Intelligent imaging system for optimal night time driving

In the recent era, vehicles become a need of public. According to Statistic Portal, in year 2018 alone, more than 81 million vehicles were sold. This results in a large number of vehicles commuting on roads, thus increases the risks of road users. Road safety is the paramount and joint responsibility of all road users, which include pedestrians and travellers using different means of transport. Safety is always a main concern for drivers. It is a complex and difficult task even for an experienced senior driver. Road accident is the most unwanted thing to happen to a road user; it was reported that most of the road users are familiar with the general rules and safety measures when using roads, nonetheless their carelessness are causing the accidents and crashes. Zhang et.al [1] proposed an intelligent driver assist system for urban driving. This system provided smart navigation for its users with intelligent parking assistance to improve driving comfort while ensure the safety of the driver. The investigations of the system performance showed high precisions in the determination of the traffic flow and parking availabilit

Diabetic foot plantar pressure monitoring system using force sensitive resistor system

Many people are suffering from diabetes; it was the major cause that has led many to hospitalization. According to [1], patients with Type 2 diabetes suffering from peripheral neuropathy are at high risks of developing diabetic foot syndrome, which leads to foot ulcerations that caused mainly by high peak plantar pressures. Without early prevention and intervention [2], diabetic foot ulcer has 15 times greater risk to cause lower limb amputation. Therefore, further studies on early prevention may help in healthcare management

The development of a portable optical system for telemonitoring of skin blood oxygen level

Oxygen is one of the keys parameters required for tissues metabolism to ensure life sustainability. Without it, human’s health would suffer and eventually result in fatal. Cells consume oxygen to break down sugar to produce adenosine triphosphate (ATP) during cellular respiration [1]. ATPs are the main source of energy for metabolic functions [2] and every cell in the body, especially muscles cell, for its ability to store and use energy; muscle would not contract or relax without ATP. Cell is not able to function well under the condition of low oxygen level, thus it would lead to hypoxemia. If left untreated, severe hypoxemia can be fatal [3]

Breast tumor diagnosis in digital mammograms

Breast cancer has been classified as the most common cancer in most part of the world [1]. Breast cancer is caused by the growth of the abnormal cells in the breast. Breast cancer not only develops in women but also on men. However, the incidents of breast cancer in women are more common than men. Breast cancer is dangerous and may take away one’s life if there is no early detection and treatment are not done to remove the cancer cell present in the breast. Although the prevention methods for breast cancer may be unclear, it is found out that the earlier the detection and treatment conducted to the patients, the higher the survivability of the patients. Digital mammography is a specific type of breast imaging that uses low-dose x-rays to detect cancer early especially before women experience any symptoms [2]. The early signs of breast cancer can be detected in mammograms. Hence, digital mammograms have been classified as one of the best methods to detect breast cancer. In the studies [2] has shown that digital mammograms produce a better result than film mammograms in a group of young women, premenopausal and perimenopausal women, and women with dense breasts. 335 women were found to be infected with breast cancer in the test. However, there is also a limitation present in digital mammograms. High breast density can affect the performance of diagnosis in digital mammography as it increases the difficulty in finding abnormalities in a mammogram. Digital mammograms are only able to yield the best accuracy in the result for the women who are under the age of 50 and absent from menopause or undergoes menopause in a period of less than one year

An Optimized Semantic Segmentation Framework for Human Skin Detection

The study incorporating optimization strategy in semantic segmentation is underexplored in dermatology. Existing approaches used complex and various heuristic designs of image processing algorithms and deep models customized for skin detection problems. This paper demonstrates Particle Swarm Optimization (PSO)-incorporated AlexNet framework for the skin segmentation task. The results from testing the trained model are promising. The model produced satisfactory performances even with a strict split of 50 %, confirming the high efficiency of the proposed framework. The mean Jaccard index and Dice similarity measures evaluated between the annotated and predicted mask ranged from 0.80 to 0.93 in the binary classification of pixels as “skin” versus “background”. This work identified that the location and color variability of skin pixels in the training data are crucial to obtaining a good skin segmentation performance. Further works that can be explored in this area include adopting a robust preprocessing strategy to increase data variability and improve model generalization or implementing an optimization-enhanced strategy on the existing segmentation models for comparison

Empirical Analysis Based on Light Attenuation Gradient of Wavelength Pairs for the Prediction of Skin Oxygen Status

An empirical technique that allows noninvasive prediction of skin oxygen saturation (SO2) using the absorption coefï¬cients of the preprocessing stored wavelength pairs is proposed. The highest probable SO2 value is decided by selecting the bin containing wavelength pairs that produce the smallest variation in the distribution of the calculated attenuation gradient value. The performance this technique was evaluated using Monte Carlo simulated data. The simulation results revealed that this technique worked reasonably well even at low SO2 condition with an overall mean error of not more than 2 %. This shows that the proposed analytic technique can potentially be used for measurement of the blood oxygen level of individuals with respiratory disease or with oxygen deprivation conditions such as hypoxic-hypoxia

Piezoelectric Photoacoustic System for Fluid Flow Monitoring

The aim of this study is to investigate the feasibility of using a laboratory assembled piezoelectric based photoacoustic (PA) system for noncontact monitoring fluid flow. This is to overcome the drawbacks of some existing fluid flow detection systems, which include expensive equipment and their maintenance cost, limited sensitivity and specificity in detecting signals from restricted regions or at low flow velocity. The produced PA signal waves detected by a piezoelectric transducer used in this study was processed to determine the required phase value (Ф), which value was found to correlate linearly with fluid flow status. The fluid pressure difference of 1.16 pascals (Pa) and 11.90 Pa applied to the developed mock circulatory system was observed to produce changes in phase value with mean ± standard deviation (SD) ΔФ of 0.79 ± 0.07 rad and 2.17 ± 0.07 rad, respectively, suggesting a linear response of the developed system with changes in circulation system. This trend was supported with the relatively low absolute difference of 0.07 ± 0.01 rad in the predicted values as compared to that of the ground truth. This work concluded that the capabilities and simplicity of the proposed PA system renders it feasible for cost effective, non-destructive assessment of fluid flow in future studies

Photoacoustic Technology for Biological Tissues Characterization

The existing PA imaging systems showed mixed performance in terms of imaging characteristic and signal-to-noise ratio (SNR). The aim of this work is to present the use of an in-house assembled photoacoustic (PA) system using a modulating laser beam of wavelength 633 nm for two-dimensional (2D) characterization of biological tissues. The differentiation of the tissues in this work is based on differences in their light absorption, wherein the produced photoacoustic signal detected by a transducer was translated into phase value (Ф) that corresponds to the peak amplitude of tissue optical absorption. This research investigated variation in PA response between the considered different parts of chicken carcasses: fat, liver and muscle. This work found fat tissue to produce the strongest PA signals with mean ± standard deviation (SD) Ф = 2.09 ± 0.31 while muscle produced the least signal strength with Ф = 1.03 ± 0.17. This work attributes these observations and the presence of stripes pattern in 2D Ф images of fat and muscle to the differences in the optical and structural properties of these samples. In addition, a comparison has been made in an attempt to better assess the performance of the developed system with the related ones.This work concluded that the developed system may be useful as an alternative means in the noninvasive and label-free visualization and characterization of intact biological tissues in terms of their structural and physiological context in the future

Forecasting electricity consumption using SARIMA method in IBM SPSS software

Forecasting is a prediction of future values based on historical data. It can be conducted using various methods such as statistical methods or machine learning techniques. Electricity is a necessity of modern life. Hence, accurate forecasting of electricity demand is important. Overestimation will cause a waste of energy but underestimation leads to higher operation costs. Univesity Tun Hussein Onn Malaysia (UTHM) is a developing Malaysian technical university, therefore there is a need to forecast UTHM electricity consumption for future decisions on generating electric power, load switching, and infrastructure development. The monthly UTHM electricity consumption data exhibits seasonality-periodic fluctuations. Thus, the seasonal Autoregressive Integrated Moving Average (SARIMA) method was applied in IBM SPSS software to predict UTHM electricity consumption for 2019 via Box-Jenkins method and Expert Modeler. There were a total of 120 observations taken from January year 2009 to December year 2018 to build the models. The best model from both methods is SARIMA(0, 1, 1)(0, 1, 1)12. It was found that the result through the Box-Jenkins method is approximately the same with the result generated through Expert Modeler in SPSS with MAPE of 8.4%

Soil moisture level prediction using optical technique and artificial neural network

This research describes the use of an optical system combined with artificial neural network (ANN) for wireless and nondestructive prediction of soil moisture level. The former system comprising of near infrared (NIR) emitters of wavelengths 1200 nm and 1450 nm, and a photodetector for near real time soil moisture measurement in loams and peats holding different amount of water. There were 63 and 90 sets of data from loams and peats, respectively, used in the development of the dual stage-multiclass ANN model, wherein measurement of light attenuation (from nondestructive system) was correlated with percent soil moisture (from destructive gold standard approach) in pre-measurement stage. The result revealed a relatively good performance in the training of the NN with regression, R, of 0.8817 and 0.8881, and satisfactory error performance of 0.7898 and 1.172, for loams and peats, respectively. The testing of the system on 50 new samples of loam and peat showed a considerably high mean accuracy of 92 % for loams while 82 % was observed for peats. This study attributes the poorer performance of the system used on peats to the detection resolution of percent soil moisture, and structure and properties of the corresponding soil. This work concluded that the developed technology may be feasible for use in the future design and improvement of agricultural soil management