The recent development of deep learning-based generative models has sharply intensified the interest in data synthesis and its applications. Data synthesis takes on an added importance especially for some pattern recognition tasks in which some classes of data are rare and difficult to collect. In an iris dataset, for instance, the minority class samples include images of eyes with glasses, oversized or undersized pupils, misaligned iris locations, and iris occluded or contaminated by eyelids, eyelashes, or lighting reflections. Such class-imbalanced datasets often result in biased classification performance. Generative adversarial networks (GANs) are one of the most promising frameworks that learn to generate synthetic data through a two-player minimax game between a generator and a discriminator. In this paper, we utilized the state-of-the-art conditional Wasserstein generative adversarial network with gradient penalty (CWGAN-GP) for generating the minority class of iris images which saves huge amount of cost of human labors for rare data collection. With our model, the researcher can generate as many iris images of rare cases as they want and it helps to develop any deep learning algorithm whenever large size of dataset is needed.

This paper proposes a method to transform a CORDIC ARMA lattice filter, which is originally realized for signal analysis, into a signal synthesis lattice filter (CORDIC ARMA lattice synthesis filter). In order to perform such a transformation and then obtain the CORDIC ARMA lattice synthesis filter, we must implement the followings with CORDIC: (1) the structure of the altered lattice filter; and (2) an angle calculation module. However, we cannot achieve such an implementation as an extension of the CORDIC ARMA lattice filter algorithm. Therefore, this paper proposes CORDIC implementation schemes for both the structure and module, and then we realize the CORDIC ARMA lattice synthesis filter. By using CORDIC processors, the elementary sections of the CORDIC ARMA lattice synthesis filter are efficiently implemented without any multipliers. Since the obtained signal synthesis lattice filter consists of dedicated CORDIC processors, it keeps the advantage of the CORDIC ARMA lattice filter, that is a simple structure.

This letter proposes a method to transform normalized ARMA lattice filters, which are originally realized for signal analysis, into signal synthesis lattice filters. Although the transformation method has been proposed for normalized ARMA lattice filters with the MA order which is greater than or equal to the AR order, it has not been done when the AR order is greater than the MA order. With the proposed method, once an ARMA lattice filter with the AR order greater than the MA order is realized, then it can be transformed to the signal synthesis filter.