Designing a Color Filter with High Overall Transmittance for Improving the Color Accuracy of Digital Cameras

Previously improved color accuracy of a given digital camera was achieved by carefully designing the spectral transmittance of a color filter to be placed in front of the camera. Specifically, the filter is designed in a way that the spectral sensitivities of the camera after filtering are approximately linearly related to the color matching functions (or tristimulus values) of the human visual system. To avoid filters that absorbed too much light, the optimization could incorporate a minimum per wavelength transmittance constraint. In this paper, we change the optimization so that the overall filter transmittance is bounded, i.e. we solve for the filter that (for a uniform white light) transmits (say) 50% of the light. Experiments demonstrate that these filters continue to solve the color correction problem (they make cameras much more colorimetric). Significantly, the optimal filters by restraining the average transmittance can deliver a further 10% improvement in terms of color accuracy compared to the prior art of bounding the low transmittance.

Reliability analysis method for redundancy systems based on GO methodology and the universal generation function

In this study, Goal-Oriented (GO) methodology, as an important technique for reliability analysis, is used in multi-state redundancy systems. However, traditional GO operation is usually not sufficiently effective for such multi-state redundancy systems because of the complex computational problem. To solve this problem, an efficient UGF-based GO operation that combines the advantages of GO methodology and the universal generation function (UGF) is proposed. The algorithm can avoid the complex calculation process caused by the algorithm-based GO operation of traditional probability formulas, and can simplify the correction of the shared signal by regarding it as the system’s conditional probability. In addition, the further correction problem in which some dependent shared signals with a common failure are caused by other shared signals is also considered. Some application examples are presented to illustrate the proposed UGF-based GO methodology, as well as its effectiveness and practicability.

Wordform as the main basis for analysis of natural language text

In the article discuss the application aspects of wordforms of natural language text for decision the mistakes correction problem. Discuss the merits and demerits of two known approaches for decision – deterministic and based on probabilities/ Construction principles of natural language corpus described, wich apply in probability approach. Declare conclusion about necessity of complex using these approaches in dependence on properties of texts.

A Do-It-Yourself Hyperspectral Imager Brought to Practice with Open-Source Python

Commercial hyperspectral imagers (HSIs) are expensive and thus unobtainable for large audiences or research groups with low funding. In this study, we used an existing do-it-yourself push-broom HSI design for which we provide software to correct for spectral smile aberration without using an optical laboratory. The software also corrects an aberration which we call tilt. The tilt is specific for the particular imager design used, but correcting it may be beneficial for other similar devices. The tilt and spectral smile were reduced to zero in terms of used metrics. The software artifact is available as an open-source Github repository. We also present improved casing for the imager design, and, for those readers interested in building their own HSI, we provide print-ready and modifiable versions of the 3D-models required in manufacturing the imager. To our best knowledge, solving the spectral smile correction problem without an optical laboratory has not been previously reported. This study re-solved the problem with simpler and cheaper tools than those commonly utilized. We hope that this study will promote easier access to hyperspectral imaging for all audiences regardless of their financial status and availability of an optical laboratory.

Automatic Word Spacing of Korean Using Syllable and Morpheme

In Korean, spacing is very important to understand the readability and context of sentences. In addition, in the case of natural language processing for Korean, if a sentence with an incorrect spacing is used, the structure of the sentence is changed, which affects performance. In the previous study, spacing errors were corrected using n-gram based statistical methods and morphological analyzers, and recently many studies using deep learning have been conducted. In this study, we try to solve the spacing error correction problem using both the syllable-level and morpheme-level. The proposed model uses a structure that combines the convolutional neural network layer that can learn syllable and morphological pattern information in sentences and the bidirectional long short-term memory layer that can learn forward and backward sequence information. When evaluating the performance of the proposed model, the accuracy was evaluated at the syllable-level, and also precision, recall, and f1 score were evaluated at the word-level. As a result of the experiment, it was confirmed that performance was improved from the previous study.

Accurate Magnetic Shell Approximations with Magnetostatic Finite Element Formulations by a Subdomain Approach

This paper presents a subproblem approach with h-conformal magnetostatic finite element formulations for treating the errors of magnetic shell approximation, by replacing volume thin regions by surfaces with interface conditions. These approximations seem to neglect the curvature effects in the vicinity of corners and edges. The process from the surface-to-volume correction problem is presented as a sequence of several subdomains, which can be composed to the full domain, including inductors and thin magnetic regions. Each step of the process will be separately performed on its own subdomain and submesh instead of solving the problem in the full domain. This allows reducing the size of matrix and time computation.

Solving Linear Second-Order Singularly Perturbed Differential Difference Equations via Initial Value Method

In this paper, an initial value method for solving a class of linear second-order singularly perturbed differential difference equation containing mixed shifts is proposed. In doing so, first, the given problem is modified in to an equivalent singularly perturbed problem by approximating the term containing the delay and advance parameters using Taylor series expansion. From the modified problem, two explicit initial value problems which are independent of the perturbation parameter are produced; namely, the reduced problem and the boundary layer correction problem. These problems are then solved analytically and/or numerically, and those solutions are combined to give an approximate solution to the original problem. An error estimate for this method is derived using maximum norm. Several test problems are considered to illustrate the theoretical results. It is observed that the present method approximates the exact solution very well.