This paper provides an overview of power line communication (PLC) applications, challenges and possible evolution. Emphasis is put on two relevant aspects: a) channel characterization and modeling, b) filter bank modulation for spectral efficient transmission. The main characteristics of both the indoor channel (in-home, in-ship, in-car) and the outdoor low voltage and medium voltage channels are reported and compared. A simple approach to statistically model the channel frequency response (CFR) is described and it is based on the generation of a vector of correlated random variables. To overcome the channel distortions, it is shown that filter bank modulation can provide robust performance. In particular, it is shown that the sub-channel spectral containment of filtered multitone modulation (FMT) can provide high notching capability and spectral efficiency. Reduced complexity can be obtained with a cyclic filter bank modulation approach that we refer to as cyclic block FMT modulation (CB-FMT) which still provides higher spectral flexibility/efficiency than OFDM.

We consider a unified approach to the tracking analysis of adaptive filters with error and matrix data nonlinearities. Using energy-conservation arguments, we not only derive earlier results in a unified manner, but we also obtain new performance results for more general adaptive algorithms without requiring the restriction of the regression data to a particular distribution. Numerical simulations support the theoretical results.

In this invited paper, software defined network (SDN)-based approaches for future cost-effective optical mobile backhaul (MBH) networks are discussed, focusing on key principles, throughput optimization and dynamic service provisioning as its use cases. We propose a novel physical-layer aware throughput optimization algorithm that confirms > 100Mb/s end-to-end per-cell throughputs with ≥2.5Gb/s optical links deployed at legacy cell sites. We also demonstrate the first optical line terminal (OLT)-side optical Nyquist filtering of legacy 10G on-off-keying (OOK) signals, enabling dynamic >10Gb/s Orthogonal Frequency Domain Multiple Access (OFDMA) λ-overlays for MBH over passive optical network (PON) with 40-km transmission distances and 1:128 splitting ratios, without any ONU-side equipment upgrades. The software defined flexible optical access network architecture described in this paper is thus highly promising for future MBH networks.

In this paper, the function expansion based topology optimization is employed to the automatic optimization of the waveguide dispersion property, and the optimum design of low-dispersion slow-light photonic crystal waveguides is demonstrated. In order to realize low-dispersion and large group index, an objective function to be optimized is expressed by the weighted sum of the objective functions for the desired group index and the low-dispersion property, and weighting coefficients are updated through the optimization process.

To implement a wideband bandpass filter with improved skirt-selectivity and out-band characteristics, a new parallel-coupled three-line unit with two short-circuited stubs symmetrically-loaded at the center line is proposed. Unlike most traditional ones, the passband of the proposed parallel-coupled three-line structure is based on the cross-coupling between non-adjacent lines rather than the direct-coupling between adjacent ones, whereas a pair of attenuation poles is found in the stopbands. After revealing its work mechanism, an efficient filter-design-scheme is correspondingly proposed for the presented structure. Firstly, based on a chebyshev-filter synthesis theory, a wideband passband filter consisting of a parallel-coupled two-line and two short-circuited stubs loaded at the input- and output- ports is designed. Furthermore, by putting a properly-designed 3/4-wavelength stepped-impedance resonator (SIR) in between the parallel-coupled two lines, two attenuation poles are then realized at the frequencies very close to the cutoff ones. Accordingly, the roll-off characteristics of the filter are significantly-improved to greater than 100,dB/GHz. Furthermore, two-section open-ended stubs are used to replace the short-circuited ones to realize a pair of extra attenuation poles in stopbands. To validate the proposed techniques, a wideband filter with a bandwidth of 3--5,GHz (Fractional bandwidth (FBW) $= (5,GHz-3,GHz)/4,GHz =50%)$ was designed, simulated, fabricated and measured. The measured responses of the filter agree well with the simulation and theoretical ones, which validates the effectiveness of the newly-proposed three-line unit and the corresponding design scheme.

Acoustic feedback is a major complaint of hearing aid users. Adaptive filters are a common method for suppressing acoustic feedback in digital hearing aids. In this letter, we propose a new variable step-size algorithm for normalized least mean square and an affine projection algorithm to combine with a variable step-size affine projection algorithm and global speech absence probability in an adaptive filter. The computer simulation used to test the proposed algorithm results in a lower misalignment error than the comparison algorithm at a similar convergence rate. Therefore, the proposed algorithm suggests an effective solution for the feedback suppression system of digital hearing aids.

This letter proposes an adaptive base plane filtering algorithm for the inter-plane estimation of RGB images in HEVC RExt. Because most high-frequency components of RGB images have low inter-plane correlation, our proposed scheme adaptively removes the high-frequency components of the base plane in order to enhance the inter-plane estimation accuracy. The experimental results show that the proposed scheme provides average BD rate gains of 0.6%, 1.0%, and 1.2% in the G, B, and R planes, respectively, with slightly decreased complexity, as compared to the previous inter-plane filtering method.

This paper addresses the problems of fingerprint identification and verification when a query fingerprint is taken under conditions that differ from those under which the fingerprint of the same person stored in a database was constructed. This occurs when using a different fingerprint scanner with a different pressure, resulting in a fingerprint impression that is smeared and distorted in accordance with a geometric transformation (e.g., affine or even non-linear). Minutiae points on a query fingerprint are matched and aligned to those on one of the fingerprints in the database, using a set of absolute invariants constructed from the shape and/or size of minutiae triangles depending on the assumed map. Once the best candidate match is declared and the corresponding minutiae points are flagged, the query fingerprint image is warped against the candidate fingerprint image in accordance with the estimated warping map. An identification/verification cost function using a combination of distance map and global directional filterbank (DFB) features is then utilized to verify and identify a query fingerprint against candidate fingerprint(s). Performance of the algorithm yields an area of 0.99967 (perfect classification is a value of 1) under the receiver operating characteristic (ROC) curve based on a database consisting of a total of 1680 fingerprint images captured from 240 fingers. The average probability of error was found to be 0.713%. Our algorithm also yields the smallest false non-match rate (FNMR) for a comparable false match rate (FMR) when compared to the well-known technique of DFB features and triangulation-based matching integrated with modeling non-linear deformation. This work represents an advance in resolving the fingerprint identification problem beyond the state-of-the-art approaches in both performance and robustness.

A 10-bit digital-to-analog converter (DAC) with a small area is proposed for data-driver integrated circuits of active-matrix liquid crystal display systems. The 10-bit DAC consists of a 7-bit resistor string, a 7-bit two-step decoder, a 2-bit logarithmic time interpolator, and a buffer amplifier. The proposed logarithmic time interpolation is achieved by controlling the charging time of a first-order low-pass filter composed of a resistor and a capacitor. The 7-bit two-step decoder that follows the 7-bit resistor string outputs an analog signal of the stepped wave with two voltage levels using the additional 1-bit digital code for the logarithmic time interpolation. The proposed 10-bit DAC is implemented using a 0.35-µm CMOS process and its supply voltage is scalable from 3.3V to 5.0V. The area of the proposed 10-bit logarithmic time interpolation DAC occupies 57% of that of the conventional 10-bit resistor-string DAC. The DNL and INL of the implemented 10-bit DAC are +0.29/-0.30 and +0.47/-0.36 LSB, respectively.

The authors have evaluated a method of expanding the bit depth of image signals called SGRAD, which requires fewer calculations, while degrading the sharpness of images less. Where noise is superimposed on image signals, the conventional method for obtaining high bit depth sometimes incorrectly detects the contours of images, making it unable to sufficiently correct the gradation. Requiring many line memories is also an issue with the conventional method when applying the process to vertical gradation. As a solution to this particular issue, SGRAD improves the method of detecting contours with transiting gradation to effectively correct the gradation of image signals which noise is superimposed on. In addition, the use of a prediction algorithm for detecting gradation reduces the scale of the circuit with less correction of the vertical gradation.

Many kinds of data can be represented as a network or graph. It is crucial to infer the latent structure underlying such a network and to predict unobserved links in the network. Mixed Membership Stochastic Blockmodel (MMSB) is a promising model for network data. Latent variables and unknown parameters in MMSB have been estimated through Bayesian inference with the entire network; however, it is important to estimate them online for evolving networks. In this paper, we first develop online inference methods for MMSB through sequential Monte Carlo methods, also known as particle filters. We then extend them for time-evolving networks, taking into account the temporal dependency of the network structure. We demonstrate through experiments that the time-dependent particle filter outperformed several baselines in terms of prediction performance in an online condition.

This paper proposes out-of-band emission reduction schemes for IEEE 802.11af based Wireless Local Area Network (WLAN) systems operating in TV White Spaces (TVWS). IEEE 802.11af adopts Orthogonal Frequency Division Multiplexing (OFDM) to exploit the TVWS spectrum effectively. The combination of the OFDM and TVWS may be able to solve the problem of frequency depletion. However the TVWS transmitter must satisfy a strict transmission spectrum mask and reduce out-of-band emission to protect the primary users. The digital convolution filter is one way of reducing the out-of-band emission. Unfortunately, implementing a strict mask needs a large number of filter taps, which causes high implementation complexity. Time-domain windowing is another effective approach. This scheme reduces out-of-band emission with low complexity but at the price of shortening the effective guard interval. This paper proposes a mechanism that jointly uses these two schemes for out-of-band emission reduction. Moreover, the appropriate windowing duration design is proposed in terms of both the out-of-band emission suppression and throughput performance for all mandatory mode of IEEE 802.11af system. The proposed time-domain windowing design reduces the number of multiplier by 96.5%.

Linear dynamical systems are basic state space models literally dealing with underlying system dynamics on the basis of linear state space equations. When the model is employed for time-series data analysis, the system identification, which detects the dimension of hidden state variables, is one of the most important tasks. Recently, it has been found that the model has singularities in the parameter space, which implies that analysis for adverse effects of the singularities is necessary for precise identification. However, the singularities in the models have not been thoroughly studied. There is a previous work, which dealt with the simplest case; the hidden state and the observation variables are both one dimensional. The present paper extends the setting to general dimensions and more rigorously reveals the structure of singularities. The results provide the asymptotic forms of the generalization error and the marginal likelihood, which are often used as criteria for the system identification.

A new type of the affine projection (AP) algorithms which incorporates the sparsity condition of a system is presented. To exploit the sparsity of the system, a weighted l1-norm regularization is imposed on the cost function of the AP algorithm. Minimizing the cost function with a subgradient calculus and choosing two distinct weightings for l1-norm, two stochastic gradient based sparsity regularized AP (SR-AP) algorithms are developed. Experimental results show that the SR-AP algorithms outperform the typical AP counterparts for identifying sparse systems.

Filter caches have been studied as an energy efficient solution. They achieve energy savings via selected access to L1 cache, but severely decrease system performance. Therefore, a filter cache system should adopt components that balance execution delay against energy savings. In this letter, we analyze the legacy filter cache system and propose Data Filter Cache with Partial Tag Cache (DFPC) as a new solution. The proposed DFPC scheme reduces energy consumption of L1 data cache and does not impair system performance at all. Simulation results show that DFPC provides the 46.36% energy savings without any performance loss.

The notion of multiple constant multiplication (MCM) is extensively adopted in digital signal processing (DSP) applications such as finite impulse filter (FIR) designs. A set of adders is utilized to replace regular multipliers for the multiplications between input data and constant filter coefficients. Though many algorithms have been proposed to reduce the total number of adders in an MCM block for area minimization, they do not consider the actual bitwidth of each adder, which may not estimate the hardware cost well enough. Therefore, in this article we propose a bitwidth-aware MCM optimization algorithm that focuses on minimizing the total number of adder bits rather than the adder count. It first builds a subexpression graph based on the given coefficients, derives a set of constraints for adder bitwidth minimization, and then optimally solves the problem through integer linear programming (ILP). Experimental results show that the proposed algorithm can effectively reduce the required adder bit count and outperforms the existing state-of-the-art techniques.

We propose a new Bloom filter that efficiently filters out non-members. With extra bits assigned and asymmetrically distributed, the new filter reduces hash computations and memory accesses. For an error rate of 10-6, the new filter reduces cost by 31.31% with 4.33% additional space, while the standard method saves offers a 20.42% reduction.

In this paper, a new evolutionary approach to recommender systems is presented. The aim of this work is to develop a new recommendation method that effectively adapts and immediately responds to the user's preference. To this end, content-based filtering is judiciously utilized in conjunction with interactive evolutionary computation (IEC). Specifically, a fitness-based truncation selection and a feature-wise crossover are devised to make full use of desirable properties of promising items within the IEC framework. Moreover, to efficiently search for proper items, the content-based filtering is modified in cooperation with data grouping. The experimental results demonstrate the effectiveness of the proposed approach, compared with existing methods.

We propose a novel adaptive filtering scheme named metric-combining normalized least mean square (MC-NLMS). The proposed scheme is based on iterative metric projections with a metric designed by combining multiple metric-matrices convexly in an adaptive manner, thereby taking advantages of the metrics which rely on multiple pieces of information. We compare the improved PNLMS (IPNLMS) algorithm with the natural proportionate NLMS (NPNLMS) algorithm, which is a special case of MC-NLMS, and it is shown that the performance of NPNLMS is controllable with the combination coefficient as opposed to IPNLMS. We also present an application to an acoustic echo cancellation problem and show the efficacy of the proposed scheme.

Tracking capsule endoscope location is one of the promising applications offered by implant body area networks (BANs). When tracking the capsule endoscope location, i.e., continuously localize it, it is effective to take the weighted sum of its past locations to its present location, in other words, to low-pass filter its past locations. Furthermore, creating an exact mathematical model of location transition will improve tracking performance. Therefore, in this paper, we investigate two tracking methods with received signal strength indicator (RSSI)-based localization in order to solve the capsule endoscope location tracking problem. One of the two tracking methods is finite impulse response (FIR) filter-based tracking, which tracks the capsule endoscope location by averaging its past locations. The other one is particle filter-based tracking in order to deal with a nonlinear transition model on the capsule endoscope. However, the particle filter requires that the particle weight is calculated according to its condition (namely, its likelihood value), while the transition model on capsule endoscope location has some model parameters which cannot be estimated from the received wireless signal. Therefore, for the purpose of applying the particle filter to capsule endoscope tracking, this paper makes some modifications in the resampling step of the particle filter algorithm. Our computer simulation results demonstrate that the two tracking methods can improve the performance as compared with the conventional maximum likelihood (ML) localization. Furthermore, we confirm that the particle filter-based tracking outperforms the conventional FIR filter-based tracking by taking the realistic capsule endoscope transition model into consideration.