The oil palm empty fruit bunch (OPEFB) natural fibres were comprehensively reviewed to assess their potential as reinforcing materials in polymer composites for energy absorption during low-velocity impact. The typical oil palm wastes include trunks, fronds, kernel shells, and empty fruit bunches. This has a tendency to burden the industry players with disposal difficulties and escalates the operating cost. Thus, there are several initiatives have been employed to convert these wastes into value added products. The objective of this study is to review the potential of oil palm empty fruit bunch (OPEFB) as natural fibre polymer composite reinforcement to absorb the energy during low-velocity impact as another option for value added products. Initially, this paper reviewed the local oil palm waste issues. Previous research works on OPEFB polymer composite, and their mechanical characterization is appraised. Their potential for energy absorption in low-velocity impact application was also elaborated. The review suggests high potential applications of OPEFB as reinforcing materials in composite structures. Furthermore, it is wisely to utilize the oil palm biomass waste into a beneficial composite, hence, promotes the green environment.

STABILIZED CONTROLLER DESIGN FOR ATTITUDE AND ALTITUDE CONTROLLING OF QUAD-ROTOR UNDER DISTURBANCE AND NOISY CONDITIONS

In recent years, the need and importance of automatically recognizing emotions from EEG signals has grown with increasing role of brain computer interface applications. The detection of fine grained changes in functional state of human brain can be detected using EEG signals when compared to other physiological signals. This paper proposes an emotion recognition system from EEG (Electroencephalogram) signals. The audio-visual induction based acquisition protocol has been designed for acquiring the EEG signals under four emotions (disgust, happy, surprise and fear) for participants. Totally, 6 healthy subjects with an age group of 21–27 using 63 biosensors are used for registering the EEG signal for various emotions. After preprocessing the signals, two different lifting based wavelet transforms (LBWT) are employed to extract the three statistical features for classifying human emotions. In this work, we used Fuzzy C-Means (FCM) clustering for classifying the emotions. Results confirm the possibility of using two different lifting scheme based wavelet transform for assessing the human emotions from EEG signals.

Now a day's Unmanned Aerial Vehicle (UAV) systems are frequently used in many commercial applications. This makes UAV application as one of the hottest topic among the researchers. The most critical task for the researchers working on UAV is its proper and stable flight controlling under uncertainty and perturbed condition. In fixed wing UAV system altitude and attitude angles (Yaw, Pitch, Roll) play a significant role for flight stabilization. This paper presents a control algorithm for fixed-wing UAV longitude and latitude angles (Yaw, Pitch and Roll) controlling. This control algorithm is based on Proportional Integral Derivate (PID) conjunction with Extended Kalman Filter (EKF). It is also describes that how an unstable system become stable after applying proposed control technique. The proposed control method is simulated on fixed-wing UAV mathematical model under perturbed and unperturbed conditions using MATLAB platform. The Simulation results shows that the proposed controller performed well and maintains the stability of an airplane and holds desired angle values under uncertainties and perturbed conditions.

Sign language is the primary communication method that impaired hearing people used in their daily life. Sign language recognition has gained a lot of attention recently by researchers in computer vision. Sign language recognition systems in general require the knowledge of the hand's position, shape, motion, orientation and facial expression. In this paper we present a simple method for converting sign language into voice signals using features obtained from head and hand gestures which can be used by hearing impaired person to communicate with an ordinary person. A simple feature extraction method based on the area of the objects in a binary image and Discrete Cosine Transform (DCT) is proposed for extracting the features from the video sign language. A simple neural network models is developed for the recognition of gestures using the features computed from the video stream. An audio system is installed to play the particular word corresponding to the gestures. Experimental results demonstrate that the recognition rate of the proposed neural network models is about 91%.