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Standing wave acoustic levitation utilizes the acoustic radiation forces generated by standing wave to hold samples against gravity. Among them, ultrasonic levitation using phased arrays has good application prospects. The principle of standing wave acoustic levitation is analyzed, and the experimental scheme of acoustic levitation using phased arrays is designed to evaluate the performance of ultrasonic levitator. The distribution of sound pressure between two arrays are approximately hourglass-shaped in space, and experimental results show that with the increase of the number of transducers, the suspension ability increases further,. The experiment of liquid droplet suspension further verifies the relationship between the voltage of excitation signal and the acoustic pressure. With the increase of the voltage of the excitation signal, the acoustic pressure amplitude increases gradually. The acoustic levitation using phased arrays is stable and insensitive to temperature, and can effectively improve the levitation ability.

A hyperspectral image classification method based on probabilistic weighted fusion of multiple spectral–spatial features is proposed in this letter. First, dimensionality reduction and feature extraction of hyperspectral images are conducted by minimum noise fraction. Then, two spectral–spatial features are composed through a combination of texture features and multiscale morphological features with characteristic images obtained by minimum noise fraction. Next, a support vector machine classifier is employed to classify each spectral–spatial feature. Finally, a probabilistic weighted fusion model is established and applied for probabilistic fusion of the classification output of every single feature. The proposed method is validated by classification experiments using Reflective Optics Spectrographic Imaging System (ROSIS) and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) images, which achieves the overall accuracy of 96.32% and 96.49%, respectively. The results indicate that the accuracy of hyperspectral image classification is improved effectively by the proposed method and the method has superior classification performance to other conventional multifeature fusion methods.