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Absorptive polarizers are pivotal components for realizing a low ambient reflection in liquid crystal displays (LCDs) and organic light-emitting diodes (OLEDs). Different types of absorptive polarizers have been proposed. Nevertheless, the realization of compact and efficient absorptive polarizers remains challenging. Wire grid polarizers (WGPs) are a promising solution because of their high durability and relatively thin thickness. In this paper, two structures of absorptive-WGPs have been proposed and optimized at the target wavelength of 532 nm: one is based on a patterned

Automatic speech recognition (ASR) based on surface electromyography (sEMG) sensors is an important technology converting electrical signals into computer-readable textual messages, which can overcome the limitation of acoustic sensors that are easily contaminated by environmental noises. However, current placements of sEMG sensors mainly depend on the experimenter’s experience, which could miss important information about the major muscular activities and lead to the decline of classification performance. In this study, 120 closely-spaced sEMG sensors were utilized to collect high-density sEMG signals for recognizing ten digits in English and Chinese. The linear discriminant analysis classifier was used to classify the speaking tasks, and the sequential forward selection algorithm was utilized for analyzing the optimal position of the sensors. The results showed that the HD sEMG energy maps could help visualize the dynamic muscle activities during the speaking process, and significantly different muscular contraction patterns were observed for different speaking tasks. The classification accuracies when using the facial sensors were significantly lower than those on the neck, although with the same number of sensors. Moreover, the classification rates could be higher than 90% with only 15 optimally selected sensors that were mainly distributed on the neck instead of the face. This study suggests that the neck muscles could be the main contributor, and more sEMG sensors should be placed on the neck to improve the ASR performance. The findings of this study could provide valuable clues for the development of a practical sEMG-based speech recognition system, especially for patients with speaking disorders.

The shape sensing technology based on multi-core fiber is attractive for many applications. A particularly important problem needing to be addressed is minimizing possible errors in shape sensing. In this paper, one of sensing errors, namely the axial strain error of multi-core fiber in shape sensing is investigated. A model of the axial strain error is established with the theoretical analysis. Furthermore, a correction method for axial strain error using measured data obtained from fiber Bragg gratings sensors is developed. In order to validate the correction method, the twist experiment of a seven-core fiber is carried out. All calculated strain values are processed using curve fitting analysis. The characteristics of the corrected data fitting present good sinusoidal properties and confirm the theoretical analysis well. The results show that the mean R2 of the fitted strain values after the correction is 0.9578. The feasibility of the proposed method is verified by experimental results.

A cost-efficient distributed temperature sensor based on Rayleigh scattering in temperature sensitive optical fibers was proposed. The temperature dependence of the irradiated commercial off-the-shelf single mode fiber was analyzed. By combining a temperature dependent model of the sensitive fibers and loss resolution model of optical time domain reflectometer, a general sensor model has been developed. In order to optimize the temperature resolution, the length of each sensing segment and the overall sensing distance, three models are derived from the general model. Based on the models and parameters optimization, a novel design method is proposed. Using the method, a prototype was built and tested. The test results indicate that the temperature resolution is better than 1 °C in the temperature range from −40 °C to 150 °C, which is consistent with the optimal results.

Absorptive polarizers are pivotal components for realizing a low ambient reflection in liquid crystal displays (LCDs) and organic light-emitting diodes (OLEDs). Different types of absorptive polarizers have been proposed. Nevertheless, the realization of compact and efficient absorptive polarizers remains challenging. Wire grid polarizers (WGPs) are a promising solution because of their high durability and relatively thin thickness. In this paper, two structures of absorptive-WGPs have been proposed and optimized at the target wavelength of 532 nm: one is based on a patterned F e 2 O 3 / A l bi-layer on top of a S i O 2 substrate, and the second one builds on the first one by depositing a S i O 2 layer in the gaps of Al. The optimal solutions exhibit a reflectance less than 5%, a transmittance over 45%, and an extinction ratio over 40 dB. To evaluate the manufacturing feasibility, their sensitivity to the wire’s dimensional parameters is investigated. Their great spectral performance and large acceptance angles demonstrate that such polarizers have the potential to significantly promote the development of current display technologies.

The convolutional neural network (CNN) is frequenctly used in silent speech recognition (SSR) based on surface electromyography (sEMG). Currently, there are two different modes when training the CNN classifier, using the sEMG signals from a single subject as the training datasets and using the mixed signals from multiple subjects as the training datasets. However, it still remains unclear how different training modes affect the performance of the CNN classifier in different classification metrics. In this study, two different training modes were used for the CNN classifier of SSR based on high-density sEMG (HD sEMG) signals. HD sEMG signals collected from six subjects were used to build two different training datasets. The HD sEMG signals from either a single subject or multiple subjects were to train the same CNN model and the performance difference was thoroughly compared in different metrics. The results showed that the CNN model trained from the signals of a single subject showed superior performance with higher average precision, average recall, and average F1 score. It also converged faster and was more stable under different signal conditions. However, it was only suitable for the SSR of the same subject, while the CNN model trained from the signals of multiple subjects showed satisfactiroy performance across all the recruited subjects. This study revealed that the CNN models trained with different training modes performed differently, and therefore the training mode could be taken into consideration in different applications of SSR based on sEMG.

A simple method is proposed to measure the total rotation of polarization axis caused by both internal and external factors during the fabrication process in polarization-maintaining fibers (PMFs). The method is suitable for any kind of PMF, and avoids the additional clamping-induced rotation of fiber with assistance of gravity. Based on this method, a polarization-maintaining photonic crystal fiber (PM-PCF) and a conventional panda PMF used in fiber-optic gyroscope (FOG) are tested. The experimental results provide foundation for the analysis of fiber quality and magnetic-induced bias error in a FOG. © 2018 The Author(s).

A radiation–temperature coupling model of optical fiber attenuation spectrum has been developed. The spectrum in Ge/P co-doped fiber ranging from 800 nm–1600 nm at different temperatures and doses was measured and decomposed according to the configurational coordinate model based on which the power-law model was employed to predict the intensity of the color center absorption band at different doses. And the fiber loss in space was predicted by the model. This work will benefit the application of fibers in a complicated radiation environment.

Objective. The auditory brainstem response (ABR) audiometry is a means of assessing the functional status of the auditory neural pathway in the clinic. The conventional click ABR test lacks good neural synchrony and it mainly evaluates high-frequency hearing while the common tone-burst ABR test only detects hearing loss of a certain frequency at a time. Additionally, the existing chirp stimuli are designed based on average data of cochlear characteristics, ignoring individual differences amongst subjects. Approach. Therefore, this study designed a new stimulus approach based on a sweep-tone concept with a time variant and spectrum characteristics that could be customized based on an individual’s cochlear characteristics. To validate the efficiency of the proposed method, we compared its performance with the click and tone-bursts using ABR recordings from 11 normal-hearing adults. Main results. Experimental results showed that the proposed sweep-tone ABR achieved a higher amplitude compared with those elicited by the click and tone-bursts. When the stimulus level or rate was varied, the sweep-tone ABR consistently elicited a larger response than the corresponding click ABR. Moreover, the sweep-tone ABR appeared earlier than the click ABR under the same conditions. Specifically, the mean wave V peak-to-peak amplitude of the sweep-tone ABR was 1.3 times that of the click ABR at 70 dB nHL (normal hearing level) and a rate of 20 s−1, in which the former saved 40% of test time. Significance. In summary, the proposed sweep-tone approach is found to be more efficient than the traditional click and tone-burst in eliciting ABR.

Objective. Silent speech recognition (SSR) based on surface electromyography (sEMG) is an attractive non-acoustic modality of human-machine interfaces that convert the neuromuscular electrophysiological signals into computer-readable textual messages. The speaking process involves complex neuromuscular activities spanning a large area over the facial and neck muscles, thus the locations of the sEMG electrodes considerably affected the performance of the SSR system. However, most of the previous studies used only a quite limited number of electrodes that were placed empirically without prior quantitative analysis, resulting in uncertainty and unreliability of the SSR outcomes. Approach. In this study, the technique of high-density sEMG was proposed to provide a full representation of the articulatory muscle activities so that the optimal electrode configuration for SSR could be systemically explored. A total of 120 closely spaced electrodes were placed on the facial and neck muscles to collect the high-density sEMG signals for classifying ten digits (0–9) silently spoken in both English and Chinese. The sequential forward selection algorithm was adopted to explore the optimal electrodes configurations. Main Results. The results showed that the classification accuracy increased rapidly and became saturated quickly when the number of selected electrodes increased from 1 to 120. Using only ten optimal electrodes could achieve a classification accuracy of 86% for English and 94% for Chinese, whereas as many as 40 non-optimized electrodes were required to obtain comparable accuracies. Also, the optimally selected electrodes seemed to be mostly distributed on the neck instead of the facial region, and more electrodes were required for English recognition to achieve the same accuracy. Significance. The findings of this study can provide useful guidelines about electrode placement for developing a clinically feasible SSR system and implementing a promising approach of human-machine interface, especially for patients with speaking difficulties.

Choices and decisions involve a series of complex cognitive processes, and the time-frequency analysis of electroencephalogram (EEG) signals can help understand the brain activities in different cognitive tasks. In this study, a decision-making cognitive task of rock-paper-scissors was designed, and the complex decision-making task was divided into three stages (decision planning, confirmation, and feedback). 64 channels of EEG signals were simultaneously recorded using the Neuroscan QuikCap system during the whole task. The average spectral power and phase synchronization values of each frequency band (delta, theta, alpha and beta band) were extracted and compared within and across different stages. The results showed the desire to win or not to lose within the first stage might be accompanied by the increase of alpha and theta components. In the second stage, the spectral power inhibition of alpha wave and phase synchronization increase of delta wave indicated that subjects would improve their attentions when they confirmed their choice. In the third stage, the theta increased effect and alpha decreased effect were related to different feedback. This pilot study suggests that the time-frequency analysis of EEG signals could be a great tool to visualize the brain activities in response to different cognitive tasks.

Speech recognition based on surface electromyography (sEMG) signals is an important research direction with potential applications in life, work and clinical. The number and placement of sEMG electrodes play a critical role in capturing the underlying sEMG activities and in turn, accurately classifying the speaking tasks. The aim of this work was to investigate the effect of the number of channels in speech recognition based on high-density (HD) sEMG. 8 healthy subjects were recruited to perform 11 English speech tasks with sEMG signals were detected from 120 electrodes covering almost the whole neck and face. The classification accuracy was evaluated in the context of a linear discriminant analysis (LDA) with different sets of EMG electrodes. By comparing the classification accuracy, the sequential forward search (SFS) algorithm was adopted to figure out the optimal combination of electrodes which realized the highest classification level. The results showed that smaller number of channels obtained by the SFS method could achieve the classification accuracy of 80%, and another few electrodes were needed to record detail information to achieve the classification accuracy of 85%, 90% and 95%. The numbers were rather smaller than 120. Considering the computation time and reliable accuracy, it is concluded that the SFS method might be helpful for standardizing the number and position of electrodes in speech recognition.

A cryogenic temperature sensor based on the temperature dependence of stable color centers in a commercial single-mode optical fiber is proposed. The radiation induced attenuation spectra at different temperatures are measured and decomposed by Ge-NBOHC and Ge(X) color centers. The configurational coordinate model is used to explain the temperature properties of the color centers. A series of experiments are conducted to evaluate its performance in the temperature range from 10°C to −196°C, and the results suggest that the temperature sensitivity is ∼0.17 dB/km/°C with a resolution of 0.034°C, and the nonlinearity and repeatability error are ±3.8% and 1.4%, respectively.

Speaking activities requires coordinated neuromuscular activation of the facial and neck muscles, so that natural and intelligible speeches could be produced for human communication. Given that any problem of these muscles will lead to speaking difficulties, understanding the contraction patterns of the facial and neck muscles is helpful to explore the muscular mechanism of various speaking problems. In this study, the high-density surface electromyography (HD sEMG) technique was proposed to examine the muscular activities associated with speaking. The HD sEMG signals were acquired when the human subjects were speaking English daily words by 120 channels of closely-spaced electrodes, symmetrically placed on the left and right sides of the facial/neck muscles. The results showed that the energy maps calculated from normalized RMS values of the sEMG signals could illustrate the dynamic spatiotemporal properties of the muscle activities during different speaking tasks. There were high left-right symmetric properties for the RMS curves and energy maps, and further analyses of the correlation coefficients confirmed a significant left-right correlation for the facial and neck muscles during the speaking. The findings of this study suggested that the HD sEMG signals would be useful to evaluate the muscle contraction patterns related to speaking activities and could be a potential tool for diagnosing the muscular functions of speaking difficulties.

A low-cost distributed temperature sensing system based on Rayleigh scattering in irradiated optical fibers was first proposed. The prototype was developed with a conventional optical time domain reflectometer and a Ge/P co-doped irradiated fiber of 100 m. Three thermal tests were conducted in the range of −40 °C ~ 60 °C. Experimental results indicate that the prototype can achieve a temperature resolution of 0.05 °C with a spatial resolution of 16.3 m at an average time of 52 s. Furthermore, the temperature resolution of the sensing system was analyzed theoretically with simulations. The simulation results show that a temperature resolution of 0.007 °C with a spatial resolution of 5 m is obtained mainly by adjusting the transmission wavelength and increasing the light source power.

Body motion registration can be applied to control computer interfaces or real devices, and force myography (FMG) is a promising modality to register real-time body motions. In this work, an approach for FMG recording was developed by using flexible piezoelectret sensors, and different lower-limb motions of three able-bodied subjects were captured. The experimental results demonstrated that the piezoelectret sensors were a suitable approach for FMG recording, and the five-channel data were possible to register the motions of leg raising, knee flexion, and knee extension. An average motion classification accuracy of 92.1% was achieved, which would be useful for the FMG-based device control in future work.

Temperature dependence of radiation induced attenuation (RIA) in commercial SMF is illustrated. Based on which both single and multipoint temperature sensing system is introduced. The system performance is satisfied and have potential to be improved.

Continuous electrocardiogram (ECG) monitoring is significant for prevention of heart disease and is becoming an important part of personal and family health care. In most of the existing wearable solutions, conventional metal sensors and corresponding chips are simply integrated into clothes and usually could only collect few leads of ECG signals that could not provide enough information for diagnosis of cardiac diseases such as arrhythmia and myocardial ischemia. In this study, a wearable 12-lead ECG acquisition system with fabric electrodes was developed and could simultaneously process 12 leads of ECG signals. By integrating the fabric electrodes into a T-shirt, the wearable system would provide a comfortable and convenient user interface for ECG recording. For comparison, the proposed fabric electrode and the gelled traditional metal electrodes were used to collect ECG signals on a subject, respectively. The approximate entropy (ApEn) of ECG signals from both types of electrodes were calculated. The experimental results show that the fabric electrodes could achieve similar performance as the gelled metal electrodes. This preliminary work has demonstrated that the developed ECG system with fabric electrodes could be utilized for wearable health management and telemedicine applications.

Motion recognition is an important application of electromyography (EMG) analysis. While discrete motions such as hand open, hand close and wrist pronation have been extensively investigated, studies on combined motions involving two or more degrees of freedom (DOFs) are relatively few and the classification accuracy of the combined motions reported in previous studies is barely satisfactory. To improve the accuracy of the combined motion recognition, common spatial pattern (CSP) was employed in this study to extract spatial features. 18 forearm motion classes, consisted of 8 discrete motions and 10 combined motions, were classified by the proposed method. Our results showed that the accuracy rate of CSP features was 96.3%, which outperformed the commonly used time-domain (TD) features by 2.4% and TD combined with auto-regression coefficients (TDAR) by 0.6%. Moreover, CSP features cost noticeable much less time than TDAR and quite less time than TD in testing. These results suggest that CSP features could be a better feature set for multi-DOF myoelectric control than conventional features.

Wireless body sensor networks (WBSNs) provide a platform to track and monitor human health status as well as feedback to the user by capturing and processing certain physiological signals. Since WBSNs need to provide efficient health information privacy, their security has been identified as one of the major challenges, especially for rehabilitation systems. Conventionally, the random numbers (RNs) based on the inter-pulse intervals (IPIs) from electrocardiogram (ECG) recordings have been widely used to secure the data in WBSNs. However, this method is limited in real-time applications such as human posture control. In this study, we proposed a novel electromyogram (EMG) based RN generation method to secure the data acquired from WBSNs systems for rehabilitation. The newly proposed security scheme was tested on EMG signals acquired from 15 healthy subjects by using EMG features. These features were coded into 128-bit RNs with entropy values ranging from 0.96 to 1.00, and hamming distances (HDs) that ranged from 41 to 83. These preliminary results showed that randomness and distinctiveness of those RNs are good enough for authentication and encryption. Findings from the current study suggest that the EMG-based RN generation method would be potential in securing the health information in WBSNs.

A wearable myoelectric device is essentially a surface electromyography(sEMG) based human machine interaction (HMI) system. The non-stationary property of sEMG could be one of the obstacles that degrade the user experience of wearable myoelectric devices because they need to be put on and taken off frequently, which brings in the time-variation effects specified in this paper. In order to reduce the influence cause by time-variation on wearable myoelectric devices, the common spatial pattern(CSP) was employed to extract relatively robust features from multichannel sEMG signals. As comparation, the conventional time domain(TD) features together with time domain auto regressive(TDAR) features were investigated. To implement the experiment, 16 electrodes were utilized to simulate a wearable myoelectric device, and an absolute value based Teager-Kaiser energy operator(TKEO) strategy was proposed to realize onset detection. The experiment was divided into 2 sessions, corresponding to the situations before and after time-variation happened respectively. The support vector machine (SVM) and linear discrimination analysis(LDA) were used to build the pattern recognition models. In session2, the average classification accuracy of CSP(95.2±2.3%) is superior to TD(90.3±4.4%) and TDAR(92.4±4.2%) under LDA. The result suggests that the CSP feature set is relatively robust against accuracy reduction resulted from time-variation in motion recognition.

Electroencephalogram (EEG) activity, recorded while performing an “odd ball” detection task, was compared between patients with early vascular dementia (VD), healthy young controls and healthy elderly controls performing the same task. The data were analyzed using the event-related synchronization/desynchronization (ERS/ERD) method. VD patients, compared with controls, showed decreased ERS effects in the delta frequency band (0.5–3.5Hz) of EEG after the target stimulus appeared in frontal, central and parietal regions. Similarly, elderly controls also showed a decreased ERS compared with young controls only in central and parietal regions. As part of this analysis, we introduce a novel quantitative index, the Event-related Energy Change Progression (EECP), which provides a reliable measure that distinguishes these groups and thereby provides a promising marker for early diagnosis of VD.

Based on the long-term data in the literature, we present evidence for long-term optical spectral index variability behaviour for BL Lacertae object OJ 287. First we find that the optical spectral index variability period of BL Lacertae object OJ 287 is in agreement with the optical flux variability period of about 11.96 yr. It is considered that different processes induce optical variability, and each process should have a definite signature in the spectral index, thus leading to the periodicity of the spectral index. We also find that the time lag between the optical spectral index and the optical flux density is half of the optical flux variability period. This result tends to support the argument that when the brightness of the long-term variations increases, the optical spectra become bluer. These variations can be explained by energy loss of the electrons during the outburst. We show that the spectral index is different during the flares compared with outside the flares; between the first and second flare peaks of the superflares, the spectrum shapes are different. Disparate processes possibly induce the two peaks of OJ 287.