Study of noise robustness of First Formant Bandwidth (F1BW) method

The performance of speech recognition application under adverse noisy condition often becomes the topic of researchers regardless of the language used. Applications that use vowel phonemes require high degree of Standard Malay vowel recognition capability.In Malaysia, researches in vowel recognition is still lacking especially in the usage of Malay vowels, independent speaker systems, recognition robustness and algorithm speed and accuracy. This paper presents a noise robustness study on an improved vowel feature extraction method called First Formant Bandwidth (F1BW) on three classifiers of Multinomial Logistic Regression (MLR), K-Nearest Neighbors (k-NN) and Linear Discriminant Analysis (LDA).Results show that LDA performs best in overall vowel classification compared to MLR and KNN in terms of robustness capability

Moving Vehicle Recognition and Classification based on Time Domain Approach

AbstractDifferentially Hearing Ability Enabled (DHAE) community cannot discriminate the sound information from a moving vehicle approaching from their behind. This research work is mainly focused on recognition of different vehicles and its position using noise emanated from the vehicle A simple experimental protocol has been designed to record the sound signal emanated from the moving vehicle under different environment conditions and also at different vehicle speed Autoregressive modeling algorithm is used for the analysis to extract the features from the recorded vehicle noise signal. Probabilistic neural network (PNN) models are developed to classify the vehicle type and its distance. The effectiveness of the network is validated through stimulation

Improved Malay vowel feature extraction method based on first and second formants

There are many speech recognition applications that use vowels phonemes.Among them are speech therapy systems that improve utterances of word pronunciation especially to children.There are also systems that teach hearing impaired person to speak properly by pronouncing words with a good degree of intelligibility.All of these systems require high degree of vowel recognition capability.This paper presents a new method of Malay vowel feature extraction based on formant and spectrum envelope called First Formant Bandwidth (F1BW).It is an effort to increase Malay vowel recognition capability by using a new speech database that consist of words uttered by Malaysian speakers from the three major races, Malay, Chinese and Indians.Based on single frame analysis, F1BW performs better than MFCC by more than 9% based on four classifiers of Levenberg-Marquart trained Neural Network, K-Nearest Neighbours, Multinomial Logistic Regression and Linear Discriminant Analysis

Hand Movement Imagery Task Classification using Fractal Dimension Feature

In this paper, a nonstimulus-based Brain Machine Interface (BMI) approach is used to acquire the brain signal from ten different subjects using 19 channel EEG electrodes while performing four different hand movement imaginary tasks. Three different Fractal Dimension algorithm namely Box counting algorithm, Higuchi algorithm, and Detrended fluctuation algorithm are used to extract fractal dimension features from recorded EEG signal and associated with the respective mental tasks. Three Feed-Forward Neural Network model is developed. The performance of the three Neural Network model is evaluated in term of classification rate and compared. The performance of the developed network models are evaluated through simulation. It is observed that the neural network model trained with Higuchi algorithm has contributed high classification accuracy with the better training and testing time for all 10 subjects. The result clearly indicates that the Higuchi fractal dimension algorithm can be used as a feature to classify motor imagery task for the proposed BMI system

Anticholinesterase and Antioxidant Activities of Spilanthes filicaulis Whole Plant Extracts for the Management of Alzheimer’s Disease

Background: Spilanthes filicaulis is a tropical herb implicated as a memory enhancer in ethnomedicine. Objective: The study investigated acetyl/butyryl cholinesterase inhibitory and antioxidant activities of different extracts of S. filicaulis whole plant and correlated them to its phytochemical constituents. Methods: The powdered whole plant was successively extracted with n-hexane, ethyl acetate and methanol. Acetyl cholinesterase (AChE) and Butyryl cholinesterase (BuChE) inhibitory activity were evaluated by Ellman colorimetry assay. Antioxidant activity was tested using 1, 1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging, ferric reducing power and nitric oxide scavenging assays. Total phenolic, flavonoid and tannin were estimated using standard methods. Correlation was determined using Quest Graph™ Regression Calculator. Results: Various extracts exhibited concentration-dependent AChE and BuChE inhibitory activity with ethyl acetate extract being the highest with IC50 of 0.77 μg/mL and 0.92 μg/mL for AChE and BuChE respectively. The ethyl acetate extract also showed the highest reducing power when compared with the other extracts. The methanol extract had slightly higher phenolic and flavonoid content and showed the highest DPPH radical scavenging effect. DPPH scavenging, AChE and BuChE inhibition had high correlation with the total flavonoid content with R2 values of 1.00, 0.800 and 0.992 respectively while nitric oxide scavenging had high correlation with phenolics and tannins with R2 = 0.942 and 0.806 respectively. Conclusion: These results show that the extracts of the whole plant of S. filicaulis possess significant AChE/BuChE inhibitory and antioxidant properties, mostly due to its flavonoid content, suggesting the possible use of the plant in neurodegenerative diseases such as AD

Recent innovations of nanotechnology in water treatment: A comprehensive review

Environmental pollution from organic and inorganic pollutants poses a threat to the ecosystem. Pollutant's prevalence and persistence have increased significantly in recent years. In order to enhance the quality of naturally accessible water to a level suitable for human consumption, a number of techniques have been employed. In this context, the use of cutting-edge nanotechnology to classical process engineering paves the way for technical encroachments in advanced water and wastewater technology. Nanotechnology has the potential to ameliorate the quality, availability, and viability of water supplies in the long run by facilitating reuse, recycling and remediation of water. The promising role of nanotechnology in wastewater remediation is highlighted in this paper, which also covers current advancements in nanotechnology-mediated remediation systems. Moreover, nano-based materials such as nano-adsorbents, photocatalysts, nano-metals and nanomembranes are discussed in this review of recent breakthroughs in nanotechnologies for water contaminant remediation. © 2021 Elsevier Lt

Segmentation and Location Computation of Bin Objects

In this paper we present a stereo vision based system for segmentation and location computation of partially occluded objects in bin picking environments. Algorithms to segment partially occluded objects and to find the object location [midpoint,x, y and z co-ordinates] with respect to the bin area are proposed. The z co-ordinate is computed using stereo images and neural networks. The proposed algorithms is tested using two neural network architectures namely the Radial Basis Function nets and Simple Feedforward nets. The training results fo feedforward nets are found to be more suitable for the current application. The proposed stereo vision system is interfaced with an Adept SCARA Robot to perform bin picking operations. The vision system is found to be effective for partially occluded objects, in the absence of albedo effects. The results are validated through real time bin picking experiments on the Adept Robot

Vowel recognition based on frequency ranges determined by bandwidth approach

Automatic speech recognition (ASR) has made great strides with the development of digital signal processing hardware and software especially using English as the language of choice.In this paper, a new feature extraction method is presented to identify vowels recorded from 80 Malaysian speakers.The features were obtained from vocal tract model based on bandwidth (BW) approach.Bandwidth approach identifies frequency bands based on the first peak of vowel frequency responses.Mean and maximum energies were calculated from these Bandwidth frequency bands. Classification results from bandwidth approach were compared with the first 3-formant features using Linear Predictive method.A multi-layer perceptron (MLP) and multinomial logistic regression (MLR) were used to classify the vowels.MLR and MLP shows comparable classification results for BW approach of 96.40% and 96.59% respectively. Bandwidth approach obtained 5.49% higher classification rate than 3-formant features using MLP