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Brain-Computer Interface (BCI) systems have obtained remarkable results in rehabilitation and robot control processes by converting brain signals into control commands. The quantity of movement speed is the fundamental issue in BCI that requires additional research. This paper investigated the classification of the slow and fast speeds of eight different upper limb movements through electroencephalogram (EEG) signals and information about the values of speed and maximum angle of movements from the MPU6050 module. Datasets were obtained by recording the EEG signals from 10 subjects and the module information connected on their right hand during movements. This study used Filter Bank Common Spatial Pattern (FBCSP) and Wavelet-Common Spatial Pattern (W-CSP) methods to extract speed features of movements. In both methods, features selected by the Mutual Information (MI) were sent to the Convolution Neural Network (CNN) and various machine learning classifiers. Due to the results of subject-independent speed classification, the FBCSP-CNN method obtained the highest accuracy of 90% with a Kappa coefficient of 0.8 for flexion/extension of the shoulder. Results from our proposed method demonstrate the ability to introduce a refined set of control commands into the BCI system by recognizing the features associated with the speed of movement parameters.

The aim of this paper proposes an orthogonal frequency-division multiplexing (OFDM) with network coding to improve the error performance of the system when the messages are transmitted from user to receiver. Two-way relay (TWR) networks are applied to reduce the transmission time slots. The exclusive-OR (XOR) coding is used for network coding in which source nodes exchange their information via TWR nodes. The XOR coded bits provides redundancy to achieve the transmit diversity gain which improves the error performance of the TWR network. OFDM is exploited for TWR to obtain the frequency selective fading nature of wireless channels. The different modulation schemes such as Quadrature Phase Shift Keying (QPSK), 16-Quadrature Amplitude Modulation (QAM) and 64-QAM with OFDM system are simulated and QPSK is selected as it gives the lowest bit error rate (BER). The multiple relaying schemes with different numbers of the information packets are also considered in this paper. Simulation results show that multiple relay schemes provide faster transmission time and better error rate performance. Moreover, different kinds of channel coding schemes such as Convolutional, Reed-Solomon (RS) and turbo codes are applied in OFDM system with network coding to compare and evaluate the BER performance of the proposed system. From the simulation results, network coded OFDM scheme with turbo codes give better BER performance for given Signal-to-Noise Ratio (SNR) in relaying scheme with different numbers of information packets compared to those of convolutional and RS codes. It shows that, the error rate performance and transmission time is reduced 10 percent than the conventional scheme at even at low SNR value.

The combination of Non-orthogonal Multiple Access (NOMA) and Multi-antenna Multi-input Multi-output (MIMO) approach has been considered as an assuring multiple access for fifth generation (5G) technology to improve the system network capacity and the efficiency of the spectrum. In this paper, we investigated a MIMO-NOMA based network code, exclusive-OR (XOR) scheme which applies physical layer network coding (PNC) in two-way relay networks to reduce the transmission time slots. The Quadrature Phase Shift Keying (QPSK) modulation technique is selected to apply to enable the data source transmitting with the carrier wave. In this paper, convolutional codes and the Soft Decision Viterbi algorithm is used to encode and decode the information bits. The coded MIMO-NOMA gives the less bit error rate compared to coded NOMA alone. Moreover, we have analyzed the spectral efficiency and energy efficiency in NOMA and MIMO-NOMA. The sum capacity and transmission rate are also compared in NOMA and MIMO-NOMA scheme. The simulation results show that MIMO-NOMA has higher spectral efficiency and faster transmission rate than NOMA scheme. Finally, the user fairness of MIMO-NOMA has been evaluated.

Orthogonal Frequency Division Multiplexing (OFDM) is one of the popular methods that used to transmit the data in high speed rate in cellular communication. However, OFDM has a high Peak to Average Power Ratio (PAPR) which caused by the non-linearity of the High Power Amplifier (HPA) that provides transmission power to the OFDM signal. The high PAPR caused the OFDM signal to be low in efficiency and giving a high bit error rate. Thus, in this paper, a method for PAPR reduction that caused by the HPA is proposed. Precoding technique is one of the popular way to compensate the bad impact of the HPA by reducing the PAPR of the signal. In this paper, the bad impact caused by the HPA model on the OFDM signal will be analyzed. Moreover, the PAPR of the OFDM signal by using precoding techniques will be compared with the PAPR of the OFDM signal without precoding technique. From the simulation results, it was found that the ZCT-OFDM has a better PAPR reduction compared to the DCT and WALSH technique. It was also found that the lower the order of modulation scheme is, the better the PAPR reduction performance for OFDM signal compared to higher order modulation scheme.

In this paper, MIMO system is used to improve the performance of transmitted signal by reducing the signal fading rate. MIMO VBLAST technique is implemented to enhance the spectral efficiency and bandwidth with spatial diversity. Multiple antennas allowed the system to transmit multiple same data signal at the same time in which the data will not fade simultaneously, hence, improving the Signal to Noise Ratio (SNR) of the system. Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) detection scheme are used to minimize the effect of channel estimation errors. The simulation was carried out in MATLAB software. Based on the simulation result, the proposed scheme for MIMO VBLAST has better network throughput in term of successful message delivery performance compared to the traditional scheme.

In this paper wireless communications network is considered to transmit source information to destination. N number sources transmitting data to a destination receiver, through M relays nodes by employing random linear network coding (RLNC). Two methods of failure decoding probability on performance of wireless communication are explained and discussed in this paper. The major parameter that will impact on the decoding probability of RLNC are quantity of relays, a finite field size, and erasure probability. The comparison of two old bounds method (Seong's approach) and new bounds method (from Khan's approach) is explored in this research. Simulation results, shows that new bounds method on probability of decoding failure having better simulation results compared to old bound method. It is also concluded that calculating the bounds of the decoding probability is an easy method to analyze failure decoding probability compared to Chiasserini's approach method which is based on network coded matrix.

This paper, we present a simulation result on retransmission rates of an HARQ (Hybrid Automatic Repeat Request) and pure ARQ schemes at different channel Signal-to-Noise ratio (SNR). We showed that retransmission is improved by as much as 2dB when HARQ was used. It was also shown that HARQ retransmission scheme perform well at lower SNR. The result of this investigation will helps us in the design of an augmented Hybrid Type-II ARQ retransmission scheme.

This paper proposes a computer vision-based approach to automatically detect human body parts and estimate the human body poses efficiently from a monocular video sequence. Human body parts detection is performed using colour, contours and silhouettes cues. It determines the 2D spatial locations of joints of a human body without any special markers on the body. The input image is segmented using silhouette extraction function to obtain a silhouette human figure. A parametric skin distribution modelling method is then utilized to detect the face and limbs of a person. Hue-Saturation-Value (HSV) colour space is chosen for this application. Radon transform is used to get more accurate orientation of the upper arms when they are inside the body perimeter. Various physical and motion constraints regarding the human body is then used to construct the upper body configuration. Our algorithm can estimate poses for the person wearing short sleeve and long sleeve shirt. It could estimate the human poses even under illumination changes, self-occlusion occurrence, and distance variations. Then, seventeen body poses are considered for classification. Eight features are extracted from each pose. Feed-forward neural network is used to classify the defined human body poses.

Among biometric systems for user verification, iris recognition systems represent a relatively new technology. Our system consists of two main parts: a localizing iris and iris pattern recognition. The raw image is captured using a digital camera. The iris is then extracted from the background after enhancement and noise elimination. Due to noise and the high degree of freedom in the iris pattern, only parts of the iris structure are selected for recognition. The selected iris structure is then reconstructed into a rectangle format. Using a trained self-organizing map neural network, iris patterns are recognized. The overall accuracy of our network is found to be about 83%.

Research and experiments were conducted to build a fuzzy controlled PID controller to control a pH neutralization process. Process modeling and control was performed chiefly with the SIMULINK software in MATLAB based on the McAvoy model. A gain scheduling method was studied to aid in tuning the P control of the PID. A PID controller was designed first, and then a fuzzy K/sub c/ controller was built using a Mamdani type fuzzy inference system (FIS) with the controller settings based on the results obtained from gain scheduling testing. Next, the Sugeno FIS was used to create a fuzzy /spl tau//sub 1/ controller to complement the fuzzy P controller. The compiled fuzzy PI controller was then tested on servo and regulator problems. The servo test used a uniform random number generator to simulate set points between pH=3 to pH= 11 and the simulation was run over 100 s. The regulator test was run at /spl plusmn/5% of acid flow, F/sub a/. The result of the servo test was a near perfect response with no overshoot or oscillatory responses. The response time was very quick with practically no lag time. The result of the regulator test was a response that changed in tandem with the disturbances but was limited to a narrow range of

Biometrics is a technology which identifies a person based on his physiology or behavioral characteristics. Fingerprint identification and recognition is a biometrics method that has been widely used in various applications because of its reliability and accuracy in the process of recognizing and verifying a person's identity. The main purpose of this paper is to develop a fingerprint identification and recognition system. The system consists of three main parts, image acquisition, processing and identification and recognition. Fingerprint images are acquired and stored in the database in the image acquisition stage. These images are then enhanced in the image processing stage by performing gray level enhancement, spatial filtering, image sharpening, edge detection, segmentation, and thinning processes. After the image has been processed, it is fed into the backpropagation neural network as input in order to train the network. After training, the neural network is ready to perform the identification and recognition operations (matching process). A neural network has been successfully developed to identify and recognize the core part of fingerprint images.