IMAGE FUSION FOR MULTIFOCUS IMAGES USING SPEEDUP ROBUST FEATURES

The multi-focus image fusion technique has emerged as major topic in image processing in order to generate all focus images with increased depth of field from multi focus photographs. Image fusion is the process of combining relevant information from two or more images into a single image. The image registration technique includes the entropy theory. Speed up Robust Features (SURF), feature detector and Binary Robust Invariant Scalable Key points (BRISK) feature descriptor is used in feature matching process. An improved RANDOM Sample Consensus (RANSAC) algorithm is adopted to reject incorrect matches. The registered images are fused using stationary wavelet transform (SWT).The experimental results prove that the proposed algorithm achieves better performance for unregistered multiple multi-focus images and it especially robust to scale and rotation translation compared with traditional direct fusion method.  Â

Learning-based auditory encoding for robust speech recognition

This paper describes ways of speeding up the optimization process for learning physiologically-motivated components of a feature computation module directly from data. During training, word lattices generated by the speech decoder and conjugate gradient descent were included to train the parameters of logistic functions in a fashion that maximizes the a posteriori probability of the correct class in the training data. These functions represent the rate-level nonlinearities found in most mammalian auditory systems. Experiments conducted using the CMU SPHINX-III system on the DARPA Resource Management and Wall Street Journal tasks show that the use of discriminative training to estimate the shape of the rate-level nonlinearity provides better recognition accuracy in the presence of background noise than traditional procedures which do not employ learning. More importantly, the inclusion of conjugate gradient descent optimization and a word lattice to reduce the number of hypotheses considered greatly increases the training speed, which makes training with much more complicated models possible. Index Terms — automatic speech recognition, discriminative training, auditory models, data analysis 1

Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease

We identified rare coding variants associated with Alzheimer’s disease (AD) in a 3-stage case-control study of 85,133 subjects. In stage 1, 34,174 samples were genotyped using a whole-exome microarray. In stage 2, we tested associated variants (P<1×10-4) in 35,962 independent samples using de novo genotyping and imputed genotypes. In stage 3, an additional 14,997 samples were used to test the most significant stage 2 associations (P<5×10-8) using imputed genotypes. We observed 3 novel genome-wide significant (GWS) AD associated non-synonymous variants; a protective variant in PLCG2 (rs72824905/p.P522R, P=5.38×10-10, OR=0.68, MAFcases=0.0059, MAFcontrols=0.0093), a risk variant in ABI3 (rs616338/p.S209F, P=4.56×10-10, OR=1.43, MAFcases=0.011, MAFcontrols=0.008), and a novel GWS variant in TREM2 (rs143332484/p.R62H, P=1.55×10-14, OR=1.67, MAFcases=0.0143, MAFcontrols=0.0089), a known AD susceptibility gene. These protein-coding changes are in genes highly expressed in microglia and highlight an immune-related protein-protein interaction network enriched for previously identified AD risk genes. These genetic findings provide additional evidence that the microglia-mediated innate immune response contributes directly to AD development

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P &lt; 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Genetically elevated high-density lipoprotein cholesterol through the cholesteryl ester transfer protein gene does not associate with risk of Alzheimer's disease

Introduction: There is conflicting evidence whether high-density lipoprotein cholesterol (HDL-C) is a risk factor for Alzheimer's disease (AD) and dementia. Genetic variation in the cholesteryl ester transfer protein (CETP) locus is associated with altered HDL-C. We aimed to assess AD risk by genetically predicted HDL-C. Methods: Ten single nucleotide polymorphisms within the CETP locus predicting HDL-C were applied to the International Genomics of Alzheimer's Project (IGAP) exome chip stage 1 results in up 16,097 late onset AD cases and 18,077 cognitively normal elderly controls. We performed instrumental variables analysis using inverse variance weighting, weighted median, and MR-Egger. Results: Based on 10 single nucleotide polymorphisms distinctly predicting HDL-C in the CETP locus, we found that HDL-C was not associated with risk of AD (P > .7). Discussion: Our study does not support the role of HDL-C on risk of AD through HDL-C altered by CETP. This study does not rule out other mechanisms by which HDL-C affects risk of AD

Research Article Improved Parallel Boost Power Converter for Power Factor Correction

Abstract: The main objective of the study is to analysis and design parallel boost power converter for power factor correction using an active filtering approach by implementing single-phase soft-switching technique with an active snubber circuit. Zero voltage transition to turn ON and zero current transition to turn OFF is implemented by the active snubber circuit for the main switches with no any further current or voltage strains. By zero-current switching without the need of added voltage stress, auxiliary switch is turned ON and OFF. The proposed converter has simple structure, low cost and ease of control. The efficiency, which is about 96% in hard switching, will increases to about 98% in the proposed soft-switching parallel boost converter

Distorted Waveform Balancing Using an Artificial Bee Colony (ABC) Based Optimal Control for Mitigating Total Harmonics in

Abstract The main objective of this paper is to reduce the total harmonics in a single phase voltage source inverter using Artificial Bee Colony (ABC) optimization technique for critical load applications. Single phase inverter is a non-linear load using power electronic components causing distortions in the load voltage and current wave patterns from the sinusoidal waveforms due to harmonics. The mapping state space model for a full bridge voltage source inverter was developed using output load resistance. An optimal ABC technique has been designed and optimized values are estimated using a full bridge voltage controlled inverter using Proportional Integral Algorithm. The MATLAB/SIMULINK tool and Experimental setup were implemented and their THD values were estimated. Also this ABC scheme is compared with the previous results such as PI Algorithm, Fuzzy logic controller and Neuro-fuzzy controllers. From the simulation and experimental results using ABC algorithm, it is observed that the total harmonics are mitigated considerably compared to previous results with respect to the power quality standards such as IEEE-519 and IEC 61000