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Widespread use of Multichannel Electroencephalograph (MCEEG) in diversified fields ranging from clinical studies to Brain Computer Interface (BCI) application, has put in a lot of thrust in data processing concepts, for effective storage and transmission. The paper proposes a computationally simple and novel methodology Normalized Spatial Pseudo Codec (n-SPC) to compress MCEEG signals. The signals are first normalized followed by two operations namely the spatial coding and pseudo coding operating on integer part and fractional part of the normalized data respectively. The proposed method was evaluated on publicly available EEG databases and results indicate that the algorithm exhibits good storage efficiency with average Compression Ratio (CR) of 4.61 with a computational complexity of only O(zN). The algorithm offers significantly a better decompressed signal quality, quantified by average Peak Signal to Noise Ratio (PSNR) of 21.42 dB. The average encoding and decoding time per sample is 0.3 and 0.04 ms, respectively with an average Percentage Root Mean Square Deviation (PRD) of 5.33. The efficacy was further evaluated using the decompressed signal to detect sleep spindle, from an excerpt of EEG recording and was compared with the visual scoring of two experts, available at the DREAMS Sleep Spindles Database. Hence, the proposed compression scheme can be used in practical MCEEG recording, archiving and BCI and neuromorphic systems. [ABSTRACT FROM AUTHOR]

The majority of the prevailing collision detection and avoidance systems render evasive maneuvers for averting front and rear vehicle collisions. This paper introduces a mid vehicle collision detection and avoidance system with a constraint-free condition that produces mid vehicle maneuvers, particularly when jammed between the front and rear vehicles. Accordingly, three novel path estimation models based on crisp, fuzzy and fuzzy regression logic are blended to formulate a mid (host) vehicle collision detection and avoidance system. At the onset, the crisp model fits an offset-based curvilinear path for the mid vehicle to avoid edge collisions. The realized crisp model is later fuzzified to address more antecedents and accordingly deliver consequents for enhancing path estimation. Finally, the fuzzified model is regressed to obtain a good fitness for real-road conditions. The fused fuzzy regression renders an approximate version of the actual road strategy to obtain collision-free trajectories. This concept is later intrinsically extended to parallel parking in reverse direction. Simulation studies using coefficient of determination (R 2) and mean square error on the field, real (next generation simulation-NGSIM) dataset reveals the goodness and the path closeness of the proposed system rendered by the novelly blended models tuned at each observation using the optimum h-uncertain factor. Also, relative mean square error analysis with state-of-art CDAS reveals the superiority of MCDAS, thereby making it amicable for real-road scenarios. [ABSTRACT FROM AUTHOR]

Intensity inhomogeneity is the prime obstacle for MR image processing like automatic segmentation, registration etc. This complication has strong dependence on the associated acquisition hardware and patient anatomy which recommends retrospective correction. In this paper, a new method is developed for correcting the intensity inhomogeneity using a non-iterative multi-scale approach that doesn't necessitate segmentation and any prior knowledge on the scanner or subject. The proposed algorithm extracts bias field at different scales using a Log-Gabor filter bank followed by smoothing operation. Later, they are combined to fit a third degree polynomial to estimate the bias field. Finally, the corrected image is estimated by performing pixel-wise division of original image and bias field. The performance of the same was tested on BrainWeb simulated data, HCP dataset and is found to provide better performance than the state-of-the-art method, N4. A good agreement between the extracted and ground truth bias field is observed through correlation coefficient on different MR modality images that include T1w, T2w and PD. Significant reduction in coefficient variation and coefficient of joint variation ratios in real data indicate an improved inter-class separation and reduced intra-class intensity variations across white and grey matter tissue regions., (Copyright © 2017 Elsevier Inc. All rights reserved.)