts drivers when their vehicle unintentionally drifts out of their lane, we use NVIDIA Jetson Nano as our main microcontroller unit (MCU) responsible for our Machine learning and Astra Pro Plus Depth camera as our RGB sensor, we use Python, Python's libraries such as OpenCV, and NumPy to implement LDW. Adaptive Cruise Control (ACC) adjusts the vehicle's speed automatically to maintain a safe distance from the vehicle ahead, we use NVIDIA Jetson Nano, Astra Pro Plus Depth camera as RGB and Depth sensor and use Custom AI Model based on YOLOv5 to detect different vehicles on the road, we use Python, TensorRT, and OpenCV. Traffic Sign Recognition can interpret and display traffic signs to keep drivers informed about speed limits, no-entry signs, and more, we use NVIDIA Jetson Nano, and Astra Pro Plus Depth camera as our RGB sensor and use Custom AI Model based on YOLOv5 to detect different Traffic Signs on the road, we use Python, TensorRT, and OpenCV. Blind Spot Detection (BSD) technology helps drivers identify vehicles in their blind spots and alerts them to avoid dangerous maneuvers, we use ESP32 as an MCU and Ultrasonic sensor, and use C++ to program the BSD feature.

关键词： YOLO Microcontrollers Robot sensing systems Cameras Feature extraction Automobiles Advanced driver assistance systems

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Optimization of Hierarchical-QAM based Downlink Multiple Access with Statistical CSI 24

Optimization of Hierarchical-QAM based Downlink Multiple Acc...

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24th IEEE International Workshop on Signal Processing Advances in Wireless communications, SPAWC 2023

作者： Zhu, Jinkang Zhou, Shengli Zhao, Ming University of Sci. and Tech. of China Cas Key Laboratory of Wireless-Optical Communications Hefei China University of Connecticut Department of Electrical and Computer Engineering Storrs United States

ISBN: (纸本)9781665496261

This paper considers a downlink multiple access scenario with OFDM transmissions in frequency selective channels. The users are assigned with different bit streams from hierarchical quadrature amplitude modulation (QAM) rather than assigned with different subcarriers as in orthogonal frequency division multiple access (OFDMA). Based on statistical channel state information in the form of the average signal-to-noise ratios of the users, we derive a closed-form solution on the hierarchical constellation design. The numerical results demonstrate the performance advantage of the proposed system over OFDMA where power control is also applied based on the same channel feedback. © 2023 IEEE.

关键词： Frequency division multiple access

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Practical Commercial 5G Standalone (SA) Uplink Throughput Prediction

arXiv

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arXiv 2023年

作者： Arunruangsirilert, Kasidis Katto, Jiro Department of Computer Science and Communications Engineering Waseda University Tokyo Japan

While the 5G New Radio (NR) network promises a huge uplift of the uplink throughput, the improvement can only be seen when the User Equipment (UE) is connected to the high-frequency millimeter wave (mmWave) band. With the rise of uplink-intensive smartphone applications such as the real-time transmission of UHD 4K/8K videos, and Virtual Reality (VR)/Augmented Reality (AR) contents, uplink throughput prediction plays a huge role in maximizing the users’ quality of experience (QoE). In this paper, we propose using a ConvLSTM-based neural network to predict the future uplink throughput based on past uplink throughput and RF parameters. The network is trained using the data from real-world drive tests on commercial 5G SA networks while riding commuter trains, which accounted for various frequency bands, handover, and blind spots. To make sure our model can be practically implemented, we then limited our model to only use the information available via Android API, then evaluate our model using the data from both commuter trains and other methods of transportation. The results show that our model reaches an average prediction accuracy of 98.9% with an average RMSE of 1.80 Mbps across all unseen evaluation scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Using Data Mining Techniques to Analyze Facial Expression Motion Vectors 6th

Using Data Mining Techniques to Analyze Facial Expression Mo...

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6th International Conference on Dynamics of Information Systems, DIS 2023

作者： Roshanzamir, Mohamad Alizadehsani, Roohallah Roshanzamir, Mahdi Shoeibi, Afshin Gorriz, Juan M. Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Fasa University Fasa74617-81189 Iran Deakin University GeelongVIC Australia Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Department of Signal Theory Networking and Communications Universidad de Granada Granada Spain Swinburne University of Technology HawthornVIC3122 Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

ISBN: (纸本)9783031503191

Automatic recognition of facial expressions is a common problem in human-computer interaction. While humans can recognize facial expressions very easily, machines cannot do it as easily as humans. Analyzing facial changes during facial expressions is one of the methods used for this purpose by the machines. In this research, facial deformation caused by facial expressions is considered for automatic facial expression recognition by machines. To achieve this goal, the motion vectors of facial deformations are captured during facial expression using an optical flow algorithm. These motion vectors are then used to analyze facial expressions using some data mining algorithms. This analysis not only determined how changes in the face occur during facial expressions but can also be used for facial expression recognition. The facial expressions investigated in this research are happiness, sadness, surprise, fear, anger, and disgust. According to our research, these facial expressions were classified into 12 classes of facial motion vectors. We applied our proposed analysis mechanism to the extended Cohen-Kanade facial expression dataset. Our developed automatic facial expression system achieved 95.3%, 92.8%, and 90.2% accuracy using Deep Learning (DL), Support Vector Machine (SVM), and C5.0 classifiers, respectively. In addition, based on this research, it was determined which parts of the face have a greater impact on facial expression recognition. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data mining

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Cache Node Placement for Maximum Traffic Minimization in Content-Centric Networking

Cache Node Placement for Maximum Traffic Minimization in Con...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Shohei Nakajima Nattapong Kitsuwan Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan

Content-centric networking (CCN) is a network architecture based on content. The conventional network architecture uses IP address to route packets based on location. In CCN, which routes packets based on content name, a cache function can be attached to nodes. Getting contents from cache nodes alleviates traffic congestion in the network. In the case that the cache function is not attached to all nodes in the network, it is necessary to decide where cache nodes are placed. Congestion tends to occur around cache nodes when using the conventional method. We formulate a problem of cache node placement as an integer linear programming (ILP) considering the population of each node. The ILP formulation determines the cache node placement to minimize the value of the maximum traffic. This result shows the proposed ILP approach reduces the maximum traffic by 59% on average.

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A Lifting-Transferring Assistive Device between Wheelchair and Car Seat for Disabled

A Lifting-Transferring Assistive Device between Wheelchair a...

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2024 International Conference on Future Technologies for Smart Society, ICFTSS 2024

作者： Sam, Toong Hai Tee, Kian Sek Wong, Whee Yen Chew, Chang Choon Soon, Chin Fhong Inti International University Faculty of Business & Communications Nilai Malaysia Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic Engineering Johor Batu Pahat Malaysia Universiti Tunku Abdul Rahman Department of Internet Engineering and Computer Science Kajang Malaysia

ISBN: (纸本)9798350373844

This study proposes a potential solution to alleviate the risk of back pain and injuries faced by caregivers in assisting wheelchair-bound patients in Malaysia, particularly those from low-income households, while getting in and out of cars. Manual lifting and transferring of patients often cause back pain or muscle injuries among caregivers. An assistive lifting and transferring device is proposed to alleviate these challenges. The device is portable and detachable. The objective is to reduce the risk of injuries among caregivers while facilitating safe transfers. Its base is adjustable for most sizes of wheelchairs and is portable and detachable, making it easy to carry in the car. It was tested and has demonstrated excellent performance in lifting and transferring patients weighing up to 100kg. The trials have suggested that the improved safety feature and convenience design could promote the mental and physical health of wheelchair-bound patients and their caregivers, aligned with the third goal of the 17 Sustainable Development Goals (SDGs). © 2024 IEEE.

关键词： Wheelchairs

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Performance Evaluation of Federated Learning in Edge Computing Environment

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Procedia computer Science 2024年 235卷 2955-2964页

作者： Prajay Kotecha Tanvi Dhoka Jitendra Bhatia Malaram Kumhar Rajesh Gupta Sudeep Tanwar Nilesh Kumar Jadav Department of Electronics and Communications Institute of Technology Nirma University Ahmedabad India Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India

In the rapidly changing world of new technologies, AI and ML have been delivered to the network’s edge, allowing edge devices to compute simple models that can be applied later in practical applications. Edge computing allows the deployment of cloudlike services to the network edge. Moreover, it allows network devices to contribute to Federated Learning (FL) by using their processing power. Federated learning was developed when privacy and security concerns emerged among people. Here, multiple clients work together to solve machine-learning problems. Instead of collecting users’ raw data, this technique protects user privacy by aggregating model parameters from each client to a central server. Even though FL techniques have several benefits, such as scalability and data privacy, when used with heterogeneous devices, they can introduce significant problems in processing complexity and speed. This paper thoroughly surveyed the important and efficient technological advancements based on edge federated learning. We review the widely used FL frameworks that effectively facilitate communication between clients and servers. We also present a case study on handwritten digit recognition using the MNIST dataset to state the effectiveness of federated learning. On applying this approach, we were able to achieve 94.56% accuracy.

关键词： Federated learning Edge computing Distributed computing MNIST Heterogeneous devices

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Intelligent Decision Support Systems: Transforming Smart Cities Management

Intelligent Decision Support Systems: Transforming Smart Cit...

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Emerging Technologies for Dependable Internet of Things (ICETI), International Conference on

作者： Zeinab E. Ahmed Aisha H. A. Hashim Rania A. Mokhtar Mamoon M. Saeed Department of Computer Engineering University of Gezira Sudan Department of Electrical and Computer Engineering International Islamic University Malaysia Malaysia Department of Computer Engineering College of Computers and Information Technology Taif University Saudi Arabia Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Yemen

ISBN: (数字)9798331533557

ISBN: (纸本)9798331533564

Since urban cities are expanding due to technology, economy, and influx of more people the need for better management systems is paramount. This paper aims to examine the use of Intelligent Decision Support Systems (IDSS) in enhancing the urban infrastructure, safety and resources in smart cities. Hereby, we present a complex framework that involves Internet of Things (IoT) devices, Neural Networks, Artificial Intelligence (AI) to make decisions in traffic controlling, environmental issues and safety. When used with machines learning (ML) algorithms and predictive analytics, the framework increases the efficiency of urban management operations. A comparison of the energy used by promised by these algorithms including LSTM, SVM, KNN, and the OPTIMUS, a system is developed that enables smart cities to significantly save energy hence highlighting its efficiency. Furthermore, a performance comparison of Random Forest, LSTM, SVM, and KNN for traffic management shows KNN achieving the highest accuracy and precision. The paper discusses data collection, processing, and integration methodologies, and presents case studies demonstrating cost reductions and sustainability improvements. The present paper provides evidence to support the assumption that the development of IDSS can greatly enhance the progress of smart cities by underpinning the process by data, feedback and other forms of information.

关键词： Support vector machines Decision support systems Energy consumption Smart cities Nearest neighbor methods Prediction algorithms Internet of Things Sustainable development Long short term memory Random forests

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Fitness Function Using Postconditions for Automated Program Repair

Fitness Function Using Postconditions for Automated Program ...

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Software engineering and Artificial Intelligence (SEAI), IEEE International Conference on

作者： Yusaku Ito Hironori Washizaki Yoshiaki Fukazawa Department of Computer Science and Communications Engineering Waseda University Tokyo Japan

Genetic algorithm-based automated program repair has the challenge that execution time tends to be extremely long. One of the causes is the low adequacy of the fitness, which represents the evaluation of program variants. To improve the efficiency of automated repair, we propose a new fitness function that uses the state of internal variables of the program in addition to the results of test runs. First, candidate specifications are randomly generated using variables in the program. Next, the correct specification is automatically estimated from the candidates, thus making the granularity of the fitness function more fine-grained. Experimental results with small programs show that the proposed method extracts the postcondition of the program appropriately in most cases. On the other hand, experiments with the Defects4J Math dataset suggest that the proposed method does not necessarily improve the efficiency of automated repair and may reduce the diversity of generated variants.

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