Wira Hidayat Mohd Saad

Bio: Wira Hidayat Mohd Saad is an academic researcher from Universiti Teknikal Malaysia Melaka. The author has contributed to research in topics: Computer science & Imaging phantom. The author has an hindex of 6, co-authored 35 publications receiving 179 citations. Previous affiliations of Wira Hidayat Mohd Saad include Universiti Putra Malaysia.

A Review of Finger-Vein Biometrics Identification Approaches

Abstract: Biometrics trait using finger-vein has attracted numerous attention from researchers all over the world since the last decade. Various approaches have been proposed in regard to improving the accuracy of identification result. This paper discusses on the approaches taken from other researches on preprocessing, feature extraction and classification stage specifically for recognizing individual identity. The strengths and weaknesses of these approaches are critically reviewed. The classification approach using machine learning method is highlighted to determine the future direction and to fill the research gap in this field.

The Evolution of Non-invasive Blood Glucose Monitoring System for Personal Application

Abstract: Glucose monitoring technology has been used by diabetic patients to monitor their blood glucose level for the past three decades. This technology is very useful for managing diet among diabetic patients. This paper reviews the fundamental technique of blood glucose detection method and the development of blood glucose monitoring systems that have been developed ever since. The most common and widely used technique is an invasive technique that requires users to prick their finger to draw the blood. However, recently a lot of new technologies have been developed for non-invasive technique to monitor blood glucose monitoring and studies in this area are growing rapidly. Among all, the optical and transdermal approach are the two most potential sensing modalities for non-invasive glucose monitoring that show a very good prospect.

Multilayer Perceptron Neural Network in Classifying Gender using Fingerprint Global Level Features

Abstract: Background/Objective: A new algorithms of gender classification from fingerprint is proposed based on Acree 25mm2 square area. The classification is achieved by extracting the global features from fingerprint images which is Ridge Density, Ridge Thickness to Valley Thickness Ratio (RTVTR) and White Lines Count. The objective of this study to test the effectiveness of the this new algorithm by looking the classification rate. Multilayer Perceptron Neural Network (MLPNN) used as a classifier. Methods: This new algorithm is tested with a database of 3000 fingerprint in which 1430 were male fingerprint and 1570 were female fingerprints. Classification part is tested with different test option. Findings: This study found that women tends to have higher Ridge Density, higher white lines count and higher ridge thickness to valley thickness ratio compared to male same as the previous study. Therefore, we can conclude that this new algorithm is very efficient and effective in classifying gender. Conclusion: The overall classification rate is 97.25% has been achieved

A new approach to highway lane detection by using hough transform technique

Abstract: This paper presents the development of a road lane detection algorithm using image processing techniques.This algorithm is developed based on dynamic videos, which are recorded using on-board cameras installed in vehicles for Malaysian highway conditions.The recorded videos are dynamic scenes of the background and the foreground, in which the detection of the objects, presence on the road area such as vehicles and road signs are more challenging caused by interference from background elements such as buildings, trees, road dividers and other related elements or objects. Thus, this algorithm aims to detect the road lanes for three significant parameter operations; vanishing point detection, road width measurements, and Region of Interest (ROI) of the road area, for detection purposes.The techniques used in the algorithm are image enhancement and edges extraction by Sobel filter, and the main technique for lane detection is a Hough Transform. The performance of the algorithm is tested and validated by using three videos of highway scenes in Malaysia with normal weather conditions, raining and a night-time scene, and an additional scene of a sunny rural road area. The video frame rate is 30fps with dimensions of 720p (1280x720) HD pixels. In the final achievement analysis, the test result shows a true positive rate, a TP lane detection average rate of 0.925 and the capability to be used in the final application implementation.

Monte Carlo Simulation on Breast Cancer Detection Using Wire Mesh Collimator Gamma Camera

Abstract: This paper presents the preliminary results of the new low energy high resolution wire-mesh collimator gamma camera in mapping breast cancer cells, by employing 140 keV photons of Technetium-99 m radionuclide tracer. The complete model of photons propagation and detection, as well as the human cells activities are simulated using Monte Carlo N-Particle code. Abnormal cells of different tumor to background values are investigated, and the results from the conventional collimator and wire-mesh collimator are compared. The results are evaluated in terms of the collimator sensitivity and the contrast to background ratio. In our assessment, the wire mesh collimator gamma camera yields slightly better results than the multihole collimator for sensitivity, however produces insignificant performance in the contrast to background evaluation.

Wearable Continuous Glucose Monitoring Sensors: A Revolution in Diabetes Treatment

Abstract: Worldwide, the number of people affected by diabetes is rapidly increasing due to aging populations and sedentary lifestyles, with the prospect of exceeding 500 million cases in 2030, resulting in one of the most challenging socio-health emergencies of the third millennium. Daily management of diabetes by patients relies on the capability of correctly measuring glucose concentration levels in the blood by using suitable sensors. In recent years, glucose monitoring has been revolutionized by the development of Continuous Glucose Monitoring (CGM) sensors, wearable non/minimally-invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose-oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1–5 min. CGM opened new challenges in different disciplines, e.g., medicine, physics, electronics, chemistry, ergonomics, data/signal processing, and software development to mention but a few. This paper first makes an overview of wearable CGM sensor technologies, covering both commercial devices and research prototypes. Then, the role of CGM in the actual evolution of decision support systems for diabetes therapy is discussed. Finally, the paper presents new possible horizons for wearable CGM sensor applications and perspectives in terms of big data analytics for personalized and proactive medicine.

A Systematic Review of Finger Vein Recognition Techniques

Abstract: Biometric identification is the study of physiological and behavioral attributes of an individual to overcome security problems. Finger vein recognition is a biometric technique used to analyze finger vein patterns of persons for proper authentication. This paper presents a detailed review on finger vein recognition algorithms. Such tools include image acquisition, preprocessing, feature extraction and matching methods to extract and analyze object patterns. In addition, we list some novel findings after the critical comparative analysis of the highlighted techniques. The comparative studies indicate that the accuracy of finger vein identification methods is up to the mark.

Simulation and Analysis of Lead based Perovskite Solar Cell using SCAPS-1D

Abstract: Objectives: Perovskite photovoltaic’s are getting to be distinctly predominant option for the conventional solar cells achieving a maximum efficiency of 22.1%. This work is concerned about the design and analyses of lead-based perovskite solar cell model with the flexible architecture of lass/FTO/PCBM/CH3NH3PbI3/PEDOT:PSS/Ag. Method/Analysis: The analysis of solar cell architecture is done using the Solar Cell Capacitance Simulator(SCAPS). It is a computer-based software tool and is well adapted for the analyses of homo and heterojunctions, multi- junctions and Schottky barrier photovoltaic devices. This software tool runs and simulates based on the Poisson’s and continuity equation of electrons and holes. For this model, it is used to optimize the various parameters such as thickness, the defect density of absorber layer, doping concentrations(ND and NA) of Electron Transport Material (ETM) and Hole Transport Material (HTM). Findings: The thickness of CH3NH3PbI3varied from 0.1μm to 0.6μm and the best results are observed at 0.3μm. The total defect density of the absorber varied from 1013 cm-3 to 1018 cm-3 and the minimum defect density of absorber layer is predicted as 1014cm-3. The ND or NA of the HTM and ETM varied from 1014 to 1019 cm-3and the PCE is maximum when ND and NA both kept at 1019cm-3. By tuning the thickness of absorber layer and doping concentrations, the predicted results are as follows; maximum power conversion efficiency(PCE)31.77%, short circuit current density (Jsc) 25.60 mA/cm2, open circuit voltage(Voc) 1.52V, fill factor(FF) 81.58%. Improvements: With this proposed simulated model, the efficiency of the perovskite solar cell reaches to the 31%, which is an improvement of 4-5%, to the previous models, with the optimization of few material parameters. Hence this simulation work will provide the handy information in fabricating perovskite solar cells to reasonably choose material parameters and to achieve the high efficiency.

Non-Invasive Glucose Monitoring: A Review of Challenges and Recent Advances

Abstract: Glucose monitoring devices represent an exciting frontier in diabetes research. Great efforts have been dedicated to the development of non-invasive glucose monitoring devices, which may considerably improve the quality of life for people suffering from diabetes and facilitate their compliance for glucose monitoring. This manuscript reviews past, current and emerging non-invasive glucose monitoring techniques and devices and presents the major challenges they face. Poor glucose specificity and sensitivity, physiological time lag, calibration process and human factors perspective are discussed. Since incorporating user requirements into device development may potentially increase user acceptance and improve patient safety and device effectiveness, special attention is given to usability, user experience and applicability for home use, thus extending previous published reviews.