Single‐cell‐gap transflective liquid‐crystal display and the use of photoalignment technology

Abstract— In this paper, transflective liquid‐crystal‐display (LCD) technology will be reviewed, and several new single‐cell‐gap transflective LCD configurations are proposed. Photoalignment technology is studied especially for transflective‐LCD applications. In order to realize the optimal performance of the display as well as a matched transmittance/reflectance voltage curve (TVC/RVC) for the transflective configurations, two different single‐cell‐gap transflective‐LCD approaches will be discussed. The first one is the dual‐mode single‐cell‐gap approach, in which different liquid‐crystal modes are applied to the transmissive and reflective subpixels of the transflective LCD. The other approach is the single‐mode s ingle‐cell‐gap approach, in which an in‐cell retardation film is applied to adjust the performance and TVC/RVC matching of a transflective LCD. Photoalignment technology is used to fabricate the dual‐mode liquid‐crystal cell in the first approach and also the in‐cell retardation film in the second approach. Prototypes of the proposed configurations have been fabricated, which show good performance and a matched TVC/RVC.     

On calibration error of randomized forecasting algorithms

It has been recently shown that calibration with an error less than Δ>0$\Delta >0$ is almost surely guaranteed with a randomized forecasting algorithm, where forecasts are obtained by random rounding the deterministic forecasts up to Δ$\Delta$. We show that this error cannot be improved for a vast majority of sequences: we prove that, using a probabilistic algorithm, we can effectively generate with probability close to one a sequence “resistant” to any randomized rounding forecasting with an error much smaller than Δ$\Delta$. We also reformulate this result by means of a probabilistic game.     

Robust resource allocation in a cluster based imaging system

Recently there has been an increased demand for imaging systems in support of high-speed digital printing. The required increase in performance in support of such systems can be accomplished through an effective parallel execution of image processing applications in a distributed cluster computing environment. The output of the system must be presented to a raster based display at regular intervals, effectively establishing a hard deadline for the production of each image. Failure to complete a rasterization task before its deadline will result in an interruption of service that is unacceptable. The goal of this research was to derive a metric for measuring robustness in this environment and to design a resource allocation heuristic capable of completing each rasterization task before its assigned deadline, thus, preventing any service interruptions. We present a mathematical model of such a cluster based raster imaging system, derive a robustness metric for evaluating heuristics in this environment, and demonstrate using the metric to make resource allocation decisions. The heuristics are evaluated within a simulation of the studied raster imaging system. We clearly demonstrate the effectiveness of the heuristics by comparing their results with the results of a resource allocation heuristic commonly used in this type of system.     

Uncertainty explicit assessment of off-the-shelf software: A Bayesian approach

Assessment of software COTS components is an essential part of component-based software development. Poorly chosen components may lead to solutions of low quality and that are difficult to maintain. The assessment may be based on incomplete knowledge about the COTS component itself and other aspects (e.g. vendor’s credentials, etc.), which may affect the decision of selecting COTS component(s). We argue in favor of assessment methods in which uncertainty is explicitly represented (‘uncertainty explicit’ methods) using probability distributions. We provide details of a Bayesian model, which can be used to capture the uncertainties in the simultaneous assessment of two attributes, thus, also capturing the dependencies that might exist between them. We also provide empirical data from the use of this method for the assessment of off-the-shelf database servers which illustrate the advantages of ‘uncertainty explicit’ methods over conventional methods of COTS component assessment which assume that at the end of the assessment the values of the attributes become known with certainty.     

VerifyThis 2015

VerifyThis 2015 was a one-day program verification competition which took place on April 12th, 2015 in London, UK, as part of the European Joint Conferences on Theory and Practice of Software (ETAPS 2015). It was the fourth instalment in the VerifyThis competition series. This article provides an overview of the VerifyThis 2015 event, the challenges that were posed during the competition, and a high-level overview of the solutions to these challenges. It concludes with the results of the competition and some ideas and thoughts for future instalments of VerifyThis.     

Knowledge transfer in SVM and neural networks

The paper considers general machine learning models, where knowledge transfer is positioned as the main method to improve their convergence properties. Previous research was focused on mechanisms of knowledge transfer in the context of SVM framework; the paper shows that this mechanism is applicable to neural network framework as well. The paper describes several general approaches for knowledge transfer in both SVM and ANN frameworks and illustrates algorithmic implementations and performance of one of these approaches for several synthetic examples.     

Hierarchical mapping of Northern Eurasian land cover using MODIS data

The Northern Eurasian land mass encompasses a diverse array of land cover types including tundra, boreal forest, wetlands, semi-arid steppe, and agricultural land use. Despite the well-established importance of Northern Eurasia in the global carbon and climate system, the distribution and properties of land cover in this region are not well characterized. To address this knowledge and data gap, a hierarchical mapping approach was developed that encompasses the study area for the Northern Eurasia Earth System Partnership Initiative (NEESPI). The Northern Eurasia Land Cover (NELC) database developed in this study follows the FAO-Land Cover Classification System and provides nested groupings of land cover characteristics, with separate layers for land use, wetlands, and tundra. The database implementation is substantially different from other large-scale land cover datasets that provide maps based on a single set of discrete classes. By providing a database consisting of nested maps and complementary layers, the NELC database provides a flexible framework that allows users to tailor maps to suit their needs. The methods used to create the database combine empirically derived climate–vegetation relationships with results from supervised classifications based on Moderate Resolution Imaging Spectroradiometer (MODIS) data. The hierarchical approach provides an effective framework for integrating climate–vegetation relationships with remote sensing-based classifications, and also allows sources of error to be characterized and attributed to specific levels in the hierarchy. The cross-validated accuracy was 73% for the land cover map and 73% and 91% for the agriculture and wetland classifications, respectively. These results support the use of hierarchical classification and climate–vegetation relationships for mapping land cover at continental scales.     

Robust Control Using Interval Analysis

The synthesis procedure of a control law that guarantees properties of robust stability with respect to structured parameter perturbations is proposed. The solution of the considered problem is based on the Razumikhin's method for functional differential equations generalized for parameter perturbation systems with time delay. The extension is obtained by using interval Lyapunov functions. The robust control law is represented through a solution of an interval matrix Riccati type equation.     

Suboptimal filter for continuous‐time linear systems with unknown parameters

The filtering problem for continuous‐time linear systems with unknown parameters is considered. A new suboptimal filter is herein proposed. It is based on the optimal mean‐square linear combination of the local Kalman filters. In contrast to the optimal weights, the suboptimal weights do not depend on current observations; thus, the proposed filter can easily be implemented in real‐time. Examples demonstrate high accuracy and efficiency of the suboptimal filter. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society