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To identify fake Malaysian banknotes, this research suggested a revolutionary fuzzy logic weighted specific (FLWS) approach in image processing techniques. The FLWS Algorithm has the benefit of a more accurate model because it is a human guidance learning algorithm that demands training to obtain the precise weights for each security feature. The trial outcomes also demonstrated that, for the purpose of detecting counterfeit Malaysian banknotes, the FLWS model outperformed the parallel fuzzy logic weighted averaging (FLWA) algorithm, MobileNet model, and VGG16 model. Its adoption of well-known watermark features, with specific weights assigned, and well-known machine learning techniques to distinguish between genuine Malaysian banknotes and counterfeit Malaysian banknotes gives it a clear advantage over earlier or current banknote counterfeit detection techniques.

This paper proposed a novel fuzzy logic weighted averaging (FLWA) algorithm in image processing techniques to detect counterfeit Malaysian banknotes. Image acquisition techniques on banknote position detection and re-adjustment, image pre-processing techniques, feature extraction methods on Malaysian banknotes’ watermarks are also covered in the paper. The FLWA Algorithm has the advantage of a much simpler model since it is a human guidance learning algorithm that does not require enrolment process to get the specific weights for each security feature. Each security feature is treated with equal weight. The experimental results also shown that FLWA model also outperform the MobileNet model and VGG16 model in Malaysian banknotes’ counterfeit detection. It has a distinct advantage over earlier or current banknote counterfeit detection techniques in that it adopted the known watermarks features, with known machine learning techniques to identify real Malaysian banknotes and detect those counterfeit Malaysian banknotes.

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).

Lane departure collisions have contributed to the traffic accidents that cause millions of injuries and tens of thousands of casualties per year worldwide. Due to vision-based lane departure warning limitation from environmental conditions that affecting system performance, a model-based vehicle dynamics framework is proposed for estimating the lane departure event by using vehicle dynamics responses. The model-based vehicle dynamics framework mainly consists of a mathematical representation of 9-degree of freedom system, which permitted to pitch, roll, and yaw as well as to move in lateral and longitudinal directions with each tire allowed to rotate on its axle axis. The proposed model-based vehicle dynamics framework is created with a ride model, Calspan tire model, handling model, slip angle, and longitudinal slip subsystems. The vehicle speed and steering wheel angle datasets are used as the input in vehicle dynamics simulation for predicting lane departure event. Among the simulated vehicle dynamic responses, the yaw acceleration response is observed to provide earlier insight in predicting the future lane departure event compared to other vehicle dynamics responses. The proposed model-based vehicle dynamics framework had shown the effectiveness in estimating lane departure using steering wheel angle and vehicle speed inputs.

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).

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).

To identify fake Malaysian banknotes, this research suggested a revolutionary fuzzy logic weighted specific (FLWS) approach in image processing techniques. The FLWS Algorithm has the benefit of a more accurate model because it is a human guidance learning algorithm that demands training to obtain the precise weights for each security feature. The trial outcomes also demonstrated that, for the purpose of detecting counterfeit Malaysian banknotes, the FLWS model outperformed the parallel fuzzy logic weighted averaging (FLWA) algorithm, MobileNet model, and VGG16 model. Its adoption of well-known watermark features, with specific weights assigned, and well-known machine learning techniques to distinguish between genuine Malaysian banknotes and counterfeit Malaysian banknotes gives it a clear advantage over earlier or current banknote counterfeit detection techniques. [ABSTRACT FROM AUTHOR]

Lane departure collisions have contributed to the traffic accidents that cause millions of injuries and tens of thousands of casualties per year worldwide. Due to vision-based lane departure warning limitation from environmental conditions that affecting system performance, a model-based vehicle dynamics framework is proposed for estimating the lane departure event by using vehicle dynamics responses. The model-based vehicle dynamics framework mainly consists of a mathematical representation of 9-degree of freedom system, which permitted to pitch, roll, and yaw as well as to move in lateral and longitudinal directions with each tire allowed to rotate on its axle axis. The proposed model-based vehicle dynamics framework is created with a ride model, Calspan tire model, handling model, slip angle, and longitudinal slip subsystems. The vehicle speed and steering wheel angle datasets are used as the input in vehicle dynamics simulation for predicting lane departure event. Among the simulated vehicle dynamic responses, the yaw acceleration response is observed to provide earlier insight in predicting the future lane departure event compared to other vehicle dynamics responses. The proposed model-based vehicle dynamics framework had shown the effectiveness in estimating lane departure using steering wheel angle and vehicle speed inputs. [ABSTRACT FROM AUTHOR]

This paper aims to present a fingerprint singular point detection algorithm and a rule-based fingerprint classification method. The singular point detection algorithm uses a quantization approach on the orientation field of the fingerprint image and seeks to locate the core and delta points via the changes of the gray levels around a 2x2 window. It has been found that with the application of an edge-trace-cum-core-delta-pairing algorithm and a merging-and-pruning heuristic as the post-processing steps, spurious singular points are removed and the final singular points are then used for classification. Fingerprint classification on NIST-4 database by rule-based method utilizes the number of singular points and three key geometry features to perform 5-class as well as 4-class classification using success rate (the accuracy) as the performance measure. It has been found to achieve 86.5% and 92.15% of success rate, respectively. The study has thus find the application of the new singular point detection algorithm via quantization and the rule-based classification to be promising as many of the fingerprint images in the NIST-4 database have been reported as poor quality, i.e. 22.35%. [ABSTRACT FROM AUTHOR]