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Integrated Circuits and Communication systems (ICICACS), IEEE International Conference on

作者： v B K L Aruna E Chitra M Padmaja ECE Depatment SRM Institute of Science and Technology Chennai India ECE Department V R Siddhartha Engineering College Vijayawada India

The demand for remote health applications is increasing as e-health monitoring devices improve. For remote health applications, e-health monitoring devices are becoming more and more popular. Cardiovascular diseases (CvDs) were recently thought to be the most common chronic illnesses among patients. Therefore, it is necessary to have more diagnosis and medical therapy and also requires transformation of information from cardiac patients to distant hospitals. There are some challenges according to the devices to take over like excess energy consumption, excessive power consumption, data protection, storage space as well as transmission rate. In addition to that, the Real-time processing of electrocardiogram (ECG) data is required, and the captured data must have high accuracy and lossless compression. A SPIHT decoder with enhanced capabilities of Set Partitioning in Hierarchical Tree has been designed for signal processing. The transmission of the ECG signal on a wireless channel is seriously impacted by numerous disturbances because of the inadequate channel in medical applications. Input database can be taken using the database named as MIT-BIH and was developed on Xilinx platform and MATLAB. SPIHT decoder results can be estimated by different algorithms and also measured the parameters like to demonstrate its effectiveness, it is compared to existing designs and measured for the Compression Ratio (CR), Signal to Noise Ratio (SNR), Percentage Root Mean Square Difference (PRD), and Root Mean Square Error (RMSE). Many ECG recordings from the Arrhythmia Database are used in the tests for this lossy compression/decompression technique. The collected results show how the suggested method can increase compression ratio while preserving high signal quality.

关键词： Wireless communication image coding Databases Measurement uncertainty Electrocardiography Decoding Noise measurement

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Transmitting image in 3D Wireless Channel using Adaptive Algorithm processing with MMSE based on MIMO principles 3

Transmitting Image in 3D Wireless Channel using Adaptive Alg...

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3rd International Scientific Conference of Engineering Sciences and Advances Technologies, IICESAT 2021

作者： Fedosov, v.P. Jameel, J.S. Kucheryavenko, S.v. Institute for Radiotechnical Systems and Control Southern Federal University 105/42 Bolshaya Sadovaya Str. Rostov-on-Don344006 Russia

With the growing evolution of wireless communication technologies, there is still a need for higher data rates, increased system capacity, and improved service quality. OFDM WiMAX technology is now regarded as one of the most common solutions for Broadband Wireless Connectivity in Urban Areas, capable of offering faster implementation and lower costs than standard wired options. This paper proposes effective adaptive algorithm processing with MMSE for use in wireless networks based on SISO and MIMO OFDM WiMAX, enabling network performance to be enhanced in the case of non-LOS wireless communications, which are standard in urban conditions. On the performance of the system, signal attenuation, the effects of several paths l, different mobility speeds and Doppler shift were studied. Combines the adaptive algorithm with MMSE, achieves improved joint channel estimation and signal detection which performs the technique effectively mobile. SNR, MSE and noise components are used to analyses mathematical models of adaptive modulation for transmitting images in SISO and MIMO systems. Simulation results show that the adaptive algorithm with MMSE would improve throughput. For example, when SNR equal 15 dB, the probability of MSE for BPSK based on MIMO principle is equal to 0.0016 with adaptive algorithm. Also, for the same value of SNR, the probability of MSE for BPSK based on MIMO principle is equal to 0.164 without adaptive algorithm. It can also be concluded that when processing signals in a receiving system under conditions of multi-path signal propagation, the use of adaptive algorithms with MMSE has a positive effect on noise immunity. © Published under licence by IOP Publishing Ltd.

关键词： MIMO systems

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Designing an AI driven intelligent Tutorial System

Designing an AI driven intelligent Tutorial System

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Recent Trends in Computer Science and Technology (ICRTCST), International Conference on

作者： Md Shamsher Alam Surjeet Kumar Zeeshan Khursheed Hemant Kumar Mahato Shadan Bashar Anjali Suman Computer Science and Engineering Associate Professor in R.V.S College of Engineering & Technology Jamshedpur India Computer Science and Engineering Assistant Professor in Government Engineering College Sheikhpura Bihar India DGM Jio Platform Limited Mumbai India Master of Computer Application Assistant Professor in NSHM Durgapur India Master of Computer Application Assistant Professor in R.V.S College of Engineering & Technology Jamshedpur India Computer Science & Engineering Student R.V.S College of Engineering & Technology Jamshedpur India

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

With the fast-evolving field of education, there is an increase in the demand for intelligent tutorial systems that can adapt to the diverse learning needs of individual students. This research paper explores the design and implementation of an AI-driven Intelligent Tutorial System (ITS) aimed at enhancing the personalized learning experience. The system leverages advanced machine learning algorithms to dynamically adjust instructional content, pace, and assessments based on real-time analysis of individual student performance and preferences. The paper delves into the key components of the proposed ITS, including adaptive learning models, natural language processing capabilities for interactive communication, and data-driven decision-making mechanisms. Emphasis is placed on the seamless integration of AI technologies to create an intuitive and responsive educational *** considerations in the development and deployment of the intelligent tutorial system are discussed, addressing issues related to data privacy, algorithmic bias, and the role of human oversight. The paper also highlights the collaborative potential between AI and human educators, emphasizing the system's capacity to support rather than replace teaching professionals. Through a combination of theoretical frameworks and practical examples, the research examines the potential impact of the AI-driven ITS on student engagement, learning outcomes, and the overall educational experience. Case studies and pilot implementations are presented to illustrate the system's efficacy in diverse educational *** research contributes to the ongoing discourse on AI in education by providing insights into the design principles, implementation challenges, and ethical considerations associated with developing an intelligent tutorial system. The findings aim to inform educators, policymakers, and technologists about the potential of AI to revolutionize personalized learning and contribute to the ong

关键词： Ethics Data privacy Machine learning algorithms image recognition Decision making Tutorials Market research

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Integration of geographic information technologies and digital image processing for responding to emergency situations at oil pipeline transportation facilities

Computer Optics

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Computer Optics 2022年第3期46卷 483-491页

作者： Pavlov, S.v. Sokolova, A.v. Khristodulo, O.I. USATU – Ufa State Aviation Technical University 12 K. Marx Ufa Bashkortostan450005 Russia

In this article, we consider an approach to integrating geoinformation technologies and digital image processing methods aimed at increasing the efficiency of computer technologies in controlling complex distributed systems (CDS) in emergency situations. The analysis is conducted using an example of the activity of oil pipeline transport enterprises, including pipelines, oil pumping stations, administrative, repair and emergency response units. The novelty of the proposed version of integration of geoinformation technologies and digital image processing consists in the application of a set-theoretic approach to a uniform formal description of various types of spatial data, digital images and their interrelationships necessary for constructing three-dimensional models of production facilities of CDS. This has allowed criteria, composition and structure of these 3D models to be defined using different levels of detail of the object models in order to reduce the computational load and speed up the 3D visualization of objects of CDS. These are necessary to support the decision-making when responding to certain types of emergency situations. The advantage of the proposed approach is that it increases the efficiency of decision-making support in countering some emergencies, which, among others, is achieved through the use of three-dimensional visualization of technological and operational (obtained in the event of an accident) information about the production facilities of the CDS, whereas the quality and fullness of the optimized 3D model directly affect the decisions made in response to the emergencies. The developed methods and algorithms have been practically implemented when building 3D geoinformation models. © Institution of Russian Academy of Sciences. All rights reserved.

关键词： Integration

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From Pixels to Payouts -A Satellite Based Solution

From Pixels to Payouts -A Satellite Based Solution

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Knowledge Engineering and Communication systems (ICKECS), International Conference on

作者： R S S v Deepthi A Leela Priya D Revanth K Ajay M Madhavi Depratment of Artificial Intelligence and Data Science Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India Depratment of Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada India

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

The Pradhan Mantri Fasal Bima Yojana (PMFBY) stands as a crucial initiative for farmers’ protection, yet grapples with challenges like delayed claims, inaccurate loss assessments, and uneven aid distribution [1]. This project proposes an innovative solution that addresses these issues by introducing a precision agriculture and risk management framework. The current crop insurance policies rely on an area-based approach and limited weather indices, resulting in an inadequate assessment of individual crop health and losses [2]. This shortfall leads to unjust aid distribution, leaving deserving farmers underserved [3]. The proposed solution advocates for a paradigm shift, incorporating a personalized and technology-driven approach. By integrating remote sensing technology, machine learning algorithms, and comprehensive data analysis, the system optimizes insurance resource allocation, assessing crop and regional risk profiles with precision. Furthermore, the project implements advanced image processing, segmentation, and machine learning models, such as SvM and CNN, for crop detection and yield estimation. The Google Maps API enhances decision support through geo-mapping, providing a transparent and data-driven basis for aid distribution. This innovative approach fosters equity, transparency, and active farmer participation, overcoming the limitations of existing methodologies. With a focus on precision, transparency, and equity, the proposed solution aims to revolutionize the effectiveness of PMFBY, ensuring accurate assessment and targeted aid distribution for farmers facing crop stress and losses.

关键词： Support vector machines Satellites Web services Crops Insurance Satellite images Yield estimation

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FPGA-based methods for high-speed processing of video images with large brightness scatter

Applied Physics

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Applied Physics 2022年第1期 34-41页

作者： Sorokin, D.v. Dragunov, D.E. Lyapustin, M.Y. Semenchenko, N.A. Sharganov, K.A. Orion R&P Association JSC 9 Kosinskaya str. Moscow111538 Russia FSBI «RF MD MHC» 13 Komarova st.Mytischi region Moscow141006 Russia

The task of detecting and identifying low contrast objects by thermal imaging optoelectronic systems in a scene with a large dynamic range requires the use of special brightness conversion algorithms. However, the most popular and high-quality methods, such as Digital Detail Enhancement (DDE), introduce large frame delays and require significant hardware resources. This article presents a review of dynamic range enhancement algorithms, among which algorithms that do not require a large number of FPGA logic elements and allow for minimal frame delay. Based on them, developed mathematical models of gradational transformation of brightness, which can detect low-contrast details of the image. The results of their implementation on FPGA as a part of domestic optoelectronic module are given. © 2022 Federal Informational-Analytical Center of the Defense Industry. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

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Advancing laser ablation assessment in hyperspectral imaging through machine learning

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Computers in Biology and Medicine 2024年 179卷 108849-108849页

作者： Danilov, viacheslav v. De Landro, Martina Felli, Eric Barberio, Manuel Diana, Michele Saccomandi, Paola Department of Mechanical Engineering Politecnico di Milano Milan Italy Department of Visceral Surgery and Medicine Inselspital Bern University Hospital University of Bern Switzerland Department of General Surgery Cardinal G. Panico Hospital Tricase Italy Research Institute against Digestive Cancer Strasbourg France ICube Laboratory University of Strasbourg Strasbourg France

Hyperspectral imaging (HSI) is gaining increasing relevance in medicine, with an innovative application being the intraoperative assessment of the outcome of laser ablation treatment used for minimally invasive tumor removal. However, the high dimensionality and complexity of HSI data create a need for end-to-end image processing workflows specifically tailored to handle these data. This study addresses this challenge by proposing a multi-stage workflow for the analysis of hyperspectral data and allows investigating the performance of different components and modalities for ablation detection and segmentation. To address dimensionality reduction, we integrated principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) to capture dominant variations and reveal intricate structures, respectively. Additionally, we employed the Faster Region-based Convolutional Neural Network (Faster R–CNN) to accurately localize ablation areas. The two-stage detection process of Faster R–CNN, along with the choice of dimensionality reduction technique and data modality, significantly influenced the performance in detecting ablation areas. The evaluation of the ablation detection on an independent test set demonstrated a mean average precision of approximately 0.74, which validates the generalization ability of the models. In the segmentation component, the Mean Shift algorithm showed high quality segmentation without manual cluster definition. Our results prove that the integration of PCA, t-SNE, and Faster R–CNN enables improved interpretation of hyperspectral data, leading to the development of reliable ablation detection and segmentation systems. © 2024 The Authors

关键词： Object detection

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Classification and detection of skin diseases based on CNN-powered image segmentation

Classification and detection of skin diseases based on CNN-p...

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Intelligent Technologies (CONIT), International Conference on

作者： T. vasudeva Reddy R. Anirudh Reddy C. S. Bhanu Teja N. Sai Charan Reddy N. Hima Chandra Sekhar Rao Department of ECE B V Raju Institute of Technology Telangana

Skin diseases have become more prevalent in recent years, posing a significant burden on healthcare systems globally. These conditions can range from noncancerous problems like acne, eczema, and vitiligo to more severe diseases such as melanoma, basal and squamous cell carcinoma. Skin ailments not only have an impact on physical health but can also affect the patient's psychological well-being, especially when the face is disfigured or damaged. Early detection and diagnosis of dermatological conditions can significantly improve survival rates as their severity can worsen over time. This article covers various methods used to identify and diagnose skin diseases, including natural diagnostic techniques and image pre-processing mechanisms and classification algorithms used globally. By understanding these techniques, healthcare professionals can detect skin diseases accurately, leading to better patient outcomes. Advancements in technology and research in this field continue to progress, providing hope to those who suffer from skin diseases.

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Handwritten Text Recognition and Conversion System

Handwritten Text Recognition and Conversion System

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： S.Dinesh Krishnan vaishnavi Tangallapelli Honey Rechal Talari vaignika Chinthakunta Shailaja Pulluri B V Raju Institute of Technology Narsapur India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Even with differences in handwriting styles and quality, handwritten text can be recognized and converted using platforms offered by handwritten text recognition and conversion systems. For offline handwritten word recognition, Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Connectionist Temporal Classification (CTC) are used. The system's goals are to increase productivity and accuracy in a number of areas, including address identification, signature verification, and application interpretation. It uses OpenCv and TensorFlow image processing algorithms for training and recognition, generating digital outputs in the process. The research also discusses the more general problem of Automated Handwritten Text Recognition (AHTR) and highlights how difficult it is to classify a variety of handwritten characters, such as numbers, symbols, and scripts written in several languages. The suggested approach makes use of CNNs to efficiently recognize handwriting in a variety of formats. Grammar mistakes are eliminated, and meaningful statements are produced. It also has other capabilities including the ability to transform text from any language to a user-specified language, which would significantly improve the system's accessibility and usefulness.

关键词： Training Productivity Handwriting recognition Text recognition Neural networks Optical character recognition Symbols Transforms Convolutional neural networks Long short term memory

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ELIχR: Eliminating Computation Redundancy in CNN-Based video processing

ELIχR: Eliminating Computation Redundancy in CNN-Based Vide...

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Conference on Redefining Scalability for Diversely Heterogeneous Architectures (RSDHA)

作者： Schmerge, Jordan Mawhirter, Daniel Holmes, Connor McClurg, Jedidiah Wu, Bo Colorado Sch Mines Dept Comp Sci Golden CO 80401 USA

ISBN: (纸本)9781665458771

video processing frequently relies on applying convolutional neural networks (CNNs) for various tasks, including object tracking, real-time action classification, and image recognition. Due to complicated network design, processing even a single frame requires many operations, leading to low throughput and high latency. This process can be parallelized, but since consecutive images have similar content, most of these operations produce identical results, leading to inefficient usage of parallel hardware accelerators. In this paper, we present ELI chi R, a software system that systematically addresses this computation redundancy problem in an architecture-independent way, using two key techniques. First, ELI chi R implements a lightweight change propagation algorithm to automatically determine which data to recompute for each new frame based on changes in the input. Second, ELI chi R implements a dynamic check to further reduce needed computations by leveraging special operators in the model (e.g., ReLU), and trading off accuracy for performance. We evaluate ELI chi R on two real-world models, Inception v3 and Resnet-50, and two video streams. We show that ELI chi R running on the CPU produces up to 3.49X speedup (1.76X on average) compared with frame sampling, given the same accuracy and real-time processing requirements, and we describe how our approach can be applied in an architecture-independent way to improve CNN performance in heterogeneous systems.

关键词： Computational modeling Scalability Redundancy Software algorithms Streaming media Throughput Software systems

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