Autonomous Abnormal Behaviour Detection Using Trajectory Analysis

Abnormal behaviour detection has attracted signification amount of attention in the past decade due to increased security concerns around the world. The amount of data from surveillance cameras have exceeded human capacity and there is a greater need for anomaly detection systems for crime monitoring. This paper proposes a solution to this problem in a reception area context by using trajectory extraction through Gaussian Mixture Models and Kalman Filter for data association. Here, trajectory analysis was performed on extracted trajectories to detect four different anomalies such as entering staff area, running, loitering and squatting down. The developed anomaly detection algorithms were tested on videos captured at Asia Pacific University’s reception area. These algorithms were able to achieve a promising detection accuracy of 89% and a false positive rate of 4.52%

Automated PCB identification and defect-detection system (APIDS)

Ever growing PCB industry requires automation during manufacturing process to produce defect free products. Machine Vision is widely used as popular means of inspection to find defects in PCBs. However, it is still largely dependent on user input to select algorithm set for the PCB under inspection prior to the beginning of the process. Continuous increase in computation power of computers and image quality of image acquisition devices demands new methods for further automation. This paper proposes a new method to achieve further automation by identifying the type of PCB under inspection prior to begin defect inspection process. Identification of PCB is achieved by using local feature detectors SURF and ORB and using the orientation data acquired to transform the PCB image to the reference image for inspection of defects. A close-loop system is produced as a prototype to reflect the practicality of the idea. A Graphical User Interface was developed using MATLAB to present the proposed system. Test data contained 29 PCBs. Each PCB was tested 5 times for camera acquired images and 3 times for database images. The identification accuracy is 98.66% for database images and 100% for images acquired from the camera. The time taken to detect the model of PCB is recorded and is significantly lower for ORB based identification than SURF based. The system is also a close loop system which detects defects in PCB units. The detection of defects has highest accuracy of 92.3% for best controlled environment scenario. With controlled environment, the system could detect defects in PCB pertaining to smallest of components such as SMDs

Small-signal analysis of a single-stage bridgeless boost half-bridge AC/DC converter with bidirectional switch

A small-signal analysis of a single-stage bridgeless boost half-bridge alternating current/direct current (AC/DC) converter with bidirectional switches is performed using circuit averaging method. The comprehensive approach to develop the small signal model from the steady state analysis is discussed. The small-signal model is then simulated with MATLAB/Simulink. The small-signal model is verified through the comparison of the bode-plot obtained from MATLAB/Simulink and the simulated large signal model in piecewise linear electrical circuit simulation (PLECS). The mathematical model obtain from the small-signal analysis is then used to determine the proportional gain Kp and integral gain Ki . In addition, the switch large-signal model is developed by considering the current and voltage waveforms during load transients and steady-state conditions