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International Conference on Advanced Communication Technology (ICACT)

作者： Nahar Sultana Farhana Huq Md. Abdur Razzaque Md. Mustafizur Rahman Department of Computer Science and Engineering Green Networking Research Group University of Dhaka Bangladesh

The deployment of Narrowband Internet of Things (NB-IoT) for the transmission of healthcare data has recently gained special attention. Existing works in the literature are limited by handling heterogeneous data in an inefficient way due to lack of prioritization of health events. In this work, we investigate the use of Device to Device (D2D) integrated NB- IoT to ensure the Quality of service (QoS) while allocating radio resources to maximize the data rate of an application’s urgency in smart healthcare. We specifically create a novel system with a two-tier structure for prioritized health data transfer across cellular user equipment (CUE), D2D, and eNB to increase throughput and develop an optimization framework using multi- objective non-integer linear programming (MNILP) for fair resource distribution among the participants. Performance analysis results demonstrate that the suggested resource allocation results in a higher percentage of data delivery ratio and offers a significantly decreased data delivery delay.

关键词： Performance evaluation Simulation Medical services Quality of service Throughput Device-to-device communication Delays

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Design of an Air Pollution Monitoring System Based on a Low-Cost Sensor Node

Design of an Air Pollution Monitoring System Based on a Low-...

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2024 IEEE International Workshop on Metrology for Living Environment, MetroLivEnv 2024

作者： Guerron, Rocio-Alejandra Carni, Domenico-Luca Lanuzza, Marco D'Amore, Francesco Bencardino, Mariantonia Lamonaca, Francesco Taco, Ramiro University of Calabria Department of Computer Engineering Modeling Electronics and Systems Rende Italy Institute for High Performance Computing and Networking Italian National Research Council Rende Italy Institute of Atmospheric Pollution Research Italian National Research Council Rende Italy

ISBN: (纸本)9798350385014

Air quality influences the life of the living beings that inhabit planet Earth. High levels of air pollutants in ambient air are in fact able to affect human health and also ecosystem integrity. Their monitoring allows for regulating the most appropriate air quality indices and verifying whether they are below the established harmful levels. However, this is a task that requires many physical and economic resources. For this reason, the research activity herein presented proposes a low-cost system, which is able to qualitatively evaluate the meteorological conditions and the main pollutants that affect a certain area. Through this proposal, the behavior of Alphasense's electrochemical gas low-cost sensors is studied. Their response to the concentrations of the main air pollutants regulated by the relevant European Directive (2008/50/CE), such as Carbon Monoxide, Nitrogen Dioxide, Ozone, and Sulfur Dioxide, is characterized and analyzed under various environmental conditions of Temperature and Relative Humidity and as a function of the electrical current signal obtained by the chemistry of the sensor. © 2024 IEEE.

关键词： Air quality

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Smart Waste Collecting Robot Integration With IoT and Machine Learning 2

Smart Waste Collecting Robot Integration With IoT and Machin...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Srinivasan, S. Jambulingam, Umamagewaran Purushothaman, K.E. Jegan Amarnath, J. Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chengalpattu India College of Computer Science King Khalid University Saudi Arabia Department of Computer Science and Engineering Kammavari Sangham Institute of Technology Karnataka Bangalore India

ISBN: (纸本)9798350305418

Non-biodegradable waste collecting from a wide area is a time-consuming process. The study aims to create an intelligent robot that can independently collect waste utilizing innovative technology. The study uses the Internet of Things (IoT) to connect and manage the various robot parts. A Raspberry Pi organizes many functions as the system's central processing unit. Real-time visual data, essential for waste detection and identification, is captured by a camera module. Ultrasonic sensors identify obstacles and measure distances so the robot can navigate its environment. The sensor helps the robot avoid collisions and navigate the predetermined space effectively. The robot's robotic arm, which enables it to pick up and collect waste materials physically, is one of its primary features. The Raspberry Pi controls the arm, allowing for accurate and synchronized motions for waste collection. They use machine learning methods to facilitate waste identification and categorization, especially a CNN model. To reliably identify various forms of waste, the CNN model is trained using a collection of waste images. These technologies may be combined to enable the automated waste-collected robot to function independently while effectively locating and collecting rubbish. By seamlessly integrating IoT, machine learning, and robots, this solution helps to save human labor and enhance waste management procedures. © 2023 IEEE.

关键词： Convolutional Neural Network (CNN) Environmental sustainability Image processing Internet of Things (IoT) Waste Collecting Robot

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Monitoring Oceanographic and Cryosphere Changes: A Remote Sensing Approach to Climate-Induced Marine and Polar Dynamics

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Remote Sensing in Earth Systems Sciences 2025年第2期8卷 496-509页

作者： Shaik, Reddi Khasim Jeslet, D. Santhi Aroulanandam, Vijay Vasanth Siva, B.A. Painam, Ranjith Kumar Thiagarajan, R. Department of Electrical and Electronics Engineering Vishnu Institute of Technology Bhimavaram India Department of Computer Science M.G.R. College Hosur India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chennai Kattakalathur India Department of ECE Chennai Institute of Tech Chennai India Department of Electronics and Communication Engineering Kallam Haranadhareddy Institute of Technology (Autonomous) Guntur Chowdavaram India Department of Information Technology Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai-62 Avadi India

Climate change has accelerated the dynamics of cryospheric and oceanographic systems, leading to rising sea levels, increased ice melt, and significant shifts in polar regions. Monitoring these changes is crucial, yet traditional observational methods are often impractical in remote, harsh environments. This study presents a novel approach to monitoring oceanographic and cryospheric changes induced by climate dynamics, using a cascaded long short-term memory (LSTM) and recurrent neural network (RNN) model with ICESat and supplementary remote sensing data. The model leverages ICESat’s high-resolution laser altimetry for precise measurements of ice thickness and ice sheet elevation, combined with data from MODIS, Sentinel-1, and CryoSat-2 for comprehensive insights. Our results indicate that the cascaded LSTM-RNN model achieves high predictive accuracy, with root mean square error (RMSE) values as low as 0.08 m for ice thickness and a mean absolute error (MAE) of 0.05 m. Additionally, it attains a correlation coefficient of 0.96, significantly outperforming other models like standalone LSTM (RMSE 0.12 m, MAE 0.09 m) and CNN-LSTM hybrid (RMSE 0.10 m, MAE 0.08 m). This model captures both seasonal and long-term trends in ice dynamics, highlighting its utility for robust climate monitoring. The successful integration of ICESat and other remote sensing data demonstrates the potential for advanced machine learning techniques to enhance predictions of polar and oceanographic changes, supporting climate adaptation efforts. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Climate change Cryosphere Ice thickness ICESat LSTM-RNN Machine learning Oceanographic changes Predictive modeling Remote sensing Sea ice extent

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Automatic Detection and Characterization of Human Veins Using Infra-Red Image Processing

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Journal of computer and Communications 2024年第9期12卷 141-159页

作者： Jean Ndoumbe Brice Ekobo Akoa Gaelle Patricia Talotsing Frederic Franck Kounga Samuel Kaissassou Bertin Chouanmo Njo Laboratory of Computer Engineering Data Science and Artificial Intelligence Department of Computer & Telecommunications Engineering National Higher Polytechnic School Douala Cameroon Laboratory of Electrical Engineering Mechatronics and Signal Processing Department of Electrical & Telecommunications Engineering National Advanced School of Engineering Yaound Cameroon Mobile Computing and Networking Research Laboratory Department of Computer and Software Engineering Polytechnique Montreal Montreal Canada DigiPlus SARL Bonamoussadi Douala Cameroon

The detection and characterization of human veins using infrared (IR) image processing have gained significant attention due to its potential applications in biometric identification, medical diagnostics, and vein-based authentication systems. This paper presents a low-cost approach for automatic detection and characterization of human veins from IR images. The proposed method uses image processing techniques including segmentation, feature extraction, and, pattern recognition algorithms. Initially, the IR images are preprocessed to enhance vein structures and reduce noise. Subsequently, a CLAHE algorithm is employed to extract vein regions based on their unique IR absorption properties. Features such as vein thickness, orientation, and branching patterns are extracted using mathematical morphology and directional filters. Finally, a classification framework is implemented to categorize veins and distinguish them from surrounding tissues or artifacts. A setup based on Raspberry Pi was used. Experimental results of IR images demonstrate the effectiveness and robustness of the proposed approach in accurately detecting and characterizing human. The developed system shows promising for integration into applications requiring reliable and secure identification based on vein patterns. Our work provides an effective and low-cost solution for nursing staff in low and middle-income countries to perform a safe and accurate venipuncture.

关键词： Vein Detection Blood Radiation Infrared Image CLAHE Algorithm Raspberry Pi

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Denoising Techniques in Healthcare Data for Improving Accuracy in Medical Applications - A Survey 7

Denoising Techniques in Healthcare Data for Improving Accura...

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7th International Conference on Communication and Electronics Systems, ICCES 2022

作者： Karthikram, A. Saravanan, M. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai Kattankulathur India Srm Institute of Science and Technology Department of Networking and Communications Chennai Kattankulathur India

ISBN: (数字)9781665496346

ISBN: (纸本)9781665496346

Bigdata alludes to a huge assortment of information that keeps on expanding dramatically over the long run. Big data sources in health care incorporate patient medical records, clinic records, clinical test results, and data gathered by medical services testing machines. The main challenge associated with the health care data is the lack of accuracy, medical errors. This can be overcome by using big data so that the health care data can be denoised and accurate data can be obtained. This helps to enhance the efficiency of diagnosis of diseases. This review paper deals with various algorithms and denoising techniques which are used to enhance the accuracy of obtained health care data. © 2022 IEEE.

关键词： Big data

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Secure Data Transmission in Wireless Adhoc Networks by Detecting Wormhole Attack 8

Secure Data Transmission in Wireless Adhoc Networks by Detec...

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8th International Conference on Smart Structures and Systems, ICSSS 2022

作者： Thilak, K. Deepa Saranya, S. Kalaiselvi, K. Anand, M. Vijay Kumaresan, K. Srm Institute of Science and Technology Department of Networking and Communications Tamil Nadu Kattankulathur Chennai India Easwari Engineering College Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India K.S.R. College of Engineering Department of Computer Science and Engineering Tamil Nadu Namakkal Tiruchengode India

ISBN: (纸本)9781665497619

Wireless Network Coding System encrypts data as it travels via intermediate nodes for secure data transmission, Wormhole attackers choose one of the nodes to target. The attacked node tries to attack other nodes, forming a wormhole tunnel in the process. When a wormhole attack is launched, Information bundles on one or the other side of the wormhole connection can be caught, sent through the wormhole connection, and rebroadcast on the other hub. Network coding has been proved to be an excellent method for improving the performance of wireless systems. To detect wormholes, a centralized approach is presented initially, and its accuracy is rigorously demonstrated. A circulated discovery calculation against wormhole in remote organization coding frameworks is proposed. The assaulting hubs are caught on the two sides of the organization, and afterward isolated from the remainder of the organization. Bundle misfortune is brought about by assaulted hubs;to avoid packet loss, data is transmitted by separating the attacked node from the network. By separating the attacked nodes from the source node to the destination node, the packets are retransmitted. The shortest path across the path that is not affected is determined and the packets are attempted to transmit from the source to send the packets effectively and successfully. © 2022 IEEE.

关键词： Wireless networks Packet loss Organizations Network coding Encoding Data communication Intelligent structures

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Cloud-Powered Patient Interaction: Humanoid Robots in Hospital Reception using NLP 10

Cloud-Powered Patient Interaction: Humanoid Robots in Hospit...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Naveenkumar, N. Priyadarshini, S. Swathy, R. Mishra, Nilamadhab Thirumaraiselvan, P. Renukadevi, S. Department of Computer Science and Engineering Tamil Nadu Namakkal India Vinayaka Mission's Kirupananda Variyar Arts & Science College Department of English Tamil Nadu Salem India Srm Institute of Science and Technology Department of Networking and Communications Chennai Tamil Nadu Kattankulathur India Vit Bhopal University School of Computing Science and Engineering Madhya Pradesh India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India Department of Mathematics Tamil Nadu Chennai India

ISBN: (纸本)9798350353068

This research presents a novel method for improving patient engagement in hospital reception areas by using cloud computing and natural language processing (NLP) approaches to power humanoid robots. In order to improve the patient experience overall, the greeting procedure is being streamlined and personalized via the deployment of humanoid robots. To facilitate an effective connection between the hospital's computer systems and the humanoid robots, cloud computing enables the processing and analysis of data in real-time. The ability to comprehend and react to patient questions, provide pertinent information, and aid with navigation inside the hospital grounds are all made possible by the robots' natural language processing skills. Robots may access and update patient data using cloud resources, proving accurate and up-to-date information transmission. and since the system is cloud-based, it can learn and adapt on the go, so the robots may improve their communication abilities in response to human input. Results in decreasing wait times, improving information accuracy, and increasing overall patient happiness have been encouraging since the deployment of this cloud-powered patient contact system. To improve healthcare administration and patient care by integrating advanced technology into healthcare environments. Adding new features and improving the system in response to input from users (both patients and doctors) are potential directions for future growth. © 2024 IEEE.

关键词： Anthropomorphic robots

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A Soft Voting Classification Model for Network Traffic Prediction in VANET/V2X

A Soft Voting Classification Model for Network Traffic Predi...

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IEEE International Conference on Wireless and Mobile Computing, networking And Communications (WiMob)

作者： Parvin Ahmadi Doval Amiri Samuel Pierre Department of Computer and Software Engineering Mobile Computing and Networking Research Laboratory Polytechnique Montreal Montreal Canada Department of Computer and Software Engineering Mobile Computing and Networking Research Laboratory (LARIM) Polytechnique Montreal Montreal Canada

Vehicular network services in the smart cities generate enormous data by vehicular road users, which is a critical challenge. Network traffic leads to a negative impact on safety applications. AI techniques are a promising solution to address network traffic in VANETs with V2X data. In this paper, we propose a soft voting classification model, which consists of hybrid supervised machine learning algorithms to predict traffic in the network. We evaluate the prediction performance of five well-known machine learning models and the proposed model based on various classification evaluation metrics. The simulation results show that the proposed network traffic prediction model performs better than other considered machine learning models in terms of accuracy (0.94%), time consumption (12.25 seconds) and AUROC (0.907) that proves its stability.

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Indoor 3D Visible Light Positioning Analysis with Channel Estimation Errors 30

Indoor 3D Visible Light Positioning Analysis with Channel Es...

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30th International Conference on Systems, Signals and Image Processing, IWSSIP 2023

作者： Bozanis, Dimitrios Evgenidis, Nikos G. Papanikolaou, Vasilis K. Bouzinis, Pavlos S. Tegos, Sotiris A. Dowhuszko, Alexis A. Diamantoulakis, Panagiotis D. Karagiannidis, George K. Department of Electrical and Computer Engineering Thessaloniki54124 Greece Germany University of Macedonia Department of Applied Informatics Thessaloniki54636 Greece Aalto University Department of Communications and Networking Espoo02150 Finland Cyber Security Systems and Applied Ai Research Center Lebanon

ISBN: (纸本)9798350337297

Since radio-frequency signals are affected signifi-cantly by multipath fading, the localization accuracy they offer cannot meet the sixth generations (6G) demanding specifications. On the other hand, visible light positioning (VLP) has emerged as a low cost alternative that takes into account the highly deterministic channel characteristics of visible light communications to provide cm-level accuracy provided that the investigated spaces are adequately covered by LEDs. However, imperfect channel state information and modeling can lead to positioning errors. In this paper, localization accuracy is quantified in terms of channel estimation errors via low complexity lateration algorithm. The channel estimation errors can be caused by a variety of issues and the provided analysis is general so that it can be easily applied to a plethora of applications. Finally, Monte Carlo simulations validate the proposed method and offer valuable insights for the design and performance bounds of VLP systems. © 2023 IEEE.

关键词： Monte Carlo methods

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