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The purpose of this paper is to present a novel and simple prediction model of long-term metal oxide semiconductor (MOS) gas sensor baseline, and it brings some new perspectives for sensor drift. MOS gas sensors, which play a very important role in electronic nose (e-nose), constantly change with the fluctuation of environmental temperature and humidity (i.e. drift). Therefore, it is very meaningful to realize the long-term time series estimation of sensor signal for drift compensation.

In the proposed sensor baseline drift prediction model, auto-regressive moving average (ARMA) and Kalman filter models are used. The basic idea is to build the ARMA and Kalman models on the short-term sensor signal collected in a short period (one month) by an e-nose and aim at realizing the long-term time series prediction in a year using the obtained model.

Experimental results demonstrate that the proposed approach based on ARMA and Kalman filter is very effective in time series prediction of sensor baseline signal in e-nose.

Though ARMA and Kalman filter are well-known models in signal processing, this paper, at the first time, brings a new perspective for sensor drift prediction problem based on the two typical models.

Output stability or drift overtime has long been a major performance deficiency for gas sensors irrespective of what technology or methodology is used for their conception. Software correction may alleviate the problem somewhat but it is not always applicable. It has long been the objective of many researchers in this field to overcome this problem fundamentally and for good. The purpose of this paper is to show that this objective has now finally been achieved.

Conventional non‐dispersive infrared (NDIR) dual beam methodology utilizes the ratio of signal channel output over reference channel output for signal processing. The signal filter overlaps the absorption band of the gas of interest while the reference filter does not. However, this ratio changes as the source ages. The current methodology uses an absorption bias between signal and reference channel outputs. This absorption bias is created by using a path length for the signal channel greater than that for the reference channel. Both the signal and reference detectors carry an identical spectral filter overlapping the absorption band of the gas to be measured.

Implementation of the currently patented NDIR gas‐sensing methodology has been carried out in different gas sensor configurations for over a year in the laboratory. Performance results for these sensors showing insignificant output drifts overtime have been repeatedly demonstrated via simulated aging for the source.

The paper puts forward the view that the recent breakthrough of the Near Zero Drift methodology for NDIR gas sensors will very quickly change the hierarchy of technology dominance and utility for gas sensors at large.

The electronic nose is an array of chemical or gas sensors and associated with a pattern-recognition framework competent in identifying and classifying odorant or non-odorant and simple or complex gases. Despite more than 30 years of research, the robust e-nose device is still limited. Most of the challenges towards reliable e-nose devices are associated with the non-stationary environment and non-stationary sensor behaviour. Data distribution of sensor array response evolves with time, referred to as non-stationarity. The purpose of this paper is to provide a comprehensive introduction to challenges related to non-stationarity in e-nose design and to review the existing literature from an application, system and algorithm perspective to provide an integrated and practical view.

The authors discuss the non-stationary data in general and the challenges related to the non-stationarity environment in e-nose design or non-stationary sensor behaviour. The challenges are categorised and discussed with the perspective of learning with data obtained from the sensor systems. Later, the e-nose technology is reviewed with the system, application and algorithmic point of view to discuss the current status.

The discussed challenges in e-nose design will be beneficial for researchers, as well as practitioners as it presents a comprehensive view on multiple aspects of non-stationary learning, system, algorithms and applications for e-nose. The paper presents a review of the pattern-recognition techniques, public data sets that are commonly referred to as olfactory research. Generic techniques for learning in the non-stationary environment are also presented. The authors discuss the future direction of research and major open problems related to handling non-stationarity in e-nose design.

The authors first time review the existing literature related to learning with e-nose in a non-stationary environment and existing generic pattern-recognition algorithms for learning in the non-stationary environment to bridge the gap between these two. The authors also present details of publicly available sensor array data sets, which will benefit the upcoming researchers in this field. The authors further emphasise several open problems and future directions, which should be considered to provide efficient solutions that can handle non-stationarity to make e-nose the next everyday device.

This study aims to design an optimized algorithm for low-cost pedestrian navigation system (PNS) to correct the heading drift and altitude error, thus achieving high-precise pedestrian location in both two-dimensional (2-D) and three-dimensional (3-D) space.

A novel heading correction algorithm based on smoothing filter at the terminal of zero velocity interval (ZVI) is proposed in the paper. This algorithm adopts the magnetic sensor to calculate all the heading angles in the ZVI and then applies a smoothing filter to obtain the optimal heading angle. Furthermore, heading correction is executed at the terminal moment of ZVI. Meanwhile, an altitude correction algorithm based on step height constraint is proposed to suppress the altitude channel divergence of strapdown inertial navigation system by using the step height as the measurement of the Kalman filter.

The verification experiments were carried out in 2-D and 3-D space to evaluate the performance of the proposed pedestrian navigation algorithm. The results show that the heading drift and altitude error were well corrected. Meanwhile, the path calculated by the novel algorithm has a higher match degree with the reference trajectory, and the positioning errors of the 2-D and 3-D trajectories are both less than 0.5 per cent.

Besides zero velocity update, another two problems, namely, heading drift and altitude error in the PNS, are solved, which ensures the high positioning precision of pedestrian in indoor and outdoor environments.

The output of nearly all non‐dispersive infrared (NDIR) gas sensors deployed in the heating, ventilation, and air conditioning (HVAC) industry today cannot maintain their accuracy specifications within six months to a year. Consequently, all installed NDIR gas sensors must be re‐checked for accuracy over time at great costs. The purpose of this paper is to advance a novel technique for expeditiously recalibrating such installed NDIR gas sensors without the need for using any gas standards.

By recognizing the fact that the calibration curve for absorption biased designed NDIR gas sensors comprises two distinct domains, namely an invariant NDIR absorption physics domain and a variant sensor components characteristics domain. By formulating a novel recalibration procedure which corrects only changes that have taken place in the variant sensor components characteristics domain over time, it is possible to recalibrate the sensor very rapidly and remotely via wireless or infrared means using only the gas concentration level surrounding the sensor as a reasonably accurate gas standard.

Implementation of the currently described recalibration technique to a large number of absorption biased designed NDIR gas sensors has been carried out for over a year in the laboratory. Results of these experiments have unambiguously confirmed the capability and the accuracy of this novel recalibration technique.

The currently presented recalibration technique for absorption biased designed NDIR gas sensors is original and has never been published elsewhere. This technique significantly reduces the maintenance costs, inclusive of labor and material, for installed NDIR gas sensors that require periodic and mandatory accuracy commissioning over time.

The purpose of this paper is to present state-of-the-art approaches for precise operation of a robotic manipulator on a macro- to micro/nanoscale.

This paper first briefly discussed fundamental issues associated with precise operation of a robotic manipulator on a macro- to micro/nanoscale. Second, this paper described and compared the characteristics of basic components (i.e. mechanical parts, actuators, sensors and control algorithm) of the robotic manipulator. Specifically, commonly used mechanisms of the manipulator were classified and analyzed. In addition, intuitive meaning and applications of its actuator explained and compared in details. Moreover, related research studies on general control algorithm and visual control that are used in a robotic manipulator to achieve precise operation have also been discussed.

Remarkable achievements in dexterous mechanical design, excellent actuators, accurate perception, optimized control algorithms, etc., have been made in precise operations of a robotic manipulator. Precise operation is critical for dealing with objects which need to be manufactured, modified and assembled. The operational accuracy is directly affected by the performance of mechanical design, actuators, sensors and control algorithms. Therefore, this paper provides a categorization showing the fundamental concepts and applications of these characteristics.

This paper presents a categorization of the mechanical design, actuators, sensors and control algorithms of robotic manipulators in the macro- to micro/nanofield for precise operation.

The purpose of this paper is to present a novel concentration estimation model for improving the accuracy and robustness of low-cost electronic noses (e-noses) with metal oxide semiconductor sensors in indoor air contaminant monitoring and overcome the potential sensor drift.

In the quantification model, a piecewise linearly weighted artificial neural network ensemble model (PLWE-ANN) with an embedded self-calibration module based on a threshold network is studied.

The nonlinear estimation problem of sensor array-based e-noses can be effectively transformed into a piecewise linear estimation through linear weighted neural networks ensemble activated by a threshold network.

In this paper, a number of experimental results have been presented, and it also demonstrates that the proposed model has very good accuracy and robustness in real-time indoor monitoring of formaldehyde.

This paper documents the development of a microcontroller‐based humidity sensing system. The humidity sensors are manufactured by thin film technology from a novel combination of SiO/In₂O₃. The fabrication and characterization of the sensor samples is presented and discussed in this paper. The sensor pattern consists of an interdigitated conductor on top of which the sensing layer is deposited. A humidity sensitivity of 0.25%/RH% and a thermal sensitivity of 0.103%/°C has been measured. The samples exhibit a low drift over a one‐year time span (0.0013RH%/yr), low hysteresis (0.34RH%), good linearity (±2RH%) and a reasonably fast time response (18 sec). The entire sensor system has been analyzed mathematically and the necessary algorithms for error‐compensation have been developed. The resulting measurement system is efficient, accurate and flexible.

The brake controller is a key component of the locomotive brake system. It is essential to study its safety.

This paper summarizes and analyzes typical faults of the brake controller, and proposes four categories of faults: position sensor faults, microswitch faults, mechanical faults and communication faults. Suggestions and methods for improving the safety of the brake controller are also presented.

In this paper, a self-judgment and self-learning dynamic calibration method is proposed, which integrates the linear error of the sensor and the manufacturing and assembly errors of the brake controller to solve the output drift. This paper also proposes a logic for diagnosing and handling microswitch faults. Suggestions are proposed for other faults of brake controller.

The methods proposed in this paper can greatly improve the usability of the brake controller and reduce the failure rate.

The purpose of this study is to investigate the effect of internal and external factors on the accuracy and consistency of the data provided by mobile-embedded micro-electromechanical system (MEMS) pressure sensors based on smartphones currently in use.

For this purpose, sensor type and smartphone model have been regarded as internal factors, whereas temperature, location and usage habits have been considered as external factors. These factors have been investigated by examining data sets provided by sensors from 14 different smartphones. In this context, internal factors have been analyzed by implementing accuracy assessment processes for three different smartphone models, whereas external factors have been evaluated by analyzing the line charts which present timely pressure changes.

The study outlined that the sensor data at different sources have different characteristics due to the affecting parameters. Even if the pressure sensors are used under similar circumstances, data of these sensors have inconsistencies because of the sensor drift originated by internal factors. This study concluded that it was not applicable to provide a common correction coefficient for pressure sensor data of each smartphone model. Therefore, relative data (pressure differences) should be taken into consideration rather than absolute data (pressure values) when developing mobile applications using sensor data.

Results of this study can be used as the guideline for developing mobile applications using MEMS pressure sensors. One of the main finding of this paper is promoting the use of relative data (pressure differences) rather than absolute data (pressure values) when developing mobile applications using smartphone-embedded sensor data. This significant result was proved by examinations applied with in the study and can be applied by future research studies.

Existing studies mostly evaluate the use of MEMS pressure sensor data obtained from limited number of smartphone models. As each smartphone model has a specific technology, factors affecting the sensor performances should be identified and analyzed precisely in terms of smartphone models for providing extensive results. In this study, five smartphone models were used fractionally. In this context, they were used for examining the common effects of the factors, and detailed accuracy assessments were applied by using two high-tech smartphones in the market.