• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.9A
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• pp.920-927
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• 2006

There are three critical considerations in developing a PCM telemetry encoder to be installed in an artillery projectile. The first is the performance consideration, such as sampling rate and data transmission rate. The second is the size consideration due to the severely limited installation space in an artillery projectile and the last is the power consumption consideration due to limitations of the munition's power supply. To meet these three considerations, the best alternative is a one-chip solution. Using a commercially available TMS320F2812 DSP, we have implemented a 30-channel PCM telemetry encoder to process randomized data frames, composed of 16-channel analog data, 14-channel digital data and 2-frame synchronization channels per data frame at 10Mbps transmitting baud rate.

• Journal of Advanced Navigation Technology
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• v.24 no.4
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• pp.305-313
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• 2020

Telemetry system is a system that transmits status information data acquired from the aircraft to the ground station. PCM encoder needs memory to store channel information in order to generate a frame format using the acquired data. Generally, telemetry systems in large aircraft require much larger memory for the increased acquisition channel information due to the increased sensors and subsystems. However, they have difficulty to store all channel information in limited memory. In this paper, we suggests and implements an advanced PCM encoder that can efficiently manage memory by minimizing duplicated channel information. This novel PCM encoder allocates duplicated channel information to memory only once. And, sub commutation channels having different information for each minor frame are allocated to the memory by multiples of sub commutation channels. Finally, the suggested PCM encoder was proved by simulation that composed channels of various measurement cycles.

• Journal of Advanced Navigation Technology
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• v.23 no.2
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• pp.186-193
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• 2019

This paper presents and implements a simple programmable PCM encoder structure uisng the commutation method. In the telemetry system, information is required to assign each data to the channel in order to generate a frame format the data acpuired from the sensor. In this case, when the number of state information is large or the data type is various, there is a necessity to input a large amount of information to each channel. However, the more the number of channels and data, the more probability the error will occur. Therefore, in this paper, the channel information is created using the program. And PCM encoder was implemented to store channel information in ROM. The proposed PCM encoder architecture reduces the likelihood of errors. And it can improve the development speed. The validity of proposed structure is proved by simulation.

• 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.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.3
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• pp.491-501
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• 2019

In side-channel analysis, which exploit physical leakage from a cryptographic device, deep learning based attack has been significantly interested in recent years. However, most of the state-of-the-art methods have been focused on classifying side-channel information in a profiled scenario where attackers can obtain label of training data. In this paper, we propose a new method based on deep learning to improve non-profiling side-channel attack such as Differential Power Analysis and Correlation Power Analysis. The proposed method is a signal preprocessing technique that reduces the noise in a trace by modifying Auto-Encoder framework to the context of side-channel analysis. Previous work on Denoising Auto-Encoder was trained through randomly added noise by an attacker. In this paper, the proposed model trains Auto-Encoder through the noise from real data using the noise-reduced-label. Also, the proposed method permits to perform non-profiled attack by training only a single neural network. We validate the performance of the noise reduction of the proposed method on real traces collected from ChipWhisperer board. We demonstrate that the proposed method outperforms classic preprocessing methods such as Principal Component Analysis and Linear Discriminant Analysis.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.565-568
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• 2005

Even though there has been a great deal of research and development for the compression techniques such as H.263, MPEG-1, and MPEG-2 in DVR systems, an efficient scheme for storing, accessing, and managing the huge amount of video data from multi-channel cameras needs to be developed. In this paper, we describe an implementation of MPEG-4 encoder for PC-based real-time multi-channel DVR systems.

• ETRI Journal
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• v.32 no.4
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• pp.493-502
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• 2010

The operation time of an encoder is one of the critical implementation issues for satisfying the timing requirements of Long Term Evolution (LTE) systems because the encoder is based on binary operations. In this paper, we propose a design and implementation of a latency efficient encoder for LTE systems. By virtue of 8-bit parallel processing of the cyclic redundancy checking attachment, code block (CB) segmentation, and a parallel processor, we are able to construct engines for turbo codings and rate matchings of each CB in a parallel fashion. Experimental results illustrate that although the total area and clock period of the proposed scheme are 19% and 6% larger than those of a conventional method based on a serial scheme, respectively, our parallel structure decreases the latency by about 32% to 65% compared with a serial structure. In particular, our approach is more latency efficient when the encoder processes a number of CBs. In addition, we apply the proposed scheme to a real system based on LTE, so that the timing requirement for ACK/NACK transmission is met by employing the encoder based on the parallel structure.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.6 no.4
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• pp.3-38
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• 1996

The mobile station shall convolutionally encode the data transmitted on the reverse traffic channel and the access channel prior to interleaving. Code symbols output from the convolutional encoder are repeated before being interleaved except the 9600 bps data rate. All the symbols are then interleaved, 64-ary orthogonal modulation, direct-sequence spreading, quadrature spreading, baseband filtering and QPSK transmission. The sync, paging, and forward traffic channel except the pilot channel in the forward CDMA channel are convolutionally encoded, block interleaved, spread with Walsh function at a fixed chip rate of 1.2288 Mcps to provide orthogonal channelization among all code channels. Following the spreading operation, the I and Q impulses are applied to respective baseband filters. After that, these impulses shall be transmitted by QPSK. Authentication in the CDMA system is the process for confirming the identity of the mobile station by exchanging information between a mobile station and the base station. The authentication scheme is to generate a 18-bit hash code from the 152-bit message length appended with 24-bit or 40-bit padding. Several techniques are proposed for the authentication data computation in this paper. To protect sensitive subscriber information, it shall be required enciphering ceratin fields of selected traffic channel signaling messages. The message encryption can be accomplished in two ways, i.e., external encryption and internal encryption.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1689-1703
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• 2020

When data is transmitted over an unreliable channel, the error of the data packet may result in serious degradation. The multiple description coding (MDC) can solve this problem and save transmission costs. In this paper, we propose a deep multiple description coding network (MDCN) to realize efficient image compression. Firstly, our network framework is based on convolutional auto-encoder (CAE), which include multiple description encoder network (MDEN) and multiple description decoder network (MDDN). Secondly, in order to obtain high-quality reconstructed images at low bit rates, the encoding network and decoding network are integrated into an end-to-end compression framework. Thirdly, the multiple description decoder network includes side decoder network and central decoder network. When the decoder receives only one of the two multiple description code streams, side decoder network is used to obtain side reconstructed image of acceptable quality. When two descriptions are received, the high quality reconstructed image is obtained. In addition, instead of quantization with additive uniform noise, and SSIM loss and distance loss combine to train multiple description encoder networks to ensure that they can share structural information. Experimental results show that the proposed framework performs better than traditional multiple description coding methods.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.379-392
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• 2024

With the rapid increase in data, saving storage space and improving the efficiency of data transmission have become critical issues, making the research on the efficiency of data compression technologies increasingly important. Lossless algorithms can precisely restore original data but have limited compression ratios, whereas lossy algorithms provide higher compression rates at the expense of some data loss. There has been active research in data compression using deep learning-based algorithms, especially the autoencoder model. This study proposes a new side-channel analysis data compressor utilizing autoencoders. This compressor achieves higher compression rates than Deflate while maintaining the characteristics of side-channel data. The encoder, using locally connected layers, effectively preserves the temporal characteristics of side-channel data, and the decoder maintains fast decompression times with a multi-layer perceptron. Through correlation power analysis, the proposed compressor has been proven to compress data without losing the characteristics of side-channel data.