Towards a general class of parametric probability weighting functions

In this study, we present a novel methodology that can be used to generate parametric probability weighting functions, which play an important role in behavioral economics, by making use of the Dombi modifier operator of continuous-valued logic. Namely, we will show that the modifier operator satisfies the requirements for a probability weighting function. Next, we will demonstrate that the application of the modifier operator can be treated as a general approach to create parametric probability weighting functions including the most important ones such as the Prelec and the Ostaszewski, Green and Myerson (Lattimore, Baker and Witte) probability weighting function families. Also, we will show that the asymptotic probability weighting function induced by the inverse of the so-called epsilon function is none other than the Prelec probability weighting function. Furthermore, we will prove that, by using the modifier operator, other probability weighting functions can be generated from the dual generator functions and from transformed generator functions. Finally, we will show how the modifier operator can be used to generate strictly convex (or concave) probability weighting functions and introduce a method for fitting a generated probability weighting function to empirical data.

Super-Resolution Reconstruction of Underwater Image Based on Image Sequence Generative Adversarial Network

Since the underwater image is not clear and difficult to recognize, it is necessary to obtain a clear image with the super-resolution (SR) method to further study underwater images. The obtained images with conventional underwater image super-resolution methods lack detailed information, which results in errors in subsequent recognition and other processes. Therefore, we propose an image sequence generative adversarial network (ISGAN) method for super-resolution based on underwater image sequences collected by multifocus from the same angle, which can obtain more details and improve the resolution of the image. At the same time, a dual generator method is used in order to optimize the network architecture and improve the stability of the generator. The preprocessed images are, respectively, passed through the dual generator, one of which is used as the main generator to generate the SR image of sequence images, and the other is used as the auxiliary generator to prevent the training from crashing or generating redundant details. Experimental results show that the proposed method can be improved on both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) compared to the traditional GAN method in underwater image SR.

Microwave Complex-Ratio-Measuring Circuits

This chapter reviews the microwave complex ratio measuring (MCRM) circuits which are used for complex reflection coefficient measurement. This MCRM circuit is relatively simple and cost-effective. There are various structures for the MCRM circuit, such as multi-probe transmission line circuits, five-port ring circuits, six-port hybrid coupler-based circuits, switched-reflector circuits, dual-generator circuits, and Wheatstone bridge-based circuits. Each structure of the circuits has its own advantages and disadvantages. In this chapter, the MCRM circuit calibration process has been described in detail.