Narrow Results

In order to further explore the intelligent research of the pressure sensor in the pipeline, based on the existing pipeline detection methods of the Internet of Things (IoT), the signal processing method and correction research in the wireless sensor network of the IoT are explored. A multi-pressure sensor detection method of negative pressure wave is proposed. Scientific and intelligent means are used to explore the pipeline remote intelligent detection. The specific research results are as follows. In the simulation study, when the working condition of pump station 1# changes, the pressure wave first reaches A₁ and B₂ sensors, and finally reaches C₁ and C₂ sensors. If there is leakage in the pipeline, the simulation results show that when the pressure of pump station rises to 5MPa, the pressure of B₁ and B₂ test points decreases to about 4.9MPa, and the pressure of A₁, A₂, C₁ and C₂ test points is about 3MPa; when the pressure of the pump station drops to 3MPa, the pressure of all test points decreases. Generally speaking, the pressure of the front-end pressure sensor of the pump station is basically unchanged while the pressure of the back end increases when the pressure increases; when the pressure decreases, the pressure of the front-end pressure sensor of the pump station is basically unchanged while the pressure of all the back-end pressure sensor decreases. After correction, the signal data is obviously smooth and the error is obviously reduced. This study can provide scientific and effective reference for the pressure monitoring of IoT sensor network.

MySIGN is a new wearable device for multiparametric remote monitoring developed by the Interdepartmental Centre for Industrial Research (CIRI) of the University of Bologna. The aim of this study is to assess the feasibility of data collection through MySIGN and the characterization of the signals and data recorded through the device. Healthy volunteers were asked to wear MySIGN and perform three tasks: Rest (R), Deep breathing test (DB), and a six-minute walking test (6MWT). During each task, the following parameters were collected via MySIGN: absolute bioimpedance value (BioZ), bioimpedance variation ($\mathrm{\Delta }$BioZ), respiratory rate, heart rate, ECG QT interval, blood oxygen saturation, and temperature. Fifteen volunteers were enrolled (nine females (60%), mean age $35.8\pm 9.3$ years). The parameters collected by the device were successfully recorded during the three tasks. BioZ values neither varied among the three tasks nor by stratifying the population according to age, whereas BioZ values were higher for subjects with a body mass index (BMI) $>23$kg/m². $\mathrm{\Delta }$BioZ remained unchanged regardless of the task, age, and BMI. MySIGN allows effective multiparametric data collection. The $\mathrm{\Delta }$BioZ might be a good value for estimating lung congestion because it is less influenced by other variables.

A path planning framework for regional surveillance of a planar advection-diffusion process by aerial mobile sensors is proposed. The goal of the path planning is to produce feasible and collision-free trajectories for a set of aerial mobile sensors that minimize some uncertainty measure of the process under observation. The problem is formulated as a dynamic optimization problem and discretized into a large-scale nonlinear programming (NLP) problem using the Petrov–Galerkin finite element method in space and simultaneous collocation in time. Receding horizon optimization problems are solved in simulations with an advection-dominated ice concentration field. Simulations illustrate the usefulness of the proposed method.

Telemedicine (TM) is used to treat patients in a remote location by using telecommunication technology. It exchanges the medical information and data from one location to another through advanced technological innovation. During this COVID-19 pandemic, there is a lockdown in almost all countries. TM is beneficial to healthcare to minimize social distance. This review paper briefs about TM and discusses how this technology works for the COVID-19 pandemic and its significant benefits. An extensive search is made on the known research engines of PubMed, SCOPUS, Google Scholar, and ResearchGate using the appropriate keywords to extract meaningful and relevant articles. Ten major applications of TM for COVID-19 are identified and discussed with a brief description of each provided. The major technological processes involved in TM, which create advancement in the medical field, are also discussed. This technology helps avoid visits to the doctor and hospital during the lockdown and provides a suitable treatment option. It collects the medical information and data, which can be helpful for better treatment of the patient. Telemedicine adopts virtualized treatment approaches for the patient. Now patients can receive better quality treatment without leaving their homes during COVID-19 lockdown.

Healthcare has always been one of the areas where the public and the government pay attention. In recent years, the use of modern technology in medicine has helped both the healthcare professionals and patients. Therefore, given the fact that these technologies are based on Internet and modern mobile devices, they allow a remote monitoring, that consists of using Internet of Things technology. With this technology, patients instead of staying in the hospital for hours and hours, can stay home and communicate with their doctors online. With this technology one can easily monitor the health status of patients in real-time. In this paper we develop a real-time multi parametric human health monitoring and prediction system. This system is inspired on/from a technique, which consists of tracking/collecting clinical data of a person in order to identify particular conditions that can help insuring fast predictability or awareness. The main advantages of the developed system are the novelty, multi functionality, and the availability at an affordable cost.

Power equipment condition monitoring diagnosis (CMD) system consists of control centre master station system, integrated processing units (IPUs), thousands of field condition monitoring devices and communication network. It is difficult to coping with these different filed devices and different communication technologies. So, we standardize the configuration file, information model and information exchange model of field conditions monitoring devices based on IEC 61850. Firstly, the specification and validation rules of configuration file is developed. Secondly, the IEC 61850 logical nodes are extended based on the function of monitoring devices. Finally, it uses IEC 61850 MMS mapping as means of communication. Using IEC 61850 would simplify implementation, reduce installation costs and reduce maintenance costs.

Most combine harvesters have not be equipped with online fault diagnosis system. A fault information acquisition and diagnosis system of the Combine Harvester based on LabVIEW is designed, researched and developed. Using ARM development board, by collecting many sensors' signals, this system can achieve real-time measurement, collection, displaying and analysis of different parts of combine harvesters. It can also realize detection online of forward velocity, roller speed, engine temperature, etc. Meanwhile the system can judge the fault location. A new database function is added so that we can search the remedial measures to solve the faults and also we can add new faults to the database. So it is easy to take precautions against before the combine harvester breaking down then take measures to service the harvester.

We propose hypothesis tests for detecting dopaminergic medication response in Parkinson disease patients, using longitudinal sensor data collected by smartphones. The processed data is composed of multiple features extracted from active tapping tasks performed by the participant on a daily basis, before and after medication, over several months. Each extracted feature corresponds to a time series of measurements annotated according to whether the measurement was taken before or after the patient has taken his/her medication. Even though the data is longitudinal in nature, we show that simple hypothesis tests for detecting medication response, which ignore the serial correlation structure of the data, are still statistically valid, showing type I error rates at the nominal level. We propose two distinct personalized testing approaches. In the first, we combine multiple feature-specific tests into a single union-intersection test. In the second, we construct personalized classifiers of the before/after medication labels using all the extracted features of a given participant, and test the null hypothesis that the area under the receiver operating characteristic curve of the classifier is equal to 1/2. We compare the statistical power of the personalized classifier tests and personalized union-intersection tests in a simulation study, and illustrate the performance of the proposed tests using data from mPower Parkinsons disease study, recently launched as part of Apples ResearchKit mobile platform. Our results suggest that the personalized tests, which ignore the longitudinal aspect of the data, can perform well in real data analyses, suggesting they might be used as a sound baseline approach, to which more sophisticated methods can be compared to.