A new linearly constrained minimum variance beamformer for reconstructing EEG sparse sources

Brain source imaging based on EEG aims to reconstruct the neural activities producing the scalp potentials. This includes solving the forward and inverse problems. The aim of the inverse problem is to estimate the activity of the brain sources based on the measured data and leadfield matrix computed in the forward step. Spatial filtering, also known as beamforming, is an inverse method that reconstructs the time course of the source at a particular location by weighting and linearly combining the sensor data. In this paper, we considered a temporal assumption related to the time course of the source, namely sparsity, in the Linearly Constrained Minimum Variance (LCMV) beamformer. This assumption sounds reasonable since not all brain sources are active all the time such as epileptic spikes and also some experimental protocols such as electrical stimulations of a peripheral nerve can be sparse in time. Developing the sparse beamformer is done by incorporating L1-norm regularization of the beamformer output in the relevant cost function while obtaining the filter weights. We called this new beamformer SParse LCMV (SP-LCMV). We compared the performance of the SP-LCMV with that of LCMV for both superficial and deep sources with different amplitudes using synthetic EEG signals. Also, we compared them in localization and reconstruction of sources underlying electric median nerve stimulation. Results show that the proposed sparse beamformer can enhance reconstruction of sparse sources especially in the case of sources with high amplitude spikes.     

An enhanced cost-aware mapping algorithm based on improved shuffled frog leaping in network on chips

The Journal of Supercomputing - Network on chip (NoC) has been of great interest in recent years. However, according to the recent studies, high communication cost has been raised as the one most...     

Preparation of hydrophobic flat sheet membranes from PVDF-HFP copolymer for enhancing the oxygen permeance in nitrogen/oxygen gas mixture

In this study, poly(vinilydene fluoride-co-hexafluoropropylene)(PVDF-HFP) was used for preparation of hydrophobic membranes using non-solvent induced phase inversion(NIPS) technique. PVDF-HFP copolymer with concentrations of 10 wt% and 12 wt% was prepared to investigate the effect of polymer concentration on pore structure,morphology, hydrophobicity and performance of prepared membranes. Besides, the use of two coagulation baths with the effects of parameters such as coagulant time, polymer type and concentration, and the amount of nonsolvent were studied. The performance of prepared membranes was evaluated based on the permeability and selectivity of oxygen and nitrogen from a gas mixture of nitrogen/oxygen under operating conditions of feed flow rate(1–5 L·min~(-1)), inlet pressure to membrane module(0.1–0.5 MPa) and temperatures between 25 and 45 °C. The results showed that the use of two coagulation baths with different compositions of distillated water and isopropanol,coagulant time, polymer type and concentration, and the amount of non-solvent additive have the most effect on pore structure, morphology, thickness, roughness and crystallinity of fabricated membranes. Porosity ranges for the three fabricated membranes were determined, where the maximum porosity was 73.889% and the minimum value was 56.837%. Also, the maximum and minimum average thicknesses of membrane were 320.85 μm and115 μm. Besides, the values of 4.7504 × 10~(-7) mol· m~(-2)· s~(-1)· Pa~(-1), 0.525 and 902.126 nm were achieved for maximum oxygen permeance, O_2/N_2 selectivity and roughness, respectively.     

Modeling and solving the bi-objective capacitated location-routing problem with probabilistic travel times

The location-routing problem (LRP) is a relatively new research area within location analysis that concerns simultaneously both the problems of location of facilities and routing of vehicles among the established facilities and the demand points. In this work, we address the capacitated LRP with probabilistic travel times, which may arise in many practical contexts in logistics and supply chain management, and present some bi-objective mathematical programming formulations to model the problem using different stochastic programming approaches. The first objective is to minimize the overall system-wide costs, while the second objective concerns minimization of the maximum delivery time to the customers. In all the cases, the deterministic equivalents of the stochastic models have been extracted. To solve the resulted models, a variable neighborhood descent-based heuristic is proposed and finally computational study is performed and numerical results are reported.     

A clustering approach to identify the time of a step change in Shewhart control charts

Control charts are the most popular statistical process control tools used to monitor process changes. When a control chart indicates an out‐of‐control signal it means that the process has changed. However, control chart signals do not indicate the real time of process changes, which is essential for identifying and removing assignable causes and ultimately improving the process. Identifying the real time of the change is known as the change‐point estimation problem. Most of the traditional methods of estimating the process change point are developed based on the assumption that the process follows a normal distribution with known parameters, which is seldom true. In this paper, we propose clustering techniques to estimate Shewhart control chart change points. The proposed approach does not depend on the true values of the parameters and even the distribution of the process variables. Accordingly, it is applicable to both phase‐I and phase‐II of normal and non‐normal processes. At the end, we discuss the performance of the proposed method in comparison with the traditional procedures through extensive simulation studies. Copyright © 2008 John Wiley & Sons, Ltd.     

A protection strategy for inverter-interfaced islanded microgrids with looped configuration

Electrical Engineering - Development of an efficient protection strategy is one of the main barriers in paving the way for the implementation of inverter-based microgrids. The limited fault current...     

Electrospun biodegradable nanofibers scaffolds for bone tissue engineering

Many polymeric materials have been developed and introduced for bone regeneration. Especially, their nanofibrous forms are mostly applied for artificial extracellular matrices. Polymeric materials in their nanofibrous form show some potent properties such as high surface‐to‐volume ratio, tunable porosity, and ease of surface functionalization. Benefiting from the properties of their main polymer and additives, they can provide new opportunities for cell seeding, proliferation, and new 3D‐tissue formation. This article focuses on most cited polymeric nanofibrous scaffolds fabricated by electrospinning and recent achievements. They were divided into two main categories: natural (collagen, silk, keratin, gelatin, chitosan, and alginate) and synthetic (e.g., polycaprolactone, polylactic acid, and polyglycolic acid) polymers. The role of several additives like hydroxyapatite, bone morphogenetic proteins (BMPs), tricalcium phosphate, and collagen type I in improving the adhesion, differentiation, and tissue formation of stem cells were discussed. Finally, the osteogenic capacity and ability of nanofibrous scaffolds to support the growth of clinically relevant bone tissue were briefly studied. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 42883.