Abstract The car braking system is a very important part of car safety. The effectiveness of Conventional braking system is strongly influenced by the ability and the driver’s experience. It is very vulnerable to accidents if the driver fails to operate the braking system. Because of that, an automation of the braking system is needed that can decrease the accident’s impact. The... The car braking system is a very important part of car safety. The effectiveness of Conventional braking system is strongly influenced by the ability and the driver’s experience. It is very vulnerable to accidents if the driver fails to operate the braking system. Because of that, an automation of the braking system is needed that can decrease the accident’s impact. The Automatic braking systems require the reasoning to manage information before making a decision that brakes or slows down. There is uncertainty in determining membership function of distance and velocity. It is difficult to determine an exact membership function. In this paper, the system used Interval type-2 fuzzy logic control that can work well in an unstructured environment and have the ability to overcome the uncertainty of information. The result is that the pressure on the brake pads is gradually increased with an average increase of 5.6%. At a distance of 34 m, the pressure is at maximum value. Read More Read Less

Abstract Epilepsy is a disease caused by abnormal electrical activity in the brain. One of the techniques for diagnosing epilepsy is by analyzing electroencephalogram (EEG) signals. Various techniques were developed by researchers to analyze epileptic seizure on EEG signals. Because of the nonlinear, non-Gaussian, and nonstationary nature of EEG signals, methods such as empirical mode decomposition (EMD) are often used for... Epilepsy is a disease caused by abnormal electrical activity in the brain. One of the techniques for diagnosing epilepsy is by analyzing electroencephalogram (EEG) signals. Various techniques were developed by researchers to analyze epileptic seizure on EEG signals. Because of the nonlinear, non-Gaussian, and nonstationary nature of EEG signals, methods such as empirical mode decomposition (EMD) are often used for analysis on EEG signals. The intrinsic mode function (IMF) of the EMD is believed to provide different information for normal EEG and seizure signals. Some features are taken from the IMF such as statistical features and spectral features. One of the differences between normal signals and abnormal signals is signal complexity where one of the metrics for measuring them is entropy. Several research used entropy combined with first and second order statistical features. In this study, only one entropy feature used to characterize each IMF produced from EMD for the classification of epileptic seizure EEG. Entropie used were Shannon entropy (ShEN), spectral entropy (SE), Renyi entropy (RE), and permutation entropy (PE). The highest accuracy produced by RE in eight IMF uses quadratic support vector machine (SVM) as the classifier. The accuracy of 97.3% with sensitivity of 97% and specificity of 99.75 % was achieved for classification in three data classes. The developed method is able to produce high accuracy with a relatively small number of features. Read More Read Less

Abstract The advancement of mobile technology with reasonable cost has indulge the mobile phone users to photograph and share their foods in social media. Since that, food recognition has become emerging research area in image processing and machine learning. Food recognition provides an automatic identification of the types of foods from an image. Further analysis of food images can be carried... The advancement of mobile technology with reasonable cost has indulge the mobile phone users to photograph and share their foods in social media. Since that, food recognition has become emerging research area in image processing and machine learning. Food recognition provides an automatic identification of the types of foods from an image. Further analysis of food images can be carried out to approximate the calories and nutrients that can be used for health-care purposes as well as the other application domains. The interest region-based detector by using Maximally Stable Extremal Region (MSER) provides distinctive interest points by representing the arbitrary shape of foods in parallelogram especially the food images with strong mixture of ingredients. However, the classification performance on food categories with less diverse texture food images by using MSER are obviously was not up to par with the rest of food categories that have more noticeable texture. The respective food objects were suffered from low volume extremal regions (ER) detection that were associated with the condition of food images that have visually texture-less objects, low contrast and brightness as well as small image pixel dimensions. Therefore, this paper proposed an adaptive interest regions detection by using MSER (aMSER) that provide an automatic MSER parameter configuration to increase the density of interest points on the targeted food images. The features are described by using Speeded-up Robust Feature Transform (SURF) and encoded by using Bag of Features (BoF) model. The classification is performed by using Linear Support Vector Machine and yield 84.20% classification rate on UEC100-Food dataset with competitive volume of ER and extraction time efficiency. Read More Read Less