Systematic Review of Indexing Spatial Skyline Queries for Decision Support

Residing in the data age, researchers inferred that huge amount of geo-tagged data is available and identified the importance of Spatial Skyline queries. Spatial or geographic location in conjunction with textual relevance plays a key role in searching Point of Interest (POI) of the user. Efficient indexing techniques like R-Tree, Quad Tree, Z-order curve and variants of these trees are widely available in terms of spatial context. Inverted file is the popular indexing technique for textual data. As Spatial skyline query aims at analyzing both spatial and skyline dominance, there is a necessity for a hybrid indexing technique. This article presents the review of spatial skyline queries evaluation that include a range of indexing techniques which concentrates on disk access, I/O time, CPU time. The investigation and analysis of studies related to skyline queries based upon the indexing model and research gaps are presented in this review.

High-capacity and Robust Image Watermarking Algorithm

With the development of internet, digital media can be manipulated, reproduced, and distributed conveniently over networks. However, illegal copy, transmission and distribution of digital media become an important security issue. In this paper, we propose a high-capacity and robustness method based on discrete wavelet transforms(DWT) and optimal discrete cosine transforms(DCT). We present two approaches, they are: DWT-ODCT(high-capacity and robust image watermarking algorithm based on DWT and optimal DCT), and(P-DWT-ODCT) high-capacity and robust image watermarking algorithm based on DWT and optimal DCT by watermark preprocessing. The watermark image is preprocessed by halftone and quad tree techniques, and the position information about the content is extracted as the actual embedded value. The cover image is transformed by DWT and optimal DCT, which provides high imperceptibility and the least image distortion. Watermark bits are not directly inserted into the frequency coefficient, but embedded by modifying the coefficient according to some rules. With this method, regardless of the approach used, our study are higher capacity and robustness than the existing schemes. The watermark extraction produces high image quality after a variety of attacks.

PKT: fast color-based spatial model for human skin detection

We present a new detection method for color-based object detection, which can improve the performance of learning procedures in terms of speed, accuracy, and efficiency, using spatial inference, and algorithm. We applied the model to human skin detection from an image; however, the method can also work for other machine learning tasks involving image pixels. We propose (1) an improved RGB/HSL human skin color threshold to tackle darker human skin color detection problem. (2), we also present a new rule-based fast algorithm (packed k-dimensional tree --- PKT) that depends on an improved spatial structure for human skin/face detection from colored 2D images. We also implemented a novel packed quad-tree (PQT) to speed up the quad-tree performance in terms of indexing. We compared the proposed system to traditional pixel-by-pixel (PBP)/pixel-wise (PW) operation, and quadtree based procedures. The results show that our proposed spatial structure performs better (with a very low false hit rate, very high precision, and accuracy rate) than most state-of-the-art models.

Hardware-Software Co-Synthesis for Distributed Memory Architectures

Hardware software co-synthesis problem is related to finding an architecture, subject to certain constraints, for a given set of tasks that are related through data dependencies. The architecture consists of a set of heterogeneous processing elements and a communication structure between these processing elements. In this thesis, a new algorithm for co-synthesis is presented that targets distributed memory architectures. The algorithm consists of four distinct phases namely, processing element selection, pipelined task allocation, scheduling and best topology selection. Selected processing elements are finally mapped to a regular distributed memory architecture comprising of mesh, hypercube or quad-tree topology. The co-synthesis method is demonstrated by applying it to MPEG encoder application and various size large random graphs.

Hardware-Software Co-Synthesis for Distributed Memory Architectures

Hardware software co-synthesis problem is related to finding an architecture, subject to certain constraints, for a given set of tasks that are related through data dependencies. The architecture consists of a set of heterogeneous processing elements and a communication structure between these processing elements. In this thesis, a new algorithm for co-synthesis is presented that targets distributed memory architectures. The algorithm consists of four distinct phases namely, processing element selection, pipelined task allocation, scheduling and best topology selection. Selected processing elements are finally mapped to a regular distributed memory architecture comprising of mesh, hypercube or quad-tree topology. The co-synthesis method is demonstrated by applying it to MPEG encoder application and various size large random graphs.

Efficient Image Inpainting Using Dimensional Space Reduction via Adaptive Patch-based Concept and Rank Lowering Technique

In this paper, we present a new algorithm for image inpainting using low dimensional feature space. In our method, projecting a low dimensional space from the original space is accomplished firstly using SVD, which is named low rank component, and then the missing pixels are filled in the new space. Finally, the original image is inpainted so that adaptive patch size is considered by quad-tree based on the previous step. In our algorithm, the missing pixels in the target region are estimated twice, one in low dimension feature space and another in the original space. It is noticeable that both processes estimate the unknown pixels using patch-based idea and rank lowering concept. Experimental results of this algorithm show better consistency in comparison with state-of-the-art methods.