• Journal of Biomedical Engineering Research
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• v.26 no.4
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• pp.199-205
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• 2005

When recording single-unit responses from neural systems, a common problem is the accurate detection of spikes (action potentials) in the presence of competing unwanted (noise) signals. While some sources of noise can be readily dealt with through filtering or 'template subtraction' techniques, other sources present a more difficult problem. In particular, noise components introduced by power supplies, which contain harmonics of the power-line frequency, can be particularly troublesome in that they can mimic the shape of the desired spikes. Thus, standard 'template subtraction' techniques or notch-filtering approaches are not appropriate. In this study, we propose the use of a novel template-subtraction scheme that involves estimating the power-line noise waveform and using cross-correlation techniques to subtract them from the recordings. This technique requires two key steps: (1) cross-correlation analysis of each recorded waveform extracts a robust representation of the power-line noise waveform and (2) a second level of cross-correlation to successfully subtract that representation from each recorded waveform. This paper describes this algorithm and provides examples of its implementation using actual recorded waveforms that are contaminated with these noise signals. An improvement (reduction) in the noise level is reported, as are suggestions for future implementation of this strategy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.7
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• pp.1239-1247
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• 2015

In frequency-division duplex (FDD) massive multiple-input multiple-output (MIMO) system, the computational complexity of downlink channel estimation is proportional to the number of antennas at a base station. Therefore, effective channel estimation techniques may have to be studied. In this paper, novel channel estimation algorithms using adaptive techniques such as Kalman and least mean square (LMS) filters are proposed in a channel model with temporal and spatial correlation.

• Journal of the Semiconductor & Display Technology
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• v.15 no.4
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• pp.97-102
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• 2016

Image stitching is a process of combining two or more images with overlapping area to create a panorama of input images, which is considered as an active research area in computer vision, especially in the field of augmented reality with 360 degree images. Image stitching techniques can be categorized into two general approaches: direct and feature based techniques. Direct techniques compare all the pixel intensities of the images with each other, while feature based approaches aim to determine a relationship between the images through distinct features extracted from the images. This paper proposes a novel image stitching method based on feature pixels with approximated clustering filter. When the features are extracted from input images, we calculate a meaning of the minutiae, and apply an effective feature extraction algorithm to improve the processing time. With the evaluation of the results, the proposed method is corresponding accurate and effective, compared to the previous approaches.

• ETRI Journal
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• v.37 no.3
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• pp.491-501
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• 2015

Spectrum sensing is an elementary function in cognitive radio designed to monitor the existence of a primary user (PU). To achieve a high rate of detection, most techniques rely on knowledge of prior spectrum patterns, with a trade-off between high computational complexity and long sensing time. On the other hand, blind techniques ignore pattern matching processes to reduce processing time, but their accuracy degrades greatly at low signal-to-noise ratios. To achieve both a high rate of detection and short sensing time, we propose fast spectrum sensing with coordinate system (FSC) - a novel technique that decomposes a spectrum with high complexity into a new coordinate system of salient features and that uses these features in its PU detection process. Not only is the space of a buffer that is used to store information about a PU reduced, but also the sensing process is fast. The performance of FSC is evaluated according to its accuracy and sensing time against six other well-known conventional techniques through a wireless microphone signal based on the IEEE 802.22 standard. FSC gives the best performance overall.

• Journal of the Korea Society of Computer and Information
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• v.22 no.5
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• pp.1-9
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• 2017

Recently, flash memory has been in a great demand from embedded system sectors for storage devices. However, program/erase (P/E) cycles per block are limited on flash memory. For the limited number of P/E cycles, many wear leveling techniques are studied. They prolonged the life time of flash memory using information tables. As one of the techniques, block erase table (BET) method using a bit array table was studied for embedded devices. However, it has a disadvantage in that performance of wear leveling is sharply low, when the consumption of memory is reduced. To solve this problem, we propose a novel wear leveling technique using Sampling-based Block Erase Table (SBET). SBET relates one bit of the bit array table to each block by using exclusive OR operation with round robin function. Accordingly, SBET enhances accuracy of cold block information and can prevent to decrease the performance of wear leveling. In our experiment, SBET prolongs life time of flash memory by up to 88%, compared with previous techniques which use a bit array table.

• Bulletin of the Korean Chemical Society
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• v.34 no.5
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• pp.1333-1338
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• 2013

Chitosan-based nanoparticles (CSNP) were prepared through ionic cross-linking and gelation of chitosan (CS) by tripolyphosphate (TPP). CS properties such as molecular weight, and preparation conditions were screened and the resulting nanoparticles were examined by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The obtained particles were consistently spherical with an overall diameter of approximately $107{\pm}20$ nm. They were successfully used as a carrier for Zidovudine, an anti-human immunodeficiency virus (HIV) which, to our knowledge, is novel. The encapsulation ability, loading capacity, and controlled release behavior for these CSNP was evaluated. Results indicated that their intrinsic properties were strongly affected by properties inherent to CS such as molecular weight, and by the preparation condition, such as cross-linking density, which depends on the concentration of the cross-linker. In vitro release tests for the entrapped zidovudine showed that the CNNP provided a continuous release that can last upwards 20 h.

• Journal of Electrical Engineering and Technology
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• v.5 no.2
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• pp.209-219
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• 2010

In this paper, a novel dynamic available transfer capability (DATC) has been computed for real time applications using three different intelligent techniques viz. i) back propagation algorithm (BPA), ii) radial basis function (RBF), and iii) adaptive neuro fuzzy inference system (ANFIS) for the first time. The conventional method of DATC is tedious and time consuming. DATC is concerned with calculating the maximum increase in point to point transfer such that the transient response remains stable and viable. The ATC information is to be continuously updated in real time and made available to market participants through an internet based Open Access Same time Information System (OASIS). The independent system operator (ISO) evaluates the transaction in real time on the basis of DATC information. The dynamic contingency screening method [1] has been utilized and critical contingencies are selected for the computation of DATC using the energy function based potential energy boundary surface (PEBS) method. The PEBS based DATC has been utilized to generate patterns for the intelligent techniques. The three different intelligent methods are tested on New England 68-bus 16 machine and 39-bus 10 machine systems and results are compared with the conventional PEBS method.

• Industrial Engineering and Management Systems
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• v.15 no.1
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• pp.110-121
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• 2016

With the growing interest in skincare and maintenance, there are increasing numbers of studies on the classification of skin type and the factors influencing each type. This study presents a novel methodology by using data mining, for the determination of the relationships between skin type, lifestyle, and patterns of cosmetic utilization. Eight skin-specific factors, which are moisture, sebum in U-zone (both cheeks), sebum in T-zone (forehead, nose, and chin), pore, melanin, wrinkle, acne, hemoglobin, were measured in 1,246 subjects living in South Korea, in conjunction with a questionnaire survey analyzing their lifestyles and pattern of cosmetic utilization. Using various multivariate statistical methods and data mining techniques, we classified the skin types based on the skin-specific values, determined the relationship between skin type and lifestyle, and accordingly sorted the subjects into clusters. Logistic regression analysis revealed gender-related differences in the skin; therefore, separate analyses were performed for males and females. Using the Gaussian Mixture Modeling (GMM) technique, we classified the subjects based on skin type (two male and four female). Using the ANOVA and decision tree techniques, we attempted to characterize the relationship between each skin type and the lifestyles of the subjects. Menstruation, eating habits, stress, and smoking were identified as the major factors affecting the skin.

• Applied Microscopy
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• v.46 no.4
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• pp.217-226
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• 2016

Monolayer hexagonal boron nitride (h-BN) is a phenomenal two-dimensional material; most of its physical properties rival those of graphene because of their structural similarities. This intriguing material has thus spurred scientists and researchers to develop novel synthetic methods to attain scalability for enabling its practical utilization. When probing the growth behaviors and structural characteristics of h-BN, the use of appropriate characterization techniques is important. In this review, we detail the use of scanning and transmission electron microscopies to investigate the atomic configurations of monolayer and bilayer h-BN grown via chemical vapor deposition. These advanced microscopy techniques have been demonstrated to provide intimate insights to the atomic structures of h-BN, which can be interpreted directly or indirectly using known growth mechanisms and existing theoretical calculations. This review provides a collective understanding of the structural characteristics and defects of synthetic h-BN films and facilitates a better perspective toward the development of new and improved synthesis techniques.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.127-136
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• 2021

Real-time human recognition is a challenging task, as the images are captured in an unconstrained environment with different poses, makeups, and styles. This limitation is addressed by generating several facial images with poses, makeup, and styles with a single reference image of a person using Generative Adversarial Networks (GAN). In this paper, we propose deep learning-based human recognition using integration of GAN and Spatial Domain Techniques. A novel concept of human recognition based on face depiction approach by generating several dissimilar face images from single reference face image using Domain Transfer Generative Adversarial Networks (DT-GAN) combined with feature extraction techniques such as Local Binary Pattern (LBP) and Histogram is deliberated. The Euclidean Distance (ED) is used in the matching section for comparison of features to test the performance of the method. A database of millions of people with a single reference face image per person, instead of multiple reference face images, is created and saved on the centralized server, which helps to reduce memory load on the centralized server. It is noticed that the recognition accuracy is 100% for smaller size datasets and a little less accuracy for larger size datasets and also, results are compared with present methods to show the superiority of proposed method.