• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권9호
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• pp.2213-2231
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• 2013

Compressed sensing is a novel technology used in the field of wireless communication and sensor networks for channel estimation, signal detection, data gathering, network monitoring, and other applications. It plays a significant role in highly secure, real-time, well organized, and cost-effective data communication in smart-grid (SG) systems, which consist of multi-tier network standards that make it challenging to synchronize in power management communication. In this paper, we present a multi-layer communication model for SG systems and propose compressed-sensing based data transmission at every layer of the SG system to improve data transmission performance. Our approach is to utilize the compressed-sensing procedure at every layer in a controlled manner. Simulation results demonstrate that the proposed monitoring devices need less transmission power than conventional systems. Additionally, secure, reliable, and real-time data transmission is possible with the compressed-sensing technique.

• 정보보호학회논문지
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• 제18권4호
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• pp.175-185
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• 2008

컴퓨터 포렌식 관점에서 디스크의 비할당 영역(unallocated space)에 존재하는 데이터를 분석하는 것은 삭제된 데이터를 조사할 수 있다는 점에서 의미가 있다. 하지만 대부분의 경우에 비할당 영역에 존재하는 데이터는 응용 프로그램으로 읽을 수 있는 완전한 파일의 형태가 아닌 단편화된 파편(Fragment)으로 존재하며 이는 암호화되거나 압축된 형식으로 존재하기도 한다. 특히 데이터의 일부만 남아있고 나머지는 다른 데이터로 덮여 쓰인 상태의 데이터 파편을 분석하는 것은 매우 어려운 일이며, 특히 존재하는 데이터 파편이 압축되거나 암호화된 경우에는 데이터가 랜덤(Random)한 특성을 가지기 때문에 통계 분석이나 시그니처 분석과 같은 기존의 데이터 파편 분석 방법만으로는 의미 있는 정보를 획득할 수 없게 된다. 따라서 파일 파편의 압축 및 암호화 여부를 판단하는 선 처리 작업이 필요하며 압축된 파편은 압축 해제를 시도해야 한다. 압축 해제로서 획득한 평문 데이터 파편은 기존에 제시된 데이터 파편 분석 방식으로 분석할 수 있다. 본 논문에서는 컴퓨터 포렌식 수사 시 비할당 영역에 존재하는 파일 파편의 분석 기술에 대해 서술한다.

• 한국정보통신설비학회:학술대회논문집
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• 한국정보통신설비학회 2008년도 정보통신설비 학술대회
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• pp.551-555
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• 2008

In these days, compressed file is useful in internet environment and has many advantages. So a lot of data hiding algorithms works on JPEG compressed file. Of course they know basic rules of transformation and quantization and they utilize those rules to implement their programming. But most of them evaluate the affection of data hiding after data modification. We propose how to predict the affection of data modification in course of data hiding process. Through some kind of experiments, several valuable facts are revealed which used in data hiding in compressed domain such as JPEG. These facts will improve existing data hiding algorithms (F3, F4 and F5 which including Matrix Encoding)[1],[5],[6].

• 정보보호학회논문지
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• 제32권5호
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• pp.869-879
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• 2022

본 연구를 통해 매우 대중적인 압축 알고리즘인 Deflate 알고리즘을 통해 생성되는 3가지 유형의 압축 데이터 블록 가운데 원본 데이터 없는 비 압축 블록(No-Payload Non-Compressed Block;NPNCB) 유형을 임의로 생성하여 정상적인 압축 블록 사이에 미리 설계된 공격 시나리오에 따라 삽입하는 방법을 통해 악의적 코드 또는 임의의 데이터를 은닉하는 취약점을 발견하였다. 비 압축 블록의 헤더에는 byte align을 위해서만 존재하는 데이터 영역이 존재하며, 본 연구에서는 이 영역을 DBA(Disposed Bit Area)라고 명명하였다. 이러한 DBA 영역에 다양한 악성 코드와 악의적 데이터를 숨길 수 있었으며, 실험을 통해 정상적인 압축 블록들 사이에 오염된 블록을 삽입했음에도 기존 상용 프로그램에서 정상적으로 경고 없이 압축 해제 되었고, 악의적 디코더로 해독하여 악성 코드를 실행할 수 있음을 보였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권1호
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• pp.58-78
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• 2016

Compressed sensing (CS) possesses the potential benefits for spectrum sensing of wideband signal in cognitive radio. The sparsity of signal in frequency domain denotes the number of occupied channels for spectrum sensing. This paper presents a scheme of adaptively adjusting the number of compressed measurements to reduce the unnecessary computational complexity when priori information about the sparsity of signal cannot be acquired. Firstly, a method of sparsity estimation is introduced because the sparsity of signal is not available in some cognitive radio environments, and the relationship between the amount of used data and estimation accuracy is discussed. Then the SNR of the compressed signal is derived in the closed form. Based on the SNR of the compressed signal and estimated sparsity, an adaptive algorithm of adjusting the number of compressed measurements is proposed. Finally, some simulations are performed, and the results illustrate that the simulations agree with theoretical analysis, which prove the effectiveness of the proposed adaptive adjusting of compressed measurements.

• Journal of Communications and Networks
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• 제12권4호
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• pp.308-316
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• 2010

The problem of compressed sensing (CS) based multiple access is studied under the assumption of random data traffic. In many multiple access systems, i.e., wireless sensor networks (WSNs), data arrival is random due to the bursty data traffic for every transmitter. Following the recently developed CS methodology, the technique of compressing the transmitter identities into data transmissions is proposed, such that it is unnecessary for a transmitter to inform the base station its identity and its request to transmit. The proposed compressed multiple access scheme identifies transmitters and recovers data symbols jointly. Numerical simulations demonstrate that, compared with traditional multiple access approaches like carrier sense multiple access (CSMA), the proposed CS based scheme achieves better expectation and variance of packet delays when the traffic load is not too small.

• 한국항해항만학회지
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• 제46권2호
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• pp.92-98
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• 2022

Maritime accidents result in enormous economic loss and loss of life; thus, such accidents must be prevented, and risks must be managed to prevent these occurrences Risk management must be based on statistical evidence such as variables. Because calculating when variables increase statistically can be difficult, compressing the designated variables is necessary to use the maritime accident data in Korea. Thus, in this study, variables of marine accident data are compressed using statistical methods. The date, ship type, and marine accident type included in all maritime accident data were extracted, the number of optimal variables was confirmed using the hierarchical clustering analysis method, and the data were compressed. For the compressed variables, the validity of the data use was statistically confirmed using analysis of variance, and the data of the variables identified using the variable compression method were designated. Consequently, among the monthly and yearly data, statistical significance was confirmed in yearly data, and compression was possible. The significance of the data was confirmed in six and eight types of ships and accidents, respectively, and these were compressed. These results can be directly used for prevention or prediction based on past maritime accident data. Additionally, the data range extracted from past maritime accidents and the number of applicable data will be studied in the future.

• International Journal of Concrete Structures and Materials
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• 제7권3호
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• pp.193-202
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• 2013

Geopolymer mortar compressed blocks were prepared using fly ash, ground granulated blast furnace slag, silica fume and metakaolin as binders and sand/quarry dust/pond ash as fine aggregate. Alkaline solution was used to activate the source materials for synthesizing the geopolymer mortar. Fresh mortar was used to obtain the compressed blocks. The strength development with reference to different parameters was studied. The different parameters considered were fineness of fly ash, binder components, type of fine aggregate, molarity of alkaline solution, age of specimen, fluid-to-binder ratio, binder-to-aggregate ratio, degree of saturation, etc. The compressed blocks were tested for compression at different ages. It was observed that some of the blocks attained considerable strength within 24 h under ambient conditions. The cardinal aim was to analyze the experimental data generated to formulate a phenomenological model to arrive at the combinations of the ingredients to produce geopolymer blocks to meet the strength development desired at the specified age. The strength data was analyzed within the framework of generalized Abrams' law. It was interesting to note that the law was applicable to the analysis of strength development of partially saturated compressed blocks when the degree of saturation was maintained constant. The validity of phenomenological model was examined with an independent set of experimental data. The blocks can replace the traditional masonry blocks with many advantages.

• ETRI Journal
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• 제38권1호
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• pp.159-163
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• 2016

Block compressed sensing (BCS) is widely used in image sampling and is an efficient, effective technique. Through the use of BCS, an image can be simultaneously compressed and encrypted. In this paper, a novel reversible data hiding (RDH) method is proposed to embed additional data into BCS images. The proposed method is the first RDH method of its kind for BCS images. Results demonstrate that our approach performs better compared with other state-of-the-art RDH methods on encrypted images.

• 전기전자학회논문지
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• 제25권1호
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• pp.76-82
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• 2021

In this paper, the compressed sensing basic pursuit denoise algorithm adopted to synthetic aperture radar imaging is investigated to improve the object recognition. From the incomplete data sets for image processing, the compressed sensing algorithm had been integrated to recover the data before the conventional back- projection algorithm was involved to obtain the synthetic aperture radar images. This method can lead to the reduction of measurement events while scanning the objects. An ultra-wideband radar scheme using a stripmap synthetic aperture radar algorithm was utilized to detect objects hidden behind the box. The Ultra-Wideband radar system with 3.1~4.8 GHz broadband and UWB antenna were implemented to transmit and receive signal data of two conductive cylinders located inside the paper box. The results confirmed that the images can be reconstructed by using a 30% randomly selected dataset without noticeable distortion compared to the images generated by full data using the conventional back-projection algorithm.