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Moisture distribution in sewage sludge was considered as the essential of thermal drying. Some methods were given in literatures to test the moisture distribution, but there was no standard method to determine the critical water content between different kinds of water. The municipal sewage sludge was dried by hot air in this work. Based on the drying curve, the derivative of drying rate with respect to dry basis moisture content was brought out to analyze the moisture distribution in sewage sludge. Results show that this method can easily determine the free water, interstitial water, surface water and bound water with a high accuracy. The present work can provide new insight to determine the moisture distribution in sewage sludge, which was still lacking in the literatures.

Carrier frequency offset (CFO) estimation is a critical problem in orthogonal frequency-division multiplexing (OFDM) systems. This letter proposes a blind CFO estimator based on smoothing the signal power spectrum. A closed-form CFO estimate is also presented, which greatly reduces the computational complexity of the proposed method. Analysis and simulation results show that the proposed estimator is very effective for OFDM systems.

This letter proposes a blind carrier frequency offset (CFO) estimator for orthogonal frequency-division multiplexing (OFDM) systems based on the frequency analysis of the received signal, and derives a closed-form CFO estimate. Since only one OFDM symbol is utilized instead of multi-symbol averaging, the proposed method is effective even when the CFO is time varying. Finally, analysis and simulation results indicate the outstanding performance of the proposed estimator.

Carrier frequency offset (CFO) is an important problem in multi-input multi-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. This letter proposes a blind CFO estimator for MIMO-OFDM systems with constant modulus constellations in the frequency-selective fading channels. A closed-form CFO estimate is also provided, which greatly reduces the computational complexity of the proposed method. Analysis and simulation results demonstrate that the proposed estimator is very effective for MIMO-OFDM systems.

The problem of approximate joint diagonalization of a set of matrices is instrumental in numerous statistical signal processing applications. This paper describes a relative gradient non-orthogonal approxi- mate joint diagonalization (AJD) algorithm based on a non-least squares AJD criterion and a special AJD us- ing a non-square diagonalizing matrix and an AJD method for ill-conditioned matrices. Simulation results demonstrate the better performance of the relative gradient AJD algorithm compared with the conventional least squares (LS) criteria based gradient-type AJD algorithms. The algorithm is attractive for practical appli- cations since it is simple and efficient.

In orthogonal frequency-division multiplexing (OFDM) systems, the carrier frequency offset (CFO) destroys the orthogonality among subcarriers which degrades system performance. Various CFO estimation methods have been developed to compensate for the CFO at the receiver. This paper describes a novel minimum output variance method for OFDM systems with CFO in additive white Gaussian noise channels. This method utilizes the phase and the amplitude of the received signal and reduces the mean square error of the CFO by about 3 dB compared with the original minimum output variance method.

Carrier frequency offset (CFO) estimation is critical in practical orthogonal frequency-division multiplexing (OFDM) systems. In this paper, we propose two novel blind CFO estimators named the variance estimator (VE) and the approximate variance estimator (AVE) respectively with high bandwidth efficiency. Both of them are based on minimization of the variance of the received symbols on each subcarrier, but the AVE only utilizes a half of OFDM symbols needed in the VE at the cost of a little degradation in performance. Simulation results show that the proposed estimators are attractively efficient for the future wireless communications.