Multiple-shot person re-identification, which is valuable for application in visual surveillance, tackles the problem of building the correspondence between images of the same person from different cameras. It is challenging because of the large within-class variations due to the changeable body appearance and environment and the small between-class differences arising from the possibly similar body shape and clothes style. A novel method named “Bi-level Relative Information Analysis” is proposed in this paper for the issue by treating it as a set-based ranking problem. It creatively designs a relative dissimilarity using set-level neighborhood information, called “Set-level Common-Near-Neighbor Modeling”, complementary to the sample-level relative feature “Third-Party Collaborative Representation” which has recently been proven to be quite effective for multiple-shot person re-identification. Experiments implemented on several public benchmark datasets show significant improvements over state-of-the-art methods.

This letter proposes a novel depth-guided inpainting scheme for the high quality hole-filling in 2D-to-3D video conversion. The proposed scheme detects and removes foreground depth layers in an image patch, enabling appropriate patch formation using only disoccluded background information. This background only patch formation helps to avoid the propagation of wrong depths over hole area, and thus improve the overall quality of converted 3D video experience. Experimental results demonstrate the proposed scheme provides visually much more pleasing inpainting results with better preserved object edges compared to the state-of-the-art depth-guided inpainting schemes.

After receiving emitted signals from various radars by electronic support measures (ESM) system, several processes are applied to signals such as: deinterleaving, recognition of pulse repetition interval (PRI) modulation, PRI estimation and etc. Indeed, recognition of PRI modulation is an essential task of ESM system. In this paper a novel and robust method for recognition of complicated PRI Modulations is presented. This method uses specifications such as distribution related to members of sequences obtained from first and second order derivation of TOAs around a constant value and continuity of these sequences to recognize the PRI modulation. Some numerical simulations are performed to illustrate the effectiveness of proposed method. Simulation results show high robustness of proposed method against noise (spurious and missing pulses) and unwanted jitter.

Network tomography is a technique for estimating internal network characteristics from end-to-end measurements. In this paper, we focus on loss tomography, which is a network tomography problem for estimating link loss rates. We study a loss tomography problem to detect links with high link loss rates in network environments with dynamically changing link loss rates, and propose a window-based sequential loss tomography scheme. The loss tomography problem is formulated as an underdetermined linear inverse problem, where there are infinitely many candidates of the solution. In the proposed scheme, we use compressed sensing, which can solve the problem with a prior information that the solution is a sparse vector. Measurement nodes transmit probe packets on measurement paths established between them, and calculate packet loss rates of measurement paths (path loss rates) from probe packets received within a window. Measurement paths are classified into normal quality and low quality states according to the path loss rates. When a measurement node finds measurement paths in the low quality states, link loss rates are estimated by compressed sensing. Using simulation scenarios with a few link states changing dynamically from low to high link loss rates, we evaluate the performance of the proposed scheme.

In this paper, we briefly review the scheme of counting statistics, in which a probability of the number of monitored or target transitions in a Markov jump process is evaluated. It is generally easy to construct a master equation for the Markov jump process, and the counting statistics enables us to straightforwardly obtain basic equations of the counting statistics from the master equation; the basic equation is used to calculate the cumulant generating function of the probability of the number of target transitions. For stationary cases, the probability is evaluated from the eigenvalue analysis. As for the nonstationary cases, we review a numerical integration scheme to calculate the statistics of the number of transitions.

We have studied photo-induced effects in a p-type transistor based on a [1]benzothieno[3,2-b]benzothiophene (BTBT) derivative. Repetition of blue light irradiation and electrical characterization under dark reveals that its threshold voltage gradually shifts in the positive direction as the cumulative exposure time increases. This shift is slowly reversed when the transistor is stored under dark. The onset voltage defined as the gate bias at which the sub-threshold current exceeds a certain level behaves in a similar manner. Mobility remains more or less the same during this exposure period and the storage period. Time evolution of the threshold voltage shift is fit by a model assuming two charged meta-stable states decaying independently. A set of parameters consists of a decay constant for each state and the ratio of the two states. A single parameter set reproduces the positive shift during the exposure period and the negative shift during the storage period. Time evolution of the onset voltage is reproduced by the same parameter set. We have also studied photo-induced effects in two types of n-type transistors where either a pure solution of a perylene derivative or a solution mixed with an insulating polymer is used for printing each semiconductor layer. A similar behavior is observed for these transistors: blue light irradiation under a negative gate bias shifts the threshold and the onset voltages in the negative direction and these shifts are reversed under dark. The two-component model reproduces the behavior of these voltage shifts and the parameter set is slightly different among the two transistors made from different semiconductor solutions. The onset voltage shift is well correlated to the threshold voltage shift for the three types of organic transistors studied here. The onset voltage is more sensitive to illumination than the threshold voltage and its sensitivity differs among transistors.

Battery life and outdoor visibility are two of the most important features for mobile applications today. It is desirable to achieve both low power consumption and excellent outdoor visibility on the display device at the same time. We have previously reported a new RGBW method to realize low power consumption and high luminance with high image quality. In this paper, the basic concept of a new RGBW calculation utilizing an “Extended HSV color space” model is described, and also its performance, such as low power consumption, color image reproducibility and outdoor visibility is presented. The new method focuses on the luminance-increase ratio by means of a White signal for the display image data, and derives the appropriate RGBW signal and backlight PWM signal for every frame period. This dynamically controlled system solves the problems of conventional RGBW systems, and realizes the same image quality as a corresponding RGB display. In order to quantify its color image reproducibility, a spectroscopic measurement has been completed using the Macbeth Color Chart. In addition, the advantages of high luminance by the new RGBW method is described. The converted tone curve with an RGBW method provides very high luminance, such as 1,000cd/m2, and improved outdoor visibility. Finally, a newly developed 4.38-inch full-HD (1,080 × 1,920) 503ppi prototype LCD utilizing this new RGBW technology is described.

Ultra-high luminance lamps emitting white light with a well-scattered blue spectrum from InGaN/GaN laser diodes and a phosphor-converted yellow spectrum show speckle contrast values as low as LED. Spectral behavior of the laser diodes is analyzed to find the reason why such low values are obtained. As a result, the PWM-driven, multi-longitudinal mode with dynamically broadened line-width is found to have a great effect on reducing speckle contrast. Despite using the lasers, such speckle-free lamps are considered to be very suitable for high-luminance and other various lighting applications.

Relay has been incorporated into standards of wireless access networks to improve the system capacity and coverage. However, the resource allocation problem to support scalable video coding (SVC) multicast for wireless relay networks is challenging due to the existence of relay stations (RSs). In this paper, we study the resource allocation problem for SVC multicast over multi-hop wireless relay networks to maximize the total utility of all users with a general non-negative, non-decreasing utility function. Since the problem is NP-hard, we simplify it with RS specification functions which specialize the relay station to receive data for each user, and convert the resource allocation problem with one RS specification function as finding a maximum spanning sub-tree of a directed graph under budget constraint. A heuristic algorithm is proposed to solve the problem with polynomial time complexity. The simulation results reveal that the proposed algorithm outperforms other algorithms under assumptions of two-hop wireless relay networks or separated transmission for relay and access links, and it keeps good approximation to the optimal results.

In this paper, a practical antenna selection (AS) scheme is investigated for downlink multiuser massive multiple input multiple output (MIMO) networks where a base station (BS) is equipped with many antennas (N) and communicates with K mobile stations (MSs) simultaneously. In the proposed antenna selection technique, S antennas (S≤N) are selected for transmission based on the knowledge of channel coefficients of each MS for reducing the number of RF chains which mainly induce cost increase in terms of size, hardware, and power. In the proposed AS technique, a BS first ranks antenna elements according to the sum of their channel gains to all MSs. Then, the BS computes the downlink sum-rate with S consecutive antenna elements in the ordered set, where the subset consisting of S consecutive antennas is called a window. The BS selects the window resulting in the highest sum-rate. The selected S antenna elements are used for transmitting signals to multiple users, while the remaining (N-S) antenna elements are turned off for the time slot. Therefore, the proposed AS technique requires only (N-S+1) sum-rate computations, while the optimal AS technique involves $inom{N}{S}$ computations. We analyze downlink sum-rate with the proposed AS technique and compare it with that of a reference system with the same number of antenna elements without AS. Our results show that the proposed AS technique significantly outperforms the reference scheme.

Electromagnetic scattering and radiation in cylindrical electromagnetic bandgap (EBG) structure is analyzed. The radiated field from a line source placed inside the eccentric configuration of the cylindrical EBG structure and plane wave incident on the cylindrical EBG structure is numerically studied based on the method proposed by the authors in their early papers. Using the developed formulation, it is shown first time that when the cylindrical EBG is illuminated by plane wave of particular resonance frequencies, the field are strongly enhanced or shaded inside the cylindrical EBG structure and this effect depends on the angle of incidence of the plane waves. We give a deep physical insight into explanation of this phenomenon based on the Lorentz reciprocity relation for cylindrical structures.

Traffic classification has recently gained much attention in both academic and industrial research communities. Many machine learning methods have been proposed to tackle this problem and have shown good results. However, when applied to traffic with out-of-sequence packets, the accuracy of existing machine learning approaches decreases dramatically. We observe the main reason is that the out-of-sequence packets change the spatial representation of feature vectors, which means the property of linear mapping relation among features used in machine learning approaches cannot hold any more. To address this problem, this paper proposes an Improved Dynamic Time Warping (IDTW) method, which can align two feature vectors using non-linear alignment. Experimental results on two real traces show that IDTW achieves better classification accuracy in out-of-sequence traffic classification, in comparison to existing machine learning approaches.

We propose a natural synchronization scheme for wireless uncoupled devices, without any signal exchange among them. Our proposed scheme only uses natural environmental fluctuations, such as the temperature or humidity of the air, the environmental sounds, and so on, for the synchronization of the uncoupled devices. This proposed synchronization is realized based on the noise-induced synchronization phenomenon, uncoupled nonlinear oscillators synchronize with each other only by adding identical common noises to each of them. Based on the theory of this phenomenon, the oscillators can also be synchronized by noise sequences, which are not perfectly identical signals. Since the environmental natural fluctuations collected at neighboring locations are similar to each other and cross-correlation becomes high, our proposed scheme enabling synchronization only by natural environmental fluctuations can be realized. As an application of this proposed synchronization, we introduce wireless sensor networks, for which synchronization is important for reducing power consumption by intermittent data transmission. We collect environmental fluctuations using the wireless sensor network devices. Our results show that the wireless sensor network devices can be synchronized only by the independently collected natural signals, such as temperature and humidity, at each wireless sensor device.

In this paper, we propose a robust registration method, named Bounded-Variables Least Median of Squares (BVLMS). It overcomes both the misassociations and the ill-conditioning due to the interactions between Bounded-Variables Least Squares (BVLS) and Least Median of Squares (LMS). Simulation results demonstrate the feasibility of this new registration method.

Channel modeling, which is quite important for wireless communications system design, is difficult to be statistically generated from experimental results due to the expense and time constraints. However, with the computational electromagnetics method, the Electro-Magnetic (EM) field can be emulated and the corresponding EM wave propagation scenario can be analyzed. In this letter, the Finite Integration Technique (FIT) method is utilized to calculate the EM wave propagation of the onboard mobile communications in the cabin of an aircraft. With the simulation results, the channel model is established. Compared with Finite-Difference Time-Domain (FDTD), the proposed scheme is more accurate, which is promising to be used in the cabin channel modeling for onboard mobile system design.

This paper presents a unified treatment of the tracking analysis of adaptive filters with data normalization and error nonlinearities. The approach we develop is based on the celebrated energy-conservation framework, which investigates the energy flow through each iteration of an adaptive filter. Aside from deriving earlier results in a unified manner, we obtain new performance results for more general filters without restricting the regression data to a particular distribution. Simulations show good agreement with the theoretical findings.

Tunneling is one of the main methods for the transition from IPv4 to IPv6 networks. By encapsulating IPv6 packets in IPv4 or UDP packets, tunnels like 6to4, Isatap and Teredo provide a feasible way for IPv4 hosts to establish IPv6 connections to hosts in IPv6 internet or IPv6 islands. For IPv4 internet, the use of tunnels varies the traffic and increases the type of packets, making the network environment more complex. In addition to common tunnels, various types of tunnels with more layers are tested in this paper. The results of successful connections prove the usefulness of multi-layer packets with diverse layer-count and type on the internet. To ensure the security of internal networks, the influence on traffic analysis in dual-stack IDS devices caused by the diversity is studied. Three spoofing attacks of “data insertion”, “data evasion” and “attacks using UDP” are proposed to show the influence on IDS caused by tunnels. Compared to the attacks without tunnels, some constraining factors are eliminated, which may increase the security risk of IDS and decrease the attacker's difficulties. To summarize this kind of problem, the concept of “Tunnel Interference” is revealed. And as solutions to this problem, two methods, RA (Record All) and HEH (Hash for Each Header), are presented in this paper which theoretically solve these problems to a great extent. RA records all headers and compares from the outermost to innermost layer. HEH is hash-based and accumulates hash values of each header. Both of them have linear time and space complexity. Experimental results show that RA and HEH will lead to minor space increase and up to 1.2% time increment in each layer compared to the original dual-stack.

Computing the level of trust between two indirectly connected users in an online social network (OSN) is a problem that has received considerable attention of researchers in recent years. Most algorithms focus on finding the most accurate prediction of trust; however, little work has been done to make them fast enough to be applied on today's very large OSNs. To address this need we propose a method for fast trust computation that is suitable for very large social networks. Our method uses min-max trust propagation strategies along with the landmark based method. Path strength of every node is pre-computed to and from a small set of reference users or landmarks. Using these pre-computed values, we estimate the strength of trust paths from the source user to in-neighbors of the target user. The final trust estimate is obtained by aggregating information from most reliable in-neighbors of the target user. We also describe how the landmark based method can be used for fast trust computation according to other trust propagation models. Experiments on a variety of real social network datasets verify the efficiency and accuracy of our method.

Machine-to-Machine (M2M) communication is expected to grow in networks of the future, where massive numbers of low cost, low function M2M terminals communicate in many-to-many manner in an extremely mobile and dynamic environment. We propose a network architecture called Data-centric Network (DCN) where communication is done using a data identifier (ID) and the dynamic data registered by mobile terminals can be retrieved by specifying the data ID. DCN mitigates the problems of prior arts, which are large size of routing table and transaction load of name resolution service. DCN introduces concept of route attraction and aggregation in which the related routes are attracted to an aggregation point and aggregated to reduce routing table size, and route optimization in which optimized routes are established routes to reduce access transaction load to the aggregation points. These allow the proposed architecture to deal with ever increasing number of data and terminals with frequent mobility and changes in data.