Momoh Jimoh Emiyoka Salami

EEG signal classification for real-time brain-computer interface applications: A review

Brain-computer interface (BCI) is linking the brain activity to computer, which allows a person to control devices directly with his brain waves and without any use of his muscles. Recent advances in real-time signal processing have made BCI a feasible alternative for controlling robot and for communication as well. Controlling devices using BCI is a crucial aid for people suffering from severe disabilities and more than that, BCIs can replace human to control robots working in dangerous or uncongenial situations. Effective BCIs demand for accurate and real-time EEG signals processing. This paper is to review the current state of research and to compare the performance of different algorithms for real-time classification of BCI-based electroencephalogram signals.

Design of an intelligent robotic donation box a case study

The design and development of an Intelligent Robotic Donation Box (IRDB) system as a typical final year project in Mechatronics engineering is presented in this paper. The developed IRDB system has the capability of collecting donation from people in an organized assembly or gathering. Also incorporated into this system is the ability to attract attention of people by making audible sound, gesture recognition as well as to avoid any obstacles on its path. New skin detection algorithms developed from artificial neural network and pseudo-modeling technique using YCbCr color spaces have also been proposed in this paper. Performance evaluation of the proposed new skin extraction techniques shows very promising results with an average accuracy of 92.75% and false positive rate of 4.5%. Similarly, performance evaluation of the IRDB system shows that the system can conveniently replace the existing manual boxes used in some organized gathering around the world and yet at a reasonable cost as compared to the existing donation boxes.

Artificial Intelligent Based Friction Modelling and Compensation in Motion Control System

The interest in the study of friction in control engineering has been driven by the need for precise motion control in most of industrial applications such as machine tools, robot systems, semiconductor manufacturing systems and Mechatronics systems. Friction has been experimentally shown to be a major factor in performance degradation in various control tasks. Among the prominent effects of friction in motion control are: steady state error to a reference command, slow response, periodic process of sticking and sliding (stickslip) motion, as well as periodic oscillations about a reference point known as hunting when an integral control is employed in the control scheme. Table 1 shows the effects and type of friction as highlighted by Armstrong et. al. (1994). It is observed that, each of task is dominated by at least one friction effect ranging from stiction, or/and kinetic to negative friction (Stribeck). Hence, the need for accurate compensation of friction has become important in high precision motion control. Several techniques to alleviate the effects of friction have been reported in the literature (Dupont and Armstrong, 1993; Wahyudi, 2003; Tjahjowidodo, 2004; Canudas, et.al., 1986). One of the successful methods is the well-known model-based friction compensation (Armstrong et al., 1994; Canudas de Wit et al., 1995 and Wen-Fang, 2007). In this method, the effect of the friction is cancelled by applying additional control signal which generates a torque/force. The generated torque/force has the same value (or approximately the same) with the friction torque/force but in opposite direction. This method requires a precise modeling of the characteristics of the friction to provide a good performance. Hence, in the context of model-based friction compensation, identification of the friction is one of the important issues to achieve high performance motion control. However, as discussed in the literatures, several types of friction models have been identified (Armstrong et al., 1994; Canudas et. al., 1995; Makkar et. al., 2005) and classified as static or dynamic friction models. Among the static models are Coulomb friction model, Tustin model, Leuven model, Karnop model, Lorentzian model. Meanwhile Dahl model, Lugre model, Seven parameters model, and the most recent Generalized Maxwell-Slip (GMS) model, are among the dynamic friction models (Tjahjowidodo, 2004). The static friction model is simple and easy in the identification process, however using such model

Time domain feature extraction technique for earth’s electric field signal prior to the earthquake

Earthquake is one of the most destructive of natural disasters that killed many people and destroyed a lot of properties. By considering these catastrophic effects, it is highly important of knowing ahead of earthquakes in order to reduce the number of victims and material losses. Earths electric field is one of the features that can be used to predict earthquakes (EQs), since it has significant changes in the amplitude of the signal prior to the earthquake. This paper presents a detailed analysis of the earths electric field due to earthquakes which occurred in Greece, between January 1, 2008 and June 30, 2008. In that period of time, 13 earthquakes had occurred. 6 of them were recorded with magnitudes greater than Ms=5R (5R), while 7 of them were recorded with magnitudes greater than Ms=6R (6R). Time domain feature extraction technique is applied to analyze the 1st significant changes in the earths electric field prior to the earthquake. Two different time domain feature extraction techniques are applied in this work, namely Simple Square Integral (SSI) and Root Mean Square (RMS). The 1st significant change of the earths electric field signal in each of monitoring sites is extracted using those two techniques. The feature extraction result can be used as input parameter for an earthquake prediction system.