A simple robust finite-time convergent observer is presented in the presence of unknown input disturbance and measurement noise. In order to achieve the robust estimation and ensure the finite-time convergence, the proposed observer is constructed by using a multiple integral observer scheme in a hybrid system framework. Comparative computer simulations and laboratory experiments have been performed to test the effectiveness of the proposed observer.

In this paper, we propose a flexible and high-capacity front-haul link for the uplink transmission of high-speed mobile signals using a cascade of radio-on-radio (RoR) and radio-over-fiber (RoF) systems. To emulate the cases that may occur in the uplink direction, we experimentally investigate the performance of superposing an uplink bursty LTE-A signal on the cascaded system using optical packet signal transmission. The performance of systems using different types of erbium-doped fiber amplifiers (EDFAs), including a high-transient EDFA, an automatic-gain-control EDFA, and a burst-mode (BM) EDFA is evaluated and compared. We confirm that the dynamic transience of the EDFAs has a significant influence on the signal performance. By using a BM-EDFA, we confirm successful transmission of the uplink packetized LTE-A signal on the cascaded system. Both the measured error vector magnitude and the received optical power range metrics exceed the requirements. We also estimate the maximum transmission range of the RoR link, and it is confirmed that a sufficiently long range could be achieved for the applications in mobile front-haul networks.

The next generation of information technology demands both high capacity and mobility for applications such as high speed wireless access capable of supporting broadband services. The transport of wireless and wireline signals is converging into a common telecommunication infrastructure. In this paper, we will present the Marie Curie Framework Program 7 project “Wireless and wireline service convergence in next generation optical access networks” (WISCON), which focuses on the conception and study of novel architectures for wavelength-division-multiplexing (WDM) optical multi-modulation format radio-over-fiber (RoF) systems; this is a promising solution to implement broadband seamless wireless -wireline access networks. This project successfully concluded in autumn 2013, and is being follow up by another Marie Curie project entitled “flexible edge nodes for dynamic optical interconnection of access and core networks” (FENDOI), which will be also briefly described.

This paper presents novel analytical results on the successful decoding probability for random linear network coding in acyclic networks. The results consist of a tight lower bound on the successful decoding probability, its convergence, and its application in constructing a practical algorithm to identify the minimum field size for random linear network coding subject to a target on the successful decoding probability. From the two characterizations of random linear network coding, namely the set of local encoding kernels and the set of global encoding kernels, we first show that choosing randomly and uniformly the coefficients of the local encoding kernels results in uniform and independent coefficients for the global encoding kernels. The set of global encoding kernels for an arbitrary destination is thus a random matrix whose invertibility is equivalent to decodability. The lower bound on the successful decoding probability is then derived in terms of the probability that this random matrix is non-singular. The derived bound is a function of the field size and the dimension of global encoding kernels. The convergence rates of the bound over these two parameters are provided. Compared to the mathematical expression of the exact probability, the derived bound provides a more compact expression and is close to the exact value. As a benefit of the bound, we construct a practical algorithm to identify the minimum field size in order to achieve a target on the successful decoding probability. Simulation and numerical results verify the validity of the derived bound as well as its higher precision than previously established bounds.

For an adaptive system identification filter with a stochastic input signal, a coefficient vector updated with an NLMS algorithm converges in the sense of ensemble average and the expected convergence vector has been revealed. When the input signal is periodic, the convergence of the adaptive filter coefficients has also been proved. However, its convergence vector has not been revealed. In this paper, we derive the convergence vector of adaptive filter coefficients updated with the NLMS algorithm in system identification for deterministic sinusoidal inputs. Firstly, we derive the convergence vector when a disturbance does not exist. We show that the derived convergence vector depends only on the initial vector and the sinusoidal frequencies, and it is independent of the step-size for adaptation, sinusoidal amplitudes, and phases. Next, we derive the expected convergence vector when the disturbance exists. Simulation results support the validity of the derived convergence vectors.

Three-dimensional (3D) movies have become very popular in movie theaters and for home viewing, To date, there has been no report of the effects of the continual vergence eye movement that occurs when viewing 3D movies from the beginning to the end. First, we analyzed the influence of viewing a 3D movie for several hours on vergence eye movement. At the same time, we investigated the influence of long viewing on the human body, using the Simulator Sickness Questionnaire (SSQ) and critical fusion frequency (CFF). It was suggested that the vergence stable time after saccade when viewing a long movie was influenced by the viewing time and that the vergence stable time after saccade depended on the content of the movie. Also the differences were seen in the SSQ and CFF between the movie's beginning and its ending when viewing a 3D movie.

This letter investigates the training convergence in range-based cooperative positioning with stochastic positional knowledge. Firstly, a closed-form of squared position-error bound (SPEB) is derived with error-free ranging. Using the derived closed-form, it is proved that the SPEB reaches its minimum when at least 2 out of N (> 2) agents send training sequences. Finally, numerical results are provided to elaborate the theoretical analysis with zero-mean Gaussian ranging errors.

In this paper, we propose an optimized virtual re-convergence system especially to reduce the visual fatigue caused by binocular stereoscopy. Our unique idea to reduce visual fatigue is to utilize the virtual re-convergence based on the optimized disparity-map that contains more depth information in the negative disparity area than in the positive area. Therefore, our system facilitates a unique search-range scheme, especially for negative disparity exploration. In addition, we used a dedicated method, using a so-called Global-Shift Value (GSV), which are the total shift values of each image in stereoscopy to converge a main object that can mostly affect visual fatigue. The experimental result, which is a subjective assessment by participants, shows that the proposed method makes stereoscopy significantly comfortable and attractive to view than existing methods.

To realize economical optical burst signal receivers for the Optical Network Unit (ONU) of the Ethernet Optical Switched Access Network (E-OSAN), we previously implemented optical burst receivers with AC-coupling and DC-coupling using off-the-shelf components, and showed that the former offers better performance. This paper proposes a new optical burst signal receiver that uses the transfer function, Gn(s) = 1-Hn(s), where Hn(s) denotes a Bessel filter transfer function of order n. We also present a method for designing the proposed receiver and clarify that it has better performance than the conventional AC-coupling one. We then present an LCR circuit synthesis of Gn(s), which is necessary to actually implement a burst receiver based on the proposal.

Let (X,Y) be a Rd R-valued random vector. In regression analysis one wants to estimate the regression function m(x):=E(Y|X=x) from a data set. In this paper we consider the convergence rate of the error for the k nearest neighbor estimators in case that m is (p,C)-smooth. It is known that the minimax rate is unachievable by any k nearest neighbor estimator for p > 1.5 and d=1. We generalize this result to any d ≥ 1. Throughout this paper, we assume that the data is independent and identically distributed and as an error criterion we use the expected L2 error.

In this letter, a novel adaptive total variation (ATV) model is proposed for image inpainting. The classical TV model is a partial differential equation (PDE)-based technique. While the TV model can preserve the image edges well, it has some drawbacks, such as staircase effect in the inpainted image and slow convergence rate. By analyzing the diffusion mechanism of TV model and introducing a new edge detection operator named difference curvature, we propose a novel ATV inpainting model. The proposed ATV model can diffuse the image information smoothly and quickly, namely, this model not only eliminates the staircase effect but also accelerates the convergence rate. Experimental results demonstrate the effectiveness of the proposed scheme.

In this paper, a new algorithm called TGA is introduced which defines the concept of time more naturally for the first time. A parameter called TimeToLive is considered for each chromosome, which is a time duration in which it could participate in the process of the algorithm. This will lead to keeping the dynamism of algorithm in addition to maintaining its convergence sufficiently and stably. Thus, the TGA guarantees not to result in premature convergence or stagnation providing necessary convergence to achieve optimal answer. Moreover, the mutation operator is used more meaningfully in the TGA. Mutation probability has direct relation with parent similarity. This kind of mutation will decrease ineffective mating percent which does not make any improvement in offspring individuals and also it is more natural. Simulation results show that one run of the TGA is enough to reach the optimum answer and the TGA outperforms the standard genetic algorithm.

Recently, Mooij et al. proposed new sufficient conditions for convergence of the sum-product algorithm, and it was also shown that if the factor graph is a tree, Mooij's sufficient condition for convergence is always activated. In this letter, we show that the converse of the above statement is also true under some assumption, and that the assumption holds for the sum-product decoding. These newly obtained fact implies that Mooij's sufficient condition for convergence of the sum-product decoding is activated if and only if the factor graph of the a posteriori probability of the transmitted codeword is a tree.

The distribution of streaming multicast and real time audio/video applications in the Internet has been quickly increased in the Internet. Commonly, these applications rarely use congestion control and do not fairly share provided network capacity with TCP-based applications such as HTTP, FTP and emails. Therefore, Internet communities will be threatened by the increase of non-TCP-based applications that likely cause a significant increase of traffics congestion and starvation. This paper proposes a set of mechanisms, such as providing various data rates, background traffics, and various scenarios, to act friendly with TCP when sending multicast traffics. By using 8 scenarios of simulations, we use 6 layered multicast transmissions with background traffic Pareto with the shape factor 1.5 to evaluate performance metrics such as throughput, delay/latency, jitter, TCP friendliness, packet loss ratio, and convergence time. Our study shows that non TCP traffics behave fairly and respectful of the co-existent TCP-based applications that run on shared link transmissions even with background traffic. Another result shows that the simulation has low values on throughput, vary in jitter (0-10 ms), and packet loss ratio > 3%. It was also difficult to reach convergence time quickly when involving only non TCP traffics.

This paper presents unconditionally stable and conformal FDTD schemes which are based on the alternating-direction implicit finite difference time domain (ADI-FDTD) method for accurate modeling of perfectly electric conducting (PEC) objects. The proposed schemes are formulated within the framework of the matrix-vector notation of the finite integration technique (FIT), which allows a systematic and consistent extension of finite difference solution of Maxwell's equations on dual grids. As possible choices of second-order convergent conformal method, we apply the partially filled cell (PFC) and the uniformly stable conformal (USC) schemes for the ADI-FDTD method. The unconditional stability and the rates of convergence of the proposed conformal ADI-FDTD (CADI-FDTD) schemes are verified by means of numerical examples of waveguide problems.

This letter proposes a new adaptive filtering method that uses the last L desired signal samples as an extra input vector, besides the existing input data, to reduce mean square error. We have improved the convergence rate by adopting the squared norm of the past error samples, in addition to the modified cost function. The modified variable error-data normalized step-size least mean square algorithm provides fast convergence, ensuring a small final misadjustment. Simulation results indicate its superior mean square error performance, while its convergence rate equals that of existing methods. In addition, the proposed algorithm shows superior tracking capability when the system is subjected to an abrupt disturbance.

This letter investigates an ADILC (Iterative Learning Control with Advanced Output Data) scheme for nonminimum phase systems using a partially known impulse response. ADILC has a simple learning structure that can be applied to both minimum phase and nonminimum phase systems. However, in the latter case, the overall control time horizon must be considered in the input update law, which makes the dimension of the matrices in the convergence condition very large. Also, this makes it difficult to find a proper learning gain matrix. In this letter, a new sufficient condition is derived from the convergence condition, which can be used to find the learning gain matrix for nonminimum phase systems if we know the first part of the impulse response up to a sufficient order. Based on this, an iterative learning control scheme is proposed using the estimation of the first part of the impulse response for nonminimum phase systems.

In this letter, we analyze the convergence speed of layered decoding of block-type low-density parity-check codes and verify that the layered decoding gives faster convergence speed than the sequential decoding with randomly selected check node subsets. Also, it is shown that using more subsets than the maximum variable node degree does not improve the convergence speed.

This paper investigates noise reduction performance and performs convergence analysis of a Variable Error Data Normalized Step-Size Least Mean Square (VEDNSS LMS) algorithm. Adopting VEDNSS LMS provides fast convergence at early stages of adaptation while ensuring small final misadjustment. An analysis of convergence and steady-state performance for zero-mean Gaussian inputs is provided. Simulation results comparing the proposed algorithm to existing algorithms indicate its superior performance under various noise and frequency environments.

Because of the fact that mobile communication channel changes by time, it is necessary to employ adaptive channel equalizers in order to combat the distorting effects of the channel. Least Mean Squares (LMS) algorithm is one of the most popular channel equalization algorithms and is preferred over other algorithms such as the Recursive Least Squares (RLS) and Maximum Likelihood Sequence Estimation (MLSE) when simplicity is the dominant decision factor. However, LMS algorithm suffers from poor performance and convergence speed within the training period specified by most of the standards. The aim of this study is to improve the convergence speed and performance of the LMS algorithm by adjusting the step size using fuzzy logic. The proposed method is compared with the Channel Matched Filter-Decision Feedback Equalizer (CMF-DFE) [1] which provides multi path propagation diversity by collecting the energy in the channel, Minimum Mean Square Error-Decision Feedback Equalizer (MMSE-DFE) [2] which is one of the most successful equalizers for the data packet transmission, normalized LMS-DFE (N-LMS-DFE) [3] , variable step size (VSS) LMS-DFE [4] , fuzzy LMS-DFE [5],[6] and RLS-DFE [7] . The obtained simulation results using HIPERLAN/1 standards have demonstrated that the proposed LMS-DFE algorithm based on fuzzy logic has considerably better performance than others.