• IEMEK Journal of Embedded Systems and Applications
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• v.7 no.5
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• pp.285-290
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• 2012

In this paper, we propose a fast encoding algorithm for scalable video encoding without compromising coding performance. Through analysis on multiple motion estimation processes performed at the enhancement layer, we show redundant motion estimations and suggest the condition under which the redundant ones can efficiently be determined without additional memory. Based on the condition, the redundant motion estimation processes are excluded in the proposed algorithm. Simulation results show that the proposed algorithm is faster than the conventional fast encoding method without performance degradation and additional memory.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.28 no.4
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• pp.154-163
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• 2020

The pilot's aeronautical decision-making during the flying greatly affects flight safety, and the importance of situational awareness has been greatly emphasized as a prerequisite for making the right decision. This is the reason why more research and interests are needed to help students entering the pilot training program develop excellent situational awareness from the initial stage of training. Situational awareness is closely related to long-term memory activities in human information processing, and pedagogy and cognitive psychology have emphasized the encoding techniques as an effective long-term memory method. This study was conducted to confirm whether pilot students' using the encoding techniques to learn flight education materials in the early stage of their training at domestic universities has a positive effect on improving their situational awareness.

• IEMEK Journal of Embedded Systems and Applications
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• v.5 no.2
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• pp.77-83
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• 2010

In this paper, we propose an efficient memory architecture for coeff_token encoding in H.264/AVC standard. The VLCTs used to encode the coeff_token syntax element are implemented with the memory. In general, the size of memory must be reduced because it affects the cost and operation speed of the system. Based on the analysis for the codewords in VLCTs, new memory architecture is designed in this paper. The proposed memory architecture results in about 24% memory saving, compared to the conventional memory architecture.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.5
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• pp.124-130
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• 2017

Character encoding is the method used to represent characters or symbols on a computer, and there are many encoding detection software tools. For the widely used encoding detection software"uchardet", the accuracy of encoding detection of unmodified normal text document is 91.39%, but the accuracy of language detection is only 32.09%. Also, if a text document is encrypted by substitution, the accuracy of encoding detection is 3.55% and the accuracy of language detection is 0.06%. Therefore, in this paper, we propose encoding and language detection of text document using the deep learning algorithm called LSTM(Long Short-Term Memory). The results of LSTM are better than encoding detection software"uchardet". The accuracy of encoding detection of normal text document using the LSTM is 99.89% and the accuracy of language detection is 99.92%. Also, if a text document is encrypted by substitution, the accuracy of encoding detection is 99.26%, the accuracy of language detection is 99.77%.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.1
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• pp.79-86
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• 1996

In this paper we propose an architecture for VLC(Variable Length Coder) and VLD(Variable Length Decoder) which is simple with respect to implementation point and efficient in memory. We implemented encoding and decoding circuit where we need only 7-bit address memory space for 114 MPEG1 DCT coefficients and employed minimal number of flip-flops and logics for an architecture to integrate a shift register for serial-to-parallel or parallel-to-serial conversion of the data in code mapping ROM. We obtained 50Mbps operating speed in both encoding and decoding process as the result of simulation using 0.80.8${\mu}m$ CMOS standard cells.

• The Korean Journal of Nuclear Medicine
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• v.34 no.1
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• pp.10-21
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• 2000

Purpose: Episodic memory is described as an 'autobiographical' memory responsible for storing a record of the events in our lives. We performed functional brain activation study using ${H_2}^{15}O$ PET to reveal the neural basis of the encoding and the retrieval of episodic memory in human normal volunteers. Materials and Methods: Four repeated ${H_2}^{15}O$ PET scans with two reference and two activation tasks were performed on 6 normal volunteers to activate brain areas engaged in encoding and retrieval with verbal materials. Images from the same subject were spatially registered and normalized using linear and nonlinear transformation. Using the means and variances for every condition which were adjusted with analysis of covariance, t-statistic analysis were performed voxel-wise. Results: Encoding of episodic memory activated the opercular and triangular parts of left inferior frontal gyrus, right prefrontal cortex, medial frontal area, cingulate gyrus, posterior middle and inferior temporal gyri, and cerebellum, and both primary visual and visual association areas. Retrieval of episodic memory activated the triangular part of left inferior frontal gyrus and inferior temporal gyrus, right prefrontal cortex and medial temporal area, and both cerebellum and primary visual and visual association areas. The activations in the opercular part of left inferior frontal gyrus and the right prefrontal cortex meant the essential role of these areas in the encoding and retrieval of episodic memory. Conclusion: We could localize the neural basis of the encoding and retrieval of episodic memory using ${H_2}^{15}O$ PET, which was partly consistent with the hypothesis of hemispheric encoding/retrieval asymmetry.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.12B
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• pp.2424-2429
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• 1999

In this paper, we present a memory efficient implementation method of channel encoder using convolutional encoding and interleaving. In conventional method, two separate RAMs must be used for the channel encoder: one RAM for storing frame data and another RAM for interleaving. In our method, without using interleaving RAM, we only use two small RAMs for buffering input frame data. We can process convolutional encoding and interleaving concurrently by using the two RAMs. There are several advantages when applying channel encoder designed using this method to several digital mobile telecommunications : the reduction of memory size ranging 33 % - 60 %, simplified procedure of receiving frame data, and resultant timing margin gained by the simplified procedure.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.832-854
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• 2019

Action recognition has been studied in computer vision field for years. We present an effective approach to recognize actions using a dimension reduction method, which is applied as a crucial step to reduce the dimensionality of feature descriptors after extracting features. We propose to use sparse matrix and randomized kd-tree to modify it and then propose modified Local Fisher Discriminant Analysis (mLFDA) method which greatly reduces the required memory and accelerate the standard Local Fisher Discriminant Analysis. For feature encoding, we propose a useful encoding method called mix encoding which combines Fisher vector encoding and locality-constrained linear coding to get the final video representations. In order to add more meaningful features to the process of action recognition, the convolutional neural network is utilized and combined with mix encoding to produce the deep network feature. Experimental results show that our algorithm is a competitive method on KTH dataset, HMDB51 dataset and UCF101 dataset when combining all these methods.

• Sleep Medicine and Psychophysiology
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• v.4 no.1
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• pp.1-14
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• 1997

This paper reviewed models to explain memory and neuropsychological tests to assess memory. Memory was explained in cognitive and neuroanatomical perspectives, Cognitive model describes memory as structure and process. In structure model, memory is divided into three systems: sensory memory, short-term memory(working memory), and long-term memory. In process model, there are broadly three categories of memory process: encoding, storage, and retrieval. Memory process work in memory structure. There are two prominent models of the neuroanatomy of memory, derived from the work of Mishkin and Appenzeller and that of Squire and Zola-Morgan. These two models are the most useful for the clinician in part because they take into account the connections between the limbic and frontal cortical regions. The major difference between the two models concerns the role of the amygdala in memory processess. Mishkin and his colleagues believe that the amygdala plays a significant role while Squire and his colleagues do not. The most popular and widely used tests of memory ability such as WMS-R, AVLT, CVLT, HVLT. RBMT, CFT, and BVRT-R, were reviewed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5A
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• pp.545-553
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• 2004

Expansion of network environment and multimedia demand universality of application service as HDTV, etc. During these processes, it is essential to process multimedia in real time in the wireless communication system based on mobile phone network and in the wire communication system due to fiber cable and xDSL. So, in this Paper the optimal memory allocation algorithm combines the merit of huffman encoding which is superior in simultaneous decoding ability and lempel-ziv that is distinguished in execution of compress is proposed to improve the channel transmission rate and processing speed in the compressing procedure and is verified in the entropy encoder of HDTV. Because the entropy encoder system using proposed optimal memory allocation algorithm has memory saturation prevention we confirms that the compressing ratio for moving pictures is superior than Huffman encoding and LZW.