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Distributed Model Architecture for Cuff-Less Blood Pressure measurement System

SN Computer Science 2022年第6期3卷 503页

作者： Dave, Tejal Joshi, Maulin Pandya, Utpal Department of Instrumentation and Control Sarvajanik College of Engineering and Technology Sarvajanik University Surat India Department of Electronics and Communication Sarvajanik College of Engineering and Technology Sarvajanik University Surat India

Continuous monitoring of blood pressure (BP) can control hypertension and cardiac diseases prevailing in many people due to luxurious lifestyle. Blood pressure measurement using cuff-based technique provides intermittent measurement and inconvenient for long-term monitoring. This work is focused on estimation of continuous blood pressure from electrocardiogram (ECG) and photoplethysmogram (PPG). The distributed model architecture is proposed to estimate blood pressure from time domain features of ECG and PPG to overcome problems with single model-based approach. The proposed work uses support vector regression and back propagation neural network for training distributed models. During testing phase, extracted features are first passed through trained pre-classifier to divert the signal to one of the trained distributed models that carries out blood pressure estimation. The proposed work is tested on 193 record segments of Multi-parameter Intelligent Monitoring in Intensive Care (MIMIC) database and also on wireless data captured from 87 subjects using prototype device. According to the British Hypertension Society (BHS) standard, the proposed method achieves grade A in the estimation of systolic and diastolic pressure for wireless data. For MIMIC database, proposed model achieves grade B for systolic, whereas grade A for diastolic pressure estimation. The values of mean error and standard deviation by proposed method are within limits of Association for the Advancement of Medical Instrumentation (AAMI) standards for wireless as well as MIMIC data. The results show improvement in the accuracy of blood pressure estimation compared to similar work and indicate high acceptability according to BHS and AAMI standards. The proposed work is helpful in wireless monitoring of patients to track the physiological conditions without interrupting their routine activities. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cuff-less blood pressure monitoring Electrocardiogram (ECG) Photoplehysmogram (PPG) Support vector machine classifier Support vector regression (SVR)

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Reconfigurable antenna beam using U-Shaped slot patch for wireless networks

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Materials Today: Proceedings 2022年 58卷 547-551页

作者： Suriyan, Kannadhasan Ramalingam, Nagarajan Venusamy, Kanagaraj Department of Electronics and Communication Engineering Cheran College of Engineering Tamilnadu India Department of Electrical and Electronics Engineering Gnanamani College of Technology Tamilnadu India Control Systems Instructor Department of Engineering University of Technology and Applied Sciences-AI Mussanah AI Muladdha Oman

A compact U-Shaped antenna was proposed in this paper. The antenna is designed with dimension of 30*44*1.8 mm. The proposed antenna is used to improvement the ground stub. The antenna works in the various band of Wi-Fi, Wi-Max, WLAN, Wireless communication and Satellite communication applications. The proposed structure having FR4 dielectric substrate with 4.2 relative permittivity and the loss tangent of 0.0025. The gain of the proposed antenna is 5.82 dB and 6.82 dB in the lower band and upper band of frequencies. The U-Shaped structure antenna operating at various bands 1.8 GHz to 2.5 GHz and 3.5 GHz to 4.5 GHz for wireless applications. The VSWR of the proposed structure is 1.4 and 1.8 respectively. The bandwidth is used to operate the U-Shaped structure is 900/1800 MHz. The reflection coefficient of the proposed structure is observed for the range of −7.08 dB and −14.00 dB. The performance parameters of the proposed structure such as good return loss, high gain, high bandwidth and good radiation pattern in nature. © 2022

关键词： Reflection

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Optimization Research on Pedestrian Multi Objects Tracking Model Based on TBD Strategy

Optimization Research on Pedestrian Multi Objects Tracking M...

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2023 International Conference on Algorithm, Imaging Processing, and Machine Vision, AIPMV 2023

作者： Wang, Shi Liu, XiangJu Liu, XinShu Chen, JiaHui Wang, XiaoHong Qilu Institute of Technology College of Computer and Communication Engineering Shandong Jinan250200 China College of Artificial Intelligence and Software Liaoning Petrochemical University Liaoning Fushun113001 China College of Information and Control Engineering Liaoning Petrochemical University Liaoning Fushun113001 China

ISBN: (纸本)9781510672444

The main task of pedestrian multi objects tracking technology is to continuously track multiple pedestrian objects simultaneously in video sequences and maintain their unique ID numbers. However, current pedestrian multi objects tracking models still have many problems, such as false detection, missed detection, and frequent ID number switching when pedestrians are obstructed or have overly similar appearances, ultimately leading to tracking failure. Therefore, this paper proposes a pedestrian multi objects tracking model based on TBD strategy. It mainly consists of two parts: pedestrian detector and pedestrian tracker. In terms of pedestrian detectors, this paper uses ES-YOLO pedestrian detectors. In terms of pedestrian trackers, this paper draws on the Omni-scale feature learning module in OSNet to redesign the StrongSORT pedestrian appearance feature extraction network, and ultimately obtains the StrongSORT pedestrian tracker based on omni-scale feature fusion, further enhancing its pedestrian feature extraction ability. In terms of experimental results. The experimental results of the pedestrian multi objects tracking model based on the TBD strategy in this paper on the MOT16 dataset show that the proposed pedestrian multi-objective tracking model can effectively improve the accuracy of pedestrian multi objects tracking and reduce the problem of frequent pedestrian ID number switching. © 2024 SPIE.

关键词： Feature extraction

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A Review of Machine Tool Motorized Spindle Variable Pressure Preload technology Research

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Recent Patents on engineering 2025年第5期19卷

作者： Dai, Ye Deng, Hao Pan, Gaofeng Chen, Xinda Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education School of Mechanical and Power Engineering Harbin University of Science and Technology Harbin150080 China School of College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China

High-precision spindles are a vital study topic because of the growing demand for high-speed, high-precision, reliable, and long-lasting machine tools. Bearing preload is the primary means of improving the accuracy of the spindle rotor system. This patent study aims to summarize the research results, systematically analyze and evaluate the machine tool motorized spindle variable pressure preload technology, and provide a scientific basis for the practical application of the motor spindle variable pressure preload technology. This study is an in-depth analysis of the basic principles of the electric spindle preloading technology, the study of its influence mechanism on the thermal deformation, stiffness, slewing error, and life of the spindle system, and a discussion of the current shortcomings in the field of spindle preloading to provide a reference for the subsequent optimization. In the spindle system, the performance of rolling bearings and the design of bearing preload have an important impact on the spindle's accuracy and stability. The traditional preload method has yet to meet the requirements of modern high-speed machine tools. Real-time controllable preload has become the new direction of development, but the current preload technology still has certain limitations and needs to be further improved. In the future, the spindle bearing variable pressure preload technology will be more efficient and intelligent. © 2025 Bentham Science Publishers.

关键词： Machine tools

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3D Imaging Sensing and Prediction technology of Large Component Surface with Multi-source Sensing 6

3D Imaging Sensing and Prediction Technology of Large Compon...

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6th Conference on Frontiers in Optical Imaging and technology: Applications of Imaging Technologies

作者： Sun, Jing Li, Lijuan Lin, Xuezhu Guo, Lili Liu, Yue Yan, Dongming Key Laboratory of Optoelectronic Measurement and Control and Optical Information Transmission Technology Ministry of Education College of Optoelectronic Engineering Changchun University of Science and Technology Changchun130022 China Zhongshan Research Institute of Changchun University of Science and Technology Guangdong Zhongshan528400 China

ISBN: (数字)9781510679757

ISBN: (纸本)9781510679740

In order to solve the problem that the surface deformation of large components cannot be photographed in real time due to the influence of assembly stress during assembly, a multi-source sensor surface deformation sensing and 3D imaging prediction technology for large components is proposed, which combined laser tracking, structured light detection and optical fiber sensing monitoring. Firstly, the multi-source sensor system is analyzed, and the multi-source sensor fusion sensing field is constructed. Secondly, the unified model of heterogeneous data is established to complete the accurate conversion of optical fiber monitoring wavelength to space point coordinates. Thirdly, multi-source data fusion is realized based on Gaussian process fusion algorithm to complete 3D imaging prediction of component surface deformation. Finally, a large skin component is taken as an example to simulate the assembly deformation experiment. The results show that the root-mean-square error is less than 0.1mm compared with the actual results. Based on the measured data of structured light scanning and multi-source sensing technology, a real-time sensing and prediction method for surface deformation imaging of large components is proposed. This method not only simplifies the scanning mode of structured light, but also provides a new idea for dynamic model building. © 2024 SPIE.

关键词： Forecasting

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Reverse Time Migration of Ultra-Wideband Ground-Penetrating Radar Based on Multiple Time-Gating Algorithm

Reverse Time Migration of Ultra-Wideband Ground-Penetrating ...

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2024 International Applied Computational Electromagnetics Society Symposium, ACES-China 2024

作者： Wu, Junjie Li, Haixiao Zhou, Yuanguo Cao, Le Wang, Yan Xi'an University of Science and Technology College of Communication and Information Engineering Xi'an China Xi'an University of Science and Technology College of Electrical and Control Engineering Xi'an China Graduate School of Chinese Aeronautical Establishment Yangzhou China Aviation Key Laboratory of Science and Technology on Electromagnetic Environment Effects Shenyang China National Key Laboratory of Electromagnetic Information Control and Effects Shenyang China

ISBN: (纸本)9798350355581

To address the issue of clutter interference in ground-penetrating radar (GPR) imaging using a VNA (Vector Network Analyzer), this paper improves the traditional reverse time migration (RTM) technique for ultra-wideband GPR imaging. The improved method retains the effective signals in the detection wavefield as the valid imaging area and suppresses signals in non-valid areas. Due to variations in the distance between the transmitting and receiving antennas and the number of anomalies in the experiment, the traditional time window cross-correlation is not ideal for this scenario. The improved method automatically applies reasonable windowing to each data channel, avoiding the simultaneous enhancement of clutter interference and effective signals through direct stacking, and preventing the filtering out of effective signals. Experimental results demonstrate that this method, when applied to real measured data and compared with the traditional cross-correlation RTM method, shows advantages in more accurately locating anomalous areas and suppressing clutter interference. This validates that the Multiple Time-Gating Reverse Time Migration (MTG-RTM) method offers better localization and higher imaging accuracy in practical engineering applications. © 2024 IEEE.

关键词： Receiving antennas

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A Novel All-phase Fast Fourier Transform-based Parameters Identification of High Frequency Oscillations 60

A Novel All-phase Fast Fourier Transform-based Parameters Id...

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60th IEEE/IAS Industrial and Commercial Power Systems Technical Conference, I and CPS 2024

作者： Sun, Feiyu Cai, Dongsheng Zhang, Zhenyuan Huang, Qi University of Electronic Science and Technology of China Sichuan Provincial Key Lab of Power System Wide-Area Measurement and Control School of Mechanical and Electrical Engineering Chengdu611731 China Chengdu University of Technology College of Nuclear Technology and Automation Engineering 610059 China Southwest University of Science and Technology School of Information Engineering Sichuan Mianyang China

ISBN: (纸本)9798350345308

Wind and solar energy inverter-based resources (IBRs) has been employed extensively with the aim of carbon neutrality. Power systems also make extensive use of power electronic converters, leading to interactions between IBRs and with the grid, causing high frequency oscillation (HFO) problems. It damages equipment and jeopardizes the security of the system. To identify HFO components above 100 Hz, this research use Phasor measurement Unit (PMU) instantaneous value data to determine the HFO associated characteristics. To reduce the influence of spectral leakage and the fence effect, as well as to improve the identification accuracy of the related parameters, this paper adopts the RV self-convolutional window based Discrete Fourier Transform (DFT) and the Hanning double-window based all-phase Fast Fourier Transform (apFFT) hybrid algorithm to identify the HFO modal parameters. We take into account the identification accuracy in complex cases, such as non-nominal frequencies, with noise effects and multiple HFO modes. The simulation results show that the proposed method has high identification bandwidth, accuracy and strong flexibility. © 2024 IEEE.

关键词： Discrete Fourier transforms

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Combined Classification of Hyperspectral and LiDAR Data based on Dual-Channel Cross-Transformer 23

Combined Classification of Hyperspectral and LiDAR Data base...

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Proceedings of the 2023 5th International Conference on Video, Signal and Image Processing

作者： Binbin Zhou Qingyan Wang Junping Zhang Yujing Wang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology China School of Electronics and Information Engineering Harbin Institute of Technology China

ISBN: (纸本)9798400709272

In the face of complex scenes, single-modal dominant classification tasks encounter limitations in performance due to insufficient information. On the other hand, joint classification of multimodal remote sensing data faces challenges such as data sample differences and lack of correlation in physical features between modalities, which can impact classification performance. To fully integrate the heterogeneous information of multimodal data and improve classification performance, we propose a dual-channel cross-transformer feature fusion extraction network. The framework leverages self-attention mechanisms to aggregate features within each modality, and the feature fusion module based on cross-modal attention fully considers the complementary information between modalities. Classification tasks are performed using the fused spatial-spectral features obtained from the joint representation of modalities. Extensive experiments conducted on the Houston and MUUFL datasets demonstrate the effectiveness of the proposed model compared to existing methods.

关键词： Hyperspectral image LiDAR data cross modality feature fusion transformer

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FPGA-Oriented Target Detection Accelerator Design

FPGA-Oriented Target Detection Accelerator Design

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Frontiers technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Weizhe Huang School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin China

Target detection is widely applied in fields such as face recognition, autonomous driving, and industrial automation. However, when deploying target detection models based on convolutional neural networks on resource-constrained embedded platforms, issues like high computational complexity, long execution times, and significant on-chip memory usage arise. To address these challenges, this paper presents a target detection accelerator design based on Field Programmable Gate Array (FPGA). Firstly, the computational workload of convolutional neural networks is reduced by merging convolutional layers with batch normalization layers. Secondly, to mitigate on-chip storage space concerns, on-chip data fixed-point quantization is employed to decrease on-chip memory consumption. Subsequently, to tackle the issue of long execution times, a convolution calculation module based on the Winograd algorithm is designed, which reduces computation latency. Finally, a dual-buffer pipeline architecture is devised to optimize memory bandwidth. Experimental results demonstrate that the proposed solution accelerates YOLOv4-tiny on the ZYNQ-7020, achieving a performance of 21.35 GOP/s. The energy efficiency is 32.56 times higher than CPU and 9.76 times higher than GPU. Additionally, the convolution calculation module in this paper reduces inference time by 15% compared to the sliding window convolution calculation method.

关键词：

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Multi-Object Detection and Classification in Construction Sites Based on YOLOv5 23

Multi-Object Detection and Classification in Construction Si...

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Proceedings of the 2023 5th International Conference on Video, Signal and Image Processing

作者： Xintao Liang Tianqi Jiang Qiang Fu Qingyan Wang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology China School of Electronics and Information Engineering Harbin Institute of Technology China

ISBN: (纸本)9798400709272

A thorough analysis of the framework structure of the YOLO algorithm is conducted, and based on the YOLOv5 algorithm, rapid detection and classification of extracted features are implemented. Addressing the issue of multi-object detection and classification in engineering sites, this study utilizes the YOLOv5 algorithm for object detection in engineering scenarios, constructs the ResNet50 network, and achieves training and recognition of categories for engineering vehicles, whether washed or unwashed. Additionally, the YOLOv5 algorithm is employed to detect whether construction personnel are wearing safety helmets and reflective clothing, enabling fast and accurate entity detection and classification at construction sites.

关键词： Deep Learning Object Detection ResNet50 YOLOv5

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