How to manage the video streaming in future networks is becoming a more and more challenging issue. Recent studies on vehicular networks depict a new picture of the next generation Intelligent Transport System (ITS), with high level road safety and more comfortable driving experience. To cope with the heterogeneous network development for the next generation cellular network, centralized medium control is promising to be employed upon Road Side Unit (RSU). To accommodate the QoS constraints posed by video services in vehicular networks, the scalable video coding (SVC) scheme in H.264/AVC standard family offers spatial and temporal scalabilities in the video dissemination. In this paper, we target the resource allocation and layer selection problem for the multi-user video streaming over highway scenario, by employing SVC coding scheme for the video contents. We propose a Resource Allocation and Layer Selection (RALS) algorithm, which explicitly takes account of the utility value of each Group Of Picture (GOP) among all the vehicular users. Simulation results show that our proposed RALS algorithm outperforms the comparison schemes in typical scenarios.

In this paper, a morpheme-based weighting and its integration method are proposed as a smoothing method to alleviate the data sparseness in Chinese-Mongolian statistical machine translation (SMT). Besides, we present source-side reordering as the pre-processing model to verify the extensibility of our method. Experi-mental results show that the morpheme-based weighting can substantially improve the translation quality.

In this letter, we present an efficient resource allocation algorithm for proportional fair schedulers in mobile multihop relay (MMR) networks. We consider a dual-hop cellular network assisted with a decode-and-forward relay station (RS). Since additional radio resources should be allocated in the wireless link between a base station (BS) and an RS, it is very important to determine the optimal amount of resources for this BS-to-RS link. The proposed resource allocation algorithm maximizes the utility of the overall MMR network in a proportionally fair point of view.

Self-luminous light sources in the real world often have nonnegligible sizes and radiate light inhomogeneously. Acquiring the model of such a light source is highly important for accurate image synthesis and understanding. In this paper, we propose an approach to measuring 4D light fields of self-luminous extended light sources by using a liquid crystal (LC) panel, i.e. a programmable optical filter and a diffuse-reflection board. The proposed approach recovers the 4D light field from the images of the board illuminated by the light radiated from a light source and passing through the LC panel. We make use of the feature that the transmittance of the LC panel can be controlled both spatially and temporally. The approach enables multiplexed sensing and adaptive sensing, and therefore is able to acquire 4D light fields more efficiently and densely than the straightforward method. We implemented the prototype setup, and confirmed through a number of experiments that our approach is effective for modeling self-luminous extended light sources in the real world.

Due to the increasing demand for 3D video transmission over wireless networks, managing the quality of experience (QoE) of wireless 3D video clients is becoming increasingly important. However, the variability of compressed 3D video bit streams and the wireless channel condition as well as the complexity of 3D video viewing experience assessment make it difficult to properly allocate wireless transmission resources. In this paper, we discuss the characteristics of H.264 3D videos and QoE assessment of 3D video clients, and further propose a transmission scheme for 3D video transmission over a wireless communication system. The purpose of our scheme is to minimize the average ratio of stalls among all video streaming clients. By taking into account the playout lead and its change, we periodically evaluate the degree of urgency of each client as regards bitstream receipt based on fuzzy logic, and then allocate the transmission resource blocks to clients jointly considering their degrees of urgency and channel conditions. The adaptive modulation and coding scheme (MCS) is applied to ensure a low transmission error rate. Our proposed scheme is suitable for practical implementation since it has low complexity, and can be easily applied in 2D video transmission and in non-OFDM systems. Simulation results, based on three left-and-right-views 3D videos and the Long Term Evolution (LTE) system, demonstrate the validity of our proposed scheme.

We introduce the design and deployment of the latest version of the VNode infrastructure, VNode-i. We present new extended VNode-i functions that offer high performance and provide convenient deep programmability to network developers. We extend resource abstraction to the transport network and achieve highly precise slice measurement for resource elasticity. We achieve precise resource isolation for VNode-i. We achieve coexistence of high performance and programmability. We also enhance AGW functions. In addition, we extend network virtualization from the core network to edge networks and terminals. In evaluation experiments, we deploy the enhanced VNode-i on the JGN-X testbed and evaluate its performance. We successfully create international federation slices across VNode-i, GENI, and Fed4FIRE. We also present experimental results on video streaming on a federated slice across VNode-i and GENI. Testbed experiments confirm the practicality of the enhanced VNode-i.

Information floating delivers information to mobile nodes in specific areas without meaningless spreading of information by permitting mobile nodes to directly transfer information to other nodes by wireless links in designated areas called transmittable areas. In this paper, we assume that mobile nodes change direction at intersections after receiving such information as warnings and local advertisements and that an information source remains in some place away from the transmittable area and continuously broadcasts information. We analyze performance of information floating under these assumptions to explore effects of the behavior changes of mobile nodes, decision deadline of the behavior change, and existence of a fixed source on information floating. We theoretically analyze the probability that a node cannot receive information and also derive the size of each transmittable area so that this probability is close to desired values.

Estimation of the time delay of arrival (TDOA) problem is important to acoustic source localization. The TDOA estimation problem is defined as finding the relative delay between several microphone signals for the direct sound. To estimate TDOA, the generalized cross-correlation (GCC) method is the most frequently used, but it has a disadvantage in terms of reverberant environments. In order to overcome this problem, the adaptive eigenvalue decomposition (AED) method has been developed, which estimates the room transfer function and finds the direct-path delay. However, the algorithm does not take into account the fact that the room transfer function is a sparse channel, and so sometimes the estimated transfer function is too dense, resulting in failure to exact direct-path and delay. In this paper, an enhanced AED algorithm that makes use of a proportionate step-size control and a direct-path constraint is proposed instead of a constant step size and the L2-norm constraint. The simulation results show that the proposed algorithm has enhanced performance as compared to both the conventional AED method and the phase-transform (PHAT) algorithm.

Logistic regression is a powerful machine learning tool to classify data. When dealing with sensitive or private data, cares are necessary. In this paper, we propose a secure system for privacy-protecting both the training and predicting data in logistic regression via homomorphic encryption. Perhaps surprisingly, despite the non-polynomial tasks of training and predicting in logistic regression, we show that only additively homomorphic encryption is needed to build our system. Indeed, we instantiate our system with Paillier, LWE-based, and ring-LWE-based encryption schemes, highlighting the merits and demerits of each instantiation. Besides examining the costs of computation and communication, we carefully test our system over real datasets to demonstrate its utility.

Massive Open Online Courses (MOOC) have been invented to support Virtual Learning Environment (VLE) for higher education. While numerous learning courses and contents were authored, most of the existing resources are now hard to reuse/redistribute among instructors due to the privacy of the contents. Therefore, Open Educational Resources (OER) and the Creative Commons license (CC) are interesting solutions available to alleviate such problems of MOOC. This research presents a new framework that effectively connects OER and MOOC for a life-long e-Learning platform for Thai people. We utilize the Fedora Commons repository for an OER back-end, and develop a new front-end to manage OER resources. In addition, we introduce a “FedX API” - including a packet encapsulation and a data transmission module - that organizes educational resources between both systems. We also proposed the CC declaring function to help participants on-the-fly declare their content license; therefore, any resources must be granted as an open licensing. Another important function is a Central Authorized System (CAS) which is applied to develop single signing-on to facilitate the OER-MOOC connection. Since the framework is designed to support the massive demand, the concurrent access capability is also evaluated to measure the performance of the proposed framework. The results show that the proposed framework can provide up to 750 concurrencies without any defects. The FedX API does not produce bottleneck trouble on the interoperability framework in any cases. In addition, resources can be exchanged among the third-party OER repositories by an OAI-PMH harvesting tool.

There have been many previous studies to facilitate the use of smartphones as remote controllers of PCs. Image-based user interfaces have been suggested to provide fully functioning remote applications. However, most previous image-based interfaces consume high battery power and network bandwidth. Also most users have specific preferences on various applications on remote PCs, but previous smartphone interface systems would not allow users to define their own smartphone interfaces to set their preferences. This paper presents a new smartphone user interface system, SmartUI, for remote PC control. SmartUI is designed as a text-oriented web-based interface, so that it can be used on any smartphone with a built-in web browser while saving battery power and network bandwidth. Moreover, SmartUI enables a user to create buttons on a smartphone; for a quick launch and for shortcut keys, associated with a specific remote PC application. As a result, SmartUI allows a user to create his or her own smartphone interface for remote PC control, while saving battery power and network bandwidth. SmartUI has been tested with various smartphones and the results are also presented in this paper.

As semiconductor technologies have advanced, the reliability problem caused by soft-errors is becoming one of the serious issues in LSIs. Moreover, multiple component errors due to single soft-errors also have become a serious problem. In this paper, we propose a method to synthesize multiple component soft-error tolerant application-specific datapaths via high-level synthesis. The novel feature of our method is speculative resource sharing between the retry parts and the secondary parts for time overhead mitigation. A scheduling algorithm using a special priority function to maximize speculative resource sharing is also an important feature of this study. Our approach can reduce the latency (schedule length) in many applications without deterioration of reliability and chip area compared with conventional datapaths without speculative resource sharing. We also found that our method is more effective when a computation algorithm possesses higher parallelism and a smaller number of resources is available.

Transfer learning extracts useful information from the related source domain and leverages it to promote the target learning. The effectiveness of the transfer was affected by the relationship among domains. In this paper, a novel multi-source transfer learning based on multi-similarity was proposed. The method could increase the chance of finding the sources closely related to the target to reduce the “negative transfer” and also import more knowledge from multiple sources for the target learning. The method explored the relationship between the sources and the target by multi-similarity metric. Then, the knowledge of the sources was transferred to the target based on the smoothness assumption, which enforced that the target classifier shares similar decision values with the relevant source classifiers on the unlabeled target samples. Experimental results demonstrate that the proposed method can more effectively enhance the learning performance.

MapReduce still suffers from a problem known as skew, where load is unevenly distributed among tasks. Existing solutions follow a similar pattern that estimates the load of each task and then rebalances the load among tasks. However, these solutions often incur heavy overhead due to the load estimation and rebalancing. In this paper, we present DynamicAdjust, a dynamic resource adjustment technique for mitigating skew in MapReduce. Instead of rebalancing the load among tasks, DynamicAdjust adjusts resources dynamically for the tasks that need more computation, thereby accelerating these tasks. Through experiments using real MapReduce workloads on a 21-node Hadoop cluster, we show that DynamicAdjust can effectively mitigate the skew and speed up the job completion time by up to 37.27% compared to the native Hadoop YARN.

The separation of signals with temporal structure from mixed sources is a challenging problem in signal processing. For this problem, blind source extraction (BSE) is more suitable than blind source separation (BSS) because it has lower computation cost. Nowadays many BSE algorithms can be used to extract signals with temporal structure. However, some of them are not robust because they are too dependent on the estimation precision of time delay; some others need to choose parameters before extracting, which means that arbitrariness can't be avoided. In order to solve the above problems, we propose a robust source extraction algorithm whose performance doesn't rely on the choice of parameters. The algorithm is realized by maximizing the objective function that we develop based on the non-Gaussianity and the temporal structure of source signals. Furthermore, we analyze the stability of the algorithm. Simulation results show that the algorithm can extract the desired signal from large numbers of observed sensor signals and is very robust to error in the estimation of time delay.

In this paper, we investigate a preference-aware multicast mechanism in active array aided LTE (Long Term Evolution) networks. An active antenna system can direct vertical beams in different horizontal and vertical directions, so the amount of energy delivered is more concentrated on the target users. The active array provides each multicast group with an individual beam with specific downtilt delivering shared video to all users in the group. For the multicast system, the objective of our proposed resource allocation scheme is to maximize the total throughput, subject to the constraints of power, subcarrier and antenna downtilt, as well as horizontal angles and the vertical half power bandwidth. To solve the problem, individual beams are steered for multicast groups. Furthermore, a novel subcarrier assignment scheme is proposed to enhance the spectrum resource utilization, and the optimal power allocation is obtained by virtue of Lagrangian method. Simulation results demonstrate the throughput and the spectral efficiency enhancement of our proposed scheme over other conditional schemes.

In this paper, by jointly considering power allocation and network selection, we address the energy efficiency maximization problem in dynamic and heterogeneous wireless networks, where user equipments are typically equipped with multi-homing capability. In order to effectively deal with the dynamics of heterogeneous wireless networks, a stochastic optimization problem is formulated that optimizes the long-term energy efficiency under the constraints of system stability, peak power consumption and average transmission rate. By adopting the parametric approach and Lyapunov optimization, we derive an equivalent optimization problem out of the original problem and then investigate its optimal resource allocation. Then, to reduce the computational complexity, a suboptimal resource allocation algorithm is proposed based on relaxed optimization, which adapts to time-varying channels and stochastic traffic without requiring relevant a priori knowledge. The simulation results demonstrate the theoretical analysis and validate the adaptiveness of our proposed algorithm.

Hierarchical scheduling for multiple resources is partially responsible for the performance achievements in large scale datacenters. However, the latest scheduling technique, Hierarchy Dominant Resource Fairness (H-DRF)[1], has some shortcomings in heterogeneous environments, such as starving certain jobs or unfair resource allocation. This is because a heterogeneous environment brings new challenges. In this paper, we propose a novel scheduling algorithm called Dominant Fairness Fairness (DFF). DFF tries to keep resource allocation fair, avoid job starvation, and improve system resource utilization. We implement DFF in the YARN system, a most commonly used scheduler for large scale clusters. The experimental results show that our proposed algorithm leads to higher resource utilization and better throughput than H-DRF.

The information maximization (Infomax) based on information entropy theory is a class of methods that can be used to blindly separate the sources. Torkkola applied the Infomax criterion to blindly separate the mixtures where the sources have been delayed with respect to each other. Compared to the frequency domain methods, this time domain method has simple adaptation rules and can be easily implemented. However, Torkkola's method works only in the real valued field. In this letter, the Infomax for blind separation of the delayed sources is extended to the complex case for processing of complex valued signals. Firstly, based on the gradient ascent the adaptation rules for the parameters of the unmixing network are derived and the steps of algorithm are given. Then, a measurement matrix is constructed to evaluate the separation performance. The results of computer experiment support the extended algorithm.

Network virtualization environments (NVEs) are emerging to meet the increasing diversity of demands by Internet users where a virtual network (VN) can be constructed to accommodate each specific application service. In the future Internet, diverse service providers (SPs) will provide application services on their own VNs running across diverse infrastructure providers (InPs) that provide physical resources in an NVE. To realize both efficient resource utilization and good QoS of each individual service in such environments, SPs should perform adaptive control on network and computational resources in dynamic and competitive resource sharing, instead of explicit and sufficient reservation of physical resources for their VNs. On the other hand, two novel concepts, software-defined networking (SDN) and network function virtualization (NFV), have emerged to facilitate the efficient use of network and computational resources, flexible provisioning, network programmability, unified management, etc., which enable us to implement adaptive resource control. In this paper, therefore, we propose an architectural design of network orchestration for enabling SPs to maintain QoS of their applications aggressively by means of resource control on their VNs efficiently, by introducing virtual network provider (VNP) between InPs and SPs as 3-tier model, and by integrating SDN and NFV functionalities into NVE framework. We define new north-bound interfaces (NBIs) for resource requests, resource upgrades, resource programming, and alert notifications while using the standard OpenFlow interfaces for resource control on users' traffic flows. The feasibility of the proposed architecture is demonstrated through network experiments using a prototype implementation and a sample application service on nation-wide testbed networks, the JGN-X and RISE.