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4506 KiB

Open AccessArticle

Motor Imagery EEG Classification for Patients with Amyotrophic Lateral Sclerosis Using Fractal Dimension and Fisher’s Criterion-Based Channel Selection

by Yi-Hung Liu, Shiuan Huang and Yi-De Huang

Sensors 2017, 17(7), 1557; https://doi.org/10.3390/s17071557 - 3 Jul 2017

Cited by 39 | Viewed by 7532

Abstract

Motor imagery is based on the volitional modulation of sensorimotor rhythms (SMRs); however, the sensorimotor processes in patients with amyotrophic lateral sclerosis (ALS) are impaired, leading to degenerated motor imagery ability. Thus, motor imagery classification in ALS patients has been considered challenging in [...] Read more.

Motor imagery is based on the volitional modulation of sensorimotor rhythms (SMRs); however, the sensorimotor processes in patients with amyotrophic lateral sclerosis (ALS) are impaired, leading to degenerated motor imagery ability. Thus, motor imagery classification in ALS patients has been considered challenging in the brain–computer interface (BCI) community. In this study, we address this critical issue by introducing the Grassberger–Procaccia and Higuchi’s methods to estimate the fractal dimensions (GPFD and HFD, respectively) of the electroencephalography (EEG) signals from ALS patients. Moreover, a Fisher’s criterion-based channel selection strategy is proposed to automatically determine the best patient-dependent channel configuration from 30 EEG recording sites. An EEG data collection paradigm is designed to collect the EEG signal of resting state and the imagination of three movements, including right hand grasping (RH), left hand grasping (LH), and left foot stepping (LF). Five late-stage ALS patients without receiving any SMR training participated in this study. Experimental results show that the proposed GPFD feature is not only superior to the previously-used SMR features (mu and beta band powers of EEG from sensorimotor cortex) but also better than HFD. The accuracies achieved by the SMR features are not satisfactory (all lower than 80%) in all binary classification tasks, including RH imagery vs. resting, LH imagery vs. resting, and LF imagery vs. resting. For the discrimination between RH imagery and resting, the average accuracies of GPFD in 30-channel (without channel selection) and top-five-channel configurations are 95.25% and 93.50%, respectively. When using only one channel (the best channel among the 30), a high accuracy of 91.00% can still be achieved by the GPFD feature and a linear discriminant analysis (LDA) classifier. The results also demonstrate that the proposed Fisher’s criterion-based channel selection is capable of removing a large amount of redundant and noisy EEG channels. The proposed GPFD feature extraction combined with the channel selection strategy can be used as the basis for further developing high-accuracy and high-usability motor imagery BCI systems from which the patients with ALS can really benefit. Full article

(This article belongs to the Special Issue Innovative Sensing Control Scheme for Advanced Materials)

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3294 KiB

Open AccessArticle

Development of an Automatic Testing Platform for Aviator’s Night Vision Goggle Honeycomb Defect Inspection

by Bo-Lin Jian and Chao-Chung Peng

Sensors 2017, 17(6), 1403; https://doi.org/10.3390/s17061403 - 15 Jun 2017

Cited by 4 | Viewed by 6038

Abstract

Due to the direct influence of night vision equipment availability on the safety of night-time aerial reconnaissance, maintenance needs to be carried out regularly. Unfortunately, some defects are not easy to observe or are not even detectable by human eyes. As a consequence, [...] Read more.

Due to the direct influence of night vision equipment availability on the safety of night-time aerial reconnaissance, maintenance needs to be carried out regularly. Unfortunately, some defects are not easy to observe or are not even detectable by human eyes. As a consequence, this study proposed a novel automatic defect detection system for aviator’s night vision imaging systems AN/AVS-6(V)1 and AN/AVS-6(V)2. An auto-focusing process consisting of a sharpness calculation and a gradient-based variable step search method is applied to achieve an automatic detection system for honeycomb defects. This work also developed a test platform for sharpness measurement. It demonstrates that the honeycomb defects can be precisely recognized and the number of the defects can also be determined automatically during the inspection. Most importantly, the proposed approach significantly reduces the time consumption, as well as human assessment error during the night vision goggle inspection procedures. Full article

(This article belongs to the Special Issue Innovative Sensing Control Scheme for Advanced Materials)

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34170 KiB

Open AccessArticle

Study of a Compression-Molding Process for Ultraviolet Light-Emitting Diode Exposure Systems via Finite-Element Analysis

by Kuo-Tsai Wu, Sheng-Jye Hwang and Huei-Huang Lee

Sensors 2017, 17(6), 1392; https://doi.org/10.3390/s17061392 - 15 Jun 2017

Cited by 2 | Viewed by 5870

Abstract

Although wafer-level camera lenses are a very promising technology, problems such as warpage with time and non-uniform thickness of products still exist. In this study, finite element simulation was performed to simulate the compression molding process for acquiring the pressure distribution on the [...] Read more.

Although wafer-level camera lenses are a very promising technology, problems such as warpage with time and non-uniform thickness of products still exist. In this study, finite element simulation was performed to simulate the compression molding process for acquiring the pressure distribution on the product on completion of the process and predicting the deformation with respect to the pressure distribution. Results show that the single-gate compression molding process significantly increases the pressure at the center of the product, whereas the multi-gate compressing molding process can effectively distribute the pressure. This study evaluated the non-uniform thickness of product and changes in the process parameters through computer simulations, which could help to improve the compression molding process. Full article

(This article belongs to the Special Issue Innovative Sensing Control Scheme for Advanced Materials)

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7093 KiB

Open AccessArticle

Inspection and Reconstruction of Metal-Roof Deformation under Wind Pressure Based on Bend Sensors

by Liman Yang, Langfu Cui, Yunhua Li and Chao An

Sensors 2017, 17(5), 1054; https://doi.org/10.3390/s17051054 - 6 May 2017

Cited by 6 | Viewed by 5033

Abstract

Metal roof sheathings are widely employed in large-span buildings because of their light weight, high strength and corrosion resistance. However, their severe working environment may lead to deformation, leakage and wind-lift, etc. Thus, predicting these damages in advance and taking maintenance measures accordingly [...] Read more.

Metal roof sheathings are widely employed in large-span buildings because of their light weight, high strength and corrosion resistance. However, their severe working environment may lead to deformation, leakage and wind-lift, etc. Thus, predicting these damages in advance and taking maintenance measures accordingly has become important to avoid economic losses and personal injuries. Conventionally, the health monitoring of metal roofs mainly relies on manual inspection, which unavoidably compromises the working efficiency and cannot diagnose and predict possible failures in time. Thus, we proposed a novel damage monitoring scheme implemented by laying bend sensors on vital points of metal roofs to precisely monitor the deformation in real time. A fast reconstruction model based on improved Levy-type solution is established to estimate the overall deflection distribution from the measured data. A standing seam metal roof under wind pressure is modeled as an elastic thin plate with a uniform load and symmetrical boundaries. The superposition method and Levy solution are adopted to obtain the analytical model that can converge quickly through simplifying an infinite series. The truncation error of this model is further analyzed. Simulation and experiments are carried out. They show that the proposed model is in reasonable agreement with the experimental results. Full article

(This article belongs to the Special Issue Innovative Sensing Control Scheme for Advanced Materials)

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7113 KiB

Open AccessArticle

A Novel Auto-Sorting System for Chinese Cabbage Seeds

by Kuo-Yi Huang and Jian-Feng Cheng

Sensors 2017, 17(4), 886; https://doi.org/10.3390/s17040886 - 18 Apr 2017

Cited by 21 | Viewed by 5686

Abstract

This paper presents a novel machine vision-based auto-sorting system for Chinese cabbage seeds. The system comprises an inlet-outlet mechanism, machine vision hardware and software, and control system for sorting seed quality. The proposed method can estimate the shape, color, and textural features of [...] Read more.

This paper presents a novel machine vision-based auto-sorting system for Chinese cabbage seeds. The system comprises an inlet-outlet mechanism, machine vision hardware and software, and control system for sorting seed quality. The proposed method can estimate the shape, color, and textural features of seeds that are provided as input neurons of neural networks in order to classify seeds as “good” and “not good” (NG). The results show the accuracies of classification to be 91.53% and 88.95% for good and NG seeds, respectively. The experimental results indicate that Chinese cabbage seeds can be sorted efficiently using the developed system. Full article

(This article belongs to the Special Issue Innovative Sensing Control Scheme for Advanced Materials)

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1908 KiB

Open AccessArticle

A Quadrilateral Geometry Classification Method and Device for Femtocell Positioning Networks

by Jeich Mar, Tsung Yu Chang and Yu Jie Wang

Sensors 2017, 17(4), 817; https://doi.org/10.3390/s17040817 - 10 Apr 2017

Cited by 2 | Viewed by 3659

Abstract

This article proposes a normalization multi-layer perception (NMLP) geometry classifier to autonomously determine the optimal four femtocell evolved Node Bs (FeNBs), which can use time difference of arrival (TDOA) to measure the location of the macrocell user equipment (MUE) with the lowest GDOP [...] Read more.

This article proposes a normalization multi-layer perception (NMLP) geometry classifier to autonomously determine the optimal four femtocell evolved Node Bs (FeNBs), which can use time difference of arrival (TDOA) to measure the location of the macrocell user equipment (MUE) with the lowest GDOP value. The iterative geometry training (IGT) algorithm is designed to obtain the training data for the NMLP geometry classifier. The architecture of the proposed NMLP geometry classifier is realized in the server of the cloud computing platform, to identify the optimal geometry disposition of four FeNBs for positioning the MUE located between two buildings. Six by six neurons are chosen for two hidden layers, in order to shorten the convergent time. The feasibility of the proposed method is demonstrated by means of numerical simulations. In addition, the simulation results also show that the proposed method is particularly suitable for the application of the MUE positioning with a huge number of FeNBs. Finally, three quadrilateral optimum geometry disposition decision criteria are analyzed for the validation of the simulation results. Full article

(This article belongs to the Special Issue Innovative Sensing Control Scheme for Advanced Materials)

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5420 KiB

Open AccessArticle

A Novel Method of Identifying Paddy Seed Varieties

by Kuo-Yi Huang and Mao-Chien Chien

Sensors 2017, 17(4), 809; https://doi.org/10.3390/s17040809 - 9 Apr 2017

Cited by 31 | Viewed by 5394

Abstract

This paper presents a novel method for identifying three varieties (Taikong 9, Tainan 11, and Taikong 14) of foundation paddy seeds. Taikong 9, Tainan 11, and Taikong 14 paddy seeds are indistinguishable by inspectors during seed purity inspections. The proposed method uses image [...] Read more.

This paper presents a novel method for identifying three varieties (Taikong 9, Tainan 11, and Taikong 14) of foundation paddy seeds. Taikong 9, Tainan 11, and Taikong 14 paddy seeds are indistinguishable by inspectors during seed purity inspections. The proposed method uses image segmentation and a key point identification algorithm that can segment paddy seed images and extract seed features. A back propagation neural network was used to establish a classifier based on seven features that could classify the three paddy seed varieties. The classification accuracies of the resultant classifier for varieties Taikong 9, Tainan 11, and Taikong 14 were 92.68%, 97.35% and 96.57%, respectively. The experimental results indicated that the three paddy seeds can be differentiated efficiently using the developed system. Full article

(This article belongs to the Special Issue Innovative Sensing Control Scheme for Advanced Materials)

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6928 KiB

Open AccessArticle

Liquid Temperature Measurements Using Two Different Tunable Hollow Prisms

by Sergio Calixto, Martha Rosete-Aguilar and Ismael Torres-Gomez

Sensors 2017, 17(2), 266; https://doi.org/10.3390/s17020266 - 29 Jan 2017

Cited by 1 | Viewed by 6232

Abstract

This paper describes the design, fabrication, and testing of two hollow prisms. One is a prism with a grating glued to its hypotenuse. This ensemble, prism + grating, is called a grism. It can be applied as an on-axis tunable spectrometer. The other [...] Read more.

This paper describes the design, fabrication, and testing of two hollow prisms. One is a prism with a grating glued to its hypotenuse. This ensemble, prism + grating, is called a grism. It can be applied as an on-axis tunable spectrometer. The other hollow prism is a constant deviation one called a Pellin-Broca. It can be used as a tunable dispersive element in a spectrometer with no moving parts. The application of prisms as temperature sensors is shown. Full article

(This article belongs to the Special Issue Innovative Sensing Control Scheme for Advanced Materials)

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