• Journal of Biomedical Engineering Research
• /
• v.16 no.3
• /
• pp.271-278
• /
• 1995

An ECG data compression algorithM using max-min slope update is proposed and a real time 3 channel ECG transmission system is implemented using the proposed algorithm. In order to effectively compress ECG data, we compare a threshold value with the max-min slope difference (MMSD) which is updated at each sample values. If this MMSD value is smaller than the threshold value, then the data is compressed. Conversely, when the MMSD value is larger than threshold value, the data is transmitted after storing the value and the length between the data which is beyond previous threshold level. As a result, it can accurately compress both the region of QRS, P, and T wave that has fast-changing and the region of the base line that slope is changing slow. Therefore, it Is possible to enhance the compression rate and the percent roms difference. In addition, because of the simplicity, this algorithm is more suitable for real-time implementation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.11 no.4
• /
• pp.421-429
• /
• 2018

While IoT device sensing data or financial transaction data is transmitted in real time, huge data traffic is generated in processing it. This huge data can be effectively compressed or transmitted using universal code, which is a real-time lossless compressor. In this paper, we propose our BL-beta code, which is newly developed universal code for compressing stock trading data, which the maximum range of measured values is difficult to predict and is generated within a relatively constant range over a very short period of time. For compressing real-time stock trading data, Compared with the fixed length bit transmission, the compression efficiency is at least 49.5% higher than that of the fixed length bit transmission, and the compression transmission performance is 16.6% better than the Exponential Golomb code.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.53 no.11
• /
• pp.581-588
• /
• 2004

Power qualify monitoring system is devoted to more concern than before, because the innovation of industrial technology needs more accurate instruments and more advanced power quality. This paper was studied on using data of the protective relay by Power Quality Monitor. This paper was proposed the wave storage condition and monitoring clauses of the protective relay as a power quality monitoring device. The protective relay will have problem to save data for PQM analysis because the protective relay memory is limited. Therefore this paper was proposed new a data compression of data got from the protective relay. This method is wave compression comparison algorithm using the DFT. The compression rate is higher than any other established method. This method can be real time storage. This algorithm is verified using the comparison among other compression rate and proved by Real Time Digital Simulator (RTDS).

• Journal of the Korea Society of Computer and Information
• /
• v.12 no.3
• /
• pp.85-93
• /
• 2007

This paper describes a compression scheme for volumetric video data(3D space X 1D time) there each frame of the volume is decompressed and rendered in real-time. Since even one frame size of volume is very large, runtime decompression can be a bottleneck for real-time playback of time-varying volume data. To increase the run-time decompression speed and compression ratio, we decompose the volume into small blocks and only update significantly changing blocks. The results show that our compression scheme compromises decompression speed and image quality well enough for interactive time-varying visualization.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2020.07a
• /
• pp.545-546
• /
• 2020

스파크는 데이터 분석을 위한 오픈소스 툴이다. 스파크에서는 실시간 데이터 분석을 위하여 스파크 스트리밍이라는 기술을 제공한다. 스파크 스트리밍은 데이터 소스가 분석서버로 데이터 스트림을 전송한다. 이때 전송하는 데이터의 크기가 커질 경우 전송과정에서 지연이 발생할 수 있다. 제안하는 기법은 전송하고자 하는 데이터의 크기가 클 때 허프만 인코딩을 이용하여 데이터를 압축하여 전송시키므로 지연시간을 줄일 수 있다.

• The KIPS Transactions:PartC
• /
• v.8C no.5
• /
• pp.525-534
• /
• 2001

Generally, compression and encryption procedures are performed independently in lossless image compression and encryption. When compression is followed by encryption, the compressed-stream should have the property of randomness because its entropy is decreased during the compression. However, when full data is compressed using image compression methods and then encrypted by encryption algorithms, real-time processing is unrealistic due to the time delay involved. In this paper, we propose to combine compression and encryption to reduce the overall processing time. It is method decomposing gray-scale image by means of quadtree compression algorithms and encrypting the structural part. Moreover, the lossless compression ratio can be increased using a transform that provides an decorrelated image and homogeneous region, and the encryption security can be improved using a reconstruction of the unencrypted quadtree data at each level. We confirmed the increased compression ratio, improved encryption security, and real-time processing by using computer simulations.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.1
• /
• pp.94-103
• /
• 2022

As a large amount of data generated by sensors or devices at the manufacturing site is transmitted to the server or client, problems arise in network processing time delay and storage resource cost increase. To solve this problem, considering the manufacturing site, where real-time responsiveness and non-disruptive processes are essential, QRC (Quotient Remainder Compression) and BL_beta compression algorithms that enable real-time and lossless compression were applied to actual manufacturing site sensor data for the first time. As a result of the experiment, BL_beta had a higher compression rate than QRC. As a result of experimenting with the same data by slightly adjusting the data size of QRC, the compression rate of the QRC algorithm with the adjusted data size was 35.48% and 20.3% higher than the existing QRC and BL_beta compression algorithms.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.5
• /
• pp.311-321
• /
• 2003

In an emergency telemedicine system such as the High-quality Multimedia based Real-time Emergency Telemedicine(HMRET) service, it is very important to examine the status of the patient continuously using the multimedia data including the biological signals(ECG, BP, Respiration, $SpO_2)$ of the patient. In order to transmit these data real time through the communication means which have the limited transmission capacity, it is also necessary to compress the biological data besides other multimedia data. For this purpose, we investigate and compare the ECG compression techniques in the time domain and in the wavelet transform domain, and present an effective lossless compression method of the biological signals using PEG Huffman table for an emergency telemedicine system. And, for the HMRET service, we developed the lossless compression and reconstruction program or the biological signals in MSVC++ 6.0 using DPCM method and JPEG Huffman table, and tested in an internet environment.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.11
• /
• pp.5476-5496
• /
• 2016

Energy efficiency of resource-constrained wireless sensor networks is critical in applications such as real-time monitoring/surveillance. To improve the energy efficiency and reduce the energy consumption, the time series data can be compressed before transmission. However, most of the compression algorithms for time series data were developed only for single variate scenarios, while in practice there are often multiple sensor nodes in one application and the collected data is actually multivariate time series. In this paper, we propose to compress the time series data by the Lasso (least absolute shrinkage and selection operator) approximation. We show that, our approach can be naturally extended for compressing the multivariate time series data. Our extension is novel since it constructs an optimal projection of the original multivariates where the best energy efficiency can be realized. The two algorithms are named by ULasso (Univariate Lasso) and MLasso (Multivariate Lasso), for which we also provide practical guidance for parameter selection. Finally, empirically evaluation is implemented with several publicly available real-world data sets from different application domains. We quantify the algorithm performance by measuring the approximation error, compression ratio, and computation complexity. The results show that ULasso and MLasso are superior to or at least equivalent to compression performance of LTC and PLAMlis. Particularly, MLasso can significantly reduce the smooth multivariate time series data, without breaking the major trends and important changes of the sensor network system.

• Proceedings of the KIEE Conference
• /
• 2002.07d
• /
• pp.2727-2729
• /
• 2002

In an emergency telemedicine system such as High-quality Multimedia based Real-time Emergency Telemedicine(HMRET) service, it is very important to examine the status of the patient continuously using the multimedia data including the biological signals(ECG, BP, Respiration, $SpO_2$) of the patient. In order to transmit these data real time through the communication means which have the limited transmission capacity. It is also necessary to compress the biological data besides other multimedia data. For the HMRET service, we developed the lossless biological signal compression program in MSVC++ 6.0 using DPCM method and JPEG Huffman table, and tested in an internet environment.