Generating robust and flexible job shop schedules using genetic algorithms

The problem of finding robust or flexible solutions for scheduling problems is of utmost importance for real-world applications as they operate in dynamic environments. In such environments, it is often necessary to reschedule an existing plan due to failures (e.g., machine breakdowns, sickness of employees, deliveries getting delayed, etc.). Thus, a robust or flexible solution may be more valuable than an optimal solution that does not allow easy modifications. This paper considers the issue of robust and flexible solutions for job shop scheduling problems. A robustness measure is defined and its properties are investigated. Through experiments, it is shown that using a genetic algorithm it is possible to find robust and flexible schedules with a low makespan. These schedules are demonstrated to perform significantly better in rescheduling after a breakdown than ordinary schedules. The rescheduling performance of the schedules generated by minimizing the robustness measure is compared with the performance of another robust scheduling method taken from literature, and found to outperform this method in many cases.     

Rendering Caustics on Non-Lambertian Surfaces

This paper presents a new technique for rendering caustics on non-Lambertian surfaces. The method is based on an extension of the photon map which removes previous restrictions limiting the usage to Lambertian surfaces. We add information about the incoming direction to the photons and this allows us to combine the photon map with arbitrary reflectance functions. By using a cone-filter we improve the quality of the radiance estimate in particular at discontinuities. Furthermore we introduce balancing of the photon map which not only reduces the memory requirements but also significantly reduces the rendering time. We have used the method to render caustics on surfaces with reflectance functions varying from Lambertian to glossy specular.     

Learning a decision maker's utility function from (possibly) inconsistent behavior

When modeling a decision problem using the influence diagram framework, the quantitative part rests on two principal components: probabilities for representing the decision maker's uncertainty about the domain and utilities for representing preferences. Over the last decade, several methods have been developed for learning the probabilities from a database. However, methods for learning the utilities have only received limited attention in the computer science community.

A promising approach for learning a decision maker's utility function is to take outset in the decision maker's observed behavioral patterns, and then find a utility function which (together with a domain model) can explain this behavior. That is, it is assumed that decision maker's preferences are reflected in the behavior. Standard learning algorithms also assume that the decision maker is behavioral consistent, i.e., given a model of the decision problem, there exists a utility function which can account for all the observed behavior. Unfortunately, this assumption is rarely valid in real-world decision problems, and in these situations existing learning methods may only identify a trivial utility function. In this paper we relax this consistency assumption, and propose two algorithms for learning a decision maker's utility function from possibly inconsistent behavior; inconsistent behavior is interpreted as random deviations from an underlying (true) utility function. The main difference between the two algorithms is that the first facilitates a form of batch learning whereas the second focuses on adaptation and is particularly well-suited for scenarios where the DM's preferences change over time. Empirical results demonstrate the tractability of the algorithms, and they also show that the algorithms converge toward the true utility function for even very small sets of observations.     

Computer-based determination of size and shape in living cells

Measurement of cell volume in living epithelial cells has become an important technique in studies of membrane transport processes that function in cell volume regulation. Planimetry of video images of optical sections enables the measurement of the cross sectional area of each section. Cell volume is calculated from the measured area of each section and the known focus displacements. In the past the measurement of cross section area has been done by manual positioning of a cursor superimposed on the video image. Each experiment generates approximately 200 images in which two or more cells may be analysed. We have developed a computer-based method that uses one image as a template, and allows automated area determination of successive images by template matching and digital image processing. This new method is comparable to the older method in speed and accuracy, but requires much less effort from the experimenter.     

1.3 V supply voltage 38 GHz static frequency divider

An ultra-low supply voltage and low power dissipation fully static frequency InP SHBT divider operating at up to 38 GHz is reported. The fully differential parallel current switched configuration of D-latch maintains the speed advantages of CML circuits while allowing full functionality at a very low supply voltage. The frequency divider operates at up to 38 GHz at a single-ended input power of 0 dBm. The power dissipation of the toggled D-flip-flop is 8 mW at a power supply voltage of 1.3 V. The authors believe this is the lowest supply voltage for static frequency dividers around this frequency in any technology. This low power configuration is suitable for any digital integrated circuit.     

Shot-noise limited,supercontinuum-based optical coherence tomography

We present the first demonstration of shot-noise limited supercontinuum-based spectral domain optical coherence tomography (SD-OCT) with an axial resolution of ...     

Highly birefringent index-guiding photonic crystal fibers

Photonic crystal fibers (PCFs) offer new possibilities of realizing highly birefringent fibers due to a higher intrinsic index contrast compared to conventional fibers. In this letter, we analyze theoretically the levels of birefringence that can be expected using relatively simple PCF designs. While extremely high degrees of birefringence may be obtained for the fibers, we demonstrate that careful design with respect to multimode behavior must be performed. We further discuss the cutoff properties of birefringent PCFs and present experimental results in agreement with theoretical predictions on both single- and multimode behavior and on levels of birefringence