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Signal Detection Method Based on Social Relationship Strength in Vehicular Ad-hoc Networks ⁎

IFAC-PapersOnLine 2024年第10期58卷 176-181页

作者： Yi Li Shuangshuang Han Yongqiang Bai Fei-Yue Wang School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China and Key Laboratory of Ocean Observation Technology MNR Tianjin China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China and Beijing Huairou Academy of Parallel Sensing Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

In the case of scarce communication resources, the importance of different vehicle communication levels is self-evident. They not only affect the efficiency of information exchange between vehicles and driving safety, but also directly affect the rational allocation and utilization of communication resources. Therefore, this paper proposes a signal detection method based on vehicle social relationship strength (SRS) in vehicular ad-hoc networks (VANETs) scenario, and integrates the SRS information into the data to be transmitted. In this method, minimum mean square error (MMSE) signal detection algorithm is selected for simulation, and Rayleigh channel and Rician channel are selected for simulation scenarios. This method makes full use of the intensity of social relationship between vehicles, and realizes the efficient transmission of data and the accurate transmission of communication level. Experimental verification shows that the method performs well in the VANETs scenario, and provides a new communication method for intelligent transportation, social network and other applications, which is expected to promote the technical progress and application development in related fields.

关键词： VANETs Social relation strength Image data transmission MMSE detection Intelligent transportation Social networking

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A Time-Efficient Path Navigating Landmine Detection Robot

A Time-Efficient Path Navigating Landmine Detection Robot

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Intelligent Signal Processing and Effective communication Technologies (INSPECT), IEEE International Conference on

作者： Rituraj Bhattacharjee Subhradip Mukherjee Hardik Gururaj Manish Rajpurohit Instrumentation & Control Engineering Department Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Electronics and communication Department MVJ College of Engineering Bangalore India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

The goal of a landmine detection robot is to map the remaining area on a visual map with millimeter precision after covering as much ground as it can show landmines. The prototype land mine detection robot model described in this study offers a visual interface for mapping landmines, modifying PIDs, and aligning cameras. It is also reasonably powerful, accurate, and easy to operate with other sensors. The manual, semi-auto, and auto modes of controlling the differential drive robot are emphasized. Image processing determines the exact location of the robot and provides live reckoning feedback to its dead reckoning servo control. A beat metal detector is a kind of quiet sensor used to locate landmines. The overall objective of the system is to provide the user with something robust, affordable, and easily comprehensible for the user.

关键词： Visualization Landmine detection Robot vision systems Prototypes Metals Detectors Manuals Signal processing Servosystems Intelligent sensors

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A High frequency millimeter wave circular patch antenna for Radar Applications and satellite communications

A High frequency millimeter wave circular patch antenna for ...

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Space, Aerospace and Defence Conference (SPACE), IEEE

作者： Manish Varun Yadav Abhi Chaurasia Salla Sai Charith Sudheendra Prabhu K Tanweer Ali Swati Varun Yadav Department of Aeronautical and Automobile Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Electronics and Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India

ISBN: (数字)9798350367386

ISBN: (纸本)9798350367393

This article proposes a millimeter wave circular patch microstrip antenna. The design illustrates a circular patch antenna which bears a range of 19.4-110GHz having a bandwidth of 90.6 GHz and having high resonance at frequencies of 51 GHz, 61.5 GHz, 71.8 GHz and 101 GHz. It depicts a good impedance matching at these frequencies, a decent gain and a directional radiation pattern. The material FR4 is utilized for designing the substrate of the antenna. The antenna shows a simulated gain of 8.8 dB, 8.9 dB, 7.7 dB, and 8.1 dB at the above specified operating frequencies.

关键词： Slot antennas Patch antennas Resonant frequency Microstrip antennas Bandwidth Millimeter wave radar Microstrip

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Performance Analysis of A Missile Guidance and control System Model for Precise Target Tracking

Performance Analysis of A Missile Guidance and Control Syste...

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Applied Intelligence and Computing (AIC), IEEE World Conference on

作者： Rituraj Bhattacharjee Subhradip Mukherjee Department of Instrumentation & Control Engineerig Manipal Institute of Technology Manipal Academy of Higher Education Manipal Karnataka India Electronics and Communication Department MVJ College of Engineering Bangalore India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

A missile navigation system model is developed and analyzed in this work. The flight command structure is a crucial component which improves the efficiency of the system. It has a major impact on both the accuracy of the strike and precise control over the missile's performance. The goal of the flight command structure is to instruct the missile to follow the steering directive produced by the control system. The system's representation is determined by how well it establishes the flying course required to intercept the target and sends the commands to the attitude control system required to keep the missile on course while taking the airframe's dynamics and relevant aerodynamic characteristics into account. The missile is guided to attack the target after it has been tracked, and its trajectory is determined by the homing guidance loop utilizing the PID controller-based autopilot mode. In the MATLAB simulation environment, dynamic performance of the missile guidance system is simulated using a step response technique based on time.

关键词： Analytical models Tracking loops Target tracking Navigation Aerodynamics Mathematical models Missile guidance Trajectory Performance analysis MATLAB

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Video Stabilization via Prediction with Time-Series Network and Image Inpainting with Pyramid Fusion

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Chinese Journal of Electronics 2021年第6期30卷 1103-1110页

作者： CHENG Keyang LI Shichao RONG Lan WANG Wenshan SHI Wenxi ZHAN Yongzhao School of Computer Science and Communication Engineering Jiangsu University Jiangsu Province Big Data Ubiquitous Perception and Intelligent Agricultural Application Engineering Research Center Cyber Space Security Academy of Jiangsu University National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data Xinjiang Lianhaichuangzhi Information Technology Co. Ltd.

Due to the poor filling effect of the video image defect commonly used in the video stabilization field, the video is seemed still unstable after the image stabilization process, which seriously affects the visual effect. To solve this problem, we improve a video stabilization method based on time-series network prediction and pyramid fusion restoration is proposed to optimize the visual effect after image *** flow of the proposed method is as follows: First,it is adaptive to determine whether the defect of the corresponding frame at the current time needs padding inpainting. Then, for the frame that needs to be inpainting,the frames generated before the current moment are sent to the model combining the convolutional neural networks and the gate recurrent unit to predict the part to be filled. Next the current defect image and the complete image to be filled are brought into the Laplacian pyramid reconstruction, and the improved weighted optimal suture is introduced for splicing during the fusion. Finally, the video frame is cut after reconstruction. The method is tested on a data set composed of videos commonly used in the field of video stabilization. The experimental results show that the average peak signal to noise ratio of the method is 2 to 5 d B higher than that of the comparison algorithm, and the average structural similarity index is improved by about 2% to 7% compared with the contrast algorithm.

关键词： image restoration image sequences video frame cutting improved weighted optimal suture gate recurrent unit video signal processing video stabilization method image fusion Laplacian pyramid reconstruction padding inpainting image resolution neural nets convolutional neural networks video image defect pyramid fusion restoration image inpainting visual effect optimization image reconstruction time-series network prediction image stabilization process

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Load Cell and FSR-Based Hand-Assistive Device 4th

Load Cell and FSR-Based Hand-Assistive Device

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4th International Conference on Advanced Computing and Intelligent engineering, ICACIE 2019

作者： Aneesha, Acharya K. Bhat, Somashekara Kanthi, M. Department of Instrumentation and Control Engineering Manipal Institute of Technology Manipal Academy of Higher Education ManipalKarnataka India Department of Electronics & Communication Engineering Manipal Institute of Technology Manipal Academy of Higher Education ManipalKarnataka India

ISBN: (纸本)9789811565830

Human upper extremity has numerous functions in day-to-day life. Strokes lead to weakening in hand muscles because of which the patient is unable to hold an object properly. Stroke related problems are treated by physiotherapy and grip strength recovery is assessed by the devices such as Jamar dynamometer and pinch meter. In this paper, load cell and Force Sensing Resistor sensors are used which mimics the use of Jamar dynamometer and assesses the patient recovery quantitatively. The sensory unit consists of the load cell which is used to sense the data and give it to the Wheatstone bridge. Participant is asked to apply force on the developed hand dynamometer and the values of grip strength forces are recorded in the system. This device transmits the participant data wirelessly using the Bluetooth module. Load cell and Force Sensing Resistors (FSR) sensor data are stored in Rapid Miner and the graphs of grip strength are plotted. Thus, the developed prototype helps to determine the grip strength of a disabled person hand using low-cost devices and possible to compare the present and previous results of the assessment. The field of application of this tool is in physiotherapy and occupational therapy. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Dynamometers

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A Hybrid Machine Learning Approach to Anomaly Detection in Industrial IoT

A Hybrid Machine Learning Approach to Anomaly Detection in I...

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Advances in Computing, communication, Embedded and Secure Systems (ACCESS), International Conference on

作者： Jayesh T P Pandiaraj K Arya Paul Ranjeesh R Chandran Prasanth P Menon Department of Electronics and Communication Kalasalingam Academy of Research and Education Srivilliputhur Tamil Nadu India Department of Electronics and Communication Adi Shankara Institute of Engineering and Technology Kerala India Department of Instrumentation and Control Engineering Kalasalingam Academy of Research and Education Srivilliputhur Tamil Nadu India

IIoT is the integration of conventional IoT principles into industrial operations. IIoT has a wide range of practical applications, including but not limited to supply chain management, connected cars, smart grids, smart cities, and smart homes. Regrettably, these systems are increasingly becoming the focus of cybercrime attacks. Machine learning is a promising technology for creating and implementing resilient security measures in IIoT networks. A new and innovative approach to detecting cyberattacks in the IIoT is proposed in this document, through the use of a hybrid machine classifier (HMC). The HMC model is a unique amalgamation of different ML models, such as K-nearest neighbor (KNN), extra trees (ET), gradient boosting (GB), AdaBoost (AB), linear discriminant analysis (LDA), ), naive Bayes (NB), support vector machine (SVM), random forest (RFlinear regression (LR), and classification and regression tree (CART). The DS2OS dataset is used to evaluate the proposed method's effectiveness. Several performance metrics, including recall, precision, accuracy, specificity, F1 score, detection rate, and ROC are used to evaluate the system's performance. The proposed model successfully distinguishes between normal and attack traffic, achieving an accuracy rate of 99.7% and 99.8%, respectively. To evaluate the effectiveness of the proposed method, its performance metrics were compared to those of other advanced attack detection algorithms. The outcomes demonstrated that the proposed model outperformed other ML and DL-based techniques

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A Horizontal Federated Learning Approach to IoT Malware Traffic Detection: An Empirical Evaluation with N-BaIoT Dataset 26

A Horizontal Federated Learning Approach to IoT Malware Traf...

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26th International Conference on Advanced communications technology, ICACT 2024

作者： Do, Phuc Hao Le, Tran Duc Vishnevsky, Vladimir Berezkin, Aleksandr Kirichek, Ruslan The Bonch-Bruevich Saint-Petersburg State University of Telecommunications Saint-Petersburg Russia Danang Architecture University Da Nang Viet Nam University of Telecommunications Department of Communication Networks and Data Transmission at Bonch-Bruevich Saint-Petersburg State Russia University of Science and Technology The University of Danang Da Nang Viet Nam The University of Danang University of Science and Technology Information Technology Faculty Danang Viet Nam V. A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences Moscow Russia Institute of Control Sciences of Russian Academy of Sciences Russia Bonch Bruevich Saint Petersburg State University of Telecommunications Department of Programming Engineering and Computer Science Russia

ISBN: (纸本)9791188428120

The increasing prevalence of botnet attacks in IoT networks has led to the development of deep learning techniques for their detection. However, conventional centralized deep learning models pose challenges in simultaneously ensuring user data privacy and detecting botnet attacks. To address this issue, this study evaluates the efficacy of Federated Learning (FL) in detecting IoT malware traffic while preserving user privacy. The study employs N-BaIoT, a dataset of real-world IoT network traffic infected by malware, and compares the effectiveness of FL models using Convolutional Neural Network, Long Short-Term Memory, and Gated Recurrent Unit models with a centralized approach. The results indicate that FL can achieve high performance in detecting abnormal traffic in IoT networks, with the CNN model yielding the best results among the three models evaluated. The study recommends the use of FL for IoT malware traffic detection due to its ability to preserve data privacy. © 2024 Global IT Research Institute - GIRI.

关键词： Malware

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Smart Supply Chain Management using Big Data Analysis and Machine Learning

Smart Supply Chain Management using Big Data Analysis and Ma...

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2022 International Conference on Edge Computing and Applications, ICECAA 2022

作者： Raviteja, Bhamidipati Pandya, Kartik A. Khan, Falak Khan, Zoheib Tufail Prajwal, R. Kekatpure, Atharva Department of Information Technolgy Gayatri Vidya Parishad College of Engineering Kommadi Visakhapatnam India Department of Mechanical Engineering Sardar Vallabhbhai Patel Institute of Technology Gujarat India Department of Business Administration Aligarh Muslim University Aligarh India Department of Mechanical Engineering Zakir Husain College of Engineering and Technology Aligarh India Department of Electronics and Communication Engineering JSS Academy of Technical Education Bengaluru India Department of Instrumentation and Control Engineering Sarvajanik College of Engineering and Technology Surat India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Supply chain management (SCM) is concerned with the movement of products, services, and information from points of origin to consumers via a network of interconnected organizations and activities. It is believed that capacity, demand, and cost are well-known variables in typical SCM challenges. In practice, however, there are uncertainties brought on by changes in client demand, supply chain management, organizational risks, and lead times. Demand uncertainty in particular has a significant impact on SC performance with wide-ranging implications for scheduling production, planning inventories, and organizing transportation. In this regard, demand forecasting is a crucial strategy for tackling supply chain uncertainty. Forecasting supply chain demand using big data analytics has been published in the supervised and unsupervised learning categories. In supervised learning, the inputs and outputs are known because the data are tagged with labels. Given a fresh unlabeled dataset, the supervised learning algorithms attempt to map the inputs to matching outputs by identifying the underlying connections between the inputs and outputs. The main motive for the authors is to prove that supply chain can be managed through machine learning and to get a novel accuracy along with comparison of the machine learning models. © 2022 IEEE.

关键词： Supply chain management

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Towards Safe Inter-train Distance Minimization via Reachability Analysis and Mixed H2/H∞control

Towards Safe Inter-train Distance Minimization via Reachabil...

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2022 Chinese Automation Congress, CAC 2022

作者： Yang, Min Lv, Jidong Liu, Hongjie Zhang, Miao Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation and Control System Beijing China China Academy of Railway Sciences Corporation Limited National Research Center of Railway Intelligence Transportation System Engineering Technology Signal and Communication Research Institute Beijing China

ISBN: (纸本)9781665465335

In high-speed railway, designing a reasonable controller of train control system is an important way to enhance the transportation capacity, while considering more dynamic behaviors of the preceding train, such as position, speed and acceleration etc, through V2V communication. In this paper we propose a novel method towards the safe inter-train distance minimization problem via reachability analysis and mixed H2/H∞ control. Firstly, by considering the disturbance behavior of the preceding train, a linear closed-loop system is modeled, in which the gains of a state feedback and a state feedback/feedforward, refer to controller A and controller B respectively, are designed using mixed H2/H∞ control theory. Then, the N-step forward reachable set for the two given controllers is presented to compute the maximum position error, which determines the minimum safe inter-train distance. Furthermore, backward reachability analysis algorithm is used to find the maximal initial state set, which guarantees that the state trajectory stay within the safe set in presence of disturbance. Finally, the proposed method is applied to a typical emergency braking scenario during two-train tracking. The experimental results show that the safety margin can be reduced by 54% and the maximal initial state set can be enlarged conspicuously when a feedforward term is used in the controller. © 2022 IEEE.

关键词： controllers

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