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Indoor location algorithm has become a research hotspot in the past decades for its wide applications, such as object tracking, personnel localization, and many other rising applications. Yet the mature commercial products are still in demand. In this paper, we present and analyze three representative and effective indoor location algorithms which are only based on angle of arrival (AOA). The three methods are the Averaging method, the Weighted Least Squares (WLS) method and the Clustering-based method. Moreover, a large number of simulation experiments are conducted to analyze their characteristics and performance.

A critical issue in image interpolation is preserving edge detail and texture information in images when zooming. In this paper, we propose a novel adaptive image zooming algorithm using weighted least-square estimation that can achieve arbitrary integer-ratio zoom (WLS-AIZ) For a given zooming ratio n, every pixel in a low-resolution (LR) image is associated with an n × n block of high-resolution (HR) pixels in the HR image. In WLS-AIZ, the LR image is interpolated using the bilinear method in advance. Model parameters of every n×n block are worked out through weighted least-square estimation. Subsequently, each pixel in the n × n block is substituted by a combination of its eight neighboring HR pixels using estimated parameters. Finally, a refinement strategy is adopted to obtain the ultimate HR pixel values. The proposed algorithm has significant adaptability to local image structure. Extensive experiments comparing WLS-AIZ with other state of the art image zooming methods demonstrate the superiority of WLS-AIZ. In terms of peak signal to noise ratio (PSNR), structural similarity index (SSIM) and feature similarity index (FSIM), WLS-AIZ produces better results than all other image integer-ratio zoom algorithms.

The aim of single image super-resolution (SISR) is to find a non-linear mapping from the low to high resolution training image samples and reconstruct a high-resolution (HR) image from a low-resolution (LR) image using the prior knowledge. As a powerful tool in solving high-level computer vision problems, deep learning can also be used to solve the low-level vision problem, such as SISR. By exploring the architecture of convolutional neural network (CNN)， we put forward a robust SISR algorithm based on CNN. Comparison to the state-of-art SISR methods, the proposed SISR algorithm obtains the best SR performance.

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