• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.471-479
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• 2018

Recently, the spread of smart TV based Android iOS Set Top box has become common. This paper propose a new approach to control the TV using gestures away from the era of controlling the TV using remote control. In this paper, the AdaBoost algorithm is applied to gesture recognition by using a mono camera. First, we use Camshift-based Body tracking and estimation algorithm based on Gaussian background removal for body coordinate extraction. Using global and local feature vectors, we recognized gestures with speed change. By tracking the time interval trajectories of hand and wrist, the AdaBoost algorithm with CART algorithm is used to train and classify gestures. The principal component feature vector with high classification success rate is searched using CART algorithm. As a result, 24 optimal feature vectors were found, which showed lower error rate (3.73%) and higher accuracy rate (95.17%) than the existing algorithm.

• Journal of IKEEE
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• v.22 no.4
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• pp.1025-1030
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• 2018

The proposed modulator is designed by utilizing a conventional structure employing time division technique to realize the 4th order delta-sigma modulator using one op-amp. In order to reduce the influence of KT/C noise, the capacitance in the first and second integrators reused was chosen to be 20pF and capacitance of third and fourth integrators was designed to be 1pF. The stage variable technique in the low power reconfigurable op-amp was used to solve the stability issue due to different capacitance loads for the reduction of KT/C noise. This technique enabled the proposed modulator to reduce the power consumption of 15% with respect to the conventional one. The proposed modulator was fabricated with 0.18um CMOS N-well 1 poly 6 metal process and consumes 305uW at supply voltage of 1.8V. The measurement results demonstrated that SNDR, ENOB, DR, FoM(Walden), and FoM(Schreier) were 66.3 dB, 10.6 bits, 83 dB, 98 pJ/step, and 142.8 dB at the sampling frequency of 256kHz, oversampling ratio of 128, clock frequency of 1.024 MHz, and input frequency of 250 Hz, respectively.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.81-86
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• 2024

The Image Signal Processor (ISP) converts RAW images captured by the camera sensor into user-preferred sRGB images. While RAW images contain more meaningful information for image processing than sRGB images, RAW images are rarely shared due to their large sizes. Moreover, the actual ISP process of a camera is not disclosed, making it difficult to model the inverse process. Consequently, research on learning the conversion between sRGB and RAW has been conducted. Recently, the ParamISP[1] model, which directly incorporates camera parameters (exposure time, sensitivity, aperture size, and focal length) to mimic the operations of a real camera ISP, has been proposed by advancing the simple network structures. However, existing studies, including ParamISP[1], have limitations in modeling the camera ISP as they do not consider the degradation caused by lens shading, optical aberration, and lens distortion, which limits the restoration performance. This study introduces Positional Encoding to enable the camera ISP neural network to better handle degradations caused by lens. The proposed positional encoding method is suitable for camera ISP neural networks that learn by dividing the image into patches. By reflecting the spatial context of the image, it allows for more precise image restoration compared to existing models.