• Journal of the Korea Society of Computer and Information
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• v.20 no.4
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• pp.95-102
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• 2015

This paper proposes a simple algorithm for circuit minimization. There are currently two effective heuristics for circuit minimization, namely manual Karnaugh maps and computable Quine-McCluskey algorithm. The latter, however, has a major defect: the runtime and memory required grow $3^n/n$ times for every increase in the number of variables n. The proposed algorithm, however, extracts the prime implicants (PI) that cover minterms of a given Boolean function by deriving an implicants table based on frequency. From a set of the extracted prime implicants, the algorithm then eliminates redundant PIs again based on frequency. The proposed algorithm is therefore capable of minimizing circuits polynomial time when faced with an increase in n. When applied to various 3-variable and 4-variable cases, it has proved to swiftly and accurately obtain the optimal solutions.

• KIISE Transactions on Computing Practices
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• v.22 no.1
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• pp.20-25
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• 2016

With the advent of the era of digital big data, the Hadoop platform has become widely used in various fields. However, the Hadoop MapReduce framework suffers from problems related to the increase of the name node's main memory and map tasks for the processing of large number of small files. In addition, a method for running C++-based tasks in the MapReduce framework is required in order to conjugate GPUs supporting hardware-based data parallelism in the MapReduce framework. Therefore, in this paper, we present a face detection system that generates a sequence file for images to process big data for images in the Hadoop platform. The system also deals with tasks for GPU-based face detection in the MapReduce framework using Hadoop Pipes. We demonstrate a performance increase of around 6.8-fold as compared to a single CPU process.

• Journal of Satellite, Information and Communications
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• v.9 no.4
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• pp.97-103
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• 2014

In order to verify normal operation of satellite OBC(On Board Computer), it is essential that processor monitoring and debugging. So we are using the GRMON of Aeroflex Gaisler. It provides a lot of features for debugging of LEON processor but we can't use that features on the NGSCB(Next Generation Spacecraft Computer Board) except a few things. So the cost-effectiveness is very low. And for hardware fault injection, we are using a method of modify satellite flight software, because we can't modify GRMON. This method can not guarantee normal operation of the satellite flight software. So in this paper we were developed the processor monitoring and fault injection tool for NGSCB.

• Journal of Korea Multimedia Society
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• v.6 no.2
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• pp.216-223
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• 2003

In this paper, we present SCCRS(Speech and Character Combined Recognition System) for speaker /writer independent. on-line multimodal interfaces. In general, it has been known that the CHMM(Continuous Hidden Markov Mode] ) is very useful method for speech recognition and on-line character recognition, respectively. In the proposed method, the same CHMM is applied to both speech and character recognition, so as to construct a combined system. For such a purpose, 115 CHMM having 3 states and 9 transitions are constructed using MLE(Maximum Likelihood Estimation) algorithm. Different features are extracted for speech and character recognition: MFCC(Mel Frequency Cepstrum Coefficient) Is used for speech in the preprocessing, while position parameter is utilized for cursive character At recognition step, the proposed SCCRS employs OPDP (One Pass Dynamic Programming), so as to be a practical combined recognition system. Experimental results show that the recognition rates for voice phoneme, voice word, cursive character grapheme, and cursive character word are 51.65%, 88.6%, 85.3%, and 85.6%, respectively, when not using any language models. It demonstrates the efficiency of the proposed system.

• Journal of Broadcast Engineering
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• v.26 no.5
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• pp.608-620
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• 2021

Recent deep learning-based face super-resolution (FSR) works showed significant performances by utilizing facial prior knowledge such as facial landmark and dictionary that reflects structural or semantic characteristics of the human face. However, most of these methods require additional processing time and memory. To solve this issue, this paper propose an efficient FSR models using knowledge distillation techniques. The intermediate features of teacher network which contains dictionary information based on major face regions are transferred to the student through adversarial multi-scale features distillation. Experimental results show that the proposed model is superior to other SR methods, and its effectiveness compare to teacher model.

• Journal of KIISE:Information Networking
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• v.30 no.2
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• pp.282-292
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• 2003

This paper proposes an efficient data structure for forwarding IPv4 and IPv6 packets at the gigabit speed in backbone routers. The LPM(Longest Prefix Matching) search becomes a bottleneck of routers' performance since the LPM complexity grows in proportion to the forwarding table size and the address length. To speed up the forwarding process, this paper introduces a data structure named BMT(Bit-Map Tie) to minimize the frequent main memory accesses. All the necessary search computations in BMT are done over a small index table stored at cache. To build the small index table from the tie representation of the forwarding table, BMT represents a link pointer to the child node and a node pointer to the corresponding entry in the forwarding table with one bit respectively. To improve the poor performance of the conventional tries when their height becomes higher due to the increase of the address length, BMT adopts a binary search algorithm for determining the appropriate level of tries to start. The simulation experiments show that BMT compacts the IPv4 backbone routers' forwarding table into a small one less than 512-kbyte and achieves the average speed of 250ns/packet on Pentium II processors, which is almost the same performance as the fastest conventional lookup algorithms.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.7
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• pp.761-768
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• 2010

This paper presents an effective method to achieve the wideband waveform for high resolution SAR(Synthetic Aperture Radar) using the frequency multiplication technique. And also this paper analyzes the root causes for the spectral regrowth due to 3rd-order intermodulation in chirp bandwidth expansion scheme using quadrature modulator and frequency multipliers. The amplitude and phase imbalance requirement are defined based on the simulation results in terms of quadrature channel imbalance. This minimizes the degradation of range resolution, peak sidelobe ratio and integrated sidelobe ratio. The wideband chirp generator using the frequency multiplier and memory map scheme was manufactured and the compensation technique was presented to reduce the spectral regrowth of SAR waveform by minimizing the amplitude and phase imbalance. After I and Q channel imbalance adjustment, the carrier level reduces －28.7 dBm to －53.4 dBm. Chirp signal with 150 MHz bandwidth at S-band expands to 600 MHz bandwidth at X-band. The sidelobe levels are reduced by about 8 to 9 dB by compensating the amplitude balance between I and Q channels.

• Journal of KIISE:Databases
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• v.29 no.4
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• pp.321-333
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• 2002

As web-based services are becoming more and more popular, concurrent updates of spatial data should be possible in the web-based environments in order to use the various services. Web-based GIS applications are characterized by large quantity of data providing and these data should be continuously updated according to various user's requirements. Faced with such an enormous data providing system, it is inefficient for a server to do all of the works of updating spatial data requested by clients. Besides, the HTTP protocol used in the web environment is established under the assumption of 'Connectionless'and 'Stateless'. Lots of problems may occur if the scheme of transaction processing based on the LAN environment is directly applied to the web environment. Especially for long transactions of updating spatial data, it is very difficult to control the concurrency among clients and to keep the consistency of the server data. This paper proposes a solution of keeping consistency during updating directly spatial data in the client-side by resolving the Dormancy Region Lock problem caused by the 'Connectionless'and 'Stateless'feature of the HTTP protocol. The RX(Region-eXclusive) lock and the periodically sending of ALIVE_CLIENTi messages can solve this problem. The protocol designed here is verified as effective enough through implementing in the main memory spatial database system, called CyberMap.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.2
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• pp.103-108
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• 2012

Recently, as the prevalence of high-performance mobile devices and the application of the multimedia content are expanded, Super Resolution (SR) technique which reconstructs low resolution images to high resolution images is becoming important. And in the mobile devices, the development of the SR algorithm considering the operation quantity or memory is required because of using the restricted resources. In this paper, we propose a new single frame fast SR technique suitable for mobile devices. In order to prevent color distortion, we change RGB color domain to HSV color domain and process the brightness information V (Value) considering the characteristics of human visual perception. First, the low resolution image is enlarged by the improved fast back projection considering the noise elimination. And at the same time, the reliable edge map is extracted by using the LoG (Laplacian of Gaussian) filtering. Finally, the high definition picture is reconstructed by using the edge information and the improved back projection result. The proposed technique removes effectually the unnatural artefact which is generated during the super resolution restoration, and the edge information which can be lost is amended and emphasized. The experimental results indicate that the proposed algorithm provides better performance than conventional back projection and interpolation methods.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.429-440
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• 2021

With the recent development of deep convolutional neural network learning, deep learning techniques applied to single image super-resolution are showing good results. One of the existing deep learning-based super-resolution techniques is RDN(Residual Dense Network), in which the initial feature information is transmitted to the last layer using residual dense blocks, and subsequent layers are restored using input information of previous layers. However, if all hierarchical features are connected and learned and a large number of residual dense blocks are stacked, despite good performance, a large number of parameters and huge computational load are needed, so it takes a lot of time to learn a network and a slow processing speed, and it is not applicable to a mobile system. In this paper, we use the residual dense structure, which is a continuous memory structure that reuses previous information, and the residual dense channel attention block using the channel attention method that determines the importance according to the feature map of the image. We propose a method that can increase the depth to obtain a large receptive field and maintain a concise model at the same time. As a result of the experiment, the proposed network obtained PSNR as low as 0.205dB on average at 4× magnification compared to RDN, but about 1.8 times faster processing speed, about 10 times less number of parameters and about 1.74 times less computation.