• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2006.06a
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• pp.25-28
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• 2006

In this paper, we say the shading correction system supporting multi-resolution for camera. The shading effect is caused by non-uniform illumination, non-uniform camera sensitivity, or even dirt and dust on glass (lens) surfaces. In general this shading effect is undesirable [1]. Eliminating it is frequently necessary for subsequent processing and especially when quantitative microscopy is the fine goal. The proposed system is available on thirty nine kinds of image resolutions scanned by interlaced and progressive type. Moreover, the system is using various continuous quadratic equations instead of using the piece-wise linear curve which is composed of multiple line segments. Finally, the system could correct the correct effect without discontinuity in any image resolution. The proposed system is also experimentally demonstrated with Xilinx Virtex FPGA XCV2000E- 6BG5560 and the TV set.

• Journal of Korean Society for Geospatial Information Science
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• v.10 no.5 s.23
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• pp.45-51
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• 2002

The goal of image merging techniques are to enhance the resolution of low-resolution images using the detail information of the high-resolution images. Among the several image merging methods, wavelet-based image merging techniques have the advantages of efficient decorrelation of image bands and time-scale analysis. However, they have no regard for spatial information between the bands. In other words, multiresolution data merging methods merge the same information-the detail information of panchromatic image-with other band images, without considering specific characteristics. Therefore, a merged image contains much unnecessary information. In this paper, we discussed this 'mixing' effect and, proposed a method to classify the detail information of the panchromatic image according to the spatial and spectral characteristics, and to minimize distortion of the merged image.

• Smart Structures and Systems
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• v.6 no.1
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• pp.1-16
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• 2010

This paper presents a novel smart MEMS accelerometer which employs a hybrid control algorithm and an estimator. This scheme is realized by adding a sliding-mode controller to a conventional PID closed loop system to achieve higher stability and higher dynamic range and to prevent pull-in phenomena by preventing finger displacement from passing a maximum preset value as well as adding an adaptive nonlinear observer to a conventional PID closed loop system. This estimator is used for online estimation of the parameter variations for MEMS accelerometers and gives the capability of self testing to the system. The analysis of convergence and resolution show that while the proposed control scheme satisfies these criteria it also keeps resolution performance better than what is normally obtained in conventional PID controllers. The performance of the proposed hybrid controller investigated here is validated by computer simulation.

• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.999-1002
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• 2002

In this paper, we proposes an adaptive method for reducing the computational overhead of fine-to-coarse MRME at the finest resolution level by considering for the spatial and spectral characteristics between wavelet decomposition levels simultaneously. As we know, there is high correlation between the adjacent blocks and it can give the very important clue to estimate motion at finest level. So, in this paper, using the initial motion vector and the adjacent motion vector in the coarsest level, we determine the optimal direction that will be minimized the estimation error in the finest level. In that direction, we define the potential searching region within the full searching region that is caused to increase much computational overhead in the FtC method. Last, in that region, we process the efficient 2-step motion estimation. and estimate the motion vector at finest resolution level. And then, this determined motion vector is scaled to coarser resolutions. As simulation result, this method is similar to computational complexity of the CtF MRME method and very significantly reduces that of the FtC MRME method. In addition, they provide higher quality than CtF MRME, both visually and quantitatively

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.4
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• pp.363-369
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• 2002

In this paper, it could improved on the arbitrary nonlinear function learning approximation which have the wavelet neural network based on Adaptive Neuro-Fuzzy Inference System(ANFIS) and the multi-resolution Analysis(MRA) of the wavelet transform. ANFIS structure is composed of a bell type fuzzy membership function, and the wavelet neural network structure become composed of the forward algorithm and the backpropagation neural network algorithm. This wavelet composition has a single size, and it is used the backpropagation algorithm for learning of the wavelet neural network based on ANFIS. It is confirmed to be improved the wavelet base number decrease and the convergence speed performances of the wavelet neural network based on ANFIS Model which is using the wavelet translation parameter learning and bell type membership function of ANFIS than the conventional algorithm from 1 dimension and 2 dimension functions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.3090-3102
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• 2015

The accuracy of an augmented reality (AR) application is highly dependent on the resolution of the object's image and the device's computational processing capability. Naturally, a mobile smart device equipped with a high-resolution camera becomes the best platform for portable AR services. AR applications require significant energy consumption and very fast response time, which are big burdens to the smart device. However, there are very few ways to overcome these burdens. Computation offloading via mobile cloud computing has the potential to provide energy savings and enhance the performance of applications executed on smart devices. Therefore, in this paper, adaptive mobile computation offloading of mobile AR applications is considered in order to determine optimal offloading points that satisfy the required quality of experience (QoE) while consuming minimum energy of the smart device. AR feature extraction based on SURF algorithm is partitioned into sub-stages in order to determine the optimal AR cloud computational offloading point based on conditions of the smart device, wireless and wired networks, and AR service cloud servers. Tradeoffs in energy savings and processing time are explored also taking network congestion and server load conditions into account.

• BMB Reports
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• v.55 no.10
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• pp.473-480
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• 2022

Neutrophils, the most abundant innate immune cells, play essential roles in the innate immune system. As key innate immune cells, neutrophils detect intrusion of pathogens and initiate immune cascades with their functions; swarming (arresting), cytokine production, degranulation, phagocytosis, and projection of neutrophil extracellular trap. Because of their short lifespan and consumption during immune response, neutrophils need to be generated consistently, and generation of newborn neutrophils (granulopoiesis) should fulfill the environmental/systemic demands for training in cases of infection. Accumulating evidence suggests that neutrophils also play important roles in the regulation of adaptive immunity. Neutrophil-mediated immune responses end with apoptosis of the cells, and proper phagocytosis of the apoptotic body (efferocytosis) is crucial for initial and post resolution by producing tolerogenic innate/adaptive immune cells. However, inflammatory cues can impair these cascades, resulting in systemic immune activation; necrotic/pyroptotic neutrophil bodies can aggravate the excessive inflammation, increasing inflammatory macrophage and dendritic cell activation and subsequent T_H1/T_H17 responses contributing to the regulation of the pathogenesis of autoimmune disease. In this review, we briefly introduce recent studies of neutrophil function as players of immune response.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.1
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• pp.54-63
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• 2011

This paper proposes a fast learning-based interpolation algorithm to up-scale an input low-resolution image into a high-resolution image. In conventional learning-based super-resolution, a certain relationship between low-resolution and high-resolution images is learned from various training images and a specific high frequency synthesis information is derived. And then, an arbitrary low resolution image can be super-resolved using the high frequency synthesis information. However, such super-resolution algorithms require heavy memory space to store huge synthesis information as well as significant computation due to two-dimensional matching process. In order to mitigate this problem, this paper presents one-dimensional patch-based learning and synthesis. So, we can noticeably reduce memory cost and computational complexity. Simulation results show that the proposed algorithm provides higher PSNR and SSIM of about 0.7dB and 0.01 on average, respectively than conventional bicubic interpolation algorithm.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.2
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• pp.187-196
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• 2017

The size of raw data has dramatically increased due to the recent trend of Synthetic Aperture Radar(SAR) development plans for high resolution and high definition image acquisition. The large raw data has an impact on satellite operability due to the limitations of storage and transmission capacity. To improve the SAR operability, the SAR raw data shall be compressed before transmission to the ground station. The Block Adaptive Quantization (BAQ) algorithm is one of the data compression algorithm and has been used for a long time in the spaceborne SAR system. In this paper, an optimization method of BAQ threshold table is introduced using real SAR raw data to prevent the degradation of signal quality caused by data compression. In this manner, a new variation estimation strategy and a new threshold method for block type decision are introduced.

• Journal of Broadcast Engineering
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• v.8 no.2
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• pp.204-208
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• 2003

In this paper, a new matrix vector formulation for a wavelet-based subband decomposition is introduced. This formulation provides a means to compute a regular multi-resolution analysis over many levels of decomposition. With this approach. any single channel linear space-invariant filtering problem can be cast into a multi-channel framework. This decomposition Is applied to the linear space-invariant image restoration problem and propose a frequency-adaptive constrained least squares(CLS) filter. In the proposed filter, we use different parameters adaptively according to subband characteristics. Experimental results are presented for the proposed frequency-adaptive CLS filter These experiments show that if accurate estimates of the subband characteristics are available, the proposed frequency adaptive CLS filter provides significant improvements over the traditional single channel filter.