• Journal of Multimedia Information System
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• v.5 no.2
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• pp.131-138
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• 2018

This work describes a binary image based fast Difference of Gaussian (DoG) filter using zero-dimensional (0-d) convolution and state machine look up tables (LUTs) for image and video stitching hardware platforms. The proposed approach for using binary images to obtain DoG filtering can significantly reduce the data size compared to conventional gray scale based DoG filters, yet binary images still preserve the key features of the image such as contours, edges, and corners. Furthermore, the binary image based DoG filtering can be realized with zero-dimensional convolution and state machine LUTs which eliminates the major portion of the adder and multiplier blocks that are generally used in conventional DoG filter hardware engines. This enables fast computation time along with the data size reduction which can lead to compact and low power image and video stitching hardware blocks. The proposed DoG filter using binary images has been implemented with a FPGA (Altera DE2-115), and the results have been verified.

• Journal of IKEEE
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• v.20 no.4
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• pp.344-350
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• 2016

In this Paper, we propose an optimized method of hardware design based on Field Programmable Gate Array (FPGA) to detect rotated angle of high definition image about Extreme Contour Point (ECP) algorithm with moving video image could be not happened to translation motion, but also physical rotation motion. It was evaluated by XC7Z020 xc7z020-3clg400 FPGA board by using xilinx 14.2 tool. The much well-known method, the Coordinate Rotation Digital Integrated Computation (CORDIC) is an algorithm to estimate rotated angle between point and point. Through the result both ECP and CORDIC, our proposed design are confirmed to have similar operating speed of about 4ns with CORDIC. However, it is verified to have high performance result in terms of the hardware cost, is much better than CORDIC with cost reduction of registers and Look Up Tables (LUTs) of 108% and 91%, respectively.

• The Journal of the Acoustical Society of Korea
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• v.37 no.4
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• pp.215-222
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• 2018

This paper reports the development and performance evaluation of a backend system for real-time IVUS (Intravascular Ultrasound) imaging. The developed backend system was designed to minimize the amount of logic and memory usage by means of efficient LUTs (Look-up Tables), and it was implemented in a single FPGA (Field Programmable Gate Array) without using external memory. This makes it possible to implement the backend system that is less expensive, smaller, and lighter. The accuracy of the backend system implemented was evaluated by comparing the output of the FPGA with the result computed using a MATLAB program implemented in the same way as the VHDL (VHSIC Hardware Description Language) code. Based on the result of ex-vivo experiment using rabbit artery, the developed backend system was found to be suitable for real-time intravascular ultrasound imaging.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.33A no.11
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• pp.140-151
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• 1996

In this study, we designed the routing structure and logic block of a SRAM cell-based FPGA with symmetrical-array architecture. The designed routing structure is composed of switch matrices, routing channels and I/O blocks, and the routing channels can be subdivided into single length channels, double length channels and global length channels. The interconnection between wires is made through SRAM cell-controlled pass transistors. To reduce the signal delay in pass transistors, we proposed a scheme raising the gate-control voltage to 7V. The designed SRAM cells have built-in shift register capability, so there is no need for separate shift registers. We designed SRAM cells in the LUTs(look-up tables) to enable the wirte operations to be performed synchronously with the clock for ease of system application. Each logic block (LFU) has four 4-input LUTs, flip-flops and other gates, and the LUTs can be used a sSRAM memory. The LFU also has a dedicated carry logic, so a 4-bit adder can be implemented in one LFU. We designed our FPGA using 0.6.mu.m CMOS technology, and simulation shows proper operation of a 4 bit counter at 100MHz.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.378-380
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• 2005

Remote sensing of atmospheric aerosol using MODIS satellite data has been proven to be very useful in global/regional scale aerosol monitoring. Due to their large spatial resolution of $10km^2$ MODIS aerosol optical thickness (AOT) data have limitations for local/urban scale aerosol monitoring applications. Modified Bremen Aerosol Retrieval (BAER) algorithm developed by von Hoyningen-Huene et al. (2003) and Lee et al. (2005) has been applied in this study to retrieve AOT in fe resolutions of $500m^2$ over Korea. Look up tables (LUTs) were constructed from the aerosol properties based on sun-photometer observation and radiation transfer model calculations. It was found that relative error between the satellite products and the ground observations was within about $15\%$. Resulting AOT products were correlated with surface PMIO concentration data. There was good correlation between MODIS AOT and surface PM concentration under certain atmospheric conditions, which supports the feasibility of using the high-resolution MODIS AOT for local and urban scale air quality monitoring

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.2
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• pp.56-64
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• 1998

Multi-layered perceptrons that are a nonlinear neural network model, have been widely used for various applications mainly thanks to good function approximation capability for nonlinear fuctions. However, for digital hardware implementation of the multi-layere perceptrons, the quantization scheme using "look-up tables (LUTs)" is commonly employed to handle nonlinear signmoid activation functions in the neworks, and thus requires large amount of storage to prevent unacceptable quantization errors. This paper is concerned with a new effective methodology for digital hardware implementation of multi-layered perceptrons, and proposes a "piecewise affine approximation" method in which input domain is divided into (small number of) sub-intervals and nonlinear sigmoid function is linearly approximated within each sub-interval. Using the proposed method, we develop an expression and an error backpropagation type learning algorithm for a multi-layered perceptron, and compare the performance with the quantization method through Monte Carlo simulations on XOR problems. Simulation results show that, in terms of learning convergece, the proposed method with a small number of sub-intervals significantly outperforms the quantization method with a very large storage requirement. We expect from these results that the proposed method can be utilized in digital system implementation to significantly reduce the storage requirement, quantization error, and learning time of the quantization method.quantization method.

• Journal of Information Display
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• v.9 no.1
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• pp.1-5
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• 2008

Two new impulsive driving technologies for 120Hz liquid crystal display (LCD) panels are proposed to improve moving picture quality. One technology generates the dark frame using an adder and a shifter simply without using any look up tables (LUTs). It results in a cost effective impulsive scheme with motion picture quality comparable to that of high speed driving. The other is a backlight flashing method designed to avoid ghost images. The issue of ghost images caused by the slow response time of liquid crystal (LC) is solved by means of 120Hz overdriving and 120Hz backlight flashing. Using the perceived blur edge time (PBET) metric, measured moving picture response time (MPRT) values were 10.8ms and 4.4ms, respectively, while that of 120Hz high speed driving was 10.1ms.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.4
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• pp.436-442
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• 2016

An electroencephalogram (EEG)-connectome processor to monitor and diagnose mental health is proposed. From 19-channel EEG signals, the proposed processor determines whether the mental state is healthy or unhealthy by extracting significant features from EEG signals and classifying them. Connectome approach is adopted for the best diagnosis accuracy, and synchronization likelihood (SL) is chosen as the connectome feature. Before computing SL, reconstruction optimizer (ReOpt) block compensates some parameters, resulting in improved accuracy. During SL calculation, a sparse matrix inscription (SMI) scheme is proposed to reduce the memory size to 1/24. From the calculated SL information, a small world feature extractor (SWFE) reduces the memory size to 1/29. Finally, using SLs or small word features, radial basis function (RBF) kernel-based support vector machine (SVM) diagnoses user's mental health condition. For RBF kernels, look-up-tables (LUTs) are used to replace the floating-point operations, decreasing the required operation by 54%. Consequently, The EEG-connectome processor improves the diagnosis accuracy from 89% to 95% in Alzheimer's disease case. The proposed processor occupies $3.8mm^2$ and consumes 1.71 mW with $0.18{\mu}m$ CMOS technology.

• Journal of Sensor Science and Technology
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• v.32 no.4
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• pp.246-251
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• 2023

Internet of Things (IoT) systems process signals from various sensors using signal processing algorithms suitable for the signal characteristics. To analyze complex signals, these systems usually use signal processing algorithms in the frequency domain, such as fast Fourier transform (FFT), filtering, and short-time Fourier transform (STFT). In this study, we propose a multi-mode sensor signal processor (SSP) accelerator with an FFT-based hardware design. The FFT processor in the proposed SSP is designed with a radix-2 single-path delay feedback (R2SDF) pipeline architecture for high-speed operation. Moreover, based on this FFT processor, the proposed SSP can perform filtering and STFT operation. The proposed SSP is implemented on a field-programmable gate array (FPGA). By sharing the FFT processor for each algorithm, the required hardware resources are significantly reduced. The proposed SSP is implemented and verified on Xilinxh's Zynq Ultrascale+ MPSoC ZCU104 with 53,591 look-up tables (LUTs), 71,451 flip-flops (FFs), and 44 digital signal processors (DSPs). The FFT, filtering, and STFT algorithm implementations on the proposed SSP achieve 185x average acceleration.