This paper presents the design of a machine learning-based classifier for the differentiation between Schizophrenia patients and healthy controls using features extracted from electroencephalograph(EEG) signals based on event related potential(ERP). A number of features are extracted from an online EEG dataset with 81 subjects, including 32 healthy controls and 49 Schizophrenia patients. The EEG signals are preprocessed and since the dataset is relatively small, the random forest machine learning algorithm is chosen to be applied on different combinations of feature sets for classification. It is found that the classification accuracy can be improved by adding certain features extracted from EEG signals.

Chaotic systems can be synchronized and used for secure communication to transmit video, audio and text files. Field Programmable Gate Arrays (FPGAs) are beneficial for the implementation of high speed, low cost and low power embedded communication systems. In this paper, a model-based design approach is presented for FPGA implementation and optimization of chaotic generators. Lorenz attractor has its significance in studying chaotic systems and is used as the design subject in this paper. The conceptual model design is built using MATLAB Simulink, and the equivalent hardware model is created using Xilinx System Generator for FPGA implementation. The design models are created with 32-bit fixed-point and 16-bit fixed-point data formats and implemented on FPGA to evaluate the design performance, including the maximum operating clock frequency, resource utilization and power consumption. The 32-bit and 16-bit fixed-point models are further optimized by using timing analysis and adding delays to break critical paths to improve timing performance. The implementation results show that the proposed design and optimization approach has achieved promising improvement on design performance by tripling the maximum operating frequency for both 32-bit and 16-bit fixed-point configurations. The FPGA hardware co-simulation results demonstrate the anticipated Lorenz chaotic generator outputs for both designs.

This paper presents the hardware implementation of single-neuron models with three types of activation functions using fixed-point data format on Field Programmable Gate Arrays (FPGA). Activation function defines the transfer behavior of a neuron model and consequently the Artificial Neural Network (ANN) constructed using it. This paper compared single neuron models designed with bipolar ramp, threshold and sigmoid activation functions. It is also demonstrated that the FPGA hardware implementation performance can be significantly improved by using 16-bit fixed-point data format instead of 32-bit floating-point data format for the neuron model with sigmoid activation function.

This paper presents an Artificial Neural Network (ANN) design for a chaotic generator, and the training performances for a three layer ANN architecture with different number of hidden neurons. Chaotic systems can be synchronized and used for secure communication. Chaotic systems such as Lorenz attractor, Rossler attractor and Chen's system are generally implemented directly based on their definitions represented by a unique group of ordinary differential equations (ODEs). An feed forward ANN can be trained using the output values of a chaotic system. The training process is carried out on a computer and the weights are generated for all neurons in an ANN architecture. These weights are then used for a trained ANN architecture model to generate the expected output for the target chaotic system. The complexity of the ANN architecture defines the implementation cost and speed. Therefore it is beneficial to use less number of hidden neurons to achieve the target training performance. Lorenz attractor has its significance in studying chaotic systems and is used as the design subject in this paper. The 3-layer ANN has one input, one hidden and one output layer. The ANN architecture with 1 to 16 hidden neurons is designed and trained respectively using MATLAB Neural Network Toolbox with three training algorithms: Levenberg-Marquardt, Scaled Conjugate Gradient algorithm and Bayesian Regulation. The optimized ANN architecture can be used to improve the efficiency of the fixed-point implementation on an Field Programmable Gates Array (FPGA) device.