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Efficient Compressed Sensing for Real-Time Electrocardiogram Acquisition on Low-Power Medical Devices

Efficient Compressed Sensing for Real-Time Electrocardiogram...

引用

IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Mitchell Terrell Jon Weissman Dept. of Computer Science and Engineering University of Minnesota Minneapolis Minnesota

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Low cost wearable and implantable cardiac monitoring devices (WCM & ICM), combined with increasingly accurate disease and arrhythmia detection algorithms have proven effective to help slow the impact of cardiovascular disease, the worlds leading cause of death in 2022 [1]. Improvements to server-side detection algorithms along with hardware limitations of these devices such as slow processor speeds and minimal battery life has led to a desire to offload data from the devices for later analysis. Paired with limited storage capacity, this has led to a push to decrease the necessary storage for electrocardiogram (EKG) signals without sacrificing disease detection accuracy and device longevity. A promising recent innovation in EKG compression has come from Compressed Sensing (CS), which exposes inherent sparseness in the signal to selectively sample for eventual server-side reconstruction. Many CS approaches have been implemented on WCM devices which demonstrate a high compression ratio (CR) and accurate signal reconstruction, but quickly become impractical for the stricter hardware constraints of ICM devices due to the increased computation on the device and slow reconstruction time. In this paper we propose a CS approach known as Tailored Sensing (TS) which combines on-device prior knowledge, a custom transform basis, and optimal sense location selection to achieve improved CR and reconstruction accuracy while eliminating on-device computational burden and slow reconstruction time. Our approach offers equivalent or better signal reconstruction at previously unfathomable CRs due to our novel signal segmentation scheme. Additionally, our approach boasts a zero overhead on-device sensing strategy and a 93 % reduction in signal reconstruction time.

关键词： Technological innovation Accuracy Medical devices Transforms Electrocardiography Signal reconstruction Hardware Sensors Detection algorithms Compressed sensing

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Towards sub-100Hz Super-Resolution Imaging Through a Novel Bi-Directional Interpolation Technique

Towards sub-100Hz Super-Resolution Imaging Through a Novel B...

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IEEE International Symposium on Applications of Ferroelectrics (ISAF)

作者： Giulia Tuccio Sajjad Afrakhteh Libertario Demi Dept. of Information Engineering and Computer Science University of Trento Italy

ISBN: (数字)9798350371901

ISBN: (纸本)9798350371918

Ultrasound Localization Microscopy (ULM) represents a safe, non-invasive and low-cost imaging modality for visualization of the microcirculation at clinically relevant dept.. In fact, by precisely localizing and tracking microbubbles (MBs) injected in the circulation, ULM characterizes the microsvascular structures. However, ULM is currently constrained to high frame rates necessary to accurately track MBs in two successive frames (kHz-range). Such high frame rates are generally beyond the reach of clinical scanners (sub-100 Hz). Here, we suggest acquiring the data at a lower frame rate followed by applying a reconstruction technique to compensate for the lost information due to the low frame rate imaging. We introduce a novel 2x2D interpolation using radial basis function (RBF)-based reconstruction to estimate unknown values in the 3D In-phase and Quadrature (IQ) data (x-z-t), thereby enhancing temporal resolution. This bidirectional approach improves the reconstruction of MBs’ dynamics by interpolating along both x and z directions. The method was tested on a rat brain data demonstrating relaxing the frame rate to 100 Hz while maintaining image quality comparable to the original high frame rate data.

关键词： Location awareness Image quality Interpolation Ultrasonic imaging Three-dimensional displays Microscopy Superresolution Bidirectional control Image reconstruction Frequency control

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Optimal Module Count for ISOP LLC Resonant Converters based on an HF Transformer Genetic Algorithm Optimization in Motorsport Applications

Optimal Module Count for ISOP LLC Resonant Converters based ...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Volpini, Andrea Tresca, Giulia Campailla, Salvatore Dappiano, Andrea Zanchetta, Pericle University of Pavia Dept. of Electrical Computer and Biomedical Engineering Pavia Italy Marelli Motorsport Research & Development Department Venaria Reale Italy Marelli Motorsport Research & Development Department Corbetta Italy

ISBN: (纸本)9798350376067

This paper investigates the selection of the optimal number of modules in an Input-Series-Output-Parallel (ISOP) LLC resonant DC-DC converter for motorsport applications. The study employs a Genetic Algorithm (GA) multi-objective optimization of the High Frequency Transformer (HFT) power losses and total volume. The analysis is repeated for converters with different numbers of modules, considering both commercially available and custom transformer cores, as well as air and water cooling systems. The Pareto front of solutions is determined and presented in each scenario. Optimization results indicate that for commercial cores, the range of optimal solutions is significantly constrained by thermal limitations. Conversely, a wider range of solutions can be obtained with custom cores. Furthermore, a lower number of modules is preferable for achieving high efficiency. On the other hand, increasing the number of modules can increase the converter power density, although this results in lower efficiency. © 2024 IEEE.

关键词： Genetic algorithms

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Plant Disease Detection for Guava and Mango using YOLO and Faster R-CNN

Plant Disease Detection for Guava and Mango using YOLO and F...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Kruthi U Shetty Rida Javed Kutty Khushi Donthi Anuj Patil Natarajan Subramanyam Dept. of Computer Science and Engineering PES University Bangalore India

Traditional methods of plant disease detection are cumbersome and prone to errors that cannot be avoided. Plant disease detection can help prevent crop losses and ensure food security. The system employs state-of-the-art deep learning techniques to automatically detect and classify plant disease in agricultural fields. The proposed system addresses the challenges associated with identifying plant diseases, which often have similar visual characteristics, by using a combination of deep learning and computer vision methods, specifically employing the YOLO (You Only Look Once) and Faster Region-Convolutional Neural Network with a ResNet-152 backbone. The system is trained on a custom dataset where the images were captured in an uncontrolled environment, specifically focusing on two plant leaves, namely Guava and Mango, to achieve high accuracy in identifying diseases in real-world field conditions. The proposed system has the potential to revolutionize plant disease detection and ensure food security.

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Genetic Algorithms and Computational Biology in Crop Improvement

Genetic Algorithms and Computational Biology in Crop Improve...

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Innovative Computing, Intelligent Communication and Smart electrical Systems (ICSES), International Conference on

作者： Pushpam Anand Shreyansh Raj Soumya Ranjan Pradhan Syed Sajid Hussain Shah Shreyash Verma Vinod Kumar Dept. of Computer Science Bachelor of Engineering Chandigarh University Punjab

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Genetic algorithms (GAs) are a subset of machine learning and can potentially solve complex biological challenges in agriculture, including crop improvement. These algorithms help simulate the process of nature selection using techniques such as Mutation, Crossover, and Selection. Integrating genetic algorithms with computational biology can help improve the agriculture field's outcomes such as crop yield, resistance to crop diseases, and crop quality, and crops are more adaptable in the environment. The role of genetic algorithms in agriculture focuses on improving breeding strategies, optimizing agricultural outputs, sustainable farming, and enhancing global food security.

关键词： Adaptation models Crops Food security Agriculture Sustainable development Genetic algorithms Computational biology Immune system Diseases Resilience

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Integrating Tradition and Technology: A Survey of AI Applications in the Design of Temple Architecture

Integrating Tradition and Technology: A Survey of AI Applica...

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Artificial Intelligence, Metaverse and Cybersecurity (ICAMAC), International Conference on

作者： J Meghana Harshavardana R Bellad Diya Pardhi Ishwari Magdum Chaitra Nagaraj dept. Computer Science Engineering PES University Bengaluru India

ISBN: (数字)9798350353488

ISBN: (纸本)9798350353495

This article will delve into the complicated inter-building of artificial intelligence and traditional skill of temple architecture of South India. It meticulously analyzes the historical roots of temple design, explaining in detail the subtle means and methods that have always been used in temple construction. Apart from detailed preparation and skilled craftsmanship, it is the cultural heritage that is demonstrated with these monumental creations. In the sequel, the paper sheds light on how AI technologies are transforming temple architecture through systems analysis and optimization, generative design and other methods. Living examples of Artificial Intelligence work do not only confirm the successful AI implementations but also illustrate the smooth link between tradition and novelty in the modern temple designs.

关键词： Surveys Technological innovation Metaverse Design methodology Cultural differences Artificial intelligence computer security System analysis and design Optimization Guidelines

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A Secure method of transferring confidential documents by leveraging Zero Trust Principles and Blockchain Technology

A Secure method of transferring confidential documents by le...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Hannah Thankam Alex Sarah Alex Swathi C G Chandrashekhar Pomu Chavan Dept. of Computer Science and Engineering PES University Bengaluru India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Ensuring regulatory compliance and maintaining client trust in the legal industry depend on the safe transfer and preservation of sensitive data. Maintaining the safety and security of confidential information is essential in a law business. The suggested concept combines zero-trust concepts with blockchain technology to develop a decentralized application (dApp) that facilitates secure document sharing between clients and law firms. To guarantee the safe transfer of private legal documents, the platform makes use of IPFS for decentralized file storage and MetaMask for authentication. OTP verification, document encryption using cryptographic methods, and MetaMask user authentication are some of its key features. Only authorized users are able to view or manage documents in the DApp thanks to smart contract access management. The requirements for security, immutability, and transparency are maintained by this method.

关键词： Industries Law Smart contracts Government Authentication Decentralized applications Blockchains Zero Trust Safety Peer-to-peer computing

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A Deep Averaged Reinforcement Learning Approach for the Traveling Salesman Problem

A Deep Averaged Reinforcement Learning Approach for the Trav...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Parasteh, Sirvan Khorram, Amin Mouhoub, Malek Sadaoui, Samira University of Regina Dept. of Computer Science Regina Canada University of Regina Dept. of Industrial Systems Engineering Regina Canada

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

This work presents a deep averaged reinforcement-learning approach to learn improvement heuristics for route planning. The proposed method is tested on the Traveling Salesman Problem (TSP). While learning improvement heuristics using machine learning models are prosperous, these methods suffer from low generalization and forgetfulness of the agents during the training process. We have applied the stochastic weight averaging method during the training phase to solve these issues, which smothers the training convergence and prevents the forgetting of optimized learned policies, and consequently provides better results. The agent can learn the optimized policy while holding a moving average of the previously learned policies during the training epochs. In order to assess the performance of our proposed approach, we conducted comparative experiments considering other known methods from the literature. The results demonstrate our proposed method's superiority in training trends and optimization. © 2022 IEEE.

关键词： Reinforcement learning

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Handcrafted Feature Assisted Light-Weight Encoder Decoder Based Classifier for Yoga Posture Recognition

Handcrafted Feature Assisted Light-Weight Encoder Decoder Ba...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Prasiddha Sarma S. Ibotombi Singh Dept. of Computer Science and Engineering Tezpur University Tezpur India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

The trend for the need for at-home workout regimens and the potential for technology to support the trend is rising as remote employment becomes more and more common. One such activity that has benefits like increased flexibility and stress alleviation is yoga. Presently, numerous self-guided yoga courses in the form of pictures and videos are available online. Consequently, there is an urgent requirement for a system that can recognize and evaluate the precision of yoga posture execution in these instructional materials. In the current scenario lightweight prediction models are very much essential because devices like smartphones, edge devices, and embedded systems that have limited processing power or battery life, lightweight models are the best option as they require less resources to train and operate. One particular approach of making the recognition system lightweight is the use of keypoint-based pose/action classifiers. In this paper, a lightweight classifier for yoga postures based on neural network architecture using MoveNet pose estimation technique is proposed. Our model is able to classify the yoga postures by learning the human joints information in a efficient manner.

关键词： Pose estimation Neural networks Predictive models Market research Sensors Artificial intelligence Task analysis

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Lung Cancer Disease Prediction using Hybrid Subtractive Clustering Method

Lung Cancer Disease Prediction using Hybrid Subtractive Clus...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Banibrata Paul Mahmood Hussain Mir Stalin Murugan Asha Kurian Dept. of Computer Science & Engineering Alliance University Bangalore India

As of right now, lung cancer is the primary cause of cancer-related deaths worldwide for both men and women. One possible explanation for lung cancer's main cause is smoking. 86% to 96% of instances of lung cancer are thought to originate in the epithelial cells that line the larger and smaller airways (bronchi and bronchioles), despite the fact that lung cancer can grow in any section of the lung. The primary objective of this work is to identify lung cancer using the ANFIS approach based on subtractive clustering method. Using Kaggle, we have made use of the Lung Cancer dataset. The network is trained using data from 1000 lung cancer patients, ranging in age from 14 to 73. The proposed system uses 11 input attributes to assess the presence and absence of lung cancer during testing, and consistently achieves 100% accuracy for each clustering radii.

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