Collisions arising from lane departures have contributed to traffic accidents causing millions of injuries and tens of thousands of casualties per year worldwide. Many related studies had shown that single vehicle lane departure crashes accounted largely in road traffic deaths that results from drifting out of the roadway. Hence, automotive safety has becoming a concern for the road users as most of the road casualties occurred due to driver's fallacious judgement of vehicle path. This paper proposes a vision-based lane departure warning framework for lane departure detection under daytime and night-time driving environments. The traffic flow and conditions of the road surface for both urban roads and highways in the city of Malacca are analysed in terms of lane detection rate and false positive rate. The proposed vision-based lane departure warning framework includes lane detection followed by a computation of a lateral offset ratio. The lane detection is composed of two stages: pre-processing and detection. In the pre-processing, a colour space conversion, region of interest extraction, and lane marking segmentation are carried out. In the subsequent detection stage, Hough transform is used to detect lanes. Lastly, the lateral offset ratio is computed to yield a lane departure warning based on the detected X-coordinates of the bottom end-points of each lane boundary in the image plane. For lane detection and lane departure detection performance evaluation, real-life datasets for both urban roads and highways in daytime and night-time driving environments, traffic flows, and road surface conditions are considered. The experimental results show that the proposed framework yields satisfactory results. On average, detection rates of 94.71% for lane detection rate and 81.18% for lane departure detection rate were achieved using the proposed frameworks. In addition, benchmark lane marking segmentation methods and Caltech lanes dataset were also considered for comparison evaluation in lane detection. Challenges to lane detection and lane departure detection such as worn lane markings, low illumination, arrow signs, and occluded lane markings are highlighted as the contributors to the false positive rates.
Visually impaired persons face challenges in running business activities, especially in handling banknotes. Malaysia researchers had proposed some Ringgit banknotes recognition systems to aid visually impaired persons recognize and classify Ringgit banknotes. However, these electronic banknote readers can only recognize Malaysian Banknotes' Ringgit value, they have no counterfeit detection features. The purpose of this study is to develop a banknote reader that not only can help visually impaired persons recognize the banknote value, but also to detect the counterfeit of the banknote, safeguarding their losses. This paper proposed a Malaysian banknote reader using backlight mechanism and image processing techniques to read and detect counterfeit for one Ringgit and five Ringgit Malaysian banknotes. The developed handheld banknote reader used visual type sensor to capture banknote image, passed to raspberry pi controller to perform image processing on banknote value and the extracted watermarks features. The developed image processing algorithm will trace out the region of interests: 1)see-thru windows, 2)Crescent and Star, 3)Perfect see though register and detect the watermarks features accordingly. The processed result will be passed back to the handheld banknote reader and broadcast on an attached mini speaker to aid the visually impaired understand the holding banknote, whether it is a real one Ringgit, real five Ringgit or none of them. The experimental result shown by this approach able to accomplish numerous round of banknote reading attempts with successful outcomes. Confusion matrix is further employed to study the performance of the banknote reader, in terms of true positive, true negative, false positive and false negative. Details analysis had been focused on the critical false positive cases (predicted real banknote and actually is fake banknote) and false negative cases (predicted fake banknote and it is actually real banknote).
Burkholderia pseudomallei infections are prevalent in Southeast Asia and northern Australia and often misdiagnosed. Diagnostics are often neither sensitive nor rapid, contributing up to 50% mortality rate. In this 2018 pilot study, we enrolled 100 patients aged 6 months-79 years from Kapit Hospital in Sarawak, Malaysia, with symptoms of B. pseudomallei infection. We used three different methods for the detection of B. pseudomallei: a real-time polymerase chain reaction (PCR) assay, a rapid lateral flow immunoassay, and the standard-of-care bacterial culture-the gold standard. Among the 100 participants, 24 (24%) were positive for B. pseudomallei by one or more of the detection methods. Comparing the two individual diagnostic methods against the gold standard-bacterial culture-of any positive test, there was low sensitivity for each test (25-44%) but high specificity (93-98%). It seems clear that more sensitive diagnostics or a sensitive screening diagnostic followed by specific confirmatory diagnostic is needed for this disease.