One of problems of OFDM systems, is the big value of peak to average power ratio. To reduce it, any attempt have been done amongst which, random phase updating is an important technique. In contrast to paper, since power variance is computable before IFFT block, the complexity of this method would be less than other phase injection methods which could be an important factor. Another interesting capability of random phase updating technique is the possibility of applying the variance of threshold power. The operation of phase injection is repeated till the power variance reaches threshold power variance. However, this may be a considered as a disadvantage for random phase updating technique. The reason is that reaching the mentioned threshold may lead to possible system delay. In this paper, in order to solve the mentioned problem, (DCT) TRANSFORM is applied on subcarrier outputs before phase injection. This leads to reduce the number of required carriers for reaching the threshold value which results in reducing system delay accordingly.

Image retrieval is one of the most applicable image processing techniques, which have been used extensively. Feature extraction is one of the most important procedures used for interpretation and indexing images in content-based image retrieval (CBIR) systems. Reducing the dimension of feature vector is one of the challenges in CBIR systems. There are many proposed methods to overcome these challenges. However, the rate of image retrieval and speed of retrieval is still an interesting field of research. In this paper, we propose a new method based on the combination of Hadamard matrix, DISCRETE wavelet TRANSFORM (HDWT2) and DISCRETE COSINE TRANSFORM ((DCT)) and we used principal component analysis (PCA) to reduce the dimension of feature vector and K-nearest neighbor (KNN) for similarity measurement.The precision at percent recall and ANR are considered as metrics to evaluate and compare different methods.Obtaining results show that the proposed method provides better performance in comparison with other methods.

Purpose: Different views of an individuals’ image may be required for proper face recognition.Recently, DISCRETE COSINE TRANSFORM ((DCT)) based method has been used to synthesize virtual views of an image using only one frontal image. In this work the performance of two different algorithms was examined to produce virtual views of one frontal image.Materials and Methods: Two new methods, based on neural networks and principle component analysis (PCA) were used to make virtual views of an image. The results were compared with those of the (DCT)-based method. Two distance metrics, i.e. mean square error (MSE) and structural similarity index measure (SSIM), were used to measure and compare image qualities. About 400 data were used to evaluate the performance of the new proposed methods.Results: The neural networks fail to improve the quality of virtually produced images. However, principle component analysis improved the quality of the synthesized images about 3%.Conclusion: Principle component analysis is better than both (DCT)-based and neural network methods for synthesizing virtual views of an image.