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MIMO: Modulation Schemes for Visible Light communication in Indoor Scenarios 1

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8th international conference on Computers, Management and Mathematical Sciences, ICCM 2022

作者： Divvala, Chiranjeevulu Vakamullu, Venkatesh Mishra, Madhusudhan Arunachal Pradesh Nirjuli India Department of Electrical Engineering Indian Institute of Technology Kharagpur West Bengal Kharagpur India Arunachal Pradesh Itanagar Nirjuli India

ISBN: (数字)9783031251948

ISBN: (纸本)9783031251931

Data traffic on mobile access networks is rapidly increasing due to high bandwidth demand caused by the increase in the number of smart devices. In addition, the saturation of radio frequency spectrum is enabling the users to switch over to optical spectrum communication as an alternative approach. In recent years, visible spectrum is grabbing the attention of researchers as it is offering versatile benefit of domestic lighting and communication. Light-emitting diodes (LEDs) play a vital role to accomplish this goal. Particularly in real-time scenarios like rooms, offices, and aeroplanes, multiple LEDs can be placed to confer the ample illumination along with the communication facility. this arrangement imparts a phenomenon of multiple-input–multiple-output (MIMO) communication through light wave. Visible light communication (VLC) using MIMO offers higher spectral efficiency by the aid of the special multiplexing techniques. Since VLC is governed by the intensity modulation and direct detection phenomenon, it needs the Hermitian symmetry operation to be carried out to communicate the complex symbols through channel. However, this can be addressed by employing multiple LEDs at the transmitter. Nevertheless, still it is novice to perceive the applicability of MIMO techniques in indoor VLC systems, since they hardly suffer from fading effects and other impairments. the present work emphasizes the design of physical layer (PHY) techniques determined for visible light communications (VLC) through wireless channel. Further, this study delineates various communication techniques governed by multi-input and multi-output paradigm for VLC. It also aims to make significant improvements in MIMO-VLC techniques to manifest the enhancement in power efficiency while maintaining the low bit error rate and bandwidth. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Visible light communication

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Reliability Analysis of Mobile Ad-Hoc networks for URLLC on the Factory Floor

Reliability Analysis of Mobile Ad-Hoc Networks for URLLC on ...

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international ITG conference on Source and Channel Coding (SCC)

作者： Frank Von Schoettler Tobias Hintz Eike Lyczkowski Peter M. Rost Research Technology SEW-EURODRIVE GmbH & Co. KG Bruchsal Germany Communications Engineering Lab Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (数字)9798331522896

ISBN: (纸本)9798331522902

Within the trend of Industry 4.0, a key requirement for wireless industrial automation is the demand for Ultra-Reliable Low-Latency communications (URLLC). However, currently deployed cellular technologies are not yet capable of providing the performance necessary to support the demands of future industrial real-time applications. To meet the increasing requirements for flexibility, coverage, and reliability, network topologies incorporating peer-to-peer (P2P) communication are under investigation. In this paper, simulations of a mobile ad hoc network (MANET) that consists of automated guided vehicles (AGVs) operating on a modern factory floor are presented, along with a reliability analysis. the movement of AGVs is simulated using an empirical mobility model, and a ray tracing (RT) simulator is employed to compute coverage maps of an industrial environment. Network performance is evaluated based on the minimum signal-to-noise ratio (SNR) required to achieve an outage probability of 10 −5 . Results indicate that increasing the number of AGVs enhances network reliability due to path diversity. However, this also increases the number of hops required for reliable communication, thereby reducing the throughput capacity of the system. Consequently, the throughput capacity of the MANET is calculated to determine the optimal number of AGVs needed to maximize throughput while meeting stringent reliability constraints.

关键词： Ultra reliable low latency communication Ray tracing throughput Production facilities Peer-to-peer computing Fourth Industrial Revolution Reliability Floors Signal to noise ratio Mobile ad hoc networks

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Research on Layout Optimization in Complex Environments Based on Genetic Algorithm 14th

Research on Layout Optimization in Complex Environments Base...

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14th international conference on Computer Engineering and networks, CENet 2024

作者： Cai, Daomeng Yang, Lin Wang, Chao Wang, Can Tong, Yue Sun, Hao Wu, Hao Lyu, Ting Beihang University No. 37 Xueyuan Road Haidian District Beijing China Systems Engineering Research Institute China State Shipbuilding Corporation No. 1 Fengxian East Road Haidian District Beijing China School of Physics University of Electronic Science and Technology of China Chengdu610054 China Department of Medical Engineering Medical Supplies Center of Chinese PLA General Hospital 28 Fuxing Road Beijing100853 China Medical Innovation Research Division Chinese PLA General Hospital 28 Fuxing Road Beijing100853 China Medical Big Data Research Center Chinese PLA General Hospital 28 Fuxing Road Beijing100853 China

ISBN: (纸本)9789819642281

In fields such as manufacturing, logistics, and communication networks, equipment layout optimization is a significant and prevalent issue. this paper investigates an equipment layout optimization method in complex environments based on a genetic algorithm (CE-GA). Initially, we formulate the layout optimization problem mathematically, defining the optimization objectives and constraints. Subsequently, we propose and improve the GA, incorporating initial population generation, fitness function design, selection, crossover, mutation operations, and elite retention strategy. Experiments validate the superiority of this method in terms of convergence speed and solution quality, comparing it with simulated annealing (SA) and particle swarm optimization (PSO) algorithms. the experimental results demonstrate that the improved GA performs excellently in handling layout optimization problems in complex environments. Finally, this paper summarizes the research findings, highlights contributions and limitations, and suggests future research directions. this study has significant practical implications for improving system efficiency and reducing operational costs, providing a valuable reference for future layout optimization research. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Genetic algorithms

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Towards a health software supporting platform for wearable devices 13th

Towards a health software supporting platform for wearable d...

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13th international conference on Emerging Ubiquitous systems and Pervasive networks, EUSPN / the 12th international conference on Current and Future Trends of Information and communication Technologies in Healthcare, ICth 2022 / Affiliated Workshops

作者： Otta, Maxmilian Department of Computer Science and Engineering University of West Bohemia Technicka 8 Plzen306 14 Czech Republic

the number of broadly available wearable devices like smart watches or fitness bands keeps growing, as well as their performance and number of provided features related to user's health. this was the reason for our decision to bring the SmartCGMS (Smart Continuous Glucose Monitoring and Controlling System) to a wearable device, in order to foster its way to practical deployment in healthcare. Currently, the SmartCGMS system is able to run on Windows, macOS, Linux, RaspberryPi or Android phones and tablets. What is currently hindering us to run SmartCGMS on a wearable device is the heterogeneity of devices and primarily lack of real-time OS features available to developers. this is natural, because device vendors aim for best user experience and so foreground tasks get the highest priority in order to maximize device responsiveness and suppress background tasks for minimum CPU load and battery drain. But running medical software on a wearable device requires almost the opposite - tasks reading sensor data and especially tasks controlling drug dosage, that are running on the wearable, require high priority and minimum interference with other running tasks. In this article, we present an initial design of a software framework in development, that will provide us the features we are currently missing in available wearable devices operating systems: a common application image that is able to run on various wearable devices and support for high priority tasks. © 2022 Elsevier B.V.. All rights reserved.

关键词： Computer operating systems

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An IoT Based Traffic Management System Using Drone and AI

An IoT Based Traffic Management System Using Drone and AI

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international conference on Computational Intelligence and communication networks (CICN)

作者： Arash Farahdel Seyed Shahim Vedaei Khan Wahid Electrical and Computer Engineering Department University of Saskatchewan Saskatoon Canada

ISBN: (纸本)9781665487726

Management of ground traffic on both urban streets and highways in a smart city setting requires collecting a huge amount of logistical data. Accessing real-time information of the traffic is essential in the event of an emergency. It requires the traffic control center to regularly monitor flows of vehicles and take suitable actions to reduce traffic jams. Several tiny devices are needed to collect and transmit real-time data from different locations. However, the bandwidth and power consumption of each device is very limited. therefore, it is essential to utilize an efficient algorithm which reduces the bandwidth, as well as power consumption. In this paper, an efficient method is proposed to reduce the transmission bandwidth while keeping the quality of the videos acceptable for image processing on the server end. To evaluate the performance of the algorithm, a framework to monitor and control the traffic on highways is developed. this framework uses a drone to fly over the traffic to capture the logistical information, and then send real-time video to the server. An object detection algorithm empowered by artificial intelligence (AI) is implemented on the cloud server that detects the number of type of vehicles, and accordingly makes decisions to manage traffic flow.

关键词： Road transportation Power demand Surveillance Bandwidth Object detection Traffic control Real-time systems

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Explanatory Case Study: Digital Forensics Investigation on DJI Spark Drone

Explanatory Case Study: Digital Forensics Investigation on D...

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international conference on Computing and Networking Technology (ICCNT)

作者： P Shashank Kumar Sriram Sankaran Center for Cyber Security Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri Kollam India

Drones are now widely used and have grown in popularity in recent years. However, as a result of their greater use, there have been more crimes involving drones. In this explanatory case study, digital forensics methods are used to analyze a DJI Spark drone discovered at a crime scene. the purpose of this inquiry is to obtain vital data that might help with the investigation of drone-related crimes. the drone’s memory card and remote controller, which have flight records, pictures, and videos on them, have important details about the crime and how the drone was used. We gather and examine this material using digital forensics techniques. Investigative personnel will then be able to recognize potential suspects and comprehend the drone’s participation in the crime. Important details, such as the drone’s flight path, the location of the murder scene, and the identities of the people involved, are revealed by the recovered data. this explanatory case study emphasizes the importance of digital forensics in the analysis of drone-related crimes. Drones will probably be employed more frequently in criminal acts as they become more prevalent in society. Investigators may keep ahead of the curve and efficiently use drones as a source of priceless evidence in criminal investigations by using competent digital forensics techniques.

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Optimizing Crop Selection through Hyperparameter Tuning of Neural networks: Development and Evaluation of Deep Learning Models for Enhancing Crop Recommendation systems

Optimizing Crop Selection Through Hyperparameter Tuning of N...

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international conference on communication systems and Network Technologies (CSNT)

作者： Sabrina Jahan Ishraque Manzur B. M. Shahria Alam Tawhidur Rahman Ishrat Jahan Momo Golam Kibria Md. Nawab Yousuf Ali Dept. of CSE East West University Dhaka Bangladesh

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Applications of Precision Agriculture are becoming more popular throughout the world due to the possibilities that this technology offers to enhance crop yields as a result of decision-making based on effective data. By leveraging data such as pH, temperature, and rainfall, the models provide precise predictions, enabling farmers to make informed crop selection decisions. In this study, we propose and test two neural network models for crop recommendation based on the soil and or environmental data. the first architecture has 5 parameters, in fact, it consists of two hidden layers; the second one has 300 parameters it consists of five hidden layers so we can observe the difference between the model complexity and performance. thus, we employed grid search and 5-fold cross-validation to optimize the use of hyperparameters such as learning rate, batch size, and activation function. Using the two-hidden-layer model, we obtained an accuracy of 98.7% percent, thereby closely performing better compared to the five-hidden-layer model that achieved 98.4% accuracy. Analyzing experimental results confirmed that larger learning rate and the Adam optimizer provided the highest average scores in both networks architecture. For example, the architecture with five hidden layers was almost five times slower, pointing to the time-space trade-off in neural network models. the results are useful in the current endeavor of deploying AI for agriculture and recommending crops by implementing this file-based approach. Further studies on the same work will involve accumulating a larger database with the soil characteristics of the Bangladesh region, with the hope of establishing a more refined and specific recommendation for the right crops to be grown.

关键词： Precision agriculture Accuracy Computational modeling Neural networks Crops Soil Data models Complexity theory Tuning Recommender systems

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An Effectual Forecasting of Oil Production Performance Using Advanced Adaptive Recurrent Neural Network Classifier

An Effectual Forecasting of Oil Production Performance Using...

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international conference on communication systems and Network Technologies (CSNT)

作者： K. Siva Kumar V. R. Nagalakshmi Nethi Korupula Siva Kumar Yagati Sai Chanikhya Mohammad Yousf Basha B P M Harshavardhan Department of Petroleum Engineering Godavari Institute of Engineering and Technology (A) Rajahmundry Andhra Pradesh

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Oil is the primary source of energy worldwide, accounting for a large percentage of global energy consumption, with significant impacts on economic, industrial, geopolitical, and social dimensions. It enables the movement of transport, driving globalization and economic integration because oil is a backbone of international trade. However, various reasons, including political, technical, natural, economic, and strategic ones, lead to the reduction of oil production. Hence, this paper proposed a comprehensive analysis and prediction of oil production using an advanced adaptive Recurrent Neural Network (RNN). Initially, diverse datasets are collected to ensure a robust foundation for analysis. Following this, preprocessing techniques, including data cleaning and filtering, are employed to enhance data quality and relevance. the exploration phase focuses on evaluating the data, identifying trends, and uncovering underlying patterns. Feature selection is critical to optimize the predictive model's performance. the Adaptive RNN is then utilized to model the relationships within the dataset, leading to the generation of predicted values. Overall, this work is implemented by python language. Finally, predicted the error value, such as the Root Mean Square Error (RMSE) of 72.9% and the Mean Absolute Error (MAE) of 26.1%, are attained. this structured approach facilitates effective data analysis and contributes to the advancement of predictive analytics in various domains.

关键词： Adaptation models Recurrent neural networks Adaptive systems Oils Production Predictive models Feature extraction Reservoirs Topology Python

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Clustering-Beamforming Algorithm for Security Performance in Multiple Flying RISs-supported systems

Clustering-Beamforming Algorithm for Security Performance in...

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international conference on Information and communication Technology Convergence (ICTC)

作者： Yuna Sim Jihun Cho Huaping Liu Sangmi Moon Seungseok Sin Kyunam Kim Jihyung Kim Intae Hwang Deptartment of ICT Convergences System Engineering Chonnam National University Gwangju Republic of Korea Oregon State University United States of America Department of IT Artificial Intelligence Korea Nazarene University Cheonan Republic of Korea Alps Electric Korea Co. Ltd. Gwangju Republic of Korea Spatial Wireless Transmission Research Section Electronics and Telecommunications Research Institute Daejeon Republic of Korea Department of Electronic Engineering and Department of ICT Convergence System Engineering Chonnam National University Gwangju Republic of Korea

this paper introduces an algorithm for improving the security performance in a system by combining unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs), which are emerging technologies in the context of 6G networks. Multiple flying RISs are utilized in this approach. Among them, a DNN-based algorithm is presented to deploy flying RISs in appropriate locations to cover the maximum number of users possible. through simulation experiments, the superiority of the proposed algorithm is confirmed in terms of accuracy and execution time. In the future, research will be conducted on algorithms optimizing beamforming from the perspective of the base station (BS) and achieving active beamforming within the RIS to further enhance the security performance.

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Twitter Spam Accounts Detection Using Machine Learning Models

Twitter Spam Accounts Detection Using Machine Learning Model...

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international conference on Computational Intelligence and communication networks (CICN)

作者： Shikah J. Alsunaidi Rawan Talal Alraddadi Hamoud Aljamaan Information and Computer Science Department King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia

ISBN: (纸本)9781665487726

the amount of spam accounts on Twitter has recently surged, which has attracted researchers' interest in seeking strategies to mitigate this problem. this paper reviews recent studies in the literature that tackled the Twitter spam accounts problem based on machine learning (ML). It then introduces an empirical study to test several ML models on a publicly access dataset. the model types were individual, ensemble, and majority voting models. It found that the ensemble ML models, and majority voting ML models can improve the prediction accuracy of Twitter spam accounts detection compared to the individual ML models. We concluded that the Random Forest model is the best for Twitter spam accounts detection using an imbalanced dataset.

关键词： Radio frequency Social networking (online) Computational modeling Blogs Predictive models communication networks Random forests

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