• Journal of the Institute of Electronics Engineers of Korea SD
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• v.47 no.12
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• pp.8-16
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• 2010

NAND multilevel cell (MLC) flash memory is widely issued because it can increase the capability of storage by storing two or more bits to a single cell. However if a number of levels in a cell increases, some physical features like cell to cell interference result cell voltage shift and it is known that a VT shift is unidirectional. To reduce errors by the effects, we can consider error correcting codes(ECC) or signal processing methods. We focus signal processing methods for the cell to cell interference voltage shift effects and propose the algorithms which reduce the effects of the voltage shift by estimating it and making level read voltages be adaptive. These new algorithms can be applied with ECC at the same time, therefore these algorithms are efficient for MLC error correcting ability. We show the bit error rate simulation results of the algorithms and compare the performance of the algorithms.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.1
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• pp.12-18
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• 2012

Stereo correspondence is the central problem of stereo vision. In this paper, we propose a stereo matching scheme based on a genetic algorithm using a multi-resolution method and AD-Census. The proposed approach considers the matching environment as an optimization problem and finds the disparity by using a genetic algorithm And adaptive chronosome structure using edge pixels and crossover mechanism are employed in this technique. A cost function is composes of certain constraints whice are commonly used in stereo matching. AD-Census measure is applied to reduce disparity error. To increase the efficiency of process, we apply image pyramid method to stereo matching and calculate the initial disparity map at the coarsest resolution. Then initial disparity map is propagated to the next finer resolution, interpolated and performed disparity refinement using local feature vector. We valid our method not only reduces the search time for correspondence compared with conventional GA-based method but also ensures the validity of matching.

• Journal of the Institute of Convergence Signal Processing
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• v.6 no.1
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• pp.15-22
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• 2005

In this paper, we propose a face recognition system by using the CCD color image. We first get the face candidate image by using YCbCr color model and adaptive skin color information. And we use it initial curve of active contour model to extract face region. We use the Eye map and mouth map using color information for extracting facial feature from the face image. To obtain center point of Log-polar image, we use extracted facial feature from the face image. In order to obtain feature vectors, we use extracted coefficients from DCT and wavelet transform. To show the validity of the proposed method, we performed a face recognition using neural network with BP learning algorithm. Experimental results show that the proposed method is robuster with higher recogntion rate than the conventional method for the rotation and scale variant.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.1
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• pp.38-43
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• 2011

Cluster-based routing is high energy consumption of cluster head nodes. A recent approach to resolving the problem is the dynamic cluster technique that periodically re-selects cluster head nodes to distribute energy consumption of the sensor nodes. However, the dynamic clustering technique has a problem that repetitive construction of clustering consumes the more energies. This paper proposes a solution to the problems described above from the energy efficiency perspective. The round-robin cluster header(RRCH) technique, which fixes the initially structured cluster and sequentially selects cluster head nodes, is suggested for solving the energy consumption problem regarding repetitive cluster construction. A simulation result were compared with the performances of two of the most widely used conventional techniques, the LEACH(Low Energy Adaptive Clustering Hierarchy) and HEED(Hybrid, Energy Efficient, Distributed Clustering) algorithms, based on energy consumption, remaining energy for each node and uniform distribution. The evaluation confirmed that in terms of energy consumption, the technique proposed in this paper was 26.5% and 20% more efficient than LEACH and HEED, respectively.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.310-319
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• 2010

In this paper, digital watermarking method using wavelet transform and quad-tree algorithm is proposed. The proposed algorithm transforms the input image by DWT(Discrete Wavelet Transform) and AWPT(Adaptive Wavelet Packet Transform), inserts the watermark by quad-tree algorithm and the Cox's algorithm. The simulation for performance analysis of the proposed algorithm is implemented about the effect of embedding watermark in each subband coefficient (HH, LH, HL) of DWT, each DWT level, and each AWPT level. The simulation result by using DWT is compared with that using AWPT in the proposed algorithm. In addition, the effect of embedding watermark in the lowest frequency band (LL) is simulated. As a simulation result using DWT, the watermarking performance of simultaneously embedding in HH, LH, and HL band of DWT(6 level) is better than that of different cases. The result of AWPT(3 level) improves the correlation value compared to that of DWT(3 level). In addition, insertion the watermark to the LL band about 30~60% of all watermarks improves the correlation value while PSNR performance decreases 1~2dB.

• Journal of the Institute of Convergence Signal Processing
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• v.11 no.4
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• pp.270-277
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• 2010

The BAMS filter is a kind of wavelet shrinkage filter based on the Bayes estimators with no simulation, therefore it can be used for a real time filter. The denoising efficiency of BAMS filter is seriously affected by the estimated noise variance in each wavelet band. To remove noise in signals in existing BAMS filter, the noise variance is estimated by using the quartile of the finest level of details in the wavelet decomposition, and with this variance, the noise of the level is removed. In this paper, to remove the image noise includingodified quartile of the level of detail is proposed. And by these techniques, the image noises of mid and high frequency bands are removed, and the results showed that the increased PSNR of ab the midband noise, the noise variance estimation method using the monotonic transform and the mout 2[dB] and the effectiveness in denosing of low noise deviation images.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.4
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• pp.202-208
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• 2020

Recently, the importance of non-face-to-face has been emphasized due to COVID-19, and the use of sharing spaces is also expanding. The use of uncontact check-in technology for access control of sharing spaces reduces waiting time and optimizes workers' efficiency, resulting in operational cost savings. In this paper, we propose a sharing space access management system based on a mobile key and RCU (Room Control Unit), access to the facility using a mobile key, and monitor the facility using an RCU. Proposal system is for shared accommodation, rental field (residence, sale-selling hotel), shared office, etc. when there is a one-time visitor on a specific day and time, the corresponding password is delivered to the mobile platform to expose and key the existing password. It is supported by a field-adaptive system that can reduce discomfort such as delivery. In order to test the operation of the proposed integrated system, tests were conducted according to scenarios to understand the overall status of the user's reservation, check-in, and check-out, and a 100% success rate was derived for each item by setting performance indicators to prove test reliability.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.4
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• pp.234-240
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• 2022

This paper proposes a customized AI exercise recommendation service for balancing the relative amount of exercise according to the working environment by each occupation. WISDM database is collected by using acceleration and gyro sensors, and is a dataset that classifies physical activities into 18 categories. Our system recommends a adaptive exercise using the analyzed activity type after classifying 18 physical activities into 3 physical activities types such as whole body, upper body and lower body. 1 Dimensional convolutional neural network is used for classifying a physical activity in this paper. Proposed model is composed of a convolution blocks in which 1D convolution layers with a various sized kernel are connected in parallel. Convolution blocks can extract a detailed local features of input pattern effectively that can be extracted from deep neural network models, as applying multi 1D convolution layers to input pattern. To evaluate performance of the proposed neural network model, as a result of comparing the previous recurrent neural network, our method showed a remarkable 98.4% accuracy.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.5
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• pp.103-111
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• 2007

In this paper, we propose a VLSI architecture of hardware optimized high quality image signal processor for a Single-chip CMOS Image Sensor(CIS). The Single-chip CIS is usually used for mobile applications, so it has to be implemented as small as possible while maintaining the image quality. Several image processing algorithms are used in ISP to improve captured image quality. Among the several image processing blocks, demosaicing and image filter are the core blocks in ISP. These blocks need line memories, but the number of line memories is limited in a low cost Single-chip CIS. In our design, high quality edge-adaptive and cross channel correlation considered demosaicing algorithm is adopted. To minimize the number of required line memories for image filter, we share the line memories using the characteristics of demosaicing algorithm which consider the cross correlation. Based on the proposed method, we can achieve both high quality and low hardware complexity with a small number of line memories. The proposed method was implemented and verified successfully using verilog HDL and FPGA. It was synthesized to gate-level circuits using 0.25um CMOS standard cell library. The total logic gate count is 37K, and seven and half line memories are used.

• Journal of the Korean Society of Radiology
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• v.84 no.1
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• pp.240-252
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• 2023

Purpose To assess the effect of deep learning image reconstruction (DLIR) for head CT in pediatric patients. Materials and Methods We collected 126 pediatric head CT images, which were reconstructed using filtered back projection, iterative reconstruction using adaptive statistical iterative reconstruction (ASiR)-V, and all three levels of DLIR (TrueFidelity; GE Healthcare). Each image set group was divided into four subgroups according to the patients' ages. Clinical and dose-related data were reviewed. Quantitative parameters, including the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), and qualitative parameters, including noise, gray matter-white matter (GM-WM) differentiation, sharpness, artifact, acceptability, and unfamiliar texture change were evaluated and compared. Results The SNR and CNR of each level in each age group increased among strength levels of DLIR. High-level DLIR showed a significantly improved SNR and CNR (p < 0.05). Sequential reduction of noise, improvement of GM-WM differentiation, and improvement of sharpness was noted among strength levels of DLIR. Those of high-level DLIR showed a similar value as that with ASiR-V. Artifact and acceptability did not show a significant difference among the adapted levels of DLIR. Conclusion Adaptation of high-level DLIR for the pediatric head CT can significantly reduce image noise. Modification is needed while processing artifacts.