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Development of a Universal Integrated Monitoring System for Communication networks

Development of a Universal Integrated Monitoring System for ...

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Communications, Information, Electronic and Energy Systems (CIEES), international conference on

作者： Krasen Angelov Panagiotis Kogias Nikolay Manchev Stanimir Sadinov Dept. of Communications Equipment and Technologies Technical University – Gabrovo Gabrovo Bulgaria Dept. of Physics Democritus University of Thrace Kavala Greece

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

Monitoring systems are a set of technical tools that continuously monitor and acquire information in a communication network, based on the analysis of statistical data, in order to identify faulty or malfunctioning nodes and processes and to alert the responsible persons. the main objective of this paper is to present the development of a hardware integrated system, mounted in a chassis for embedding in a 19" communications cabinet, which allows the monitoring of the server room in which it is located, by means of built-in sensors, as well as the monitoring of external networks and systems of different types (wireless, optical, etc.) and with different purposes. the applicability and functionality of the developed integrated system for monitoring communication networks is demonstrated through several use cases presented as examples in the paper.

关键词： Performance evaluation Wireless communication Wireless sensor networks Hardware sensor systems Communication networks Optical sensors Monitoring System implementation Standards

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Sustainable Cluster Head Selection in Wireless sensor networks through Bio-Inspired Optimization

Sustainable Cluster Head Selection in Wireless Sensor Networ...

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Trends in Material Science and Inventive Materials (ICTMIM), international conference on

作者： Bandi Rambabu Lendale Venkateswarlu G.Ravi Srujana Inturi R Ravinder Reddy Satyanarayana Nimmala CVR College of Engineering Hyderabad Telangana India Geethanjali College of Engineering and Technology Hyderabad Telangana India Malla Reddy College of Engineering and Technology Hyderabad Telangana India Chaitanya Bharathi Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

the selection of cluster heads is a critical challenge in Wireless sensor networks (WSNs) because it has a direct impact on the overall performance, network lifetime, and energy efficiency. the Crayfish Optimization Algorithm (COA) is a bio-inspired metaheuristic approach that is introduced in this study. It is intended to improve cluster head selection by emulating the foraging and decision-making behavior of crayfish. COA optimizes the selection of cluster heads by considering residual energy, connectivity, and network coverage. this is achieved by utilizing a pheromone-like memory mechanism to balance exploration and exploitation, thereby facilitating adaptive learning and enhanced convergence. the simulation results indicate that COA substantially improves data transmission efficiency, extends the lifespan of networks, and reduces energy consumption in comparison to current methods. It is well-suited for large-scale, dynamic WSN environments due to its robustness and scalability.

关键词： Wireless sensor networks Energy consumption Scalability Heuristic algorithms Decision making Clustering algorithms throughput Magnetic heads Energy efficiency Optimization

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E-Health Care Patient Information Retrieval and Monitoring System Using SVM 1

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5th international conference on Smart Technologies in Data Science and Communication, SMART-DSC 2022

作者： Sumathi, K. Pandiaraja, P. Department of Computer Science and Engineering KSR Institute for Engineering and Technology TamilNadu Thiruchencode India Department of Computer Science and Engineering M.Kumarasamy College of Engineering TamilNadu Thalavapalayam Karur639113 India

ISBN: (数字)9789811968808

ISBN: (纸本)9789811968792

In healthcare, the modern technologies and the smart devices have brought an excellent results. In Intensive Care Unit, these technologies brought a more facilities to take care of patient health. the Internet of things helps the gadgets to fit with Internet. this provide a conjoin between the care taker and sick people which leads to the duplex communication. the aim of the patient surveilling device is to protect the patient in the intensive care unit. this system is used for analyzing the patient essential movements and sends the report continuously to the doctor through the help of the cloud. With the help of support vector machine algorithm, the data get compared with available dataset. If the compared value gets reached above its threshold value or below its threshold value the precaution message is send to the server. the server sends the notification message to the care taker and provides guidance for giving first aid. To collect these vital information, we need some sensors. these sensors sense various body parameters such as the blood pressure, temperature (body heat), heart rate, and sugar level. In addition, our system also analyzes the comma patient movement with the help of three-axis accelerometer sensor. the heart rate is measured by using the pulse oximeter sensor;blood pressure is monitored by blood pressure sensor. these sensors generate the report frequently. this system mainly helps the patient by sending the message not only in emergency case it also provides the precaution message if the value reached above or below its threshold. the surveilling system helps the care taker and reduces the work pressure and also this system overcomes the nursing staff’s shortage problem. the biomedical data of the patient send through the server with the help of wireless communication network and the data will be displayed on the mobile phone as well as laptop using web browser. this multitasking implementation in our system helps the health care especially in

关键词： Internet of things Microcontroller sensor network three axis accelerometer

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2D/3D ResNet Deep Neural Network for 4G and 5G NR Wireless Channel Estimation 25

2D/3D ResNet Deep Neural Network for 4G and 5G NR Wireless C...

引用

25th international conference on Digital Signal processing and its Applications, DSPA 2023

作者： Usatyuk, Vasiliy S. Egorov, Sergey I. T8 Sensor R&d Department Moscow Russia South-West State University Department of Computer Science Kursk Russia

ISBN: (纸本)9798350337235

For estimating the MIMO channels for both the uplink and the downlink, we used residual deep neural network. the proposed residual deep neural network (ResNET) channel estimation outperforms the MMSE channel estimation by more than 2 dB under EPA uplink channels and is capable of operating on untrained wireless channels. According to the 3GPP NR release 15 standard, the proposed 3D ResNET channel estimation approach exhibits a 0.5 to 1 dB gap to the ideal channel estimation and a 1 to 1.6 dB gain over MMSE channel estimation for 5G New Radio (NR) physical layer links, including demodulation reference symbols (DM-RS Type 2) and synchronization signal (SS) bursts periodicity (30 ms), DL-SCH transport channel coding modulation and coding scheme 13 (MCS), code rate 490 over 1024 under 15 iterations of Normalize Min-sum 5G eMBB LDPC decoder with scale 0.75, HARQ off (no retransmissions), PDSCH precoding using SVD (PDSCH Mapping Type A, 4×4 antennas), 20 resource blocks (240 subcariers), clustered delay line (CDL-C) channel with subcarier spacing 30kHZ, delay spread 300e-9, doppler shift 5Hz, central frequency 3.5GHz demonstrates a 0.5 to 1 dB gap to the perfect channel estimate and a gain of between 1 and 1.6 dB over MMSE channel estimation. the suggested solution enables a 25% boost in 5G NR throughput. © 2023 IEEE.

关键词： Deep neural networks

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Sine Cosine Algorithm based Optimal Cluster Head Selection in Wireless sensor networks

Sine Cosine Algorithm based Optimal Cluster Head Selection i...

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international conference on Smart Systems and Inventive Technology (ICSSIT)

作者： B. Pitchaimanickam P. Muthuvel R. Rajasekar Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Chennai India Department of CSE School of Computing Kalasalingam Academy of Research Kalasalingam Academy of Research Krishnankoil Tamil Nadu India

the sensor nodes are dispersed all through the environment to track changes in the physical and environmental conditions. Information from the environment is gathered with the help of sensors and transmitted to the Base Station (BS). the sensor node operates its sensing, computing, and communication processes using battery power. these procedures shorten the lifetime of the network and demand extra energy. the lifespan of the network problem can be effectively solved using the clustering strategy. In this paper, the Sine Cosine Algorithm (SCA) is recommended in the LEACH-C algorithm to discover efficient Cluster Head (CH). the proposed algorithm is developed by using the mathematical sine and cosine functions for finding the best solution. the proposed methodology finds the minimum distance and residual energy. this algorithm is implemented in Ns-2 for measuring the alive nodes and energy depletion. this technique minimizes energy utilization and maximizes network lifespan.

关键词： Wireless sensor networks Base stations Metaheuristics Clustering algorithms Energy measurement Heterogeneous networks sensors

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Enhanced Simulation Tool for ZigBee Security

Enhanced Simulation Tool for ZigBee Security

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Communications, Information, Electronic and Energy Systems (CIEES), international conference on

作者： Marieta Yordanova Aydan Haka Veneta Aleksieva Hristo Valchanov Faculty of Computer Sciences and Automation Technical University of Varna Varna Bulgaria

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

the significance of ZigBee security is on the rise because of the growing reliance on wireless sensor networks (WSNs) and the need for secure communication. Ensuring security in ZigBee networks involves elements like key management, encryption, and safeguarding data across various stack layers. Developing simulators to replicate and visually represent network security processes is essential for enhancing the security and understanding of these networks. Lately, numerous simulators have been created to assess the efficiency of Wireless sensor networks (WSNs). However, among the simulators examined, none focused on the communication security of ZigBee devices at the application layer of the ZigBee *** paper presents an enhanced simulator to help analyze and improve network security by showing the key generation and exchange process, along with application layer encryption. the simulator employs 128-bit AES encryption in accordance with ZigBee standards and shows a graphical depiction of the application layer key request, generation, and receipt sequence. this allows users to examine diverse security contexts and visualize the results of standard and non-standard operational approaches.

关键词： Training Wireless sensor networks Zigbee Network security Encryption Security Standards

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Research on Outdoor Mobile Robot Localization and Mapping Algorithm Based on Multi-sensor Fusion

Research on Outdoor Mobile Robot Localization and Mapping Al...

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Science and Technology Innovation (IAECST), international Academic Exchange conference on

作者： Biao Liu Shuqin Xiang Guangzhou Gosuncn Robot Co.Ltd Guangdong China Chongqing University Chongqing China

An accurate localization and mapping method based on multi-sensor data fusion is proposed for mobile robots in outdoor open scenes. By collecting and pre-processing multi-sensor data, combining with LIO-SAM to build 3D point cloud maps and optimizing point cloud maps, it helps mobile robots to complete high-precision localization and mapping in outdoor environment. Finally, the mobile robot platform is built and combined with the robot operating system to verify the effectiveness and accuracy of the scheme in outdoor environments.

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FIREnet 2.0: Advanced Neural Framework for Smart Fire Detection & Localization 2

FIREnet 2.0: Advanced Neural Framework for Smart Fire Detect...

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2nd international conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Aruthaveni, R.M. Dhivya, B. Hariharan, M. Siva, R. Department of Computer Science Dr. SNS Rajalakshmi College of Arts and Science Tamilnadu Coimbatore India

ISBN: (纸本)9798350340235

Fire safety plays an important role in smart environments, and traditional fire detection systems frequently demonstrate limits regarding reaction time and false alarms. the proposed study offers FIREnet 2.0, an innovative fire detection and localization system that harnesses the power of Edge AI and 5G connection. S uch a system is driven by the need for faster and more accurate fire detection in smart cities, industrial settings, and beyond. Existing fire detection systems usually suffer from high false alarm rates, slow reaction times, and restricted scalability. FIREnet 2.0, on the other hand, provides an innovative solution. the proposed study describes the system's novel technique: data collection, Edge AI processing with modified YOLO-based CNNs, 5G data transfer, fire localization, and reaction coordination. Extensive testing, with controlled, real-world fire tests, shows that FIREnet 2.0 delivers high accuracy, lowers false alarms, and considerably shortens reaction times compared to existing systems. the novel method of fire detection has the potential to revolutionize fire safety in smart environments and critical infrastructure, boosting safety and response times. © 2023 IEEE.

关键词： 5th generation connectivity Convolutional Neural networks Edge Artificial Intelligence Fire detection Fire Localization Smart environments

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A Case Study of Geoacoustic Inversion Based on Convolutional Neural Network Using Vertical Array Data 5

A Case Study of Geoacoustic Inversion Based on Convolutional...

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5th international conference on Information Communication and Signal processing, ICICSP 2022

作者： Liu, Mingda Niu, Haiqiang Li, Zhenglin Guo, Yonggang Institute of Acoustics Chinese Academy of Sciences University of Chinese Academy of Sciences State Key Laboratory of Acoustics Beijing China School of Ocean Engineering and Technology Sun Yat-sen University Zhuhai China

ISBN: (纸本)9781665485890

Geoacoustic inversion is regarded as a global or local optimization problem for the conventional matched-field inversion (MFI), while it is treated as a nonlinear regression problem in a machine learning (ML) framework in this paper. A case study is performed to evaluate the feasibility of these two inversion methods when the two-layer bottom is assumed to be a half space model in a shallow water environment. A convolutional neural network (CNN) with multi-task learning is used to estimate geoacoustic parameters simultaneously in shallow water. the network input is the normalized sample covariance matrices of the broadband data received by a vertical line array in frequency domain. the training data, validation data, and test data are generated by an acoustic propagation model. Localization and transmission loss (TL) for different typical bottom models are used to verify the performance of CNN and MFI. Simulation results demonstrate that the trained CNN is robust in geoacoustic inversion even on noisy test data with a moderate Signal-to-Noise Ratio (SNR) and achieve inversion performance comparable to the MFI. © 2022 IEEE.

关键词： Convolutional neural networks

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Signal processing and Connectivity Analysis in Calcium Imaging Data: Unveiling Neuronal Network Dynamics 25

Signal Processing and Connectivity Analysis in Calcium Imagi...

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2025 5th international conference on Bioinformatics and Intelligent Computing, BIC 2025

作者： Jia, Yuanhang Yang, Ruinan Institute of Architecture Tianjin University Tianjin Tianjin China Institute of Biomedical Engineering Sichuan University Sichuan Chengdu China

ISBN: (纸本)9798400712203

Calcium imaging, as a means of high temporal and spatial resolution, has been widely used in neuroscience research to monitor the dynamic activity of neuronal networks. In this paper, a comprehensive analysis process for calcium imaging data is proposed to improve the accuracy and effectiveness of data processing, and the analysis is performed using OASIS. First, Z-score standardization is applied to normalize the signal of each neuron to eliminate the difference in signal amplitude between different neurons. then, we design and implement a high-pass filtering method based on the frequency domain to extract high-frequency components and remove low-frequency noise and background signals by fast Fourier transform (FFT), thus enhancing the signal features of neuronal activity. the method was verified on two sets of calcium imaging data, and the results show that the signal after filtering is clearer and can capture the instantaneous activity and synchronization pattern of neurons more effectively. Finally, the result displays that the highly connected neurons in the primary visual cortex mainly interact with a specific population of neurons, indicating the existence of a specific local network structure. this study provides an effective tool for calcium imaging data analysis in the field of neuroscience and is expected to be widely used in the dynamic study of complex neural networks. © 2025 Copyright held by the owner/author(s).

关键词： Calcium imaging Deconvolution Denoising processing Mouse primary visual cortex Network connectivity analysis Z-score standardization

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