The need for publishing maps in secure digital format, especially guarantees data integrity which motivated us to propose a scheme that detects and locates modification data with high accuracy while ensuring exact rec...
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The need for publishing maps in secure digital format, especially guarantees data integrity which motivated us to propose a scheme that detects and locates modification data with high accuracy while ensuring exact recovery of the original content. In particular, using fragile watermarking algorithm based on reversible manner to embed hidden data in 2D vector map for each spatial features. In this paper, a reversible data-hiding scheme is explored based on the idea of difference expansion with Manhattan distances. A set of invertible integer mappings is defined to extract Manhattan distances from coordinates and the hidden data are embedded by modifying the differences between the adjacent distances. Experiments results show that the proposed scheme has good performance in term invisibility and tamper modification ability. The scheme could detect modification data such addition and deletion some features, and exactly recovery the original content of the 2D vector map.
Internet of Things (IoT) is increasingly used in a plethora of fields to enable radically new ways for various purposes, ranging from monitoring the environment to enhancing the wellbeing of human life. With the ever-...
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Internet of Things (IoT) is increasingly used in a plethora of fields to enable radically new ways for various purposes, ranging from monitoring the environment to enhancing the wellbeing of human life. With the ever-increasing size of such networks, it is fundamental to understand the issues that come with scaling on different networking layers. A cost-efficient approach to examine large-scale networks is to use simulators or emulators to test the infrastructure and its ability to support the desired applications. In this paper, we investigate and compare the currently available simulation/emulation software. We found out that the current solutions are mostly appropriate for small- and medium-scale emulation, however they are not suitable for large-scale testing that reaches millions of node running concurrently. We then propose a large-scale IoT emulator called MAMMotH and present a brief overview of its design. Finally we discuss some of the current issues and future directions, e.g. radio link simulation.
Wireless sensor networks using transmit-only sensor nodes are a promising new approach for applications such as environmental monitoring because they can provide high network scalability and long life-time at low cost...
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ISBN:
(纸本)9781467345217
Wireless sensor networks using transmit-only sensor nodes are a promising new approach for applications such as environmental monitoring because they can provide high network scalability and long life-time at low cost. But achieving reliable data delivery in a transmit-only network is a challenging problem because there is no feedback channel for reporting lost or damaged packets. Taking a systems approach, this paper identifies three specific error control strategies that are applicable in this design space. The strategies are drawn from the areas of temporal diversity, spatial diversity and coding-based methods. An empirical investigation of these strategies is performed using two case study deployments in indoor and outdoor settings, running from two weeks to several months. In both case studies data replication in successive packets and receiver diversity performed best. In most cases, but not all, coding-based methods offered a poor balance between overhead and efficiency.
Advances in mobile networking and information processing technologies have triggered vehicular ad hoc networks (VANETs) for traffic safety and value-added applications. Most efforts have been made to address the secur...
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data collected from mobile phones have potential knowledge to provide with important behavior patterns of individuals. In this paper, we present approaches to discovering personal mobility and characteristics based on...
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Ultrasound (US) images have been widely used in the diagnosis of breast cancer in particular. While experienced doctors may locate the tumor regions in a US image manually, it is highly desirable to develop algorithms...
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ISBN:
(纸本)9781457718571
Ultrasound (US) images have been widely used in the diagnosis of breast cancer in particular. While experienced doctors may locate the tumor regions in a US image manually, it is highly desirable to develop algorithms that automatically detect the tumor regions in order to assist medical diagnosis. In this paper, we propose a novel algorithm for automatic detection of breast tumors in US images. We formulate the tumor detection as a two step learning problem: tumor localization by bounding box and exact boundary delineation. Specifically, the proposed method uses an AdaBoost classifier on Harr-like features to detect a preliminary set of tumor regions. The preliminarily detected tumor regions are further screened with a support vector machine using quantized intensity features. Finally, the random walk segmentation algorithm is performed on the US image to retrieve the boundary of each detected tumor region. The proposed method has been evaluated on a data set containing 112 breast US images, including histologically confirmed 80 diseased ones and 32 normal ones. The data set contains one image from each patient and the patients are from 31 to 75 years old. Experiments demonstrate that the proposed algorithm can automatically detect breast tumors, with their locations and boundary shapes retrieved with high accuracy.
To generate large number of reports in a limited time window, four techniques were proposed, including ROLAP&SQL, Shared Scanning, Hadoop based Solution, and MOLAP&Cube Sharding, an algorithm that performs in ...
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To generate large number of reports in a limited time window, four techniques were proposed, including ROLAP&SQL, Shared Scanning, Hadoop based Solution, and MOLAP&Cube Sharding, an algorithm that performs in memory aggregation was designed for the second solution. The experiment results show that all techniques except ROLAP&SQL can meet the time window constraint, the Hadoop based solution is a promising technique owe to its highly scalability. Considering maturity of the techniques and their performance, we put MOLAP&Cube Sharding into practice while keeping an eye on Hadoop for future adoption.
Wireless sensor networks generate volumes of scientific observations. However, gathered data is typically not published for use by other scientists. This paper analyses the reasons that so little sensor network data i...
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Orthodontic craniometric landmarks provide critical information in oral and maxillofacial imaging diagnosis and treatment planning. The Dent-landmark, defined as the odontoid process of the epistropheus, is one of the...
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ISBN:
(纸本)9781424441211
Orthodontic craniometric landmarks provide critical information in oral and maxillofacial imaging diagnosis and treatment planning. The Dent-landmark, defined as the odontoid process of the epistropheus, is one of the key landmarks to construct the midsagittal reference plane. In this paper, we propose a learning-based approach to automatically detect the Dent-landmark in the 3D cone-beam computed tomography (CBCT) dental data. Specifically, a detector is learned using the random forest with sampled context features. Furthermore, we use spacial prior to build a constrained search space other than use the full three dimensional space. The proposed method has been evaluated on a dataset containing 73 CBCT dental volumes and yields promising results.
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