• Journal of the Korea Society of Computer and Information
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• v.17 no.12
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• pp.149-158
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• 2012

Proposed is a hybrid randomizing function using two asymptotically optimal randomizing functions: Elias function and Peres function. Randomizing function is an mathematical abstraction of producing a uniform random bits from a source of randomness with bias. It is known that the output rate of Elias function and Peres function approaches to the information-theoretic upper bound. Especially, for each fixed input length, Elias function is optimal. However, its computation is relatively complicated and depends on input lengths. On the contrary, Peres function is defined by a simple recursion. So its computation is much simpler, uniform over the input lengths, and runs on a small footprint. In view of this tradeoff between computational complexity and output efficiency, we propose a hybrid randomizing function that has strengths of the two randomizing functions and analyze it.

• ETRI Journal
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• v.33 no.2
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• pp.283-286
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• 2011

Path loss plays fundamental roles in system design, spectrum management, and performance evaluation. The traditional path loss model has a slight inconvenience; it depends on the unknown distance. In this letter, we explore the probability distribution function (PDF) of path loss in an indoor office environment by randomizing out the distance variable. It is shown that the resulting PDF is not Gaussian-like but is skewed to the right, and both the PDF and the moments are related to the size of the office instead of the unknown distance. To be specific, we incorporate the IEEE 802.15.4a channel parameters into our model and tabulate the cumulative distribution function with respect to different room sizes. Through a simple example, we show how our model helps a cognitive spectrum user to infer path loss information of primary users without necessarily knowing their transmitter-receiver distance.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.228-228
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• 2002

The DLS(Data Link System) is located in the PDTS(Payload Data Transmission Subsystem) of KOMPSAT-II, and its main function is to provide communication link with Ground Segment as a space segment. DLS receive the data of MSC, OBC from DCSU(Data Compression Storage Unit) and transmit to the Ground Station by X-Band RF link. DLS is consist of CCU(Channel Coding Unit), QTX(QPSK Transmitter, ASU(Antenna Switch Unit) CCU makes a packet for communication after several kind of data processing such like Ciphering, RS Coding. QTX transmit PDTS data by OQPSK. Modulation. ASU is the unit for reliability of antenna switching. So, DLS's function is consists of ciphering, RS coding, CCSDS packetizing, randomizing, modulation and switching to antenna. These DLS's functions are controlled by PMU(Payload Management Unit). All commands to DLS are sent by PMU and all telemetries of DLS are sent to the PMU. The PMU receives commands from OBC and sends telemetries to the OBC. The OBC communicates with Ground Station by S-Band RF link. This paper presents the on-orbit DLS operation concept through the ground segment.

• Journal of the Korea Society of Computer and Information
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• v.18 no.2
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• pp.19-30
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• 2013

We study sub-Peres functions that are defined recursively as Peres function for random number generation. Instead of using two parameter functions as in Peres function, the sub-Peres functions uses only one parameter function. Naturally, these functions produce less random bits, hence are not asymptotically optimal. However, the sub-Peres functions runs in linear time, i.e., in O(n) time rather than O(n logn) as in Peres's case. Moreover, the implementation is even simpler than Peres function not only because they use only one parameter function but because they are tail recursive, hence run in a simple iterative manner rather than by a recursion, eliminating the usage of stack and thus further reducing the memory requirement of Peres's method. And yet, the output rate of the sub-Peres function is more than twice as much as that of von Neumann's method which is widely known linear-time method. So, these methods can be used, instead of von Neumann's method, in an environment with limited computational resources like mobile devices. We report the analyses of the sub-Peres functions regarding their running time and the exact output rates in comparison with Peres function and other known methods for random number generation. Also, we discuss how these sub-Peres function can be implemented.