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 letter, we propose a novel framework to estimate the joint distribution of multiple Local Binary Patterns (LBPs). Multiple LBPs extracted from the same central pixel are first encoded using handcrafted encoding schemes to achieve rotation invariance, and the outputs are further encoded through a pre-trained Restricted Boltzmann Machine (RBM) to reduce the dimension of features. RBM has been successfully used as binary feature detectors and the binary-valued units of RBM seamlessly adapt to LBP. The proposed feature is called RBM-LBP. Experiments on the CUReT and Outex databases show that RBM-LBP is superior to conventional handcrafted encodings and more powerful in estimating the joint distribution of multiple LBPs.

Many important problems in communication networks, transportation networks, and logistics networks are solved by the minimization of cost functions. In general, these can be complex optimization problems involving many variables. However, physicists noted that in a network, a node variable (such as the amount of resources of the nodes) is connected to a set of link variables (such as the flow connecting the node), and similarly each link variable is connected to a number of (usually two) node variables. This enables one to break the problem into local components, often arriving at distributive algorithms to solve the problems. Compared with centralized algorithms, distributed algorithms have the advantages of lower computational complexity, and lower communication overhead. Since they have a faster response to local changes of the environment, they are especially useful for networks with evolving conditions. This review will cover message-passing algorithms in applications such as resource allocation, transportation networks, facility location, traffic routing, and stability of power grids.

Automatic detection of immunoreactive areas in fluorescence microscopic images is becoming a key technique in the field of biology including neuroscience, although it is still challenging because of several reasons such as low signal-to-noise ratio and contrast variation within an image. In this study, we developed a new algorithm that exhaustively detects co-localized areas in multi-channel fluorescence images, where shapes of target objects may differ among channels. Different adaptive binarization thresholds for different local regions in different channels are introduced and the condition of each segment is assessed to recognize the target objects. The proposed method was applied to detect immunoreactive spots that labeled membrane receptors on dendritic spines of mouse cerebellar Purkinje cells. Our method achieved the best detection performance over five pre-existing methods.

A black mask (BM) is a layer used to improve the display quality by suppressing light leakage. In general, the BM is formed by a photolithography process. In this study, a novel technique for the fabrication of a quasi-black mask (q-BM) is proposed; the q-BM was composed of vertical and hybrid orientation areas, patterned by a separation coating technique using an electro-spray deposition method. Using our technique, the q-BM can be formed easily without the additional masks used for the BM.

In order to realize high-yield speckle modulation, we developed a novel spatial light modulator using zinc oxide single crystal doped with nitrogen ions. The distribution of dopants was optimized to induce characteristic optical functions by applying an annealing method developed by us. The device is driven by a current in the in-plane direction, which induces magnetic fields. These fields strongly interact with the doped material, and the spatial distribution of the refractive index is correspondingly modulated via external control. Using this device, we experimentally demonstrated speckle modulation, and we discuss the quantitative superiority of our approach.

In a network with dense deployment of multiple-input multiple-output (MIMO) small cells, coverage overlap between the small cells produces intercell-interference, which degrades system capacity. This paper proposes an intercell-interference management (IIM) scheme that aims to maximize system capacity by using both power control for intercell-interference coordination (ICIC) on the transmitter side and interference cancellation (IC) on the receiver side. The power control determines transmit power levels at the base stations (BSs) by employing a neural network (NN) algorithm over the backhaul. To further improve the signal to interference plus noise ratio (SINR), every user terminal (UT) employs a multiuser detector (MUD) as IC. The MUD detects not only the desired signals, but also some interfering signals to be cancelled from received signals. The receiver structure consists of branch metric generators (BMGs) and MUD. BMGs suppress residual interference and noise in the received signals by whitening matched filters (WMFs), and then generate metrices by using the WMFs' outputs and symbol candidates that the MUD provides. On the basis of the metrices, the MUD detects both the selected interfering signals and the desired signals. In addition, the MUD determines which interfering signals are detected by an SINR based replica selection algorithm. Computer simulations demonstrate that the SINR based replica selection algorithm, which is combined with channel encoders and packet interleavers, can significantly improve the system bit error rate (BER) and that combining IC at the receiver with NN power control at the transmitter can considerably increase the system capacity. Furthermore, it is shown that choosing the detected interfering signals by the replica selection algorithm can obtain system capacity with comparable loss and less computational complexity compared to the conventional greedy algorithm.

Rain attenuation can drastically impact the service availability of satellite communication, especially in the higher frequency bands above 20 GHz, such as the Ka-band. Several countermeasures, including site and time diversity, have been proposed to maintain satellite link service. In this paper, we evaluate the performance of a power boost beam method, which is an adaptive satellite power control technology based on using rain radar data obtained throughout Japan to forecast the power margin. Boost beam analysis is considered for different beam sizes (50, 100, 150, and 200km) and beam numbers (1-4 beams) for a total of 16 cases. Moreover, we used a constant boost power corresponding to the rainfall rate of 20mm/h. The obtained results show that in comparison to the case with no boost, the effective rain intensity in each boost case was reduced.

The segmentation of Mycobacterium tuberculosis images forms the basis for the computer-aided diagnosis of tuberculosis. The segmented objects are often broken due to the low-contrast objects and the limits of segmentation method. This will result in decreasing the accuracy of segmentation and recognition. A simple and effective post-processing method is proposed to connect the broken objects. The broken objects in the segmented binary images are connected based on the information obtained from their skeletons. Experimental results demonstrate the effectiveness of our proposed method.

In this paper, a new scheme for personalized web page recommendation using multi-user search engine query information is proposed. Our contribution is a scheme that improves the accuracy of personalization for various types of contents (e.g., documents, images and music) without increasing user burden. The proposed scheme combines “preference footprints” for browsed pages with collaborative filtering. We acquire user interest using words that are relevant to queries submitted by users, attach all user interests to a page as a footprint when it is browsed, and evaluate the relevance of web pages in relation to words in footprints. The performance of the scheme is evaluated experimentally. The results indicate that the proposed scheme improves the precision and recall of previous schemes by 1%-24% and 80%-107%, respectively.

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.

There are often low contrast Mycobacterium tuberculosis (MTB) objects in the MTB images. Based on improved histogram equalization (HE), a framework of contrast enhancement is proposed to increase the contrast of MTB images. Our proposed algorithm was compared with the traditional HE and the weighted thresholded HE. The experimental results demonstrate that our proposed algorithm has better performance in contrast enhancement, artifacts suppression, and brightness preserving for MTB images.

In this paper, an insect classification method using scanning electron microphotographs is presented. Images taken by a scanning electron microscope (SEM) have a unique problem for classification in that visual features differ from each other by magnifications. Therefore, direct use of conventional methods results in inaccurate classification results. In order to successfully classify these images, the proposed method generates an optimal training dataset for constructing a classifier for each magnification. Then our method classifies images using the classifiers constructed by the optimal training dataset. In addition, several images are generally taken by an SEM with different magnifications from the same insect. Therefore, more accurate classification can be expected by integrating the results from the same insect based on Dempster-Shafer evidence theory. In this way, accurate insect classification can be realized by our method. At the end of this paper, we show experimental results to confirm the effectiveness of the proposed method.

In this paper, we give a formalization of the behavior of the Content-Centric Networking (CCN) protocol with parameterizing content managements. CCN is a communications architecture that is based on the names of contents, rather than on addresses. In the protocol used in CCN, each node sends packets to the nodes that are connected to it, which communicate with further nodes that are connected to them. This kind of behaviors prevents formalizing the CCN protocol as end-to-end communications. In our previous work, we formalized the CCN protocol using the proof assistant Coq. However, in this model, each node in the network can store any number of contents. The storage for each node is usually limited and the node may drop some of the contents due to its filled storage. The model proposed in this paper permits a node to have its own content management method, and still keeps the temporal properties that are also valid in the previous model. To demonstrate difference between these models, we give a specification that is valid in the previous model but invalid in the proposed model, called orthogonality. Since it is generally invalid in CCN, the proposed model is more precise than the previous one.

Inspired from the idea of data representation in manifold learning, we derive a novel model which combines the original training images and their tangent vectors to represent each image in the testing set. Different from the previous methods, the L1 norm is used to control the reconstruction error. Considering the fact that the objective function in the proposed model is non-smooth, we utilize the majorization minimization (MM) method to solve the proposed optimization model. It is interesting to note that at each iteration a quadratic optimization problem is formulated and its analytical solution can be achieved, thereby making the proposed algorithm effective. Extensive experiments on face images demonstrate that our method achieves better performance than some previous methods.

Measurement matrix construction is critically important to signal sampling and reconstruction for compressed sensing. From a practical point of view, deterministic construction of the measurement matrix is better than random construction. In this paper, we propose a novel deterministic method to construct a measurement matrix for compressed sensing, CS-FF (compressed sensing-finite field) algorithm. For this proposed algorithm, the constructed measurement matrix is from the finite field Quasi-cyclic Low Density Parity Check (QC-LDPC) code and thus it has quasi-cyclic structure. Furthermore, we construct three groups of measurement matrices. The first group matrices are the proposed matrix and other matrices including deterministic construction matrices and random construction matrices. The other two group matrices are both constructed by our method. We compare the recovery performance of these matrices. Simulation results demonstrate that the recovery performance of our matrix is superior to that of the other matrices. In addition, simulation results show that the compression ratio is an important parameter to analyse and predict the recovery performance of the proposed measurement matrix. Moreover, these matrices have less storage requirement than that of a random one, and they achieve a better trade-off between complexity and performance. Therefore, from practical perspective, the proposed scheme is hardware friendly and easily implemented, and it is suitable to compressed sensing for its quasi-cyclic structure and good recovery performance.

In data center networks, Quantized Congestion Notification (QCN) has been ratified as the standard congestion management mechanism in the link layer. Since QCN nonlinearly switches between the rate increase and decrease stages, it is very difficult to understand QCN in depth and provide theoretical guidelines on setting the buffer size of the QCN switch. This paper gives an explicit formula for the boundary of buffer occupancy of the QCN switch. Specifically, based on the fluid model of QCN, we first derive the uniformly asymptotic stability condition of the QCN system. Then, under the condition that QCN is uniformly asymptotically stable, we analyze the buffer occupancy of the QCN switch theoretically and show that the classic rule-of-thumb for buffer sizing is not suitable for QCN. Finally, simulations validate the accuracy of our theoretical results.

Although many approaches about ideal channels have been proposed in previous researches, few authors considered the situation of nonideal communication links. In this paper, we study the problem of distributed decision fusion over nonideal channels by using the scan statistics. In order to obtain the fusion rule under nonideal channels, we set up the nonideal channels model with the modulation error, noise and signal attenuation. Under this model, we update the fusion rule by using the scan statstics. We firstly consider the fusion rule when sensors are distributed in grid, then derive the expressions of the detection probability and false alarm probability when sensors follow an uniform distribution. Extensive simulations are conducted in order to investigate the performance of our fusion rule and the influence of signal-noise ratio (SNR) on the detection and false alarm probability. These simulations show that the theoretical values of the global detection probability and the global false alarm probability are close to the experimental results, and the fusion rule also has high performance at the high SNR region. But there are some further researches need to do for solving the large computational complexity.

Sound localization systems are widely studied and have several potential applications, including hearing aid devices, surveillance and robotics. However, few proposed solutions target portable systems, such as wearable devices, which require a small unnoticeable platform, or unmanned aerial vehicles, in which weight and low power consumption are critical aspects. The main objective of this research is to achieve real-time sound localization capability in a small, self-contained device, without having to rely on large shaped platforms or complex microphone arrays. The proposed device has two surface-mount microphones spaced only 20 mm apart. Such reduced dimensions present challenges for the implementation, as differences in level and spectra become negligible, and only time-difference of arrival (TDoA) can be used as a localization cue. Three main issues have to be addressed in order to accomplish these objectives. To achieve real-time processing, the TDoA is calculated using zero-crossing spikes applied to the hardware-friendly Jeffers model. In order to make up for the reduction in resolution due to the small dimensions, the signal is upsampled several-fold within the system. Finally, a coherence-based spectral masking is used to select only frequency components with relevant TDoA information. The proposed system was implemented on a field-programmable gate array (FPGA) based platform, due to the large amount of concurrent and independent tasks, which can be efficiently parallelized in reconfigurable hardware devices. Experimental results with white-noise and environmental sounds show high accuracies for both anechoic and reverberant conditions.

A continuous flow reactor equipped with a low-loss flow channel and a microwave cavity was developed for synthesizing nanophosphors. A continuous solution synthesis of YVO4:Eu,Bi nanophosphor was succeeded through the rapid hydrothermal method using this equipment. Internal quantum efficiency of YVO4:Eu,Bi nanophosphor obtained by 20 minutes microwave heating is about 30% at 320 nm as high as that obtained by 6 hours hydrothermal treatment in autoclave.