Momoh Jimoh Eyiomika Salami

Dynamic keystroke analysis using AR model

The design and development of a pressure sensor based typing biometrics authentication system (BAS) is discussed in this paper. The dynamic keystroke, represented by its time duration and force generates a waveform, which when concatenated results in a users typing pattern for the typed password. The design of the BAS is in two stages, whereby the hardware comprising the pressure sensor and the associated data acquisition system (DAS) is first implemented. The system DAS has been designed using LabVIEW software. Furthermore several data preprocessing techniques have been used to improve the quality of the acquired waveforms. The second stage involves a classifier to authenticate the users. This paper discusses a new data classifier technique based on autoregressive signal modeling (AR), which has been developed so as to correctly identify and authenticate the users of the system. Some experiments have been conducted to show the validity of the overall BAS performance. The results obtained have shown that this proposed system is reliable with many potential applications for computer security.

Hardware Implementation of ANFIS Controller for Gas-Particle Separations in Wet Scrubber System

Wet scrubber system has been used for the control of gas and particulate matter (PM) emissions from production industries. Due to non-linear characteristics, wet scrubbers are limited to the control of PM that is less than 5μm. In this study, an intelligent control technique based on Adaptive Neuro-Fuzzy Inference System (ANFIS) has been designed using MATLAB software. The ANFIS Controller has the advantage of solving non-linearities in the proposed wet scrubber system by manipulating the scrubbing liquid droplet size for the effective control of particulate matter that is less than 5μm. From the simulation results, the controller was able to set PM concentration below the setpoint and provides smooth control response within short settling time. Hardware implementation of the ANFIS controller was performed using prototype wet scrubber system by considering Arduino Duemilanove microcontroller and MATLAB interface. The results show that the intelligent controller has achieved the desired objectives of controlling the PM concentration effectively by setting the value below the set point (20μg/m3) which is the allowable PM concentration standard recommended by World Health Organization.

An effective vision technique for microchip lead inspection

A new effective method for the microchip lead inspection for the chip manufacturing industry has been developed in this work. In contrast to the gray scale pattern matching technique this approach employs selected parameters of binary blobs to perform fault detection and measurements. This leads to a significant reduction of image processing time. A special combination of gray level filtering techniques with gray morphological operations enhances the borders of the lead images. Newly developed threshold calibration technique significantly improves the measurement accuracy. A unique statistical analysis has been developed to identify all possible lead defects in the chips. This method is rotationally and scale invariant and able to detect defective leads for the chips with different specifications. The minimum required information about the microchip is the number of leads.