Beam steering with high front-to-back ratio and high directivity on small platforms using decoupled antenna pairs

Beam steering with high front-to-back ratio and high directivity on a small platform is proposed. Two closely spaced antenna pairs with eigenmode port decoupling are used as the basic radiating elements. Two orthogonal radiation patterns are obtained for each antenna pair. High front-to-back ratio and high directivity are achieved by combining the two orthogonal radiation patterns. With an infinite groundplane, a front-to-back ratio of 21 dB with a directivity of 9.8 dB can be achieved. Beam steering, at the expense of a slight decrease in directivity, is achieved by placing the two antenna pairs 0.5λ apart. The simulated half power beamwidth is 58°. A prototype was designed and the 2-D radiation patterns were measured. The prototype supports three directions of beam steering. The half power beamwidth was measured as 46°, 48°, and 50° for the three respective beam directions. The measured front-to-back ratio in azimuth plane is 8.5 dB, 8.0 dB and 7.6 dB, respectively.

Cross likelihood ratio based speaker clustering using eigenvoice models

This paper proposes the use of eigenvoice modeling techniques with the Cross Likelihood Ratio (CLR) as a criterion for speaker clustering within a speaker diarization system. The CLR has previously been shown to be a robust decision criterion for speaker clustering using Gaussian Mixture Models. Recently, eigenvoice modeling techniques have become increasingly popular, due to its ability to adequately represent a speaker based on sparse training data, as well as an improved capture of differences in speaker characteristics. This paper hence proposes that it would be beneficial to capitalize on the advantages of eigenvoice modeling in a CLR framework. Results obtained on the 2002 Rich Transcription (RT-02) Evaluation dataset show an improved clustering performance, resulting in a 35.1% relative improvement in the overall Diarization Error Rate (DER) compared to the baseline system.

Effects of corrugation frequency and aspect ratio on natural convection within an enclosure having sinusoidal corrugation over a heated top surface

Natural convection of a two-dimensional laminar steady-state incompressible fluid flow in a modified rectangular enclosure with sinusoidal corrugated top surface has been investigated numerically. The present study has been carried out for different corrugation frequencies on the top surface as well as aspect ratios of the enclosure in order to observe the change in hydrodynamic and thermal behavior with constant corrugation amplitude. A constant flux heat source is flush mounted on the top sinusoidal wall, modeling a wavy sheet shaded room exposed to sunlight. The flat bottom surface is considered as adiabatic, while the both vertical side walls are maintained at the constant ambient temperature. The fluid considered inside the enclosure is air having Prandtl number of 0.71. The numerical scheme is based on the finite element method adapted to triangular non-uniform mesh element by a non-linear parametric solution algorithm. The results in terms of isotherms, streamlines and average Nusselt numbers are obtained for the Rayleigh number ranging from 10^3 to 10^6 with constant physical properties for the fluid medium considered. It is found that the convective phenomena are greatly influenced by the presence of the corrugation and variation of aspect ratios.

Models of collective cell motion for cell populations with different aspect ratio : diffusion, proliferation and travelling waves

Continuum, partial differential equation models are often used to describe the collective motion of cell populations, with various types of motility represented by the choice of diffusion coefficient, and cell proliferation captured by the source terms. Previously, the choice of diffusion coefficient has been largely arbitrary, with the decision to choose a particular linear or nonlinear form generally based on calibration arguments rather than making any physical connection with the underlying individual-level properties of the cell motility mechanism. In this work we provide a new link between individual-level models, which account for important cell properties such as varying cell shape and volume exclusion, and population-level partial differential equation models. We work in an exclusion process framework, considering aligned, elongated cells that may occupy more than one lattice site, in order to represent populations of agents with different sizes. Three different idealizations of the individual-level mechanism are proposed, and these are connected to three different partial differential equations, each with a different diffusion coefficient; one linear, one nonlinear and degenerate and one nonlinear and nondegenerate. We test the ability of these three models to predict the population level response of a cell spreading problem for both proliferative and nonproliferative cases. We also explore the potential of our models to predict long time travelling wave invasion rates and extend our results to two dimensional spreading and invasion. Our results show that each model can accurately predict density data for nonproliferative systems, but that only one does so for proliferative systems. Hence great care must be taken to predict density data for with varying cell shape.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

Analysis of the damping properties of high-transmission-ratio Cycloidal drives

Cycloidal drives are widely used in today’s industries for drives where large reduction ratios are required. Drive-train dynamics plays an important role in their design. This paper presents a new methodology for assessing damping characteristics of Cycloidal drives and compares the natural frequencies obtained from experiments and theoretical/numerical calculations using Fast-Fourier-Transforms.

Suicide rates in Shandong, China, 1991–2010 : rapid decrease in rural rates and steady increase in male–female ratio

Background China has one of the highest suicide rates in the world; however, the recent trends in suicide have not been adequately studied. This study aimed to examine the potential changes in the rates and characteristics in a Chinese population. Methods Data on suicide deaths in 1991–2010 were extracted from the Shandong Disease Surveillance Point (DSP) mortality dataset based on ICD-10 codes. The temporal trend in age-adjusted suicide rates for each subpopulation was tested using log-linear Poisson regression analysis. Results From 1991 to 2010, there was a marked decrease in the overall suicide rate in Shandong, with an average reduction of 8% per year. The decrease trend was stronger in rural than in urban areas and more evident in females than in males. Similar decreases were observed for all age groups. Pesticide ingestion and hanging remained the top two methods for suicide. Limitations There are likely quality concerns in the morality data, such as underreporting and misclassification, as well as low accuracy in determining the underlying causes of deaths. The representativeness of the DSP system may also be problematic due to the rapid changes in economy and demography. Conclusions Completed suicides in Shandong have sharply declined over the past 20 years. Higher rates in females versus males and in rural versus urban areas, which were previously considered to be distinguishing features of suicide in China, are becoming less pronounced.

Automatic Audio Segmentation Using the Generalized Likelihood Ratio

This paper presents a novel technique for segmenting an audio stream into homogeneous regions according to speaker identities, background noise, music, environmental and channel conditions. Audio segmentation is useful in audio diarization systems, which aim to annotate an input audio stream with information that attributes temporal regions of the audio into their specific sources. The segmentation method introduced in this paper is performed using the Generalized Likelihood Ratio (GLR), computed between two adjacent sliding windows over preprocessed speech. This approach is inspired by the popular segmentation method proposed by the pioneering work of Chen and Gopalakrishnan, using the Bayesian Information Criterion (BIC) with an expanding search window. This paper will aim to identify and address the shortcomings associated with such an approach. The result obtained by the proposed segmentation strategy is evaluated on the 2002 Rich Transcription (RT-02) Evaluation dataset, and a miss rate of 19.47% and a false alarm rate of 16.94% is achieved at the optimal threshold.

Open design of high pressure ratio radial-inflow turbine for academic validation

Computational Fluid Dynamics (CFD) simulations are widely used in mechanical engineering. Although achieving a high level of confidence in numerical modelling is of crucial importance in the field of turbomachinery, verification and validation of CFD simulations are very tricky especially for complex flows encountered in radial turbines. Comprehensive studies of radial machines are available in the literature. Unfortunately, none of them include enough detailed geometric data to be properly reproduced and so cannot be considered for academic research and validation purposes. As a consequence, design improvements of such configurations are difficult. Moreover, it seems that well-developed analyses of radial turbines are used in commercial software but are not available in the open literature especially at high pressure ratios. It is the purpose of this paper to provide a fully open set of data to reproduce the exact geometry of the high pressure ratio single stage radial-inflow turbine used in the Sundstrand Power Systems T-100 Multipurpose Small Power Unit. First, preliminary one-dimensional meanline design and analysis are performed using the commercial software RITAL from Concepts-NREC in order to establish a complete reference test case available for turbomachinery code validation. The proposed design of the existing turbine is then carefully and successfully checked against the geometrical and experimental data partially published in the literature. Then, three-dimensional Reynolds-Averaged Navier-Stokes simulations are conducted by means of the Axcent-PushButton CFDR CFD software. The effect of the tip clearance gap is investigated in detail for a wide range of operating conditions. The results confirm that the 3D geometry is correctly reproduced. It also reveals that the turbine is shocked while designed to give a high-subsonic flow and highlight the importance of the diffuser.

Eigenvoice modeling for cross likelihood ratio based speaker clustering : a Bayesian approach

This paper proposes the use of Bayesian approaches with the cross likelihood ratio (CLR) as a criterion for speaker clustering within a speaker diarization system, using eigenvoice modeling techniques. The CLR has previously been shown to be an effective decision criterion for speaker clustering using Gaussian mixture models. Recently, eigenvoice modeling has become an increasingly popular technique, due to its ability to adequately represent a speaker based on sparse training data, as well as to provide an improved capture of differences in speaker characteristics. The integration of eigenvoice modeling into the CLR framework to capitalize on the advantage of both techniques has also been shown to be beneficial for the speaker clustering task. Building on that success, this paper proposes the use of Bayesian methods to compute the conditional probabilities in computing the CLR, thus effectively combining the eigenvoice-CLR framework with the advantages of a Bayesian approach to the diarization problem. Results obtained on the 2002 Rich Transcription (RT-02) Evaluation dataset show an improved clustering performance, resulting in a 33.5% relative improvement in the overall Diarization Error Rate (DER) compared to the baseline system.

Assessing the Impact of Sensor Accuracy and Sensor Choice on Air Traffic Conflict Risk Estimation using Risk-Ratio Concepts

One of the primary desired capabilities of any future air traffic separation management system is the ability to provide early conflict detection and resolution effectively and efficiently. In this paper, we consider the risk of conflict as a primary measurement to be used for early conflict detection. This paper focuses on developing a novel approach to assess the impact of different measurement uncertainty models on the estimated risk of conflict. The measurement uncertainty model can be used to represent different sensor accuracy and sensor choices. Our study demonstrates the value of modelling measurement uncertainty in the conflict risk estimation problem and presents techniques providing a means of assessing sensor requirements to achieve desired conflict detection performance.

Classifier selection using sequential error ratio criterion for multi-instance and multi-sample fusion

Classifier selection is a problem encountered by multi-biometric systems that aim to improve performance through fusion of decisions. A particular decision fusion architecture that combines multiple instances (n classifiers) and multiple samples (m attempts at each classifier) has been proposed in previous work to achieve controlled trade-off between false alarms and false rejects. Although analysis on text-dependent speaker verification has demonstrated better performance for fusion of decisions with favourable dependence compared to statistically independent decisions, the performance is not always optimal. Given a pool of instances, best performance with this architecture is obtained for certain combination of instances. Heuristic rules and diversity measures have been commonly used for classifier selection but it is shown that optimal performance is achieved for the `best combination performance' rule. As the search complexity for this rule increases exponentially with the addition of classifiers, a measure - the sequential error ratio (SER) - is proposed in this work that is specifically adapted to the characteristics of sequential fusion architecture. The proposed measure can be used to select a classifier that is most likely to produce a correct decision at each stage. Error rates for fusion of text-dependent HMM based speaker models using SER are compared with other classifier selection methodologies. SER is shown to achieve near optimal performance for sequential fusion of multiple instances with or without the use of multiple samples. The methodology applies to multiple speech utterances for telephone or internet based access control and to other systems such as multiple finger print and multiple handwriting sample based identity verification systems.

Hybrid antibacterial fabrics with extremely high aspect ratio Ag/AgTCNQ nanowires

This study reports the synthesis of extremely high aspect ratios (>3000) organic semiconductor nanowires of Ag–tetracyanoquinodimethane (AgTCNQ) on the surface of a flexible Ag fabric for the first time. These one-dimensional (1D) hybrid Ag/AgTCNQ nanostructures are attained by a facile, solution-based spontaneous reaction involving immersion of Ag fabrics in an acetonitrile solution of TCNQ. Further, it is discovered that these AgTCNQ nanowires show outstanding antibacterial performance against both Gram negative and Gram positive bacteria, which outperforms that of pristine Ag. The outcomes of this study also reflect upon a fundamentally important aspect that the antimicrobial performance of Ag-based nanomaterials may not necessarily be solely due to the amount of Ag+ ions leached from these nanomaterials, but that the nanomaterial itself may also play a direct role in the antimicrobial action. Notably, the applications of metal-organic semiconducting charge transfer complexes of metal-7,7,8,8-tetracyanoquinodimethane (TCNQ) have been predominantly restricted to electronic applications, except from our recent reports on their (photo)catalytic potential and the current case on antimicrobial prospects. This report on growth of these metal-TCNQ complexes on a fabric not only widens the window of these interesting materials for new biological applications, it also opens the possibilities for developing large-area flexible electronic devices by growing a range of metal-organic semiconducting materials directly on a fabric surface.

Ambiguity Acceptance Testing : A Comparison of the Ratio Test and Difference Test

Integer ambiguity resolution is an indispensable procedure for all high precision GNSS applications. The correctness of the estimated integer ambiguities is the key to achieving highly reliable positioning, but the solution cannot be validated with classical hypothesis testing methods. The integer aperture estimation theory unifies all existing ambiguity validation tests and provides a new prospective to review existing methods, which enables us to have a better understanding on the ambiguity validation problem. This contribution analyses two simple but efficient ambiguity validation test methods, ratio test and difference test, from three aspects: acceptance region, probability basis and numerical results. The major contribution of this paper can be summarized as: (1) The ratio test acceptance region is overlap of ellipsoids while the difference test acceptance region is overlap of half-spaces. (2) The probability basis of these two popular tests is firstly analyzed. The difference test is an approximation to optimal integer aperture, while the ratio test follows an exponential relationship in probability. (3) The limitations of the two tests are firstly identified. The two tests may under-evaluate the failure risk if the model is not strong enough or the float ambiguities fall in particular region. (4) Extensive numerical results are used to compare the performance of these two tests. The simulation results show the ratio test outperforms the difference test in some models while difference test performs better in other models. Particularly in the medium baseline kinematic model, the difference tests outperforms the ratio test, the superiority is independent on frequency number, observation noise, satellite geometry, while it depends on success rate and failure rate tolerance. Smaller failure rate leads to larger performance discrepancy.