Low-power DWT-based quasi-averaging algorithm and architecture for epileptic seizure detection

In this paper, we have developed a low-complexity algorithm for epileptic seizure detection with a high degree of accuracy. The algorithm has been designed to be feasibly implementable as battery-powered low-power implantable epileptic seizure detection system or epilepsy prosthesis. This is achieved by utilizing design optimization techniques at different levels of abstraction. Particularly, user-specific critical parameters are identified at the algorithmic level and are explicitly used along with multiplier-less implementations at the architecture level. The system has been tested on neural data obtained from in-vivo animal recordings and has been implemented in 90nm bulk-Si technology. The results show up to 90 % savings in power as compared to prevalent wavelet based seizure detection technique while achieving 97% average detection rate. Copyright 2010 ACM.

An optimal algorithm for low power multiplierless FIR filter design using chebychev criterion

In this paper, we propose a novel finite impulse response (FIR) filter design methodology that reduces the number of operations with a motivation to reduce power consumption and enhance performance. The novelty of our approach lies in the generation of filter coefficients such that they conform to a given low-power architecture, while meeting the given filter specifications. The proposed algorithm is formulated as a mixed integer linear programming problem that minimizes chebychev error and synthesizes coefficients which consist of pre-specified alphabets. The new modified coefficients can be used for low-power VLSI implementation of vector scaling operations such as FIR filtering using computation sharing multiplier (CSHM). Simulations in 0.25um technology show that CSHM FIR filter architecture can result in 55% power and 34% speed improvement compared to carry save multiplier (CSAM) based filters.

Inhibition of Rho-kinase protects cerebral barrier from ischaemia-evoked injury through modulations of endothelial cell oxidative stress and tight junctions

Ischaemic strokes evoke blood-brain barrier (BBB) disruption and oedema formation through a series of mechanisms involving Rho-kinase activation. Using an animal model of human focal cerebral ischaemia, this study assessed and confirmed the therapeutic potential of Rho-kinase inhibition during the acute phase of stroke by displaying significantly improved functional outcome and reduced cerebral lesion and oedema volumes in fasudil- versus vehicle-treated animals. Analyses of ipsilateral and contralateral brain samples obtained from mice treated with vehicle or fasudil at the onset of reperfusion plus 4 h post-ischaemia or 4 h post-ischaemia alone revealed these benefits to be independent of changes in the activity and expressions of oxidative stress- and tight junction-related parameters. However, closer scrutiny of the same parameters in brain microvascular endothelial cells subjected to oxygen-glucose deprivation ± reperfusion revealed marked increases in prooxidant NADPH oxidase enzyme activity, superoxide anion release and in expressions of antioxidant enzyme catalase and tight junction protein claudin-5. Cotreatment of cells with Y-27632 prevented all of these changes and protected in vitro barrier integrity and function. These findings suggest that inhibition of Rho-kinase after acute ischaemic attacks improves cerebral integrity and function through regulation of endothelial cell oxidative stress and reorganization of intercellular junctions. Inhibition of Rho-kinase (ROCK) activity in a mouse model of human ischaemic stroke significantly improved functional outcome while reducing cerebral lesion and oedema volumes compared to vehicle-treated counterparts. Studies conducted with brain microvascular endothelial cells exposed to OGD ± R in the presence of Y-27632 revealed restoration of intercellular junctions and suppression of prooxidant NADPH oxidase activity as important factors in ROCK inhibition-mediated BBB protection.

PKC-β exacerbates in vitro brain barrier damage in hyperglycemic settings via regulation of RhoA/Rho-kinase/MLC2 pathway

Stroke patients with hyperglycemia (HG) develop higher volumes of brain edema emerging from disruption of blood-brain barrier (BBB). This study explored whether inductions of protein kinase C-β (PKC-β) and RhoA/Rho-kinase/myosin-regulatory light chain-2 (MLC2) pathway may account for HG-induced barrier damage using an in vitro model of human BBB comprising human brain microvascular endothelial cells (HBMEC) and astrocytes. Hyperglycemia (25 mmol/L D-glucose) markedly increased RhoA/Rho-kinase protein expressions (in-cell westerns), MLC2 phosphorylation (immunoblotting), and PKC-β (PepTag assay) and RhoA (Rhotekin-binding assay) activities in HBMEC while concurrently reducing the expression of tight junction protein occludin. Hyperglycemia-evoked in vitro barrier dysfunction, confirmed by decreases in transendothelial electrical resistance and concomitant increases in paracellular flux of Evan's blue-labeled albumin, was accompanied by malformations of actin cytoskeleton and tight junctions. Suppression of RhoA and Rho-kinase activities by anti-RhoA immunoglobulin G (IgG) electroporation and Y-27632, respectively prevented morphologic changes and restored plasma membrane localization of occludin. Normalization of glucose levels and silencing PKC-β activity neutralized the effects of HG on occludin and RhoA/Rho-kinase/MLC2 expression, localization, and activity and consequently improved in vitro barrier integrity and function. These results suggest that HG-induced exacerbation of the BBB breakdown after an ischemic stroke is mediated in large part by activation of PKC-β.

Small GTPase RhoA and its effector rho kinase mediate oxygen glucose deprivation-evoked in vitro cerebral barrier dysfunction

BACKGROUND AND PURPOSE: Enhanced vascular permeability attributable to disruption of blood-brain barrier results in the development of cerebral edema after stroke. Using an in vitro model of the brain barrier composed of human brain microvascular endothelial cells and human astrocytes, this study explored whether small GTPase RhoA and its effector protein Rho kinase were involved in permeability changes mediated by oxygen-glucose deprivation (OGD), key pathological phenomena during ischemic stroke.

METHODS: OGD increased RhoA and Rho kinase protein expressions in human brain microvascular endothelial cells and human astrocytes while increasing or unaffecting that of endothelial nitric oxide synthase in respective cells. Reperfusion attenuated the expression and activity of RhoA and Rho kinase in both cell types compared to their counterparts exposed to equal periods of OGD alone while selectively increasing human brain microvascular endothelial cells endothelial nitric oxide synthase protein levels. OGD compromised the barrier integrity as confirmed by decreases in transendothelial electric resistance and concomitant increases in flux of permeability markers sodium fluorescein and Evan's blue albumin across cocultures. Transfection of cells with constitutively active RhoA also increased flux and reduced transendothelial electric resistance, whereas inactivation of RhoA by anti-RhoA Ig electroporation exerted opposite effects. In vitro cerebral barrier dysfunction was accompanied by myosin light chain overphosphorylation and stress fiber formation. Reperfusion and treatments with a Rho kinase inhibitor Y-27632 significantly attenuated barrier breakdown without profoundly altering actin structure.

CONCLUSIONS: Increased RhoA/Rho kinase/myosin light chain pathway activity coupled with changes in actin cytoskeleton account for OGD-induced endothelial barrier breakdown.

DFIG Machine Design for Maximizing Power Output Based on Surrogate Optimization Algorithm

This paper presents a surrogate-model based optimization of a doubly-fed induction generator (DFIG) machine winding design for maximizing power yield. Based on site-specific wind profile data and the machine’s previous operational performance, the DFIG’s stator and rotor windings are optimized to match the maximum efficiency with operating conditions for rewinding purposes. The particle swarm optimization (PSO)-based surrogate optimization techniques are used in conjunction with the finite element method (FEM) to optimize the machine design utilizing the limited available information for the site-specific wind profile and generator operating conditions. A response surface method in the surrogate model is developed to formulate the design objectives and constraints. Besides, the machine tests and efficiency calculations follow IEEE standard 112-B. Numerical and experimental results validate the effectiveness of the proposed technologies.

IDS: A Divide-and-Conquer Algorithm for Inference in Polytree-Shaped Credal Networks

A credal network is a graph-theoretic model that represents imprecision in joint probability distributions. An inference in a credal net aims at computing an interval for the probability of an event of interest. Algorithms for inference in credal networks can be divided into exact and approximate. The selection of an algorithm is based on a trade off that ponders how much time someone wants to spend in a particular calculation against the quality of the computed values. This paper presents an algorithm, called IDS, that combines exact and approximate methods for computing inferences in polytree-shaped credal networks. The algorithm provides an approach to trade time and precision when making inferences in credal nets

Generalized loopy 2U: A new algorithm for approximate inference in credal networks

Credal networks generalize Bayesian networks by relaxing the requirement of precision of probabilities. Credal networks are considerably more expressive than Bayesian networks, but this makes belief updating NP-hard even on polytrees. We develop a new efficient algorithm for approximate belief updating in credal networks. The algorithm is based on an important representation result we prove for general credal networks: that any credal network can be equivalently reformulated as a credal network with binary variables; moreover, the transformation, which is considerably more complex than in the Bayesian case, can be implemented in polynomial time. The equivalent binary credal network is then updated by L2U, a loopy approximate algorithm for binary credal networks. Overall, we generalize L2U to non-binary credal networks, obtaining a scalable algorithm for the general case, which is approximate only because of its loopy nature. The accuracy of the inferences with respect to other state-of-the-art algorithms is evaluated by extensive numerical tests.

Generalized Loopy 2U: A New Algorithm for Approximate Inference in Credal Networks

Credal nets generalize Bayesian nets by relaxing the requirement of precision of probabilities. Credal nets are considerably more expressive than Bayesian nets, but this makes belief updating NP-hard even on polytrees. We develop a new efficient algorithm for approximate belief updating in credal nets. The algorithm is based on an important representation result we prove for general credal nets: that any credal net can be equivalently reformulated as a credal net with binary variables; moreover, the transformation, which is considerably more complex than in the Bayesian case, can be implemented in polynomial time. The equivalent binary credal net is updated by L2U, a loopy approximate algorithm for binary credal nets. Thus, we generalize L2U to non-binary credal nets, obtaining an accurate and scalable algorithm for the general case, which is approximate only because of its loopy nature. The accuracy of the inferences is evaluated by empirical tests.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.