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This paper presents on-wafer measurement methods for the scattering coefficient, power, and noise figure of a semiconductor circuit in the D band (110∼170 GHz). The calibration algorithms for the S-parameter measurement were compared, and the effect of the total coplanar waveguide (CPW) width of the commercial calibration standards was examined. In the power measurement, the accuracy of the measurement value was improved through precision path loss evaluation. In addition, in the noise figure measurement, the system configuration and calibration method are presented, and the path loss compensation is examined. The detailed configuration and consid- erations for each measurement system are presented, and the measured results of the designed low-noise amplifier (LNA) chip are presented. [ABSTRACT FROM AUTHOR]

Portable systems with built-in active noise control is required low power operation. Excessive anti noise search operation can lead to rapid battery consumption. A method that can adaptively cancel noise according to the operating conditions of the system is required and the methods of reducing power are becoming very important key feature in today's portable systems. In this paper, we propose the method of active noise control(ANC) using binary search algorithm in noisy systems. The implemented architecture detects a frequency component considered as noise from the input signal and by using the binary search algorithm, the system find out an appropriate amplitude value for anti-noise in a much faster time than the general linear search algorithm. Through the experimental results, it was confirmed that the proposed algorithm performs a successful functional operation. [ABSTRACT FROM AUTHOR]

Recently, with the development of artificial intelligence and IoT technology, automation, and unmanned work are progressing in various fields, and the importance of image processing, which is the basis of AI object recognition, is increasing. In particular, in systems that require detailed data processing, noise removal is used as a preprocessing step, but the existing algorithm does not consider the noise level of the image, so it has the disadvantage of blurring in the filtering process. Therefore, in this paper, we propose a modified Gaussian filter that determines the weight by determining the noise level of the image. The proposed algorithm obtains the noise estimate for the AWGN of the image using quadtree segmentation, determines the Gaussian weight and the pixel weight, and obtains the final output by convolution with the local mask. To evaluate the proposed algorithm, it was simulated compared to the existing method, and superior performance was confirmed compared to the existing method. [ABSTRACT FROM AUTHOR]

In this paper, we propose an algorithm to remove AWGN by considering the characteristics of noise present in the image. The proposed algorithm uses block matching to calculate the output, and calculates an estimate by determining the similarity between the center mask and the matching mask. The output of the filter is calculated by adding or subtracting the estimated value and the input pixel value, and weighting is given according to the standard deviation of the center mask and the noise constant to obtain the final output. In order to evaluate the proposed algorithm, the simulation was performed in comparison with the existing methods, and analyzed through the enlarged image and PSNR comparison. The proposed algorithm minimizes the effect of noise, preserves important characteristics of the image, and shows the performance of removing noise efficiently. [ABSTRACT FROM AUTHOR]

In a multi-function radar, the adaptive beamforming algorithm is one of the several methods that can be applied to restore the degraded SINR(Signal-to-Interference Noise Ratio) to its original level. This algorithm is employed for the regular operation of radars in jamming situations, when unwanted high-power signals are received. In this paper, we introduce a few adaptive beamforming algorithms that can be applied to a multi-function digital radar in jamming situations. Furthermore, we compare and analyze the adaptive beamforming performance in terms of SINR and sidelobe level characteristics by applying each algorithm to a specific planar array comprising optimized subarray structures. [ABSTRACT FROM AUTHOR]

Fuzzy clustering, represented by FCM(Fuzzy C-Means), is a simple and efficient clustering method. However, the object function in FCM makes clusters affect clustering results proportional to the density of clusters, which can distort clustering results due to density difference between clusters. One method to alleviate this density problem is EDI-FCM(Extended Density-Independent FCM), which adds additional terms to the objective function of FCM to compensate for the density difference. In this paper, proposed is an enhanced EDI-FCM using regularization, Regularized EDI-FCM. Regularization is commonly used to make a solution space smooth and an algorithm noise insensitive. In clustering, regularization can reduce the effect of a high-density cluster on clustering results. The proposed method converges quickly and accurately to real centers when compared with FCM and EDI-FCM, which can be verified with experimental results. [ABSTRACT FROM AUTHOR]