Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

The Brain Research Institute (BRI) uses various types of indirect measurements, including EEG and fMRI, to understand and assess brain activity and function. As well as the recovery of generic information about brain function, research also focuses on the utilisation of such data and understanding to study the initiation, dynamics, spread and suppression of epileptic seizures. To assist with the future focussing of this aspect of their research, the BRI asked the MISG 2010 participants to examine how the available EEG and fMRI data and current knowledge about epilepsy should be analysed and interpreted to yield an enhanced understanding about brain activity occurring before, at commencement of, during, and after a seizure. Though the deliberations of the study group were wide ranging in terms of the related matters considered and discussed, considerable progress was made with the following three aspects. (1) The science behind brain activity investigations depends crucially on the quality of the analysis and interpretation of, as well as the recovery of information from, EEG and fMRI measurements. A number of specific methodologies were discussed and formalised, including independent component analysis, principal component analysis, profile monitoring and change point analysis (hidden Markov modelling, time series analysis, discontinuity identification). (2) Even though EEG measurements accurately and very sensitively record the onset of an epileptic event or seizure, they are, from the perspective of understanding the internal initiation and localisation, of limited utility. They only record neuronal activity in the cortical (surface layer) neurons of the brain, which is a direct reflection of the type of electrical activity they have been designed to record. Because fMRI records, through the monitoring of blood flow activity, the location of localised brain activity within the brain, the possibility of combining fMRI measurements with EEG, as a joint inversion activity, was discussed and examined in detail. (3) A major goal for the BRI is to improve understanding about ``when'' (at what time) an epileptic seizure actually commenced before it is identified on an eeg recording, ``where'' the source of this initiation is located in the brain, and ``what'' is the initiator. Because of the general agreement in the literature that, in one way or another, epileptic events and seizures represent abnormal synchronisations of localised and/or global brain activity the modelling of synchronisations was examined in some detail. References C. M. Michel, G. Thut, S. Morand, A. Khateb, A. J. Pegna, R. Grave de Peralta, S. Gonzalez, M. Seeck and T. Landis, Electric source imaging of human brain functions, Brain Res. 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An algebraic approach for determination of DG parameters to support voltage profiles in radial distribution networks

Rapidly increasing electricity demands and capacity shortage of transmission and distribution facilities are the main driving forces for the growth of Distributed Generation (DG) integration in power grids. One of the reasons for choosing a DG is its ability to support voltage in a distribution system. Selection of effective DG characteristics and DG parameters is a significant concern of distribution system planners to obtain maximum potential benefits from the DG unit. This paper addresses the issue of improving the network voltage profile in distribution systems by installing a DG of the most suitable size, at a suitable location. An analytical approach is developed based on algebraic equations for uniformly distributed loads to determine the optimal operation, size and location of the DG in order to achieve required levels of network voltage. The developed method is simple to use for conceptual design and analysis of distribution system expansion with a DG and suitable for a quick estimation of DG parameters (such as optimal operating angle, size and location of a DG system) in a radial network. A practical network is used to verify the proposed technique and test results are presented.

A detailed investigation of nominally 2-D radical-farming scramjet combustion

This paper reports on an investigation of the flow/chemistry coupling inside a nominally two-dimensional inlet-fuelled scramjet configuration. The experiments were conducted at a freestream Mach number of 7.3 and a total flow enthalpy of 4.3MJ/kg corresponding to a Mach 9.7 flight condition. The phenomenon of radical-farming has been studied in detail using two-dimensional OH* chemiluminescence imaging and emission spectroscopy. High signal levels of excited OH (OH*) were detected behind the first shock reflections inside the combustion chamber upstream of any measurable pressure rise from combustion, which occurred towards the rear of the combustor. The production of OH in the first hot pocket initiates the ignition process and then accelerates the combustion process in the next downstream hot pocket. This was confirmed by numerical simulations of premixed hydrogen/air flow through the scramjet. Chemical kinetics analyses reveal that the ignition process is governed by the interaction between various reaction groups leading to a chainbranching explosion for low mean temperature and pressure combustion flowfields.

High-level expression of Rhodotorula gracilis d-amino acid oxidase in Pichia pastoris

By combining gene design and heterologous over-expression of Rhodotorula gracilis D-amino acid oxidase (RgDAO) in Pichia pastoris, enzyme production was enhanced by one order of magnitude compared to literature benchmarks, giving 350 kUnits/l of fed-batch bioreactor culture with a productivity of 3.1 kUnits/l h. P. pastoris cells permeabilized by freeze-drying and incubation in 2-propanol (10% v/v) produce a highly active (1.6 kUnits/g dry matter) and stable oxidase preparation. Critical bottlenecks in the development of an RgDAO catalyst for industrial applications have been eliminated.

Stepwise engineering of a Pichia pastoris D-amino acid oxidase whole cell catalyst

Trigonopsis variabilis D-amino acid oxidase (TvDAO) is a well characterized enzyme used for cephalosporin C conversion on industrial scale. However, the demands on the enzyme with respect to activity, operational stability and costs also vary with the field of application. Processes that use the soluble enzyme suffer from fast inactivation of TvDAO while immobilized oxidase preparations raise issues related to expensive carriers and catalyst efficiency. Therefore, oxidase preparations that are more robust and active than those currently available would enable a much broader range of economically viable applications of this enzyme in fine chemical syntheses. A multi-step engineering approach was chosen here to develop a robust and highly active Pichia pastoris TvDAO whole-cell biocatalyst. As compared to the native T. variabilis host, a more than seven-fold enhancement of the intracellular level of oxidase activity was achieved in P. pastoris through expression optimization by codon redesign as well as efficient subcellular targeting of the enzyme to peroxisomes. Multi copy integration further doubled expression and the specific activity of the whole cell catalyst. From a multicopy production strain, about 1.3 x 103 U/g wet cell weight (wcw) were derived by standard induction conditions feeding pure methanol. A fed-batch cultivation protocol using a mixture of methanol and glycerol in the induction phase attenuated the apparent toxicity of the recombinant oxidase to yield final biomass concentrations in the bioreactor of >or= 200 g/L compared to only 117 g/L using the standard methanol feed. Permeabilization of P. pastoris using 10% isopropanol yielded a whole-cell enzyme preparation that showed 49% of the total available intracellular oxidase activity and was notably stabilized (by three times compared to a widely used TvDAO expressing Escherichia coli strain) under conditions of D-methionine conversion using vigorous aeration. Stepwise optimization using a multi-level engineering approach has delivered a new P. pastoris whole cell TvDAO biocatalyst showing substantially enhanced specific activity and stability under operational conditions as compared to previously reported preparations of the enzyme. The production of the oxidase through fed-batch bioreactor culture and subsequent cell permeabilization is high-yielding and efficient. Therefore this P. pastoris catalyst has been evaluated for industrial purposes.

Polarized infrared absorption spectrum of matrix-isolated methylperoxyl radicals, CH3OO (X)over-tilde 2A ''

We have used a tandem pair of supersonic nozzles to produce clean samples of CH3OO radicals in cryogenic matrices. One hyperthermal nozzle decomposes azomethane (CH3NNCH3) to generate intense pulses of CH3 radicals, While the second nozzle alternately fires a burst Of O-2/Ar at the 20 K matrix. The CH3/O-2/20 K argon radical sandwich acts to produce target methylperoxyl radicals: CH3 + O-2 --> CH3OO. The absorption spectra of the radicals are monitored with a Fourier transform infrared spectrometer. We report 10 of the 12 fundamental infrared bands of the methylperoxyl radical CH3OO, (X) over tilde (2)A", in an argon matrix at 20 K. The experimental frequencies (cm(-1)) and polarizations follow: the a' modes are 3032, 2957, 1448, 1410, 1180, 1109, 90, 492, while the a" modes are 3024 and 1434. We cannot detect the asymmetric CH3 rocking mode, nu(11), nor the torsion, nu(12). The infrared spectra of (CH3OO)-O-18-O-18, (CH3OO)-C-13, and CD3OO have been measured as well in order to determine the isotopic shifts. The experimental frequencies, {nu}, for the methylperoxyl radicals are compared to harmonic frequencies, {omega}, resulting from a UB3LYP/6-311G(d,p) electronic structure calculation. Linear dichroism spectra were measured with photooriented radical samples in order to establish the experimental polarizations of most vibrational bands. The methylperoxyl radical matrix frequencies listed above are within +/-2% of the gas-phase vibrational frequencies. A final set of vibrational frequencies for the H radical, are recommended. See also http://ellison.colorado.edu/methylperoxyl.

Sensor fault-resilient control of interior permanent-magnet synchronous motor drives

In a conventional ac motor drive using field-oriented control, a dc-link voltage, speed, and at least two current sensors are required. Hence, in the event of sensor failure, the performance of the drive system can be severely compromised. This paper presents a sensor fault-tolerant control strategy for interior permanent-magnet synchronous motor (IPMSM) drives. Three independent observers are proposed to estimate the speed, dc-link voltage, and currents of the machine. If a sensor fault is detected, the drive system isolates the faulty sensor while retaining the remaining functional ones. The signal is then acquired from the corresponding observer in order to maintain the operation of the drive system. The experimental results provided verify the effectiveness of the proposed approach.

A SiC-based matrix converter topology for inductive power transfer system

Typical inductive power transfer (IPT) systems employ two power conversion stages to generate a high-frequency primary current from low-frequency utility supply. This paper proposes a matrix-converter-based IPT system, which employs high-speed SiC devices to facilitate the generation of high-frequency current through a single power conversion stage. The proposed matrix converter topology transforms a three-phase low-frequency voltage system to a high-frequency single-phase voltage, which, in turn, powers a series compensated IPT system. A comprehensive mathematical model is developed and power losses are evaluated to investigate the efficiency of the proposed converter topology. Theoretical results are presented with simulations, which are performed in MATLAB/Simulink, in comparison to a conventional two-stage converter. Experimental evident of a prototype IPT system is also presented to demonstrate the applicability of the proposed concept.

Inpatient facial expression recognition with SMS alert

Facial expression recognition (FER) has been dramatically developed in recent years, thanks to the advancements in related fields, especially machine learning, image processing and human recognition. Accordingly, the impact and potential usage of automatic FER have been growing in a wide range of applications, including human-computer interaction, robot control and driver state surveillance. However, to date, robust recognition of facial expressions from images and videos is still a challenging task due to the difficulty in accurately extracting the useful emotional features. These features are often represented in different forms, such as static, dynamic, point-based geometric or region-based appearance. Facial movement features, which include feature position and shape changes, are generally caused by the movements of facial elements and muscles during the course of emotional expression. The facial elements, especially key elements, will constantly change their positions when subjects are expressing emotions. As a consequence, the same feature in different images usually has different positions. In some cases, the shape of the feature may also be distorted due to the subtle facial muscle movements. Therefore, for any feature representing a certain emotion, the geometric-based position and appearance-based shape normally changes from one image to another image in image databases, as well as in videos. This kind of movement features represents a rich pool of both static and dynamic characteristics of expressions, which playa critical role for FER. The vast majority of the past work on FER does not take the dynamics of facial expressions into account. Some efforts have been made on capturing and utilizing facial movement features, and almost all of them are static based. These efforts try to adopt either geometric features of the tracked facial points, or appearance difference between holistic facial regions in consequent frames or texture and motion changes in loca- facial regions. Although achieved promising results, these approaches often require accurate location and tracking of facial points, which remains problematic.

A dual inverter-based supercapacitor direct integration scheme for wind energy conversion systems

Interfacing converters used in connecting energy storage systems like supercapacitors and battery banks to wind power systems introduce additional cost and power losses. This paper therefore presents a direct integration scheme for supercapacitors used in mitigating short-term power fluctuations in wind power systems. This scheme uses a dual inverter topology for both grid connection and interfacing a supercapacitor bank. The main inverter of the dual inverter system is powered by the rectified output of a wind turbine-coupled permanent-magnet synchronous generator. The auxiliary inverter is directly connected to the supercapacitor bank. With this approach, an interfacing converter is not required, and there are no associated costs and power losses incurred. The operation of the proposed system is discussed in detail. Simulation and experimental results are presented to verify the efficacy of the proposed system in suppressing short-term wind power fluctuations.

A series-connected photovoltaic distributed generator capable of enhancing power quality

Design of a series-connected photovoltaic generator (SPVG) capable of enhancing power quality is investigated. Analysis of the SPVG operations under disturbance conditions shows explicitly how achievable network voltage quality is affected by the SPVG injected power and its apparent power rating, and that voltage quality can be significantly improved even with a modest level of energy storage capacity incorporated in the SPVG. A control system for the SPVG is also proposed. Both simulation and laboratory tests confirm the efficacy of the distributed generator system.

Modeling and sensorless direct torque and flux control of a dual-airgap axial flux permanent-magnet machine with field-weakening operation

This paper presents the modeling and motion-sensorless direct torque and flux control of a novel dual-airgap axial-flux permanent-magnet machine optimized for use in flywheel energy storage system (FESS) applications. Independent closed-loop torque and stator flux regulation are performed in the stator flux ( x-y) reference frame via two PI controllers. This facilitates fast torque dynamics, which is critical as far as energy charging/discharging in the FESS is concerned. As FESS applications demand high-speed operation, a new field-weakening algorithm is proposed in this paper. Flux weakening is achieved autonomously once the y-axis voltage exceeds the available inverter voltage. An inherently speed sensorless stator flux observer immune to stator resistance variations and dc-offset effects is also proposed for accurate flux and speed estimation. The proposed observer eliminates the rotary encoder, which in turn reduces the overall weight and cost of the system while improving its reliability. The effectiveness of the proposed control scheme has been verified by simulations and experiments on a machine prototype.

Design of a least-cost battery-supercapacitor energy storage system for realizing dispatchable wind power

A statistical approach is used in the design of a battery-supercapacitor energy storage system for a wind farm. The design exploits the technical merits of the two energy storage mediums, in terms of the differences in their specific power and energy densities, and their ability to accommodate different rates of change in the charging/discharging powers. By treating the input wind power as random and using a proposed coordinated power flows control strategy for the battery and the supercapacitor, the approach evaluates the energy storage capacities, the corresponding expected life cycle cost/year of the storage mediums, and the expected cost/year of unmet power dispatch. A computational procedure is then developed for the design of a least-cost/year hybrid energy storage system to realize wind power dispatch at a specified confidence level.

Power buffer with model predictive control for stability of vehicular power systems with constant power loads

Electrification of vehicular systems has gained increased momentum in recent years with particular attention to constant power loads (CPLs). Since a CPL potentially threatens system stability, stability analysis of hybrid electric vehicle with CPLs becomes necessary. A new power buffer configuration with battery is introduced to mitigate the effect of instability caused by CPLs. Model predictive control (MPC) is applied to regulate the power buffer to decouple source and load dynamics. Moreover, MPC provides an optimal tradeoff between modification of load impedance, variation of dc-link voltage and battery current ripples. This is particularly important during transients or starting of system faults, since battery response is not very fast. Optimal tradeoff becomes even more significant when considering low-cost power buffer without battery. This paper analyzes system models for both voltage swell and voltage dip faults. Furthermore, a dual mode MPC algorithm is implemented in real time offering improved stability. A comprehensive set of experimental results is included to verify the efficacy of the proposed power buffer.

A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended Kalman filter

Interior permanent-magnet synchronous motors (IPMSMs) become attractive candidates in modern hybrid electric vehicles and industrial applications. Usually, to obtain good control performance, the electric drives of this kind of motor require one position, one dc link, and at least two current sensors. Failure of any of these sensors might lead to degraded system performance or even instability. As such, sensor fault resilient control becomes a very important issue in modern drive systems. This paper proposes a novel sensor fault detection and isolation algorithm based on an extended Kalman filter. It is robust to system random noise and efficient in real-time implementation. Moreover, the proposed algorithm is compact and can detect and isolate all the sensor faults for IPMSM drives. Thorough theoretical analysis is provided, and the effectiveness of the proposed approach is proven by extensive experimental results.