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This paper investigates the finite-time robust tracking problem for a 5-DOF (degrees of freedom) upper-limb exoskeleton robot subjected to parametric uncertainties, unmodelled dynamics, and unknown human efforts. By developing the non-singular terminal sliding mode control approach, three innovative schemes of robust torques are proposed to steer configuration variables (angular displacements of joints) of the 5-DOF robotic exoskeleton to the reference trajectories within adjustable finite times. Based on mathematical analysis, it is proven that all suggested schemes of control inputs (input torques) accomplish and provide the mentioned tracking objective accurately. In addition, several new formulas (in the form of inequalities) are derived to determine and tune the needed finite times for achieving the tracking objective. Finally, three numerical examples are given to show the appropriate performance and the acceptable effectiveness of the proposed finite-time robust control schemes. [ABSTRACT FROM AUTHOR]

Background: Brain-computer interfaces (BCIs) based on steady-state visual evoked potentials (SSVEPs) provide high rates of accuracy and information transfer rate, but need user's attention to flickering visual stimuli. This quickly leads to eye-fatigue when the flickering frequency is in the low-frequency range. High-frequency flickering stimuli (>30 Hz) have been proposed with significantly lower eye-fatigue. However, SSVEP responses in this frequency range are remarkably weaker, leading to doubts about usability of high-frequency stimuli to develop efficient BCI systems. The purpose of this study was to evaluate if a practical SSVEP Speller can be developed with Repetitive Visual Stimuli in the high-frequency range. Methods: An asynchronous high-frequency (35-40 Hz) speller for typing in Persian language was developed using five flickering visual stimuli. Least absolute shrinkage and selection operator algorithm with two user-calibrated thresholds was used to detect the user's selections. A total of 14 volunteers evaluated the system in an ordinary office environment to type 9 sentences consist of 81 characters with a multistage virtual keyboard. Results: Despite very high performance of 6.9 chars/min overall typing speed, average accuracy of 98.3%, and information transfer rate of 64.9 bpm for eight of the participants, the other six participants had serious difficulty in spelling with the system and could not complete the typing experiment. Conclusions: The results of this study in accordance with some previous studies suggest that high rate of illiteracy in high-frequency SSVEP-based BCI systems may be a major burden for their practical application. [ABSTRACT FROM AUTHOR]

The aim of this paper is to improve the performance of the conventional Goertzel algorithm in determining the protein coding regions in deoxyribonucleic acid (DNA) sequences. First, the symbolic DNA sequences are converted into numerical signals using electron ion interaction potential method. Then by combining the modified anti-notch filter and linear predictive coding model, we proposed an efficient algorithm to achieve the performance improvement in the Goertzel algorithm for estimating genetic regions. Finally, a thresholding method is applied to precisely identify the exon and intron regions. The proposed algorithm is applied to several genes, including genes available in databases BG570 and HMR195 and the results are compared to other methods based on the nucleotide level evaluation criteria. Results demonstrate that our proposed method reduces the number of incorrect nucleotides which are estimated to be in the noncoding region. In addition, the area under the receiver operating characteristic curve has improved by the factor of 1.35 and 1.12 in HMR195 and BG570 datasets respectively, in comparison with the conventional Goertzel algorithm. [ABSTRACT FROM AUTHOR]

A motion energy image (MEI) is a spatial template that collapses regions of motion into a single image in which more moving pixels are brighter than others. The forward single-step history image (fSHI) is a spatiotemporal template that shows the presence and direction of motion. Each video can be described using these templates. Recently, the popularity of deep learning architectures for human activity recognition encourages us to explore the effectiveness of combining them with these templates. Hence, three new methods are introduced to convert the problem of human activity recognition in video into image templates classification. In method 1, each video is split into N groups of consecutive frames, and the MEI is computed for each group. Transfer learning with the fine-tuning technique is used for classifying these templates. fSHIs or spatiotemporal templates are used for classification in method 2, similar to method 1. Fusing the two streams of these templates is introduced as method 3. Among these methods, method 3 outperforms the others and is called the proposed method. It achieves recognition accuracies of 92.60% and 93.40% for the UCF Sport and UCF-11 action datasets, respectively. Also, it is compared with state-of-the-art approaches, and the results show that the proposed method has the best performance. [ABSTRACT FROM AUTHOR]

Objective: Conners Adult ADHD Rating Scale (CAARS) is among the valid questionnaires for evaluating Attention-Deficit/Hyperactivity Disorder in adults. The aim of this paper is to evaluate the validity of the estimation of missed answers in scoring the screening version of the Conners questionnaire, and to extract its principal components., Method: This study was performed on 400 participants. Answer estimation was calculated for each question (assuming the answer was missed), and then a Kruskal-Wallis test was performed to evaluate the difference between the original answer and its estimation. In the next step, principal components of the questionnaire were extracted by means of Principal Component Analysis (PCA). Finally the evaluation of differences in the whole groups was provided using the Multiple Comparison Procedure (MCP)., Results: Findings indicated that a significant difference existed between the original and estimated answers for some particular questions. However, the results of MCP showed that this estimation, when evaluated in the whole group, did not show a significant difference with the original value in neither of the questionnaire subscales. The results of PCA revealed that there are eight principal components in the CAARS questionnaire., Conclusion: The obtained results can emphasize the fact that this questionnaire is mainly designed for screening purposes, and this estimation does not change the results of groups when a question is missed randomly. Notwithstanding this finding, more considerations should be paid when the missed question is a critical one.

Several researches have been done to identify visual system characteristics. Some of them are based on the processing of the brain signal recordings. Visual evoked potentials (VEPs) are electrical signals which are produced in response to the visual stimuli and recorded by means of electrodes placed on the head. These signals are usually characterized by the amplitude and latency of their peaks. Different types of visual stimuli and visual system characteristics can affect the shape and hence the characteristics of VEPs. In this paper, proper visual stimuli were used and VEPs were recorded in order to classify visual acuity. To achieve this goal, visual evoked potentials were recorded and processed in time, frequency and time-frequency domains. In order to preserve dynamics of the recorded signals, two algorithms for single-trial VEP extraction were used. The results of the classification of visual acuity in both average and single-trial VEPs are acceptable.