The application of intelligent sensors in medical research:a review

The rapid advancement of biomedicine in the twenty-first century has been facilitated by the constant innovation in biomedical technology.The most crucial issue in the field of medicine is to use sensor technology to gather information from primitive organisms,particularly the human body.Design,development,and application of biomedical sensors in the study of clinical diseases’diagnosis and therapy have all been significantly aided by the advancement of medicine.The interest in creating sensors significantly increased in the 1960s.Chemical and biological sensors have been swiftly created in response to an urgent practical necessity,enabling the creation of selective sensors for the direct detection of diverse ions and compounds.The traditional large-size sensors are quickly turning into miniature sensors and are rapidly applied in biological and medical fields.Currently,wearable electronic blood pressure monitors,home blood glucose meters,and quick body surface digital thermometers are commonly used.The advent of a wide variety of medical-grade wearable sensors that will enable real-time biometric data tracking of a large range of physiological characteristics will likely be one of the most revolutionary,exciting,and difficult changes to come to medicine over the next several years.For possible uses in the entertainment,health monitoring,and medical care industries,high-performance flexible strain sensors connected to clothing or human skin are necessary.The use of sensors in the development of biomedical diagnostic tools and medical equipment will enhance human quality of life in the twenty-first century.This article will introduce the current medical sensor field related to sensors for physical quantities,sensors for chemical quantities,sensors for biological quantities such as electronic nose,electronic tongue,and their applications.

Application of Zero-Watermarking for Medical Image in Intelligent Sensor Network Security

The field of healthcare is considered to be the most promising application of intelligent sensor networks.However,the security and privacy protection ofmedical images collected by intelligent sensor networks is a hot problem that has attracted more and more attention.Fortunately,digital watermarking provides an effective method to solve this problem.In order to improve the robustness of the medical image watermarking scheme,in this paper,we propose a novel zero-watermarking algorithm with the integer wavelet transform(IWT),Schur decomposition and image block energy.Specifically,we first use IWT to extract low-frequency information and divide them into non-overlapping blocks,then we decompose the sub-blocks by Schur decomposition.After that,the feature matrix is constructed according to the relationship between the image block energy and the whole image energy.At the same time,we encrypt watermarking with the logistic chaotic position scrambling.Finally,the zero-watermarking is obtained by XOR operation with the encrypted watermarking.Three indexes of peak signal-to-noise ratio,normalization coefficient(NC)and the bit error rate(BER)are used to evaluate the robustness of the algorithm.According to the experimental results,most of the NC values are around 0.9 under various attacks,while the BER values are very close to 0.These experimental results show that the proposed algorithm is more robust than the existing zero-watermarking methods,which indicates it is more suitable for medical image privacy and security protection.

Study of Detecting Impact Damage for Composite Material Based on Intelligent Sensor

A system of impact damage detection for composite material structures by using an intelligent sensor embedded in composite material is described. In the course of signal processing, wavelet transform has the exceptional property of temporal frequency localization, whereas Kohonen artificial neural networks have excellent characteristics of self-learning and fault-tolerance. By combining the merits of abstracting time-frequency domain eigenvalues and improving the ratio of signal to noise in this system, impact damage in composite material can be properly recognized.

Sonar Image Target Detection for Underwater Communication System Based on Deep Neural Network

Target signal acquisition and detection based on sonar images is a challenging task due to the complex underwater environment.In order to solve the problem that some semantic information in sonar images is lost and model detection performance is degraded due to the complex imaging environment,we proposed a more effective and robust target detection framework based on deep learning,which can make full use of the acoustic shadow information in the forward-looking sonar images to assist underwater target detection.Firstly,the weighted box fusion method is adopted to generate a fusion box by weighted fusion of prediction boxes with high confidence,so as to obtain accurate acoustic shadow boxes.Further,the acoustic shadow box is cut down to get the feature map containing the acoustic shadow information,and then the acoustic shadow feature map and the target information feature map are adaptively fused to make full use of the acoustic shadow feature information.In addition,we introduce a threshold processing module to improve the attention of the model to important feature information.Through the underwater sonar dataset provided by Pengcheng Laboratory,the proposed method improved the average accuracy by 3.14%at the IoU threshold of 0.7,which is better than the current traditional target detection model.

Prototyping low-cost automatic weather stations for natural disaster monitoring

Weather events put human lives at risk mostly when people might occupy areas susceptible to natural disasters.Deploying Professional Weather Stations(PWS)in vulnerable areas is key for monitoring weather with reliable measurements.However,such professional instrumentation is notably expensive while remote sensing from a number of stations is paramount.This imposes challenges on the large-scale weather station deployment for broad monitoring from large observation networks such as in Cemaden—The Brazilian National Center for Monitoring and Early Warning of Natural Disasters.In this context,in this paper,we propose a Low-Cost Automatic Weather Station(LCAWS)system developed from Commercial Off-The-Shelf(COTS)and open-source Internet of Things(IoT)technologies,which provides measurements as reliable as a reference PWS for natural disaster monitoring.When being automatic,LCAWS is a stand-alone photovoltaic system connected wirelessly to the Internet in order to provide real-time reliable end-to-end weather measurements.To achieve data reliability,we propose an intelligent sensor calibration method to correct measures.From a 30-day uninterrupted observation with sampling in minute resolution,we show that the calibrated LCAWS sensors have no statistically significant differences from the PWS measurements.As such,LCAWS has opened opportunities for reducing maintenance costs in Cemaden's observational network.

An intelligent MXene/MoS_(2)acoustic sensor with high accuracy for mechano-acoustic recognition

Auditory systems are the most efficient and direct strategy for communication between human beings and robots.In this domain,flexible acoustic sensors with magnetic,electric,mechanical,and optic foundations have attracted significant attention as key parts of future voice user interfaces(VUIs)for intuitive human–machine interaction.This study investigated a novel machine learning-based voice recognition platform using an MXene/MoS_(2) flexible vibration sensor(FVS)with high sensitivity for acoustic recognition.The performance of the MXene/MoS_(2) FVS was systematically investigated both theoretically and experimentally,and the MXene/MoS_(2) FVS exhibited high sensitivity(25.8 mV/dB).An MXene/MoS_(2) FVS with a broadband response of 40–3,000 Hz was developed by designing a periodically ordered architecture featuring systematic optimization.This study also investigated a machine learning-based speaker recognition process,for which a machine-learning-based artificial neural network was designed and trained.The developed neural network achieved high speaker recognition accuracy(99.1%).

Research and application on wireless remote measurement system of drill hydrology

The configuration, function, principle of operation and the main design of the wireless remote measurement system of drill hydrology based on GPRS were introduced in this paper. The current resources of GPIRS network was used by the system, and water level, water temperature and turbidity were measured by the intelligent sensors. Then the data were transmitted to the monitoring computer by the GPRS modem in wireless, which processed the data, forecasted and predicted water disaster. The monitoring computer software has the Chinese operation interface in the windows circumstance with simple and convenience using. The managers can operate every function by the Chinese cue. The data communications between the remote indicating instrument distributing in every drill and the monitoring computer is built only by one monitoring computer. The technology of data collection, GPRS wireless communication, computer, data processing, database were collected by the system, some functions such as real time supervising, early-warning, decision-making supporting, and so on had been achieved. The system has such merits as high precision, low cost, flexible distributing, credible transmitting and simple operation.

Exponential Stabilization and Estimation for Sampled Observer Design of Surface Mounted Permanent Magnet Synchronous Motor

A nonlinear state observer design with sampled and delayed output measurements for variable speed and external load torque estimations of SPMSM drive system has been addressed, successfully. Sampled output state predictor is re-initialized at each sampling instant and remains continuous between two sampling instants. Throughout this study, a positive constant to satisfy an upper limit of the sampling period between sampling instants and allowable timing delay in terms of observer parameters has been prepared such that the exponential stable of the closed-loop system is guaranteed, based on Lyapunov stability tools. In order to validate the theoretical results introduced by main fundamental theorem to prove the observer convergence, the proposed sampled-data observer is demonstrated through a sample study application to variable speed SPMSM drive system.

Emerging robot swarm traffic

Purpose-This paper aims to discuss traffic patterns generated by swarms of robots while commuting to and from a base station.Design/methodology/approach-The paper adopts a mathematical evaluation and robot swarm simulation.The swarm approach is bottom-up:designing individual agents the authors are looking for emerging group behaviour patterns.Examples of group behaviour patterns are human-driven motorized traffic which is rigidly structured in two lanes,while army ants develop a three-lane pattern in their traffic.The authors copy army ant characteristics onto their robots and investigate whether the three lane traffic pattern may emerge.They follow a three-step approach.The authors first investigate the mathematics and geometry of cases occurring when applying the artificial potential field method to three“perfect”robots.Any traffic pattern(two,three or more lanes)appears to be possible.Next,they use the mathematical cases to study the impact of limited visibility by defining models of sensor designs.In the final step the authors implement ant inspired sensor models and a trail following mechanism on the robots in the swarm and explore which traffic patterns do emerge in open space as well as in bounded roads.Findings-The study finds that traffic lanes emerge in the swarm traffic;however the number of lanes is dependent on the initial situation and environmental conditions.Intrinsically the applied robot models do not determine a specific number of traffic lanes.Originality/value-The paper presents a method for studying and simulating robot swarms.

Development of sensor system for real-time measurement of droplet deposition of agricultural sprayers

During the chemical application process,droplet deposition on a target is an important reference indicator for evaluating the spraying technique and its performance.In order to quickly obtain deposition results in the field,this study proposed a novel system based on surface humidity sensors.The basic principle is to convert the measured physical quantity change into a capacitance change,thereby realizing the physical quantity to electrical signal conversion.An Android application for mobile terminal and the corresponding coordinator were developed,which allowed operators to control multiple sensors simultaneously through the Bluetooth.The soluble tracer detected by spectrophotometer was used to calibrate the system.The obtained results indicated a good correlation between deposition volume and voltage increment output from the newly developed system(R2 of the six nozzles with Dv0.5 ranging from 107.28μm to 396.20μm were 0.8674-0.9729),and a power regression model based on the least squares technique(R2=0.8022)was developed.In the field test,the system exhibited an optimal performance in predicting the deposition volume.Compared with the conventional method of measuring tracer concentration,the deviation was less than 10%.In addition,the system exhibited good fitting curve of the deposition distribution with droplet number results measured by the water sensitive paper method.