Remote Monitoring Network Research Articles

Deep Learning Network for Remote Monitoring of Thermal Exchange Tunnels

Monitoring and predicting ground settlement throughout tunnel construction is critical to ensuring the safe and accurate use of urban tunnel systems. The accurate and efficient diagnosis of such settlement can decrease hazards while improving the safety and dependability of these initiatives. However, typical tunnel inspection procedures are time-consuming, costly, and heavily reliant on human subjectivity. The trained model's accuracy was evaluated by comparing its findings across extended operating durations using the same and different thermal operational patterns as those utilized for training. Deep learning, one of the most powerful Artificial Intelligence approaches, is required for the tunnel's settlement predicting challenge. Nevertheless, deep neural networks frequently want huge quantities of training data. In the method we used, CNN-LSTM models were trained on datasets of various sizes and attributes. The results suggest that both of the proposed models may achieve a little inaccuracy under specific situations.

DEVELOPMENT OF A LOCAL BIOMEDICAL SIGNAL COLLECTION NETWORK BASED ON ZIGBEE TECHNOLOGY

The article describes a methodology for developing a local network for remote monitoring of patient biomedical signals based on low-speed energy-saving ZigBee technology. The local network is based on two transmitters and one receiving module connected to the server. Transmitters transmit information from sensors about the condition of two patients (temperature, pulse, blood flow) to the on-site server. Information is displayed in a specially designed interface that is easy to visualize. In addition, audio elements are connected to the transmitting equipment to alert medical personnel if biomedical signal thresholds are exceeded. Thus, it is possible to monitor the condition of two or more patients (in one department, the number of transmitters can be in the dozens, thanks to the branched ZigBee topology). In the future, it is planned to increase the number of transmitting modules to create a large local monitoring network in a single medical institution. In addition, there is the prospect of collecting more biosignals, including ECG, body position, oxygen saturation of the patient’s blood, etc.

Smart-Systems for Precision Agriculture, Environmental Protection and Healthcare

Appearance of innovative tools of informatics in the world is determined by development of information-communication technology, microelectronics, sensor and biosensor technologies. Spreading of these technologies on precision agriculture, environmental protection and health care gives opportunity to create smart fields, gardens, greenhouses, forests and parks, and also smart health monitors, which estimate the health state of person as in rehabilitation, so in emergency situations. Results of research of authors in the development of new information technologies and creation of main components of the smart systems for different purposes on this base are shows in this paper. Main requirements for knowledge bank for precision agriculture and ecological monitoring are defined. Main principles of creating knowledge bank on base of requirements are proposed. The typical smart system models and their development stages are considered. Structure of wireless multilevel networks for the estimation of state of biological objects of different origin and their nodes are described. Developed sensor networks and smart systems for agriculture are considered. Structure of proposed smart system for agriculture and ecological monitoring is given. Approaches for chlorophyll fluorescence induction curves analysis are studied. Results of network testing for the estimation of the autonomous work time of network nodes and possible errors are given. Wireless sensor network and smart system for remote medical monitoring are described. Diagnostic smart systems for estimation of quality of life are considered. Medical communicator, computer device on base of tablet computer, was used for their development. The short form of Survey Instrument (SF-36) for life quality estimation and abnormal uterine bleeding questionnaire, which was developed in State Scientific Institution "Center for Innovative Medical Technologies of the National Academy of Sciences of Ukraine", were embedded in communicator. STEPS questionnaire which was designed for epidemiological monitoring of the prevalence of noncommunicable diseases and their risk factors in the target subpopulations could be added to communicator. Development of smart systems for estimation of quality of life and prevalence of noncommunicable diseases was made in cooperation with R.E. Kavetsky Institute of Experimental Pathology, Oncology and Radiobiology NAS of Ukraine (department of research management and innovation), Chebotarev Institute of Gerontology of the National Academy of Medical Sciences of Ukraine, Center for Innovative Medical Technologies of the National Academy of Sciences of the Ukraine. Keywords: wireless sensor network, precision agriculture, chlorophyll fluorescence induction, express-diagnostics of plant state, quality of life, SF-36.

Research on Urban Monitoring Network Management Platform based on AI Data Stream Processing

In recent years, with the upsurge of the continuous development of high-tech information industry, the security industry has also made a new leap in technology, products, industry and so on. The traditional single small-scale monitoring and management software obviously cannot meet the actual needs of users in terms of system scale and functional requirements. This paper puts forward a network management platform for urban monitoring based on AI data stream processing. With the help of comprehensive processing and analysis of urban complex information, through network remote monitoring means, information supervision and public opinion trends are used to control social environmental security, timely warning and response to unexpected accidents, so as to ensure that governance departments can fully control and prevent possible problems such as road traffic, population distribution and disaster emergency.

Creation of pacemaker remote monitoring networks between hospital and pharmacies during Covid-19 outbreak

IntroductionRemote Monitoring (RM) of Cardiac Implantable Electronic Devices (CIEDs) is proven to be safe and efficient. It has been adopted in our center since years. At the time of the recent Covid-19 outbreak, we introduced and tested a collaborative organizational model, through a new RM device (Totem), creating a network with the surrounding territory and limiting CIED patients’ presence in hospital. MethodsWe involved 4 neighbor pharmacies where Totem devices were installed; we called and informed 64 patients with Totem compatible pacemaker (PM) about the possibility to perform their PM follow-up (FU) in-pharmacy; 58 gave their consent and their data were inserted into our RM database. ResultsDuring an 18-month FU period, a total of 70 RM transmissions have been received: one alert of high atrial burden triggering a pharmacological optimization, one alert of high ventricular impedance leading to a new ventricular lead implantation and four alerts of elective replacement indicator. Fulfilled questionnaires revealed complete patient satisfaction. ConclusionsA collaborative network between our hospital and the surrounding territory to perform RM FUs of CIEDs during Covid-19 pandemic was feasible, leading to patient compliance and satisfaction and revealing important technical and clinical alerts.

Room temperature mixed-potential solid-electrolyte NO2 sensor for environmental monitoring

Mixed-potential solid-electrolyte (MPSE) gas sensors have important applications for the detection of hazardous NO2. However, limited by the poor conductivity of conventional electrolytes at low temperatures, the realization of high performance of MPSE gas sensors usually requires a compromise to high working temperatures, hindering their application. To this end, we developed a novel ultrasensitive and selective MPSE NO2 sensor based on K2Fe4O7 solid electrolyte and Ni-MOF|N2 sensing electrode (SE), which can achieve a low detection limit (LOD) as low as 5 ppb at room temperature (RT) without additional energy supply. Simultaneously, the selectivity of the sensor to the interfering gas NO achieves a 31-fold improvement by the enhanced participation of H2O molecules and unsaturated coordinated Ni ions, which are obtained by the sintering in anoxic atmospheres like N2 or Ar. Moreover, the sensor is further used as the information collection terminal of the Internet of Things (IoT) system, building a NO2 monitoring network for remote monitoring, different environment evaluation, and information sharing. This work fills the gap of the ultrasensitive MPSE NO2 sensor in the RT field and provides new insights for the construction of NO2 portable sensor networks.

A combined strategy for dynamic probabilistic risk assessment of fission battery designs using EMRALD and DEPM

The notion of nuclear reactors with battery-like capabilities, called fission batteries, puts forth system requirements and design constraints that have so far been unseen in the nuclear power production industry. Such restrictions require fission batteries to be modular, integrated, autonomous, tamper-proof (i.e., resilient, fault-tolerant, all-weather, and safe), and affordable. With design requirements specifying no human intervention for operation, and minimal connectivity to remote monitoring networks, fission batteries are unique among existing nuclear power plants and emerging advanced reactor designs. Given these attributes, traditional probabilistic risk assessment (PRA) of fission batteries is expected to require dynamic methods to model advanced aspects, such as self-diagnosis, self-adjustment, and duration-prediction capabilities, as they are key ingredients for unattended operations. In addition, availability models need to integrate autonomous control, associated error-detection algorithms, and adversarial human actions. Currently, no existing framework demonstrably assesses these advanced attributes. This paper introduces and demonstrates an integrated framework for the dynamic modeling of fission battery designs. The proposed framework comprises a combined modeling strategy that uses the dual-graph error propagation methodology (DEPM) based on the continuous-time Markov chain (CTMC) models implemented in OpenPRA Error Propagation (OpenErrorPro) and the dynamic PRA tool, Event Modeling Risk Assessment using Linked Diagrams (EMRALD), based on discrete dynamic event trees (DDET). This combination overcomes some of the limitations of the tools when used independently. It enables detailed dynamic analysis to produce time explicit results to support the development of fission battery traditional PRA models. To evaluate the utility of this novel approach, a demonstration case is shown that models the hypothesized response of a fission battery design to an external fire event. DEPM CTMCs and alternative failure approaches are coupled with EMRALD to characterize and quantify the likelihood of the event sequences. The results show that the combined framework effectively captures the dynamic aspects of fission battery design in terms of the timing and realism of modeled events. Given the complexity of the failure scenarios, we believe that EMRALD and DEPM are necessary and complementary when the need for high-resolution analysis offsets the challenges of detailed modeling.

Unmanned-Surface-Vehicle-Aided Maritime Data Collection Using Deep Reinforcement Learning

Employing unmanned surface vehicles (USVs) as marine data collectors is promising for large-scale environment sensing in remote ocean monitoring network. In this article, we consider a USV-aided marine data collection network, where a USV collects data from multiple monitoring terminals while avoiding collisions with monitoring terminals and obstacles. Aiming at minimizing energy consumption and data loss, we formulate a trajectory optimization problem with practical constraints, including collision avoidance, steering angle, and velocity limitation. The problem is intractable due to the stochastic arrived data and the random emergence and movement of dynamic obstacles. To efficiently solve it, we transform it as a constrained Markov decision process (MDP) problem and address it using a target-oriented double deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> -learning network (D2QN)-based collision avoidance and trajectory planning algorithm. In the proposed algorithm, the USV acts as an agent to explore and learn its trajectory planning policy by utilizing the causal knowledge. Numerical results demonstrate that the performance of the proposed algorithm is superior in terms of successful probability, energy consumption, and data loss.

Designing of Fiber Bragg Gratings for Long-Distance Optical Fiber Sensing Networks

Most optical sensors on the market are optical fiber Bragg grating (FBG) sensors with low reflectivity (typically 7-40%) and low side-lobe suppression (SLS) ratio (typically SLS &lt;15 dB), which prevents these sensors from being effectively used for long-distance remote monitoring and sensor network solutions. This research is based on designing the optimal grating structure of FBG sensors and estimating their optimal apodization parameters necessary for sensor networks and long-distance monitoring solutions. Gaussian, sine, and raised sine apodizations are studied to achieve the main requirements, which are maximally high reflectivity (at least 90%) and side-lobe suppression (at least 20 dB), as well as maximally narrow bandwidth (FWHM&lt;0.2 nm) and FBGs with uniform (without apodization). Results gathered in this research propose high-efficiency FBG grating apodizations, which can be further physically realized for optical sensor networks and long-distance (at least 40 km) monitoring solutions.

Environmental Monitoring and Restoration of Ecological Cycle Index System Based on IoT Agriculture

Environmental factors have a direct impact on the development of agriculture, so it is particularly important to detect the environment of the agroecological cycle index system. Although there are some plans for environmental monitoring, most of them are environmental monitoring for a larger concept, but this study is specific to the actual object. This article takes farmland as the research object and comprehensively uses embedded technology, information monitoring technology, and network technology based on the analysis of the research status and system application of the farmland environmental monitoring system. This article studies and designs a real-time online remote monitoring system for farmland ecological environment based on embedded architecture and GPRS technology. By using sensors to obtain farmland information, it is displayed on the monitoring center and mobile client, and data information is obtained in real time. It manages and protects the farmland environment in advance. This article measures and analyzes the data from the final experiment. The experimental results show that the monitoring system can accurately collect farmland data in real time. And through the embedded server and the Internet, the data can be remotely transmitted in real time and displayed on the monitoring center and mobile client software. The results showed that PM2.5 was 30 μg/m3 at 20:00. The experimental data have the characteristics of real time and stability, which can meet the requirements of real-time network remote monitoring and transmission of data.

An Analysis of Internet of Things Computer Network Security and Remote Control Technology

In order to solve the problem of the specific application of Internet of Things computer network security monitoring technology in infrastructure construction, a method of remote network monitoring system design is proposed, taking tunnel security construction as an example. According to the characteristics of construction tunnel safety monitoring, this method provides theoretical guidance from several aspects, such as computer remote monitoring, data decision analysis, safety control, personnel positioning, and data management. Through experiments, it is found that in the safety detection experiment, when the data is extracted within 2 minutes for detection, the detection accuracy is higher than 80%, and if it is 4 minutes, the detection accuracy is higher than 90%; in the response time test, the local control response time is generally less than 1 s. In the remote control response time test, the response time can generally be within 3 s, and it may be greater than 5 s. The designed construction tunnel monitoring system transmits the collected data to the monitoring terminal remotely. Then, the sensor data is analyzed for timing decision-making. This method can effectively calculate the safety status of each monitoring unit of the current tunnel according to a certain algorithm and formulate corresponding emergency measures according to the safety status.

Non-Contact Atrial Fibrillation Detection From Face Videos by Learning Systolic Peaks.

We propose a non-contact approach for atrial fibrillation (AF) detection from face videos. Face videos, electrocardiography (ECG), and contact photoplethysmography (PPG) from 100 healthy subjects and 100 AF patients are recorded. Data recordings from healthy subjects are all labeled as healthy. Two cardiologists evaluated ECG recordings of patients and labeled each recording as AF, sinus rhythm (SR), or atrial flutter (AFL). We use the 3D convolutional neural network for remote PPG monitoring and propose a novel loss function (Wasserstein distance) to use the timing of systolic peaks from contact PPG as the label for our model training. Then a set of heart rate variability (HRV) features are calculated from the inter-beat intervals, and a support vector machine (SVM) classifier is trained with HRV features. Our proposed method can accurately extract systolic peaks from face videos for AF detection. The proposed method is trained with subject-independent 10-fold cross-validation with 30 s video clips and tested on two tasks. 1) Classification of healthy versus AF: the accuracy, sensitivity, and specificity are 96.00%, 95.36%, and 96.12%. 2) Classification of SR versus AF: the accuracy, sensitivity, and specificity are 95.23%, 98.53%, and 91.12%. In addition, we also demonstrate the feasibility of non-contact AFL detection. We achieve good performance of non-contact AF detection by learning systolic peaks. non-contact AF detection can be used for self-screening of AF symptoms for suspectable populations at home or self-monitoring of AF recurrence after treatment for chronic patients.

Aquarium Monitoring System Based on Internet of Things

With the ever-increasing richness of social resources, the number of devices using the Internet of Things is also increasing. Currently, many people keep pets such as fish in their homes, and they need to be carefully taken care of. In particular, it is necessary to create a safe and comfortable environment for them and to maintain this environment continuously. An adverse environment can affect the growth of fish and may even result in their death. This study used the LinkIt 7697 module and the BlocklyDuino editor to produce a control system for a smart aquarium. The purpose of this system is to monitor the temperature, light intensity, and water level in an aquarium, as well as to provide alerts to presence of intruders; therefore, temperature, light, ultrasonic, and infrared sensing modules are used. The system has set aquarium environment thresholds, and it processes the signals obtained by the sensors to control and optimize the outputs to loads using data fusion calculations so that the aquarium has the most comfortable environment for the fish. An automatic feeder is also included in the system, and this uses a servo motor. The data from the system is uploaded to a back-end computer through the built-in Wi-Fi system of the LinkIt 7697 module. The Cloud Sandbox platform is used to display the results in real time, achieving the purpose of remote network monitoring.

Remote Network Monitoring Technology Based on Computer Technology

With the rapid development of Internet technology, using images to express the characteristics of things more direct, compared with text, audio, image expression content is more ambiguous, which makes the rapid increase of digital images on the Internet. Nowadays one of the hot directions of computer vision research is how to accurately and quickly retrieve the target image from a large amount of image data. This paper summarizes the development of image retrieval technology at home and abroad, and proposes an image search method based on color histogram and Chi-square distance. This paper discusses how to construct an image search system, which can search the image quickly, describe the color distribution of the photo with color histogram, divide the image into five regions, extract image features from the color histogram of each region, and then get the data set of multi-dimensional image features. Then the chi-square distance is used to calculate the similarity of color histogram, and the closest image is selected as the first similar image, which realizes the necessary logic of receiving query image and returning related results.

Research on Image Fusion Algorithm Based on Nonsubsampled Shear Wave Transform and Principal Component Analysis

With the development of network remote monitoring technology, its application in all walks of life has become more and more extensive. The application of remote monitoring technology makes the development of all walks of life more intelligent and informatized. This paper analyzes the development status of remote monitoring technology, and studies the design and implementation of remote network monitoring system based on computer technology. It also conducts related research on the future development trend of remote monitoring technology.

Statement of retraction: a wireless network remote monitoring method driven by artificial intelligence

This article refers to:RETRACTED ARTICLE: A wireless network remote monitoring method driven by artificial intelligence

Development of Bioimpedance Sensors and Measurement System for Biomedical In-Vitro Applications

Abstract Bioimpedance analysis is a label-free and easy approach to obtain information on cellular barrier integrity and cell viability more broadly. In this work, we introduce a small, low-cost, portable in vitro impedance measurement system for studies where a shadow-free exposure of the cells is a requirement. It can be controlled by a user-friendly web interface and can perform measurements automated and autonomously at short intervals. The system can be integrated into an existing IoT network for remote monitoring and indepth analyses. A single-board computer (SBC) serves as the central unit, to control, analyze, store and forward the measurement data from the single-chip impedance analyzer. Various materials and manufacturing methods were used to produce a purpose-built lid on top of a modified 24-well microtiter plate in a “do it yourself” fashion. Furthermore, three different sensor designs were developed utilizing anodic aluminum oxide (AAO) membranes and gold-plated electrodes. Preliminary tests with potassium chloride (KCl) showed first promising results.

PA during the COVID-19 outbreak in China: a cross-sectional study.

COVID-19 has undergone several mutations and is still spreading in most countries now. PA has positive benefits in the prevention of COVID-19 infection and counteracting the negative physical and mental effects caused by COVID-19. However, relevant evidence has indicated a high prevalence of physical inactivity among the general population, which has worsened due to the outbreak of the pandemic, and there is a severe lack of exercise guidance and mitigation strategies to advance the knowledge and role of PA to improve physical and mental health in most countries during the epidemic. This study surveyed the effects of COVID-19 on PA in Chinese residents during the pandemic and provided important reference and evidence to inform policymakers and formulate policies and planning for health promotion and strengthening residents’ PA during periods of public health emergencies. ANOVA, Kolmogorov–Smirnov, the chi-square test and Spearman correlation analysis were used for statistical analysis. A total of 14,715 participants were included. The results show that nearly 70% of Chinese residents had inadequate PA (95%CI 58.0%–82.19%) during the COVID-19 outbreak, which was more than double the global level (27.5%, 95%CI 25.0%–32.2%). The content, intensity, duration, and frequency of PA were all affected during the period of home isolation, and the types of PA may vary among different ages. The lack of physical facilities and cultural environment is the main factor affecting PA. However, there was no significant correlation between insufficient PA and the infection rate. During the period of home isolation and social distance of epidemic prevention, it is necessary to strengthen the scientific remote network monitoring and guidance for the process of PA in China.

Irradiance Sensing through PV Devices: A Sensitivity Analysis.

In this work, a sensitivity analysis for the closed-form approach of irradiance sensing through photovoltaic devices is proposed. A lean expression to calculate irradiance on a photovoltaic device, given its operating point, temperature and equivalent circuit model, is proposed. On this expression, the sensitivity towards errors in the measurement of the photovoltaic device operating point and temperature is analyzed, determining optimal conditions to minimize sensitivity. The approach is studied for two scenarios, a stand-alone sensor and irradiance sensing on an operating power-producing photovoltaic device. A low-cost realization of a virtual sensor employing the closed form for monitoring performance of photovoltaic module is also presented, showing the advantage of this kind of simple solution. The proposed solution can be used to create a wireless sensor network for remote monitoring of a photovoltaic plant, assessing both electrical and environmental conditions of the devices in real time.

Optimal percentage of biventricular pacing to obtain CRT response: how high is high enough

Abstract Funding Acknowledgements Type of funding sources: None. Background The greatest benefit with cardiac resynchronization therapy (CRT) is achieved when biventricular pacing (BivP) percentage (%) is close to 100%. However, in some patients that goal can be challenging to obtain. Purpose Determine whether a lower BivP% could lead to similar CRT response and events, as compared with patients with BivP% &amp;gt;98%. Methods Patients with CRT followed up in a remote-monitoring network were retrospectively analyzed. BivP% was assessed and response to CRT was defined as an absolute increase in left ventricle ejection fraction (LVEF) &amp;gt;5% or a relative increase in LVEF &amp;gt;15%. Low BivP% was defined as &amp;lt;98%. Clinical, echocardiographic data and all-cause death during follow-up were evaluated. ROC curve and AUC were obtained to determine the discriminative power of BivP% as predictor of CRT response. Optimal cut-point value was obtained and patients were divided according to this value. Kaplan-Meyer survival function was used to compare survival in the different groups and the Log-rank test was used for comparison between the groups. Results 88 patients, 76% male, median age 73.5 (IQR 65.75-79.25) years were included. A CRT-D was implanted in 69%. Etiology was ischemic in 44%. 93% were under beta-blockers. Median LVEF before CRT was 27% (IQR 20.25-32). 44 patients (50%) had low BivP% (median 91%, IQR 96-99), 55% due to atrial fibrillation and 52% due to frequent premature ventricular complexes. After optimization of medical therapy, device programming and/or interventional procedures, we obtained a BivP &amp;gt;98% in 26 out of the 44 patients (59%). However, in 18 patients (20%) BivP% was &amp;lt;98% (median 95, IQR 92.25-96). 66% patients were CRT responders. Median follow-up was 36 (IQR 23.75-84) months. During follow-up, all-cause mortality was 27% (24 patients). Optimal cut-point value for predicting CRT response was 91% BivP% (AUC 0.644, p-value 0.047, 95% CI 0.496-0.792). The characteristics of the two groups didn’t differ significantly (Table). Survival was significantly higher in patients with BivP% &amp;gt;91% (Log-rank 3.667, p-value 0.050) – Figure. Conclusion In this population, BivP% &amp;gt;91% was sufficient to achieve CRT-response and was associated with a better survival. BivP% &amp;lt;91%(n = 4)BivP% &amp;gt;91%(n = 84)p-valueAge in years, median (IQR)72.50 (70.50-73.75)74.00 (65.00-80.00)0.666CRT-D, n (%)3 (75.0)58 (69.0)0.999Ischemic cardiopathy, n (%)3 (75.0)35 (41.7)0.311LVEF before CRT, median (IQR)27 (19-39)27 (20-32)0.795Beta-blockers, n (%)4 (100.0)78 (95.1)0.999Abstract Figure.