Current Issue : October - December Volume : 2012 Issue Number : 4 Articles : 6 Articles
In downlink multiuser multiple-input multiple-output (MU-MIMO) systems, the zero-forcing (ZF) transmission is a\r\nsimple and effective technique for separating users and data streams of each user at the transmitter side, but its\r\nperformance depends greatly on the accuracy of the available channel state information (CSI) at the transmitter\r\nside. In time division duplex (TDD) systems, the base station estimates CSI based on uplink pilots and then uses it\r\nthrough channel reciprocity to generate the precoding matrix in the downlink transmission. Because of the\r\nconstraints of the TDD frame structure and the uplink pilot overhead, there inevitably exists CSI delay and channel\r\nestimation error between CSI estimation and downlink transmission channel, which degrades system performance\r\nsignificantly. In this article, by characterizing CSI inaccuracies caused by CSI delay and channel estimation error, we\r\ndevelop a novel bit error rate (BER) expression for M-QAM signal in TDD downlink MU-MIMO systems. We find that\r\nchannel estimation error causes array gain loss while CSI delay causes diversity gain loss. Moreover, CSI delay\r\ncauses more performance degradation than channel estimation error at high signal-to-noise ratio for time varying\r\nchannel. Our research is especially valuable for the design of the adaptive modulation and coding scheme as well\r\nas the optimization of MU-MIMO systems. Numerical simulations show accurate agreement with the proposed\r\nanalytical expressions....
In this article, we propose a method for computing convolution of large 3D images. The convolution is performed\r\nin a frequency domain using a convolution theorem. The algorithm is accelerated on a graphic card by means of\r\nthe CUDA parallel computing model. Convolution is decomposed in a frequency domain using the decimation in\r\nfrequency algorithm. We pay attention to keeping our approach efficient in terms of both time and memory\r\nconsumption and also in terms of memory transfers between CPU and GPU which have a significant inuence on\r\noverall computational time. We also study the implementation on multiple GPUs and compare the results between\r\nthe multi-GPU and multi-CPU implementations...
An efficient algorithm to generate three-dimensional (3D) video sequences is presented in this work. The algorithm\r\nis based on a disparity map computation and an anaglyph synthesis. The disparity map was first estimated by\r\nemploying the wavelet atomic functions technique at several decomposition levels in processing a 2D video\r\nsequence. Then, we used an anaglyph synthesis to apply the disparity map in a 3D video sequence reconstruction.\r\nCompared with the other disparity map computation techniques such as optical flow, stereo matching, wavelets,\r\netc., the proposed approach produces a better performance according to the commonly used metrics (structural\r\nsimilarity and quantity of bad pixels). The hardware implementation for the proposed algorithm and the other\r\ntechniques are also presented to justify the possibility of real-time visualization for 3D color video sequences...
The time-frequency R�©nyi entropy provides a measure of complexity of a nonstationary multicomponent signal in\r\nthe time-frequency plane. When the complexity of a signal corresponds to the number of its components, then\r\nthis information is measured as the R�©nyi entropy of the time-frequency distribution (TFD) of the signal. This article\r\npresents a solution to the problem of detecting the number of components that are present in short-time interval\r\nof the signal TFD, using the short-term R�©nyi entropy. The method is automatic and it does not require a prior\r\ninformation about the signal. The algorithm is applied on both synthetic and real data, using a quadratic separable\r\nkernel TFD. The results confirm that the short-term R�©nyi entropy can be an effective tool for estimating the local\r\nnumber of components present in the signal. The key aspect of selecting a suitable TFD is also discussed....
This article proposed a novel human identification method based on retinal images. The proposed system\r\ncomposed of two main parts, feature extraction component and decision-making component. In feature extraction\r\ncomponent, first blood vessels extracted and then they have been thinned by a morphological algorithm. Then,\r\ntwo feature vectors are constructed for each image, by utilizing angular and radial partitioning. In previous studies,\r\nManhattan distance has been used as similarity measure between images. In this article, a fuzzy system with\r\nManhattan distances of two feature vectors as input and similarity measure as output has been added to decisionmaking\r\ncomponent. Simulations show that this system is about 99.75% accurate which make it superior to a great\r\nextent versus previous studies. In addition to high accuracy rate, rotation invariance and low computational\r\noverhead are other advantages of the proposed systems that make it ideal for real-time systems....
Explicit motion estimation is considered a major factor in the performance of classical motion-based super\r\nresolution (SR) algorithms. To reconstruct video frames sequentially, we applied a dynamic SR algorithm based on\r\nthe Kalman recursive estimator. Our approach includes a novel measurement validation process to attain robust\r\nimage reconstruction results under inexplicit motion estimation. In our method, the suitability for high-resolution\r\npixel estimation is determined by the accuracy of motion estimation. We measured the accuracy of the image\r\nregistration result using the Mahalanobis distance between the input low-resolution frame and the motion\r\ncompensated high-resolution estimation. We also incorporate an effective scene change detection method\r\ndedicated to the proposed SR approach for minimizing erroneous results when abrupt scene changes occur in the\r\nvideo frames. According to the ratio of well-aligned pixels (i.e., motion is compensated accurately) to the total\r\nnumber of pixels, we are able to detect sudden changes of scene and context in the input video. Representative\r\nexperiments on synthetic and real video data show robust performance of the proposed algorithm in terms of its\r\nreconstruction quality even with errors in the estimated motion....
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