Bo-Wen Wu

Optical design of automotive headlight system incorporating digital micromirror device

In recent years, the popular adaptive front-lighting automobile headlight system has become a main emphasis of research that manufacturers will continue to focus great efforts on in the future. In this research we propose a new integral optical design for an automotive headlight system with an advanced light-emitting diode and digital micromirror device (DMD). Traditionally, automobile headlights have all been designed as a low beam light module, whereas the high beam light module still requires using accessory lamps. In anticipation of this new concept of integral optical design, we have researched and designed a single optical system with high and low beam capabilities. To switch on and off the beams, a DMD is typically used. Because DMDs have the capability of redirecting incident light into a specific angle, they also determine the shape of the high or low light beam in order to match the standard of headlight illumination. With collocation of the multicurvature reflection lens design, a DMD can control the light energy distribution and thereby reinforce the resolution of the light beam.

Optimizing chromatic aberration calibration using a novel genetic algorithm

Advances in digitalized image optics has increased the importance of chromatic aberration. The axial and lateral chromatic aberrations of an optical lens depends on the choice of optical glass. Based on statistics from glass companies worldwide, more than 300 optical glasses have been developed for commercial purposes. However, the complexity of optical systems makes it extremely difficult to obtain the right solution to eliminate small chromatic aberration. Even the damped least-squares technique, which is a ray-tracing-based method, is limited owing to its inability to identify an enhanced optical system configuration. Alternatively, this study instead attempts to eliminate even negligible axial and lateral colour aberration by using algorithms involving the theories of geometric optics in triplet lens, binary and real encoding, multiple dynamic crossover and random gene mutation techniques.

Prediction of the Thermal Imaging Minimum Resolvable (Circle) Temperature Difference with Neural Network Application

Thermal imaging is an important technology in both national defense and the private sector. An advantage of thermal imaging is its ability to be deployed while fully engaged in duties, not limited by weather or the brightness of indoor or outdoor conditions. However, in an outdoor environment, many factors, including atmospheric decay, target shape, great distance, fog, temperature out of range and diffraction limits can lead to bad image formation, which directly affects the accuracy of object recognition. The visual characteristics of the human eye mean that it has a much better capacity for picture recognition under normal conditions than artificial intelligence does. However, conditions of interference significantly reduce this capacity for picture recognition for instance, fatigue impairs human eyesight. Hence, psychological and physiological factors can affect the result when the human eye is adopted to measure MRTD (minimum resolvable temperature difference) and MRCTD (minimum resolvable circle temperature difference). This study explores thermal imaging recognition, and presents a method for effectively choosing the characteristic values and processing the images fully. Neural network technology is successfully applied to recognize thermal imaging and predict MRTD and MRCTD (Appendix A), exceeding thermal imaging recognition under fatigue and the limits of the human eye.

Eliminating lateral color aberration of a high-resolution digital projection lens using a novel genetic algorithm

Advances in digital image optics have increased the significance of lateral color aberration because it is easily seen in the projected area. The choice of optical glass plays a role in the elimination of lateral color aberration. Current optical software still has difficulty in finding the optimal combination of optical glasses for twelve or more elements in a projection lens, the choice being among at least 300 optical glasses that have been developed. Even the modern damped least squares, a ray-tracing-based method, is limited, owing to its inability to identify an enhanced optical system configuration. As an alternative, this research proposes a new optimization process by using algorithms involving the theory of geometric optics in a projector lens, real encoding, multiple dynamic crossover, and random gene mutation techniques. Results and conclusions show that attempts to achieve negligible axial and lateral color aberration are successful.

Study on human vision model of the multi-parameter correction factor

This paper is a study, based on the limitation of human vision characteristic, of image recognition through the take account of correction factor. Those aspects that have been explored focus on human eye modelings, including human vision recognition characteristics and various mathematical modeling verify. By using Modulation Transfer Function (MTF) curve evaluation recognition capability on the studied models, an optimum recognition model most compatible to human eye physiology is summed up.

Applications of neural networks in human shape visual perception.

Advances in optical and electronic technology can immensely reduce noise in images and greatly enhance human visual recognition. However, it is still difficult for human eyes to identify low-resolution thermal images, due to the limits imposed by psychological and physiological factors. In addition, changes in monitor brightness and lens resolution may also interfere with visual recognition abilities. To overcome these limitations, we devised a suitable and effective recognition method which may help the military in revising the shape parameters of long-range targets. The modulation transfer function was used as a basis to extend the visual characteristics of the human visual model and a new model was produced through the incorporation of new shape parameters. The new human visual model was next used in combination with a backpropagation neural network for better recognition of low-resolution thermal images. The new model was then tested in experiments and the results showed that the accuracy rate of recognition steadily rose by over 95%.

Optimization of optics with micro diffractive optical element via a hybrid Taguchi genetic algorithm

This paper proposes a new method for optimization optics with a diffractive optical element (DOE) via a Hybrid Taguchi Genetic Algorithm. A Diffractive Optical Element, based the theory of wave phase difference, takes advantage of the negative Abbe number which might significantly eliminate the axial chromatic aberrations of optics. Following the advanced technology applied to the micro lens and etching process, precisely-made micro DOEs can now be manufactured in large numbers. However, traditional least damping square has its limitations for the optimization of axial and chromatic aberrations with DOE. In this research, we adopted the genetic algorithm (GA) and incorporated the steady Taguchi method into GA. Combining the two methods produced a new hybrid Taguchi-genetic algorithm (HTGA). Suitable glass combinations and DOE positions were selected to minimize both axial and lateral chromatic aberration in the optical system. This new method carries out the task of eliminating both axial and lateral chromatic aberration, unlike DOE optimization by LDS, which works for axial aberration only and with less efficiency. Experiments show that the surface position of the DOE could be determined first; in addition, regardless of whether chromatic aberration was axial or longitudinal, issues concerning the optical lenss chromatic aberration could be significantly reduced, compared to results from the traditional least damping square (LDS) method.

Primary Chromatic Aberration Elimination via Optimization Work with Genetic Algorithm

Chromatic Aberration plays a part in modern optical systems, especially in digitalized and smart optical systems. Much effort has been devoted to eliminating specific chromatic aberration in order to match the demand for advanced digitalized optical products. Basically, the elimination of axial chromatic and lateral color aberration of an optical lens and system depends on the selection of optical glass. According to reports from glass companies all over the world, the number of various newly developed optical glasses in the market exceeds three hundred. However, due to the complexity of a practical optical system, optical designers have so far had difficulty in finding the right solution to eliminate small axial and lateral chromatic aberration except by the Damped Least Squares (DLS) method, which is limited in so far as the DLS method has not yet managed to find a better optical system configuration. In the present research, genetic algorithms are used to replace traditional DLS so as to eliminate axial and lateral chromatic, by combining the theories of geometric optics in Tessar type lenses and a technique involving Binary/Real Encoding, Multiple Dynamic Crossover and Random Gene Mutation to find a much better configuration for optical glasses. By implementing the algorithms outlined in this paper, satisfactory results can be achieved in eliminating axial and lateral color aberration.

Optical computing for application to reducing the thickness of high-power-composite lenses

With the adoption of polycarbonate lens material for injection molding of greater accuracy and at lower costs, polycarbonate has become very suitable for mass production of more economical products, such as diving goggles. However, with increasing requirements for visual quality improvement, lenses need to have not only refractive function but also thickness and spherical aberration, which are gradually being taken more seriously. For a high-power-composite lens, meanwhile, the thickness cannot be substantially reduced, and there is also the issue of severe spherical aberration at the lens edges. In order to increase the added value of the product without changing the material, the present research applied the eye model and Taguchi experiment method, combined with design optimization for hyperbolic-aspherical lens, to significantly reduce the lens thickness by more than 30%, outperforming the average thickness reduction in general aspherical lens. The spherical aberration at the lens edges was also reduced effectively during the optimization process for the nonspherical lens. Prototypes made by super-finishing machines were among the results of the experiment. This new application can be used in making a large amount of injection molds to substantially increase the economic value of the product.

New Application of Selection of Customized Lens Fitting in Optometry Services

Judging from existing technologies, using human eye measurement devices to obtain individual relevant optical feature parameters is no longer difficult. These parameters allow us to construct an eye model for individual eyeballs. Such customized eye models are used to select appropriate glass material for each lens fitting to achieve a relatively minimal degree of spherical aberration. However, due to the variety of glass materials, the selection process is time consuming and not cost-effective. Therefore, it is necessary to design a set of feasible methods to select appropriate glass material quickly. In this study, then, we used the CODE V macro programming language to develop a new set of methods for glass selection. Glass with a smaller degree of spherical aberration and lower cost was selected via automatic algorithm to be applied in customized lens fitting. This method was validated as feasible and practical based on experimental results, and this method can greatly increase the degree of customization in lens fitting.