Consumers undergo an intellectual burden when working with technological programs. Mostly in situations of several activities. For instance, while communicating when driving with the navigation device. It is not necessary to divert users from their primary duties in such circumstances. In memory cycles and related workload, the pre-frontal cortex (PFC) has a significant role to play. In this study, we have used data from 10 participants to evaluate the task behaviors in PFC with usable near-infrared spectroscopy (fNIRS), which is a non-invasive imaging modality. In classification, CNN research has been state of the art. This has undermined the need to extract features manually. In order to assess the mental workload, we implemented a time-frequency approach with CNN approach. Rather than traditional CNN network we used ResNet50 pretrained network here. Application of Wigner-Ville Distribution in Functional Imaging is introduced here. The proposed CNN approach achieves a considerable average improvement relative to conventional methods. The results across differences in time window length are benchmarked. Satisfactory result obtained with twenty five second window for which the CNN yields 98% correct classification where traditional CNN achieved 89% accuracy.

Image quality assessment (IQA) is a process of measurement of the image quality using the evaluations of subjective value with the model of computation. The quality of the image can be calculated by using different types of method where each method works with using isolated features of image. One very renowned method is structural similarity index (SSIM) which measured the quality of image comparing structure of image and the structure stage is obtained from pixel-based stage. FSIM (Feature Similarity Index) measured image quality using low level feature and Gradient magnitude (GM) act as primary feature of image. In this work, a novel MFSIM (Moderate Feature Similarity Index) is introduced which work with full reference IQA, HVS (Human Visual System) and low-level feature of images. In MFSIM the Phase Congruency (PC) is used as primary feature where the PC is dimensionless contrast invariant. In the moderated FSIM the Gradient Magnitude (GM) of the image is considered as the feature of secondary. For application IQA, we applied into segmented image with original image using MRI images. The distortion level of the segmented image is calculated using different image quality index measurement techniques. The image can be used in numerous purposes and the quality of image is distorted for different reason. There are lots of applications where noise less of perfect image is used for getting exact result. So it is very important to find out the distortion level of image. For instance during the segmentation of MRI image for brain tumor detection, the exactness of image need to calculate so that the brain tumor can be find out accurately. So the main purpose of this research work is to introduce a new image quality index and find out the brain tumor and the segmented image quality.

The brain imaging device, Electroencephalography (EEG) provides several advantages over other brain signals like Functional Near-infrared Spectroscopy (fNIRS) and Functional Magnetic Resonance Imaging (fMRI). It is non-invasive and easily applicable. EEG provides high temporal resolution with a low setup cost. EEG signals of several subjects which record electric potential caused by neurons firing in the brain are undergone a Hidden Markov Model (HMM) classification technique. We are particularly interested to detect the brain diseases from EEG signals by an HMM probabilistic model. This HMM model is built with a given initial probability matrix of five different states, namely, epilepsy, seizure, dementia, stroke and normality. The transition probability matrix is updated after each iteration of parameter estimation using Baum-Welch algorithm (B-W algorithm).

Digital Signal Processing (DSP) is one of the fastest growing techniques in the electronics industry. The signal-rate system in digital signal processing has evolved the key of fastest speed in digital signal processor. Field Programmable Gate Array (FPGA) offers good solution for addressing the needs of high performance DSP systems. The focus of this paper is on the basic DSP functions, namely filtering signals to remove unwanted frequency bands. Multi-rate Digital Filters (MDFs) are the main theme to build bio-chip design in this paper. For different purposes DSP systems need to change the sampling rate of the signal to achieve some applications. This can be done using multi-rate system where designers can increase or decrease the operating sampling rate. This bio-chip has attractive features like, low requirement of the coefficient word lengths, significant saving in computation time and storage which results in a reduction in its dynamic power consumption. This paper introduces an efficient FPGA realization of multi-rate digital filter with narrow pass-band and narrow transition band to reduce noises and changing the frequency sampling rate by factor which is required according to application. This bio-chip works on bio-signals like EEG signal.

Multi-scale transform (MST) and sparse representation (SR) techniques are used in an image representation model. Image fusion is used especially in medical, military and remote sensing areas for high resolution vision. In this paper an image fusion technique based on shearlet transformation and sparse representation is proposed to overcome the natural defects of both MST and SR based methods. The proposed method is also used in different transformations and SR for comparison purposes. This research also investigate denoising techniques with additive white Gaussian noise into source images and perform threshold for de-noised into the proposed method. The image quality assessments for the fused image are used for the performance of proposed method and compared with others.