Functional Gait Can Be Affected by Noise: Effects of Age and Cognitive Function: A Pilot Study

Background: The ageing process may degrade an individual's balance control, hearing capacity, and cognitive function. Older adults perform worse on simultaneously executed balance and secondary tasks (i.e., dual-task performance) than younger adults and may be more vulnerable to auditory distraction.Aim: The purpose of this study was to determine the effect of passive listening on functional gait in healthy older vs. younger adults, and to investigate the effect of age, functional gait, hearing ability and cognitive functioning on dual-task performance.Methods: Twenty young and 20 older healthy adults were recruited. Functional gait (Functional Gait Assessment in silent and noisy condition), hearing function (audiogram; Speech in Babble test), and cognitive ability (Cambridge Neuropsychological Test Automated Battery) were measured.Results: Overall, a significant difference between functional gait performance in silent vs. noisy conditions was found (p = 0.022), with no significant difference in dual-task cost between the two groups (p = 0.11). Correlations were found between increasing age, worse functional gait performance, poorer hearing capacity and lower performance on cognitive function tasks. Interestingly, worse performance on attention tasks appeared to be associated with a worse functional gait performance in the noisy condition.Conclusion: Passive listening to multi-talker babble noise can affect functional gait in both young and older adults. This effect could result from the cognitive load of the babble noise, due to the engagement of attention networks by the unattended speech.

Design of automation control thermal system integrated with parabolic trough collector based solar plant

This paper presents enhanced design for Automation control of processes involved in a solar system which utilizes programmable logic controller to automate tracking system for obtaining maximum solar radiation. Three areas are involved in this proposed multi area system where first and second area considers solar power plant with thermal system based parabolic trough collector with fixed solar isolation and random isolation of solar energy whereas third area comprises of solar thermal system with dish Stirling realistic unit. Energy efficiency can be increased by using solar concentrator along with Stirling engine. Optimization of gain of the controller is by utilizing crow search novel algorithm. Crow search algorithm is an optimization technique, which provides better performance at complex time varying noisy condition and time in-varying noisy condition. The Proposed controller is evaluated by obtaining the optimized parameters of the system whose comparison is done by operating proposed controller with & without renewable sources of energy thereby revealing better performance for both conditions. Testing is done in different areas with fixed solar isolation and random stisolation of solar energy involved in solar thermal power plant based on parabolic trough collector. Gain and parameters of the controller of the solar power plant are optimized by utilizing automation for operation of solar concentrator with parabolic Trough collector. Data acquisition and monitoring is done by human machine interface (HMI) in order to report safe operation. The Simulation results of integrated solar thermal system involving dish Stirling with parabolic trough collector, shows that dynamic response of the proposed controller operating with renewable solar energy is better than that of non-renewable energy source.

Robustness Speaker Recognition Based on Feature Space in Clean and Noisy Condition

Background & Objective: Speaker Recognition (SR) techniques have been developed into a relatively mature status over the past few decades through development work. Existing methods typically use robust features extracted from clean speech signals, and therefore in idealized conditions can achieve very high recognition accuracy. For critical applications, such as security and forensics, robustness and reliability of the system are crucial. Methods: The background noise and reverberation as often occur in many real-world applications are known to compromise recognition performance. To improve the performance of speaker verification systems, an effective and robust technique is proposed to extract features for speech processing, capable of operating in the clean and noisy condition. Mel Frequency Cepstrum Coefficients (MFCCs) and Gammatone Frequency Cepstral Coefficients (GFCC) are the mature techniques and the most common features, which are used for speaker recognition. MFCCs are calculated from the log energies in frequency bands distributed over a mel scale. While GFCC has been acquired from a bank of Gammatone filters, which was originally suggested to model human cochlear filtering. This paper investigates the performance of GFCC and the conventional MFCC feature in clean and noisy conditions. The effects of the Signal-to-Noise Ratio (SNR) and language mismatch on the system performance have been taken into account in this work. Conclusion: Experimental results have shown significant improvement in system performance in terms of reduced equal error rate and detection error trade-off. Performance in terms of recognition rates under various types of noise, various Signal-to-Noise Ratios (SNRs) was quantified via simulation. Results of the study are also presented and discussed.

Data-driven rational feedforward tuning: With application to an ultraprecision wafer stage

Rational basis functions are introduced into iterative learning control to enhance the flexibility towards nonrepeating tasks. At present, the application of rational basis functions either suffers from nonconvex optimization problem or requires the predefinition of poles, which restricts the achievable performance. In this article, a new data-driven rational feedforward tuning approach is developed, in which convex optimization is realized without predefining the poles. Specifically, the optimal parameter which eliminates the reference-induced error is directly solved using the least square method. No parametric model is involved in the parameter tuning process and the optimal parameter is estimated using the measured data. In the noisy condition, it is proved that the estimated optimal parameter is unbiased and the estimation accuracy in terms of variance is analysed. The performance of the proposed approach is tested on an ultraprecision wafer stage. The experimental results confirm that high performance is achieved using the proposed approach.

Improved Permutation Entropy for Measuring Complexity of Time Series under Noisy Condition

Measuring complexity of observed time series plays an important role for understanding the characteristics of the system under study. Permutation entropy (PE) is a powerful tool for complexity analysis, but it has some limitations. For example, the amplitude information is discarded; the equalities (i.e., equal values in the analysed signal) are not properly dealt with; and the performance under noisy condition remains to be improved. In this paper, the improved permutation entropy (IPE) is proposed. The presented method combines some advantages of previous modifications of PE. Its effectiveness is validated through both synthetic and experimental analyses. Compared with PE, IPE is capable of detecting spiky features and correctly differentiating heart rate variability (HRV) signals. Moreover, it performs better under noisy condition. Ship classification experiment results demonstrate that IPE achieves 28.66% higher recognition rate than PE at 0dB. Hence, IPE could be used as an alternative of PE for analysing time series under noisy condition.

Classification improvement of spoken arabic language based on radial basis function

The important task in the computer interaction is the languages recognition and classification. In the Arab world, there is a persistent need for the Arabic spoken language recognition To help those who have lost the upper parties in doing what they want through speech computer interaction. While, the Arabic automatic speech recognition (AASR) did not receive the desired attention from the researchers. In this paper, the Radial Basis Function(RBF) is used for the improvement of the Arabic spoken language letter. The recognition and classification process are based on three steps; these are; preprocessing, feature extraction and classification (Recognition). The Arabic Language Letters (ALL) recognition is done by using the combination between the statistical features and the Temporal Radial Basis Function for different letter situation and noisy condition. The recognition percent are from 90% - 99.375% has been gained with independent speaker, where these results are over-perform the earlier works by nearly 2.045%. The simulati.on has been made by using Matlab 2015b.