• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.11 no.1
• /
• pp.1-6
• /
• 2007

In this paper, a simplified new modulation classification method that utilizes likelihood function for received signal in an impaired AWGN channel environment. The proposed method provides the superior to ML method, although the likelihood under the assumption that each modulated signal is sent utilized. On the other hand, the ML method gets the performance characteristics of high computational complexity and weakness to channel impairment such as phase offsets and frequency offsets. The proposed method has lower computational complexity than that of the ML method. Moreover, the proposed method is robust to the channel impairment such as phase offsets and frequency offsets. The correct classification probabilities of the proposed method and the ML method are given for an AWGN channel with phase offsets and frequency offsets, which were simulated with extensive Monte-Carlo simulation. As shown in simulation resole, a more accurate classification performance both in phase offset environment and in frequency offset can be achieved with the low computational complexity of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.9
• /
• pp.1176-1182
• /
• 2021

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.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.11
• /
• pp.1468-1473
• /
• 2018

The importance of communication and data processing is increasing with the advance of the Fourth Industrial Revolution. Hence, the importance of video and data processing technologies, which directly influence the accuracy and reliability of equipment, is also increasing. In this research report we propose an algorithm for calculating the final output by estimating the standard deviation and estimate required for removing AWGN while adapting to changes in the frequency factors of video. This algorithm calculates the final output by checking an estimated value against the effective pixel range, which is obtained from the standard deviation of mask factors. Subsequently, the weighted value is computed, taking into account the filter output. To evaluate the functionality of this algorithm, it is compared with the most-commonly used present method through simulation. The simulation results show that the important features of the image are preserved and efficient noise cancellation performance is demonstrated.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.10
• /
• pp.1310-1316
• /
• 2021

Recently, with the development of artificial intelligence and IoT technology, the importance of video processing such as object tracking, medical imaging, and object recognition is increasing. In particular, the noise reduction technology used in the preprocessing process demands the ability to effectively remove noise and maintain detailed features as the importance of system images increases. In this paper, we provide a modified weight filter based on pixel matching in an AWGN environment. The proposed algorithm uses a pixel matching method to maintain high-frequency components in which the pixel value of the image changes significantly, detects areas with highly relevant patterns in the peripheral area, and matches pixels required for output calculation. Classify the values. The final output is obtained by calculating the weight according to the similarity and spatial distance between the matching pixels with the center pixel in order to consider the edge component in the filtering process.

• Journal of the Institute of Convergence Signal Processing
• /
• v.23 no.1
• /
• pp.44-49
• /
• 2022

Abstract Recently, with the development of IoT technology and communication media, various video equipment is being used in industrial fields. Image data acquired from cameras and sensors are easily affected by noise during transmission and reception, and noise removal is essential as it greatly affects system reliability. In this paper, we propose a weight filter algorithm based on the pixel value distribution pattern to preserve details in the process of restoring images damaged in AWGN. The proposed algorithm calculates weights according to the pixel value distribution pattern of the image and restores the image by applying a filtering mask. In order to analyze the noise removal performance of the proposed algorithm, it was simulated using enlarged image and PSNR compared to the existing method. The proposed algorithm preserves important characteristics of the image and shows the performance of efficiently removing noise compared to the existing method.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.26 no.2
• /
• pp.207-213
• /
• 2022

Recently, with the improvement of the performance of IoT technology and AI, automation and unmanned work are progressing in a wide range of fields, and interest in image processing, which is the basis of automation such as object recognition and object classification, is increasing. Image noise removal is an important process used as a preprocessing step in an image processing system, and various studies have been conducted. However, in most cases, it is difficult to preserve detailed information due to the smoothing effect in high-frequency components such as edges. In this paper, we propose an algorithm to restore damaged images in AWGN(additive white Gaussian noise) using fuzzy weights based on Gaussian distribution. The proposed algorithm switched the filtering process by comparing the filtering mask and the noise estimate with each other, and reconstructed the image by calculating the fuzzy weights according to the low-frequency and high-frequency components of the image.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.25 no.12
• /
• pp.1846-1852
• /
• 2021

In modern society, various digital devices are being distributed in a wide range of fields due to the fourth industrial revolution and the development of IoT technology. However, noise is generated in the process of acquiring or transmitting an image, and not only damages the information, but also affects the system, causing errors and incorrect operation. AWGN is a representative noise among image noise. As a method for removing noise, prior research has been conducted, and among them, AF, A-TMF, and MF are the representative methods. Existing filters have a disadvantage that smoothing occurs in areas with high frequency components because it is difficult to consider the characteristics of images. Therefore, the proposed algorithm calculates the standard deviation distribution to effectively eliminate noise even in the high frequency domain, and then calculates the final output by applying the probability density function weight of the Laplace distribution using the curve fitting method.

• Proceedings of the IEEK Conference
• /
• 1998.06a
• /
• pp.7-10
• /
• 1998

The error performance of 16 Square-QAM signal with Optimum Threshold Detection(OTD) under the consideration of AWGN and Rician fading was analyzed for various value of Rician fading depth K. And error performance of 16-QAM signal with OTD, which considered in AWGN and Rician fading, was compared with that of 16-QAM signal with OTD, which considered in Rician fading only. And BCH coding is adopted. From the results, the error perfomance evlauated by proposed OTD was superior to that of 16-QAM signal with OTD, which considered in Rician fading without AWGN, in low CNR.

• Proceedings of the IEEK Conference
• /
• 1999.06a
• /
• pp.77-80
• /
• 1999

This paper deals with a design of a symbol timing recovery circuit of QAM using the interpolation in AWGN channel. To reduce timing jitter and the amount of processing data, we employ MGA (Modified Gardner Algorithm) as a symbol timing error detector which is called NDA(Nondecision Directed Algorithm). We show the characteristics (S-curve and the variance) of timing error detector with the roll-off factor of a shaping filter, which are compared with GA. Also, we compare the BER curve of interpolation method with that of ideal case. The performance of the STR is shown to be close to that of ideal case. This result shows that this method can be useful to implement symbol timing recovery circuit for multi-level modulation.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.4A
• /
• pp.312-317
• /
• 2011

In this paper, we studied the frequency hopping(FH) performance on the FH radio communication system via M&S of initial synchronization architecture. It is difficult to test the synchronization performance on the real FH communication environment. Thus, we have modeled the synchronization system similar to the real radio communication structure, and simulated the optimum correlation value and the synchronization performance on AWGN channel. As the result, we could obtain the thresholds of correlation synchronization and the FH synchronization performance under the low SNR.