This paper presents the analytical study of flexible stimulation waveforms in muscle fatigue reduction for functional electrical stimulator (FES)-assisted hemiplegic muscle activities. The major challenge of muscle contraction induced by FES is early muscle fatigue which greatly limits activities such as FES-assisted standing and walking. The fixed stimulation pattern applied on a same motor unit has resulted the motor unit to be overworked and fatigue easily. Therefore, in this work, the stimulus parameters, which include the pulse width duration and the frequency were varied to create a few flexible stimulation waveforms using MATLAB/Simulink. The pulse width duration was modulated from 100µs -500µs to generate five types of flexible stimulation waveforms such as Rectangular, Trapezoidal, Ramp Up, Ramp Down and Triangular. Concurrently, a few ranges of stimulus frequency were also used, which include 20Hz, 30Hz and 50Hz. The generated flexible stimulation waveforms were applied onto a humanoid muscle model to investigate and analyse the muscle output response and early muscle fatigue reduction. From the conducted simulation results and analyses, it was observed that flexible stimulation waveforms such as Triangular, Ramp Up and Ramp Down could reduce early muscle fatigue phenomenon by having lower average of negative slope, in the range of 0.012 to 0.013 for the muscle fitness. In contrast, the Rectangular and Trapezoidal shapes were found to have higher negative slope of muscle fitness in the range of 0.028 to 0.031. The Ramp Down shape was found to have the lowest average of negative slope (0.012) while Rectangular was found to have the highest average of negative slope (0.031). Therefore, it can be concluded that flexible stimulation waveforms such Ramp Down, Ramp Up and Triangular shapes could reduce early muscle fatigue phenomenon with Ramp Down shape having the highest muscle fatigue reduction.

Purpose Porous silicon (PS) was successfully fabricated using an alternating current photo-assisted electrochemical etching (ACPEC) technique. This study aims to compare the effect of different crystal orientation of Si n(100) and n(111) on the structural and optical characteristics of the PS. Design/methodology/approach PS was fabricated using ACPEC etching with a current density of J = 10 mA/cm2 and etching time of 30 min. The PS samples denoted by PS100 and PS111 were etched using HF-based solution under the illumination of an incandescent white light. Findings FESEM images showed that the porous structure of PS100 was a uniform circular shape with higher density and porosity than PS111. In addition, the AFM indicated that the surface roughness of porous n(100) was less than porous n(111). Raman spectra of the PS samples showed a stronger peak with FWHM of 4.211 cm−1 and redshift of 1.093 cm−1. High resolution X-ray diffraction revealed cubic Si phases in the PS samples with tensile strain for porous n(100) and compressive strain for porous n(111). Photoluminescence observation of porous n(100) and porous n(111) displayed significant visible emissions at 651.97 nm (Eg = 190eV) and 640.89 nm (Eg = 1.93 eV) which was because of the nano-structure size of silicon through the quantum confinement effect. The size of Si nanostructures was approximately 8 nm from a quantized state effective mass theory. Originality/value The work presented crystal orientation dependence of Si n(100) and n(111) for the formation of uniform and denser PS using new ACPEC technique for potential visible optoelectronic application. The ACPEC technique has effectively formed good structural and optical characteristics of PS.

<p><span lang="EN-US">Long term load forecasting data is important for grid expansion and power system operation. Besides, it also important to ensure the generation capacity meet electricity demand at all times. In this paper, Least-Square Support Vector Machine (LSSVM) is used to predict the long-term load demand. Four inputs are considered which are peak load demand, ambient temperature, humidity and wind speed. Total load demand is set as the output of prediction in LSSVM. In order to improve the accuracy of the LSSVM, Grey Wolf Optimizer (GWO) is hybridized to obtain the optimal parameters of LSSVM namely GWO-LSSVM. Mean Absolute Percentage Error (MAPE) is used as the quantify measurement of the prediction model. The objective of the optimization is to minimize the value of MAPE. The performance of GWO-LSSVM is compared with other methods such as LSSVM and Ant Lion Optimizer – Least-Square Support Vector Machine (ALO-LSSVM). From the results obtained, it can be concluded that GWO-LSSVM provide lower MAPE value which is 0.13% as compared to other methods.</span></p>

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Functional electrical stimulation (FES) device has been widely used by spinal cord injury (SCI) patients in their rehab exercises to restore motor function to their paralysed muscles. The major challenge of muscle contraction induced by FES is early muscle fatigue due to the open-loop stimulation strategy. To reduce the early muscle fatigue phenomenon, a closed-loop FES system is proposed to track the angle of the limb’s movement and provide an accurate amount of charge according to the desired reference angle. Among the existing feedback controllers, fuzzy logic controller (FLC) has been found to exhibit good control performance in handling complex non-linear systems without developing any complex mathematical model. Recently, there has been considerable interest in the implementation of FLC in hardware embedded systems. Therefore, in this paper, a digital fuzzy feedback controller (FFC) embedded in a field-programmable gate array (FPGA) board was proposed. The digital FFC mainly consists of an analog-to-digital converter (ADC) Data Acquisition and FLC sub-modules. The FFC was designed to monitor and control the progress of knee extension movement by regulating the stimulus pulse width duration to meet the target angle. The knee is expected to extend to a maximum reference angle setting (70°, 40° or 30°) from its normal position of 0° once the stimulus charge is applied to the muscle by the FES device. Initially, the FLC was modelled using MATLAB Simulink. Then, the FLC was hardcoded into digital logic using hardware description language (HDL) Verilog codes. Thereafter, the performance of the digital FLC was tested with a knee extension model using the HDL co-simulation technique in MATLAB Simulink. Finally, for real-time verification, the designed digital FFC was downloaded to the Intel FPGA (DE2-115) board. The digital FFC utilized only 4% of the total FPGA (Cyclone IV E) logic elements (LEs) and required 238 µs to regulate stimulus pulse width data, including 3 µs for the FLC computation. The high processing speed of the digital FFC enables the stimulus pulse width duration to be updated every stimulation cycle. Furthermore, the implemented digital FFC has demonstrated good control performance in accurately controlling the stimulus pulse width duration to reach the desired reference angle with very small overshoot (1.4°) and steady-state error (0.4°). These promising results are very useful for a real-world closed-loop FES application.

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Despite abundant growth in automatic emotion recognition system (ERS) studies using various techniques in feature extractions and classifiers, scarce sources found to improve the system via pre-processing techniques. This paper proposed a smart pre-processing stage using fuzzy logic inference system (FIS) based on Mamdani engine and simple time-based features i.e. zero-crossing rate (ZCR) and short-time energy (STE) to initially identify a frame as voiced (V) or unvoiced (UV). Mel-frequency cepstral coefficients (MFCC) and linear prediction coefficients (LPC) were tested with K-nearest neighbours (KNN) classifiers to evaluate the proposed FIS V-UV segmentation. We also introduced two feature fusions of MFCC and LPC with formants to obtain better performance. Experimental results of the proposed system surpassed the conventional ERS which yielded a rise in accuracy rate from 3.7% to 9.0%. The fusion of LPC and formants named as SFF LPC-fmnt indicated a promising result between 1.3% and 5.1% higher accuracy rate than its baseline features in classifying between neutral, angry, happy and sad emotions. The best accuracy rates yielded for male and female speakers were 79.1% and 79.9% respectively using SFF MFCC-fmnt fusion technique.

Gender discrimination and awareness are essentially practiced in social, education, workplace, and economic sectors across the globe. A person manifests this attribute naturally in gait, body gesture, facial, including speech. For that reason, automatic gender recognition (AGR) has become an interesting sub-topic in speech recognition systems that can be found in many speech technology applications. However, retrieving salient gender-related information from a speech signal is a challenging problem since speech contains abundant information apart from gender. The paper intends to compare the performance of human vocal tract-based model i.e., linear prediction coefficients (LPC) and human auditory-based model i.e., Mel-frequency cepstral coefficients (MFCC) which are popularly used in other speech recognition tasks by experimentation of optimal feature parameters and classifier’s parameters. The audio data used in this study was obtained from 93 speakers uttering selected words with different vowels. The two feature vectors were tested using two classification algorithms namely, discriminant analysis (DA) and artificial neural network (ANN). Although the experimental results were promising using both feature parameters, the best overall accuracy rate of 97.07% was recorded using MFCC-ANN techniques with almost equal performance for male and female classes.