Performance Of Sensing System Research Articles

Joint Communication and Sensing System Performance Evaluation and Testbed: A Communication-Centric Approach

Joint Communication and Sensing System Performance Evaluation and Testbed: A Communication-Centric Approach

DETERMINATION OF THE THRESHOLD SOLUTION FOR A CHANNEL WITH RAY-LEIG FADING WHEN PROBING THE COGNITIVE RADIO ENERGY DETECTOR SPECTRUM

Spectrum sensor system performance and detection of spectrum band occupancy in cognitive radio are among of the main research aspects. When considering a system in which the primary user changes or there are multiple types of primary users, an energy detector is used. For energy detector operation, the main parameters that determine probabilistic characteristics of detection (probability of user detection, probability of a «false alarm» error and probability of a «missing target» error) will depend on the correct determination of the decision threshold. This article reviews an analytical approach to the normalized decision threshold determination. The purpose is to analyze and determine the optimal decision threshold value for an energy detector in a channel with Rayleigh fading. A spectrum sensing system in cognitive radio is considered. Normalized values of the threshold solution for a channel with Rayleigh fading are determined. Graphic illustrations of the analysis results and calculations are presented. Results obtained for determining threshold solutions may be considered a proper approximation for calculating the characteristics of sensing systems in cognitive radio in channels with Rayleigh fading, providing the opportunity to determine frequency bands free of primary users more likely, thereby increasing the efficiency of use of the radio frequency spectrum.

Laboratory testing methods to evaluate the reliability of occupancy sensors for commercial building applications

The energy performance of commercial buildings is greatly influenced by occupants which are highly variable and among the most unpredictable components of a building's operation. While most building control systems use fixed, predetermined occupancy schedules, these fixed occupancy levels can be quite different from actual occupancy. This can cause unnecessary energy consumption, particularly from heating, ventilation, and air conditioning (HVAC) and lighting systems which are responsible for approximately 60% of commercial buildings' energy use. The use of occupancy counting sensor systems integrated with building management system controls is one method that can be used to improve the energy-consuming performance of buildings. However, there is no standardized universal methodology and metrics to evaluate their reliability. The aim of this research is to develop a uniform evaluation methodology to assess the reliability of occupancy counting sensor systems in a controlled laboratory environment. The developed testing methodology includes both “typical” scenarios representing the occupancy scenarios of a typical commercial building, and “failure” testing scenarios which represent a range of potential scenarios that may impact a sensor system's reliability. These methods were then implemented in a case study to evaluate the performance of two novel occupancy counting sensor systems (i.e., door-centric, and camera-based). Results suggest that typical testing results can be used to compare the overall performance of the occupancy counting sensor systems; however, failure testing is also important to understand the weaknesses of the sensor system in order to select the suitable one for the intended use of the commercial building. In addition, the proposed methodology includes a modified confusion matrix which enables the ability to identify if failures are caused by over or under counting occupants and to what extent this occurs over the testing period.

Development of ASTM International D8405—Standard Test Method for Evaluating PM2.5 Sensors or Sensor Systems Used in Indoor Applications

Sensors and sensor systems for monitoring fine particles with aerodynamic diameters smaller than 2.5 µm can provide real-time feedback on indoor air quality and thus can help guide actions to manage indoor air pollutant concentrations. Standardized verification of the performance and accuracy of sensors and sensor systems is crucial for predicting the efficacy of such monitoring. A new ASTM International standard test method (ASTM D8405) was created for this need and is the most exacting laboratory protocol published to date for evaluating indoor air quality sensors and sensor systems measuring particles smaller than 2.5 µm in diameter. ASTM D8405 subjects sensors and sensor systems to five test phases: (1) an initial particle concentration ramp; (2) exposure to various temperature and humidity conditions; (3) exposure to interfering particles; (4) temperature cycling; and (5) a final particle concentration ramp to assess drift. This paper discusses the development of the standard test method, key aspects of the testing process, example evaluation results, and a comparison of this standard test method against peer evaluation protocols.

Advantages and Challenges of Composting Reactors for Household Use: Smart Reactor Concept

In the European Union, 88 Mt of food waste is generated annually, accounting for 6% of total EU greenhouse gas emissions. To reduce the amount of bio-waste going into the landfills, the composting of bio-waste at a household level must be facilitated. Traditional composting devices for garden and household biological waste solely rely on natural processes and do not hold online process control features or require energy input. This study describes a design and construction of a smart composting reactor for improved composting process control and compares the developed system with other laboratory-scale reactors and commercial devices available for this purpose. The Alternative Hierarchy Process (AHP) method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria analysis method were used to assess the importance of various parameters and devices. The results showed good thermal insulation by reducing thermal transmittance from 1.87 W/m²K to 1.27 W/m²K, the effective sensor system performance of the constructed system, providing continuous data logging of temperature, moisture, and gas concentration levels. The system demonstrated 58% proximity to the ideal solution.

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.

Towards Personalization of Indoor Air Quality: Review of Sensing Requirements and Field Deployments.

As humans spend more time indoors, ensuring acceptable indoor air quality (IAQ) through ubiquitous sensing systems has become a necessity. Although extensive studies have been conducted on the IAQ sensing systems, a holistic review of the performance and deployment of Ubiquitous IAQ Sensing (UIAQS) systems with associated requirements in IAQ sensing standards is still lacking. In this study, we first reviewed IAQ pollutants and other IAQ-related factors and the associated requirements in the prominent IAQ sensing standards. We found that while non-pollutant factors are influential on occupants’ perception of IAQ and their satisfaction, they do not have evaluation metrics in the IAQ standards. Then, we systematically reviewed field studies on UIAQS technologies in the literature. Specific classes of information were recorded and analyzed further. We found that the majority of the UIAQS systems did not meet the requirements of the prominent IAQ sensing standards and identified four primary research gaps. We concluded that a new holistic and personalized approach that incorporates UIAQS measurements and subjective feedback is needed. This study provides valuable insights for researchers and policymakers to better improve UIAQS technologies by developing personalized IAQ sensors and sensing standards.

Proximity-field nanopatterning for high-performance chemical and mechanical sensor applications based on 3D nanostructures

In this era of the Internet of Things, the development of innovative sensors has rapidly accelerated with that of nanotechnology to accommodate various demands for smart applications. The practical use of three-dimensional (3D) nanostructured materials breaks several limitations of conventional sensors, including the large surface-to-volume ratio, precisely tunable pore size and porosity, and efficient signal transduction of 3D geometries. This review provides an in-depth discussion on recent advances in chemical and mechanical sensors based on 3D nanostructures, which are rationally designed and manufactured by advanced 3D nanofabrication techniques that consider structural factors (e.g., porosity, periodicity, and connectivity). In particular, we focus on a proximity-field nanopatterning technique that specializes in the production of periodic porous 3D nanostructures that satisfy the structural properties universally required to improve the performance of various sensor systems. State-of-the-art demonstrations of high-performance sensor devices such as supersensitive gas sensors and wearable strain sensors realized through designed 3D nanostructures are summarized. Finally, challenges and outlooks related to nanostructures and nanofabrication for the practical application of 3D nanostructure-based sensor systems are proposed.

Edge Effects of an Eddy-Current Thickness Sensor During Chemical Mechanical Polishing

Accurate and in-situ thickness measurements of copper films on Si-based wafers are employed to improve global planarization, adjust polishing pressure, and avoid over-polishing and under-polishing during chemical mechanical polishing (CMP). However, edge effects cause inaccurate thickness measurements at the wafer edge, and greatly decrease the effective area of integrated circuit (IC) manufacturing. In this study, edge effects were studied using both a trans-dimensional simulation model of electromagnetic fields and circuit coupling in eddy-current thickness measurement and an experimental nanoscale thickness-measurement sensor system with a resolution of 2.07 Å /mV. The simulation and experimental results were in close agreement when the sensor coil was located at different edge positions. The influence of different factors such as copper film thickness, edge positions, calibrated thickness, lift-off distance, and excitation voltages on thickness-measurement performance in the edge region were also examined. Copper film surface current density was calculated to clearly evaluate eddy-current distortion at different edge positions. The determined influences of these parameters on edge effects could be used to improve the edge-measurement performance of sensor systems by adjusting coil and circuit parameters or propose an edge signal compensation algorithm during CMP.

Influential variables impacting the reliability of building occupancy sensor systems: A systematic review and expert survey

Occupancy information is critical for buildings, as it can substantially impact energy consumption. However, occupancy is highly unpredictable, and depends on occupants’ schedules and their interactions with building systems. Occupancy sensor systems have been deployed in buildings for many years, using a broad range of sensor types, most typically for lighting control. Similarly, many research studies have been conducted that use a range of sensor modalities for occupancy sensing. These studies have assessed reliability considering diverse variables, testing methods, and evaluation metrics. However, no comprehensive review of occupancy sensor system reliability has been compiled. In this research, a review of the literature on occupancy sensor systems is presented, focusing on influential variables that may cause systems to incorrectly represent occupancy. Next, a survey was developed and completed by researchers, practitioners, and other stakeholders, ranking the most influential variables. The results of this effort provide a tiered list of what both literature and experts suggest are the most influential factors on occupancy sensor system performance. Future work includes the development of a standard methodology to evaluate the reliability of different occupancy sensor systems based on these efforts.

Performance of Sentinel-2A Remote Sensing System for Urban Area Mapping in Malaysia via Pixel-Based and OBIA Methods

Sentinel-2A remote sensing satellite system was recently launched, providing free global remote sensing data similar to Landsat systems. Although the mission enables the acquisition of 10 m spatial resolution global data, the assessment of Sentinel-2A data performance for mapping in Malaysia is still limited. This study aimed to investigate and assess the capability of Sentinel-2A imagery in mapping urban areas in Malaysia by comparing its performance against the established Landsat-8 data as well as the fusion datasets from combining Landsat-8 and Sentinel-2A datasets and using Wavelet transform (WT), Brovey transform (BT) and principal component analysis. Pixel-based and object-based image analysis (OBIA) classification approaches combined with support vector machine (SVM) and decision tree (DT) algorithms were utilized in this assessment, and the accuracy generated was analysed. The Sentinel-2A data provided superior urban mapping output over the use of Landsat-8 alone, and the fusion datasets do not yield advantages for single-scene urban mapping. The highest overall accuracy (OA) for pixel-based classification of Sentinel-2A images is 84.77 % by SVM, followed by 65.27 % using DT. BT produced the highest OA for the fusion images of 78.40 % with SVM and 52.21 % with DT. For the object-based classification of Sentinel-2A images, the highest OA is 71.33 % by SVM, followed by 76.38 % using DT. Similarly, the highest OA of fusion images is obtained by BT of 50.35 % with SVM, followed by 65.66 % with DT. From the analysis, the use of SVM pixel-based classification for medium spatial resolution Sentinel-2A data is effective for urban mapping in Malaysia and useful for future long-term mapping applications. HIGHLIGHTS An accurate mapping of urban land is still challenging and requires high image quality of spectral and spatial aspects to identify features Single and fusion image analysis conducted in order to investigate and assess the most performing interpretation result by grouping out the features classes Statistical performance and image classification comparison is relevant to prove the most effective result among the images GRAPHICAL ABSTRACT

Analytical Evaluation of Mobile In Situ Soil Nitrate Infrared Sensor Designs for Precision Agriculture

One approach to improving nitrogen fertilizer use efficiency in crop production is to use infrared (IR) soil nitrate sensors to analyze soil nitrate and then spatially modulate fertilizer application rates. The most commonly used Fourier transform IR (FTIR) spectrometers are sensitive to vibration and hence not suitable for real-time measurements in agricultural fields when mounted on tractors that experience severe vibrations as they are driven across a field. In this paper, we propose IR sensor designs to address the vibration issues currently limiting the use of IR spectroscopy for precision nitrogen fertilizer management in agriculture. We evaluate the performance of different nitrate sensor configurations based on diamond attenuated total reflection (D-ATR) spectrometers, that do not have moving parts and less sensitive to vibrations than FTIRs. We study sensor configurations based on the following infrared light sources: commercially available tunable quantum cascade lasers (QCLs), thermal sources, superluminescent diodes (SLDs), and superlattice light emitting diodes (SLEDs). Performance of the different sensor systems is evaluated and optimized to achieve maximum signal-to-noise ratio (SNR). We also propose a sensing limit variable, S, to characterize different sensor configurations. The work presented in this manuscript provides design options for agricultural nitrate sensors that can be rugged in the field and collect spectral data at fast rates with high SNR.

A Graphics Processing Unit (GPU) Approach to Large Eddy Simulation (LES) for Transport and Contaminant Dispersion

Recent advances in the development of large eddy simulation (LES) atmospheric models with corresponding atmospheric transport and dispersion (AT&D) modeling capabilities have made it possible to simulate short, time-averaged, single realizations of pollutant dispersion at the spatial and temporal resolution necessary for common atmospheric dispersion needs, such as designing air sampling networks, assessing pollutant sensor system performance, and characterizing the impact of airborne materials on human health. The high computational burden required to form an ensemble of single-realization dispersion solutions using an LES and coupled AT&D model has, until recently, limited its use to a few proof-of-concept studies. An example of an LES model that can meet the temporal and spatial resolution and computational requirements of these applications is the joint outdoor-indoor urban large eddy simulation (JOULES). A key enabling element within JOULES is the computationally efficient graphics processing unit (GPU)-based LES, which is on the order of 150 times faster than if the LES contaminant dispersion simulations were executed on a central processing unit (CPU) computing platform. JOULES is capable of resolving the turbulence components at a suitable scale for both open terrain and urban landscapes, e.g., owing to varying environmental conditions and a diverse building topology. In this paper, we describe the JOULES modeling system, prior efforts to validate the accuracy of its meteorological simulations, and current results from an evaluation that uses ensembles of dispersion solutions for unstable, neutral, and stable static stability conditions in an open terrain environment.

Highly sensitive detection for alkaline phosphatase using doped ZnS quantum dots with room temperature phosphorescence and its logic gate function

This paper presents a highly sensitive sensing system for alkaline phosphatase by room temperature phosphorescence of Mn doped ZnS quantum dots and pyrophosphate. The sensing system has intense room temperature phosphorescence emission in the absence of alkaline phosphatase. The phosphorescence is quenched gradually with the addition of alkaline phosphatase. The emission “on” without alkaline phosphatase may be attributed to the increased probability of charge transfer from one of surface traps to the dopant bands of another resulted from the shortened dot-to-dot distance by the strong chelation of pyrophosphate and Zn2+ ion and the hydrogen bonding between pyrophosphate and β-cyclodextrin. The addition of alkaline phosphatase causes pyrophosphate hydrolyzed to orthophosphate and the dot-to-dot distance of quantum dots back to the normal, and then the phosphorescence “off”. The factors affecting the sensing system performance were also optimized. Under the optimal experimental conditions, the linear range for alkaline phosphatase is determined as 0.2−10 U/L with a LOD at 0.045 U/L. The recovery of human serum was determined from 93.75%–103.03%, indicating a potential application in biomedical diagnosis. Furthermore, an RTP-based “INHIBIT” logic gate using the doped ZnS quantum dots was also presented.

Urban Mapping Performance of Sentinel-2A Remote Sensing System in Malaysia

Sentinel-2A remote sensing satellite system was recently launched, thereby providing free global remote sensing data in a similar way to Landsat systems. The mission enables the acquisition of 10 m spatial resolution global data; however, the assessment of Sentinel-2A data performance for mapping in Malaysia is still limited. This study aimed to investigate and assess the capability of Sentinel-2A imagery in mapping urban area in Malaysia by comparing its performance against the established Landsat-8 data as well as the fusion datasets from combining Landsat-8 and Sentinel-2A datasets by use of Wavelet transform (WT), Brovey transform (BT), and principal component analysis. Pixel- and object-based classification approaches combined with support vector machine (SVM) and decision tree (DT) algorithms were utilized in this assessment, and the accuracy generated was analysed. The Sentiel-2A data provide superior urban mapping output over the use of Landsat-8 alone, and the fusion datasets do not yield advantages for single-scene urban mapping. The highest overall accuracy (OA) for pixel-based classification of Sentinel-2A images is 84.77% by SVM, followed by 65.27% using DT. BT produces the highest OA for the fusion images of 78.40% with SVM and 52.21% with DT. For the object-based classification of Sentinel-2A images, the highest OA is 71.33% by SVM, followed by 76.38% using DT. Similarly, the highest OA of fusion images is obtained by BT of 50.35% with SVM, followed by 65.66% with DT. From the analysis, the use of SVM pixel-based classification for medium spatial resolution Sentinel-2A data is effective for urban mapping in Malaysia and is useful for future long-term mapping applications

Assessment and Improvement of Two Low-Cost Particulate Matter Sensor Systems by Using Spatial Interpolation Data from Air Quality Monitoring Stations

Two low-cost fine particulate matter (PM2.5) sensor systems have been established by the government and community in Taiwan. Each system combines hundreds of PM2.5 sensors through an Internet of Things architecture. Since these sensors have not been calibrated, their performance has been questioned. In this study, the spatial interpolation data from air quality monitoring stations (AQMSs) was used to quantify the performances of the two sensor systems. The linearity, sensitivity, offset, precision, accuracy, and bias of the two sensor systems were estimated. The results indicate that the linearity of the government’s sensor system was higher than that of the community sensor system. However, the sensitivity of the government’s system was lower than that of the community system. The relative standard deviation, relative error, offset, and bias of the community sensor system were higher than those of the government sensor system. However, the government sensor system exhibited superior spatial interpolation results for the AQMS data than the community sensor system did. The precision and accuracy of the two sensor systems were poor during a period of low PM2.5 concentrations. A working platform of improvements consisting of monitoring the operation loop and automatic correction loop is proposed. The monitoring operation loop comprises five modules, namely outlier detection, temporal anomaly analysis, spatial anomaly analysis, spatiotemporal anomaly analysis, and trajectory analysis modules. The automatic correction loop contains spatial interpolation module, a sensor performance detection module, and a correction module. The proposed working platform can enhance the performance of low-cost sensor systems, especially as alert systems for reportable events.

Assessment of air quality sensor system performance after relocation

Sensor based monitoring systems have been indicated as a promising tool to increase information on spatio-temporal distribution of air pollution but several issues have been raised about the accuracy of such monitors when used in the field. The study aimed at assessing the performance of sensor based systems after multiple relocation in different seasons and sites. The systems included electrochemical sensors to measure NO2 and O3 concentrations. The approach consisted in two-week field calibration of each device at a reference monitoring station and the test of the calibrated device at different reference station sites. The main specific goal was a comparison of sensor performance considering site-specific (SS) and no site-specific (no_SS) calibration, i.e. calibration and testing carried out or not at sites with similar characteristics. Calibration was performed by season using random forest (RF) models. Very good performance was found for calibrated O3 sensors with R2 ≥ 0.82 regardless of seasons and sites. Mean normalized root mean square error (nRMSE) was around 7% and 6% in winter and summer tests, respectively. Very good performance of sensor systems was observed also for NO2 during winter (R2 ≥ 0.84) with much better accuracy for SS compared to no_SS calibration (nRMSE equal to 6% and 17%, respectively). A marked decrease of performance was observed for NO2 sensors during summer. Our results show a good potential of sensor based systems after SS field calibration in increasing information on the distribution of air pollution at high spatial and temporal resolution.

Simulation of a Smart Sensor Detection Scheme for Wireless Communication Based on Modeling

The principle of sensing and sending information applies to all kinds of communications in different media like air, water, etc. During data transmission through sensors, the most important issue is sensing failure problems. In communication systems, sensing failure problems generally occur and affect sensor system performance. In this study, sensing failure issues are discussed, and a smart sensor is introduced that detects the communication signals. The characteristics of these sensors are identified with signal-to-noise ratio (SNR) readings. The proposed sensor system has high performance and it is better than many systems in the literature. Graphical findings show that the suggested scheme increases device efficiency and has more performance than the energy detector (ED) and ED-ADT-2013 by 33.5%, adaptive spectrum sensing (ASS)-2012 by 41.5%, energy detecting technique for adaptive spectrum sensing (EDT-ASS)-2015 by 28.2%, energy detector, and cyclo-2010 by 57.0%, and energy-SNR adaptive threshold (ADT) detector-2016 by 52.8% at −12 dB SNR, respectively. The proposed sensor detection scheme performs well compared to other existing sensors regarding sensing time. It was found that cooperative spectrum sensing (CSS) with the proposed method outperforms HwQ-2012 and CSS-EDT-ASS-2015 by 22.70% and 16.10% in terms of probability at −12 dB SNR, respectively, and detects the signal at −19.70 dB SNR.

A Noise-Reduced Light-to-Frequency Converter for Sub-0.1% Perfusion Index Blood SpO[Formula: see text] Sensing.

To improve the SpO 2 sensing system performance for hypoperfusion (low perfusion index) applications, this paper proposes a low-noise light-to-frequency converter scheme from two aspects. First, a low-noise photocurrent buffer is proposed by reducing the amplifier noise floor with a transconductance-boost ( gm-boost) circuit structure. Second, a digital processing unit of pulse-frequency-duty-cycle modulation is proposed to minimize the quantization noise in the following timer by limiting the maximum output frequency. The proposed light-to-frequency sensor chip is designed and fabricated with a 0.35- μm CMOS process. The overall chip area is 1 × 0.9 mm 2 and the typical total current consumption is about 1.8 mA from a 3.3-V power supply at room temperature. The measurement results prove the proposed functionality of output pulse duty cycle modulation, while the SNR of a typical 10-kHz output frequency is 59 dB with about 9-dB improvement when compared with the previous design. Among them, 2-3 dB SNR improvement stems from the gm-boosting and the rest comes from the layout design. In-system experimental results show that the minimum measurable PI using the proposed blood SpO 2 sensor could be as low as 0.06% with 2-percentage-point error of SpO 2. The proposed chip is suitable for portable low-power high-performance blood oximeter devices especially for hypoperfusion applications.

Magnetic Position Sensors

Position processors with in-built linearity compensation enable improvements in sensing system performance, vital to levels of precision required by modern automotive applications