Vision-based frontal vehicle detection and tracking

This paper presents a vision-based driver assistance system composing of vehicle detection using knowledge-based method and vehicle tracking using Kalman filtering.First, a preceding vehicle is localized by a proposed detection scheme, consisting of shadow detection and brake lights detection.Second, the possible vehicle region is extracted for verification. Symmetry analysis includes contour and brake lights symmetries are performed and followed by an asymmetry contour analysis in order to obtain vehicle’s center.The center of vehicle is tracked continuously using Kalman filtering within a predicted subwindow in consecutive frames.It reduces the scanning process and maximizes the computational speed of vehicle detection. Simulation results demonstrate good performance of the proposed system

Coal-Fired Boiler Fault Prediction using Artificial Neural Networks

Boiler fault is a critical issue in a coal-fired power plant due to its high temperature and high pressure characteristics. The complexity of boiler design increases the difficulty of fault investigation in a quick moment to avoid long duration shut-down. In this paper, a boiler fault prediction model is proposed using artificial neural network. The key influential parameters analysis is carried out to identify its correlation with the performance of the boiler. The prediction model is developed to achieve the least misclassification rate and mean squared error. Artificial neural network is trained using a set of boiler operational parameters. Subsequenlty, the trained model is used to validate its prediction accuracy against actual fault value from a collected real plant data. With reference to the study and test results, two set of initial weights have been tested to verify the repeatability of the correct prediction. The results show that the artificial neural network implemented is able to provide an average of above 92% prediction rate of accuracy

Multi-level Signal Decomposition for Power Quality Disturbance Classification

The introduction of electric vehicles impose large disturbance to the grid-level power signal due to the charging and discharging mechanism. Power signal monitoring in the electrical grid can provide several insights such as power quality disturbance detection, major power consumption area, peak power usage period, and their potential catastrophic failure conditions. As for preventive maintenance purpose, automatic classification of power quality disturbance using a hybrid method incorporating wavelet transform and deep LSTM network is proposed in this paper. Multi-level signal decomposition is applied to input signal to increase the resolution of input decomposing into multiple frequency bands. Subsequently, these multi-level frequency components are fed into deep LSTM layer to further extract useful higher order latent feature. Classification performance of the proposed wavelet-based LSTM (WTLSTM) network is bench-marked with deep LSTM method. Additive white Gaussian noise (AWGN) with signal-to-noise (SNR) levels between 20-50dB are inserted during the training process to increase the generalization of signal learning with the realistic scenarios. The classification performance of both WT-LSTM and Deep LSTM networks are tested with 20,30,40,50dB SNR AWGN and noiseless conditions. As a result, the WT-LSTM network obtains an overall classification performance of 89.77% on 20dB and 99.21% on noiseless condition as compared to Deep LSTM, with 88.48% and 98.54% respectively

Feasibility of Visible Near-Infrared Hyperspectral Imaging in Detection of Calcium Hypochlorite in Sago Flour

The general public perspective on sago flour quality is based on the perceived colour appearances. This contributed to the potential of food fraud by excessive usage of bleaching agents such as calcium hypochlorite (CHC) to alter the product’s colour. Conventional methods to detect and quantify CHC such as titration and chromatography are time-consuming, expensive and limited to laboratory setups only. In this research, visible near-infrared hyperspectral imaging (Vis-NIR HSI) was combined with partial least squares regression (PLSR) model to quantify CHC in pure sago flour accurately and rapidly. Hyperspectral images with the spectral region of 400 nm to 1000 nm were captured for CHC-pure sago mixture samples with CHC concentration ranging from 0.005 w/w% to 2 w/w%. Mean reflectance spectral data was extracted from the hyperspectral images, and was used as inputs to develop the PLSR model to predict the CHC concentration. The PLSR model achieved the commendable predictive results in this study, with Rp = 0.9509, RMSEP = 0.1655 and MAPEP of 3.801%, proving that Vis-NIR HSI can effectively predict the concentration of CHC in sago flour

Growth Study and Biological Hydrogen Production by novel strain Bacillus paramycoides

Industrial revolution has created high dependent on fossil fuels for energy creation. However, combustion of fossil fuels has created excessive amount of greenhouse gases, hence led to climate change. Thus, renewable energy has been proposed to alleviate the environmental pollution issues around the globe. One of the promising renewable energies is green hydrogen energy. Commercialized technologies such as electrolysis and thermochemical reaction are utilized to form hydrogen energy. Nonetheless, these processes require high energy and yet producing greenhouse gases that harm the environment. In this study, biodegradation process to produce hydrogen energy has been explored. To our knowledge, Bacillus paramycoides strain has not yet been investigated for biological hydrogen evolution. Therefore, in this paper, the ability of Bacillus paramycoides to produce biological hydrogen has been studied. The rod-shaped and gram-positive Bacillus paramycoides was identified under scanning electron microscope and gram staining procedure. Furthermore, biological hydrogen generation by Bacillus sp. was experimented for 96 hours. The result shows that 4668 ± 120 ppm cumulative hydrogen gas was generated through dark fermentation process. For Bacillus sp. growth study, lag, log, and stationary phase have been achieved in 96 hours. In a summary, metabolic engineering to degrade abundant biomass wastes is a sustainable pathway to produce hydrogen energy, simultaneously resolve waste management issue around the globe

Development and user testing study of MozzHub : a bipartite network-based dengue hotspot detector

Traditionally, dengue is controlled by fogging, and the prime location for the control measure is at the patient’s residence. However, when Malaysia was hit by the first wave of the Coronavirus disease (COVID-19), and the government-imposed movement control order, dengue cases have decreased by more than 30% from the previous year. This implies that residential areas may not be the prime locations for dengue-infected mosquitoes. The existing early warning system was focused on temporal prediction wherein the lack of consideration for spatial component at the microlevel and human mobility were not considered. Thus, we developed MozzHub, which is a web-based application system based on the bipartite network-based dengue model that is focused on identifying the source of dengue infection at a small spatial level (400 m) by integrating human mobility and environmental predictors. The model was earlier developed and validated; therefore, this study presents the design and implementation of the MozzHub system and the results of a preliminary pilot test and user acceptance of MozzHub in six district health offices in Malaysia. It was found that the MozzHub system is well received by the sample of end-users as it was demonstrated as a useful (77.4%), easy-to-operate system (80.6%), and has achieved adequate client satisfaction for its use (74.2%)

Robotic Vision System Design for Black Pepper Harvesting

Robotic vision system design is developed in this paper to locate the coordinate of pepper fruits from trees and leaves, and identify pepper ripeness for harvest in Sarawak region, Malaysia. The vision system comprises of three stages, i.e. salient point localization, contour extraction and pepper verification. First, ripe peppers are spotted using visual saliency detection based on color, intensity and orientation. Three most salient regions are then determined by red component detection, whereas red element indicates a ripe pepper region. The detected red salient region is therefore shrunk to pepper edges using active contour method. To further verify the correct detection of peppers, the extracted edges are required to match with pre-defined shape, and to check neighborhoods similarity surrounding the region of interest. Preliminary simulation results showed that the vision system spotted the salient regions with pepper in 91.3% of success rate; contour extractions covering a pepper boundary with 84.35% of success rate and the results for pepper verification stage are promising

Ground-Image Plane Mapping for Lane marks detection

Autonomous vehicles are equipped with optical sensors and micro-processing units to perform intelligent visual analysis of its surroundings. Due to the high speed of moving vehicle, the captured information has to be processed in a short duration to avoid possible collision. In this paper, aground-image plane mapping technique is proposed to quickly locate detected object if the object’s position is known in the real world. A three dimensional (3D) world coordinate is mathematically derived to an image plane using pinhole camera model. Several 3D perspective parameters such as vehicle’s steering angle and its velocity, sensor’s height and tilting angle are encompassed in the ground plane measurement. The optical sensor’s intrinsic parameters such as focal length, principal point, pixel’s height and width are also inserted for the mathematical model derivation. The importance of this ground to image plane mapping enables a rapid search of an object in a moving scene to achieve fast object identification during sensor acquisition. Experimental results have been carried on the application of lane marks detection with 93.82% correct mapping, using approximately 20% less processing time

Measuring Human Balance on an Instrumented Dynamic Platform: A Postural Sway Analysis

A system to monitor the trajectory and distribution of Center of Pressure (CoP) oscillations in real-time was designed. The system used a custom built force plate that measured sway area and sway velocity based on the measured CoP. A stable posture is reflected by a controlled CoP oscillation, where the oscillation lies within the limits of stability. Large magnitudes of CoP oscillations (large sway area) indicate weak proprioception strength and a heightened risk of falls. Experiments carried out involved self-induced perturbations that destabilized postural control among volunteers with active and inactive lifestyles. The observed results from the experiment indicate that individuals with active lifestyle have better postural control than individuals with inactive lifestyle. Subjectswith active lifestyles demonstrated greater sway velocities, while maintaining a small sway area