Recent developments in cloud computing and big data have spurred the emergence of data-intensive applications for which massive scientific datasets are stored in globally distributed scientific data centers that have a high frequency of data access by scientists worldwide. Multiple associated data items distributed in different scientific data centers may be requested for one data processing task, and data placement decisions must respect the storage capacity limits of the scientific data cen...

Big Personal Data is growing explosively. Consequently, an increasing number of internet users are drowning in a sea of data. Big Personal Data has enormous commercial value; it is a new kind of data asset. An urgent problem has thus arisen in the data market: How to price Big Personal Data fairly and reasonably. This paper proposes a pricing model for Big Personal Data based on tuple granularity, with the help of comparative analysis of existing data pricing models and strategies. This model...

Cloud data centers now provide a plethora of rich online applications such as web search, social networking, and cloud computing. A key challenge for such applications, however, is to meet soft real-time constraints. Due to the deadline-agnostic congestion control in Transmission Control Protocol (TCP), many deadline-sensitive flows cannot finish transmission before their deadlines. In this paper, we propose an SDN-based Explicit-Deadline-aware TCP (SED) for cloud Data Center Networks (DCN). ...

The use of a Traffic Matrix (TM) to describe the characteristics of a global network has attracted significant interest in network performance research. Due to the high dimensionality and sparsity of network traffic, Principal Component Analysis (PCA) has been successfully applied to TM analysis. PCA is one of the most common methods used in analysis of high-dimensional objects. This paper shows how to apply PCA to TM analysis and anomaly detection. The experiment results demonstrate that the...

This paper presents a cloud-based multiple-route recommendation system, xGo, that enables smartphone users to choose suitable routes based on knowledge discovered in real taxi trajectories. In modern cities, GPS-equipped taxicabs report their locations regularly, which generates a huge volume of trajectory data every day. The optimized routes can be learned by mining these massive repositories of spatio-temporal information. We propose a system that can store and manage GPS log files i...

Indoor navigation has received much attention by both industry and academia in recent years. To locate users, a number of existing methods use various localization algorithms in combination with an indoor map, which require expensive infrastructures deployed in advance. In this study, we propose the use of existing indoor objects with attached RFID tags and a reader to navigate users to their destinations, without the need for any additional hardware. The key insight upon which our proposal i...

In fifth-generation wireless communication networks, Non-Orthogonal Multiple Access (NOMA) has attracted much attention in both academic and industrial fields because of its higher spectral efficiency in comparison with orthogonal multiple access. Recently, numerous uplink NOMA techniques have been proposed, some of which are based on Successive Interference Cancellation (SIC) and others on Joint Decoding (JD, or simultaneous decoding). In this study, we analyze the outage capacities of SIC a...

In this paper, we propose and construct an observer design based on a Self-Recurrent Consequent-Part Fuzzy Wavelet Neural Network (SRCPFWNN) for a class of nonlinear system. We use a Self-Recurrent Wavelet Neural Network (SRWNN) to construct a self-recurrent consequent part for each rule of the Takagi-Sugeno-Kang (TSK) model in the SRCPFWNN and analyze the structure of the fuzzy wavelet neural network model. Based on the Direct Adaptive Control Theory (DACT) and a back propagation-based learn...

In this paper, we propose a fast registration scheme for remote-sensing images for use as a fundamental technique in large-scale online remote-sensing data processing tasks. First, we introduce priori-information images, and use machine learning techniques to identify robust remote-sensing image features from state-of-the-art Scale-Invariant Feature Transform (SIFT) features. Next, we apply a hierarchical coarse-to-fine feature matching and image registration scheme on the basis of additional...

In this paper, we present the modeling and optimization of a Real-Time Protocol (RTP) used in Train Communication Networks (TCN). In the proposed RTP, message arbitration is represented by a probabilistic model and the number of arbitration checks is minimized by using the probability of device activity. Our optimized protocol is fully compatible with the original standard and can thus be implemented easily. The experimental results demonstrate that the proposed algorithm can reduce the numbe...

Energy efficiency is one of the most important issues for High Performance Computing (HPC) today. Heterogeneous HPC platform with some energy-efficient customizable cores (as application-specific accelerators) is believed as one of the promising solutions to meet ever-increasing computing needs and to overcome power density limitations. In this paper, we focus on using customizable processor cores to optimize the typical stencil computations — the kernel of many high-performance applications....