Current Issue : July - September Volume : 2017 Issue Number : 3 Articles : 5 Articles
Discrete cosine transform(DCT) has been an international standard in Joint Photographic Experts Group (JPEG) format to reduce\nthe blocking effect in digital image compression. This paper proposes a fast discrete cosine transform(FDCT) algorithmthat utilizes\nthe energy compactness and matrix sparseness properties in frequency domain to achieve higher computation performance. For\na JPEG image of 8 Ã?â?? 8 block size in spatial domain, the algorithm decomposes the two-dimensional (2D) DCT into one pair of\none-dimensional (1D) DCTs with transform computation in only 24 multiplications.The 2D spatial data is a linear combination of\nthe base image obtained by the outer product of the column and row vectors of cosine functions so that inverse DCT is as efficient.\nImplementation of the FDCT algorithm shows that embedding a watermark image of 32 Ã?â?? 32 block pixel size in a 256 Ã?â?? 256 digital\nimage can be completed in only 0.24 seconds and the extraction of watermark by inverse transform is within 0.21 seconds. The\nproposed FDCT algorithm is shown more efficient than many previous works in computation....
Estimation problems in the presence of deterministic linear nuisance parameters arise in a variety of fields. To cope\nwith those, three common methods are widely considered: (1) jointly estimating the parameters of interest and the\nnuisance parameters; (2) projecting out the nuisance parameters; (3) selecting a reference and then taking differences\nbetween the reference and the observations, which we will refer to as ââ?¬Å?differential signal processing.ââ?¬Â A lot of literature\nhas been devoted to these methods, yet all follow separate paths.\nBased on a unified framework, we analytically explore the relations between these three methods, where we\nparticularly focus on the third one and introduce a general differential approach to cope with multiple distinct\nnuisance parameters. After a proper whitening procedure, the corresponding best linear unbiased estimators (BLUEs)\nare shown to be all equivalent to each other. Accordingly, we unveil some surprising facts, which are in contrast to\nwhat is commonly considered in literature, e.g., the reference choice is actually not important for the differencing\nprocess. Since this paper formulates the problem in a general manner, one may specialize our conclusions to any\nparticular application. Some localization examples are also presented in this paper to verify our conclusions....
This paper considers the robust waveform design of multiple-input multiple-output (MIMO) radar to enhance targets\ndetection in the presence of signal-dependent interferences assuming the knowledge of steering vectors is imprecise.\nSpecifically, resorting to semidefinite programming (SDP)-related technique, we first maximize the worst-case\nsignal-to-interference-plus-noise ratio (SINR) over uncertain region to optimize waveform covariance matrix forcing a\nuniform elemental power requirement. Then, based on least square (LS) approach, we devise the waveform\naccounting for constant modulus and similarity constraints by the obtained waveform covariance matrix using cyclic\nalgorithm (CA). Finally, we assess the effectiveness of the proposed technique through numerical simulations in terms\nof non-uniform point-like clutter and uniform clutter....
In this paper, we study the performance of asynchronous and nonlinear FBMC-based multi-cellular networks. The\nconsidered system includes a reference mobile perfectly synchronized with its reference base station (BS) and K\ninterfering BSs. Both synchronization errors and high-power amplifier (HPA) distortions will be considered and a\ntheoretical analysis of the interference signal will be conducted. On the basis of this analysis, we will derive an\naccurate expression of signal-to-noise-plus-interference ratio (SINR) and bit error rate (BER) in the presence of a\nfrequency-selective channel. In order to reduce the computational complexity of the BER expression, we applied an\ninteresting lemma based on the moment generating function of the interference power. Finally, the proposed model\nis evaluated through computer simulations which show a high sensitivity of the asynchronous FBMC-based\nmulti-cellular network to HPA nonlinear distortions....
Conventional algorithms used for parameter estimation in colocated multiple-input-multiple-output (MIMO) radars\nrequire the inversion of the covariance matrix of the received spatial samples. In these algorithms, the number of\nreceived snapshots should be at least equal to the size of the covariance matrix. For large size MIMO antenna arrays,\nthe inversion of the covariance matrix becomes computationally very expensive. Compressive sensing (CS) algorithms\nwhich do not require the inversion of the complete covariance matrix can be used for parameter estimation with fewer\nnumber of received snapshots. In this work, it is shown that the spatial formulation is best suitable for large MIMO arrays\nwhen CS algorithms are used. A temporal formulation is proposed which fits the CS algorithms framework, especially\nfor small size MIMO arrays. A recently proposed low-complexity CS algorithm named support agnostic Bayesian\nmatching pursuit (SABMP) is used to estimate target parameters for both spatial and temporal formulations for the\nunknown number of targets. The simulation results show the advantage of SABMP algorithm utilizing low number of\nsnapshots and better parameter estimation for both small and large number of antenna elements. Moreover, it is\nshown by simulations that SABMP is more effective than other existing algorithms at high signal-to-noise ratio....
Loading....