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Top Documents Accessed: Oct 2009

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1.	Collaborative Cyclostationary Spectrum Sensing for Cognitive Radio Systems Lunden, J.; Koivunen, V.; Huttunen, A.; Poor, H.V. Signal Processing, IEEE Transactions on Volume 57, Issue 11, Date: Nov. 2009, Pages: 4182-4195 Digital Object Identifier 10.1109/TSP.2009.2025152 Summary: This paper proposes an energy efficient collaborative cyclostationary spectrum sensing approach for cognitive radio systems. An existing statistical hypothesis test for the presence of cyclostationarity is extended to multiple cyclic frequencies and i...

	Abstract \| Full Text: PDF (1436 KB)

2.	A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking Arulampalam, M.S.; Maskell, S.; Gordon, N.; Clapp, T. Signal Processing, IEEE Transactions on Volume 50, Issue 2, Date: Feb 2002, Pages: 174-188 Digital Object Identifier 10.1109/78.978374 Summary: Increasingly, for many application areas, it is becoming important to include elements of nonlinearity and non-Gaussianity in order to model accurately the underlying dynamics of a physical system. Moreover, it is typically crucial to process data on-...

	Abstract \| Full Text: PDF (355 KB)

3.	Decomposable Principal Component Analysis Wiesel, A.; Hero, A.O. Signal Processing, IEEE Transactions on Volume 57, Issue 11, Date: Nov. 2009, Pages: 4369-4377 Digital Object Identifier 10.1109/TSP.2009.2025806 Summary: In this paper, we consider principal component analysis (PCA) in decomposable Gaussian graphical models. We exploit the prior information in these models in order to distribute PCA computation. For this purpose, we reformulate the PCA problem in the s...

	Abstract \| Full Text: PDF (395 KB)

4.	Matching pursuits with time-frequency dictionaries Mallat, S.G.; Zhifeng Zhang Signal Processing, IEEE Transactions on Volume 41, Issue 12, Date: Dec 1993, Pages: 3397-3415 Digital Object Identifier 10.1109/78.258082 Summary: The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen in order to best match the signal struc...

	Abstract \| Full Text: PDF (2488 KB)

5.	Parameter Estimation of Phase-Modulated Signals Using Bayesian Unwrapping Morelande, M.R. Signal Processing, IEEE Transactions on Volume 57, Issue 11, Date: Nov. 2009, Pages: 4209-4219 Digital Object Identifier 10.1109/TSP.2009.2025801 Summary: Parametric estimation of phase-modulated signals (PMS) in additive white Gaussian noise is considered. The prohibitive computational expense of maximum likelihood estimation for this problem has led to the development of many suboptimal estimators whi...

	Abstract \| Full Text: PDF (614 KB)

6.	An Iterative Bayesian Algorithm for Sparse Component Analysis in Presence of Noise Zayyani, H.; Babaie-Zadeh, M.; Jutten, C. Signal Processing, IEEE Transactions on Volume 57, Issue 11, Date: Nov. 2009, Pages: 4378-4390 Digital Object Identifier 10.1109/TSP.2009.2025154 Summary: We present a Bayesian approach for sparse component analysis (SCA) in the noisy case. The algorithm is essentially a method for obtaining sufficiently sparse solutions of underdetermined systems of linear equations with additive Gaussian noise. In gen...

	Abstract \| Full Text: PDF (660 KB)

7.	Optimal Multiband Joint Detection for Spectrum Sensing in Cognitive Radio Networks Zhi Quan; Shuguang Cui; Sayed, A.H.; Poor, H.V. Signal Processing, IEEE Transactions on Volume 57, Issue 3, Date: March 2009, Pages: 1128-1140 Digital Object Identifier 10.1109/TSP.2008.2008540 Summary: Spectrum sensing is an essential functionality that enables cognitive radios to detect spectral holes and to opportunistically use under-utilized frequency bands without causing harmful interference to legacy (primary) networks. In this paper, a novel...

	Abstract \| Full Text: PDF (884 KB)

8.	On the Statistics of Spectral Amplitudes After Variance Reduction by Temporal Cepstrum Smoothing and Cepstral Nulling Gerkmann, T.; Martin, R. Signal Processing, IEEE Transactions on Volume 57, Issue 11, Date: Nov. 2009, Pages: 4165-4174 Digital Object Identifier 10.1109/TSP.2009.2025795 Summary: In this paper, we derive the signal power bias that arises when spectral amplitudes are smoothed by reducing their variance in the cepstral domain (often referred to as cepstral smoothing) and develop a power bias compensation method. We show t...

	Abstract \| Full Text: PDF (1205 KB)

9.	Channel Estimation for OFDM Modulated Two-Way Relay Networks Feifei Gao; Rui Zhang; Ying-Chang Liang Signal Processing, IEEE Transactions on Volume 57, Issue 11, Date: Nov. 2009, Pages: 4443-4455 Digital Object Identifier 10.1109/TSP.2009.2026537 Summary: In this work, we introduce the model of employing orthogonal-frequency-division multiplexing (OFDM) for transmission over time-dispersive channels in the two-way relay network (TWRN), where two source terminals exchange their information through a rel...

	Abstract \| Full Text: PDF (630 KB)

10.	Radiological Source Detection and Localisation Using Bayesian Techniques Morelande, M.R.; Ristic, B. Signal Processing, IEEE Transactions on Volume 57, Issue 11, Date: Nov. 2009, Pages: 4220-4231 Digital Object Identifier 10.1109/TSP.2009.2026618 Summary: The problem considered in this paper is detection and estimation of multiple radiation sources using a time series of radiation counts from a collection of sensors. A Bayesian framework is adopted. Source detection is approached as a model selection p...

	Abstract \| Full Text: PDF (543 KB)

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