In mobile communication it is necessary to remove acoustic echo. Most acoustic echo cancellation algorithms are sensitive to double-talk. Recursive prediction error (RPE) algorithm is one of the best solutions to such problem. However integrating the algorithm into headsets is a challenging task due to conversion from floating-point to fixed-point. To address this problem, the simplification approach and fixed-point implementation are proposed. Simulation results validate the simplification and its fixed-point implementation.
Wireless sensor networks (WSNs) suffer from limited power and large amounts of redundant data. This paper describes a multisource data fusion method for WSNs that can be combined with the characteristics of a profile detection system. First, principal component analysis is used to extract sample features and eliminate redundant information. Feature samples from different sources are then fused using a method of superposition to reduce the amount of data transmitted by the network. Finally, a mathematical model is proposed. On the basis of this model, a novel method of special object recognition based on sparse representation is developed for multisource data fusion samples according to the distribution of nonzero coefficients under an overcomplete dictionary. The experimental results from numerical simulations show that the proposed recognition method can effectively identify special objects in the fusion samples, and the overall performance is better than that of traditional methods.
Traditional single-pixel imaging systems are aimed mainly at relatively static or slowly changing targets. When there is relative motion between the imaging system and the target, sizable deviations between the measurement values and the real values can occur and result in poor image quality of the reconstructed target. To solve this problem, a novel dynamic compressive imaging system is proposed. In this system, a single-column digital micro-mirror device is used to modulate the target image, and the compressive measurement values are obtained for each column of the image. Based on analysis of the measurement values, a new recovery model of dynamic compressive imaging is given. Differing from traditional reconstruction results, the measurement values of any column of vectors in the target image can be used to reconstruct the vectors of two adjacent columns at the same time. Contingent upon characteristics of the results, a method of image quality enhancement based on an overlapping average algorithm is proposed. Simulation experiments and analysis show that the proposed dynamic compressive imaging can effectively reconstruct the target image; and that when the moving speed of the system changes within a certain range, the system reconstructs a better original image. The system overcomes the impact of dynamically changing speeds, and affords significantly better performance than traditional compressive imaging.
Using the characteristics of a moving object, this paper presents a compressive imaging method for moving objects based on a linear array sensor. The method uses a higher sampling frequency and a traditional algorithm to recover the image through a column-by-column process. During the compressive sampling stage, the output values of the linear array sensor are multiplied by a coefficient that is a measurement matrix element, and then the measurement value can be acquired by adding all the multiplication values together. During the reconstruction stage, the orthogonal matching pursuit algorithm is used to recover the original image when all the measurement values are obtained. Numerical simulations and experimental results show that the proposed compressive imaging method not only effectively captures the information required from the moving object for image reconstruction but also achieves direct separation of the moving object from a static scene.
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