• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.11
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• pp.9-14
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• 2016

In the holographic data storage system, the data can be stored more than one bit per pixel and the storage capacity and transmission rate can be increased. In this paper, we proposed a sparse-ON pixel 4/6 balanced-modulation code that the code rate is 1.33 (bit/pixel) with uniform page density. Even though the performance of the proposed sparse-ON pixel 4/6 balanced-code is similar to 2/3 and 6/9 modulation codes, it can increase the storage capacity more than these modulation codes and also store more pages in a volume by reducing the rate of ON pixels to mitigate IPI (inter-page interference).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.10
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• pp.833-837
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• 2013

Since holographic data storages read and write information on a volume and the information is processed per page, it has the advantage of high recording density and data transfer rate. However, there are two major drawbacks like 2-dimensional intersymbol interference and interpage interference as the density between pixels increases. Furthermore, a bright page that contains many ON pixels influences the reliable detection of the neighboring pages, which causes the less number of pages stored in the storage volume. We propose a sparse-ON pixel two-dimensional modulation code with the code rate 6/8 for increasing the number of pages stored in the volume. The proposed code is compared to conventional 6/8 balanced code, and it shows similar or a little bit better performance than that of the balanced code. Therefore, the proposed code can increase the recording capacity without loss of the performance.

• Journal of the Korea Computer Graphics Society
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• v.22 no.3
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• pp.21-29
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• 2016

Cell segmentation is an important but time-consuming and laborious task in biological image analysis. An automated, robust, and fast method is required to overcome such burdensome processes. These needs are, however, challenging due to various cell shapes, intensity, and incomplete boundaries. A precise cell segmentation will allow to making a pathological diagnosis of tissue samples. A vast body of literature exists on cell segmentation in microscopy images [1]. The majority of existing work is based on input images and predefined feature models only - for example, using a deformable model to extract edge boundaries in the image. Only a handful of recent methods employ data-driven approaches, such as supervised learning. In this paper, we propose a novel data-driven cell segmentation algorithm for bright-field microscopy images. The proposed method minimizes an energy formula defined by two dictionaries - one is for input images and the other is for their manual segmentation results - and a common sparse code, which aims to find the pixel-level classification by deploying the learned dictionaries on new images. In contrast to deformable models, we do not need to know a prior knowledge of objects. We also employed convolutional sparse coding and Alternating Direction of Multiplier Method (ADMM) for fast dictionary learning and energy minimization. Unlike an existing method [1], our method trains both dictionaries concurrently, and is implemented using the GPU device for faster performance.