Results 211 to 220 of about 17,994 (253)

Automatic recognition of digitally modulated communications signals

ISSPA '99. Proceedings of the Fifth International Symposium on Signal Processing and its Applications (IEEE Cat. No.99EX359), 2003
This paper introduces an algorithm that extends the capability of digital modulations classifiers to cope with signals that have memory incorporated in their modulation scheme. The algorithm employs the decision-theoretic approach where the identification of different modulation types is performed by developing a set of decision criteria.
V. Ramakomar, Daryoush Habibi, Abdesselam Bouzerdoum   +2 more
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A novel method for automatic modulation recognition

Applied Soft Computing, 2012
Automatic recognition of the digital modulation plays an important role in various applications. This paper investigates the design of an accurate system for recognition of digital modulations. First, it is introduced an efficient pattern recognition system that includes two main modules: the feature extraction module and the classifier module. Feature
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Revised architecture for automatic modulation recognition

International Journal of Information Technology, 2019
Cognition and adaptability is a prominent aspect of Communication Intelligence (COMINT). Automatic Modulation Recognition (AMR) is one such aspect. AMR focuses on identifying modulation technique used at a particular carrier frequency which is got by spectral analysis (SA).
Sunil S. Mathad, C. Vijaya
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Modified Automatic Modulation Recognition Algorithm

2009 5th International Conference on Wireless Communications, Networking and Mobile Computing, 2009
The modulation identification algorithm proposed by Asoke K. Nandi and E. E. Azzouz is one of the important methods in modulation identification field. A solution to the problem you may meet in realizing this algorithm is presented in this paper. A few modifications are made to the identification parameters gamma_max, sigma_ap and sigma_dp.
Jie Yang, Xumeng Wang, Hongli Wu
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Automatic Modulation Recognition with Deep Learning Algorithms

2024 32nd Signal Processing and Communications Applications Conference (SIU)
In this study, an automatic modulation classifier based on Convolutional Neural Network (CNN) was developed using deep learning algorithms. A synthetic dataset generated with GNU Radio consisting of eleven modulations at varying signal-to-noise ratios was used for classification. The time-frequency domain images of the complex signals are generated. An
Çamlıbel, Ayşenur, Karakaya, Bahattin, Tanç, Yeşim Hekim   +2 more
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On properties of modulation spectrum for robust automatic speech recognition

Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181), 2002
We report on the effect of band-pass filtering of the time trajectories of spectral envelopes on speech recognition. Several types of filter (linear-phase FIR, DCT, and DFT) are studied. Results indicate the relative importance of different components of the modulation spectrum of speech for ASR.
Noboru Kanedera, Hynek Hermansky, Takayuki Arai   +2 more
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Automatic Generation of Modules of Visual Recognition

Applied Mechanics and Materials, 2013
We consider the problem of automatic generation of visual recognition modules. In particular, we consider a self-learning algorithm for visual recognition and system of automatic generation that based on some biological observations.
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Automatic Modulation Recognition Using Deep Learning Architectures

2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2018
In this paper, we present an automatic modulation recognition framework for the detection of radio signals in a communication system. The framework considers both a deep convolutional neural network (CNN) and a long short term memory network. Further, we propose a pre-processing signal representation that combines the in-phase, quadrature and fourth ...
Meng Zhang, Yuan Zeng 0001, Zidong Han, Yi Gong 0001   +3 more
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Sparsely Connected CNN for Efficient Automatic Modulation Recognition

IEEE Transactions on Vehicular Technology, 2020
This paper proposes a convolutional neural network (CNN), called SCGNet, for low-complexity and robust modulation recognition in intelligent communication receivers. Principally, the network combines two types of sparse convolutional layers–depthwise and regular grouped in an architecture to achieve high recognition accuracy while keeping the network ...
Godwin Brown Tunze, Thien Huynh-The, Jae-Min Lee 0001, Dong-Seong Kim 0002   +3 more
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The modulation spectrum in the automatic recognition of speech

1997 IEEE Workshop on Automatic Speech Recognition and Understanding Proceedings, 2002
The article questions the reliability of the short term spectral envelope as the dominant carrier of the phonetic identity of a given speech instant and suggests the temporal dynamics of components of the spectral envelopes as more reliable means for deriving the linguistic context of the speech message.
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