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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Manjunatha, D.V. Maindola, Meenakshi Jose, Reny Kaliappan, S. Patel, Mitul Maranan, Ramya Department of Electronics & Communication Engineering Alvas Institute of Engineering & Technology Moodabidri India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Idukki India Division of Research and Development Lovely Professional University Phagwara India Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Vadodara India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9798331504960

In order to improve the visual quality of low-resolution video frames, this study introduces a new superresolution method for real-time video processing that is powered by artificial intelligence. With little computational overhead, the suggested approach uses a hybrid deep learning model to reconstruct high-resolution frames. This model mixes Convolutional Neural Networks (CNNs) with Generative Adversarial Networks (GANs). The suggested method outperforms state-of-the-art methods by 12.3% in PSNR and 8.7% in SSIM, according to experimental results on benchmark video datasets. The PSNR is 34.56 dB and the SSIM is 0.94. Also, running on a GTX 1080Ti GPU, the system shows an average processing speed of 30 fps, which is great for real-time apps. The suggested strategy is effective in eliminating artifacts, improving visual clarity, and retaining fine details in various real-time video processing contexts, according to both quantitative and qualitative results. © 2024 IEEE.

关键词： Convolutional neural networks

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Hall effect diameter sensor integration in DIY filament extruder 26

Hall effect diameter sensor integration in DIY filament extr...

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26th International Conference on Knowledge-Based and Intelligent Information and engineering Systems, KES 2022

作者： Piotr, Zmuda Trzebiatowski Marcin, Walczak Lukasz, Bazych Aleksandra, Janisiak Tomasz, Królikowski Sebastian, Banucha Andrzej, Blarzejewski Faculty of Mechanical Engineering Koszalin University of Technology Koszalin75-453 Poland Faculty of Electronics and Computer Science Koszalin University of Technology Koszalin75-453 Poland

The paper presents the concept and modernization of filament extruder for use in FDM technology, through the implementation of the filament diameter sensor. Issues related to the process of designing solutions and their adaptation to the existing device were described, and the decision to develop their own design was justified. The issues, advantages and disadvantages of the solutions used from the perspective of reducing the costs of producing materials on site were also raised. © 2022 The Authors. Published by Elsevier B.V.

关键词： Frequency division multiplexing

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Performance enhancement of text-independent speaker recognition in noisy and reverberation conditions using Radon transform with deep learning

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International Journal of Speech technology 2022年第3期25卷 679-687页

作者： El-Moneim, Samia Abd El-Mordy, Eman Abd Nassar, M.A. Dessouky, Moawad I. Ismail, Nabil A. El-Fishawy, Adel S. El-Dolil, Sami El-Dokany, Ibrahim M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt High Institute of Engineering and Technology Tanta Egypt Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Recognition (ASR) in mismatched conditions is a challenging task, since robust feature extraction and classification techniques are required. Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) is an efficient network that can learn to recognize speakers, text-independently, when the recording circumstances are similar. Unfortunately, when the recording circumstances differ, its performance degrades. In this paper, Radon projection of the spectrograms of speech signals is implemented to get the features, since Radon Transform (RT) has less sensitivity to noise and reverberation conditions. The Radon projection is implemented on the spectrograms of speech signals, and then 2-D Discrete Cosine Transform (DCT) is computed. This technique improves the system recognition accuracy, text-independently with less sensitivity to noise and reverberation effects. The ASR system performance with the proposed features is compared to that of the system that depends on Mel Frequency Cepstral Coefficients (MFCCs) and spectrum features. For noisy utterances at 25 dB, the recognition rate with the proposed feature reaches 80%, while it is 27% and 28% with MFCCs and spectrum, respectively. For reverberant speech, the recognition rate reaches 80.67% with the proposed features, while it reaches 54% and 62.67% with the MFCCs and spectrum, respectively. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Feature extraction

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Aerial Vehicle Detection and Classification Through Fusion of Multi-Domain Features of Acoustic Signals

Aerial Vehicle Detection and Classification Through Fusion o...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Sumble, Malaika Aziz, Sumair Khan, Muhammad Umar Iqtidar, Khushbakht Fernandez-Rojas, Raul University of Engg. and Tech. Taxila Department of Electronics Engineering Taxila Pakistan University of Canberra Faculty of Science and Technology CanberraACT Australia National University of Sciences & Tech. Dept. of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9798350349719

Detection of aerial vehicles is a challenging task. Over time, the significance of developing such vehicles has increased rapidly. However, an utmost concern arises from the potential misuse of these small-sized vehicles in illicit activities such as unauthorized surveillance, smuggling contraband, or disrupting critical infrastructure significant security threats. Recognizing the gravity of this issue, we have opted to introduce an innovative approach to automate the detection processes for aerial vehicles. This initiative aims to deploy effective drone detection systems to mitigate risks and safeguard against illegal activities. We put use to two datasets from GitHub depot. Cepstral, spectral, and time domain features were extracted from the data, followed by classification. We conducted two experiments, with the first (Drone, No-Drone) yielding 98.6% accuracy, using the Ensemble (Bagged Trees) classifier. The second set of experimentation addressed five classes: Background Noises, Bebop Drone, Drone, Helicopter, and Mambo Drone. Ensemble (Bagged trees) again outperformed all other classifiers and achieved 98.3% accuracy. The results highlight that our proposed framework gives effective results based on audio signals of different aerial vehicles. © 2024 IEEE.

关键词： Drones

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Artificial Intelligence Based Solar Radiation Predictive Model Using Weather Forecasts

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computers, Materials & Continua 2022年第4期71卷 109-124页

作者： Sathish Babu Pandu A.Sagai Francis Britto Pudi Sekhar P.Vijayarajan Amani Abdulrahman Albraikan Fahd N.Al-Wesabi Mesfer Al Duhayyim Department of Electrical and Electronics Engineering University College of EngineeringPanruti607106India Department of Mechanical Engineering Rohini College of Engineering&TechnologyPalkulam629401India Department of Electrical and Electronics Engineering Vignan’s Institute of Information TechnologyAndra Pradesh530046India Department of Electrical and Electronics Engineering University College of EngineeringBIT CampusTiruchirappalli620024India Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia&Faculty of Computer and ITSana’a UniversitySana’aYemen Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Solar energy has gained attention in the past two decades,since it is an effective renewable energy source that causes no harm to the *** Irradiation Prediction(SIP)is essential to plan,schedule,and manage photovoltaic power plants and grid-based power generation *** models have been proposed for SIP in the literature while such studies demand huge volumes of weather data about the target location for a lengthy period of *** this scenario,commonly available Artificial Intelligence(AI)technique can be trained over past values of irradiance as well as weatherrelated parameters such as temperature,humidity,wind speed,pressure,and ***,in current study,the authors aimed at developing a solar irradiance prediction model by integrating big data analytics with AI models(BDAAI-SIP)using weather forecasting *** order to perform long-term collection of weather data,Hadoop MapReduce tool is *** proposed solar irradiance prediction model operates on different ***,data preprocessing take place using various sub processes such as data conversion,missing value replacement,and data ***,Elman Neural Network(ENN),a type of feedforward neural network is also applied for predictive *** is divided into input layer,hidden layer,loadbearing layer,and output *** overcome the insufficiency of ENN in choosing the value of weights and hidden layer neuron count,Mayfly Optimization(MFO)algorithm is *** order to validate the performance of the proposed model,a series of experiments was *** experimental values infer that the proposed model outperformed other methods used for comparison.

关键词： Solar irradiation prediction weather forecast artificial intelligence Elman neural network mayfly optimization

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Pelican optimized extreme learning machine based prognosis of heart failure using textural patterns in CMR images

Pelican optimized extreme learning machine based prognosis o...

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2023 IEEE technology and engineering Management Conference - Asia Pacific, TEMSCON-ASPAC 2023

作者： Muthulakshmi, M. Ashwini, K. Jansi, R. Kiran, Batchu Bhanu Sai Mani Greeshma, Nimmagadda Keerthi, Pulimi Amrita School of Engineering Dept. of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Chennai India Amrita School of Computing Dept. of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology Dept. of Electronics and Communication Engineering Kattankulathur Chennai603203 India Amrita School of Engineering Dept. of Computer and Communication Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350384659

One of the abnormal cardiovascular conditions with the greatest rate of increase is heart failure (HF). There is a lot of correlation in the exterior symptoms observed that are typically ignored, particularly when there is progression from one stage to another. Planning an efficient treatment requires grouping patients into different phenotypic categories. In CMR imaging, these conditions show damaged heart muscles. Understanding the classification performance of ELM aided by optimal texture features is the main aim of the proposed work. In this study, Krawtchouk moment and the co-occurrence of neighboring sparse local ternary pattern descriptors are used to extract texture and anatomical information of LV. However, an effective feature selection strategy is required to handle the high dimensionality of the feature vector. The primary benefit of the Pelican optimization algorithm (POA), which takes inspiration from nature, is its capacity to carry out both global and local searches effectively. This paper suggests usage of multi-objective POA to optimize the proportion of extracted features and improve the performance of the ELM classifier. The people with various HF stages have been diagnosed with the maximum accuracy of 95.7 % as a result of this inclusion. The enhanced traits have distinguished moderate and severe HF patients from control participants with significant performance. The proposed work further shows the impact of tissue characterization from CMR image and optimized features-based classification on the HF stage detection. © 2023 IEEE.

关键词： Magnetic resonance

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Performance Analysis of AODV Routing forWireless Sensor Network in FPGA Hardware

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computer Systems Science & engineering 2022年第3期40卷 1073-1084页

作者： Namit Gupta Kunwar Singh Vaisla Arpit Jain Adesh Kumar Rajeev Kumar Department of Computer Science&Engineering Uttarakhand Technical UniversityDehradun248007India Department of Computer Science B T Kumaon Institute of Technology DwarahatIndia Faculty of Engineering&CS Teerthanker Mahaveer UniversityMoradabadIndia Department of Electrical&Electronics Engineering School of EngineeringUniversity of Petroleum and Energy StudiesDehradun248007India

Wireless sensor network(WSN)is a group of interconnected sensor nodes that work wirelessly to capture the information of *** routing of the network is a challenging *** routing of WSN is classified as proactive,reactive,and *** on-demand distance vector(AODV)routing is an example of reactive routing based on the demand route formations among different nodes in the *** research article emphasizes the design and simulation of the AODV routing hardware chip using very-high-speed integrated circuit hardware description language(VHDL)programming in Xilinx integrated synthesis environment(ISE)14.7 *** performance of the chip is studied based on the field-programmable gate array(FPGA)hardware parameters such as slices,lookup table(LUTs),input/output block(IOB),flipflops,and memory for the different configurations of the network(N=10,20….100).The delay and frequency are also estimated on the Virtex-5 *** performance of the WSN with AODV routing is also analyzed based on the packet delivery ratio,throughput,delay,and control *** simulation test cases verified the 8-bit,64-bit,and 128-bit data communication within the nodes.

关键词： Wireless sensor network FPGA synthesis AODVrouting simulation and synthesis VHDL simulation

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An Enhanced System on Chip-Based Sobel Edge Detector

An Enhanced System on Chip-Based Sobel Edge Detector

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2023 International Telecommunications Conference, ITC-Egypt 2023

作者： Khalil, Ahmed S. Shalaby, Mohamed Hegazi, Emad Ain-Shams University Faculty of Engineering Electronics & Communications Dept Cairo Egypt Arab Academy for Science Technology Maritime Transport Heliopolis Computer Science Dept Cairo Egypt

ISBN: (纸本)9798350326062

Nowadays real-time image processing has become a great challenge because of the use of high-resolution camera sensors, which produce HD video streaming with high resolution and frame rate. As a result, processing this huge amount of data on serial processor became a big challenge. Programmable logic such as FPGA plays an important role in solving this problem by using parallel processing techniques like pipelining. The advances in the system on chip (SOC) technology allow researchers to develop complex heterogeneous systems that can be used in real-time image processing. In this paper, we present a hardware design implementation for accelerating Sobel edge detector by applying some optimization techniques such as line buffer and memory window technique, pipeline technique, unrolling for parallel processing technique, and loop-flatten technique. The experimental results of this design are presented and a comparison between this design and related work is discussed. © 2023 IEEE.

关键词： System-on-chip

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Formation of Specific Competences in the engineering Career by Educational Software - methodological landmarks applied to the 'Initiation in research' discipline 15

Formation of Specific Competences in the Engineering Career ...

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15th International Conference on electronics, computers and Artificial Intelligence, ECAI 2023

作者： Schiopu, Adriana-Gabriela Oproescu, Mihai Bulgaru, Ionut Constantin Novac, Ovidiu Cornelia Novac, Mihaela Iana, Gabriel-Vasile University of Pitesti Faculty of Mechanics and Technology Pitesti Romania University of Pitesti Faculty of Electronics Communication and Computer Science Arges Pitesti Romania University of Pitesti Departament of Teacher's Training Pitesti Romania University of Oradea Faculty of Electrical Engineering and Information Technology Oradea Romania

ISBN: (纸本)9798350321388

Since a curricular concept oriented towards the formation of competences is very topical, the informational and educational technologies are characterized as a transdisciplinary and interdisciplinary field, which provides tools for the elaboration of educational applications, which can eventually be implemented in technical disciplines. Since both teachers and students often see only a part of the TIC (Information and Communication Technologies) possibilities, we believe it is necessary to research and bring to the attention of the pedagogical opinion the formative valences of modern technologies, which are no longer just an element of impact, but tend to already become everyday tools with perspective in the formation of transversal competences in most technical disciplines. In the context of formative-productive education, in this paper, we are concerned with the study of engineering disciplines through traditional strategies and methods, the formation of positive attitudes of students towards the immanent values of engineering and their correlation with the techniques and strategies for the development of informational and communication skills. Thus, there is a need to use the computer as a tool for learning, taking into account the fact that it is precisely this type of study that is of great value from a functional point of view. Specifically, the aim of this research is to define the methodological configuration of the development of educational software (SE) in the modern educational system through the prism of the transdisciplinary and interdisciplinary character of informatics and TIC, on the example of the discipline Initiation in research in the University of Pitesti, through the act of implementing software products, profiling the contribution on the formation of transversal competences to the aforementioned discipline. © 2023 IEEE.

关键词： Students

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Random Forest Predictive Model for Ventilator-Associated Pneumonia Prediction with IoT Data Analytics

Random Forest Predictive Model for Ventilator-Associated Pne...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Ramadevi, R. Kishoresonti, V.J.K. Jain Jacob, M. Vaidehi, V. Mohankumar, N. Rajmohan, M. Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Biomedical Engineering Tamil Nadu Chennai India Sathyabama Institute Of Science And Technology Department Of Electronics And Communication Engineering Tamil Nadu Chennai India Srm Institute Of Science And Technology Faculty Of Management Tamil Nadu Chennai India Department Of Computer Science Tamil Nadu Salem India B.V. Raju Institute Of Technology Department Of Electronics And Communication Engineering Telangana India Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

To identify and forecast ventilator-associated pneumonia (VAP) early on, this study investigates the use of random forest predictive models in combination with Internet of Things (IoT) data analytics. Patients on mechanical ventilation are particularly vulnerable to VAP, a serious nosocomial infection. It uses Random Forest algorithms to examine a wide range of factors, such as vital signs, patient demographics, and ventilator settings, by using the abundance of healthcare data supplied by the IoT. The goal is to create reliable prediction models that can identify people who are at risk of getting VAP before any symptoms appear. Using data gathered from ventilator-integrated real-time monitoring devices, it creates a complete dataset. The findings reveal that random forest models are reliable and very accurate in predicting VAP. Integrating analytics from the IoT improves prediction accuracy, which in turn helps healthcare providers make better, timelier preventative decisions. Improving patient outcomes and optimizing resource allocation in critical care units may be possible as a result of this study's contributions to the expanding body of work on using sophisticated data-driven methodologies for infectious illness prediction in clinical settings. © 2024 IEEE.

关键词： Internet of things

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