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This paper aims to provide a review of different types of non-wearable human identification sensors which can be applied for smart home environment.

The authors performed a systematic review to assess and compare different types of non-wearable and non-intrusive human identification sensors used in smart home environment. The literature research adds up to 5,567 records from 2000 to 2016, out of which 40 articles were screened and selected for this review.

In this review, the authors classified non-wearable human identification technologies into four main groups, namely, object-based, footstep-based, body shape-based and gait-based identification technologies. Assessing these four group of identification technologies showed that the maturity of non-wearable identification is not high and most of these technologies are verified in a lab environment. Additionally, footstep-based identification is the most popular identification approach listed in the literature.

This study contributes to the literature on human identification technologies in several ways. This paper identifies the state-of-the-art regarding non-wearable technologies which can be used in smart home environment. Moreover, the results of this paper can provide a better understanding of advantages and disadvantages of the non-wearable identification technologies.

The flexible eddy current array sensor has the characteristics of lightweight and flexibility, which has a great application prospect in the field of fatigue crack monitoring. But the exciting layout and feature signal extraction have a great influence on the crack monitoring characteristics of the sensor. This paper aims to propose a method using crack disturbed voltage as sensitivity to characterize crack propagation.

Flexible eddy current array sensors with reverse and codirectional exciting layout are proposed, and the advantages and disadvantages of three characterization methods based on the change of trans-impedance amplitude, the change of the trans-impedance’s real and imaginary part and the crack disturbed voltage are compared and analyzed by finite element simulation. Finally, the fatigue crack monitoring experiment is carried out.

The crack disturbed voltage and the change of trans-impedance’s imaginary part can effectively characterize the crack propagation for sensors with different exciting layouts. The codirectional exciting layout sensor has better crack identification sensitivity than the reverse exciting layout sensor, especially the induction coil 2. When the distance between the exciting coil and the induction coil is 0.1, 0.2 and 0.3 mm, it is increased by 372.09%, 295.24% and 231.43%, respectively.

Crack disturbed voltage can effectively characterize the crack propagation for sensors with two different exciting layouts.

This paper aims to study the influence of different reinforcement methods on crack monitoring characteristics of eddy current array sensors, and the sensors with two different reinforcement methods, SUS304 reinforcement and permalloy reinforcement, are proposed.

First, the finite element model of the sensor is established to analyze the influence of the reinforcement plate’s electromagnetic parameters on the crack identification sensitivity. Then, the crack monitoring accuracy test of sensors with two reinforcement methods is carried out. Finally, the fatigue crack monitoring experiments with bolt tightening torques of 45 and 63 N · m are carried out, respectively.

In this study, it is found that the crack identification sensitivity of the sensor can be improved by increasing the relative permeability of the reinforcement plate. The crack monitoring accuracy of the sensors with two different reinforcement methods is about 1 mm. And the crack identification sensitivity of the sensor reinforced by permalloy reinforcement plate is significantly higher than that of the sensor reinforced by SUS304 reinforcement plate.

The sensor reinforced by reinforcement plate can work normally under the squeezing action of the bolt, and the crack monitoring sensitivity of the sensor can be significantly improved by using the reinforcement plate with high relative permeability.

The purpose of this paper is to design and validate an electronic nose (E-nose) prototype using commercially available metal oxide gas sensors (MOX). This prototype has a sensor array board that integrates eight different MOX gas sensors to handle multi-purpose applications. The number of sensors can be adapted to match different requirements and classification cases. The paper presents the validation of this E-nose prototype when used to identify three gas samples, namely, alcohol, butane and cigarette smoke. At the same time, it discusses the discriminative abilities of the prototype for the identification of alcohol, acetone and a mixture of them. In this respect, the selection of the appropriate type and number of gas sensors, as well as obtaining excellent discriminative abilities with a miniaturized design and minimal computation time, are all drivers for such implementation.

The suggested prototype contains two main parts: hardware (low-cost components) and software (Machine Learning). An interconnection printed circuit board, a Raspberry Pi and a sensor chamber with the sensor array board make up the first part. Eight sensors were put to the test to see how effective and feasible they were for the classification task at hand, and then the bare minimum of sensors was chosen. The second part consists of machine learning algorithms designed to ensure data acquisition and processing. These algorithms include feature extraction, dimensionality reduction and classification. To perform the classification task, two features taken from the sensors’ transient response were used.

Results reveal that the system presents high discriminative ability. The K-nearest neighbor (KNN) and support vector machine radial basis function based (SVM-RBF) classifiers both achieved 97.81% and 98.44% mean accuracy, respectively. These results were obtained after data dimensionality reduction using linear discriminant analysis, which is more effective in terms of discrimination power than principal component analysis. A repeated stratified K-cross validation was used to train and test five different machine learning classifiers. The classifiers were each tested on sets of data to determine their accuracy. The SVM-RBF model had high, stable and consistent accuracy over many repeats and different data splits. The total execution time for detection and identification is about 10 s.

Using information extracted from transient response of the sensors, the system proved to be able to accurately classify the gas types only in three out of the eight MQ-X gas sensors. The training and validation results of the SVM-RBF classifier show a good bias-variance trade-off. This proves that the two transient features are sufficiently efficient for this classification purpose. Moreover, all data processing tasks are performed by the Raspberry Pi, which shows real-time data processing with miniaturized architecture and low prices.

Amperometric gas sensors are commonly used in air quality monitoring in long-term measurements. Baseline shift of sensor responses and power failure may occur over time, which is an obstacle for reliable operation of the entire system. The purpose of this study is to check the possibility of using PCA method to detect defected samples, identify faulty sensor and correct the responses of the sensor identified as faulty.

In this work, the authors present the results obtained with six amperometric sensors. An array of sensors was exposed to sulfur dioxide at the following concentrations: 0 ppm (synthetic air), 50 ppb, 100 ppb, 250 ppb, 500 ppb and 1000 ppb. The damage simulation consisted in adding to the sensor response a value of 0.05 and 0.1 µA and replacing the responses of one of sensors with a constant value of 0 and 0.15 µA. Sensor validity index was used to identify a damaged sensor in the matrix, and its responses were corrected via iteration method.

The results show that the methods used in this work can be potentially applied to detect faulty sensor responses. In the case of simulation of damage by baseline shift, it was possible to achieve 100% accuracy in damage detection and identification of the damaged sensor. The method was not very successful in simulating faults by replacing the sensor response with a value of 0 µA, due to the fact that the sensors mostly gave responses close to 0 µA, as long as they did not detect SO₂ concentrations below 250 ppb and the failure was treated as a correct response.

This work was inspired by methods of simulating the most common failures that occurs in amperometric gas sensors. For this purpose, simulations of the baseline shift and faults related to a power failure or a decrease in sensitivity were performed.

The purpose of this paper is to identify the root cause of low yield problems in the semiconductor manufacturing process using sensor data continuously collected from manufacturing equipment and describe the process environment in the equipment.

This paper proposes a sensor data mining process based on the sequential modeling of random forests for low yield diagnosis. The process consists of sequential steps: problem definition, data preparation, excursion time and critical sensor identification, data visualization and root cause identification.

A case study is conducted using real-world data collected from a semiconductor manufacturer in South Korea to demonstrate the effectiveness of the diagnosis process. The proposed model successfully identified the excursion time and critical sensors previously identified by domain engineers using costly manual examination.

The proposed procedure helps domain engineers narrow down the excursion time and critical sensors from the massive sensor data. The procedure's outcome is highly interpretable, informative and easy to visualize.

This work analyzes a pharmaceutical supply chain (PSC) in terms of supply chain visibility (SCV). The current good distribution practice (GDP) guideline demands increased visibility from firms. The purpose of this paper is to propose a solution for SCV enhancements based on automatic identification (Auto-ID) technologies.

The authors qualitatively analyze data from ten case studies of actors in a PSC. A review of Auto-ID technologies supports the derivation of solutions to enhance SCV.

This work shows that the functionalities of Auto-ID technologies offered by current practical monitoring solutions and challenges created by the GDP guideline necessitate further SCV enhancements. To enhance SCV, the authors propose three solutions: securPharm with passive radio frequency identification tags, transport containers with sensor nodes, and an SCV dashboard.

This study is limited to a PSC in Germany and is therefore not intended to be exhaustive. Thus, the results serve as a foundation for further analyses.

This study provides an overview of the functionality of Auto-ID technologies. In juxtaposition with the influence of the GDP guideline, the use of our Auto-ID-based solutions can help to enhance SCV.

This work analyzes a PSC in Germany, with consideration given to the influence of current legislation. Based on a multiple-case-study design, the authors derive three Auto-ID-based solutions for enhancing SCV.

This paper aims to study different possibilities for implementing easy-to-use and cost-effective micro-systems to detect and trace expelled gases from rotten food. The paper covers various radio-frequency identification (RFID) technologies and gas sensors as the two promoting feasibilities for the tracing of packaged food. Monitoring and maintaining quality and safety of food in transport and storage from producer to consumer are the most important concerns in food industry. Many toxin gases, even in parts per billion ranges, are produced from corrupted and rotten food and can endanger the consumers’ health. To overcome the issues, intelligent traceability of food products, specifically the packaged ones, in terms of temperature, humidity, atmospheric conditions, etc., has been paid attention to by many researchers.

Food poisoning is a serious problem that affects thousands of people every year. Poisoning food must be recognized early to prevent a serious health problem.

Contaminated food is usually detectable by odor. A small gas sensors and low-cost tailored to the type of food packaging and a communication device for transmitting alarm output to the consumer are key factors in achieving intelligent packaging.

Conducting polymer composite, intrinsically conducting polymer and metal oxide conductivity gas sensors, metal–oxide–semiconductor field-effect transistor (MOSFET) gas sensors offer excellent discrimination and lead the way for a new generation of “smart sensors” which will mould the future commercial markets for gas sensors.

Small size, low power consumption, short response time, wide operating temperature, high efficiency and small area are most important features of introduced system for using in package food.

The practice of facility management (FM) has been evolving with the rapid development of pervasive sensing technologies (PSTs) such as sensors, automatic identification (auto-ID), laser scanning and photogrammetry. Despite the proliferation of research on the use of PSTs for FM, a comprehensive review of such research is missing from the literature. This study aims to cover the knowledge void by examining the status quo and challenges of the selected PSTs with a focus on FM.

This paper reviewed 204 journal papers recounting cases of using PSTs for FM. The reviewed papers were extracted from Elsevier Scopus database using the advanced search.

Findings of this study revealed that PSTs and FM applications form a many-to-many mapping, i.e. one PST could facilitate many FM applications, and one application can also be supported by various PSTs. It is also found that energy modeling and management is the most referred purpose in FM to adopt PSTs, while space management, albeit important, received the least attention. Five challenges are identified, which include high investment on PSTs, data storage problem, absence of proper data exchange protocols for data interoperability, a lack of mature data processing methods for data utilization and privacy of users.

This paper paints a full picture of PSTs adoption for FM. It pinpoints the promising explorations for tackling the key challenges to future development.

This paper aims to introduce an aircraft engine inspection robot (AEIR) which can go in the internal of the aircraft engine without collision and detect damage for engine blades.

To obtain the position and pose information of the blades inside the engine, a novel tactile sensor based on electrical impedance tomography (EIT) is developed, which could provide location and direction information when it contacts with an unknown object. In addition, to navigate the continuum robot, a control method is proposed to control the continuum robot, which can control the continuum robot to move along the pre-planned path and reduce the deviation from the planned path.

Experiment results show that the average error of contact location measurement of the tactile sensor is 0.8 mm. The average error relative to the size (diameter of 18 mm) of the sensor is 4.4%. The continuum robot can successfully reach the target position through a gap of 30 mm and realize the spatial positioning of blades. The validity of the AEIR for engine internal blade detection is verified.

The aero-engine inspection robot developed in this paper can replace human to detect engine blades and complete different detection tasks with different kinds of sensors.