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Limited memory size is considered as a major bottleneck in data centers for intelligent urban computing. It is shown that there exist a large number of duplicated pages when various processes working with big data are hosted in data centers. Memory deduplication aims to automatically eliminate duplicate data in memory. It is an efficient technique to reduce memory requirement. In memory deduplication, pages with same content are detected and merged into a single copy. Recently, several system-level techniques have been proposed to address this issue, in which content-based page sharing (CBPS) is most widely used, since CBPS can be performed transparently in the hypervisor layer without any modification to guest operating systems of data center. In this paper, we survey several techniques for memory deduplication. We also classify these techniques based on their characteristics to highlight their similarities and differences. The aim of this paper is to provide insights to researchers into working of memory deduplication techniques and also to motivate them to propose better intelligent urban computing systems. [ABSTRACT FROM AUTHOR]

With the rapid growth of the number of images on the Internet, it has become more necessary to ensure the content security of images. The key problem is retrieving relevant images from the large database. Binary embedding is an effective way to improve the efficiency of calculating similarities for image content security as binary code is storage efficient and fast to compute. It tries to convert real-valued signatures into binary codes while preserving similarity of the original data, and most binary embedding methods quantize each projected dimension to one bit (presented as 0/1). As a consequence, it greatly decreases the discriminability of original signatures. In this paper, we first propose a novel triple-bit quantization strategy to solve the problem by assigning 3-bit to each dimension. Then, asymmetric distance algorithm is applied to re-rank candidates obtained from Hamming space for the final nearest neighbors. For simplicity, we call the framework triple-bit quantization with asymmetric distance (TBAD). The inherence of TBAD is combining the best of binary codes and real-valued signatures to get nearest neighbors quickly and concisely. Moreover, TBAD is applicable to a wide variety of embedding techniques. Experimental comparisons on BIGANN set show that the proposed method can achieve remarkable improvement in query accuracy compared to original binary embedding methods. [ABSTRACT FROM AUTHOR]

This paper proposes a 3D laser scanning technology for the pinhole camera with lens distortion. On the basis of the pinhole camera mathematical model, we improve the extraction algorithm of Harris corner points, and introduce the professional 2D RAC calibration, 3D reconstruction, and 3D volume calculation. With the help of stepper motor, and the camera, we can capture a series of images and obtain the object 3D surface information. Furthermore, we use the line laser with Gaussian distribution as the measurement illuminant, and select the samples at the peak of intensity distribution. This brings both high accuracy and speed of measurement. [ABSTRACT FROM AUTHOR]