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47th ICT and Electronics Convention, MIPRO 2024

作者： Bajalcaliev, K. Mileski, D. Gushev, P. Gusev, M. Jakimovski, B. Innovation Dooel Skopje Macedonia Sts Cyril And Methodius University In Skopje Faculty Of Computer Science And Engineering Skopje Macedonia

ISBN: (纸本)9798350382495

This paper explores the transformation of electrocardiogram (ECG) monitoring from traditional offline to Real-Time analysis, enabled by high-speed mobile networks and affordable data plans. The transition to live monitoring presents challenges in data streaming and processing and the necessity of balancing immediacy with accuracy. We optimize two critical aspects of cloud architecture and scalability under the broader umbrella of cloud efficiency by evaluating the architecture's components and their contribution to overall efficiency. The focus is on accommodating over a thousand concurrent patients streaming ECG data while maintaining cost-effectiveness, constrained by Near Real-Time Round Trip Time (RTT) of ≤ 3 seconds, achieving a throughput of ≥ 333.333( msgs/s). © 2024 IEEE.

关键词： Electrocardiograms

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Position correction of mobile robot by detecting line landmark using Hough line transform 1

Position correction of mobile robot by detecting line landma...

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1st International Conference on Electronic and computer engineering, ECE 2023

作者： Anggoro, Bagus I.J. Setiawan, Iwan Nurhayati, Oky Dwi Faculty of Electrical Engineering University of Diponegoro Semarang Indonesia Faculty of Computer Science University of Diponegoro Semarang Indonesia

Mobile robots are used in a laboratory automation system to transport specimens or experiments to their destinations. When moving, the robot has some error positions because of the wheels skid on the floor and the inaccuracy direction of the robot. The robot needs to correct its position and return to the track. By reading the landmarks in the environment, the robot can correct its position. This research proposes to do the position correction of the robot using a camera by reading the orange lines as an artificial landmark on the laboratory environment floor. The camera detects the orange lines as two parallel edges. And by Hough line transform (HLT) it produces Δρ value between line edges. The robot calculates the error position using the Δρ value and the biggest ρ (ρMAX) as the distance prediction robot to the line. Compared to the original robot movement on the Webots simulator application, this method can help the robot keep its position on the track. On the goal position, the robot can reduce 90% the error position. This error improvement makes the error position of the robot becomes around 5 mm from the target position. © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Mobile robots

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Performance Evaluation of YOLO Models for Automatic Crop and Weed Detection 33

Performance Evaluation of YOLO Models for Automatic Crop and...

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33rd IEEE International Conference on Microelectronics, MIEL 2023

作者： Stankovic, L. Nikolic, T. Talaska, T. Dlugosz, R. University of Nis Faculty of Electronic Engineering Nis Serbia Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz Poland

ISBN: (纸本)9798350347760

Accurate weed mapping is essential for the implementation of weed management in order to maintain high crop yields and less chemical pollution. This paper focuses on the performance evaluation of YOLO models for automatic crop and weed detection. The research aims to develop weed detection models using one-stage deep learning-based object detectors by transferring pre-trained object detection models to a selected weed dataset. The models are trained on annotated images from a public dataset and evaluated based on detection accuracy. Results demonstrate promising training performance, with approximately 70% mean average precision achieved in 100 training epochs. YOLOv5x exhibits the best performance among the studied models, achieving the highest precision, recall, and mean average precision (0.72, 0.75, and 0.74, respectively). The findings indicate that models with deeper or larger backbone networks tend to provide higher detection accuracy. © 2023 IEEE.

关键词： Crops

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Hybrid Chaotic Salp Swarm with Crossover Algorithm for Underground Wireless Sensor Networks

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computers, Materials & Continua 2022年第8期72卷 2963-2980页

作者： Mariem Ayedi Walaa H.ElAshmawi Esraa Eldesouky First Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia MEDIATRON Lab. SUP’COMCarthage UniversityTunis2083Tunisia Department of Computer Science Faculty of Computers and InformaticsSuez Canal UniversityIsmailia41522Egypt Faculty of Computer Science Misr International UniversityCairoEgypt

Resource management in Underground Wireless Sensor Networks(UWSNs)is one of the pillars to extend the network *** intriguing design goal for such networks is to achieve balanced energy and spectral resource *** paper focuses on optimizing the resource efficiency in UWSNs where underground relay nodes amplify and forward sensed data,received from the buried source nodes through a lossy soil medium,to the aboveground base station.A new algorithm called the Hybrid Chaotic Salp Swarm and Crossover(HCSSC)algorithm is proposed to obtain the optimal source and relay transmission powers to maximize the network resource *** proposed algorithm improves the standard Salp Swarm Algorithm(SSA)by considering a chaotic map to initialize the population along with performing the crossover technique in the position updates of *** experimental results,the HCSSC algorithm proves its outstanding superiority to the standard SSA for resource efficiency ***,the network’s lifetime is ***,the proposed algorithm achieves an improvement performance of 23.6%and 20.4%for the resource efficiency and average remaining relay battery per transmission,***,simulation results demonstrate that the HCSSC algorithm proves its efficacy in the case of both equal and different node battery capacities.

关键词： Underground wireless sensor networks resource efficiency chaotic theory crossover algorithm salp swarm algorithm

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Squeezing the Lemon: Using Accident Analysis for Recommendations to Improve the Resilience of Telecommunications Organizations 26

Squeezing the Lemon: Using Accident Analysis for Recommendat...

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26th International Conference on Enterprise Information Systems, ICEIS 2024

作者： Wienen, Hans C.A. Bukhsh, Faiza A. Vriezekolk, Eelco Pires, Luís Ferreira Wieringa, Roel J. Faculty of Electrical Engineering. Mathematics and Computer Science University of Twente Netherlands Dutch Authority for Digital Infrastructure Netherlands

ISBN: (纸本)9789897586927

Telecommunications networks form critical infrastructure, since accidents in these networks can severely impact the functioning of society. Structured accident analysis methods can help draw lessons from accidents, giving valuable information to improve the resilience of telecommunications networks. In this paper, we introduce a method (TRAM) for accident analysis in the Telecommunication domain by improving AcciMap, which is a popular method for analyzing accidents. The improvements have made AcciMap more efficient and instructive by explicitly identifying ICT aspects of the accidents, extending the method to support the evaluation of crisis organizations and introducing additional notation for feedback loops. This resulted in TRAM, a method with a 25% improved efficiency over AcciMap, while also addressing ICT aspects, leading to concrete actionable results that can help telecommunication organizations grow more resilient. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Accidents

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A Method for Shape Retrieval of Flying Objects Using Satellite Communication Signals

A Method for Shape Retrieval of Flying Objects Using Satelli...

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2023 IEEE/CIC International Conference on Communications in China, ICCC Workshops 2023

作者： Li, Shuangyang Shen, Xi Huang, Defeng David Technical University of Berlin Faculty of Electrical Engineering and Computer Science Berlin Germany University of Western Australia Department of Electrical Electronic and Computer Engineering Perth Australia

ISBN: (纸本)9798350345407

The successful commercialization of satellite communication systems using low earth orbit (LEO) satellites has received significant attention. In this paper, we study the ability of shape retrieval for flying objects using LEO satellite signals. We first show that the object shape can be extracted from the received satellite signals via exploiting the diffraction pattern. Then, we derive a discrete system model that relates the shape of the flying object with the received signal at ground stations, where the impact of the satellite elevation angle is considered. We discuss the 'coherence time' for the considered shape retrieval problem and further propose a simple shape retrieval scheme using a linear filter to the combined received samples. Our numerical results verify the effectiveness of the proposed scheme, and demonstrate promising retrieval performance under various conditions. © 2023 IEEE.

关键词： Satellite communication systems

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Smart Street Lighting for Enhancing Energy Efficiency in Urban Environments 32

Smart Street Lighting for Enhancing Energy Efficiency in Urb...

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32nd International Conference on Software, Telecommunications and computer Networks, SoftCOM 2024

作者： Krizanovic, Visnja Pejkovic, Ana Pracny, Patrik J.J. Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

ISBN: (纸本)9789532901382

In the ever-evolving landscape of urban development, innovative solutions are constantly sought to improve the quality of life of residents. Moving away from conventional static street lighting, one such transformative solution for residential environments is smart street lighting. This intelligent solution brings an increase in energy efficiency. In this paper, the goal is to analyze the implementation of smart street lighting within the concept of a smart city based on the possibilities of saving and efficient use of energy. In selected case studies of urban scenarios, through simulation procedures, the possibility of implementing smart lighting was analyzed from the point of view of energy saving, indicating the importance of this aspect in the context of building smart energy lighting systems in urban environments. The discussions given in the paper show a quantitative assessment of energy savings in urban areas by replacing traditional lighting systems. The results of the analyzes show that the analyzed solutions provide useful guidelines for the application of the concept of smart lighting in urban environments and the selection of suitable solutions with regard to possible reductions in energy costs. © 2024 University of Split, FESB.

关键词： energy efficiency smart cities Smart street lighting

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A Comparative Study of IDS-Based Deep Learning Models for IoT Network

A Comparative Study of IDS-Based Deep Learning Models for Io...

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2023 International Conference on Advances in Artificial Intelligence and Applications, AAIA 2023

作者： Shaker, Bassam Noori Al-Musawi, Bahaa Qasim Hassan, Mohammed Falih Faculty of Computer Science and Information Technology University of Al-Qadisiyah Iraq Faculty of Engineering University of Kufa Iraq

ISBN: (纸本)9798400708268

The proliferation of connected devices within Internet of Things (IoT) networks has underscored the critical necessity of developing efficient Intrusion Detection Systems (IDS) that can adeptly identify and counteract threats. Deep learning algorithms are better than classical machine learning at automatically learning patterns and representations from raw data, enabling them to detect complex attacks. This research aims to conduct a comparative study of IDSs implemented through three deep learning models: Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Recurrent Neural Networks (RNN). The study employs three datasets (NF-UNSW-NB15, NF-BoT-IoT, and NF-ToN-IoT) to assess model performance in both binary and multiclass classification scenarios. The findings indicate that for binary traffic classification, DNN outperforms the other two models with an accuracy bout (98%) for all datasets. In the context of multiclass traffic classification, the DNN model surpasses the performance of the other model except for NF-ToN-IoT a CNN model score (69.08%). © 2023 Copyright held by the owner/author(s).

关键词： Internet of things

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Accurate Phase Detection for ZigBee Using Artificial Neural Network

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Intelligent Automation & Soft Computing 2023年第6期36卷 2505-2518页

作者： Ali Alqahtani Abdulaziz A.Alsulami Saeed Alahmari Mesfer Alrizq Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The IEEE802.15.4 standard has been widely used in modern industry due to its several benefits for stability,scalability,and enhancement of wireless mesh *** standard uses a physical layer of binary phase-shift keying(BPSK)modulation and can be operated with two frequency bands,868 and 915 *** frequency noise could interfere with the BPSK signal,which causes distortion to the signal before its arrival at ***,filtering the BPSK signal from noise is essential to ensure carrying the signal from the sen-der to the receiver with less ***,removing signal noise in the BPSK signal is necessary to mitigate its negative sequences and increase its capability in industrial wireless sensor ***,researchers have reported a posi-tive impact of utilizing the Kalmen filter in detecting the modulated signal at the receiver side in different communication systems,including ***-while,artificial neural network(ANN)and machine learning(ML)models outper-formed results for predicting signals for detection and classification *** paper develops a neural network predictive detection method to enhance the performance of BPSK ***,a simulation-based model is used to generate the modulated signal of BPSK in the IEEE802.15.4 wireless personal area network(WPAN)***,Gaussian noise was injected into the BPSK simulation *** reduce the noise of BPSK phase signals,a recurrent neural networks(RNN)model is implemented and integrated at the receiver side to esti-mate the BPSK’s phase *** evaluated our predictive-detection RNN model using mean square error(MSE),correlation coefficient,recall,and F1-score *** result shows that our predictive-detection method is superior to the existing model due to the low MSE and correlation coefficient(R-value)metric for different signal-to-noise(SNR)*** addition,our RNN-based model scored 98.71%and 96.34%based on recall and F1-score,respectively.

关键词： Neural networks RNN BPSK phasedetection WPAN IEEE802.15.4 signal demodulation

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An EfficientNet to Classify Monkeypox-Comparable Skin Lesions Using Transfer Learning

An EfficientNet to Classify Monkeypox-Comparable Skin Lesion...

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2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Hossen, Md. Rifat Alfaz, Nazia Sami, Adnan Tanim, Sharia Arfin Bin Sarwar, Talha Islam, Md. Kamrul Department of Computer Science and Engineering Dhaka Bangladesh Department of Computer Science Dhaka Bangladesh College of Computing and Applied Science Universiti Malaysia Pahang Faculty of Computing Pekan Malaysia

ISBN: (纸本)9798350346473

Monkeypox is an infectious illness caused by the DNA-based monkeypox virus, which has raised public health concerns due to its rapid transmission to over 50 countries. Direct physical interaction with infected humans or infected animals is the main reason behind the spread of this virus. The appearance of skin problems such as smallpox and rashes are the most frequently reported symptoms of this virus. Since cases of monkeypox are increasing rapidly around the world, stopping the spread of this zoonosis by providing early diagnosis and treatment is crucial before the emergence of a pandemic similar to COVID-19. In this study, we aim to propose a transfer learning-based approach using the EfficientNet-B0 architecture to identify monkeypox subjects by using skin lesion image samples. However, distinguishing monkeypox from other comparable infectious skin illnesses like chickenpox and measles is challenging. Therefore, additionally, this study identifies other diseases that also cause blisters and rashes on the skin, like chickenpox, and measles. During the data distribution phase, 5-fold cross-validation is used to validate the work's reliability by assuring that every sample is utilized for training and validation. For the evaluation of the model's classification performance, accuracy and loss are recorded for each training epoch. Moreover, precision, recall, F1-score, and confusion matrix are generated upon completion of the model training. This proposed approach is experimented on a public dataset and has shown remarkable performance by providing an overall 96.53% classification accuracy, 96.57% precision, 96.53% recall, and 96.52% F1-score. © 2023 IEEE.

关键词： Viruses

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