Multiple-input multiple-output (MIMO) radar is getting more and more applications over the last decade. In near field imaging using a linear MIMO array, the azimuth sampling is non-uniform, resulting in spatially variant point spread function (PSF) over a large imaging zone. In this work, an azimuth sidelobe suppression technique is proposed where apodization or complex amplitude weighting is applied to the multiple channel data prior to image reconstruction. For best sidelobe suppression, the optimal channel weights <i>w<sub>opt</sub></i> are obtained through mathematical optimization. The overall process mainly includes three steps. Firstly, the expression of PSF in azimuth is acquired by the azimuth focusing process; Secondly, based on the fact that, for an ideal PSF the maximum value of the mainlobe should be one and the values of sidelobes should be zeros, the problem of finding <i>w<sub>opt</sub></i> is mathematically fomulated as an optimization problem; Lastly, by setting proper mainlobe width and sidelobe level, the optimal weights can be solved through convex optimization algorithm. Simulations of a MIMO radar system where channel amplitude-phase error and antenna elements position deviation exist are presented and the performance of the proposed technique is studied.
At present, the most technology of counting money is to use the money counter in financial fields. The paper presents a
new method for automatic counting paper money which is based on image processing technology. Firstly, the paper
money image is acquired by CCD. After analyzing the feature of image, we find that in Cr-space the edge of each paper
money is enhanced. Then we use the north-west sobel operator for filtering and north sobel operator for detecting edge.
Although the image-processed better highlight the edge of each paper money, the edge is rough and its variance is high.
It is hardly to threshold the image for getting the single-pixel edge linked. After Different segmentation algorithm was
been used for deriving the edge of paper money, we find the Two-dimensional Histogram θ-division algorithm is suitable
for our purpose. The experimental result is proved satisfied. The detecting rate reached 100% in controlled environment
for RMB. However, if we want to detect other kinds of paper money such as dollar, there also have several problems to