• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.5
• /
• pp.1035-1040
• /
• 2009

We propose a new encoder/decoders based on an tune able wavelength converter(TWC) and an arrayed waveguide grating(AWG) router for large capacity optical CDMA networks. The proposed encoder/decoder treats codewords of wavelength/time 2-D code simultaneously using the dynamic code allocation property of the TWC and the cyclic property of the AWG router, and multiple subscribers can share the encoder/decoder in networks. Feasibility of the structure of the proposed encoder/decoder for dynamic code allocation is tested through simulations using two wavelength/time 2-D codes, which are the generalized multi-wavelength prime code(GMWPC) and the generalized multi-wavelength Reed-Solomon code(GMWRSC). Test results show that the proposed encoder/decoder can increase the channel efficiency not only by increasing the number of simultaneous users without any multiple-access interference but by using a relatively short length CDMA codes.

• Journal of the Korean Institute of Telematics and Electronics C
• /
• v.34C no.11
• /
• pp.39-46
• /
• 1997

In this paper, we propose an area-efficient architecture of a reed-solomon (RS) decoder/encoder for digital VCRs. The new architecture of the decoder/encoder targeted to reduce the circit size and decoding latency has the following two features. First, area-efficeincy has been significantly improved by sharing a functional block for encoding, modified syndrome computation, and erasure locator polynomial evaluation. Second, modified euclid's algorithms has been implemented by using a new architecture. Experimental results have showed that the decoder/encoder designed by using the proposed method has been implemented with 25% smaller sie over straight forware implementation based on the conventional method [1] and the decoding latency has been reduced.

• Journal of Korea Multimedia Society
• /
• v.21 no.8
• /
• pp.849-857
• /
• 2018

In this paper, we propose an automatic composition method using time series embedding of RNN Auto-Encoder. RNN Auto-Encoder can learn existing songs and can compose new songs from the trained RNN decoder. If one song is fully trained in the RNN Auto-Encoder, the song is embedded into the vector values of RNN nodes in the Auto-Encoder. If we train a lot of songs and apply a specific vector to the decoder of Auto-Encoder, then we can obtain a new song that combines the features of trained multiple songs according to the given vector. From extensive experiments we could find that our method worked well and generated various songs by selecting of the composition vectors.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.31 no.1C
• /
• pp.8-13
• /
• 2006

A new RS(Reed Solomon) Decoder design method, using Galois Subfield GF($2^4$) Multiplier, is described. The Decoder is designed using Normalized error position stored ROM. Here New Inverse Calculator in GF($2^8$) is designed, which is simpler and faster than the classical GF($2^8$) direct inverse calculator, using the Galois Subfield GF($2^4$) Arithmatic operator.

• Journal of Positioning, Navigation, and Timing
• /
• v.8 no.4
• /
• pp.193-200
• /
• 2019

The auto-encoder network which is a good candidate to handle the modeling of the signal strength attenuation is designed for denoising and compensating the distortion of the received data. It provides a non-linear mapping function by iteratively learning the encoder and the decoder. The encoder is the non-linear mapping function, and the decoder demands accurate data reconstruction from the representation generated by the encoder. In addition, the adaptive network width which supports the automatic generation of new hidden nodes and pruning of inconsequential nodes is also implemented in the proposed algorithm for increasing the efficiency of the algorithm. Simulation results show that the proposed method can improve the neural network training surface to achieve the highest possible accuracy of the signal modeling compared with the conventional modeling method.

• Proceedings of the IEEK Conference
• /
• 2003.07e
• /
• pp.2351-2354
• /
• 2003

A new transform coder for arbitrarily shaped image segments is proposed. In the encoder, a block-based DCT is applied to the resulting image block after shifting pixels within the image segment to block border and padding the mean value of the pixels to empty region. For reducing the transmission bit rate, the transform coefficients located in padded region are truncated and only the remaining transform coefficients are transmitted to the decoder. In the decoder, the transform coefficients truncated in the encoder are recovered using received transform coefficients and a block-based inverse DCT is performed.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.7
• /
• pp.1706-1725
• /
• 2024

The unique U-shaped structure of U-Net network makes it achieve good performance in image segmentation. This network is a lightweight network with a small number of parameters for small image segmentation datasets. However, when the medical image to be segmented contains a lot of detailed information, the segmentation results cannot fully meet the actual requirements. In order to achieve higher accuracy of medical image segmentation, a novel improved U-Net network architecture called multi-scale encoder-decoder U-Net+ (MEDU-Net+) is proposed in this paper. We design the GoogLeNet for achieving more information at the encoder of the proposed MEDU-Net+, and present the multi-scale feature extraction for fusing semantic information of different scales in the encoder and decoder. Meanwhile, we also introduce the layer-by-layer skip connection to connect the information of each layer, so that there is no need to encode the last layer and return the information. The proposed MEDU-Net+ divides the unknown depth network into each part of deconvolution layer to replace the direct connection of the encoder and decoder in U-Net. In addition, a new combined loss function is proposed to extract more edge information by combining the advantages of the generalized dice and the focal loss functions. Finally, we validate our proposed MEDU-Net+ MEDU-Net+ and other classic medical image segmentation networks on three medical image datasets. The experimental results show that our proposed MEDU-Net+ has prominent superior performance compared with other medical image segmentation networks.

• Journal of the Institute of Electronics Engineers of Korea TC
• /
• v.42 no.11
• /
• pp.13-20
• /
• 2005

For Modern Consumer and Communication Elecronic Devices, Always Error Protecting HW and SW is used. The Core is RS(Reed Solomon) Codec in Galois Field GF($2^8$). Here New 2 to 3 Symbol RS Decoder Design and Encoder design Method using Normalized error position Value is described. Examples are given to show the methods are working well.

• Smart Media Journal
• /
• v.2 no.2
• /
• pp.39-43
• /
• 2013

In this paper, we suggest a new technique for WPC parity-check matrix (H-matrix) generation and a corresponding decoding process. The key idea is to construct WPC H-matrix by using a convolutional encoder. It is easy to have many different coderates from a mother code with convolutional codes. However, it is difficult to have many different coderates with LDPC codes. Constructing LDPC Hmatrix based on a convolutional code can easily bring the advantage of convolutional codes to have different coderates. Moreover, both LDPC and convolutional decoding algorithms can be applied altogether in the decoding part. This process prevents the performance degradation of short-length WPC code.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.45 no.1
• /
• pp.11-21
• /
• 2008

In conventional video coding, the complexity of encoder is much higher than that of decoder. However, as more needs arises for extremely simple encoder in environments having constrained energy such as sensor network, much investigation has been carried out for eliminating motion prediction/compensation claiming most complexity and energy in encoder. The Wyner-Ziv coding, one of the representative schemes for the problem, reconstructs video at decoder by correcting noise on side information using channel coding technique such as turbo code. Since the encoder generates only parity bits without performing any type of processes extracting correlation information between frames, it has an extremely simple structure. However, turbo decoding errors occur in noisy side information. When there are high-motion or occlusion between frames, more turbo decoding errors appear in reconstructed frame and look like Salt & Pepper noise. This severely deteriorates subjective video quality even though such noise rarely occurs. In this paper, we propose a computationally extremely light encoder based on symbol-level Wyner-Ziv coding technique and a new corresponding decoder which, based on a decision whether a pixel has error or not, applies median filter selectively in order to minimize loss of texture detail from filtering. The proposed method claims extremely low encoder complexity and shows improvements both in subjective quality and PSNR. Our experiments have verified average PSNR gain of up to 0.8dB.