Multiple Sensor Systems Research Articles

Formulation of Envelope Correlation Coefficient for Multiple Sensors Based on Scattering Parameter

The Envelope Correlation Coefficient (ECC) is crucial for assessing multiple sensor systems in wireless communication, sensing, and microwave imaging. ECC measures signal similarity between sensors, with higher values (close to 1.00) indicating strong correlation. Efficient power transmission, reception, and multiband path transmission enhance sensor network performance by improving data rates and reliability through multiple frequency bands. This study uses scattering parameters to calculate ECC for four different sensors in a single arrangement. Most of the sensors exhibit higher correlation at f2 and f3, while f1 shows a low ECC.

Measurements of the Limit of Detection for Electrochemical Gas Sensors

Abstract Electrochemical amperometric gas cells are becoming the sensor of choice when measuring polluting gases using low-cost air quality networks. A number of technical issues remain to be resolved to deliver fit-for-purpose monitoring systems: humidity corrections are needed but not well understood, interfering gases such as ozone can have variable cross-sensitivity and calibration intervals, and procedures are still being investigated. Another unanswered question is the limit of detection (LOD) for electrochemical gas sensors. Estimates range from hundreds of equivalent parts per billion (ppbv) to single-digit ppbv concentrations. We discuss the LOD for nitrogen dioxide (NO2), an important gas when monitoring air quality. Multiple NO2 sensor systems were tested in an environmental chamber to determine, among other parameters, the LOD for NO2 electrochemical gas sensors. Low-noise electronics and battery powering further reduced electronic noise, allowing the intrinsic LOD of the electrochemical cell to be determined. Noise, quantified as the standard deviation in zero air in a very stable temperature and relative humidity–controlled chamber was <500 pA, which translated into 1.6 ppbv, so the LOD, 3 × standard deviation, was 4.8 ppb. Interestingly, the LOD calculated with 300 ppbv NO2 test gas was the same (±0.1 ppbv). Further tests with a higher resolution analog-to-digital converter resulted in the same LOD, further leading to the conclusion that for the Alphasense NO2-A43F NO2 sensor, the limiting value for LOD is 4.8 ppbv.

A Localized Quantitative Precipitation Estimation for S-band Polarimetric Radar in Taiwan

Abstract A polarimetric radar quantitative precipitation estimation to estimate rain rate (R) from specific attenuation (A) has been applied in Taiwan's operational Quantitative Precipitation Estimation and Segregation Using Multiple Sensors (QPESUMS) system since 2016. A 3-yrs' (2016-2018) drop size distribution dataset from an operational Parsivel network was used to derive a localized coefficient as well as the α(K) function in the R(A) scheme for S-band radar, where α is a key parameter in the estimation of A and K is the linear fitted slope of differential reflectivity (ZDR) versus reflectivity (Z). The local DSD data was also used to derive localized R(Z) and R(KDP) relationships, and the relationships were evaluated using radar observations in heavy rain cases. A synthetic QPE combining the localized R(A), R(Z), and R(KDP) relationships is compared to its operational counterpart and showed about 8 % reduction in normalized mean error for the Mei-Yu cases. Typhoon cases exhibited similar improvements by the localized QPE relationships, but showed higher uncertainties than in the Mei-Yu cases. The higher uncertainties in the typhoon QPE verification was likely due to the stronger winds in typhoons than in the Mei-Yu events that caused greater mismatches between the radar observations at an altitude and the gauges at the ground. Overall, the results demonstrated advantages of localized radar rainfall relationships derived from the disdrometer data to improve the accuracy of the operational rainfall estimation products.

Concept of a Satellite Cross-Calibration Radiometer for In-Orbit Calibration of Commercial Optical Satellites

The satellite Earth observation (EO) sector is burgeoning with hundreds of commercial satellites being launched each year, delivering a rich source of data that could be exploited for societal benefit. Data streams from the growing number of commercial satellites are of variable quality, limiting the potential for their combined use in science applications that need long time-series data from multiple sources. The quality of calibration performed on optical sensors onboard many satellite systems is highly variable due to calibration methods, sensor design, mission objective, budget, or other operational constraints. A small number of currently operating well-characterised satellite systems with onboard calibration, such as Landsat-8/9 and Sentinel-2, and planned future missions, like the NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder, the European Space Agency (ESA)’s Traceable Radiometry Underpinning Terrestrial and Helio Studies (TRUTHS), and LIBRA from China, are considered benchmarks for optical data quality due to their traceability to international measurement standards. This paper describes the concept of a space-based transfer calibration radiometer called the Satellite Cross-Calibration Radiometer (SCR) that would enable the calibration parameters from satellites such as Landsat-8/9, Sentinel-2, or other benchmark systems to be transferred to a range of commercial optical EO satellite systems while in orbit. A description of the key characteristics of the SCR to successfully operate in orbit and transfer calibration from reference systems to client systems is presented. A system like the SCR in orbit could complement SI-Traceable satellites (SITSats) to improve data quality and consistency and facilitate the interoperable use of data from multiple optical sensor systems for delivering higher returns on the global investment in EO.

Multiple-Sensor System Detection via Feature Combination Selection and Low-Redundancy Optimization

Multiple-Sensor System Detection via Feature Combination Selection and Low-Redundancy Optimization

1 Creating Sensor Systems for Real Time Animal Management

Abstract Sustainable animal production needs to consider all aspects of sustainability: economic, environmental, and social. Important aspects include 1) waste and odor management, greenhouse gas emissions; 2) animal care and well-being, labor needs, worker safety, and satisfaction; 3) careful use of antibiotics, food safety, and foreign animal disease; and 4) all while the producer needs to make the business show a profit. New technological approaches for building management, animal monitoring, and care should be carefully considered to help satisfy these pressures and concerns. Increases in computing power, sensor systems, and modeling techniques lend themselves to technological advancements in animal agriculture. Research has shown promising results for implementing multiple sensor systems. Precision animal management systems use a variety of sensors to capture real-time data on individual animals and new modeling techniques to process the data into valuable information to aid management decisions. However, data streams from different sources can be problematic. This presentation aims to provide an understanding of current and future technologies and the advantages, difficulties, and pitfalls of processing and combining different types of real-time data. Current technologies used in precision animal management systems include sound, images, and radio frequency identification (RFID) systems. Steps to a successful system include 1) evaluating the needs of the producers and the animals, 2) selecting appropriate sensors, and 3) understanding both the value and the limitation of the captured data and the data processing.

Placement Method of Multiple Lidars for Roadside Infrastructure in Urban Environments.

Sensors on autonomous vehicles have inherent physical constraints. To address these limitations, several studies have been conducted to enhance sensing capabilities by establishing wireless communication between infrastructure and autonomous vehicles. Various sensors are strategically positioned within the road infrastructure, providing essential sensory data to these vehicles. The primary challenge lies in sensor placement, as it necessitates identifying optimal locations that minimize blind spots while maximizing the sensor's coverage area. Therefore, to solve this problem, a method for positioning multiple sensor systems in road infrastructure is proposed. By introducing a voxel grid, the problem is formulated as an optimization challenge, and a genetic algorithm is employed to find a solution. Experimental findings using lidar sensors are presented to demonstrate the efficacy of this proposed approach.

Efficient antibiotics, small organic molecules and inorganic anions detection with a fluorescent Ni(II) coordination polymer based multiple sensor system

As a hotspot issue of global concern, the abuse of environmental and biological related molecules, including antibiotics, small organic molecules and inorganic anions poses a severe threat to the biological health and ecological environment. Accurate and effective monitoring of these species is of great significance. In this study, one novel nickel(II)-based coordination polymer [Ni(H2edda)2(Hbmoe)2] (Ni-CP) has been successfully designed and synthesized by using the mixed ligands 5,5′-(ethane-1,2-diylbis(oxy)) diisophthalic acid (H4edda) and 1,1′-bis(1H-benzimidazolyl) oxydiethane (bmoe). Significantly, this framework reveals great thermal and chemical stability and can retain its structural integrity when immersed in water or even in a certain acid/base aqueous solution (pH = 2–13) for a period of time. As expected, this material also exhibits strong fluorescence. Further investigations indicate that as-synthesized Ni-CP can be served as a multi-responsive sensing platform for the highly selective and sensitive detection of nitrofurazone (NFZ), acetylacetone (ACAC), MnO4− and Cr(VI) in aqueous media. The mechanisms for fluorescence quenching have been disclosed through thorough experimental and computational investigations.

IOT Based Indoor Lighting Performance of Led With Different Color Temperatures System Based on Human Activity

Numerous LED light brands are available on the market, each offering a variety of light colors, including warm white (2700 K), white (3000 K), cool white (5000 K), and daylight (6000 K). Each color of light produces heat in the form of different lights in the room. Living things have a physiological ability called circadian rhythm to adapt to changes in their environment due to rhythmic physiological influences on hormonal secretion, temperature, wake-sleep rhythm, glucose balance, and cell regulation cycles. Lighting sources should also be human-friendly, especially with regard to the increased hazards from shortwave emissions. Extended periods of exposure to white light that contains blue and/or violet wavelengths can suppress the production of melatonin, leading to higher secretion of female hormones. This, in turn, elevates the likelihood of developing breast cancer. This paper aims to design a lighting regulation system based on human activity and light color regulation with circadian rhythm for 24 hours a day. This system employs the Internet of Things (IoT) along with multiple sensor systems and WS2812B LEDs. By applying fuzzy logic, it generates outputs that effectively regulate the brightness and color of the lights. The research findings demonstrate that indoor lighting can be easily adapted by utilizing various sensors and fuzzy logic techniques to detect human activity. As a result, the intensity and color of the lights can be automatically adjusted based on human activity, and the IoT allows for remote control of the system.

Smart Tool Wear Monitoring of CFRP/CFRP Stack Drilling Using Autoencoders and Memory-Based Neural Networks

The drilling of carbon fiber-reinforced plastic (CFRP) materials is a key process in the aerospace industry, where ensuring high product quality is a critical issue. Low-quality of final products may be caused by the occurrence of drilling-induced defects such as delamination, which can be highly affected by the tool conditions. The abrasive carbon fibers generally produce very fast tool wear with negative effects on the hole quality. This suggests the need to develop a method able to accurately monitor the tool wear development during the drilling process in order to set up optimal tool management strategies. Nowadays, different types of sensors can be employed to acquire relevant signals associated with process variables which are useful to monitor tool wear during drilling. Moreover, the increasing computational capacity of modern computers allows the successful development of procedures based on Artificial Intelligence (AI) techniques for signal processing and decision making aimed at online tool condition monitoring. In this work, an advanced tool condition monitoring method based on the employment of autoencoders and gated recurrent unit (GRU) recurrent neural networks (RNN) is developed and implemented to estimate tool wear in the drilling of CFRP/CFRP stacks. This method exploits the automatic feature extraction capability of autoencoders to obtain relevant features from the sensor signals acquired by a multiple sensor system during the drilling process and the memory abilities of GRU to estimate tool wear based on the extracted sensor signal features. The results obtained with the proposed method are compared with other neural network approaches, such as traditional feedforward neural networks, and considerations are made on the influence that memory-based hyperparameters have on tool wear estimation performance.

Development of a Multiple RGB-D Sensor System for ADHD Screening and Improvement of Classification Performance Using Feature Selection Method

Attention deficit and hyperactivity disorder (ADHD) is a mixed behavioral disorder that exhibits symptoms, such as carelessness and hyperactivity–impulsivity. To date, existing ADHD diagnosis methods rely on observations by observers, such as parents and teachers, which limits the ability to reflect objective evaluation. In this study, to overcome this limitation, we proposed a multiple RGB-D sensor system that can objectively measure the amount of action and attention of children playing a robot-led game. In addition, a classifier was developed to classify children into ADHD, ADHD risk, and normal groups using the multilayer perceptron and data obtained through sensors. The effectiveness of the developed system for ADHD screening was verified. In this study, the priority of abnormal behavior indicators designed for ADHD screening was measured, the features with the highest priority were selected using a feature selection method. Eight hundred and twenty-eight children participated and were classified into the ADHD, ADHD risk, and normal groups, and the results were compared with the diagnosis by clinicians. The proposed system achieved sensitivity of 97.06% and 100%, and specificity of 96.42% and 94.68% in the ADHD and ADHD risk groups, respectively.

Perspectives on AI-driven systems for multiple sensor data fusion

Abstract Artificially intelligent automation has not only impact on sensor technologies, but also on comprehensive multiple sensor systems for assisting situational awareness and decision-making. This is particularly true for integrated Manned-unManned-Teaming (MuM-T), for example. From a systems engineering perspective which does not exclude applications in the defence domain, three tasks need to be fulfilled: (1) Design artificially intelligent automation in a way that human beings are mentally and emotionally able to master each situation. (2) Identify technical design principles to facilitate the responsible use of AI in ethically critical applications. (3) Guarantee that human decision makers always have full superiority of information, decision-making, and options of action. Our discussion of AI-driven systems for multiple sensor data fusion results in recommendations and key results. We are addressing the algorithms needed, the data to be processed, the programming skills required, the computing devices to be used, the inevitable anthropocentric design, the reviewing of R & D efforts necessary, and the integration of different dimensions in a systems-of-systems point of view.

Smart Home Environment Monitoring System Based on Microcontroller

More than 90% of a person’s lifetime is spent indoors in a variety of activities, thus the parameters of the home environment affect human physical and mental health. With the development of Internet of things technology, people put forward new and higher requirements for the smart home. Based on the microcontroller, in this paper, we use multiple sensor systems to monitor various indoor environmental parameters, that is, to collect and display the environmental temperature, humidity, light intensity, CO concentration, and smoke concentration accurately and in real time. In the process of monitoring, the limits of various environmental parameters can be set artificially. Different types of alarms will be activated when one or several parameters are detected to exceed the limits. The microcontroller transmits the monitoring data to mobile terminals such as smartphones and others through Bluetooth modules, and the display interface is clear and intuitive. After testing, the system has the advantages of accurate measurement results, stable performance, and simple operation, which has a certain promotion and use value in smart home environment monitoring.

Low frequency error analysis and calibration for multiple star sensors system of GaoFen7 satellite

ABSTRACT The GaoFen7 (GF7) optical satellite is the first Chinese civilian sub-meter high-resolution stereo mapping satellite and is equipped with a double linear array camera and laser altimeter to achieve large-scale topographic mapping. To improve the accuracy of attitude determination, an attitude determination system comprised of four star sensors is loaded. According to the measurement accuracy and steady performance, the star sensors 1a and 1b is usually used together for satellite attitude calculation, which is called the conventional mode of attitude determination. Then, the combination of star sensors 2a and 2b is called the unconventional mode of attitude determination. Affected by variations in the incident angle of sunlight and solar radiation, thermal deformation occurs in the body and installation structure of the star sensor, which causes Attitude Low-Frequency Error (ALFE) and seriously influences the consistency of attitude determination results of different combination modes for multiple star sensors system. This study proposes an ALFE analysis and calibration approach for the multiple star sensors system of GF7 satellite to ensure the consistency of attitude determination results of different combination modes. Based on the statistical characteristics of the angles of the three axes, the installation parameters of the four star sensors are first calibrated. After analyzing the characteristics of the optical axis angles within 1420 orbit periods over 135 days, the segmented ALFE compensation model between the unconventional and conventional modes is proposed based on the Fourier series model and input parameter of latitude. Based on the on-orbit installation parameters and the ALFE model, the precise attitude determination results of the unconventional mode are calculated. Experimental results show that the attitude determination consistency after compensation is better than 2”. Moreover, the reliable application time range of the compensation model is 30 days to satisfy the requirements for high-precision attitude determination of GF7 satellite.

Development and testing of a performance evaluation methodology to assess the reliability of occupancy sensor systems in residential buildings

With the emergence of advanced occupancy sensor technologies to better detect occupancy in buildings, a universal methodology and metrics are required to evaluate and report sensor systems’ reliability and compare the performance across multiple sensor systems. This research presents a methodology to assess the reliability of occupancy sensor systems in residential buildings in a controlled laboratory environment, including both “typical” and “failure” testing scenarios. The developed methodology was then implemented to evaluate a novel occupancy detection sensor system’s reliability. “Typical” testing evaluates the overall accuracy of the sensor system, which suggest how reliable the occupancy sensor system is over time. Results show that on average, the precision and recall are 0.75 and 0.70, indicating similar numbers of false positives and false negatives across the dataset. The overall accuracy of the tested sensor system was 62.4% to 76.4%. Failure testing results indicate whether there are influential variables impacting the sensor performance. For the tested sensor system, the number of occupants, presence of large objects, presence of interior light sources, and number of doors are not influential, while lighting level, location of occupants, additional door in the entry/exit area, and having the TV on are variables determined to impact the sensor system performance.

Gait phase detection by using a portable system and artificial neural network

Gait phases are important to evaluate the walking function and to identify the characteristics of pathological gaits. However, it is difficult to differentiate gait phases outside gait laboratories, thus, this study aimed to develop a method to detect 8 gait sub-phases using a wearable multiple sensor system and artificial neural network (ANN). Motion sensors were used to acquire the acceleration of lower limbs, and force sensitive resistors were used to detect contact state and force between the foot and the ground. Walking was recorded using a high-speed camera. Two feed forward back-propagation (BP) neural networks were developed. The resilient BP algorithm was used to train ANN. A total of 66 volunteers participated in this study. For the stance and swing phase detection, simulation of the training data showed an accuracy of 98.0 ​%. The data from the test set showed a recognition accuracy of 97.75 ​%. Because the ending point of the last phase ‘Terminal Swing’ is always 100 ​% GC, we only listed seven phases. The prediction accuracy of seven phases were: 35.9 ​%, 63.8 ​%, 93.6 ​%, 94.9 ​%, 94.8 ​%, 97.9 ​% and 98 ​% using the limb acceleration data only. The average accuracy for seven phases were 68 ​%, 91.3 ​%, 97.8 ​%, 98.9 ​%, 98.8 ​%, 99.1 ​%, and 99.5 ​% using the limb acceleration and foot pressure data for fast, normal, and slow gait speeds. This study provides a new method for eight gait sub-phases detection with high accuracy combining a wearable system and ANN, which may make gait phase analysis possible under free-living conditions.

RTVEMVS: Real-time modeling and visualization system for vehicle emissions on an urban road network

To improve the real-time and precision of vehicle emission monitoring and estimation for supporting variable and speedy response on vehicle emission control work, a real-time modeling and visualization system for vehicle emissions on an urban road network (RTVEMVS) was designed and built with Web platform by the development of three modules: real-time data collection, real-time modeling and real-time visualization. The real-time mapping of vehicle emission by vehicle types and road links and real-time“precise vehicles emissions impact estimation” technology which can trace key roads and vehicles induced high air pollution were innovated on system to provide the sensitive perception for government decision makers on speedy vehicles emission control. The detailed vehicle information, traffic flow, speed on each road link and weather data were accessed and fused in real-time from multiple urban sensor systems together with road network information. Integrating an emission calculation model with a high spatiotemporal resolution and the AREMOD dispersion model was running on real-time in RTVEMVS. Based on the Web interface and WebGIS technology (ArcGIS API for JavaScript), real-time vehicle emissions and estimation results can be visualized on a map. The RTEMVS was demonstrated by application on Chongqing of China, hourly based-links vehicle emission by vehicle types on road network and the top 10 of key road links and vehicles induced high air pollution per hour were clearly reveal and visually viewed, and indicate that the real-time system with real-time mapping and precise estimation can provide direct support to the variable vehicle emission control. The RTVEMVS is a useful platform for supporting decision-making in the precise control of urban vehicle emissions pollution.

TreeMMoSys: A low cost sensor network to measure wind-induced tree response

Severe storms caused the largest amount of damaged timber in European forests in the past 70 years. Storm damage occurs when wind loads exceed the failure limits of trees. A decisive factor in assessing storm damage is comprehensive knowledge of interactions between the aerial parts of trees and the high-impact airflow. This paper describes the inexpensive multiple sensor system TreeMMoSys that can measure aerial tree parts' wind-induced reactions, including branches and the stem. The output of TreeMMoSys includes acceleration and angular rate data converted to tilt angles in the post-processing. The system consists of a scalable number of light-weight tree response sensors and ground receivers that communicate through a WLAN network. The weatherproofed system is highly portable, reusable, and allows for an efficient monitoring and a maximization of the number of study trees. Due to the stable measurement performance and accuracy of TreeMMoSys, it can be deployed in the field for long-term monitoring of single tree reactions or neighboring trees' reactions to wind excitation.

HF-SPHR: Hybrid Features for Sustainable Physical Healthcare Pattern Recognition Using Deep Belief Networks

The daily life-log routines of elderly individuals are susceptible to numerous complications in their physical healthcare patterns. Some of these complications can cause injuries, followed by extensive and expensive recovery stages. It is important to identify physical healthcare patterns that can describe and convey the exact state of an individual’s physical health while they perform their daily life activities. In this paper, we propose a novel Sustainable Physical Healthcare Pattern Recognition (SPHR) approach using a hybrid features model that is capable of distinguishing multiple physical activities based on a multiple wearable sensors system. Initially, we acquired raw data from well-known datasets, i.e., mobile health and human gait databases comprised of multiple human activities. The proposed strategy includes data pre-processing, hybrid feature detection, and feature-to-feature fusion and reduction, followed by codebook generation and classification, which can recognize sustainable physical healthcare patterns. Feature-to-feature fusion unites the cues from all of the sensors, and Gaussian mixture models are used for the codebook generation. For the classification, we recommend deep belief networks with restricted Boltzmann machines for five hidden layers. Finally, the results are compared with state-of-the-art techniques in order to demonstrate significant improvements in accuracy for physical healthcare pattern recognition. The experiments show that the proposed architecture attained improved accuracy rates for both datasets, and that it represents a significant sustainable physical healthcare pattern recognition (SPHR) approach. The anticipated system has potential for use in human–machine interaction domains such as continuous movement recognition, pattern-based surveillance, mobility assistance, and robot control systems.

A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis

This paper presents a novel method for fusing information from multiple sensor systems for bearing fault diagnosis. In the proposed method, a convolutional neural network is exploited to handle multiple signal sources simultaneously. The most important finding of this paper is that a deep neural network with wide structure can extract automatically and efficiently discriminant features from multiple sensor signals simultaneously. The feature fusion process is integrated into the deep neural network as a layer of that network. Compared to single sensor cases and other fusion techniques, the proposed method achieves superior performance in experiments with actual bearing data.