Research of Ring MEMS Rate Integrating Gyroscopes

This paper To get the angle value with a MEMS rate gyroscope in some specific field, the usual method is to make an integral operation to the rate output, which will lead the error cumulating effect. So the rate gyro is not suitable. MEMS rate integrating gyroscope (MRIG) will solve this problem. A DSP system has been developed to implement the control arithmetic. The system can measure the angle of rotation directly by the control loops that make the sensor work in whole-angle mode. Modeling the system with MATLAB, desirable results of angle outputs are got, which prove the feasibility of the control arithmetic.

Optometric-lab: a Stereophotogrammetry Tool for Eye Movements Records

In this paper as showed a non-invasive 3D eye tracker for optometry clinical applications. Measurements of biomechanical variables in clinical practice have many font of errors associated with traditional procedments such cover test (CT), near point of accommodation (NPC), eye ductions (ED), eye vergences (EG) and, eye versions (ES). Ocular motility should always be tested but all evaluations have a subjective interpretations by practitioners, the results is based in clinical experiences, repeatability and accuracy don-t exist. Optometric-lab is a tool with 3 (tree) analogical video cameras triggered and synchronized in one acquisition board AD. The variables globe rotation angle and velocity can be quantified. Data record frequency was performed with 27Hz, camera calibration was performed in a know volume and image radial distortion adjustments.

Improving Spatiotemporal Change Detection: A High Level Fusion Approach for Discovering Uncertain Knowledge from Satellite Image Database

This paper investigates the problem of tracking spa¬tiotemporal changes of a satellite image through the use of Knowledge Discovery in Database (KDD). The purpose of this study is to help a given user effectively discover interesting knowledge and then build prediction and decision models. Unfortunately, the KDD process for spatiotemporal data is always marked by several types of imperfections. In our paper, we take these imperfections into consideration in order to provide more accurate decisions. To achieve this objective, different KDD methods are used to discover knowledge in satellite image databases. Each method presents a different point of view of spatiotemporal evolution of a query model (which represents an extracted object from a satellite image). In order to combine these methods, we use the evidence fusion theory which considerably improves the spatiotemporal knowledge discovery process and increases our belief in the spatiotemporal model change. Experimental results of satellite images representing the region of Auckland in New Zealand depict the improvement in the overall change detection as compared to using classical methods.

Synthesis of ZnO Nanostructures via Gel-casting Method

In this study, ZnO nano rods and ZnO ultrafine particles were synthesized by Gel-casting method. The synthesized ZnO powder has a hexagonal zincite structure. The ZnO aggregates with rod-like morphology are typically 1.4 μm in length and 120 nm in diameter, which consist of many small nanocrystals with diameters of 10 nm. Longer wires connected by many hexahedral ZnO nanocrystals were obtained after calcinations at the temperature over 600° C.The crystalline structures and morphologies of the powder have been characterized by X-ray diffraction(XRD) and Scaning electron microscopy (SEM).The result shows that the different preparation conditions such as concentration H2O, calcinations time and calcinations temperature have a lot of influences upon the properties of nano ZnO powders, an increase in the temperature of the calcinations results in an increase of the grain size and also the increase of the calcinations time in high temperature makes the size of the grains bigger. The existences of extra watter prevent nano grains from improving like rod morphology. We have obtained the smallest grain size of ZnO powder by controlling the process conditions. Finally In a suitable condition, a novel nanostructure, namely bi-rod-like ZnO nano rods was found which is different from known ZnO nanostructures.

SELF-Cured Alkali Activated Slag Concrete Mixes- An Experimental Study

Alkali Activated Slag Concrete (AASC) mixes are manufactured by activating ground granulated blast furnace slag (GGBFS) using sodium hydroxide and sodium silicate solutions. The aim of the present experimental research was to investigate the effect of increasing the dosages of sodium oxide (Na2O, in the range of 4 to 8%) and the activator modulus (Ms) (i.e. the SiO2/Na2O ratio, in the range of 0.5 to 1.5) of the alkaline solutions, on the workability and strength characteristics of self-cured (air-cured) alkali activated Indian slag concrete mixes. Further the split tensile and flexure strengths for optimal mixes were studied for each dosage of Na2O.It is observed that increase in Na2O concentration increases the compressive, split-tensile and flexural strengths, both at the early and later-ages, while increase in Ms, decreases the workability of the mixes. An optimal Ms of 1.25 is found at various Na2O dosages. No significant differences in the strength performances were observed between AASCs manufactured with alkali solutions prepared using either of potable and de-ionized water.

The Effects of Roots Action of Tropical Green Roofs–Replication of German FLL in Singapore

Green Roofs offers numerous advantages, including lowering ambient temperature, which is of increasing interest due to global warming concerns. However, there are technical problems pertaining to waterproofing to be resolved. Currently, the only recognized green roof waterproofing test is the German standard FLL. This paper examines the potential of replicating the test in tropical climate and reducing the test duration by using pre-grown plants. A three year old sample and a new setup were used for this experimental study. The new setup was prepared with close reference to the FLL standards and was compared against the three year old sample. Results showed that the waterproofing membrane was damaged by plant roots in both setups. Joints integrity was also challenged.

Human Growth Curve Estimation through a Combination of Longitudinal and Cross-sectional Data

Parametric models have been quite popular for studying human growth, particularly in relation to biological parameters such as peak size velocity and age at peak size velocity. Longitudinal data are generally considered to be vital for fittinga parametric model to individual-specific data, and for studying the distribution of these biological parameters in a human population. However, cross-sectional data are easier to obtain than longitudinal data. In this paper, we present a method of combining longitudinal and cross-sectional data for the purpose of estimating the distribution of the biological parameters. We demonstrate, through simulations in the special case ofthePreece Baines model, how estimates based on longitudinal data can be improved upon by harnessing the information contained in cross-sectional data.We study the extent of improvement for different mixes of the two types of data, and finally illustrate the use of the method through data collected by the Indian Statistical Institute.

The Study of Synbiotic Dairy Products Rheological Properties during Shelf-Life

The influence of lactulose and inulin on rheological properties of fermented milk during storage was studied.Pasteurized milk, freeze-dried starter culture Bb-12 (Bifidobacterium lactis, Chr. Hansen, Denmark), inulin – RAFTILINE®HP (ORAFI, Belgium) and syrup of lactulose (Duphalac®, the Netherlands) were used for experiments. The fermentation process was realized at 37 oC for 16 hours and the storage of products was provided at 4 oC for 7 days. Measurements were carried out by BROOKFIELD standard methods and the flow curves were described by Herschel-Bulkley model. The results of dispersion analysis have shown that both the concentration of prebiotics (p=0.04

On Simulation based WSN Multi-Parametric Performance Analysis

Optimum communication and performance in Wireless Sensor Networks, constitute multi-facet challenges due to the specific networking characteristics as well as the scarce resource availability. Furthermore, it is becoming increasingly apparent that isolated layer based approaches often do not meet the demands posed by WSNs applications due to omission of critical inter-layer interactions and dependencies. As a counterpart, cross-layer is receiving high interest aiming to exploit these interactions and increase network performance. However, in order to clearly identify existing dependencies, comprehensive performance studies are required evaluating the effect of different critical network parameters on system level performance and behavior.This paper-s main objective is to address the need for multi-parametric performance evaluations considering critical network parameters using a well known network simulator, offering useful and practical conclusions and guidelines. The results reveal strong dependencies among considered parameters which can be utilized by and drive future research efforts, towards designing and implementing highly efficient protocols and architectures.

Gravitino Dark Matter in (nearly) SLagy D3/D7 m-Split SUSY

In the context of large volume Big Divisor (nearly) SLagy D3/D7 μ-Split SUSY [1], after an explicit identification of first generation of SM leptons and quarks with fermionic superpartners of four Wilson line moduli, we discuss the identification of gravitino as a potential dark matter candidate by explicitly calculating the decay life times of gravitino (LSP) to be greater than age of universe and lifetimes of decays of the co-NLSPs (the first generation squark/slepton and a neutralino) to the LSP (the gravitino) to be very small to respect BBN constraints. Interested in non-thermal production mechanism of gravitino, we evaluate the relic abundance of gravitino LSP in terms of that of the co-NLSP-s by evaluating their (co-)annihilation cross sections and hence show that the former satisfies the requirement for a potential Dark Matter candidate. We also show that it is possible to obtain a 125 GeV light Higgs in our setup.

Deterministic Method to Assess Kalman Filter Passive Ranging Solution Reliability

For decades, the defense business has been plagued by not having a reliable, deterministic method to know when the Kalman filter solution for passive ranging application is reliable for use by the fighter pilot. This has made it hard to accurately assess when the ranging solution can be used for situation awareness and weapons use. To date, we have used ad hoc rules-of-thumb to assess when we think the estimate of the Kalman filter standard deviation on range is reliable. A reliable algorithm has been developed at BAE Systems Electronics & Integrated Solutions that monitors the Kalman gain matrix elements – and a patent is pending. The “settling" of the gain matrix elements relates directly to when we can assess the time when the passive ranging solution is within the 10 percent-of-truth value. The focus of the paper is on surface-based passive ranging – but the method is applicable to airborne targets as well.

Risk-Management by Numerical Pattern Analysis in Data-Mining

In this paper a new method is suggested for risk management by the numerical patterns in data-mining. These patterns are designed using probability rules in decision trees and are cared to be valid, novel, useful and understandable. Considering a set of functions, the system reaches to a good pattern or better objectives. The patterns are analyzed through the produced matrices and some results are pointed out. By using the suggested method the direction of the functionality route in the systems can be controlled and best planning for special objectives be done.

Investigation of Chaotic Behavior in DC-DC Converters

DC-DC converters are widely used in regulated switched mode power supplies and in DC motor drive applications. There are several sources of unwanted nonlinearity in practical power converters. In addition, their operation is characterized by switching that gives birth to a variety of nonlinear dynamics. DC-DC buck and boost converters controlled by pulse-width modulation (PWM) have been simulated. The voltage waveforms and attractors obtained from the circuit simulation have been studied. With the onset of instability, the phenomenon of subharmonic oscillations, quasi-periodicity, bifurcations, and chaos have been observed. This paper is mainly motivated by potential contributions of chaos theory in the design, analysis and control of power converters, in particular and power electronics circuits, in general.

Improved Fuzzy Neural Modeling for Underwater Vehicles

The dynamics of the Autonomous Underwater Vehicles (AUVs) are highly nonlinear and time varying and the hydrodynamic coefficients of vehicles are difficult to estimate accurately because of the variations of these coefficients with different navigation conditions and external disturbances. This study presents the on-line system identification of AUV dynamics to obtain the coupled nonlinear dynamic model of AUV as a black box. This black box has an input-output relationship based upon on-line adaptive fuzzy model and adaptive neural fuzzy network (ANFN) model techniques to overcome the uncertain external disturbance and the difficulties of modelling the hydrodynamic forces of the AUVs instead of using the mathematical model with hydrodynamic parameters estimation. The models- parameters are adapted according to the back propagation algorithm based upon the error between the identified model and the actual output of the plant. The proposed ANFN model adopts a functional link neural network (FLNN) as the consequent part of the fuzzy rules. Thus, the consequent part of the ANFN model is a nonlinear combination of input variables. Fuzzy control system is applied to guide and control the AUV using both adaptive models and mathematical model. Simulation results show the superiority of the proposed adaptive neural fuzzy network (ANFN) model in tracking of the behavior of the AUV accurately even in the presence of noise and disturbance.

Using Dempster-Shafer Theory in XML Information Retrieval

XML is a markup language which is becoming the standard format for information representation and data exchange. A major purpose of XML is the explicit representation of the logical structure of a document. Much research has been performed to exploit logical structure of documents in information retrieval in order to precisely extract user information need from large collections of XML documents. In this paper, we describe an XML information retrieval weighting scheme that tries to find the most relevant elements in XML documents in response to a user query. We present this weighting model for information retrieval systems that utilize plausible inferences to infer the relevance of elements in XML documents. We also add to this model the Dempster-Shafer theory of evidence to express the uncertainty in plausible inferences and Dempster-Shafer rule of combination to combine evidences derived from different inferences.

Adaptive Nonlinear Backstepping Control

This paper presents an adaptive nonlinear position controller with velocity constraint, capable of combining the input-output linearization technique and Lyapunov stability theory. Based on the Lyapunov stability theory, the adaptation law of the proposed controller is derived along with the verification of the overall system-s stability. Computer simulation results demonstrate that the proposed controller is robust and it can ensure transient stability of BLDCM, under the occurrence of a large sudden fault.

Design of Digital IIR filters with the Advantages of Model Order Reduction Technique

In this paper, a new model order reduction phenomenon is introduced at the design stage of linear phase digital IIR filter. The complexity of a system can be reduced by adopting the model order reduction method in their design. In this paper a mixed method of model order reduction is proposed for linear IIR filter. The proposed method employs the advantages of factor division technique to derive the reduced order denominator polynomial and the reduced order numerator is obtained based on the resultant denominator polynomial. The order reduction technique is used to reduce the delay units at the design stage of IIR filter. The validity of the proposed method is illustrated with design example in frequency domain and stability is also examined with help of nyquist plot.

On the Learning of Causal Relationships between Banks in Saudi Equities Market Using Ensemble Feature Selection Methods

Financial forecasting using machine learning techniques has received great efforts in the last decide . In this ongoing work, we show how machine learning of graphical models will be able to infer a visualized causal interactions between different banks in the Saudi equities market. One important discovery from such learned causal graphs is how companies influence each other and to what extend. In this work, a set of graphical models named Gaussian graphical models with developed ensemble penalized feature selection methods that combine ; filtering method, wrapper method and a regularizer will be shown. A comparison between these different developed ensemble combinations will also be shown. The best ensemble method will be used to infer the causal relationships between banks in Saudi equities market.

Convergence and Divergence in Telephone Conversations: A Case of Persian

People usually have a telephone voice, which means they adjust their speech to fit particular situations and to blend in with other interlocutors. The question is: Do we speak differently to different people? This possibility has been suggested by social psychologists within Accommodation Theory [1]. Converging toward the speech of another person can be regarded as a polite speech strategy while choosing a language not used by the other interlocutor can be considered as the clearest example of speech divergence [2]. The present study sets out to investigate such processes in the course of everyday telephone conversations. Using Joos-s [3] model of formality in spoken English, the researchers try to explore convergence to or divergence from the addressee. The results propound the actuality that lexical choice, and subsequently, patterns of style vary intriguingly in concordance with the person being addressed.

Bearing Fault Feature Extraction by Recurrence Quantification Analysis

In rotating machinery one of the critical components that is prone to premature failure is the rolling bearing. Consequently, early warning of an imminent bearing failure is much critical to the safety and reliability of any high speed rotating machines. This study is concerned with the application of Recurrence Quantification Analysis (RQA) in fault detection of rolling element bearings in rotating machinery. Based on the results from this study it is reported that the RQA variable, percent determinism, is sensitive to the type of fault investigated and therefore can provide useful information on bearing damage in rolling element bearings.