We propose an edge-preserving multiscale image decomposition method using filters for non-equispaced signals. It is inspired by the domain transform, which is a high-speed edge-preserving smoothing method, and it can be used in many image processing applications. One of the disadvantages of the domain transform is sensitivity to noise. Even though the proposed method is based on non-equispaced filters similar to the domain transform, it is robust to noise since it employs a multiscale decomposition. It uses the Laplacian pyramid scheme to decompose an input signal into the piecewise-smooth components and detail components. We design the filters by using an optimization based on edge-preserving smoothing with a conversion of signal distances and filters taking into account the distances between signal intervals. In addition, we also propose construction methods of filters for non-equispaced signals by using arbitrary continuous filters or graph spectral filters in order that various filters can be accommodated by the proposed method. As expected, we find that, similar to state-of-the-art edge-preserving smoothing techniques, including the domain transform, our approach can be used in many applications. We evaluated its effectiveness in edge-preserving smoothing of noise-free and noisy images, detail enhancement, pencil drawing, and stylization.

Light field data, which is composed of multi-view images, have various 3D applications. However, the cost of acquiring many images from slightly different viewpoints sometimes makes the use of light fields impractical. Here, compressive sensing is a new way to obtain the entire light field data from only a few camera shots instead of taking all the images individually. In paticular, the coded aperture/mask technique enables us to capture light field data in a compressive way through a single camera. A pixel value recorded by such a camera is a sum of the light rays that pass though different positions on the coded aperture/mask. The target light field can be reconstructed from the recorded pixel values by using prior information on the light field signal. As prior information, the current state of the art uses a dictionary (light field atoms) learned from training datasets. Meanwhile, it was reported that general bases such as those of the discrete cosine transform (DCT) are not suitable for efficiently representing prior information. In this study, however, we demonstrate that a 4D-DCT basis works surprisingly well when it is combined with a weighting scheme that considers the amplitude differences between DCT coefficients. Simulations using 18 light field datasets show the superiority of the weighted 4D-DCT basis to the learned dictionary. Furthermore, we analyzed a disparity-dependent property of the reconstructed data that is unique to light fields.

This paper considers a massive multiple-input-multiple-output (MIMO) relaying system with multi-pair single-antenna users. The relay node adopts maximum-ratio combining/maximum-ratio transmission (MRC/MRT) stratagem for reception/transmission. We analyze the spectral efficiency (SE) and power scaling laws with respect to the number of relay antennas and other system parameters. First, by using the law of large numbers, we derive the closed-form expression of the SE, based on which, it is shown that the SE per user increases with the number of relay antennas but decreases with the number of user pairs, both logarithmically. It is further discovered that the transmit power at the source users and the relay can be continuously reduced as the number of relay antennas becomes large while the SE can maintains a constant value, which also means that the energy efficiency gain can be obtained simultaneously. Moreover, it is proved that the number of served user pairs can grow proportionally over the number of relay antennas with arbitrary SE requirement and no extra power cost. All the analytical results are verified through the numerical simulations.

In this paper, we study the spectral efficiency of the uplink multi-user large-scale distributed antenna systems (DAS) with imperfect channel state information. We propose the system model of multi-user DAS and illustrate the necessity of pilot reuse. Then, we derive the sum-rate of the system under pilot contamination. Furthermore, we investigate the asymptotical performance when the number of antennas goes to infinity. To reduce the pilot contamination, we present two novel pilot assignment algorithms to improve the spectral efficiency. Finally, we evaluate our proposed strategies through extensive simulations which show that compared with random pilot reuse, the min-max algorithm shows impressive performance with low complexity.

The limitation of the GPS in urban canyon has led to the rapid development of Wi-Fi positioning system (WPS). The fingerprint-based WPS could be divided into calibration and positioning stages. In calibration stage, several grid points (GPs) are selected, and their position tags and featured access points (APs) are collected to build fingerprint database. In positioning stage, real time measurement of APs are compared with the feature of each GP in the database. The k weighted nearest neighbors (KWNN) algorithm is used as pattern matching algorithm to estimate the final positioning result. However, the performance of outdoor fingerprint-based WPS is not good enough for pedestrian navigation. The main challenge is to build a robust fingerprint database. The received number of APs in outdoor environments has large variation. In addition, positioning result estimated by GPS receiver is used as position tag of each GP to automatically build the fingerprint database. This paper studies the lifecycle of fingerprint database in outdoor environment. We also shows that using long time collected data to build database could improve the positioning accuracy. Moreover, a new 3D-GNSS (3D building models aided GNSS) positioning method is used to provide accurate position tags. In this paper, the fingerprint-based WPS has been developed in an outdoor environment near the center of Tokyo city. The proposed WPS can achieve around 17 meters positioning accuracy in urban canyon.

With the appearance of large OLED panels, the OLED TV industry has experienced significant growth. However, this technology is still in the early stages of commercialization, and some technical challenges remain to be overcome. During the development phase of a product, power consumption is one of the most important considerations. To reduce power consumption in OLED displays, we propose a method based on just-noticeable difference (JND). JND refers to the minimum visibility threshold when visual content is altered and results from physiological and psychophysical phenomena in the human visual system (HVS). A JND model suitable for OLED displays is derived from numerous experiments with OLED displays. With the use of JND, it is possible to reduce power consumption while minimizing perceptual image quality degradation.

An organization may have two or more similar workflows as a result of workflow evolutions or mergers and acquisitions. We should grasp the common behavior of those workflows to consolidate the management of them and/or to do business process reengineering. Workflows can be modeled as a particular class of Petri nets, called workflow nets. The common behavior of two or more workflow nets can be represented as a superclass under the behavioral inheritance of those workflow nets. In this paper, we tackled a problem of extracting a superclass from two workflow nets, named Superclass Extraction problem. We first gave a definition of the problem. Next we proposed a procedure to solve the problem on the basis of process mining technique. Then we gave an application of the proposed procedure.

In this paper, a new non-uniform weight-updating scheme for adaptive digital beamforming (DBF) is proposed. The unique feature of the letter is that the effective working range of the beamformer is extended and the computational complexity is reduced by introducing the robust DBF based on worst-case performance optimization. The robust parameter for each weight updating is chosen by analyzing the changing rate of the Direction of Arrival (DOA) of desired signal in LEO satellite communication. Simulation results demonstrate the improved performance of the new Non-Uniform Weight-Updating Beamformer (NUWUB).

In this paper, we propose a compressed sensing scheme using sparse-graph codes and peeling decoder (SGPD). By using a mix method for construction of sensing matrices proposed by Pawar and Ramchandran, it generates local sensing matrices and implements sensing and signal recovery in an adaptive manner. Then, we show how to optimize the construction of local sensing matrices using the theory of sparse-graph codes. Like the existing compressed sensing schemes based on sparse-graph codes with “good” degree profile, SGPD requires only O(k) measurements to recover a k-sparse signal of dimension n in the noiseless setting. In the presence of noise, SGPD performs better than the existing compressed sensing schemes based on sparse-graph codes, still with a similar implementation cost. Furthermore, the average variable node degree for sensing matrices is empirically minimized for SGPD among various existing CS schemes, which can reduce the sensing computational complexity.

In this letter, a method of wideband direction of arrival (DOA) estimation based on co-prime arrays with sub-Nyquist sampling is proposed. Previous works have employed co-prime arrays for wideband DOA estimation, which can increase the degrees of freedom (DOFs) in the spatial domain. However, they are all based on Nyquist sampling. Different from existing methods, we incorporate a sub-Nyquist sampling scheme called multicoset sampling for DOA estimation to relax hardware condition. Simulation results show the correctness and effectiveness.

In this paper, an infinite-horizon team-optimal incentive Stackelberg strategy is investigated for a class of stochastic linear systems with many non-cooperative leaders and one follower. An incentive structure is adopted which allows for the leader's team-optimal Nash solution. It is shown that the incentive strategy set can be obtained by solving the cross-coupled stochastic algebraic Riccati equations (CCSAREs). In order to demonstrate the effectiveness of the proposed strategy, a numerical example is solved.

This paper proposes a circular bit-vector-mismatches (CBVM) algorithm for approximate circular string matching with k-mismatches. We develop the proposed CBVM algorithm based on the rotation feature of the circular pattern. By reusing the matching information of the previous substring, the next substring of the input string can be processed in parallel.

In this research, we investigated the reliability of a 1-out-of-2 system with two-stage repair comprising hardware restoration and data reconstruction modes. Hardware restoration is normally independently executed by two modules. In contrast, we assumed that one of the modules could omit data reconstruction by replicating the data from the module during normal operation. In this 1-out-of-2 system, the two modules mutually cooperated in the recovery mode. As a first step, an evaluation model using Markov chains was constructed to derive a reliability measure: “unavailability in steady state.” Numerical examples confirmed that the reliability of the system was improved by the use of two cooperating modules. As the data reconstruction time increased, the gains in terms of system reliability also increased.

Quadriphase sequences with good correlation properties are required in higher order digital modulation schemes, e.g., for timing measurements, channel estimation or synchronization. In this letter, based on interleaving technique and pairs of mismatched binary sequences with perfect cross-correlation function (PCCF), two new methods for constructing quadriphase sequences with mismatched filtering which exist for even length N ≡ 2(mod4) are presented. The resultant perfect mismatched quadriphase sequences have high energy efficiencies. Compared with the existing methods, the new methods have flexible parameters and can give cyclically distinct perfect mismatched quadriphase sequences.

In this letter, standard particle swarm optimization (PSO) with the center-symmetric trimmed correlation matrix and the orthogonal projection technique is firstly presented for blind carrier frequency offset estimation under interleaved orthogonal frequency division multiple access (OFDMA) uplink systems. It doesn't require eigenvalue decomposition and only needs a single OFDMA data block. Second, this letter also presents adaptive multiple inertia weights with Newton method to speed up the convergence of standard PSO iteration process. Meanwhile, the advantage of inherent interleaved OFDMA signal structure also is exploited to conquer the problems of local optimization and the effect of ambiguous peaks for the proposed approaches. Finally, several simulation results are provided for illustration and comparison.

In this letter, we present a spectrally efficient multicast method which enables a transmitter to simultaneously transmit multiple multicast streams without any interference among multicast groups. By using unique combiners at receivers with multiple antennas within each multicast group, the proposed method simplifies multiple channels between the transmitter and the receivers to an equivalent channel. In addition, we establish the sufficient condition for the system configuration which should be satisfied for the channel simplification and provide a combiner design technique for the receivers. To remove interference among multicast groups, the precoder for the transmitter is designed by utilizing the equivalent channels. By exploiting time resources efficiently, the channel simplification (CS) based method achieves a higher sum rate than the time division multiplexing (TDM) based method, which the existing multicast techniques fundamentally employ, at high signal-to-noise ratio (SNR) regime. Furthermore, we present a multicast method combining the CS based method with the TDM based method to utilize the benefits of both methods. Simulation results successfully demonstrate that the combined multicast method obtains a better sum rate performance at overall SNR regime.

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.

One problem in the use of wireless Content Centric Networks (CCNs) is the need for substantial overhead to achieve reliability in content delivery due to the requirement for a request packet per each data packet transmission. This paper introduces a novel protocol to reduce overhead and achieve reliability. The protocol allows an interest packet to request multiple data packets and an intermediate node, rather than the provider, to send the data packet. Simulation results show that the proposed protocol improves the performance of wireless CCN.

In this paper, we propose a method for color error diffusion based on the Neugebauer model for color halftone printing. The Neugebauer model expresses an arbitrary color as a trilinear interpolation of basic colors. The proposed method quantizes the color of each pixel to a basic color which minimizes an accumulated quantization error, and the quantization error is diffused to the ratios of basic colors in subsequent pixels. Experimental results show that the proposed method outperforms conventional color error diffusion methods including separable method in terms of eye model-based mean squared error.