• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.4
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• pp.187-194
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• 2018

The operating system for IoT should have a small memory footprint and provide low power state, real-time, multitasking, various network protocols, and security. Although the Zephyr kernel, an operating system for IoT, released by the Linux Foundation in February 2016, has these features but errors generated by the user code can generate fatal problems in the system because the Zephyr kernel adopts a single-space method that both the user code and kernel code execute in the same space. In this research, we propose a space separation method, which separates kernel space and user space, to solve this problem. The space separation that we propose consists of three modifications in Zephyr kernel. The first is the code separation that kernel code and user code execute in each space while using different stacks. The second is the kernel space protection that generates an exception by using the MPU (Memory Protection Unit) when the user code accesses the kernel space. The third is the SVC based system call that executes the system call using the SVC instruction that generates the exception. In this research, we implemented the space separation in Zephyr v1.8.0 and evaluated safety through abnormal execution of the user code. As the result, the kernel was not crashed by the errors generated by the user code and was normally executed.

• ETRI Journal
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• v.38 no.3
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• pp.510-517
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• 2016

Recent developments in the field of separation of mixed signals into music/voice components have attracted the attention of many researchers. Recently, iterative kernel back-fitting, also known as kernel additive modeling, was proposed to achieve good results for music/voice separation. To obtain minimum mean square error (MMSE) estimates of short-time Fourier transforms of sources, generalized spatial Wiener filtering (GW) is typically used. In this paper, we propose an advanced music/voice separation method that utilizes a generalized weighted ${\beta}$-order MMSE estimation (WbE) based on iterative kernel back-fitting (KBF). In the proposed method, WbE is used for the step of mixed music signal separation, while KBF permits kernel spectrogram model fitting at each iteration. Experimental results show that the proposed method achieves better separation performance than GW and existing Bayesian estimators.

• Journal of the Chungcheong Mathematical Society
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• v.23 no.1
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• pp.59-63
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• 2010

In this paper, we introduce the concept of weakly-ultra-${\alpha}{\psi}$-separation of two sets in a topological space using ${\alpha}{\psi}$-open sets. The ${\alpha}{\psi}$-closure and the ${\alpha}{\psi}$-kernel are defined in terms of this weakly ultra-${\alpha}{\psi}$-separation. We also investigate some of the properties of the ${\alpha}{\psi}$-kernel and the ${\alpha}{\psi}$-closure.

• Journal of the Optical Society of Korea
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• v.12 no.3
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• pp.121-125
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• 2008

We address the problem of Blind Source Separation(BSS) of superimposed signals in situations where one signal has constant or slowly varying intensities at some consecutive locations and at the corresponding locations the other signal has highly varying intensities. Independent Component Analysis(ICA) is a major technique for Blind Source Separation and the existing ICA algorithms fail to estimate the original intensities in the stated situation. We combine the advantages of existing sparse methods and Kernel ICA in our technique, by proposing wavelet packet based sparse decomposition of signals prior to the application of Kernel ICA. Simulations and experimental results illustrate the effectiveness and accuracy of the proposed approach. The approach is general in the way that it can be tailored and applied to a wide range of BSS problems concerning one-dimensional signals and images(two-dimensional signals).

• The Journal of the Acoustical Society of Korea
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• v.36 no.4
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• pp.261-266
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• 2017

In this paper, we propose a vocal and nonvocal separation method which uses a combination of kernel model and LSTM (Long-Short Term Memory) networks. Conventional vocal and nonvocal separation methods estimate the vocal component even in sections where only non-vocal components exist. This causes a problem of the source estimation error. Therefore we combine the existing kernel based separation method with the vocal/nonvocal classification based on LSTM networks in order to overcome the limitation of the existing separation methods. We propose a parallel combined separation algorithm and series combined separation algorithm as combination structures. The experimental results verify that the proposed method achieves better separation performance than the conventional approaches.

• The Journal of the Acoustical Society of Korea
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• v.35 no.1
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• pp.49-54
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• 2016

In this paper, we propose a vocal separation method using weighted ${\beta}$-order minimum mean wquare error estimation (WbE) based on kernel back-fitting algorithm. In spoken speech enhancement, it is well-known that the WbE outperforms the existing Bayesian estimators such as the minimum mean square error (MMSE) of the short-time spectral amplitude (STSA) and the MMSE of the logarithm of the STSA (LSA), in terms of both objective and subjective measures. In the proposed method, WbE is applied to a basic iterative kernel back-fitting algorithm for improving the vocal separation performance from monaural music signal. The experimental results show that the proposed method achieves better separation performance than other existing methods.

• The Journal of the Acoustical Society of Korea
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• v.34 no.3
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• pp.227-233
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• 2015

In this paper, we propose music and voice separation using kernel sptectrogram models backfitting based on log-spectral amplitude estimator. The existing method separates sources based on the estimate of a desired objects by training MSE (Mean Square Error) designed Winer filter. We introduce rather clear music and voice signals with application of log-spectral amplitude estimator, instead of adaptation of MSE which has been treated as an existing method. Experimental results reveal that the proposed method shows higher performance than the existing methods.

• ETRI Journal
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• v.29 no.3
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• pp.270-280
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• 2007

In Linux, real-time tasks are supported by separating real-time task priorities from non-real-time task priorities. However, this separation of priority ranges may not be effective when real-time tasks make the system calls that are taken care of by the kernel threads. Thus, Linux is considered a soft real-time system. Moreover, kernel threads are configured to have static priorities for throughputs. The static assignment of priorities to kernel threads causes trouble for real-time tasks when real-time tasks require kernel threads to be invoked to handle the system calls because kernel threads do not discriminate between real-time and non-real-time tasks. We present a dynamic kernel thread scheduling mechanism with weighted average priority inheritance protocol (PIP), a variation of the PIP. The scheduling algorithm assigns proper priorities to kernel threads at runtime by monitoring the activities of user-level real-time tasks. Experimental results show that the algorithms can greatly improve the unexpected execution latency of real-time tasks.

• Korean Journal of Food Science and Technology
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• v.23 no.5
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• pp.593-598
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• 1991

The effect of emulsion treatment on the separation of quick-cooking rice kernel after drying and the quality of reconstituted quick-cooking rice made of a Japonica variety were investigated. Among the several stages of emulsion treatment tested, immersion of cooked rice before drying was the most effective on the separation index. Immersion condition of 3 min at $30^{\circ}C$ was found to be the most desirable. Emulsion composed of 5% soybean oil and 0.5% sucrose fatty acid ester (HLB : 9.5) was found to be the most effective to yield the separation index of 86%. By applying the above mentioned emulsion, the separation index was improved by 30 compared with untreated one. The quality of the quick-cooking rice manufactured by the emulsion treatment was found to be as good as untreated one.

• The KIPS Transactions:PartB
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• v.13B no.1 s.104
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• pp.27-34
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• 2006

In this study, we propose this new algorithm that generates score function in ICA(Independent Component Analysis) using entropy theory. To generate score function, estimation of probability density function about original signals are certainly necessary and density function should be differentiated. Therefore, we used kernel density estimation method in order to derive differential equation of score function by original signal. After changing formula to convolution form to increase speed of density estimation, we used FFT algorithm that can calculate convolution faster. Proposed score function generation method reduces the errors, it is density difference of recovered signals and originals signals. In the result of computer simulation, we estimate density function more similar to original signals compared with Extended Infomax and Fixed Point ICA in blind source separation problem and get improved performance at the SNR(Signal to Noise Ratio) between recovered signals and original signal.