• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.5 no.5
• /
• pp.409-412
• /
• 2004

This paper shows that the proposed Turbo TCM(Turbo Trellis Coded Modulation) has a good performance with a little complexity of decoder. The encoder structure, which is connected with Turbo Codes, is the proposed modulation technique for an efficient bandwidth, This method is used symbol by symbol MAP decoder of iteration similar to binary Turbo Codes in the receiver. The result shows that the BER performance according to iteration is improved about 2,5dB at $BER=10^{-2}$ compared to Turbo Codes with Gray mapping.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.40 no.6
• /
• pp.84-89
• /
• 2003

In this paper, we real-time implemented to the TMS320C5416 the vocoder of variable bit rate applied the SOLA-B algorithm by Henja to the ITU-T G.729A vocoder of 8kbps transmission rate. This proposed method using the SOLA-B algorithm is that it is reduced the duration of the speech in encoding and is played at the speed of normal by extending the duration of the speech in decoding. At this time, we bandied that the interval of cross correlation function if skipped every 3 sample for decreasing the computational amount of SOLA-B algorithm. The real-time implemented vocoder of C.729A and SOLA-B algorithm is represented the complexity of maximum that is 10.2MIPS in encoder and 2.8MIPS in decoder of 8kbps transmission rate. Also, it is represented the complexity of maximum that is 18.5MIPS in encoder and 13.1MIPS in decoder of 6kbps, it is 18.5MIPS in encoder and 13.1MIPS in decoder of 4kbps. The used memory is about program ROM 9.7kwords, table ROM 4.5kwords, RAM 5.1 kwords. The waveform of output is showed by the result of C simulator and Bit Exact. Also, for evaluation of speech quality of the vocoder of real-time implemented variable bit rate, it is estimated the MOS score of 3.69 in 4kbps.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.42 no.4 s.304
• /
• pp.167-176
• /
• 2005

This paper proposes a new power minimization technique for the frame-based multimedia signal processing. The derivation of the technique is based on the newly proposed microscopic DVS(Dynamic Voltage Scaling) method, where, the operating frequency and the supply voltage levels are dynamically controlled according to the processing requirement for each frame of multimedia data. The multimedia signal processing algorithms are also redesigned and optimized to maximize the power saving efficiency of the microscopic DVS technology. The characterization of the mean/variance distribution of the processing load in the frame-based multimedia signal processing provides the major basis not only for the optimized application of the microscopic DVS technology but also for the optimization of the multimedia algorithms. The power saying efficiency of the proposed DVS approach is experimentally tested with the algorithms of MPEG-2 video decoder and MPEG-2 AAC audio encoder on the ARM9 RISC processor. The experimental results with the diverse MPEG-2 video and audio files show The average power saving efficiencies of 50$\%$ and 30$\%$, respectively. The results also agree very well with those of the analytic derivations.

• Journal of Industrial Convergence
• /
• v.20 no.12
• /
• pp.195-202
• /
• 2022

Underground Geospatial Information Map Management System(UGIMMS) integrates various underground facilities in the underground space into 3D mesh data, and supports to check the 3D image and location of the underground facilities in the mobile app. However, there is a problem that it takes a long time to run in the app because various underground facilities can exist in some areas executed by the app and can be seen layer by layer. In this paper, we propose a deep learning-based K-means vertex clustering algorithm as a method to reduce the execution time in the app by reducing the size of the data by reducing the number of vertices in the 3D mesh data within the range that does not cause a problem in visibility. First, our proposed method obtains refined vertex feature information through a deep learning encoder-decoder based model. And second, the method was simplified by grouping similar vertices through K-means vertex clustering using feature information. As a result of the experiment, when the vertices of various underground facilities were reduced by 30% with the proposed method, the 3D image model was slightly deformed, but there was no missing part, so there was no problem in checking it in the app.

• Information Systems Review
• /
• v.25 no.1
• /
• pp.129-143
• /
• 2023

In the era of big data, the field of artificial intelligence is showing remarkable growth, and in particular, the image classification learning methods by deep learning are becoming an important area. Various studies have been actively conducted to further improve the performance of CNNs, which have been widely used in image classification, among which a representative method is the Convolutional Recurrent Neural Network (CRNN) algorithm. The CRNN algorithm consists of a combination of CNN for image classification and RNNs for recognizing time series elements. However, since the inputs used in the RNN area of CRNN are the flatten values extracted by applying the convolution and pooling technique to the image, pixel values in the same phase in the image appear in different order. And this makes it difficult to properly learn the sequence of arrangements in the image intended by the RNN. Therefore, this study aims to improve image classification performance by proposing a novel hybrid method of CNN and RNN applying the concepts of encoder and decoder. In this study, the effectiveness of the new hybrid method was verified through various experiments. This study has academic implications in that it broadens the applicability of encoder and decoder concepts, and the proposed method has advantages in terms of model learning time and infrastructure construction costs as it does not significantly increase complexity compared to conventional hybrid methods. In addition, this study has practical implications in that it presents the possibility of improving the quality of services provided in various fields that require accurate image classification.

• Annual Conference of KIPS
• /
• 2023.11a
• /
• pp.30-33
• /
• 2023

The recent scholarly focus has been directed towards the expeditious and accurate detection of salient objects, a task that poses considerable challenges for resource-limited edge devices due to the high computational demands of existing models. To mitigate this issue, some contemporary research has favored inference speed at the expense of accuracy. In an effort to reconcile the intrinsic trade-off between accuracy and computational efficiency, we present novel model for salient object detection. Our model incorporate group-wise attentive module within the decoder of the encoder-decoder framework, with the aim of minimizing computational overhead while preserving detection accuracy. Additionally, the proposed architectural design employs attention mechanisms to generate boundary information and semantic features pertinent to the salient objects. Through various experimentation across five distinct datasets, we have empirically substantiated that our proposed models achieve performance metrics comparable to those of computationally intensive state-of-the-art models, yet with a marked reduction in computational complexity.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.9 no.3
• /
• pp.120-126
• /
• 1984

In this paper, there is presented the novel encoder circuit design method in the realization of exponential adaption process on the delta modulation coding of speech signals. The digital implementation has been adapted for the illustration of above, especially using a rate multiplier end a double integration circuit. The use of a double integration of the local decoder included in the ADM encoder in prove the undesirable characteristics which the low switching speed of the ratemultiplier couses the SQNR to decreuse, and the SQNR of the decoding signal by above realization is relatively uniformed in wide range of signal levels. The validity of the above design is verified by laboratory experiments.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2011.07a
• /
• pp.582-584
• /
• 2011

This paper presents a Channel DiVision (CDV) scheme for transform-domain distributed video coding. In the proposed system, we employ the symmetric motion estimation to generate high quality side information for Wyner-Ziv (WZ) frames. Also, the decoder estimates the distortion of the side information, which is used to classify the transmitting channels for WZ frames. Each channel has a different expected noise. Then, the encoder allocates an appropriate number noise. Then, the encoder allocates an appropriate number present rate-distortion performance results and comparisons with existing state-of-the-art algorithms and H.264.

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.113.3-113
• /
• 2001

In this paper, we have designed the VHDL module of DS/SS QPSK wireless modem processor for digital data communication. The spread spectrum method is used for modern processor, because this method guarantees good frequency efficiency and higher security. Also, it guarantees good performance in digital communication system under multi-path interferences. The differential encoder and decoder are used for simple circuit composition in the signal detection. For the synchronization of receiver, matched filter and power detector are used. And the IF modulation/demodulation of QPSK method is used in the digital level. The transmitter of VHDL modem processor consists of differential encoder, PN code generator, and QPSK ...

• Journal of Korea Multimedia Society
• /
• v.23 no.2
• /
• pp.216-226
• /
• 2020

In this paper, a stacked convolutional autoencoder model is proposed in order to classify Alzheimer's disease with high accuracy in PET/CT images. The proposed model makes use of the latent space representation - which is also called the bottleneck, of the encoder-decoder architecture: The input image is sent through the pipeline and the encoder part, using stacked convolutional filters, extracts the most useful information. This information is in the bottleneck, which then uses Softmax classification operation to classify between Alzheimer's disease, Mild Cognitive Impairment, and Normal Control. Using the data from Dong-A University, the model performs classification in detecting Alzheimer's disease up to 98.54% accuracy.