Exploring Digital Image Dithering Techniques on a Broken Foot Image

The contouring effects appear when an image is quantized rudely irrespective of the uniform or non-uniform quantization. To mitigate the effects of contouring, a small amount of random noise is added (dithered) to the original image before quantization. Techniques such as dithering and half-toning are widely used strategies in obtaining images and texts in magazines, newspapers, books, printers, computer monitors, and LCDs. This study explores the dithering technique on a broken foot image with more elaborative methods and results. All the experiments involved in this study, such as quantization, dithering, no dithering, and dithering, quantized, and filtered techniques, are conducted using the Matlab R2016b tool. Overall information and details are retained with the aid of lowpass filtering and highpass filtering, respectively. Simulation results such as Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) are obtained in every stage of the dithering procedure to analyze and compare the performance or accuracy.

Electroencephalogram based communication system for locked in state person using mentally spelled tasks with optimized network model

Due to growth in population, Individual persons with disabilities are increasing daily. To overcome the disability especially in Locked in State (LIS) due to Spinal Cord Injury (SCI), we planned to design four states moving robot from four imagery tasks signals acquired from three electrode systems by placing the electrodes in three positions namely T1, T3 and FP1. At the time of the study we extract the features from Continuous Wavelet Transform (CWT) and trained with Optimized Neural Network model to analyze the features. The proposed network model showed the highest performances with an accuracy of 93.86 % then that of conventional network model. To confirm the performances we conduct offline test. The offline test also proved that new network model recognizing accuracy was higher than the conventional network model with recognizing accuracy of 97.50 %. To verify our result we conducted Information Transfer Rate (ITR), from this analysis we concluded that optimized network model outperforms the other network models like conventional ordinary Feed Forward Neural Network, Time Delay Neural Network and Elman Neural Networks with an accuracy of 21.67 bits per sec. By analyzing classification performances, recognizing accuracy and Information Transformation Rate (ITR), we concluded that CWT features with optimized neural network model performances were comparably greater than that of normal or conventional neural network model and also the study proved that performances of male subjects was appreciated compared to female subjects.