Earticle

Cyclic prefix (CP) is one of the most important technique to OFDM system and is reducing inter-symbol interference (ISI) effects in high speed wireless mobile communication system. At the time varying channel condition, however, fixed CP length is not only increasing power consumption but also reducing data transmission rate. So in this paper, we propose the system that has adaptive CP length for high speed data transmission system. We don’t control CP length of every symbol but adjust symbol interval depending on channel condition to CP reconstruction.

In this study, the correlation among the changes of Modulation Transfer Function(MTF) in the noise and high-contrast resolution and the change of Contrast to noise ratio(CNR) in the low-contrast resolution will be examined to investigate the estimation of image quality according to the type of algorithms. The image data obtained by scanning American Association of Physicists in Medicine(AAPM) phantom was applied to each algorithm and the exposure condition of 120 kVp, 250 mAs, and then the CT number and noise were measured. The MTF curved line of the high-contrast resolution was calculated with Point Spread Function(PSF) by using the analysis program by Philips, resulting in 0.5 MTF, 0.1 MTF and 0.02 MTF respectively. The low-contrast resolution was calculated with CNR and the uniformity was measured to each algorithm. Since the measurement value for the uniformity of the equipment was below ± 5 HU, which is the criterion figure, it was found to belong to the normal range. As the algorithm got closer from soft to edge, the standard deviation of CT number increased, which indicates that the noise increased as well. As for MTF, 0.5 MTF, 0.1 MTF and 0.02 MTF were all sharp algorithms, and as the algorithm got closer from soft to edge, it was possible to distinguish more clearly with the naked eye. On the other hand, CNR gradually decreased, because the difference between the contrast hole CT number and the acrylic CT number was the same while the noise of hole increased.

본 연구는 환자 피폭선량의 감소를 위한 목적으로 일개 병원에서 시행되는 경부 ct 검사의 촬영조건에서 관 전압과 영상처리 알고리즘의 적절한 적용으로 기존 영상 화질과 유사한 촬영조건과 방사선량을 평가하였다. 2021년 6월부터 11월까지 내원한 여성 환자 150명을 대상으로 경부 CT 검사를 시행하였다. 촬영조건은 80kV, 100kV, 120kV 자동 노출 조절 방법으로 영상을 획득하였다. 쵤영 후 영상 재구성 알고리즘인 SAFIRE를 단계적 0, 1, 3, 5 로 적용하였고, 경부 주변 조직에 관심 영역을 설정하여 신호대잡음비(Signal to noise, SNR)와 대조도비(Contrast to noise ratio, CNR)로 영상을 정량적 분석하였다. 그룹 간 비교에서 SAFIRE의 강도가 증가할수록 노이즈가 감소 하였으며 신호대잡음비, 대조도비가 증가하는 양상을 보였다. 또한 120kV의 자동 노출 조절 방법과 SAFIRE 강도 3과 비교적 유사한 영상의 화질을 가지는 조건은 100kV의 자동 노출 조절 방법과 SAFIRE 강도 3으로 나타났다. 120kV 자동 노출 조절 방법에서 100kV 자동 노출 조절 방법으로 낮추었더니 방사선량이 13.73%로 감소하였다.

This study evaluated the imaging conditions and radiation dose similar to the existing image quality by appropriate application of tube voltage and image processing algorithm in the imaging conditions of a cervical ct test performed at a hospital for the purpose of reducing the patient's exposure dose. Cervical CT scans were performed on 150 female patients who visited the hospital from June to November 2021. Images were acquired by automatic exposure control method of 80kV, 100kV, and 120kV under the shooting conditions. SAFIRE, a post-image reconstruction algorithm, was applied in steps of 0, 1, 3, and 5, and the region of interest was set in the surrounding tissues of the neck to obtain images with Signal to Noise (SNR) and Contrast to Noise Ratio (CNR). was quantitatively analyzed. In comparison between groups, as the intensity of SAFIRE increased, the noise decreased, and the signal-to-noise ratio and contrast ratio increased. In addition, the conditions of the automatic exposure control method of 120kV and the image quality relatively similar to SAFIRE intensity 3 were the automatic exposure control method of 100kV and SAFIRE intensity 3. When it was lowered from the 120kV automatic exposure control method to the 100kV automatic exposure control method, the radiation dose was reduced to 13.73%.

광기전공학 기술이 융합된 광학계의 발달은 광학계를 구성하는 부품의 표면이 비구면 또는 자유곡면으로 진화하고 있다. 본 논문에서는 광학식 자유곡면의 국부영역으로부터 직교하는 2방향의 곡률을 정의하여 전체 형상 을 복원하는 알고리즘을 제안하였다. 8.4 m 자유곡면 형상을 가진 반사거울에 구현된 알고리즘을 적용한 결과 형상 복원 최대오차 0.065 nm, 평균제곱근 오차 0.013 nm로 복원됨을 확인하였다. 프루브의 위치오차 발생에 대한 노이 즈 민감도를 해석한 결과, 2 mm 오차에 대해서도 형상복원 최대오차 30 nm, 제곱평균제곱근 오차 8.7 nm로 위치 오차에 매우 둔감한 알고리즘임을 확인하였다.

The demand for accurate freeform apsheric surface is increasing to satisfy the optical performance. In this paper, we develop the algorithm for opto-mechatronics convergence, that reconstruct the surface 3D profiles from the curvarure data along two orthogonal directions. A synthetic freeform surface with 8.4 m diameter was simulated for the testing. The simulation results show that the reconstruction error is 0.065 nm PV(Peak-to-valley) and 0.013 nm RMS(Root mean square) residual difference. Finally the sensitivity to noise is diagnosed for probe position error, the simulation results proving that the suggested method is robust to position error.

본 연구는 얼굴인식의 복구 처리과정 중 본 연구자의 개발된 고유 해결 일반 알고리즘을 얼굴인식의 고유얼굴 생성 을 위해 적용하였다. 얼굴 복구의 결과를 통한 본 알고리즘의 신뢰성과 오차의 한계들을 살펴볼 수 있었으며 주어 진 얼굴 인식 문제에서도 본 알고리즘을 통해 병렬처리가 가능함을 제시하고 있다. 본 연구는 얼굴이미지를 다양한 동작과 형태로 25개의 실험 데이터를 적용했으며, 실제 얼굴 이미지와 복원된 얼굴 이미지 비교 과정도 아울러 소 개하고 있다. 특히 얼굴인식의 주성분알고리즘에서 요구하는 최고값의 고유 값들을 순차적으로 생성하는 과정을 본 연구자의 고유문제해결의 알고리즘을 통해 병렬적 방법으로 최고값들의 고유치들을 생성할 수 있는 강점을 제시하 고 있다.

Abstract This paper introduces the application of a new algorithm developed by the author that has solved the eigenproblem to find the eigenface, which is created in face recognition, within the process of reconstruction processing of face recognition. The reliability and limit of error of this algorithm can be observed from the face reconstruction results. Also, parallel processing can be performed through this algorithm in the given face recognition problem. A simulation was operated on 25 face images with different motions and various forms. Furthermore, this paper presents the process of comparing the original image with the reconstructed image. Moreover, it presents the advantage of creating the eigenvalues of the largest values through the algorithm of solving the eigenproblem, which the author introduces, in the process of sequentially creating the eigenvalues of the largest values, which is demanded in principle component algorithm of face recognition.

Compressed sensing (CS) provides a new solution for the problems of requiring large amount of measurements data and long data acquisition time in radar application, and both issues also exist in ground penetrating radar (GPR). Aiming at this problem, we adopt impulse radar with CS framework, and transform the GPR imaging into sparse constraint optimization problem performed on time-domain sub-sampling in this paper. Specifically, it focuses on the impulse GPR imaging method based on CS under double underground targets condition containing noise and abundant clutter. Furthermore, the performance of matching reconstruction algorithms under the different signal to noise ratios (SNR), measurement dimensions and sparseness values is also presented. The experimental results show that CS algorithms based on matching reconstruction can obviously reduce measurement data, improve the image quality and make a better anti-noise performance. When SNR of measurement data is 1dB, the probability of accurate imaging can still reach 95%. So we may reasonably conclude that the regularized orthogonal matching pursuit algorithm has a better performance than the other matching algorithms.

To solve the "soft field" effect and the ill-posed problem in electrical capacitance tomography technology, a novel image reconstruction algorithm based on population entropy and adaptive differential evolution for Electrical Capacitance Tomography is proposed in this study. The algorithm uses all the gray pixels as the initial population’s individual. After finite iterations, the algorithm mutates and makes crossover of the population in order to obtain the optimal species populations. That is the optimal value for the ECT imaging pixels. The population entropy and the variation factor make the range of each searching generation decreasing. In the simulation, the improved adaptive differential evolution algorithm will be compared with the LBP algorithm. The result shows that the new algorithm has better image quality and more stable boundary than the LBP Algorithm, which provides a new way to reconstruct images for ECT.

According to the image reconstruction accuracy influenced by the “soft field” nature and the limited projection data in electrical capacitance tomography, based on the working principle of the electrical capacitance tomography system, a Novel image reconstruction algorithm based on compressed sensing is proposed in the paper. The method based on ART (algebra reconstruction technique) organically combines the gradient sparse of image and ART, and reduces the norm of image gradient with full-variational method, and improves the accuracy and speed of image reconstruction. Experimental results and simulation data indicate that the imaging accuracy is markedly improved, and the image is closed to the prototype. This new algorithm presents a feasible and effective way to research on image reconstruction algorithm for Electrical Capacitance Tomography System.

In order to solve the problem of image reconstruction in electrical capacitance tomography (ECT) technology, the feasibility of applying convolutional neural network (CNN) to ECT image reconstruction is studied. The convolution layer and the training of the structure of the sub sampling method is improved based on deep research for convolution neural network for the more time-consuming process of deep structure and training issues, and a fast convergence convolution neural network (FCCNN) image reconstruction method is proposed. Matlab was used to build a ECT simulation system. For each algorithm, the simulation results were compared and analyzed. The experimental results show that our algorithm improved the image reconstruction efficiency and quality of the common flow pattern.

Due to the problem of low imaging accuracy and slow imaging speed when applying SVM image reconstruction algorithm in ECT system to dealing with a large amount of sample data set, the method of combining feature dimension reduction with SVM algorithm is proposed. This method classifies the sample data by using the way of clustering and extracts the feature parameter, finds out the connection between each sample and the feature, and deals the sample data with dimension reduction, thus finally getting the high-quality training sample. Then it trains the simplified sample data by applying SVM algorithm and obtains decision function, then the decision function is used to predict and image. The experimental results of image reconstruction show that this method greatly reduces the running time and improves the accuracy of imaging compared to using the SVM algorithm alone.

Superresolution image reconstruction technique uses single or a series of low-resolution images to reconstruct a high resolution image without changing the hardware devices, while improving image quality and the spatial resolution of the image. High resolution means the image with a higher pixel density, can provide more details. In this paper, a novel image superresolution algorithm based on sparse representation is studied. During over-complete dictionary of the training phase, the proposed method improves two aspects including feature extraction and dimension reduction. In the feature extraction process, combining the second derivative with the gradient direction, we construct a new descent direction to improve gradient method. The convergence speed of the new algorithm is faster than the gradient method and can get better results. Then improved two-dimensional Principal Component Analysis (2DPCA) algorithm is used to reduce the dimension, it could eliminate the correlation of the image lines and column. Experiment results show that this method of image reconstruction is better and faster for high resolution image reconstruction.

The paper introduces the Keren registration method and points out its disadvantage which means it will become inaccuracy on the large scale parameters. To reduce the error on large scale parameters of Keren registration, a two step method is proposed, which the phase correlation algorithm is used to estimate the large translation and rotation angle roughly and the improved Keren algorithm is used to estimate accurately the small translation and rotation angle. The experimental results show that the two step method makes less absolute error of angle than Keren method in the situation of large translation and rotation angle. A new method of estimating the standard deviation of noise is introduced to the robust certainty function, which reduces the impact of noise in the process of interpolation using normalized convolution algorithm. By the edge detection of fusion image in the first stage of the interpolation process of normalized convolution algorithm, a calculation method of long axis and short axis of the structure self-adaptive function is improved. The experimental results show that the proposed interpolation method can improve the performance of the original algorithm and enhance the effect of image super-resolution reconstruction.

Image super resolution reconstruction has important significance in remote sensing image feature extraction and classification etc.. Because the remote sensing image size is larger, it is difficult to super resolution reconstruction using multiple images, the compressed sensing (CS) theory was introduced into the super-resolution reconstruction. Algorithm designed the low pass filter to reduce the sample correlation matrix and wavelet, at the same time, the algorithm selects the partial Hadamard-matrix as the measurement matrix, it has faster reconstruction speed and low storage requirements, which ensure that the image reconstruction keep with the RIP criterion of compressed sensing theory . Finally, this paper realizes the remote sensing image super resolution reconstruction through the improved iterative algorithm. Experiments show that the reconstructed images of the PSNR value has increased, the reconstructed image has a better visual effect.

This paper presents a self-organized fuzzy neural network (SOFNN) surface reconstruction algorithm suitable for point clouds without normal. It overcomes the defect of traditional Delaunay triangulation which is difficult to reconstruct point clouds with noises and implicit function which is limited to the number of point clouds and point clouds are required very strict. The SOFNN is based on the fuzzy clustering method optimizing training data before learning fuzzy rules, in order to remove noise data and resolve conflicts in data. The approach not only reduce computational burden of neural network, but also make it easy to fit the surface for point clouds without normal and suitable for mass point clouds. The feature of the SOFNN has dynamic self-organized structure, fast learning speed and flexibility in learning. The experiment results show that is very fine.