Project Detail
Supreme Software Technologies combines powerful and effective identity verification, validation and fraud detection tools to instantly authenticate businesses and individuals. With access to the industry’s most robust database and the Customer Digital Recognizer Network, you can instantly connect to aggregated information from over 37 billion public and proprietary records that cover 95% of the U.S. adult population, including entities with limited credit and financial histories. We deliver analytics backed, our proprietary advanced linking technology that synthesizes disparate data sets and intelligently examines data interconnections to provide meaningful identity insight and help uncover indicators of potential fraud.
Automatic recognition of the communication signals plays an important role for various applications. Most of the existing techniques require high levels of signal to noise ratio (SNR). In our solution, we propose a high efficient technique for classification of the digital modulations that requires a low level of SNRs. This technique includes two main modules: feature extraction module and the classifier module. In the feature extraction module we use the auto-regressive modeling together other useful features. These features are a combination set of the entropy and energy of the signal, variance of the coefficients wavelet packet transform, fourth order of moment and zero-crossing rate. In the classifier module we have used the two structures of the neural networks: multi-layer perceptron (MLP) neural network and radial basis neural networks. Simulation results show the proposed technique has very high recognition accuracy for identification of the considered digital modulations even at very low SNRs.
