Suchergebnisse

<p class="p1">With the tremendous development of computer graphic rendering technology, photorealistic computer graphic images are difficult to differentiate from photo graphic images. In this article, a method is proposed based on discrete wavelet transform based binary statistical image features to distinguish computer graphic from photo graphic images using the support vector machine classifier. Textural descriptors extracted using binary statistical image features are different for computer graphic and photo graphic which are based on learning of natural image statistic filters. Input RGB image is first converted into grayscale and decomposed into sub-bands using Haar discrete wavelet transform and then binary statistical image features are extracted. Fuzzy entropy based feature subset selection is employed to choose relevant features. Experimental results using Columbia database show that the method achieves good detection accuracy.</p>

<p>Wireless communication applications with large signal bandwidth are developed tremendously in recent times. Due to large bandwidth the wide band communication causes huge power consumption and signal deterioration after addition of narrow band interference (NBI). The ultra wide band (UWB) energy detector, which is highly robust against NBI signal is presented. Compressed sensing is implemented to reduce the power consumption at the analog to digital converter with approximated message passing reconstruction. In addition to this, digital notch is employed to eliminate the NBI affected measurements from compressed version of the received signal before applying it to the energy detector. To analyze the efficiency of the detector, the energy detection and bit error probability of the detector in the absence of NBI and after mitigating NBI is compared. The simulation results are the evidence of effectiveness of the presented energy detector.</p><p> </p>