Study of Two Writing Schemes for a Magnetic Tunnel Junction Based On Spin Orbit Torque

MRAM technology provides a combination of fast access time, non-volatility, data retention and endurance. While a growing interest is given to two-terminal Magnetic Tunnel Junctions (MTJ) based on Spin-Transfer Torque (STT) switching as the potential candidate for a universal memory, its reliability is dramatically decreased because of the common writing/reading path. Three-terminal MTJ based on Spin-Orbit Torque (SOT) approach revitalizes the hope of an ideal MRAM. It can overcome the reliability barrier encountered in current two-terminal MTJs by separating the reading and the writing path. In this paper, we study two possible writing schemes for the SOT-MTJ device based on recently fabricated samples. While the first is based on precessional switching, the second requires the presence of permanent magnetic field. Based on an accurate Verilog-A model, we simulate the two writing techniques and we highlight advantages and drawbacks of each one. Using the second technique, pioneering logic circuits based on the three-terminal architecture of the SOT-MTJ described in this work are under development with preliminary attractive results.

Analysis and Measuring Surface Roughness of Nonwovens Using Machine Vision Method

Concerning the measurement of friction properties of textiles and fabrics using Kawabata Evaluation System (KES), whose output is constrained to the surface friction factor of fabric, and no other data would be generated; this research has been conducted to gain information about surface roughness regarding its surface friction factor. To assess roughness properties of light nonwovens, a 3-dimensional model of a surface has been simulated with regular sinuous waves through it as an ideal surface. A new factor was defined, namely Surface Roughness Factor, through comparing roughness properties of simulated surface and real specimens. The relation between the proposed factor and friction factor of specimens has been analyzed by regression, and results showed a meaningful correlation between them. It can be inferred that the new presented factor can be used as an acceptable criterion for evaluating the roughness properties of light nonwoven fabrics.

Optimization of Growth of Rhodobacter Sphaeroides Using Mixed Volatile Fatty Acidsby Response Surface Methodology

A combination of photosynthetic bacteria along with anaerobic acidogenic bacteria is an ideal option for efficient hydrogen production. In the present study, the optimum concentration of substrates for the growth of Rhodobacter sphaeroides was found by response surface methodology. The optimum combination of three individual fatty acids was determined by Box Behnken design. Increase of volatile fatty acid concentration decreased the growth. Combination of sodium acetate and sodium propionate was most significant for the growth of the organism. The results showed that a maximum biomass concentration of 0.916 g/l was obtained when the concentrations of acetate, propionate and butyrate were 0.73g/l,0.99g/l and 0.799g/l, respectively. The growth was studied under an optimum concentration of volatile fatty acids and at a light intensity of 3000 lux, initial pH of 7 and a temperature of 35°C.The maximum biomass concentration of 0.92g/l was obtained which verified the practicability of this optimization.

Access Policy Specification for SCADA Networks

Efforts to secure supervisory control and data acquisition (SCADA) systems must be supported under the guidance of sound security policies and mechanisms to enforce them. Critical elements of the policy must be systematically translated into a format that can be used by policy enforcement components. Ideally, the goal is to ensure that the enforced policy is a close reflection of the specified policy. However, security controls commonly used to enforce policies in the IT environment were not designed to satisfy the specific needs of the SCADA environment. This paper presents a language, based on the well-known XACML framework, for the expression of authorization policies for SCADA systems.

Robust Human Rights Governance: Developing International Criteria

Many states are now committed to implementing international human rights standards domestically. In terms of practical governance, how might effectiveness be measured? A facevalue answer can be found in domestic laws and institutions relating to human rights. However, this article provides two further tools to help states assess their status on the spectrum of robust to fragile human rights governance. The first recognises that each state has its own 'human rights history' and the ideal end stage is robust human rights governance, and the second is developing criteria to assess robustness. Although a New Zealand case study is used to illustrate these tools, the widespread adoption of human rights standards by many states inevitably means that the issues are relevant to other countries. This is even though there will always be varying degrees of similarity-difference in constitutional background and developed or emerging human rights systems.

Hydrodynamic Modeling of a Surface Water Treatment Pilot Plant

A mathematical model for the hydrodynamics of a surface water treatment pilot plant was developed and validated by the determination of the residence time distribution (RTD) for the main equipments of the unit. The well known models of ideal/real mixing, ideal displacement (plug flow) and (one-dimensional axial) dispersion model were combined in order to identify the structure that gives the best fitting of the experimental data for each equipment of the pilot plant. RTD experimental results have shown that pilot plant hydrodynamics can be quite well approximated by a combination of simple mathematical models, structure which is suitable for engineering applications. Validated hydrodynamic models will be further used in the evaluation and selection of the most suitable coagulation-flocculation reagents, optimum operating conditions (injection point, reaction times, etc.), in order to improve the quality of the drinking water.

Optimal Preventive Maintenance of the Reserve Source in the Industrial Electric Network

The great majority of the electric installations belong to the first and second category. In order to ensure a high level of reliability of their electric system feeder, two power supply sources are envisaged, one principal, the other of reserve, generally a cold reserve (electric diesel group). The principal source being under operation, its control can be ideal and sure, however for the reserve source being in stop, a preventive maintenance-s which proceeds on time intervals (periodicity) and for well defined lengths of time are envisaged, so that this source will always available in case of the principal source failure. The choice of the periodicity of preventive maintenance of the source of reserve influences directly the reliability of the electric feeder system. On the basis of the semi-markovians processes, the influence of the periodicity of the preventive maintenance of the source of reserve is studied and is given the optimal periodicity.

A Grid-based Neural Network Framework for Multimodal Biometrics

Recent scientific investigations indicate that multimodal biometrics overcome the technical limitations of unimodal biometrics, making them ideally suited for everyday life applications that require a reliable authentication system. However, for a successful adoption of multimodal biometrics, such systems would require large heterogeneous datasets with complex multimodal fusion and privacy schemes spanning various distributed environments. From experimental investigations of current multimodal systems, this paper reports the various issues related to speed, error-recovery and privacy that impede the diffusion of such systems in real-life. This calls for a robust mechanism that caters to the desired real-time performance, robust fusion schemes, interoperability and adaptable privacy policies. The main objective of this paper is to present a framework that addresses the abovementioned issues by leveraging on the heterogeneous resource sharing capacities of Grid services and the efficient machine learning capabilities of artificial neural networks (ANN). Hence, this paper proposes a Grid-based neural network framework for adopting multimodal biometrics with the view of overcoming the barriers of performance, privacy and risk issues that are associated with shared heterogeneous multimodal data centres. The framework combines the concept of Grid services for reliable brokering and privacy policy management of shared biometric resources along with a momentum back propagation ANN (MBPANN) model of machine learning for efficient multimodal fusion and authentication schemes. Real-life applications would be able to adopt the proposed framework to cater to the varying business requirements and user privacies for a successful diffusion of multimodal biometrics in various day-to-day transactions.

Scatterer Density in Edge and Coherence Enhancing Nonlinear Anisotropic Diffusion for Medical Ultrasound Speckle Reduction

This paper proposes new enhancement models to the methods of nonlinear anisotropic diffusion to greatly reduce speckle and preserve image features in medical ultrasound images. By incorporating local physical characteristics of the image, in this case scatterer density, in addition to the gradient, into existing tensorbased image diffusion methods, we were able to greatly improve the performance of the existing filtering methods, namely edge enhancing (EE) and coherence enhancing (CE) diffusion. The new enhancement methods were tested using various ultrasound images, including phantom and some clinical images, to determine the amount of speckle reduction, edge, and coherence enhancements. Scatterer density weighted nonlinear anisotropic diffusion (SDWNAD) for ultrasound images consistently outperformed its traditional tensor-based counterparts that use gradient only to weight the diffusivity function. SDWNAD is shown to greatly reduce speckle noise while preserving image features as edges, orientation coherence, and scatterer density. SDWNAD superior performances over nonlinear coherent diffusion (NCD), speckle reducing anisotropic diffusion (SRAD), adaptive weighted median filter (AWMF), wavelet shrinkage (WS), and wavelet shrinkage with contrast enhancement (WSCE), make these methods ideal preprocessing steps for automatic segmentation in ultrasound imaging.

Solar Cell Parameters Estimation Using Simulated Annealing Algorithm

This paper presents Simulated Annealing based approach to estimate solar cell model parameters. Single diode solar cell model is used in this study to validate the proposed approach outcomes. The developed technique is used to estimate different model parameters such as generated photocurrent, saturation current, series resistance, shunt resistance, and ideality factor that govern the current-voltage relationship of a solar cell. A practical case study is used to test and verify the consistency of accurately estimating various parameters of single diode solar cell model. Comparative study among different parameter estimation techniques is presented to show the effectiveness of the developed approach.

Variable Structure Model Reference Adaptive Control for Vehicle Steering System

A variable structure model reference adaptive control (VS-MRAC) strategy for active steering assistance of a two wheel steering car is proposed. An ideal steering system with fixed properties and moving on an ideal road is used as the reference model, and the active steering assistance system is forced to attain the same behavior as the reference model. The proposed system can treat the nonlinear relationships between the side slip angles and lateral forces on tire, and the uncertainties on friction of the road surface, whose compensation are very important under critical situations. Simulation results show improvements on yaw rate and side slip.

Analytical Solutions for Geodesic Acoustic Eigenmodes in Tokamak Plasmas

The analytical solutions for geodesic acoustic eigenmodes in tokamak plasmas with circular concentric magnetic surfaces are found. In the frame of ideal magnetohydrodynamics the dispersion relation taking into account the toroidal coupling between electrostatic perturbations and electromagnetic perturbations with poloidal mode number |m| = 2 is derived. In the absence of such a coupling the dispersion relation gives the standard continuous spectrum of geodesic acoustic modes. The analysis of the existence of global eigenmodes for plasma equilibria with both off-axis and on-axis maximum of the local geodesic acoustic frequency is performed.

A Note on Characterization of Regular Γ-Semigroups in terms of (∈,∈ ∨q)-Fuzzy Bi-ideal

The purpose of this note is to obtain some properties of (∈,∈ ∨q)- fuzzy bi-ideals in a Γ-semigroup in order to characterize regular and intra-regular Γ-semigroups.

Helicopter Adaptive Control with Parameter Estimation Based on Feedback Linearization

This paper presents an adaptive feedback linearization approach to derive helicopter. Ideal feedback linearization is defined for the cases when the system model is known. Adaptive feedback linearization is employed to get asymptotically exact cancellation for the inherent uncertainty in the knowledge of the given parameters of system. The control algorithm is implemented using the feedback linearization technique and adaptive method. The controller parameters are unknown where an adaptive control law aims to drive them towards their ideal values for providing perfect model matching between the reference model and the closed-loop plant model. The converged parameters of controller would then provide good estimates for the unknown plant parameters.

Intuitionistic Fuzzy Points in Semigroups

The notion of intuitionistic fuzzy sets was introduced by Atanassov as a generalization of the notion of fuzzy sets. Y.B. Jun and S.Z. Song introduced the notion of intuitionistic fuzzy points. In this paper we find some relations between the intuitionistic fuzzy ideals of a semigroup S and the set of all intuitionistic fuzzy points of S.

A Numerical Approach for Static and Dynamic Analysis of Deformable Journal Bearings

This paper presents a numerical approach for the static and dynamic analysis of hydrodynamic radial journal bearings. In the first part, the effect of shaft and housing deformability on pressure distribution within oil film is investigated. An iterative algorithm that couples Reynolds equation with a plane finite elements (FE) structural model is solved. Viscosity-to-pressure dependency (Vogel- Barus equation) is also included. The deformed lubrication gap and the overall stress state are obtained. Numerical results are presented with reference to a typical journal bearing configuration at two different inlet oil temperatures. Obtained results show the great influence of bearing components structural deformation on oil pressure distribution, compared with results for ideally rigid components. In the second part, a numerical approach based on perturbation method is used to compute stiffness and damping matrices, which characterize the journal bearing dynamic behavior.

Modeling and Analysis of Twelve-phase (Multi- Phase) DSTATCOM for Multi-Phase Load Circuits

This paper presents modeling and analysis of 12-phase distribution static compensator (DSTATCOM), which is capable of balancing the source currents in spite of unbalanced loading and phase outages. In addition to balance the supply current, the power factor can be set to a desired value. The theory of instantaneous symmetrical components is used to generate the twelve-phase reference currents. These reference currents are then tracked using current controlled voltage source inverter, operated in a hysteresis band control scheme. An ideal compensator in place of physical realization of the compensator is used. The performance of the proposed DTATCOM is validated through MATLAB simulation and detailed simulation results are given.

Assessment of Time-Lapse in Visible and Thermal Face Recognition

Although face recognition seems as an easy task for human, automatic face recognition is a much more challenging task due to variations in time, illumination and pose. In this paper, the influence of time-lapse on visible and thermal images is examined. Orthogonal moment invariants are used as a feature extractor to analyze the effect of time-lapse on thermal and visible images and the results are compared with conventional Principal Component Analysis (PCA). A new triangle square ratio criterion is employed instead of Euclidean distance to enhance the performance of nearest neighbor classifier. The results of this study indicate that the ideal feature vectors can be represented with high discrimination power due to the global characteristic of orthogonal moment invariants. Moreover, the effect of time-lapse has been decreasing and enhancing the accuracy of face recognition considerably in comparison with PCA. Furthermore, our experimental results based on moment invariant and triangle square ratio criterion show that the proposed approach achieves on average 13.6% higher in recognition rate than PCA.

Design of Digital Differentiator to Optimize Relative Error

It is observed that the Weighted least-square (WLS) technique, including the modifications, results in equiripple error curve. The resultant error as a percent of the ideal value is highly non-uniformly distributed over the range of frequencies for which the differentiator is designed. The present paper proposes a modification to the technique so that the optimization procedure results in lower maximum relative error compared to the ideal values. Simulation results for first order as well as higher order differentiators are given to illustrate the excellent performance of the proposed method.