Siti Anom Ahmad

Infrared Heating in Food Drying: An Overview

This article aims to review and analyze the aspects and characteristics related to infrared food drying. Indeed, with a review of 100 relevant publications all dealing with infrared food drying, this article notes that infrared drying has several advantages over other common food drying methods. Shorter drying time, a better final dried product quality, and more energy savings in the process are revealed as the most important advantages of infrared drying over convective heat drying. Infrared dryers can also be easily combined with other drying methods such as hot air, microwave, vibration, and vacuum. This article clearly shows that using infrared heating for food drying purposes has become more popular in the last decade and its application in the industrial drying of different foodstuffs has been employed widely.

Pressure Sensor: State of the Art, Design, and Application for Robotic Hand

We survey the state of the art in a variety of force sensors for designing and application of robotic hand. Most of the force sensors are examined based on tactile sensing. For a decade, many papers have widely discussed various sensor technologies and transducer methods which are based on microelectromechanical system (MEMS) and silicon used for improving the accuracy and performance measurement of tactile sensing capabilities especially for robotic hand applications. We found that transducers and materials such as piezoresistive and polymer, respectively, are used in order to improve the sensing sensitivity for grasping mechanisms in future. This predicted growth in such applications will explode into high risk tasks which requires very precise purposes. It shows considerable potential and significant levels of research attention.

Selection of Mother Wavelet Functions for Multi-Channel EEG Signal Analysis during a Working Memory Task

We performed a comparative study to select the efficient mother wavelet (MWT) basis functions that optimally represent the signal characteristics of the electrical activity of the human brain during a working memory (WM) task recorded through electro-encephalography (EEG). Nineteen EEG electrodes were placed on the scalp following the 10–20 system. These electrodes were then grouped into five recording regions corresponding to the scalp area of the cerebral cortex. Sixty-second WM task data were recorded from ten control subjects. Forty-five MWT basis functions from orthogonal families were investigated. These functions included Daubechies (db1–db20), Symlets (sym1–sym20), and Coiflets (coif1–coif5). Using ANOVA, we determined the MWT basis functions with the most significant differences in the ability of the five scalp regions to maximize their cross-correlation with the EEG signals. The best results were obtained using “sym9” across the five scalp regions. Therefore, the most compatible MWT with the EEG signals should be selected to achieve wavelet denoising, decomposition, reconstruction, and sub-band feature extraction. This study provides a reference of the selection of efficient MWT basis functions.

A Review of Classification Techniques of EMG Signals during Isotonic and Isometric Contractions.

In recent years, there has been major interest in the exposure to physical therapy during rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and human machine interface (HMI) applications. An automated system will guide the user to perform the training during rehabilitation independently. Advances in engineering have extended electromyography (EMG) beyond the traditional diagnostic applications to also include applications in diverse areas such as movement analysis. This paper gives an overview of the numerous methods available to recognize motion patterns of EMG signals for both isotonic and isometric contractions. Various signal analysis methods are compared by illustrating their applicability in real-time settings. This paper will be of interest to researchers who would like to select the most appropriate methodology in classifying motion patterns, especially during different types of contractions. For feature extraction, the probability density function (PDF) of EMG signals will be the main interest of this study. Following that, a brief explanation of the different methods for pre-processing, feature extraction and classifying EMG signals will be compared in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

Role of EEG as Biomarker in the Early Detection and Classification of Dementia

The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.

Cognitive impairment and memory dysfunction after a stroke diagnosis: a post-stroke memory assessment

Cognitive impairment and memory dysfunction following stroke diagnosis are common symptoms that significantly affect the survivors’ quality of life. Stroke patients have a high potential to develop dementia within the first year of stroke onset. Currently, efforts are being exerted to assess stroke effects on the brain, particularly in the early stages. Numerous neuropsychological assessments are being used to evaluate and differentiate cognitive impairment and dementia following stroke. This article focuses on the role of available neuropsychological assessments in detection of dementia and memory loss after stroke. This review starts with stroke types and risk factors associated with dementia development, followed by a brief description of stroke diagnosis criteria and the effects of stroke on the brain that lead to cognitive impairment and end with memory loss. This review aims to combine available neuropsychological assessments to develop a post-stroke memory assessment (PSMA) scheme based on the most recognized and available studies. The proposed PSMA is expected to assess different types of memory functionalities that are related to different parts of the brain according to stroke location. An optimal therapeutic program that would help stroke patients enjoy additional years with higher quality of life is presented.

Surface EMG pattern analysis of the wrist muscles at different speeds of contraction

The aim of this study is to investigate the characteristics of surface electromyographic signals, particularly in pattern analysis. The data were collected from the wrist muscles (flexor carpi ulnaris and extensor carpi radialis) of 20 healthy participants. The study focuses on the movement of the wrist muscles at different frequencies. Participants were asked to contract their muscles at four different speeds (60, 90 and 120 cycles a minute and maximum speed) during wrist flexion and extension, co-contraction and isometric contraction. In this work, moving approximate entropy, mean absolute value and kurtosis are evaluated from the surface electromyographic signals at the four speeds. Moving approximate entropy and kurtosis analysis show that there are significant differences at three states of contraction; start, middle and end. It is shown that there are more regular data in a surface electromyographic signal at the beginning and end of a muscle contraction with low regularity during the middle part.

Using accelerometers to analyse slip for prosthetic application

A slip sensor, using accelerometers, has been investigated for use in prosthetic design. The basis of this report is the characterization of this sensor including how it performs in re-gripping a falling object. The possibility of using three-axis vibration control is investigated and the limitations of this method are reported. A controller was produced to determine how reliable the sensor is when using both open- and closed-loop control methods. The conclusion is that the sensor is robust, and in addition to basic vibration, it is possible to use the sensor to calculate a reliable value for the distance of slip. Using statistical measures, a minimum grip force is given for successful re-grip without knowledge of the tangential friction forces.

A sensitivity study of piezoresistive pressure sensor for robotic hand

In this paper, we have studied and surveyed the field of robotic hand and the works that have been done in this area related to types of materials such as piezoresistive, piezoelectric and capacitive as well as a few types of pressure sensors. It indicates that piezoresistive pressure sensor is the best technique that can be used to implement a robotic hand for pick and place application. An adequate experiment of pressure sensor interfacing and calibration have been done in this paper. As a preliminary result of the works, output voltage (V) of the pressure sensor versus applied force input (N) are presented. Furthermore, this framework can be used to derive a new approach of pressure sensor distribution on the robotic hand based on complex algorithm of controlling applied pressures.

Selection of mother wavelets thresholding methods in denoising multi-channel EEG signals during working memory task

The aim of this pilot study was to select the most similar mother wavelet function and the most efficient threshold in order to use with wavelet basis function for the human brain electrical activity during working memory task. A 60 seconds was recorded from the scalp using the Electroencephalography (EEG). 19 electrodes were placed over different sites on the scalp where analyzed for one control subject and one post-stroke patients in the first week of his stroke onset. In this study, forty-five mother wavelet basis functions from orthogonal families with four thresholding methods were used. The selection of mother wavelet functions like Daubechies (db), symlet (sym) and coiflet (coif) and the thresholding methods these are sqtwolog, rigrsure, heursure and minimax are to check mother wavelet functions similarity with the recorded EEG signals during working memory task. The test have been done using four evaluating criteria, namely signal to noise ratio (SNR), peak signal to noise ratio (PSNR) mean square error (MSE) and crosscorelation method (xcorr). Symlet mother wavelet of order 9 (sym9) is the most compatible for all the 19 channels for both EEG datasets that selected to be examined and the best results have been obtained by using the rigrsure thresholding method.