• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.3
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• pp.427-432
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• 2021

In this paper, we conduct performance analysis in automatic modulation classification of unknown communication signal to identify its modulation types based on deep neural network. The modulation classification performance was verified using time domain digital sample data of the modulated signal, frequency domain data to which FFT was applied, and time and frequency domain mixed data as neural network input data. For 11 types of analog and digitally modulated signals, the modulation classification performance was verified in various SNR environments ranging from -20 to 18 dB and reason for false classification was analyzed. In addition, by checking the learning speed according to the type of input data for neural network, proposed method is effective for constructing an practical automatic modulation recognition system that require a lot of time to learn.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.4
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• pp.22-30
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• 2008

In this paper, we deal with modulation classification method using WT capable of classifying incident digital signals without a priori information. These key features should have good properties of sensitive with modulation types and insensitive with SNR variation. The 4 key features for modulation recognition are selected using WT coefficients, which have the property of insentive to the changing of noise. The numerical simulations for classifying 8 digital modulation types using these features are peformed. The numerical simulations of the 3 types (i.e. DTC, MDC, and SVMC) of modulation classifiers are performed the investigation of classification accuracy and execution time to design the modulation classification module in software radio. The simulation result indicated that the execution time of MDC and DTC was best and MDC and SVMC showed good classification performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4C
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• pp.425-432
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• 2007

In this paper, we deals with automatic modulation classification capable of classifying incident signals without a priori information. The 7 key features which have good properties of sensitive with modulation types and insensitive with SNR variation are selected. The numerical simulations for classifying 9 modulation types using the these features are performed. The numerical simulations of the 4 types of modulation classifiers are performed the investigation of classification accuracy and execution time to implement the fast modulation classifier in software radio. The simulation result indicated that the execution time of DTC was best and SVC and MDC showed good classification performance. The prototype was implemented with DTC type. With the result of field trials, we confirmed the performance in the prototype was agreed with the numerical simulation result of DTC.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.381-384
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• 2004

본 논문에서는 변조 함수법을 이용하여 비선형 연속시스템의 퍼지모델 파라미터 인식을 위한 새로운 알고리즘을 제시하였다. 동력학 미분방정식은 미분항을 가지고 있기 때문에 입출력 데이터를 이용하여 퍼지모델 파라미터를 인식하는 경우 외란의 영향을 무시할 수 없으므로 퍼지모델 파라미터 인식이 어렵다. 그러나 변조 함수법을 이용하면 미분항을 소거할 수 있어 미분항이 없는 연립방정식으로부터 쉽게 퍼지모델 파라미터 인식이 가능하다 몇 개의 시뮬레이션을 통해 제안한 변조 함수법을 이용한 퍼지모델 파라미터 인식의 정확성과 유효성을 확인할 수 있었다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.23 no.4
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• pp.805-814
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• 1998

An efficient image processing algorithm is proposed to recognize both the height and width modulated barcodes which are rotated and printed at an arbitrary position. The main feature of this algorithm is to utilize the gradient information of a rotated barcode with a Sobel operator. The barcode area is extracted using the gradient information, and the barcode is decoded from the binary image of the extracted area. Theis algorithm is successfully applied to the 4 state and width modulated barcodes. It takes 0.86 secoden to process a letter, and the recognition rate reaches above 98% under various testing conditions. Since both the width and height modulated barcodes are processed with the proposed algorithm, it can be applied to postal service automation.

• Review of KIISC
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• v.29 no.3
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• pp.44-50
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• 2019

최근 딥러닝 기술은 다양한 분야에서 놀라운 성능을 보여주고 있어 많은 서비스에 적용되고 있다. 얼굴인식 또한 딥러닝 기술을 접목하여 높은 수준으로 얼굴인식이 가능해졌다. 하지만 딥러닝 기술은 원본 이미지를 최소한으로 변조시켜 딥러닝 모델의 오인식을 발생시키는 적대적 예제에 취약하다. 이에 따라, 본 논문에서는 딥러닝 기반 얼굴인식 시스템에 대해 적대적 예제를 이용하여 기만공격 실험을 수행하였으며 실제 얼굴에 분장할 수 있는 영역을 고려하여 설정된 변조 영역에 따른 기만공격 성능을 분석한다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.3 s.309
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• pp.61-67
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• 2006

In electronic support systems, the analysis of PRI modulation characteristics for radar signals has attracted much interest because of the problem of the identification ambiguity in dense electronic warfare signal environments. A new method of recognizing the PRI modulation types of radar pulse signals is proposed for electronic support. The proposed method recognizes the PRI modulation types using the classifiers which are based on the property of the linear autocorrelation of the PRI sequences for each PRI modulation type. In addition, the proposed method estimates the PRI modulation period for the PRI modulation types with the periodicity. Simulation results are presented to show the performance of the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.10
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• pp.1275-1281
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• 2019

The automatic modulation recognition of a radio signal is a major task of an intelligent receiver, with various civilian and military applications. In this paper, we propose a method to recognize the modulation of radio signals in wireless communication based on the deep neural network. We classify the modulation pattern of radio signal by using the LSTM model, which can catch the long-term pattern for the sequential data as the input data of the deep neural network. The amplitude and phase of the modulated signal, the in-phase carrier, and the quadrature-phase carrier are used as input data in the LSTM model. In order to verify the performance of the proposed learning method, we use a large dataset for training and test, including the ten types of modulation signal under various signal-to-noise ratios.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.9
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• pp.2009-2019
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• 2013

In this paper, we propose an automatic modulation recognition algorithm based on cyclic moment and new modified cumulant for analog and digital modulation signals. It is noteworthy that each modulated signal has different cycle frequency characteristics according to its order of cyclic moment. By means of this characteristics as classification features, various modulated signals can be efficiently classified. Also, to identify modulated signals having the same cycle frequency characteristics, we take advantage of the additional classification factors such as variations of envelope and phase as well as modified cumulant. The proposed algorithm was evaluated by considering the number of symbols, SNR, and frequency offset. In the simulation condition where the number of gathered symbols was about 819, and SNR and frequency offset were above 10dB and below 25%, respectively, the average accuracy of the proposed algorithm was more than 95%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.1C
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• pp.73-81
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• 2005

We propose an automatic modulation recognition scheme which extracts pre-defined key features from the received signal and then applies equal gain combining method to determine the used modulation. Moreover, we compare and analyze the performance of the proposed algorithm with that of decision-theoretic algorithm. Our scheme extracts five pre-defined key features from each data segment, a data unit for the key feature extraction, which are then averaged over all the segments to recognize the modulation according to the decision procedure. We check the performance of the proposed algorithm through computer simulations for analog modulations such as AM, FM, SSB and for digital modulations such as FSK2, FSK4, PSK2, and PSK4, by measuring recognition success rate varying SNR and data collection time. The result shows that the performance of the proposed scheme is comparable to that of the decision-theoretic algorithm with less complexity.