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Microcontroller-Based Implementation of the DRT for Bioimpedance Spectroscopy

Microcontroller-Based Implementation of the DRT for Bioimped...

引用

ieee SSD international Multi-conference on systems, Signals and Devices

作者： Ayoub Trabelsi Nour Ammar Cherif Ouni Ahmed Yahia Kallel Ahmed Fakhfakh Salwa Sahnoun Nabil Derbel Olfa Kanoun Professorship Measurement and Sensor Technology Chemnitz University of Technology Chemnitz Germany Laboratory of Signals Systems Artificial Intelligence and Networks Digital Research Centre of Sfax National School of Electronics and Telecommunications of Sfax Technopole of Sfax Ons City Tunisia Control and Energy Management Laboratory School of Sfax University of Sfax Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Bio-Impedance Spectroscopy (BIS) is a non-invasive method for characterizing biological systems. Converting impedance spectra into the distribution of relaxation times (DRT) allows the separation of overlapping effects without relying on equivalent circuits as impedance models. This transformation significantly reduces signal processing time from several seconds to milliseconds. Therefore, there is a significant need for embedded solutions that implement DRT in microcontroller-based systems. Following a detailed evaluation of various methodologies, NNLS combined with Tikhonov regularization was selected for its ability to achieve an 83.33% reduction in execution time. This paper presents a novel approach for implementing this optimized DRT extraction in STM32-based embedded systems. The novel implementation achieves processing times of 74 ms for a 101-frequency data set on the STM32 while utilizing only 2.69 kB of RAM. The system successfully extracts DRT peaks with different magnitudes and their relative relaxation times for 51-frequency and 101-frequency datasets. We demonstrate equivalent circuit parameter extraction with relative errors below 3.5%. This work establishes a foundation for portable BIS analysis, with potential applications in areas such as medical diagnostics, food state characterization, and plant monitoring.

关键词： Spectroscopy embedded systems Microcontrollers Signal processing algorithms Random access memory Bioimpedance Biological systems Signal processing Impedance Equivalent circuits

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Design and Analysis of a Smart Irrigation Control System

Design and Analysis of a Smart Irrigation Control System

引用

ieee Students' conference on Electrical, Electronics and Computer Science (SCEECS)

作者： Chakravarthi Jada Nandaluru Varshitha Electrical and Electronics Engineering Rajiv Gandhi University of Knowledge Technologies Nuzvid

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

The development of a smart irrigation system is critical in addressing water conservation and enhancing agri-cultural practices. The design and implementation of a control system aimed at regulating water delivery to plants based on critical factors such as soil moisture, weather conditions and specific plant requirements is presented in this paper. The system assimilates various control methodologies, including PID control, root locus analysis, frequency response analysis, state space representation, and digital control techniques. For modeling, simulation and testing of the system, ensuring a comprehensive evaluation of its performance, Simulink is used. The proposed smart irrigation system demonstrates substantial improvements in water use efficiency and plant health by dynamically adjusting irrigation schedules in response to real-time environmental data. To further promote this technology in sustainable agriculture, future enhancements and real-world applications are discussed.

关键词： Irrigation Soil moisture Moisture Weather forecasting Water conservation Control systems real-time systems Sensor systems Water resources Intelligent sensors

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Research on Automatic Driving System Based on End-to-End and real-time Path Planning

Research on Automatic Driving System Based on End-to-End and...

引用

Power Electronics, Computer applications (ICPECA), ieee international conference on

作者： Dongrun Liu Chunnian Ren College of Information Science and Technology Qingdao University of Science and Technology Qingdao China Qingdao University of Science and Technology Qingdao China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

With the development of autonomous driving technology, visual perception systems face challenges in target detection and localization tasks in complex traffic scenes, deep learning provides new ways to solve this problem, but single-phase and two-phase target detection algorithms need to balance speed and accuracy. In this paper, we study the related work of machine vision in the field of autonomous driving, based on which we propose path planning methods based on multiple neural networks, including CNN, RNN, Transformer and Occupancy Network, etc., and design an integrated framework to make the perception and path planning modules work closely together. The experimental results show that in the end-to-end model performance comparison, the CNN-LSTM-Transformer model converges fast and has the lowest loss and error in the validation set, and has the highest path prediction accuracy and robustness; the evaluation of the real-time path planning algorithm reveals that it has a high real-time performance with an average time delay of only 0.0003 seconds, an average mean square error of 0.0081, an average absolute error of 0.0723, and an average path length of 0.0081, and an average path length of 0.0723. The average mean square error is 0.0081, the average absolute error is 0.0723, and the average path length is 12.3465, so the path quality is good. Future research can further optimize the algorithm to improve the model adaptability and detection efficiency, in order to promote the development of automatic driving technology.

关键词： Deep learning Adaptation models Accuracy Data integration Predictive models Transformers Path planning real-time systems Autonomous vehicles Visual perception

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real-time Monitoring of DC Microgrids of Solar Farms Using an MQTT-Based IIoT Device with Node-RedIntegration

Real-Time Monitoring of DC Microgrids of Solar Farms Using a...

引用

Smart and Sustainable Developments in Electrical Engineering (SSDEE), ieee international conference on

作者： Soumyadip Banerjee Priyanka Kumari Tanmoy Maity Deparment of Electrical Engineering IIT (ISM) Dhanbad Jharkhand India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

This paper presents the design and development of a real-time monitoring system for DC microgrids in solar farms using an MQTT-based IIoT device integrated with Node-RED. The system incorporates voltage and current sensors connected to solar panels, with an ESP32 microcontroller reading data from the sensors to calculate power output. This data is transmitted to a Raspberry Pi server via a WiFi network, utilizing the MQTT protocol for efficient and reliable communication. Node-RED manages and visualizes the data flow, providing a user-friendly interface through its dashboard for monitoring real-time voltage, current, and power readings. This integration of IIoT devices with Node-RED streamlines data collection, processing, and visualization, enhancing solar farms' overall monitoring and control. The proposed system's scalability allows for the seamless addition of new sensors and microgrids without substantial modifications, addressing the need for flexible expansion in large-scale solar farms. Detailed flow diagrams and performance evaluations are included to demonstrate the system's effectiveness and reliability in real-time applications. Compared to existing monitoring solutions, this system offers lower costs, higher scalability, and enhanced real-time data processing capabilities, making it suitable for deployment in various renewable energy environments.

关键词： Renewable energy sources Protocols Scalability Solar farms Microgrids real-time systems Sensor systems Sensors Monitoring Industrial Internet of Things

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Electrowetting-on-Dielectric-Based Microbubble Patterning for Spatial Ultrasound Modulation

Electrowetting-on-Dielectric-Based Microbubble Patterning fo...

引用

ieee international conference on Micro Electro Mechanical systems

作者： Subeen Kim Sangho Bang Yehhyun Jo Hyunjoo J. Lee School of Electrical Engineering Korea Advanced Institute of Science and Technology Korea

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

We report a dynamic spatial ultrasound modulator (SUM) based on an electrowetting-on-dielectric (EWOD) technique that enables binary modulation of ultrasound transmission. By employing spatial air-bubble manipulation using an EWOD method, the proposed system exhibits faster performance than the current state-of-the-art single-element dynamic SUMs, with a response time of tens of milliseconds. In addition, the proposed system is significantly smaller than conventional phased-array systems, with a pixel size of a few hundred microns. The application of a double-layer Parylene-C (k = 3.14) and sputtered Al 2 O 3 (k = 7.70) using standard microfabrication processes enables low leakage current in a wet environment, which is 10 times lower compared to a single-layer Parylene-C method. Our EWOD-based SUM is capable of real-time acoustic modulation in a miniaturized package, with high design flexibility and compatibility with existing single-element ultrasound transducers. This will allow for expansive ultrasound applications such as acoustic tweezers, cell assembly, and focused ultrasound stimulation.

关键词： Micrometers Ultrasonic imaging Transducers Amplitude modulation Acoustics real-time systems Spatiotemporal phenomena time factors Leakage currents Standards

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Target Classification in Radar Using Deep Learning

Target Classification in Radar Using Deep Learning

引用

Smart and Sustainable Developments in Electrical Engineering (SSDEE), ieee international conference on

作者： Suchitra Sahu Perumulla Abhiram P M Menghal Department of Electrical and Electronics Engineering Pilani - Hyderabad Campus Birla Institute of Technology and Science Hyderabad India Faculty of Electronics Military College of Electronics and Mechanical Engineering Secunderabad Hyderabad Telangana India

ISBN: (数字)9798331542108

ISBN: (纸本)9798331542115

In radar systems, precise target classification is essential for effective situational awareness. We developed a Convolutional Neural Network (CNN)-based approach to classify radar targets using their signal characteristics automatically. Compared to traditional methods, our solution demonstrates higher accuracy, robustness, and computational efficiency while addressing challenges like data labelling, interpretability, and signal variability. The results highlight the potential of deep learning to transform radar systems, enhancing applications in military surveillance and autonomous navigation. This work marks a significant step toward seamlessly integrating deep learning with radar technologies.

关键词： Deep learning Accuracy Surveillance Transforms Feature extraction Robustness real-time systems Data models Radar applications Convolutional neural networks

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Multichannel Object Detection with Event Camera

Multichannel Object Detection with Event Camera

引用

international Image Processing, applications and systems conference (IPAS)

作者： Rafael Iliasov Alessandro Golkar Chair of Spacecraft Systems Technical University of Munich Munich Germany

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

object detection based on event vision has been a dynamically growing field in computer vision for the last 16 years. In this work, we create multiple channels from a single event camera and propose an event fusion method (EFM) to enhance object detection in event-based vision systems. Each channel uses a different accumulation buffer to collect events from the event camera. We implement YOLOv7 for object detection, followed by a fusion algorithm. Our multichannel approach outperforms single-channel-based object detection by 0.7% in mean Average Precision (mAP) for detection overlapping ground truth with IOU = 0.5.

关键词： Computer vision Event detection Machine vision AI accelerators Object detection Cameras Feature extraction real-time systems

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Advanced Automated Detection of Heart Diseases Using TQWT and Dual-Task Deep Learning Model

Advanced Automated Detection of Heart Diseases Using TQWT an...

引用

Industrial Electronics: Developments & applications (ICIDeA), ieee international conference on-

作者： Soubhagya Ranjan Prusty Sudhakar Sahu Sarita Nanda Satyaranjan Jena Chitralekha Jena Pradeep Kumar Sahu School of Electrical Engineering KIIT Deemed to be University Bhubaneswar India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

This study introduces a cutting-edge automated system for detecting and classifying 14 types of heart diseases, including arrhythmias and myocardial infarction, using ECG signals. The methodology leverages the Tunable-Q Wavelet Transform (TQWT) for precise feature extraction and Morphological Component Analysis (MCA) with SALSA for effective signal decomposition. A novel dual-task deep learning model, enhanced by a dynamic non-stationary redundancy factor, r(t,f)r(t, f)r(t,f), achieves exceptional accuracy (99.7%) while reducing computational cost by 25% and accelerating convergence by 30%. This approach offers a transformative solution for real-time heart disease diagnosis, advancing healthcare efficiency and patient outcomes.

关键词： Heart Deep learning Wavelet transforms Accuracy Computational modeling Redundancy Electrocardiography Wavelet analysis real-time systems Diseases

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Abnormal Network Traffic Detection Based on Multi-Pooling Cascaded MPCNN

Abnormal Network Traffic Detection Based on Multi-Pooling Ca...

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Power Electronics, Computer applications (ICPECA), ieee international conference on

作者： Kefeng He Yarong Shi China Engineering Group Gansu Electric Power Design Institute Co. Ltd. Lanzhou China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

Traditional network traffic detection methods based on machine learning have been widely used, but the accuracy of identifying and detecting abnormal traffic is not high. Compared with machine learning methods, deep learning has made significant progress in fields such as text processing, video generation, and other fields, bringing new ideas to abnormal traffic detection. This paper adopts an abnormal traffic classification method based on multi-pooling cascaded convolutional neural network (MPCNN). The accuracy of extensive experiments is 0.9914, the precision is 0.9984, and the F1 score is 0.9962. These experiments achieved the company's goal of accuracy greater than 0.98. It is used for real-time monitoring of daily network traffic to recognize abnormal traffic virtually, alert topic timely, and prevent potential network threats.

关键词： Accuracy Telecommunication traffic Companies Network security Feature extraction Data models real-time systems Power electronics Protection Text processing

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A Motion Injury Detection and Prevention System Based on Image Recognition and Deep Learning

A Motion Injury Detection and Prevention System Based on Ima...

引用

Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Kang Shao Limin Zhang Yunlong Ma Weifang Engineering Vocational College Weifang Shandong China

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

With the popularization of sports and fitness activities, how to effectively monitor and prevent sports injuries has become an important challenge. This article proposes a sports injury detection and prevention system. Firstly, this system collects real-time motion image data of athletes through high-definition cameras and uses YOLOv5 (You Only Look Once v5) for efficient object detection and action recognition. Then, combining long short-term memory networks (LSTM) to analyze time series data during the motion process. Finally, multimodal data fusion technology was introduced to comprehensively analyze the data. The experimental results show that the system achieves an accuracy of 88% when processing 5000 samples. In terms of real-time performance, the system has a response time of 0.60 seconds under 5000 samples. In the above data conclusions, the proposed system outperforms traditional models in terms of accuracy, real-time performance, and computational resource consumption in sports injury detection, demonstrating its effectiveness and feasibility in practical applications.

关键词： YOLO Accuracy Image recognition Prevention and mitigation Computational modeling real-time systems Integrated circuit modeling Injuries Long short term memory Sports

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