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Sensors, Volume 19, Issue 20 (October-2 2019) – 251 articles

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Cover Story (view full-size image) One of the open challenges of environmental monitoring concerns the design of new analytical tools. [...] Read more.
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Open AccessArticle
Structure from Articulated Motion: Accurate and Stable Monocular 3D Reconstruction without Training Data
Sensors 2019, 19(20), 4603; https://doi.org/10.3390/s19204603 - 22 Oct 2019
Cited by 3 | Viewed by 1110
Abstract
Recovery of articulated 3D structure from 2D observations is a challenging computer vision problem with many applications. Current learning-based approaches achieve state-of-the-art accuracy on public benchmarks but are restricted to specific types of objects and motions covered by the training datasets. Model-based approaches [...] Read more.
Recovery of articulated 3D structure from 2D observations is a challenging computer vision problem with many applications. Current learning-based approaches achieve state-of-the-art accuracy on public benchmarks but are restricted to specific types of objects and motions covered by the training datasets. Model-based approaches do not rely on training data but show lower accuracy on these datasets. In this paper, we introduce a model-based method called Structure from Articulated Motion (SfAM), which can recover multiple object and motion types without training on extensive data collections. At the same time, it performs on par with learning-based state-of-the-art approaches on public benchmarks and outperforms previous non-rigid structure from motion (NRSfM) methods. SfAM is built upon a general-purpose NRSfM technique while integrating a soft spatio-temporal constraint on the bone lengths. We use alternating optimization strategy to recover optimal geometry (i.e., bone proportions) together with 3D joint positions by enforcing the bone lengths consistency over a series of frames. SfAM is highly robust to noisy 2D annotations, generalizes to arbitrary objects and does not rely on training data, which is shown in extensive experiments on public benchmarks and real video sequences. We believe that it brings a new perspective on the domain of monocular 3D recovery of articulated structures, including human motion capture. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Detection of Simulated Fukushima Daichii Fuel Debris Using a Remotely Operated Vehicle at the Naraha Test Facility
Sensors 2019, 19(20), 4602; https://doi.org/10.3390/s19204602 - 22 Oct 2019
Viewed by 972
Abstract
The use of robotics in harsh environments, such as nuclear decommissioning, has increased in recent years. Environments such as the Fukushima Daiichi accident site from 2011 and the Sellafield legacy ponds highlight the need for robotic systems capable of deployment in hazardous environments [...] Read more.
The use of robotics in harsh environments, such as nuclear decommissioning, has increased in recent years. Environments such as the Fukushima Daiichi accident site from 2011 and the Sellafield legacy ponds highlight the need for robotic systems capable of deployment in hazardous environments unsafe for human workers. To characterise these environments, it is important to develop robust and accurate localization systems that can be combined with mapping techniques to create 3D reconstructions of the unknown environment. This paper describes the development and experimental verification of a localization system for an underwater robot, which enabled the collection of sonar data to create 3D images of submerged simulated fuel debris. The system was demonstrated at the Naraha test facility, Fukushima prefecture, Japan. Using a camera with a bird’s-eye view of the simulated primary containment vessel, the 3D position and attitude of the robot was obtained using coloured LED markers (active markers) on the robot, landmarks on the test-rig (passive markers), and a depth sensor on the robot. The successful reconstruction of a 3D image has been created through use of a robot operating system (ROS) node in real-time. Full article
(This article belongs to the collection Positioning and Navigation)
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Open AccessArticle
A Simple, Low-Cost Micro-Coating Method for Accuracy Improvement and Its Application in Pressure Sensors
Sensors 2019, 19(20), 4601; https://doi.org/10.3390/s19204601 - 22 Oct 2019
Cited by 1 | Viewed by 818
Abstract
The demand for high-accuracy pressure sensors has increased with the advancement of technology in a wide variety of applications. However, it is generally difficult and expensive to improve the accuracy of the pressure sensor because it usually depends on the sensing principle and [...] Read more.
The demand for high-accuracy pressure sensors has increased with the advancement of technology in a wide variety of applications. However, it is generally difficult and expensive to improve the accuracy of the pressure sensor because it usually depends on the sensing principle and the internal physical structure of the pressure sensor, varying with its material and production process. Thus, a simple, low-cost, and generally applied post-processing method is proposed to improve the accuracy of pressure sensors. In this method, a micro-coating is cladded on the surface of the sensor, which effectively isolates the adverse effect of the external environment, similar to applying a “micro-protective clothing” on the pressure sensor. Experiments on seven pressure sensors are conducted, in which the micron-thin parylene polymer is utilized as the surface-deposited coating layer to demonstrate the improvement of accuracy. Results show that the accuracy was improved, with an average increase of approximately 62.54% than before cladding, while the sensitivity was almost unchanged. The principle of improving the accuracy of this method was also analyzed. The proposed simple, efficient, and low-cost method of cladding micro-coating for enhancing the accuracy of sensors can be widely applied in various fields of industrial automatic control. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
WaistonBelt X: A Belt-Type Wearable Device with Sensing and Intervention Toward Health Behavior Change
Sensors 2019, 19(20), 4600; https://doi.org/10.3390/s19204600 - 22 Oct 2019
Cited by 2 | Viewed by 1413
Abstract
Changing behavior related to improper lifestyle habits has attracted attention as a solution to prevent lifestyle diseases, such as diabetes, heart disease, arteriosclerosis, and stroke. To drive health behavior changes, wearable devices are needed, and they must not only provide accurate sensing and [...] Read more.
Changing behavior related to improper lifestyle habits has attracted attention as a solution to prevent lifestyle diseases, such as diabetes, heart disease, arteriosclerosis, and stroke. To drive health behavior changes, wearable devices are needed, and they must not only provide accurate sensing and visualization functions but also effective intervention functions. In this paper, we propose a health support system, WaistonBelt X, that consists of a belt-type wearable device with sensing and intervention functions and a smartphone application. WaistonBelt X can automatically measure a waistline with a magnetometer that detects the movements of a blade installed in the buckle, and monitor the basic activities of daily living with inertial sensors. Furthermore, WaistonBelt X intervenes with the user to correct lifestyle habits by using a built-in vibrator. Through evaluation experiments, we confirmed that our proposed device achieves measurement of the circumference on the belt position (mean absolute error of 0.93 cm) and basic activity recognition (F1 score of 0.95) with high accuracy. In addition, we confirmed that the intervention via belt vibration effectively improves the sitting posture of the user. Full article
(This article belongs to the Special Issue Wearable Sensors and Systems in the IOT)
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Open AccessArticle
Fruit Detection and Segmentation for Apple Harvesting Using Visual Sensor in Orchards
Sensors 2019, 19(20), 4599; https://doi.org/10.3390/s19204599 - 22 Oct 2019
Cited by 11 | Viewed by 1249
Abstract
Autonomous harvesting shows a promising prospect in the future development of the agriculture industry, while the vision system is one of the most challenging components in the autonomous harvesting technologies. This work proposes a multi-function network to perform the real-time detection and semantic [...] Read more.
Autonomous harvesting shows a promising prospect in the future development of the agriculture industry, while the vision system is one of the most challenging components in the autonomous harvesting technologies. This work proposes a multi-function network to perform the real-time detection and semantic segmentation of apples and branches in orchard environments by using the visual sensor. The developed detection and segmentation network utilises the atrous spatial pyramid pooling and the gate feature pyramid network to enhance feature extraction ability of the network. To improve the real-time computation performance of the network model, a lightweight backbone network based on the residual network architecture is developed. From the experimental results, the detection and segmentation network with ResNet-101 backbone outperformed on the detection and segmentation tasks, achieving an F 1 score of 0.832 on the detection of apples and 87.6% and 77.2% on the semantic segmentation of apples and branches, respectively. The network model with lightweight backbone showed the best computation efficiency in the results. It achieved an F 1 score of 0.827 on the detection of apples and 86.5% and 75.7% on the segmentation of apples and branches, respectively. The weights size and computation time of the network model with lightweight backbone were 12.8 M and 32 ms, respectively. The experimental results show that the detection and segmentation network can effectively perform the real-time detection and segmentation of apples and branches in orchards. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Securing Cryptographic Chips against Scan-Based Attacks in Wireless Sensor Network Applications
Sensors 2019, 19(20), 4598; https://doi.org/10.3390/s19204598 - 22 Oct 2019
Cited by 2 | Viewed by 827
Abstract
Wireless sensor networks (WSN) have deeply influenced the working and living styles of human beings. Information security and privacy for WSN is particularly crucial. Cryptographic algorithms are extensively exploited in WSN applications to ensure the security. They are usually implemented in specific chips [...] Read more.
Wireless sensor networks (WSN) have deeply influenced the working and living styles of human beings. Information security and privacy for WSN is particularly crucial. Cryptographic algorithms are extensively exploited in WSN applications to ensure the security. They are usually implemented in specific chips to achieve high data throughout with less computational resources. Cryptographic hardware should be rigidly tested to guarantee the correctness of encryption operation. Scan design improves significantly the test quality of chips and thus is widely used in semiconductor industry. Nevertheless, scan design provides a backdoor for attackers to deduce the cipher key of a cryptographic core. To protect the security of the cryptographic system we first present a secure scan architecture, in which an automatic test control circuitry is inserted to isolate the cipher key in test mode and clear the sensitive information at mode switching. Then, the weaknesses of this architecture are analyzed and an enhanced scheme using concept of test authorization is proposed. If the correct authorization key is applied within the specific time, the normal test can be performed. Otherwise, only secure scan test can be performed. The enhanced scan scheme ensures the security of cryptographic chips while remaining the advantages of scan design. Full article
(This article belongs to the Special Issue Security and Privacy in Wireless Sensor Network)
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Open AccessArticle
Hybrid Wireless Fingerprint Indoor Localization Method Based on a Convolutional Neural Network
Sensors 2019, 19(20), 4597; https://doi.org/10.3390/s19204597 - 22 Oct 2019
Cited by 5 | Viewed by 811
Abstract
In the indoor location field, the quality of received-signal-strength-indicator (RSSI) fingerprints plays a key role in the performance of indoor location services. However, changes in an indoor environment may lead to the decline of location accuracy. This paper presents a localization method employing [...] Read more.
In the indoor location field, the quality of received-signal-strength-indicator (RSSI) fingerprints plays a key role in the performance of indoor location services. However, changes in an indoor environment may lead to the decline of location accuracy. This paper presents a localization method employing a Hybrid Wireless fingerprint (HW-fingerprint) based on a convolutional neural network (CNN). In the proposed scheme, the Ratio fingerprint was constructed by calculating the ratio of different RSSIs from important contribution access points (APs). The HW-fingerprint combined the Ratio fingerprint and the RSSI to enhance the expression of indoor environment characteristics. Moreover, a CNN architecture was constructed to learn important features from the complex HW-fingerprint for indoor locations. In the experiment, the HW-fingerprint was tested in an actual indoor scene for 15 days. Results showed that the average daily location accuracy of the K-Nearest Neighbor (KNN), Support Vector Machines (SVMs), and CNN was improved by 3.39%, 8.03% and 9.03%, respectively, when using the HW-fingerprint. In addition, the deep-learning method was 4.19% and 16.37% higher than SVM and KNN in average daily location accuracy, respectively. Full article
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Open AccessReview
Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation
Sensors 2019, 19(20), 4596; https://doi.org/10.3390/s19204596 - 22 Oct 2019
Cited by 12 | Viewed by 1481
Abstract
Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such [...] Read more.
Upper limb amputation is a condition that significantly restricts the amputees from performing their daily activities. The myoelectric prosthesis, using signals from residual stump muscles, is aimed at restoring the function of such lost limbs seamlessly. Unfortunately, the acquisition and use of such myosignals are cumbersome and complicated. Furthermore, once acquired, it usually requires heavy computational power to turn it into a user control signal. Its transition to a practical prosthesis solution is still being challenged by various factors particularly those related to the fact that each amputee has different mobility, muscle contraction forces, limb positional variations and electrode placements. Thus, a solution that can adapt or otherwise tailor itself to each individual is required for maximum utility across amputees. Modified machine learning schemes for pattern recognition have the potential to significantly reduce the factors (movement of users and contraction of the muscle) affecting the traditional electromyography (EMG)-pattern recognition methods. Although recent developments of intelligent pattern recognition techniques could discriminate multiple degrees of freedom with high-level accuracy, their efficiency level was less accessible and revealed in real-world (amputee) applications. This review paper examined the suitability of upper limb prosthesis (ULP) inventions in the healthcare sector from their technical control perspective. More focus was given to the review of real-world applications and the use of pattern recognition control on amputees. We first reviewed the overall structure of pattern recognition schemes for myo-control prosthetic systems and then discussed their real-time use on amputee upper limbs. Finally, we concluded the paper with a discussion of the existing challenges and future research recommendations. Full article
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Open AccessArticle
Topological Frontier-Based Exploration and Map-Building Using Semantic Information
Sensors 2019, 19(20), 4595; https://doi.org/10.3390/s19204595 - 22 Oct 2019
Cited by 1 | Viewed by 703
Abstract
Exploration of unknown environments is a fundamental problem in autonomous robotics that deals with the complexity of autonomously traversing an unknown area while acquiring the most important information of the environment. In this work, a mobile robot exploration algorithm for indoor environments is [...] Read more.
Exploration of unknown environments is a fundamental problem in autonomous robotics that deals with the complexity of autonomously traversing an unknown area while acquiring the most important information of the environment. In this work, a mobile robot exploration algorithm for indoor environments is proposed. It combines frontier-based concepts with behavior-based strategies in order to build a topological representation of the environment. Frontier-based approaches assume that, to gain the most information of an environment, the robot has to move to the regions on the boundary between open space and unexplored space. The novelty of this work is in the semantic frontier classification and frontier selection according to a cost–utility function. In addition, a probabilistic loop closure algorithm is proposed to solve cyclic situations. The system outputs a topological map of the free areas of the environment for further navigation. Finally, simulated and real-world experiments have been carried out, their results and the comparison to other state-of-the-art algorithms show the feasibility of the exploration algorithm proposed and the improvement that it offers with regards to execution time and travelled distance. Full article
(This article belongs to the Special Issue Mobile Robot Navigation)
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Open AccessArticle
Field Programmable Gate Array-Embedded Platform for Dynamic Muscle Fiber Conduction Velocity Monitoring
Sensors 2019, 19(20), 4594; https://doi.org/10.3390/s19204594 - 22 Oct 2019
Cited by 1 | Viewed by 748
Abstract
This paper proposes a novel architecture of a wearable Field Programmable Gate Array (FPGA)-based platform to dynamically monitor Muscle Fiber Conduction Velocity (MFCV). The system uses a set of wireless sensors for the detection of muscular activation: four surface electromyography electrodes (EMGs) and [...] Read more.
This paper proposes a novel architecture of a wearable Field Programmable Gate Array (FPGA)-based platform to dynamically monitor Muscle Fiber Conduction Velocity (MFCV). The system uses a set of wireless sensors for the detection of muscular activation: four surface electromyography electrodes (EMGs) and two footswitches. The beginning of movement (trigger) is set by sensors (footswitches) detecting the feet position. The MFCV value extraction exploits an iterative algorithm, which compares two 1-bit digitized EMG signals. The EMG electrode positioning is ensured by a dedicated procedure. The architecture is implemented on FPGA board (Altera Cyclone V), which manages an external Bluetooth module for data transmission. The time spent for data elaboration is 63.5 ms ± 0.25 ms, matching real-time requirements. The FPGA-based MFCV estimator has been validated during regular walking and in the fatigue monitoring context. Six healthy subjects contributed to experimental validation. In the gait analysis, the subjects showed MFCV evaluation of about 7.6 m/s ± 0.36 m/s, i.e., <0.1 m/s, a typical value for healthy subjects. Furthermore, in agreement with current research methods in the field, in a fatigue evaluation context, the extracted data showed an MFCV descending trend with the increment of the muscular effort time (Rested: MFCV = 8.51 m/s; Tired: 4.60 m/s). Full article
(This article belongs to the Special Issue Advances in Sensors for Context-Aware, Mobile and Smart Healthcare)
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Open AccessArticle
Evaluating Soil-Borne Causes of Biomass Variability in Grassland by Remote and Proximal Sensing
Sensors 2019, 19(20), 4593; https://doi.org/10.3390/s19204593 - 22 Oct 2019
Cited by 1 | Viewed by 712
Abstract
On a grassland field with sandy soils in Northeast Germany (Brandenburg), vegetation indices from multi-spectral UAV-based remote sensing were used to predict grassland biomass productivity. These data were combined with soil pH value and apparent electrical conductivity (ECa) from on-the-go proximal sensing serving [...] Read more.
On a grassland field with sandy soils in Northeast Germany (Brandenburg), vegetation indices from multi-spectral UAV-based remote sensing were used to predict grassland biomass productivity. These data were combined with soil pH value and apparent electrical conductivity (ECa) from on-the-go proximal sensing serving as indicators for soil-borne causes of grassland biomass variation. The field internal magnitude of spatial variability and hidden correlations between the variables of investigation were analyzed by means of geostatistics and boundary-line analysis to elucidate the influence of soil pH and ECa on the spatial distribution of biomass. Biomass and pH showed high spatial variability, which necessitates high resolution data acquisition of soil and plant properties. Moreover, boundary-line analysis showed grassland biomass maxima at pH values between 5.3 and 7.2 and ECa values between 3.5 and 17.5 mS m−1. After calibrating ECa to soil moisture, the ECa optimum was translated to a range of optimum soil moisture from 7% to 13%. This matches well with to the plant-available water content of the predominantly sandy soil as derived from its water retention curve. These results can be used in site-specific management decisions to improve grassland biomass productivity in low-yield regions of the field due to soil acidity or texture-related water scarcity. Full article
(This article belongs to the Special Issue Smart Sensing Technologies for Agriculture) Printed Edition available
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Open AccessArticle
Simultaneous EEG Acquisition System for Multiple Users: Development and Related Issues
Sensors 2019, 19(20), 4592; https://doi.org/10.3390/s19204592 - 22 Oct 2019
Viewed by 911
Abstract
Social interaction is one of humans’ most important activities and many efforts have been made to understand the phenomenon. Recently, some investigators have attempted to apply advanced brain signal acquisition systems that allow dynamic brain activities to be measured simultaneously during social interactions. [...] Read more.
Social interaction is one of humans’ most important activities and many efforts have been made to understand the phenomenon. Recently, some investigators have attempted to apply advanced brain signal acquisition systems that allow dynamic brain activities to be measured simultaneously during social interactions. Most studies to date have investigated dyadic interactions, although multilateral interactions are more common in reality. However, it is believed that most studies have focused on such interactions because of methodological limitations, in that it is very difficult to design a well-controlled experiment for multiple users at a reasonable cost. Accordingly, there are few simultaneous acquisition systems for multiple users. In this study, we propose a design framework for an acquisition system that measures EEG data simultaneously in an environment with 10 or more people. Our proposed framework allowed us to acquire EEG data at up to 1 kHz frequency from up to 20 people simultaneously. Details of our acquisition system are described from hardware and software perspectives. In addition, various related issues that arose in the system’s development—such as synchronization techniques, system loads, electrodes, and applications—are discussed. In addition, simultaneous visual ERP experiments were conducted with a group of nine people to validate the EEG acquisition framework proposed. We found that our framework worked reasonably well with respect to less than 4 ms delay and average loss rates of 1%. It is expected that this system can be used in various hyperscanning studies, such as those on crowd psychology, large-scale human interactions, and collaborative brain–computer interface, among others. Full article
(This article belongs to the Special Issue Biomedical Signal Processing)
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Open AccessArticle
Fully Transparent Gas Sensor Based on Carbon Nanotubes
Sensors 2019, 19(20), 4591; https://doi.org/10.3390/s19204591 - 22 Oct 2019
Cited by 1 | Viewed by 732
Abstract
In this paper, we demonstrate the feasibility of realization of transparent gas sensors based on carbon nanotubes (CNTs). Both sensing layer and electrodes consist of CNTs deposited by spray deposition. The transparent sensor—with a transmittance higher than 60% in both sensing layer and [...] Read more.
In this paper, we demonstrate the feasibility of realization of transparent gas sensors based on carbon nanotubes (CNTs). Both sensing layer and electrodes consist of CNTs deposited by spray deposition. The transparent sensor—with a transmittance higher than 60% in both sensing layer and electrodes—is characterized towards NH3 and CO2 and compared with a reference sensor with the same active layer but evaporated Au electrodes. In particular, the sensitivity towards NH3 is virtually identical for both reference and transparent sensors, whereas the transparent device exhibits higher sensitivity to CO2 than the reference electrode. The effect of the spacing among consecutive electrodes is also studied, demonstrating that a wider spacing in fully CNT based sensors results in a higher sensitivity because of the higher sensing resistance, whereas this effect was not observed in gold electrodes, as their resistance can be neglected with respect to the resistance of the CNT sensing layer. Overall, the transparent sensors show performance comparable—if not superior—to the traditionally realized ones, opening the way for seamlessly integrated sensors, which do not compromise on quality. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle
Passively Addressable Ultra-Low Volume Sweat Chloride Sensor
Sensors 2019, 19(20), 4590; https://doi.org/10.3390/s19204590 - 22 Oct 2019
Cited by 1 | Viewed by 785
Abstract
This work demonstrates a novel electrochemical biosensor for the detection of chloride ion levels in ultra-low volumes (1–3 microliters) of passively expressed human sweat. We present here a hydration monitor that the pediatric, geriatric, and other immune-compromised or physically inactive/sedentary population cohort can [...] Read more.
This work demonstrates a novel electrochemical biosensor for the detection of chloride ion levels in ultra-low volumes (1–3 microliters) of passively expressed human sweat. We present here a hydration monitor that the pediatric, geriatric, and other immune-compromised or physically inactive/sedentary population cohort can utilize, for whom the current methods of chloride quantification of active stimulation of sweat glands through iontophoresis or treadmill runs are unsuitable. In this work, non-faradaic electroanalysis using gold microelectrodes deposited on a flexible nanoporous substrate, for high nanoscale surface area to volume enhancement, was leveraged to operate in ultra-low sweat volumes of <3 µL eluted at natural rates. The specific chloride ionophore-based affinity of chloride ions resulted in the modulation of charge transfer within the electrical double layer at the electrode–sweat buffer interface, which was transduced using electrochemical impedance spectroscopy (EIS) and chronoamperometry (CA). Linear calibration dose responses with R-squared values of 0.9746 and 0.9403 for EIS and CA respectively were obtained for a dynamic range of 10–100 mM. The surface charge and the binding chemistry of the capture probe were studied using zeta potential studies and UV-Vis. The dynamic sweat chloride-tracking capability of the sensor was evaluated for a duration of 180 min. Studies were conducted to probe the efficacy of the developed sensor for passive ultra-low sweat chloride assessment on human subjects (n = 3). Full article
(This article belongs to the Section Biosensors)
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Open AccessLetter
Piezoresistive Sensors Based on Electrospun Mats Modified by 2D Ti3C2Tx MXene
Sensors 2019, 19(20), 4589; https://doi.org/10.3390/s19204589 - 22 Oct 2019
Cited by 3 | Viewed by 919
Abstract
The preparation methodology and properties of electroconductive, electrospun mats composed of copolyamide 6,10 and Ti3C2Tx are described in this paper. Mats of several compositions were prepared from a solution of n-propanol. The obtained electrospun mats were then tested [...] Read more.
The preparation methodology and properties of electroconductive, electrospun mats composed of copolyamide 6,10 and Ti3C2Tx are described in this paper. Mats of several compositions were prepared from a solution of n-propanol. The obtained electrospun mats were then tested as piezoresistive sensors. The relative resistance (AR) of the sensor increased with an increase in the Ti3C2Tx content, and materials with relatively higher electrical conductivity displayed noticeably higher sensitivity to applied pressure. The pressure-induced changes in resistivity increased with an increment in the applied force. Full article
(This article belongs to the Section Chemical Sensors)
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Open AccessArticle
Robust Vehicle Detection and Counting Algorithm Employing a Convolution Neural Network and Optical Flow
Sensors 2019, 19(20), 4588; https://doi.org/10.3390/s19204588 - 22 Oct 2019
Cited by 8 | Viewed by 1047
Abstract
Automatic vehicle detection and counting are considered vital in improving traffic control and management. This work presents an effective algorithm for vehicle detection and counting in complex traffic scenes by combining both convolution neural network (CNN) and the optical flow feature tracking-based methods. [...] Read more.
Automatic vehicle detection and counting are considered vital in improving traffic control and management. This work presents an effective algorithm for vehicle detection and counting in complex traffic scenes by combining both convolution neural network (CNN) and the optical flow feature tracking-based methods. In this algorithm, both the detection and tracking procedures have been linked together to get robust feature points that are updated regularly every fixed number of frames. The proposed algorithm detects moving vehicles based on a background subtraction method using CNN. Then, the vehicle’s robust features are refined and clustered by motion feature points analysis using a combined technique between KLT tracker and K-means clustering. Finally, an efficient strategy is presented using the detected and tracked points information to assign each vehicle label with its corresponding one in the vehicle’s trajectories and truly counted it. The proposed method is evaluated on videos representing challenging environments, and the experimental results showed an average detection and counting precision of 96.3% and 96.8%, respectively, which outperforms other existing approaches. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle
Real-Time Correction and Stabilization of Laser Diode Wavelength in Miniature Homodyne Interferometer for Long-Stroke Micro/Nano Positioning Stage Metrology
Sensors 2019, 19(20), 4587; https://doi.org/10.3390/s19204587 - 22 Oct 2019
Cited by 1 | Viewed by 613
Abstract
A low-cost miniature homodyne interferometer (MHI) with self-wavelength correction and self-wavelength stabilization is proposed for long-stroke micro/nano positioning stage metrology. In this interferometer, the displacement measurement is based on the analysis of homodyne interferometer fringe pattern. In order to miniaturize the interferometer size, [...] Read more.
A low-cost miniature homodyne interferometer (MHI) with self-wavelength correction and self-wavelength stabilization is proposed for long-stroke micro/nano positioning stage metrology. In this interferometer, the displacement measurement is based on the analysis of homodyne interferometer fringe pattern. In order to miniaturize the interferometer size, a low-cost and small-sized laser diode is adopted as the laser source. The accuracy of the laser diode wavelength is real-time corrected by the proposed wavelength corrector using a modified wavelength calculation equation. The variation of the laser diode wavelength is suppressed by a real-time wavelength stabilizer, which is based on the principle of laser beam drift compensation and the principle of automatic temperature control. The optical configuration of the proposed MHI is proposed. The methods of displacement measurement, wavelength correction, and wavelength stabilization are depicted in detail. A laboratory-built prototype of the MHI is constructed, and experiments are carried out to demonstrate the feasibility of the proposed wavelength correction and stabilization methods. Full article
(This article belongs to the Section Optical Sensors)
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Open AccessArticle
An Augmented Reality Based Human-Robot Interaction Interface Using Kalman Filter Sensor Fusion
Sensors 2019, 19(20), 4586; https://doi.org/10.3390/s19204586 - 22 Oct 2019
Cited by 4 | Viewed by 1164
Abstract
In this paper, the application of Augmented Reality (AR) for the control and adjustment of robots has been developed, with the aim of making interaction and adjustment of robots easier and more accurate from a remote location. A LeapMotion sensor based controller has [...] Read more.
In this paper, the application of Augmented Reality (AR) for the control and adjustment of robots has been developed, with the aim of making interaction and adjustment of robots easier and more accurate from a remote location. A LeapMotion sensor based controller has been investigated to track the movement of the operator hands. The data from the controller allows gestures and the position of the hand palm’s central point to be detected and tracked. A Kinect V2 camera is able to measure the corresponding motion velocities in x, y, z directions after our investigated post-processing algorithm is fulfilled. Unreal Engine 4 is used to create an AR environment for the user to monitor the control process immersively. Kalman filtering (KF) algorithm is employed to fuse the position signals from the LeapMotion sensor with the velocity signals from the Kinect camera sensor, respectively. The fused/optimal data are sent to teleoperate a Baxter robot in real-time by User Datagram Protocol (UDP). Several experiments have been conducted to test the validation of the proposed method. Full article
(This article belongs to the Section Intelligent Sensors)
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Open AccessArticle
Integrated Robotic and Network Simulation Method
Sensors 2019, 19(20), 4585; https://doi.org/10.3390/s19204585 - 21 Oct 2019
Viewed by 717
Abstract
The increasing use of mobile cooperative robots in a variety of applications also implies an increasing research effort on cooperative strategies solutions, typically involving communications and control. For such research, simulation is a powerful tool to quickly test algorithms, allowing to do more [...] Read more.
The increasing use of mobile cooperative robots in a variety of applications also implies an increasing research effort on cooperative strategies solutions, typically involving communications and control. For such research, simulation is a powerful tool to quickly test algorithms, allowing to do more exhaustive tests before implementation in a real application. However, the transition from an initial simulation environment to a real application may imply substantial rework if early implementation results do not match the ones obtained by simulation, meaning the simulation was not accurate enough. One way to improve accuracy is to incorporate network and control strategies in the same simulation and to use a systematic procedure to assess how different techniques perform. In this paper, we propose a set of procedures called Integrated Robotic and Network Simulation Method (IRoNS Method), which guide developers in building a simulation study for cooperative robots and communication networks applications. We exemplify the use of the improved methodology in a case-study of cooperative control comparison with and without message losses. This case is simulated with the OMNET++/INET framework, using a group of robots in a rendezvous task with topology control. The methodology led to more realistic simulations while improving the results presentation and analysis. Full article
(This article belongs to the Special Issue Smart Mobile and Sensor Systems)
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Open AccessArticle
Behavior-Based Control for an Aerial Robotic Swarm in Surveillance Missions
Sensors 2019, 19(20), 4584; https://doi.org/10.3390/s19204584 - 21 Oct 2019
Cited by 2 | Viewed by 695
Abstract
Aerial robotic swarms have shown benefits for performing search and surveillance missions in open spaces in the past. Among other properties, these systems are robust, scalable and adaptable to different scenarios. In this work, we propose a behavior-based algorithm to carry out a [...] Read more.
Aerial robotic swarms have shown benefits for performing search and surveillance missions in open spaces in the past. Among other properties, these systems are robust, scalable and adaptable to different scenarios. In this work, we propose a behavior-based algorithm to carry out a surveillance task in a rectangular area with a flexible number of quadcopters, flying at different speeds. Once the efficiency of the algorithm is quantitatively analyzed, the robustness of the system is demonstrated with 3 different tests: loss of broadcast messages, positioning errors, and failure of half of the agents during the mission. Experiments are carried out in an indoor arena with micro quadcopters to support simulation results. Finally, a case study is proposed to show a realistic implementation in the test bed. Full article
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Open AccessArticle
Hierarchical Classification of Urban ALS Data by Using Geometry and Intensity Information
Sensors 2019, 19(20), 4583; https://doi.org/10.3390/s19204583 - 21 Oct 2019
Cited by 1 | Viewed by 678
Abstract
Airborne laser scanning (ALS) can acquire both geometry and intensity information of geo-objects, which is important in mapping a large-scale three-dimensional (3D) urban environment. However, the intensity information recorded by ALS will be changed due to the flight height and atmospheric attenuation, which [...] Read more.
Airborne laser scanning (ALS) can acquire both geometry and intensity information of geo-objects, which is important in mapping a large-scale three-dimensional (3D) urban environment. However, the intensity information recorded by ALS will be changed due to the flight height and atmospheric attenuation, which decreases the robustness of the trained supervised classifier. This paper proposes a hierarchical classification method by separately using geometry and intensity information of urban ALS data. The method uses supervised learning for stable geometry information and unsupervised learning for fluctuating intensity information. The experiment results show that the proposed method can utilize the intensity information effectively, based on three aspects, as below. (1) The proposed method improves the accuracy of classification result by using intensity. (2) When the ALS data to be classified are acquired under the same conditions as the training data, the performance of the proposed method is as good as the supervised learning method. (3) When the ALS data to be classified are acquired under different conditions from the training data, the performance of the proposed method is better than the supervised learning method. Therefore, the classification model derived from the proposed method can be transferred to other ALS data whose intensity is inconsistent with the training data. Furthermore, the proposed method can contribute to the hierarchical use of some other ALS information, such as multi-spectral information. Full article
(This article belongs to the Special Issue LiDAR-Based Creation of Virtual Cities)
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Open AccessArticle
Direct Wideband Coherent Localization by Distributed Antenna Arrays
Sensors 2019, 19(20), 4582; https://doi.org/10.3390/s19204582 - 21 Oct 2019
Cited by 2 | Viewed by 684
Abstract
We address wideband direct coherent localization of a radio transmitter by a distributed antenna array in a multipath scenario with spatially-coherent line-of-sight (LoS) signal components. Such a signal scenario is realistic in small cells, especially indoors in the mmWave range. The system model [...] Read more.
We address wideband direct coherent localization of a radio transmitter by a distributed antenna array in a multipath scenario with spatially-coherent line-of-sight (LoS) signal components. Such a signal scenario is realistic in small cells, especially indoors in the mmWave range. The system model considers collocated time and phase synchronized receiving front-ends with antennas distributed in 3D space at known locations connected to the front-ends via calibrated coaxial cables or analog radio frequency over fiber links. The signal model assumes spherical wavefronts. We propose two ML-type algorithms (for known and unknown transmitter waveforms) and a subspace-based SCM-MUSIC algorithm for wideband direct coherent position estimation. We demonstrate the performance of the methods by Monte Carlo simulations. The results show that even in multipath environments, it is possible to achieve localization accuracy that is much better (by two to three orders of magnitude) than the carrier wavelength. They also suggest that the methods that do not exploit knowledge of the waveform have mean-squared errors approaching the Cramér–Rao bound. Full article
(This article belongs to the Special Issue Multi-Sensor Systems for Positioning and Navigation)
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Open AccessArticle
Semi-Automatic Calibration Method for a Bed-Monitoring System Using Infrared Image Depth Sensors
Sensors 2019, 19(20), 4581; https://doi.org/10.3390/s19204581 - 21 Oct 2019
Cited by 2 | Viewed by 674
Abstract
With the aging of society, the number of fall accidents has increased in hospitals and care facilities, and some accidents have happened around beds. To help prevent accidents, mats and clip sensors have been used in these facilities but they can be invasive, [...] Read more.
With the aging of society, the number of fall accidents has increased in hospitals and care facilities, and some accidents have happened around beds. To help prevent accidents, mats and clip sensors have been used in these facilities but they can be invasive, and their purpose may be misinterpreted. In recent years, research has been conducted using an infrared-image depth sensor as a bed-monitoring system for detecting a patient getting up, exiting the bed, and/or falling; however, some manual calibration was required initially to set up the sensor in each instance. We propose a bed-monitoring system that retains the infrared-image depth sensors but uses semi-automatic rather than manual calibration in each situation where it is applied. Our automated methods robustly calculate the bed region, surrounding floor, sensor location, and attitude, and can recognize the spatial position of the patient even when the sensor is attached but unconstrained. Also, we propose a means to reconfigure the spatial position considering occlusion by parts of the bed and also accounting for the gravity center of the patient’s body. Experimental results of multi-view calibration and motion simulation showed that our methods were effective for recognition of the spatial position of the patient. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
Doppler Differential Positioning Technology Using the BDS/GPS Indoor Array Pseudolite System
Sensors 2019, 19(20), 4580; https://doi.org/10.3390/s19204580 - 21 Oct 2019
Cited by 4 | Viewed by 641
Abstract
A Global Satellite Navigation System (GNSS) cannot provide normal location services in an indoor environment because the signals are blocked by buildings. The Beidou satellite navigation system (BDS)/GPS indoor array pseudolite system is proposed to overcome the problems of indoor positioning with conventional [...] Read more.
A Global Satellite Navigation System (GNSS) cannot provide normal location services in an indoor environment because the signals are blocked by buildings. The Beidou satellite navigation system (BDS)/GPS indoor array pseudolite system is proposed to overcome the problems of indoor positioning with conventional pseudolite, such as time synchronization, ambiguity resolution and base stations. At the same time, an algorithm for Doppler differential positioning is proposed to improve the indoor positioning accuracy and the positioning coverage of the system, which uses the Doppler difference equation and Known Point Initialization (KPI) to determinate the velocity and position of the receiver. Experiments were conducted to verify the proposed system under different conditions; the average positioning error of the Doppler differential positioning algorithm was 7.86 mm in the kinematic test and 2.9 mm in the static test. The results show that BDS/GPS indoor array pseudolite system has the potential to make indoor positioning achieve sub-centimeter precision. Finally, the positioning error of the proposed algorithm is also analyzed, and the data tests show that the dilution of precision (DOP) and cycle- slips have a significant impact on the indoor positioning accuracy; a cycle-slip of a half-wavelength can cause positioning errors of tens of millimeters. Therefore, the Doppler-aided cycle-slip detection method (DACS) is proposed to detect cycle-slips of one cycle or greater than one, and the carrier phase double difference cycle-slip detection method (CPDD) is used to detect cycle slips of a half-wavelength. Full article
(This article belongs to the Section Remote Sensors)
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Open AccessArticle
An Improved Energy-Efficient Routing Protocol for Wireless Sensor Networks
Sensors 2019, 19(20), 4579; https://doi.org/10.3390/s19204579 - 21 Oct 2019
Cited by 9 | Viewed by 1068
Abstract
Cluster-based hierarchical routing protocols play an essential role in decreasing the energy consumption of wireless sensor networks (WSNs). A low-energy adaptive clustering hierarchy (LEACH) has been proposed as an application-specific protocol architecture for WSNs. However, without considering the distribution of the cluster heads [...] Read more.
Cluster-based hierarchical routing protocols play an essential role in decreasing the energy consumption of wireless sensor networks (WSNs). A low-energy adaptive clustering hierarchy (LEACH) has been proposed as an application-specific protocol architecture for WSNs. However, without considering the distribution of the cluster heads (CHs) in the rotation basis, the LEACH protocol will increase the energy consumption of the network. To improve the energy efficiency of the WSN, we propose a novel modified routing protocol in this paper. The newly proposed improved energy-efficient LEACH (IEE-LEACH) protocol considers the residual node energy and the average energy of the networks. To achieve satisfactory performance in terms of reducing the sensor energy consumption, the proposed IEE-LEACH accounts for the numbers of the optimal CHs and prohibits the nodes that are closer to the base station (BS) to join in the cluster formation. Furthermore, the proposed IEE-LEACH uses a new threshold for electing CHs among the sensor nodes, and employs single hop, multi-hop, and hybrid communications to further improve the energy efficiency of the networks. The simulation results demonstrate that, compared with some existing routing protocols, the proposed protocol substantially reduces the energy consumption of WSNs. Full article
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Open AccessArticle
Indoor Intruder Tracking Using Visible Light Communications
Sensors 2019, 19(20), 4578; https://doi.org/10.3390/s19204578 - 21 Oct 2019
Cited by 4 | Viewed by 825
Abstract
This paper proposes a comprehensive study of indoor intruder tracking using visible light communication (VLC). A realistic indoor VLC channel was developed, taking into consideration reflections, shadowing, and ambient noise. The intruder was considered smart and aiming to escape tracking. This was modelled [...] Read more.
This paper proposes a comprehensive study of indoor intruder tracking using visible light communication (VLC). A realistic indoor VLC channel was developed, taking into consideration reflections, shadowing, and ambient noise. The intruder was considered smart and aiming to escape tracking. This was modelled by adding noise and disturbance to the intruder’s trajectory. We propose to extend the application of minimax filtering from state estimation in the radio frequency (RF) domain to intruder tracking using VLC. The performance of the proposed method was examined and compared with Kalman filter for both VLC and RF. The simulation results showed that the minimax filter provided marginally better tracking and was more robust to the adversary behavior of the intruder than Kalman filter, with less than 0.5 cm estimation error. In addition, minimax was significantly better than Kalman filter for RF tracking applications. Full article
(This article belongs to the Special Issue Free-Space Optical and Visible Light Communications)
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Open AccessArticle
An Alignment Method for Strapdown Inertial Navigation Systems Assisted by Doppler Radar on a Vehicle-Borne Moving Base
Sensors 2019, 19(20), 4577; https://doi.org/10.3390/s19204577 - 21 Oct 2019
Cited by 2 | Viewed by 670
Abstract
In this study, we investigated a novel method for high-accuracy autonomous alignment of a strapdown inertial navigation system assisted by Doppler radar on a vehicle-borne moving base, which effectively avoids the measurement errors caused by wheel-slip or vehicle-sliding. Using the gyroscopes in a [...] Read more.
In this study, we investigated a novel method for high-accuracy autonomous alignment of a strapdown inertial navigation system assisted by Doppler radar on a vehicle-borne moving base, which effectively avoids the measurement errors caused by wheel-slip or vehicle-sliding. Using the gyroscopes in a strapdown inertial navigation system and Doppler radar, we calculated the dead reckoning, analyzed the error sources of the dead reckoning system, and established an error model. Then the errors of the strapdown inertial navigation system and dead reckoning system were treated as the states. Besides velocity information, attitude information was cleverly introduced into the alignment measurement to improve alignment accuracy and reduce alignment time. Therefore, the first measurement was the difference between the output attitude and velocity of the strapdown inertial navigation system and the corresponding signals from the dead reckoning system. In order to further improve the alignment accuracy, more measurement information was introduced by using the vehicle motion constraint, that is, the velocity output projection of strapdown inertial navigation system along the transverse and vertical direction of the vehicle body was also used as the second measurement. Then the corresponding state and measurement equations were established, and the Kalman filter algorithm was used for assisted alignment filtering. The simulation results showed that, with a moving base, the misalignment angle estimation accuracy was better than 0.5’ in the east direction, 0.4’ in the north direction, and 3.2’ in the vertical direction. Full article
(This article belongs to the Section Physical Sensors)
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Open AccessArticle
A SINS/DVL Integrated Positioning System through Filtering Gain Compensation Adaptive Filtering
Sensors 2019, 19(20), 4576; https://doi.org/10.3390/s19204576 - 21 Oct 2019
Cited by 1 | Viewed by 623
Abstract
Because of the complex task environment, long working distance, and random drift of the gyro, the positioning error gradually diverges with time in the design of a strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated positioning system. The use of velocity information [...] Read more.
Because of the complex task environment, long working distance, and random drift of the gyro, the positioning error gradually diverges with time in the design of a strapdown inertial navigation system (SINS)/Doppler velocity log (DVL) integrated positioning system. The use of velocity information in the DVL system cannot completely suppress the divergence of the SINS navigation error, which will result in low positioning accuracy and instability. To address this problem, this paper proposes a SINS/DVL integrated positioning system based on a filtering gain compensation adaptive filtering technology that considers the source of error in SINS and the mechanism that influences the positioning results. In the integrated positioning system, an organic combination of a filtering gain compensation adaptive filter and a filtering gain compensation strong tracking filter is explored to fuse position information to obtain higher accuracy and a more stable positioning result. Firstly, the system selects the indirect filtering method and uses the integrated positioning error to model the navigation parameters of the system. Then, a filtering gain compensation adaptive filtering method is developed by using the filtering gain compensation algorithm based on the error statistics of the positioning parameters. The positioning parameters of the system are filtered and information on errors in the navigation parameters is obtained. Finally, integrated with the positioning parameter error information, the positioning parameters of the system are solved, and high-precision positioning results are obtained to accurately position autonomous underwater vehicles (AUVs). The simulation results show that the SINS/DVL integrated positioning method, based on the filtering gain compensation adaptive filtering technology, can effectively enhance the positioning accuracy. Full article
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Open AccessArticle
Sensors for Expert Grip Force Profiling: Towards Benchmarking Manual Control of a Robotic Device for Surgical Tool Movements
Sensors 2019, 19(20), 4575; https://doi.org/10.3390/s19204575 - 21 Oct 2019
Cited by 1 | Viewed by 766
Abstract
STRAS (Single access Transluminal Robotic Assistant for Surgeons) is a new robotic system based on the Anubis® platform of Karl Storz for application to intra-luminal surgical procedures. Pre-clinical testing of STRAS has recently permitted to demonstrate [...] Read more.
STRAS (Single access Transluminal Robotic Assistant for Surgeons) is a new robotic system based on the Anubis® platform of Karl Storz for application to intra-luminal surgical procedures. Pre-clinical testing of STRAS has recently permitted to demonstrate major advantages of the system in comparison with classic procedures. Benchmark methods permitting to establish objective criteria for ‘expertise’ need to be worked out now to effectively train surgeons on this new system in the near future. STRAS consists of three cable-driven sub-systems, one endoscope serving as guide, and two flexible instruments. The flexible instruments have three degrees of freedom and can be teleoperated by a single user via two specially designed master interfaces. In this study, small force sensors sewn into a wearable glove to ergonomically fit the master handles of the robotic system were employed for monitoring the forces applied by an expert and a trainee (complete novice) during all the steps of surgical task execution in a simulator task (4-step-pick-and-drop). Analysis of grip-force profiles is performed sensor by sensor to bring to the fore specific differences in handgrip force profiles in specific sensor locations on anatomically relevant parts of the fingers and hand controlling the master/slave system. Full article
(This article belongs to the Special Issue Wearable and Nearable Biosensors and Systems for Healthcare)
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Open AccessArticle
Dropping Counter: A Detection Algorithm for Identifying Odour-Evoked Responses from Noisy Electroantennograms Measured by a Flying Robot
Sensors 2019, 19(20), 4574; https://doi.org/10.3390/s19204574 - 21 Oct 2019
Cited by 1 | Viewed by 735
Abstract
The electroantennogram (EAG) is a technique used for measuring electrical signals from the antenna of an insect. Its rapid response time, quick recovery speed, and high sensitivity make it suitable for odour-tracking tasks employing mobile robots. However, its application to flying robots has [...] Read more.
The electroantennogram (EAG) is a technique used for measuring electrical signals from the antenna of an insect. Its rapid response time, quick recovery speed, and high sensitivity make it suitable for odour-tracking tasks employing mobile robots. However, its application to flying robots has not been extensively studied owing to the electrical and mechanical noises generated. In this study, we investigated the characteristics of the EAG mounted on a tethered flying quadcopter and developed a special counter-based algorithm for detecting the odour-generated responses. As the EAG response is negative, the algorithm creates a window and compares the values inside it. Once a value is smaller than the first one, the counter will increase by one and finally turns the whole signal into a clearer odour stimulated result. By experimental evaluation, the new algorithm gives a higher cross-correlation coefficient when compared with the fixed-threshold method. The result shows that the accuracy of this novel algorithm for recognising odour-evoked EAG signals from noise exceeds that of the traditional method; furthermore, the use of insect antennae as odour sensors for flying robots is demonstrated to be feasible. Full article
(This article belongs to the Section Chemical Sensors)
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