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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Poongothai P Sabareswari T C R Ramesh Veeramalai Sankaradass R. Ponnusamy Dept of Mathematics Chennai Institute of Technology Tamil Nadu India Dept of Electronics and Communication Engineering Chennai Institute of Technology Tamil Nadu India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Dept of Computer Science and Engineering Chennai Institute of Technology Tamil Nadu India Dept. of Artificial Intelligence and Data Science Madha Engineering College Tamil Nadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Undeniably, autonomous vehicles are the most dramatic evolution of transportation yet. They offer new safety, efficiency, and convenience levels that have never been seen before. Still, some natural and unexpected conditions continually strike a blow against the safety of self-driving systems, such as potholes on roads. This paper introduces a new, cloud-based safety solution for autonomous vehicles through artificial intelligence and implementation with the YOLOv8 object detection model, coupled with the deep learning platform Weights & Biases, or wandb. As the real-time pothole-detecting system is developed, issues related to navigation appear while increasing the safety level. This work adopts the YOLOv8 object detection model in the use case due to its higher accuracy and much faster object detection rate. This can classify and predict the object boundaries of a single network, thus being a good fit for quick and accurate detection. Upgrades will be made in sync with new conditions in roads and types of potholes as the right solution gains ready to change the future of transportation about autonomous transportation, paving the path for safer and more reliable self-driving vehicles. It directly addresses the acute challenge of pothole-related hazards. It builds the way to an even safer and more efficient future for autonomous mobility, thus showing tremendous potential for transforming advanced AI technologies.

关键词： Accuracy Navigation Roads Transportation Object detection Real-time systems Safety Reliability Artificial intelligence Autonomous vehicles

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A Web and Mobile Application to Identify the Availability of Drinking Water Information During a Water Crisis

A Web and Mobile Application to Identify the Availability of...

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2023 Intelligent Computing and Control for engineering and Business systems, ICCEBS 2023

作者： Razeeth, Suhail Shihara, Fathima Pavithra, Kasuni Kaliyamoorthy, Maran Nawaz, Samsudeen Sabraz Kariyapper, R.K.A.R. South Eastern University of Sri Lanka Dept. of Information and Communication Technology Sri Lanka Uva Wellassa University of Sri Lanka Dept. of Computer Science and Informatics Sri Lanka Sri Sai Ram Institute of Management Studies Sri Sai Ram Engineering College India South Eastern University of Sri Lanka Department of Management and Information Technology Sri Lanka

ISBN: (纸本)9798350394580

Sri Lanka faces drinking water related issues often due to many reasons like weather changes, floods, droughts, and tsunamis, affecting the drinking water availability. In such instances, people find difficult to gather information regarding consumable drinking water availability. In order to facilitate the meeting of commercial water sellers and customers and to engage the general public and social services in addressing the drinking water problem, this study is focused on building both web and mobile applications. The web application was implemented with a selected set of functionalities such as social login, social sharing, geocoding, reverse geocoding, viewing, and filtering posts using Angular 6, *** web framework, and Leaflet. The mobile application was developed to direct the customers to the water availability location with a shortest path using the Dijkstra's algorithm. Case evaluation was conducted with the Rotaract Club members in Badulla and thinking aloud method was used to pick up the user's ideas while interacting with the system. It can be concluded that the created web and mobile applications would offer the public a clever way to draw attention to an issue with water availability, and the commercial water service providers are given a venue to market their services and locate potential customers. © 2023 IEEE.

关键词： crowdsourcing GIS mobile application water availability web application

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Efficient GPU-based Gridding and Segmentation of Microarray Images via K-Means and Fuzzy C-Means

Efficient GPU-based Gridding and Segmentation of Microarray ...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： J Eric Clapten M M Sasimekala C Rajeswari K Sanjay Kumar Department of Electronics Communication Engineering Chennai India Department of Biomedical Engineering Chennai India Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India

The analysis of microarray image helps in identifying new genes that enables us to understand their functions and gene expression levels simultaneously at different conditions. This is also useful in diagnosing the various diseases and helps in develop the treatment for the required disease. In general it has 3 stages, the first one is gridding, and the second segmentation and the third one is intensity extraction. In this paper we are going to discuss various machine learning techniques in segmenting the microarray images. This proposed algorithm eliminates the noise and enhances the image quality by finding the nearest spots in segmentation and enhancing the spot using KNN algorithm. The performance can be improved further by the efficient utilization of a graphics processing unit (GPU). It is also shown that the method can be employed to produce a model on health based on a PET-MRI dataset which can be utilized to automatically detect pathology in the imaging.

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Four-port printed MIMO Dipole Antennas at Wi-Fi 6E/7 Bands for Wireless Modem Applications

Four-port printed MIMO Dipole Antennas at Wi-Fi 6E/7 Bands f...

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International Workshop on Antenna Technology: Small Antennas and Novel Metamaterials, iWAT

作者： Shiang-An Shie Wen-Shan Chen Wen-Hsiu Hsu Department of Electronic Engineering Southern Taiwan University of Science and Technology Tainan City Taiwan Dept. Computer and Communication Shu-Te Univesrsity Kaohsiung City Taiwan

ISBN: (数字)9798350314755

ISBN: (纸本)9798350314762

A design of four-port MIMO (multiple-input multiple-output) dipole antennas at Wi-Fi 6E/7 bands (2.4-2.484 GHz, 5.15 – 7.125 GHz) for wireless modem applications is proposed. The printed dipole is printed on an FR4 substrate with a dielectric constant of 4.4, a thickness of 1.6 mm and a loss tangent of 0.0245. Positioned around the base plate, four printed dipole antennas are employed. The dipole antenna consists of a two-arm dipole and a parasitic C-shaped strip. The dipole is excited by a mini-cable. The two-arm dipole antenna is responsible for exciting Wi-Fi 6E/7 bands and enhancing low-frequency matching by using a parasitic C-shaped strip around the two-arm dipole. The dipole antenna with a reflector improves antenna gain and directivity. The distance between the dipole antenna and the reflector is 15.625mm. The overall dimensions of the design are 123.2 mm x 81.6 mm x 71.6 mm. From the measured results, The S parameters within the frequency bands are all lower than -10dB, and the measured isolation parameters are all lower than -15dB. The simulated gain falls within the range of 3.6 – 4dBi for low frequencies and 4.1 – 6.4dBi for high frequencies. The design exhibits good performances is good for wireless modem applications at the Wi-Fi 6E/7 bands.

关键词： Wireless communication Antenna measurements Dipole antennas Reflector antennas Modems Frequency measurement Scattering parameters

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Smart Energy Efficient Non-Terrestrial Networks: Towards 6G

Smart Energy Efficient Non-Terrestrial Networks: Towards 6G

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Future communications and Networks (FCN), International Conference on

作者： S. Zammit V. Buttigieg H. Fenech E. Depasquale A. Sciberras M. Cordina J. M. Mifsud F. Davoli N. Badini P. Yang Dept. of Computer and Communication Engineering University of Malta Msida Malta University of Genoa and CNIT Genoa Italy University of Electronic Science and Technology of China Sichuan China

ISBN: (数字)9798331512880

ISBN: (纸本)9798331512897

The concept of Non-Terrestrial Networks (NTNs) as a component of 5G mobile networks was initially studied by the 3GPP during the work on Release 15, was codified in Release 17, and is expected to feature prominently in 5G Advanced (5GA) and 6G releases. AI/ML techniques play a crucial role in ensuring that 5GA and 6G networks will be energy efficient. Thus, there is a pressing need to explore how AI/ML techniques will impact the energy efficiency in ground systems, unmanned aerial systems, and satellite systems as constituent components of NTNs. Moreover, real-time Edge AI applications will impose a significant burden on the NTN infrastructure, including from an energy demand perspective. The Smart Energy Efficient NTN for 6G (SMARTEN6G) project is developing a methodology that will be utilized to emulate 5G, 5GA and 6G NTNs for planning and monitoring the energy efficiency of AI/ML techniques in transmission and service delivery chains, and to manage the energy demands of Edge AI applications. SMARTEN6G will employ a Digital Twin to emulate NTNs, especially where the layers are challenging to interface with for R&D purposes, such as geostationary and non-geostationary satellite systems and high-altitude platforms. The paper describes the main architectural choices adopted in the project to design a Digital Twin for the emulation of the NTN environment in the Open RAN (O-RAN) context, and for its integration in a cyberphysical system to construct a Proof-of-Concept prototype for the experimental evaluation of energy-aware AI/ML solutions.

关键词： 6G mobile communication Energy consumption Satellites 5G mobile communication Edge AI Open RAN Energy efficiency Real-time systems Digital twins Research and development

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Investigating the Effectiveness of Artificial Intelligence in Watermarking and Steganography for Digital Media Security

Investigating the Effectiveness of Artificial Intelligence i...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Shadan M.J.a Abdalwahid Wassan Adnan Hashim Mohammed Ganim Saeed Sarmad A. Altaie Shahab Wahhab Kareem Information System Engineering Dept. Engineering College Erbil Polytechnic University Erbil Iraq Medical Instruments techniques Dept AIQAlam University College Kirkuk Iraq Department of Information Technology Dohuk Polytechnic University Dohuk Iraq Computer Engineering Department University of Technology Baghdad Iraq Information System Engineering Dept. Erbil Technical Engineering College Erbil Polytechnic University Erbil Iraq

ISBN: (数字)9798350374131

ISBN: (纸本)9798350374148

Watermarking and Steganography are methods of embedding digital information within images or other media, such as text or audio, for the purpose of Copyright Protection or covert communication. This field of study is not recent and has been ongoing for several years, culminating in its current advanced stage. The utilization of Artificial Intelligence algorithms has played a pivotal role in revolutionizing various aspects, including security concerns and the precision of outcomes, as compared to traditional methods. This paper focuses on doing an in-dept. analysis of cutting-edge research, techniques, and methodologies employed in the domain of Watermarking and Steganography, specifically in conjunction with Artificial Intelligence. By thoroughly examining and evaluating a collection of recent studies in this domain, we have scrutinized the outcomes of each study with respect to its research goal, the acquired results, the employed algorithm, and the research's robustness in terms of susceptibility to various forms of attacks and the technique of data embedding. Our findings indicate that the use of Artificial Intelligence algorithms has a substantial influence on enhancing result precision, system resilience, and establishing data ownership. Deep Neural Networks (DNN) are essential in Watermarking and Steganography due to their robustness, effectiveness, and accuracy. Novel methodologies and systems improve security, incorporation rates, precision of detection, and speed of convergence. Deep learning is being investigated in techniques such as data concealment, information hiding, and steganography to improve security.

关键词： Deep learning Steganography Fault tolerance Reviews Measurement standards Watermarking Artificial neural networks

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Optimization of Imprecise Multiplier Circuits by using Binary Decision Diagram

Optimization of Imprecise Multiplier Circuits by using Binar...

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IEEE International Symposium on Smart Electronic systems (iSES)

作者： Pooja Choudhary Lava Bhargava G.U. Vinod Ashok Kumar Suhag Mashahiro Fujita Virendra Singh Dept. of Electronics and Communication Engineering Swami Keshvanand Institute of Technology Management and Gramothan Jaipur India Malaviya National Institute of Technology Jaipur India Dept. of Electronics and Communication Engineering Malaviya National Institute of Technology Jaipur India Qualcomm Bangalore India Dept. of Electronics and Communication Engineering BML Munjal University Haryana India VLSI Design and Education Center University of Tokyo Japan Dept. of Electrical Engineering & Dept. of Computer Science Engineering Indian Institute of Technology Bombay India

Imprecise multipliers are employed to enhance the efficiency and reduce the power consumption of arithmetic circuits while tolerating acceptable levels of inaccuracy in the outcomes. The multiplier circuit has a broader range of applications within embedded systems, particularly in the fields of digital signal processing, IoT and multimedia, etc. We proposed a systematic formal approach utilizing the Look-Up Table (LUT) employing the Burch method to evaluate the imprecise multiplier based on the Binary Decision Diagram (BDD) considering Worst Case Error constraint. Implemented 13 bit multiplier using normal and proposed imprecise multiplier shows 98% reduction in number of nodes. The proposed 25 bit imprecise multiplier based on Burch HL shows an 89% decrement in a number of nodes w.r.t. Burch LH method. BDD generation for imprecise multiplier is 75% faster than normal.

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Gammatone Visualization based Cough Sound Classification: Performance Comparison with Delta and Delta-Delta Features

Gammatone Visualization based Cough Sound Classification: Pe...

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Smart Technologies and systems for Next Generation Computing (ICSTSN), International Conference on

作者： B.Lakshmi Priya S. Jayalakshmy D. Saraswath School of Computer Science and Engineering Vellore Institute of Technology Chennai Campus Chennai India Dept. of Electronics and Communication Engineering IFET College of Engineering Villupuram India

Cough being a common symptom for most respiratory disease is considered as a predictor in the diagnosis of the diseases. In recent years, time frequency representations of signals are acclaimed for its efficacy in the classification of signals. This work explores the potential of time frequency representation derived from gammatone features in the classification of cough signals. Accordingly, visualization of gammatone cepstral coefficients (GTCC) and its delta and delta-delta variants are employed for classifying cough signals using GoogLeNet, a prominent pre-trained CNN architecture. The results of this study evinces that the delta-delta variant of GTCC with a classification accuracy of 98.02% has significantly outperformed GTCC and its delta variant which recorded accuracies of 97.22% and 94.44% respectively.

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Fastplan: A Three-Step Framework for Accelerating Drone-Centric Search Operations in Post-Disaster Relief

SSRN

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SSRN 2024年

作者： Lim, Sunho Lee, Ingyu Choi, Gyu Sang Chae, Jinseok Ashish, Ellora Ward, Eric Pu, Cong T2WISTOR: TTU Wireless Mobile Networking Laboratory Dept. of Computer Science Texas Tech University LubbockTX79409 United States Dept. of Information and Communication Engineering Yeungnam University Gyeongsan38541 Korea Republic of Dept. of Computer Science and Engineering Incheon National University Incheon22012 Korea Republic of Dept. of Computer Science Oklahoma State University StillwaterOK74078 United States

Commercially well-known drones are increasingly popular and have been deployed in post-disaster relief to support traditional search and rescue operations. However, there are still barriers to conducting a drone-centric search operation, including but not limited to operational difficulty for non-professional drone pilots, inefficient pre-planned path, and more importantly initial setup delay. In this paper, we propose a small-scale prototype of a mobile framework, called FastPlan, to facilitate the development for expediting drone-centric search decisions and operations. The basic idea is to automate the required operations with no or minimized user intervention. The framework consists of three major operations: points-of-interest (POIs) extraction, clustering, and path planning. We extract a set of target POIs from a public map (i.e., Google Maps) to decide what and where to search. Then we deploy simple density-based clustering and search priority-based path planning strategies to efficiently group and cover the POIs. We implement the framework as an Android-based mobile software (App) for a proof-of-the-concept and conduct extensive simulation experiments for performance evaluation. We analyze the different performance behavior and their implication and applicability. The results indicate that the proposed framework can be extended flexibly depending on the search priority and be a viable approach in post-disaster relief. © 2024, The Authors. All rights reserved.

关键词： Motion planning

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Development of a Neural Network system for Predicting Topsoil Moisture Using Remote Sensing and Rainfall Forecast Data

Development of a Neural Network System for Predicting Topsoi...

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International Conference on Embedded & Distributed systems (EDiS)

作者： Miriam Zambudio Martínez Larissa Haringer Martins Da Silveira Rafael Marin-Perez Antonio Fernando Skarmeta Gomez Faculty of Computer Science University of Murcia Murcia Spain Odin Solutions SL Murcia Spain Dept. of Information and Communication Engineering University of Murcia Murcia Spain

ISBN: (数字)9798331519872

ISBN: (纸本)9798331519889

With only 1% of the planet's water resources accessible in rivers and lakes, and with droughts on the rise, the scarcity of fresh water is becoming an increasingly serious problem for the world's population. Of this 1%, 70% is used by the agricultural sector, which tends to consume more of this scarce resource due to population growth and the resulting pressure to increase agricultural production. One of the fundamental variables in this context is soil surface moisture, which is particularly important for the management of water resources and has a direct impact on the interaction between the soil and the atmosphere. Therefore, this work discusses the current advances made in the measurement of this variable, which is fundamental for precision agriculture, and creates a model capable of predicting topsoil moisture in a study basin located in the southeast of the Iberian Peninsula, characterised by its predominantly agricultural. To develop the model, we used topsoil moisture data from the Sentinel-1 satellite, probes distributed over the study area, precipitation measurements from meteorological stations and ensemble forecasts from meteorological models. The final model predicted surface soil moisture (SSM) with an average Root Mean Squared Error (RMSE) of 0.15 and a maximum difference between observed and predicted data of 1.37%, demonstrating the ability to predict from one to ten days in advance.

关键词： Precipitation Satellites Soil measurements Moisture measurement Soil moisture Moisture Predictive models Data models Probes Water resources

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