Progression of Alzheimer’s disease (AD) bears close proximity with the tissue loss in the medial temporal lobe (MTL) and enlargement of lateral ventricle (LV). The early stage of AD, mild cognitive impairment (MCI), can be traced by diagnosing brain MRI scans with advanced fuzzy c-means clustering algorithm that helps to take an appropriate intervention. In this paper, firstly the sparsity is initiated in clustering method that too rician noise is also incorporated for brain MR scans of AD subject. Secondly, a novel neighbor pixel constrained fuzzy c-means clustering algorithm is designed where topoloty-based selection of parsimonious neighbor pixels is automated. The adaptability in choice of neighbor pixel class outliers more justified object edge boundary which outperforms a dynamic cluster output. The proposed adaptive neighbor constrained deviation sparse variant fuzzy c-means clustering (AN_DsFCM) can withhold imposed sparsity and withstands rician noise at imposed sparse environment. This novel algorithm is applied for MRI of AD subjects and normative data is acquired to analyse clustering accuracy. The data processing pipeline of theoretically plausible proposition is elaborated in detail. The experimental results are compared with state-of-the-art fuzzy clustering methods for test MRI scans. Visual evaluation and statistical measures are studied to meet both image processing and clinical neurophysiology standards. Overall the performance of proposed AN_DsFCM is significantly better than other methods.

Orthogonal frequency division multiplexing (OFDM), being a significant aspect of multicarrier modulation (MCM), is accomplished to encounter the effect of multipath reception by splitting the entire allotted bandwidth into several narrow subbands which leads to an advancement in spectral efficiency and diminishes the effect of intersymbol interference (ISI). However, OFDM fails to meet the requirements of high data rate communication networks such as 5G. To achieve the ever increasing demand of 5G cellular networks, this paper presents an innovative filtered-OFDM (F-OFDM) technique based on narrow-band finite impulse response filter which is operated on interpolated band-pass method (IBM). Simulation results show that the F-OFDM with IBM based narrow-band finite impulse response filter (FIR) accomplishes shorter out-of-band emission (OOBE) compared to the F-OFDM designed with some state-of-the-art narrow transition band filtering techniques.

This paper proposes a new technique, called conditional differential coefficients method (CDCM), for hardware-efficient realization of finite impulse response filters using differential coefficients. In connection to this, a group of differential coefficients are extracted from the entire set which aims to reduce the total number of ones in the coefficient representation. Selection of the differential coefficients has been done in accordance with its difference with the original coefficient. It has the consequent effect of minimizing the total number of full adders with respect to direct method, minimum index floating point representation scheme, minimal difference DCM and so on.