Myunghee Jung

Microfabrication and characteristics of low-power high-performance magnetic thin-film transformers

We investigated low-power (1.5-W), solenoid-type magnetic thin-film transformers with a Ni/sub 81/Fe/sub 19/ core for a 5-MHz drive dc-dc converter application. We used 2-/spl mu/m-thick Ni/sub 81/Fe/sub 19/ films covered by 20-/spl mu/m-thick copper coils as the core materials. The transformers fabricated in this study range in size from 3.08 mm /spl times/ 25.5 mm to 6.15 mm /spl times/ 12.75 mm. The design of the transformers was optimized by utilizing the conventional equations, a Maxwell three-dimensional field simulator, and parameters obtained from the magnetic properties of NiFe magnetic core materials. The 6.15 mm /spl times/ 12.75 mm transformers exhibit inductance (L) of 0.83 /spl mu/H, dc resistance of 2.3 /spl Omega/, coupling factor of 0.91, and gain of -1 dB at 5 MHz. These results are comparable to those reported in recent literature for various types of transformers sandwiched by more complex magnetic core materials. The calculated data obtained by using the well-known equation of L and the equivalent circuit agreed well with the high-frequency data for L and gain of the transformers.

Characterization of Al-As codoped p-type ZnO films by magnetron cosputtering deposition

We report the preparation of Al–As codoped p-type ZnO films by rf magnetron cosputtering deposition. The p-type conductivity of the films was revealed by Hall measurements, x-ray photoelectron spectroscopy (XPS), and photoluminescence measurements after being annealed in O2. It was observed by XPS that Al content increased with increasing AlAs target power from 80 to 160 W and reached a maximum value at an AlAs target power of 160 W. Hole concentration decreased with increasing Al content. With increasing AlAs target power greater than 160 W, the samples exhibit increases in As and O contents and decreases in Al and Zn contents, which contribute to the increase in hole concentration. A high hole concentration of 2.354×1020 cm−3, a low resistivity of 2.122×10−2 Ω cm, and a Hall mobility of 0.13 cm2/V s for the films with high As content of 16.59% were obtained. XPS has also been used to address the unresolved issues related to the p-type formation mechanism of As-doped ZnO, supporting that the acceptor is ...

Multiresolution approach for texture segmentation using MRF models

Texture is an important characteristic of an image. Texture segmentation/classification have been widely utilized in remote sensing application such as scene classification and feature extraction since remotely sensed data include abundant texture characteristics. Markov Random Field (MRF) models have been successfully employed in texture modeling since they exploit contextual behavior. In this study, the method for characterization and segmentation of textured image in multiresolution MRF frame is proposed. The discrete wavelet transform (DWT) is utilized as a multiresolution technique to enable utilization of feature information at multiple scales and provide computational efficiency. Here, the sub-images of a textured image decomposed with the DWT are modeled with MRFs. Texture segmentation is successively carried out with MAP criterion from the coarse resolution to a finer resolution refining segmentation map. This method can be applied to change detection and feature extraction based on remotely sensed data.

Development of High-Performance Solenoid-Type RF Chip Inductors Utilizing Low-Loss Al2O3 Core Materials

In this study, small size, high-performance, solenoid-type RF chip inductors utilizing low-loss Al2O3 core materials were investigated. Copper coils with a diameter of 40 µm were used and the dimensions of the RF chip inductors fabricated were 2.1×1.5×1.0 mm3. The high-frequency characteristics of the inductance, quality factor, and impedance of the developed inductors were measured using a RF impedance/material analyzer (HP4291B with a HP16193A test fixture). The developed inductors have a self-resonant frequency of 1 to 3.5 GHz and exhibit an inductance of 22 to 150 nH. The inductors have a quality factor of 60 to 90 over the frequency ranges of 500 MHz to 1.5 GHz. The capacitance–frequency data is well described in the experimental data. It was suggested that reducing the number of turns of the coil and increasing the space between the coils are important measures for improving the high-frequency limit of the RF chip inductors.

NDVI-based land-cover change detection using harmonic analysis

This study presents a normalized difference vegetation index (NDVI)-based land-cover change detection method based on harmonic analysis. Multi-temporal NDVI data show seasonal variation characteristics in the time domain. A harmonic model represents the characterization of the temporal variability in a data set over a local region corresponding to a pixel through its harmonic components. In this research, annual land-cover change detection is performed by tracking the temporal dynamics through analysing harmonic components. A simple but effective noise reduction process is also proposed to provide the necessary high-quality data stream for the multi-temporal NDVI analysis based on the statistics of the observed oscillations. The proposed algorithm was tested and evaluated with the multi-temporal Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI time series of the MYD13Q1, 16 day L3 global 250 m SIN grid (v005) VI data set. The results indicate that the proposed algorithm provides a computationally inexpensive automatic method to monitor vegetation conditions and long-term land-cover change over large regions. The method described here is particularly useful for monitoring changes in well-established deciduous forests with developed canopies.

Detection of Harmful Content Using Multilevel Verification in Visual Sensor Data

Various types of harmful content such as adult images and video clips have been increasingly distributed through wired or wireless visual sensor-based networks. In this paper, we propose a new algorithm for extracting human nipple regions, representing the harmfulness of the images, by using a multilevel verification technique in visual sensor-based image data. The proposed algorithm first detects human face regions including eyes and lips from input images. The method then generates a nipple map utilizing representative features that female nipples have and detects candidate nipple regions by applying the generated nipple map to segmented skin regions followed by morphological operations. Subsequently, the proposed method selects real nipple areas after eliminating non-nipple regions at multiple levels by applying geometrical information and an average color filter to the detected candidate nipple regions. Experimental results show that the proposed method can robustly extract female nipple regions in various types of input images captured in environments where certain constraints are not imposed on.

An adaptive camera-selection algorithm to acquire higher-quality images

Various types of three-dimensional (3D) cameras have been used to analyze real-world objects or environments effectively. However, because most existing 3D cameras capture scenes by statically using one type of camera, there may be a limit to the quality of the captured images. Therefore, in this paper, we build a hybrid camera system that combines passive triangulation (PT)- and active triangulation (AT)-based cameras and suggest a new mechanism of estimating accurate 3D depth by adaptively switching between the two types of cameras depending on the complexity of the environment. The suggested method initially uses initial input images to extract brightness and texture, which are major features representing the current state of the surrounding environment. The method subsequently generates a set of rules that dynamically select the PT- or AT-based camera, whichever can operate more suitably in the current environment, by analyzing the two extracted features. In experimental results, we demonstrate that the proposed adaptive camera-selection approach can be applied to extract 3D depth reliably with reasonable performance in terms of accuracy and time.

Personalized e-learning process using effective assessment and feedback

The amount and quality of feedback provided to the learner has an impact on the learning process. Personalized feedback is particularly important to the effective delivery of e-learning courses. E-learning delivery methods such as web-based instruction are required to overcome the barriers to traditional-type classroom feedback. Thereby, the feed-back for a learner should consist not only of adaptive information about his errors and performance, but also of adaptive hints for the improvement of his solution. Furthermore, the tutoring component is required to individually motivate the learners. In this paper, an adaptive assessment and feedback process model for personalized e-learning is proposed and developed for the purpose of maximizing the effects of learning.

Robust detection of mosaic regions in visual image data

Due to the explosive increase in the production and sharing of digital visual media such as photos, animations, films and video clips, the need to intentionally or unintentionally create mosaic blocks to effectively cover user-designated regions within images has grown. In this paper, a method using boundary characteristics to effectively detect the grid-type mosaic blocks existing within the input image is proposed. Initially, the Canny edge is detected from the input image. Then, the boundary characteristics of mosaic blocks are extracted from the detected edges, and the candidate regions which may contain mosaic blocks are detected. After this stage, geometric characteristics are used to eliminate non-mosaic blocks and select actual mosaic blocks. In the experiment performed in this paper, it was observed that the method using boundary characteristics achieved more robust detection of the grid-type mosaic blocks from various input images than other existing methods. The mosaic-detection method proposed in this paper is expected to be valuable for applications in various fields.

Exposed body component-based harmful image detection in ubiquitous sensor data

At present, users are able to obtain a variety of image contents easily via visual sensor networks. Under this circumstance, the detection of adult or harmful images has become a highly important issue. This paper proposes an algorithm for analyzing images in the visual sensor network and robustly detecting the human nipple and navel regions, which can be used for harmful image detection. From a color image, the proposed algorithm first detects skin color areas and locates human facial regions, including the eyes and lips. The algorithm then creates a color-based nipple map for detecting candidate regions of nipples from the extracted skin regions. If a detected candidate nipple region is located within the detected facial region, the candidate nipple region is removed because it has been incorrectly detected. Subsequently, by utilizing geometrical features and the mean color filter of the nipples, the method filters the candidate regions of nipples in two steps and detects the actual nipple regions. Lastly, this method exploits the structural relation between the detected nipple regions and the navel areas to be detected, and uses the edge and saturation images to detect the navel region robustly. Experimental results reveal that the proposed adult image algorithm detects the human nipple and navel regions from various types of images more reliably than existing algorithms. Since the proposed approach uses the human structure information of the nipple and navel regions to filter the candidate navel regions effectively, it removes many incorrectly detected regions, which results in high accuracy. We expect that the proposed nipple and navel region detection algorithm will be successfully employed to detect and block harmful images in various real applications.