• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.8
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• pp.1780-1784
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• 2015

Traditional IP routing, see only the Destination Address When Forwarding Layer 3 routing and exchange information and Destination-Based Routing Lookup is required for all Hop. Thus, all routers Full Internet routing information, the route information of more than about 120,000 may require. Therefore, the router configuration, which can be dispersed in the environment, the traffic load is required in accordance with this congestion. In this study, a unique characteristic of the Internet in the environment of an existing network Best Effect for QoS guarantee and hardware high speed switching of large multimedia data transmitted using a Labeling for forwarding a packet environment configuration is required. Video Stream Broadcast Transport Labeling rather than in much of the higher performance of the multi-step policy to most of the Video Stream Packet deulim was fixed to Labeling Header Format proposes a method of applying an effective QoS policy to a more simplified policy.

• Journal of KIISE:Information Networking
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• v.28 no.2
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• pp.262-276
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• 2001

Recently much research for developing forwarding table that support fast router without employing both special hardware and new protocols. This article introduces a new forwarding data structure based on the software to enable forwarding lookup to be penormed at giga-bit speed. The forwarding table is known as a bottleneck of the routers penormance due to its high complexity proportional to the forwarding table size. The recent research that based on the software uses a Patricia trie and its variants. and also uses a hash function with prefix length key and others. The proposed forwarding table structure construct a forwarding table by the bit stream array in which it constructs trie from routing table prefix entries and it represents each pointer pointing the child node and the associated forwarding table entry with one bit The trie structure and routing prefix pointer need a large memory when representing those by linked-list or array. but in the proposed data structure, the needed memory size is small enough since it represents information with one bit. Additionally, by use a lookup method that start searching at desired middle level we can shorten the search path. The introduced data structure. called bit-map trie shows that we can implement a fast forwarding engine on the conventional Pentium processor by reducing the backbone routing table fits into Level 2 cache of Pentium II processor and shortens the searching path. Our experiments to evaluate the performance of proposed method show that this bit-map trie accomplishes 5.7 million lookups per second.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.46 no.3
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• pp.20-25
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• 2009

We propose hardware architecture of floating-point square root and inverse square root arithmetic units using lookup tables. They are used for lighting engines and shader processor for 3D graphic processing. The architecture is based on Taylor series expansion and consists of lookup tables and correction units so that the size of look-up tables are reduced. It can be applied to 32 bit floating point formats of IEEE-754 and reduced 24 bit floating point formats. The square root and inverse square root arithmetic units for 32 bit and 24 bit floating format number are designed as the proposed architecture. They can operation in a single cycle, and satisfy the precision of $10^{-5}$ required by OpenGL 1.x ES. They are designed using Verilog-HDL and the RTL codes are verified using an FPGA.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.334-337
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• 2001

In this paper we implemented DDFS and gam-phase dectector which use output of DDFS or any sinusoidal signal input to broaden the usability of DDFS. DDFS is composed of a 32 bits phase accumulator, phase increment registers, ROM and several registers for controlling the operations. It generates the digital data for sine wave up to the half of the clock frequency. To reduce the ROM size and increase the speed, we adopt the algorithms based on Taylor's series expansion method. Data at sparse phase intervals are stored in ROM and sine data between intervals are calculated in hardware. Function of Gain-Phase Extraction consists of sine lookup of DDFS and the optimized multipliers.

• The Korean Journal of Nuclear Medicine
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• v.34 no.6
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• pp.439-444
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• 2000

PACS (Picture Archiving and Communication System) is being rapidly spread and installed in many hospitals, but most of the system do not include nuclear medicine field. Although additional costs of hardware for nuclear medicine PACS is low, the complexity in developing viewing software and little market have made the nuclear medicine PACS not popular. Most PACS utilize DICOM 3.0 as standard format, but standard format in nuclear medicine has been Interfile. Interfile should be converted into DICOM format if nuclear images are to be stored and visualized in most PACS. Nowadays, many vendors supply the DICOM option in gamma camera and PET. Several hospitals in Korea have already installed nuclear PACS with DICOM, but only the screen captured images are supplied. Software for visualizing pseudo-color with color lookup tables and expressing with volume view should be developed to fulfill the demand of referring physicians and nuclear medicine physicians. PACS is going to integrate not only radiologic images but also endoscopic and pathologic images. Web and PC based PACS is now a trend and is much compatible with nuclear medicine PACS. Most important barrier for nuclear medicine PACS that we encounter is not a technical problem, but indifference of investor such as administrator of hospital or PACS. Now it is time to support and invest for the development of nuclear medicine PACS.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.115-117
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• 2022

Whitebox encryption technique is a method of preventing exposure of encryption keys by mixing encryption key information with a software-based encryption algorithm. Whitebox encryption technique is attracting attention as a technology that replaces conventional hardware-based security encryption techniques by making it difficult to infer confidential data and keys by accessing memory with unauthorized reverse engineering analysis. However, in the encryption and decryption process, a large lookup table is used to hide computational results and encryption keys, resulting in a problem of slow encryption and increased memory size. In particular, it is difficult to apply whitebox cryptography to low-cost, low-power, and light-weight Internet of Things products due to limited memory space and battery capacity. In addition, in a network environment that requires real-time service support, the response delay time increases due to the encryption/decryption speed of the whitebox encryption, resulting in deterioration of communication efficiency. Therefore, in this paper, we analyze whether the AES-based whitebox(WBC-AES) proposed by S.Chow can satisfy the speed and memory requirements based on the experimental results.

• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.4
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• pp.189-198
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• 2015

Most IoT-related approaches have tried to establish the relation by connecting the network between things. The proposed research will present how the pervasive interaction of eco-system formed by touching the objects between humans and things can be recognized on purpose. By collecting and sharing the detected patterns among all kinds of things, we can construct the environment which enables individualized interactions of different objects. To perform the aforementioned, we are going to utilize technical procedures such as event-driven signal processing, pattern matching for signal recognition, and hardware in the loop simulation. We will also aim to implement the prototype of sensor processor based on Arduino MCU, which can be integrated with system using Arduino-Matlab/Simulink hybrid-interoperation environment. In the experiment, we use piezo transducer to detect the vibration or vibrates the surface using acoustic wave, which has specific frequency spectrum and individualized signal shape in terms of time axis. The signal distortion in time and frequency domain is recorded into memory tracer within sensor processor to extract the meaningful pattern by comparing the stored with lookup table(LUT). In this paper, we will contribute the initial prototypes for the acoustic touch processor by using off-the-shelf MCU and the integrated framework based on Matlab/Simulink model to provide the individualization of the touch-sensing for the user on purpose.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.975-976
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• 1993

This talk presents the overview of the author's research and development activities on fuzzy inference hardware. We involved it with two distinct approaches. The first approach is to use application specific integrated circuits (ASIC) technology. The fuzzy inference method is directly implemented in silicon. The second approach, which is in its preliminary stage, is to use more conventional microprocessor architecture. Here, we use a quantitative technique used by designer of reduced instruction set computer (RISC) to modify an architecture of a microprocessor. In the ASIC approach, we implemented the most widely used fuzzy inference mechanism directly on silicon. The mechanism is beaded on a max-min compositional rule of inference, and Mandami's method of fuzzy implication. The two VLSI fuzzy inference chips are designed, fabricated, and fully tested. Both used a full-custom CMOS technology. The second and more claborate chip was designed at the University of North Carolina(U C) in cooperation with MCNC. Both VLSI chips had muliple datapaths for rule digital fuzzy inference chips had multiple datapaths for rule evaluation, and they executed multiple fuzzy if-then rules in parallel. The AT & T chip is the first digital fuzzy inference chip in the world. It ran with a 20 MHz clock cycle and achieved an approximately 80.000 Fuzzy Logical inferences Per Second (FLIPS). It stored and executed 16 fuzzy if-then rules. Since it was designed as a proof of concept prototype chip, it had minimal amount of peripheral logic for system integration. UNC/MCNC chip consists of 688,131 transistors of which 476,160 are used for RAM memory. It ran with a 10 MHz clock cycle. The chip has a 3-staged pipeline and initiates a computation of new inference every 64 cycle. This chip achieved an approximately 160,000 FLIPS. The new architecture have the following important improvements from the AT & T chip: Programmable rule set memory (RAM). On-chip fuzzification operation by a table lookup method. On-chip defuzzification operation by a centroid method. Reconfigurable architecture for processing two rule formats. RAM/datapath redundancy for higher yield It can store and execute 51 if-then rule of the following format: IF A and B and C and D Then Do E, and Then Do F. With this format, the chip takes four inputs and produces two outputs. By software reconfiguration, it can store and execute 102 if-then rules of the following simpler format using the same datapath: IF A and B Then Do E. With this format the chip takes two inputs and produces one outputs. We have built two VME-bus board systems based on this chip for Oak Ridge National Laboratory (ORNL). The board is now installed in a robot at ORNL. Researchers uses this board for experiment in autonomous robot navigation. The Fuzzy Logic system board places the Fuzzy chip into a VMEbus environment. High level C language functions hide the operational details of the board from the applications programme . The programmer treats rule memories and fuzzification function memories as local structures passed as parameters to the C functions. ASIC fuzzy inference hardware is extremely fast, but they are limited in generality. Many aspects of the design are limited or fixed. We have proposed to designing a are limited or fixed. We have proposed to designing a fuzzy information processor as an application specific processor using a quantitative approach. The quantitative approach was developed by RISC designers. In effect, we are interested in evaluating the effectiveness of a specialized RISC processor for fuzzy information processing. As the first step, we measured the possible speed-up of a fuzzy inference program based on if-then rules by an introduction of specialized instructions, i.e., min and max instructions. The minimum and maximum operations are heavily used in fuzzy logic applications as fuzzy intersection and union. We performed measurements using a MIPS R3000 as a base micropro essor. The initial result is encouraging. We can achieve as high as a 2.5 increase in inference speed if the R3000 had min and max instructions. Also, they are useful for speeding up other fuzzy operations such as bounded product and bounded sum. The embedded processor's main task is to control some device or process. It usually runs a single or a embedded processer to create an embedded processor for fuzzy control is very effective. Table I shows the measured speed of the inference by a MIPS R3000 microprocessor, a fictitious MIPS R3000 microprocessor with min and max instructions, and a UNC/MCNC ASIC fuzzy inference chip. The software that used on microprocessors is a simulator of the ASIC chip. The first row is the computation time in seconds of 6000 inferences using 51 rules where each fuzzy set is represented by an array of 64 elements. The second row is the time required to perform a single inference. The last row is the fuzzy logical inferences per second (FLIPS) measured for ach device. There is a large gap in run time between the ASIC and software approaches even if we resort to a specialized fuzzy microprocessor. As for design time and cost, these two approaches represent two extremes. An ASIC approach is extremely expensive. It is, therefore, an important research topic to design a specialized computing architecture for fuzzy applications that falls between these two extremes both in run time and design time/cost. TABLEI INFERENCE TIME BY 51 RULES {{{{Time }}{{MIPS R3000 }}{{ASIC }}{{Regular }}{{With min/mix }}{{6000 inference 1 inference FLIPS }}{{125s 20.8ms 48 }}{{49s 8.2ms 122 }}{{0.0038s 6.4㎲ 156,250 }} }}