Monitoring Requirements Research Articles

Hyperspectral multivariate linear prediction model of tobacco (nicotiana tabacum L.) Leaf nitrogen content

In order to accurately and effectively obtain the nitrogen content of tobacco leaves during the whole growth period, in the present study the field canopy spectrum of the three critical periods of tobacco rosette stage, vigorous growth stage and topping stage were used. The correlation analysis of field canopy spectrum, first derivative spectrum, hyperspectral parameters and vegetation index with the nitrogen content of tobacco leaves was carried out one by one, and the prediction model was established by multiple linear regression using the variables with the best correlation coefficient. Results showed that the first derivative spectrum, EVI II and green peak position had strong correlation, which is suitable for introducing multivariate equations as independent variables. Finally, the modeling determination coefficient (R2) was 0.66, RMSE was 0.40, and MAPE was 11%. The validation results showed that R2 was 0.73, RMSE was 0.38, and MAPE was 8.33%, which proved that this model could accurately predict the nitrogen content of tobacco leaves and could meet the requirements of large-scale statistical monitoring of tobacco quality indicators in the field. Bangladesh J. Bot. 52(2): 575-584, 2023 (June) Special

UAV Architecture for Patrolling Air in Geotourism Zone at Disaster Risk with the Characteristics of GPS Signal Limit

The development of geotourism is a trend in the field of tourism that is always being developed. The exoticism of natural tours in the environment of geothermal and volcanology activities will be proportioned to the potential risks that appear. The records of tracking from several countries show the fatal conditions from the effects of carbon dioxide emissions such as H2S, SO2, and CO that are emitted from unexpected geophysical activity, odorless so that they are not detected properly in geotourism zones that cause casualties of inhabitant and tourist. The aim of this paper is to provide a reference of the framework for the drone explore architecture for the requirement of monitoring in the environmental conditions of the limited GPS signal. This study implemented the Research & Development (R&D) method by using the Zachman framework. The study found that the development of the Early Warning System (EWS) as a part of mitigation using robotic technology such as the UAV drone becomes the solution requirement of the problem because the drone is capable of carrying sensors that detect harmful gases with a radius that is relatively low. The drone sensor will process the gas data with the permitted threshold gas ratio and send the collected data to the administration system. This case becomes a part of the disaster mitigation process in the geotourism field.

A high-toughness, tailorable, wearable multifunctional sensor based on multisynergistic fabric-hydrogel constructed via dual-function boric acid bridge

Flexible wearable electronic devices have attracted immense interest in human rehabilitation and motion detection to their intrinsic characteristics. However, with the sticky feeling and low strength of universal hydrogels, the flexible sensor made it difficult to satisfy the requirements of comfort for the human body. Here is a novel alcohol polyvinyl-boric acid-polyaniline (PVA-BA-PANI/PBA) fabric-hydrogel multi-functional sensor inspired by the microstructure of natural tendon based on cotton fabric fibers as muscle fibers and PVA hydrogel as extracellular matrix. The dual-function “bridge” molecule BA played the role of hydrogel cross-linker and provided a stable protonated environment for PANI. The three-dimensional pore structure of hydrogels was modulated by adjusting the polymerization time and boric acid concentration. The tailorable fabric-hydrogel composite PBA showed excellent electrical conductivity (0.52 S/m), good mechanical properties (tensile strength of 6.42 MPa and toughness of 6.16 MJ m−3), high-temperature resistance coefficient (TCR of −1588.011 and −4.252% °C−1 in the temperature range of −20 − 0 °C and 0 − 50 °C, respectively) and repeatable stability. Synchronously, the resultant PBA sensors revealed superb thermosensitive performances for real-time and continuous temperature monitoring requirements, providing a promising route to develop wearable artificial intelligence in human rehabilitation and motion detection.

Transforming marine monitoring: Using eDNA metabarcoding to improve the monitoring of the Mediterranean Marine Protected Areas network

The creation of Marine Protected Areas (MPAs) is central to the European Union’s strategy for protecting and restoring marine environments. However, current research indicates that monitoring of MPAs is insufficient to ensure their protective role. In response, the European Commission encourages the development and adoption of more effective monitoring tools for MPAs. Environmental DNA (eDNA) methods may serve this function, but their use in MPA monitoring and management remains relatively unexplored. The current study addresses this gap by examining the potential for eDNA to enhance monitoring within Mediterranean MPAs, where monitoring challenges are especially severe. We addressed the gap by synthesizing the results of two analyses. First, a systematic literature review examined existing applications of eDNA metabarcoding to monitor marine habitats. From the experiences documented in the reviewed literature, we applied a SWOT analysis (a framework used to identify and analyze strengths, weaknesses, opportunities and threats) to assess the tool’s appropriateness for EU monitoring requirements. Second, we probed the tool’s suitability for Mediterranean MPAs with a questionnaire on monitoring and other challenges completed by the managers of 29 MPAs in the region. The findings of our SWOT analysis demonstrated that when compared to conventional monitoring methods, eDNA frequently outperformed them or acted as a crucial supplement. We also found that eDNA is suitable for most criteria set forth in EU requirements. Our questionnaire revealed monitoring strategy limitations that eDNA approaches may alleviate. Still, eDNA approaches do have drawbacks which may jeopardize its accuracy and sufficiency. Incorporating eDNA monitoring methods has great potential to enhance protection efforts in MPAs in the Mediterranean and elsewhere. Their successful adoption will require improved coordination among stakeholders and current, comprehensive, and customized recommendations for MPA managers. This will improve the overall functioning of MPA networks and thus support the role of MPAs in safeguarding vulnerable marine environments.

Dynamic evaluation method for Time-variant reliability of structural safety of Concrete-faced rockfill dam

The evaluation of the structural safety risk for concrete-faced rockfill dams (CFRDs) is susceptible to the time-varying parameters and the environments of topography, geology, and operations. Traditional evaluation methods, due to their involvement in numerical simulation, cannot meet the current requirements for intelligent monitoring in terms of timeliness and practicality. Therefore, a dynamic evaluation method of reliability in CFRDs is proposed in this paper. Due to the nonlinear mapping relationship between the time-variant reliability and the monitoring characterizations of CFRDs, the long and short-term memory neural network (LSTM) and support vector machine (SVM) models are introduced to construct the potential functional relationships between the reliability sequences and the monitoring characterizations, and the matching between the accuracy of the two models and the frequency of monitoring characterizations has been fully studied, forming an adaptive evaluation model of safety risk for the CFRDs according to the different monitoring frequencies of different monitoring items. The application of Sanbanxi CFRD shows an average relative error of less than 1% and 5% for dam slope stability and slab cracking simulations, respectively. These results demonstrate that the LSTM and SVM models with adaptive monitoring frequency exhibit high fitting and prediction accuracy and applicability, and possess theoretical and engineering application value.

Exploring the usage intention of AI-powered devices in smart homes among millennials and zillennials: the moderating role of trust

PurposeThis study aims to investigate how artificial intelligence (AI)-powered smart home devices affect young consumers' requirements for convenience, support, security and monitoring, as well as their ability to advance environmental sustainability. This study also examines the variables that impact users' motivation to use AI-powered smart home devices, such as perceived value, ease of use, social presence, identity, technology security and the moderating impact of trust.Design/methodology/approachThe responses from residents of Sarawak, Malaysia, were collected through online questionnaires. This study aimed to examine the perceptions of millennials and zillennials towards their trust and adoption of AI-powered devices. This study used a quantitative approach, and the relationships among the study constructs were analysed using partial least squares - structural equation modelling.FindingsThe present study found that perceived usefulness, ease of use and social presence were the main motivators among actual and potential users of smart home devices, especially in determining their intentions to use and actual usage. Additionally, there was a moderating effect of trust on the relationship between perceived ease of use, social presence, social identity and intention to use AI-powered devices in smart homes.Originality/valueTo the best of the authors’ knowledge, this is one of the first studies to examine the factors influencing smart technology adoption. This study provided meaningful insights on the development of strategies for the key stakeholders to enhance the adoption and usage of AI-powered smart home devices in Sarawak, one of the promising Borneo states. Additionally, this study contributed to the growing body of knowledge on the associations between technology acceptance model dimensions, intention and actual usage of smart technology, with the moderating impact of trust.

Real-time reading system for pointer meter based on YolactEdge

Despite the extensive deployment of digital instruments in modern times, their stability is challenging to maintain in adverse environmental conditions such as extreme temperatures, pressure, or powerful electromagnetic radiation. Analog meters, owing to their mechanical resilience and electromagnetic impedance, persistently find usage across nuclear power plants, petroleum, and chemical industries. However, under these harsh conditions, manual reading of the instruments may prove to be difficult and dangerous while failing to meet the requirements of real-time monitoring. In recent years, several machine vision-based meter reading systems have been proposed, however, achieving high accuracy through camera-based methods under varying angles and lighting conditions poses significant challenges. Cloud deployment may compromise plant privacy, while edge computing faces limitations in real-time meter reading due to limited computing power. To address these issues, we propose a real-time reading system based on the YolactEdge instance segmentation framework for single-point analog meters. Our system is more accurate than previous studies and is implemented and deployed on the Jetson Xavier NX edge computing device. Our performance evaluation shows that our model outperforms other baselines, with low reference values and relative errors of 0.0237% and 0.0300%, respectively, and an average inference speed of 10.26 FPS with INIT 8 linear acceleration on Nvidia Jetson NX.

SSSH: Responsive soothing bassinet feasibility study for infants with congenital heart disease after cardiac surgery

PurposeFor infants with congenital heart disease (CHD) requiring surgery, prolonged hospital stays, intermittent caregiver visitation, and constrained unit staffing ratios present barriers to adequately address post-operative stressors and associated need to retain cognitive and physiological reserves. Similar patients requiring high-engagement interventions, such as hospitalized infants with neonatal abstinence syndrome, have found success in utilizing responsive bassinets to soothe infants and save floor nurses' time. However, it remains unclear if such technology can be leveraged in the CHD population given their complex hemodynamics, feeding intolerance, and monitoring requirements. MethodsThis multidisciplinary feasibility study evaluated responsive bassinet use in a cohort of infants with CHD <6 months of age in a medical-surgical unit at a midwestern children's hospital. Specifically assessing 1) implementation requirements, challenges, and potential of utilizing the device, together with 2) ability to perform bedside monitoring (monitoring) and 3) measuring physiologic trends during use. ResultsBetween 11/2020–1/2022, nine infants utilized a responsive bassinet over 599 h (mean 13, range 4–26 days per infant). No increase in monitoring alarms and accurate vital signs monitoring during bassinet activity were noted with appropriate physiologic responses for infants with single ventricle and biventricular surgeries. ConclusionsFeasibility of introducing new technology into care, and successful use of its functionality for soothing was found to be plausible for infants with CHD. Practice implicationsAfter cardiac surgery, infants with CHD have need for interventions to reduce stress. Use of a soothing bassinet has the potential to aid in doing so without interference with monitoring requirements.

Real-Time Fire Detection Method Based on Computer Vision for Electric Vehicle Charging Safety Monitoring

In the process of charging and using electric vehicles, lithium battery may cause hazards such as fire or even explosion due to thermal runaway. Therefore, a target detection model based on the improved YOLOv5 (You Only Look Once) algorithm is proposed for the features generated by lithium battery combustion, using the K-means algorithm to cluster and analyse the target locations within the dataset, while adjusting the residual structure and the number of convolutional kernels in the network and embedding a convolutional block attention module (CBAM) to improve the detection accuracy without affecting the detection speed. The experimental results show that the improved algorithm has an overall mAP evaluation index of 94.09%, an average F1 value of 90.00%, and a real-time detection FPS (frames per second) of 42.09, which can meet certain real-time monitoring requirements and can be deployed in various electric vehicle charging stations and production platforms for safety detection and will provide a guarantee for the safe production and development of electric vehicles in the future.

Use of digital technology to improve the way asphalt roads are constructed

Digital technology has the potential to improve the way asphalt roads are constructed and maintained. The application of technologies, such as remote monitoring and telemetry, are being used to improve the quality of asphalt materials during the construction phase. Based on real-time data, the movement and arrival of delivery vehicles can be monitored and material temperatures can be recorded at each stage of the paving process. Similarly, roller compactor passes can be mapped to show the number of passes, compaction temperature and area of mat covered. Importantly, the technology has the capacity to create a safer working environment for road workers. This paper describes a study that builds on previous research, with the aim of developing notes for guidance and compliance requirements for electronic data monitoring. A series of criteria are proposed to approve the level of compaction based on electronic data. The latter includes the adoption of a similar approach to that being used in the United States, although certain aspects have been adapted for the type of materials and equipment available in Scotland. It is intended that the compliance requirements will be trialled on forthcoming paving schemes to determine their effectiveness and whether further amendments are required.

Fluence requirements in existing UV disinfection facilities to comply with EU validation performance targets for reclaimed water: a case study.

Since 2020, there is a new European Regulation (EU, 2020/741) on minimum requirements for water reuse, where routine and validation monitoring requirements (log reductions of indicator microorganisms and reference pathogens) have been established. Many reclamation facilities that are already in operation might have difficulties to comply with these performance targets. Existing disinfection systems must be expanded and upgraded. In the case of UV disinfection systems, fluence requirements must be determined to properly design with a focus on the safety and economic-environmental viability of reclaimed water. This study can be used as a reference to develop fluence-response curves for Clostridium perfringens spores, Escherichia coli, and total and F-specific coliphages, indicator microorganisms referred to in the new European Regulation. Eight UV-LED collimated beam tests were performed. Samples were obtained from filtered effluent of secondary treatment from two wastewater treatment plants (WWTPs) which ranged between 30 and 54%. Results showed UV sensitivity of 33.46 mJ/cm2 log I for C. perfringens spores and 2.86 mJ/cm2 log I for E. coli, both from environmental origin. Coliphages were inactivated below the limit of quantitation. The non-dominance of MS2 phages in environmental F-specific coliphages was observed.

3D visualization technology for rubber tree forests based on a terrestrial photogrammetry system

IntroductionRubber trees are an important cash crop in Hainan Province; thus, monitoring sample plots of these trees provides important data for determining growth conditions. However, existing monitoring technology and rubber forest sample plot analysis methods are relatively simple and present widespread issues, such as limited monitoring equipment, transportation difficulties, and relatively poor three-dimensional visualization effects in complex environments. These limitations have complicated the development of rubber forest sample plot monitoring.MethodThis study developed a terrestrial photogrammetry system combined with 3D point-cloud reconstruction technology based on the structure from motion with multi-view stereo method and sample plot survey data. Deviation analyses and accuracy evaluations of sample plot information were performed in the study area for trees to explore the practical significance of this method for monitoring rubber forest sample plots. Furthermore, the relationship between the height of the first branch, diameter at breast height (DBH), and rubber tree volume was explored, and a rubber tree standard volume model was established.ResultsThe Bias, relative Bias, RMSE, and RRMSE of the height of the first branch measured by this method were −0.018 m, −0.371%, 0.562 m, and 11.573%, respectively. The Bias, relative Bias, RMSE, and RRMSE of DBH were −0.484 cm, −1.943%, −2.454 cm, and 9.859%, respectively, which proved that the method had high monitoring accuracy and met the monitoring requirements of rubber forest sample plots. The fitting results of rubber tree standard volume model had an R2 value of 0.541, and the estimated values of each parameter were 1.745, 0.115, and 0.714. The standard volume model accurately estimated the volume of rubber trees and forests using the first branch height and DBH.DiscussionThis study proposed an innovative planning scheme for a terrestrial photogrammetry system for 3D visual monitoring of rubber tree forests, thus providing a novel solution to issues observed in current sample plot monitoring practices. In the future, the application of terrestrial photogrammetry systems to monitor other types of forests will be explored.

Sound Identification Method for Gas and Coal Dust Explosions Based on MLP.

To solve the problems of backward gas and coal dust explosion alarm technology and single monitoring means in coal mines, and to improve the accuracy of gas and coal dust explosion identification in coal mines, a sound identification method for gas and coal dust explosions based on MLP in coal mines is proposed, and the distributions of the mean value of the short-time energy, zero crossing rate, spectral centroid, spectral spread, roll-off, 16-dimensional time-frequency features, MFCC, GFCC, short-time Fourier coefficients of gas explosion sound, coal dust sound, and other underground sounds were analyzed. In order to select the most suitable feature vector to characterize the sound signal, the best feature extraction model of the Relief algorithm was established, and the cross-entropy distribution of the MLP model trained with the different numbers of feature values was analyzed. In order to further optimize the feature value selection, the recognition results of the recognition models trained with the different numbers of sound feature values were compared, and the first 35-dimensional feature values were finally determined as the feature vector to characterize the sound signal. The feature vectors are input into the MLP to establish the sound recognition model of coal mine gas and coal dust explosion. An analysis of the feature extraction, optimal feature extraction, model training, and time consumption for model recognition during the model establishment process shows that the proposed algorithm has high computational efficiency and meets the requirement of the real-time coal mine safety monitoring and alarm system. From the results of recognition experiments, the sound recognition algorithm can distinguish each kind of sound involved in the experiments more accurately. The average recognition rate, recall rate, and accuracy rate of the model can reach 95%, 95%, and 95.8%, respectively, which is obviously better than the comparison algorithm and can meet the requirements of coal mine gas and coal dust explosion sensing and alarming.

Two-stage automated diagnosis framework for urogenital schistosomiasis in microscopy images from low-resource settings.

Automated diagnosis of urogenital schistosomiasis using digital microscopy images of urine slides is an essential step toward the elimination of schistosomiasis as a disease of public health concern in Sub-Saharan African countries. We create a robust image dataset of urine samples obtained from field settings and develop a two-stage diagnosis framework for urogenital schistosomiasis. Urine samples obtained from field settings were captured using the Schistoscope device, and S. haematobium eggs present in the images were manually annotated by experts to create the SH dataset. Next, we develop a two-stage diagnosis framework, which consists of semantic segmentation of S. haematobium eggs using the DeepLabv3-MobileNetV3 deep convolutional neural network and a refined segmentation step using ellipse fitting approach to approximate the eggs with an automatically determined number of ellipses. We defined two linear inequality constraints as a function of the overlap coefficient and area of a fitted ellipses. False positive diagnosis resulting from over-segmentation was further minimized using these constraints. We evaluated the performance of our framework on 7605 images from 65 independent urine samples collected from field settings in Nigeria, by deploying our algorithm on an Edge AI system consisting of Raspberry Pi + Coral USB accelerator. The SH dataset contains 12,051 images from 103 independent urine samples and the developed urogenital schistosomiasis diagnosis framework achieved clinical sensitivity, specificity, and precision of 93.8%, 93.9%, and 93.8%, respectively, using results from an experienced microscopist as reference. Our detection framework is a promising tool for the diagnosis of urogenital schistosomiasis as our results meet the World Health Organization target product profile requirements for monitoring and evaluation of schistosomiasis control programs.

Multi-rate Kalman filtering for structural dynamic response reconstruction by fusing multi-type sensor data with different sampling frequencies

In this paper, a novel dynamic response reconstruction method based on multi-rate Kalman filtering (MRKF) is presented. The proposed method starts with representing the structural system by the state-space equation. Then, different observation equations are defined, and that selection is based on the availability of sensor types at a specific time. Not only can the multi-type sensor data sampled at different rates be fused directly, but the presented method also relaxes the collocated monitoring requirement. In addition, future observations are used to benefit the current state estimation by the Rauch, Tung, and Striebel smoothing procedure. The unobserved structural dynamic responses are estimated using the MRKF virtual sensing technique with multi-rate sensor data. Several demonstrative numerical tests are performed to verify the superiority and robustness of the presented MRKF method on one benchmark shear frame model. The experimental test employed a computer-vision-based displacement tracking technique. Results show that the proposed method surmounts the obstacle to deploying consumer-grade cameras in structural health monitoring applications, which provide a low-cost sensing solution without sacrificing response estimation accuracies.

Flexible Finger-Shaped Active Dry EEG Electrode With a Configurable Application-Specific Integrated Circuit and Embedded Leadoff Detection

Dealing with the discomfort and complex setting-up of conventional wet electrodes in the electroencephalography (EEG) monitoring for personal daily healthcare, a flexible finger-shaped active dry EEG electrode is developed and tested in this paper. The flexible finger-shaped dry electrodes, made of polydimethylsiloxane (PDMS) and elastic electrically conductive composite (ECC), penetrate through the hair to achieve stable contact with the scalp. To improve the quality of weak signal acquisition, a customized configurable application-specific integrated circuit (ASIC) is integrated using ECC based fabrication process, which amplifies the signal by 100 V/V, detects the lead-off states of electrodes, communicates with the back-end electronic module for configuration and status feedback. The amplifiers have >100 MΩ @ 50 Hz input impedance to match the large interface impedance of dry electrodes. An I2C slave controller with a dedicated wake-up oscillator is introduced for excellent configurability with a reduced number of wires and lower power consumption. Modulus of the proposed electrodes are similar as human tissue, ensuring remarkable user comfort. Besides, the process costs low and mold-casting is suitable for mass production. Over all, the easy-to-use active dry electrode could meet the requirements for daily EEG monitoring.

Safe Distance Monitoring of Live Equipment Based Upon Instance Segmentation and Pseudo-LiDAR

Electrocution accidents caused by operation and maintenance personnel and high-voltage live equipment frequently occur in substations. Although many cameras have been installed for the surveillance of critical energized equipment, they cannot complete the monitoring of safe distance. In addition, the current research based on vision or LiDAR suffers from computationally intensive, lack of real-time or expensive equipment. In order to meet the low cost, easy maintenance and real-time requirements of substation safe distance monitoring, a monocular vision method based on 2D-3D fusion is proposed in this study. Specifically, instance segmentation, depth estimation, depth reconstruction, and back projection transformation are used to predict the 3D distance of objects in 2D images. The work is mainly implemented in two aspects: a) To obtain the high-quality masks of Power Transformers and Person, SOLOv2 is optimized on three aspects: feature extraction, feature fusion and ability to cope with object geometric transformation. b) Pseudo-LiDAR output from RGB images via depth estimation and back projection techniques. Subsequently, combined with the point cloud of the object and the actual-to-virtual conversion ratio, the actual distance between Power Transformer and Person can be calculated. Experimental results show that the maximum error of ranging is within 9%, with an average error rate of 5.34% on the Power Transformer-Person dataset. It can be seen that the proposed method has achieved good ranging effects, and can realize the automatic measurement of the safe distance of Power Transformer.

Monitoring Ground Displacement in Mining Areas with Time-Series Interferometric Synthetic Aperture Radar by Integrating Persistent Scatterer/Slowly Decoherent Filtering Phase/Distributed Scatterer Approaches Based on Signal-to-Noise Ratio

During the interferometric synthetic aperture radar (InSAR)-based ground displacement monitoring in mining areas, the overlying land is mainly covered by low vegetation and arable land, which makes interferograms acquired by InSAR techniques easily susceptible to decorrelation, resulting in the quantity and density of highly coherent points (CPs) are not enough to reflect the spatial location and spatio-temporal evolution process of ground displacement, which is hardly meeting requirements of high-precision ground displacement monitoring. In this study, we developed an approach for monitoring ground displacement in mining areas by integrating Persistent Scatterer (PS), Slowly Decoherent Filtering Phase (SDF), and Distributed Scatterer (DS) based on signal-to-noise ratio (SNR) to increase the spatial density of CPs. A case study based on a mining area in Heze was carried out to verify the reliability and feasibility of the proposed method in practical applications. Results showed that there were four significant displacement areas in the study area and the quantity of CPs acquired by the proposed method was maximum 6.7 times that of conventional PS-InSAR technique and maximum 2.3 times that of SBAS-InSAR technique. The density of CPs acquired by the proposed method increased significantly. The acquired ground displacement information of the study area was presented in more detail. Moreover, the monitoring results were highly consistent with ground displacement results extracted by PS-InSAR and SBAS-InSAR methods in terms of displacement trends and magnitudes.

RETRACTED ARTICLE: Fusion of transformer and ML-CNN-BiLSTM for network intrusion detection

Network intrusion detection system (NIDS) can effectively sense network attacks, which is of great significance for maintaining the security of cyberspace. To meet the requirements of efficient and accurate network status monitoring, a NIDS model using Transformer-based fusion deep learning architecture is proposed. Firstly, GAN-Cross is used to expand minority class sample data, thereby alleviating the issues of imbalanced minority class about the original dataset. Then, the Transformer module is used to adjust the ML-CNN-BiLSTM model to enhance the feature encoding ability of the intrusion model. Finally, the data enhancement model and feature enhancement model are integrated into the NIDS model, the detection model is optimized, the features of network state data are extracted at a deeper level, and the generalization ability of the detection model is enhanced. Some simulation experiments using UNSW-NB15 datasets show that the proposed fusion architecture can achieve efficient analysis of complex network traffic datasets, with an accuracy of 0.903, effectively improving the detection accuracy of NIDS and its ability to detect unknown attacks. The proposed model has good application value in ensuring the stable operation of network systems.

Nmr-VSM: Non-Touch Motion-Robust Vital Sign Monitoring via UWB Radar Based on Deep Learning.

In recent years, biometric radar has gained increasing attention in the field of non-touch vital sign monitoring due to its high accuracy and strong ability to detect fine-grained movements. However, most current research on biometric radar can only achieve heart rate or respiration rate monitoring in static environments, which have strict monitoring requirements and single monitoring parameters. Moreover, most studies have not applied the collected data despite their significant potential for applications. In this paper, we introduce a non-touch motion-robust vital sign monitoring system via ultra-wideband (UWB) radar based on deep learning. Nmr-VSM not only enables multi-dimensional vital sign monitoring under human motion environments but also implements cardiac anomaly detection. The design of Nmr-VSM includes three key components. Firstly, we design a UWB radar that can perform multi-dimensional vital sign monitoring, including heart rate, respiratory rate, distance, and motion status. Secondly, we collect real experimental data and analyze the impact of eight factors, such as motion status and distance, on heart rate monitoring. We then propose a deep neural network (DNN)-based heart rate data correction model that achieves high robustness in motion environments. Finally, we model the heart rate variability (HRV) of the human body and propose a convolutional neural network (CNN)-based anomaly detection model that achieves low-latency detection of heart diseases, such as ventricular tachycardia and ventricular fibrillation. Experimental results in a real environment demonstrate that Nmr-VSM can not only accurately monitor heart rate but also achieve anomaly detection with low latency.