Long-term Real-time Monitoring Research Articles

Assessment of Tunnel Lining Stability through Integrated Monitoring of Fiber Bragg Grating Strain and Structural Deformation.

Tunnel excavation induces the stress redistribution of the surrounding rock. Structural cracks may develop in the secondary lining due to this stress redistribution and bias pressure, consequently affecting the overall construction safety of the tunnel. This paper aims to achieve real-time monitoring of the excavation stability of the lining structure by integrating two monitoring technologies: structural deformation monitoring and fiber grating strain monitoring. Additionally, it proposes a method to simultaneously measure the thermal strain and applied stress-strain of the structure. By analyzing the displacement and deformation of the lining structure, its stability can be preliminarily evaluated in the short term. To achieve long-term real-time monitoring and a more accurate assessment of the tunnel structure's stability, the paper introduces fiber Bragg grating (FBG) strain sensor monitoring technology. First, based on the geological stratigraphy information obtained from the exploration, a simulation model of the tunnel under different section bias angles is established. The displacement and stress concentration areas of the lining structure are then analyzed to optimize the sensor deployment array and provide a theoretical basis for the sensor arrangement. FBG strain sensors are installed on the surface of the structure to measure thermal strain and loading stress-strain, whereas FBG temperature sensors measure local temperature. The findings indicate that following tunnel excavation, the maximum daily strain differences at K107+043 and K107+240 were 126.87 µε and 209.38 µε, respectively. After a period of rock disturbance, the average daily strain differences due to applied stress-strain were 16.8 µε and 12.65 µε, respectively. The thermal strain was close to the daily strain difference. Therefore, after the rock disturbance subsided, the strain fluctuations in the lining structure were mainly caused by local temperature changes, and the surrounding rock tended to stabilize. This offers a viable method for evaluating structural stability post-tunnel excavation.

Design and testing of a high-precision generating voltmeter for metal-enclosed megavolt level DC voltage source.

An extremely stable megavolt (MV) level DC voltage source is the key foundation for many scientific instruments, and the need for accurate measurement and long-term real-time monitoring of its output voltage is increasingly urgent. The utilization of conventional resistive voltage dividers for measurements introduces leakage currents, resulting in considerable measurement errors. The non-contact generating voltmeter (GVM) sensor based on electric field measurement has a simple structure and a low cost, making it expected to be an effective solution. Currently, most research on GVM sensors focuses on the measurement of weak electric fields at kV/m levels with significant interference. In this paper, an improved high-precision non-contact GVM sensor was designed. A DC voltage test platform was built, and the effects of the sampling resistor and motor rotation speed on the measurement results were discussed. The relative combined uncertainty of the improved GVM sensor reached 0.042%, which satisfied the urgent need for MV level DC voltage source measurement. The improved GVM sensor can provide an effective reference for measuring the output voltage of a metal-enclosed MV level DC voltage source or the potential of a suspended electrode.

Directional statistics-inspired end-to-end atrial fibrillation detection model based on ECG rhythm

Numerous algorithms designed for detecting atrial fibrillation (AF) often exhibit limitations in extracting essential rhythm features, leading to challenges in accurately discerning ectopic beats and consequently yielding suboptimal detection performance. In this study, we explored the distribution patterns of R-R intervals (RRIs) in both AF and specific rhythms, such as atrial premature beats (APBs) and normal sinus rhythm (NSR). For the first time, we employed the Sobolev test statistics, a method used in directional statistics to assess spherical uniformity, to quantify the irregularity and variability characteristics of RRIs. We developed an end-to-end learnable model for detecting AF by leveraging this approach. A cross-dataset validation method is employed to train and test the proposed model. This involved the use of a simulated dataset and eight distinct real-world databases. Notably, when trained on the Computing in Cardiology Challenge 2017 (C2017) and tested on the MIT-BIH Atrial Fibrillation Database (AFDB), our model, designed to take only a sequence of 32 RRIs as input, achieved a sensitivity of 96.2%, specificity of 98.2%, and accuracy of 97.3%. The results illustrate its competitive standing against the existing methods for AF detection and its enhanced resilience to ectopic beats. Unlike existing deep learning-based AF detection models, our model prioritizes interpretability, boasts lower computational complexity (with fewer than 2000 learnable parameters), and demonstrates superior generalization capabilities. This will help improve the quality of long-term real-time monitoring and management of AF, reduce the burden on clinicians, and ultimately improve patient outcomes.

A buffering fluorogenic probe for real-time lysosomal pH monitoring

Real-time monitoring of lysosomal pH is critical for understanding lysosome-related biological processes and diseases, but few lysosome probes can achieve this response effectively. Therefore, it is essential to develop pH-sensitive probes with better performance. In this study, we designed and synthesized a lysosome-targeting “buffering fluorogenic probe” (Lyso-BFP) by introducing a salicylamide group to naphthalimide for buffering strategy and a hydroxyethyl piperazine unit to target lysosome as well as specific pH response. Lyso-BFP demonstrated excellent photostability, pH specificity, and responsiveness to lysosomal acidification in living cells, thanks to its well-suit pKa. Density functional theory calculation revealed that Lyso-BFP’s excellent pH sensing performance resulted from the inhibition of the photo-induced electron transfer (PET) process from hydroxyethyl piperazine to the naphthalimide moiety after the piperazine ring’s protonation at low pH. Remarkably, Lyso-BFP allowed for wash-free imaging and long-term real-time monitoring of lysosome pH changes based on its off-on fluorescence behavior and buffer strategy. With its superior pH selectivity and real-time pH monitoring capability, Lyso-BFP holds great potential in the diagnosis of lysosome-related disease, and the design concept of this fluorogenic probe provides a new perspective for real-time tracking of cell organelles.

Acid-Resistant Near-Infrared II Ag2Se Quantum Dots for Gastrointestinal Imaging.

With the development of near-infrared II (NIR-II) fluorescence imaging, Ag2Se quantum dots (QDs) have become promising label candidates due to their negligible toxicity and narrow band gap. Despite their potential for gastrointestinal (GI) imaging, the application of Ag2Se QDs still presents significant challenges due to issues such as fluorescence extinction or poor stability in the complex digestive microenvironment. Herein, we have proposed a novel approach to the continuous production of Se precursors using glutathione (GSH) as the reductant under acidic conditions, realizing the continuous growth of water-dispersible Ag2Se QDs. The Ag2Se QDs emitting at 600-1100 nm have been successfully synthesized. Meanwhile, the silver-rich surface of the synthesized NIR-II Ag2Se QDs has been passivated well with the dense GSH, resulting in exceptional colloidal stability and photostability and endowing them with acid resistance. As a result, the obtained NIR-II Ag2Se QDs have exhibited remarkable stability in gastric acid, thus enabling their utilization for long-term real-time monitoring of GI peristalsis via NIR-II fluorescence imaging. Moreover, in contrast to conventional barium meal-based X-ray imaging, NIR-II fluorescence imaging with as-prepared NIR-II Ag2Se QDs can offer clearer visualization of fine intestinal structures, with a width as small as 1.07 mm. The developed strategy has offered a new opportunity for the synthesis of acid-resistant nanocrystals, and the acid-resistant, low-toxicity, and biocompatible NIR-II Ag2Se QDs synthesized in this work show a great promise for GI imaging and diagnosis of GI diseases in vivo.

A real-time quantitative acceleration monitoring method based on triboelectric nanogenerator for bridge cable vibration

Real-time monitoring of vibration acceleration of civil infrastructure is imperative for effective management and safe operation of structures. Although the triboelectric nanogenerator (TENG) shows potential for self-powered sensing, it faces challenges in correlating limited experimental electrical amplitudes to structural responses, thereby hindering comprehensive performance analysis of civil infrastructure. Herein, a self-driven acceleration TENG sensor (A-TENG) is designed and manufactured, comprising an outer shell and an inner mass-spring-damper system. By establishing a sensing model based on vibration theory and the TENG mechanism, the electrical signals generated by the sensors can be correlated with structural acceleration, enabling self-driven real-time quantitative characterization. Indoor and on-site experiments demonstrate that the A-TENG sensor is capable of continuously monitoring the acceleration profile with excellent consistency compared to commercial sensors. The non-contact free-standing sliding mode design and ultra-lightweight construction of the A-TENG sensor (∼8 g) enhance its start-up sensitivity (∼0.1 m/s2), long-term stability (∼30,000 loading cycles) while minimizing mass interference. The proposed sensing theory renders a novel approach to offering complete time-domain information, which is vital for the precise analysis of structural behavior. This work facilitates understanding of self-driven sensors utilizing TENG technology and provides a useful tool for long-term real-time quantitative structural health monitoring.

Long-term real-time monitoring of cell migration under β-amyloid treatment.

Alzheimer's disease (AD) is the most common age-related neurodegenerative disease and is clinically characterized by progressive loss of memory in patients. Neuropathological features of AD include the presence of amyloid plaques and neurofibrillary tangles, and loss of neurons and synapses in brain regions. According to the amyloid cascade hypothesis, the accumulation of amyloid beta (Aβ) plays a central role in the pathogenesis of AD, and that oligomer was found to be neurotoxicity and destroy nerve cells. Therefore, microscopic observation of the morphological and behavioral changes of cells induced by Aβ, will help to elucidate the detailed mechanism of the effect of Aβ on AD. Herein, we presented a chip-based method for long-term real-time cellular imaging, which could be performed at a whole field of view at 1 cm2 with ultrahigh resolution of 500 nm. Neuronal-like cell lines were employed to investigate the cellular migrations under Aβ 42 oligomer treatment. Both the cellular differentiation and representative cellular migrations under oligomer stimuli were tracked in a few days’ time, showing abnormal cellular behaviors with a high oligomer concentration. Our results would shed new light on the cellular evidence of the amyloid cascade hypothesis.

Long-term spatiotemporal and highly specific imaging of the plasma membrane of diverse plant cells using a near-infrared AIE probe.

Fluorescent probes are valuable tools to visualize plasma membranes intuitively and clearly and their related physiological processes in a spatiotemporal manner. However, most existing probes have only realized the specific staining of the plasma membranes of animal/human cells within a very short time period, while almost no fluorescent probes have been developed for the long-term imaging of the plasma membranes of plant cells. Herein, we designed an AIE-active probe with NIR emission to achieve four-dimensional spatiotemporal imaging of the plasma membranes of plant cells based on a collaboration approach involving multiple strategies, demonstrated long-term real-time monitoring of morphological changes of plasma membranes for the first time, and further proved its wide applicability to plant cells of different types and diverse plant species. In the design concept, three effective strategies including the similarity and intermiscibility principle, antipermeability strategy and strong electrostatic interactions were combined to allow the probe to specifically target and anchor the plasma membrane for an ultralong amount of time on the premise of guaranteeing its sufficiently high aqueous solubility. The designed APMem-1 can quickly penetrate cell walls to specifically stain the plasma membranes of all plant cells in a very short time with advanced features (ultrafast staining, wash-free, and desirable biocompatibility) and the probe shows excellent plasma membrane specificity without staining other areas of the cell in comparison to commercial FM dyes. The longest imaging time of APMem-1 can be up to 10 h with comparable performance in both imaging contrast and imaging integrity. The validation experiments on different types of plant cells and diverse plants convincingly proved the universality of APMem-1. The development of plasma membrane probes with four-dimensional spatial and ultralong-term imaging ability provides a valuable tool to monitor the dynamic processes of plasma membrane-related events in an intuitive and real-time manner.

Ultrasound Imaging With a Flexible Probe Based on Element Array Geometry Estimation Using Deep Neural Network.

Conventionally, ultrasound (US) diagnosis is performed using hand-held rigid probes. Such devices are difficult to be used for long-term monitoring because they need to be continuously pressed against the body to remove the air between the probe and body. Flexible probes, which can deform and effectively adhere to the body, are a promising technology for long-term monitoring applications. However, owing to the flexible element array geometry, the reconstructed image becomes blurred and distorted. In this study, we propose a flexible probe U.S. imaging method based on element array geometry estimation from radio frequency (RF) data using a deep neural network (DNN). The input and output of the DNN are the RF data and parameters that determine the element array geometry, respectively. The DNN was first trained from scratch with simulation data and then fine-tuned with in vivo data. The DNN performance was evaluated according to the element position mean absolute error (MAE) and the reconstructed image quality. The reconstructed image quality was evaluated with peak-signal-to-noise ratio (PSNR) and mean structural similarity (MSSIM). In the test conducted with simulation data, the average element position MAE was 0.86 mm, and the average reconstructed image PSNR and MSSIM were 20.6 and 0.791, respectively. In the test conducted with in vivo data, the average element position MAE was 1.11 mm, and the average reconstructed image PSNR and MSSIM were 19.4 and 0.798, respectively. The average estimation time was 0.045 s. These results demonstrate the feasibility of the proposed method for long-term real-time monitoring using flexible probes.

Non-contact Blood Pressure Measurement Method Using Frequency Modulated Continuous Wave Radar

Non-contact continuous blood pressure monitoring is significant in vital sign monitoring. Frequency modulated continuous wave (FMCW) radar is suitable for non-contact wave signal extraction. A heartbeat-guided blood pressure monitoring algorithm using FMCW radar is proposed. The target heart rate is detected and pulse wave signal is extracted based on FMCW reflected signals. The variational mode decomposition (VMD) is introduced to alleviate the interferences of human breath and slight body movements. The pulse wave signal is extracted based on target heart rate. Blood pressure related features of pure pulse waveform are extracted to obtain blood pressure based on its estimation model. Experiments are conducted indoor among 15 participants sitting in a nature state. The average accuracy of diastolic blood pressure (DBP) is 94.3% and that of systolic blood pressure is 94.4%. The experimental results demonstrate the robustness and effectiveness of the proposed algorithm, which makes it possible to further achieve long-term real-time non-contact blood pressure monitoring.

A real-time pluripotency reporter for the long-term and real-time monitoring of pluripotency changes in induced pluripotent stem cells

To master the technology of reprogramming mouse somatic cells to induced pluripotent stem cells (iPSCs), which will lay a good foundation for setting up a technology platform on reprogramming human cancer cells into iPSCs. Mouse iPSCs (i.e., Oct4-GFP miPSCs) was successfully generated from mouse embryonic fibroblasts (MEFs) harboring Oct4-EGFP transgene by introducing four factors, Oct4, Sox2, c-Myc and Klf4, under mESC (Murine embryonic stem cells) culture conditions. Oct4-GFP miPSCs were similar to mESCs in morphology, proliferation, mESC-specific surface antigens and gene expression. Additionally, Oct4-GFP miPSCs could be cultured in suspension to form embryoid bodies (EBs) and differentiate into cell types of the three germ layers in vitro. Moreover, Oct4-GFP miPSCs could develop to teratoma and chimera in vivo. Unlike cell cycle distribution of MEFs, Oct4-GFP miPSCs are similar to mESCs in the cell cycle structure which consists of higher S phase and lower G1 phase. More importantly, our data demonstrated that MEFs harboring Oct4-EGFP transgene did not express GFP, until they were reprogrammed to the pluripotent stage (iPSCs), while the GFP expression was progressively lost when these pluripotent Oct4-GFP miPSCs exposed to EB-mediated differentiation conditions, suggesting the pluripotency of Oct4-GFP miPSCs can be real-time monitored over long periods of time via GFP assay. Altogether, our findings demonstrate that Oct4-GFP miPSC line is successfully established, which will lay a solid foundation for setting up a technology platform on reprogramming cancer cells into iPSCs. Furthermore, this pluripotency reporter system permits the long-term real-time monitoring of pluripotency changes in a live single-cell, and its progeny.

Advances in Printable, Flexible and Transparent Graphene Photodetectors for Optoelectronics Applications

Photodetectors, often known as photosensors, are light or electromagnetic radiation sensors. Graphene is a carbon allotrope made up of a single layer of carbon atoms that are linked together in a hexagonal configuration. Carbon nanotubes, another emerging material, are made from rolled graphene. The high carrier mobility, linear dispersion, and 0 bandgap of graphene contribute to the ultra-fast, wide-spectrum response of graphene photodetectors from visible to infrared. The high responsivity of graphene photodetectors is 500 A/W for light of 450 nm and 4 A/W for light of 1064 nm. The strong compatibility of graphene with silicon technology has been explored, and recent discoveries open the way for chip integrated photodetectors with high-performance. This article will be covering different types of photodetectors which differ in type of materials used, along with graphene and type of methods of manufacture. It gives each device construction and working along with results on its responsivity and sensitivity. We then cover the innumerable applications of graphene-based photodetectors in communication sector, medical sector, and in flexible electronics which are discussed briefly. We also go through the advancements in materials and manufacturing processes, emphasizing that graphene photodetector devices are gaining popularity due to their capacity to provide long-term real-time precise monitoring. Lastly, we discuss advantages and hurdles that are to be overcome.

Online monitoring and early warning technology for the status of earth wire for overhead line

Overhead lines for wind power transmission in the southern region have the characteristics of large tower-line elevation, across thick ice areas, harsh natural environments, and difficult maintenance. Unpredictable line breakage accidents often occur, affecting the stability of the social power supply. This paper designs and introduces a technology based on intelligent perception technique for online monitoring of the earth wire, which can realize long-term real-time online monitoring of the state of the earth wire of overhead lines and solve the problem of line monitoring from wind power transmission and fault inspection and troubleshooting. Now the test and monitoring work of an overhead transmission line has been completed during the ice period, and the test results show that the technology has good monitoring performance.

Train-Induced Vibration Monitoring of Track Slab under Long-Term Temperature Load Using Fiber-Optic Accelerometers.

The train-induced vibration response provides a flexible solution for the real-time monitoring deformation of high-speed railway track slab in actual operation. This paper proposes a long-term real-time monitoring method for track slab deformation based on wavelet packet energy (WPE) using fiber optic accelerometers to record train-induced vibration. We found that the vibration response law of track slab deformation could be established by using the WPE of the frequency band covering the first- and second-order frequencies induced by the adjacent carriages. A field test was carried out for more than one year on the Beijing–Shanghai high-speed railway to investigate the train-induced vibration response law of track slab that was continuously deformed under a long-term temperature load. The maximum values of the WPE characteristic index appeared in winter and summer, and they were positively correlated with the temperature difference between the air environment and the track slab under the daily temperature load. These results were demonstrated to be consistent with the track slab deformation law for long-term and daily temperature loads. The novel method based on fiber optic accelerometers and WPE provides a new method for the long-term and real-time monitoring of track slab deformation.

The Design of Energy-efficient Portable Node for Long-term ECG Monitoring

As an important metric of observing heart rate changes, electrocardiogram (ECG) can reflect the current changes in heart rate in real time, and is an important basis for predicting cardiovascular diseases. This paper presents a portable ECG monitoring node with small size, low power consumption and long-term real-time monitoring ability. With the raw data amplified, filtered and A/D converted, the effective ECG data is obtained, and then transmitted to the Android client through the HC-06 Bluetooth module, which can be continuously updated on the client. The heart rate and ECG waveform updated per second allowing the user to observe their heart rate changes in real time and their physical conditions. Due to the specific design of signal processing unit, the proposed ECG monitoring node can achieve low power consumption and portability. The experimental results show that the node is suitable for long-term ECG monitoring in all kinds of environments with energy-efficient ability.

A multifunctional targeted nanoprobe with high NIR-II PAI/MRI performance for precise theranostics of orthotopic early-stage hepatocellular carcinoma.

Early diagnosis and effective treatment of hepatocellular carcinoma (HCC) is quite critical for improving patients' prognosis. The combination of second near-infrared window photoacoustic imaging (NIR-II PAI) and T2-magnetic resonance imaging (T2-MRI) is promising for achieving omnibearing information on HCC diagnosis due to the complementary advantages of outstanding optical contrast, high temporospatial resolution and soft-tissue resolution. Thus, the rational design of a multifunctional targeted nanoplatform with outstanding performance in dual-modal NIR-II PAI/T2-MRI is particularly valuable for precise diagnosis and imaging-guided non-invasive photothermal therapy (PTT) of early-stage HCC. Herein, a versatile targeted organic-inorganic hybrid nanoprobe was synthesized as a HCC-specific contrast agent for sensitive and efficient theranostics. The developed multifunctional targeted nanoprobe yielded superior HCC specificity, reliable stability and biocompatibility, high imaging contrast in both NIR-II PAI and T2-MRI, and an excellent photothermal conversion efficiency (74.6%). Furthermore, the theranostic efficiency of the targeted nanoprobe was systematically investigated using the orthotopic early HCC-bearing mice model. The NIR-II PAI exhibited sensitive detection of ultra-small HCCs (diameter less than 1.8 mm) and long-term real-time monitoring of the tumor and nanoprobe targeting process in deep tissues. The T2-MRI demonstrated clear imaging contrast and a spatial relationship between micro-HCC and adjacent structures for a comprehensive description of the tumor. Moreover, when using the targeted nanoprobe, the non-invasively targeted PTT of orthotopic early HCC was carried out under reliable dual-modal imaging guidance with remarkable anti-tumor efficiency and biosafety. This study provides an insight for constructing a multifunctional targeted nanoplatform for precise and comprehensive theranostics of early-stage HCC, which would greatly benefit the patients in the era of precision medicine.

Characteristics, evolution, and potential source regions of submicron aerosol in Beijing, China

Atmospheric fine particulate pollution has been a dramatically environmental concern in all seasons in the megacity Beijing in recent years. A long-term real-time monitoring campaign of non-refractory submicron aerosol (NR-PM1) at a southeastern urban site in Beijing from July 13, 2017 to May 26, 2018 using an Aerodyne Aerosol Chemical Speciation Monitor (ACSM) was carried out to investigate the characteristics and evolution processes of NR-PM1 and its potential source effects. The annual average concentration of NR-PM1 during the entire study period was 40.8 μg/m3, lower than that of NR-PM1 from July 2011 to June 2012, and its seasonal variation presented the order of autumn and spring > winter > summer. The average concentration of organic species Org in the entire study was 15.1 μg/m3 and accounted for 37% of NR-PM1, being consistently a significant component of NR-PM1 in the four seasons. The average concentrations of SO42− and NO3− were 8.6 and 11.1 μg/m3, respectively, accounting for 21% and 27% of NR-PM1, and NO3− and its contribution to NR-PM1 exceeded those of SO42−, suggesting that secondary nitrate became a more dramatic pollutant. Note that SO42− in summer accounted for 27% of NR-PM1, higher than those in the other seasons, indicating that secondary sulfate formation was greatly important in summer, however, NO3− in summer accounted for 22% of NR-PM1, lower than those in the other seasons, more likely due to relatively high temperature in summer being favorable for dissociation of nitrate. NH4+ and Cl− species presented lower mass concentrations and mass fractions, and the seasonal variation of NH4+ was similar to NO3−, yet Cl− presented a typical seasonal change related to intense fossil fuel combustion during the heating period in winter and biomass burning in autumn with an order of winter > autumn > spring > summer. All aerosol species exhibited different diurnal cycles, of which Org presented a bimodal mode, SO42− and Cl− showed a flatter diurnal pattern, however, NO3− and NH4+ displayed more similar single-peak patterns. The conversion of SO2 to SO42− presented an obvious seasonal variation with summer > autumn > spring > winter driven by different factors. The conversion of NO2 to NO3− also showed a significant seasonal change characterized by autumn > summer and spring > winter controlled by different factors. Relative humidity, temperature and wind speed during the study period were significant meteorological elements influencing NR-PM1 and its species, and heavy haze pollution episodes largely occurred under high relative humidity and low wind speed. High potential source regions influencing NR-PM1 pollution in Beijing in all seasons presented dramatically distributions related to wind directions.

DYNAMIC MONITORING METHOD OF ILLEGAL BUILDINGS USING SPATIOTEMPORAL BIG DATA BASED ON URBAN HIGH LYING ZONES

Abstract. China's urban illegal buildings are emerging in an endless stream with a large number. There is a wide demand for urban illegal buildings monitoring in urban management departments, including Beijing, Shanghai, Guangzhou and other regions where urban management is facing increasingly serious problems of illegal buildings. It is urgent to solve the common problem of "urban disease" caused by urban illegal buildings, and a new automatic monitoring method that can reduce the cost of urban management is urgently needed. This kind of automated monitoring method of illegal buildings has a wide market demand in Urban Management and Law Enforcement and the Ministry of Land and Resources. Existing technologies cannot realize long-term, autonomous, rapid and intelligent dynamic real-time monitoring of urban illegal buildings, which leads to the problem that illegal buildings’ behaviors cannot be stopped in time. There is a possibility to solve these problems using the ubiquitous network of base stations in cities to monitor illegal buildings.This paper proposes a dynamic monitoring method of illegal buildings using spatiotemporal big data based on urban high lying zones. Through the Spatiotemporal sensor network technology, the tilt-type stereo camera is set up at the high lying zones around the survey area. According to the real-time ambient temperature and humidity numerical data fed back by the temperature and humidity sensor, the tilt-type stereo camera uses intelligent time-lapse photogrammetry technology to obtain multiple stereo pairs. The tilted remote sensor transmits the multiple stereo pairs to the urban dynamic analysis service network using wireless transmission. The urban dynamic analysis service network will complete a series of analysis and processing operations without any human intervention, and then transmit the results of the analysis to the early-warning terminal successively through the base station, WIFI and other wireless transmission methods. Finally, the illegal building data is uploaded to the terminal. That is to say, the urban dynamic analysis service network can realize the intelligent, automatic analysis and processing of spatial analysis server and the operation of calling all database data and storing data.The method proposed in this paper uses the high lying zones around the survey area to expand the monitoring range, improve the accuracy of monitoring data, realize long-term real-time monitoring, and fully utilize the characteristics of Spatiotemporal sensing network technology intelligent, autonomous, wireless transmission, etc., significantly reducing labor. It greatly shortens the process from the emergence to the discovery of illegal buildings in cities. The workload of monitoring has improved the efficiency of dynamic monitoring and warehousing of illegal buildings data in cities.

An Active Self-Driven Piezoelectric Sensor Enabling Real-Time Respiration Monitoring.

In this work, we report an active respiration monitoring sensor based on a piezoelectric-transducer-gated thin-film transistor (PTGTFT) aiming to measure respiration-induced dynamic force in real time with high sensitivity and robustness. It differs from passive piezoelectric sensors in that the piezoelectric transducer signal is rectified and amplified by the PTGTFT. Thus, a detailed and easy-to-analyze respiration rhythm waveform can be collected with a sufficient time resolution. The respiration rate, three phases of respiration cycle, as well as phase patterns can be further extracted for prognosis and caution of potential apnea and other respiratory abnormalities, making the PTGTFT a great promise for application in long-term real-time respiration monitoring.

FPGA-based system for artificial neural network arrhythmia classification

The automatic detection and cardiac classification are essential tasks for real-time cardiac diseases diagnosis. In this context, this paper describes a field programmable gates array (FPGA) implementation of arrhythmia recognition system, based on artificial neural network. Firstly, we have developed an optimized software-based medical diagnostic approach, capable of defining the best electrocardiogram (ECG) signal classes. The main advantage of this approach is the significant features minimization, compared to the existing researches, which leads to minimize the FPGA prototype size and saving energy consumption. Secondly, to provide a continuous and mobile arrhythmia monitoring system for patients, we have performed a hardware implementation. The FPGA has been referred due to their easy testing and quick implementation. The optimized approach implementation has been designed on the Nexys4 Artix7 evaluation kit using the Xilinx System Generator for DSP. In order to evaluate the performance of our proposal system, the classification performances of proposed FPGA fixed point have been compared to those obtained from the MATLAB floating point. The proposed architecture is validated on FPGA to be a customized mobile ECG classifier for long-term real-time monitoring of patients.