Emotion assessment in stroke patients gives meaningful information to physiotherapists to identify the appropriate method for treatment. This study was aimed to classify the emotions of stroke patients by applying bispectrum features in electroencephalogram (EEG) signals. EEG signals from three groups of subjects, namely stroke patients with left brain damage (LBD), right brain damage (RBD), and normal control (NC), were analyzed for six different emotional states. The estimated bispectrum mapped in the contour plots show the different appearance of nonlinearity in the EEG signals for different emotional states. Bispectrum features were extracted from the alpha (8–13) Hz, beta (13–30) Hz and gamma (30–49) Hz bands, respectively. The k-nearest neighbor (KNN) and probabilistic neural network (PNN) classifiers were used to classify the six emotions in LBD, RBD and NC. The bispectrum features showed statistical significance for all three groups. The beta frequency band was the best performing EEG frequency-sub band for emotion classification. The combination of alpha to gamma bands provides the highest classification accuracy in both KNN and PNN classifiers. Sadness emotion records the highest classification, which was 65.37% in LBD, 71.48% in RBD and 75.56% in NC groups.

The exhaust system in any Internal Combustion (IC) engine is a critical component that affects the engine’s performance. A poorly designed exhaust system generally results in an increment of exhaust backpressure. Backpressure is one of the fluid’s characteristics that acts as a resistance to exhaust gas flow. Relatively higher backpressure blocks the exhaust gas flow from discharging efficiently, decreasing the engine’s performance. In general, higher backpressure results in power and torque loss as well as higher fuel consumption and emission to the environment. This review paper aims to elucidate the relationship between exhaust backpressure and the performance of IC engine. Various past studies were conducted to study the effect of exhaust backpressure on the performance of IC engine through Computational Fluid Dynamic (CFD) simulation, engine simulation and experimental analysis. Some studies used Taguchi’s method to optimize the exhaust manifold’s design in respect to backpressure decrement. It was found that 0.22 kW to 0.45 kW of engine’s power increases for every 1 kPa of exhaust backpressure decrement. At the same time, 1.5% to 3% of fuel consumption decreases for every 10 kPa of backpressure decrement. In contrast, higher backpressure does reduce the Nitrous Oxides (NOx) emission in the exhaust gas due to higher temperature. Therefore, exhaust backpressure must be minimized to improve any IC engine’s performance if the NOx emission is neglected. This review paper is expected to provide a better understanding of the impact of exhaust backpressure on IC engine’s performance.

Driving cycle is a series of data points representing the speed of vehicle versus time and used to determine the performance of vehicle in general. One of the critical portions of driving cycle development is route selection methodology. This paper describes the efficient methodology of route selection for driving cycle development. Previous data from JKR Road Traffic Volume Malaysia (RTVM) in 2015 is studied and analysed to propose the methodology in route selection. The selected routes are then analysed by using Google Maps. For each region, four (4) routes are selected for each urban and rural. For this paper, the selection of route is focused on northern region of Malaysia specifically in Penang. Penang is chosen for this study because it is one of the developed state in Malaysia that has many urban and rural routes. The methods of route selection constructed in this study could be used by other region to develop their own driving cycles.