In this paper, we propose a new joint transmit and receive spatial/frequency-domain filtering for single-carrier (SC) multiple-input multiple-output (MIMO) eigenmode transmission using iterative interference cancellation (IC). Iterative IC is introduced to a previously proposed joint transmit and receive spatial/frequency-domain filtering based on minimum mean square error criterion (called joint Tx/Rx MMSE filtering) to reduce the residual inter-symbol interference (ISI) after the Rx filtering. The optimal Tx/Rx filters are derived based on the MMSE criterion taking into account the iterative IC. The superiority of our proposed technique is confirmed by computer simulation.

The Lyapunov stability theory-based adaptive filter (LST-AF) is a robust filtering algorithm which the tracking error quickly converges to zero asymptotically. Recently, the software module of the LST-AF algorithm is effectively used in engineering applications such as tracking, prediction, noise cancellation and system identification problems. Therefore, hardware implementation becomes necessary in many cases where real time procedure is needed. In this paper, an implementation of the LST-AF algorithm on Field Programmable Gate Arrays (FPGA) is realized for the first time to our knowledge. The proposed hardware implementation on FPGA is performed for two main benchmark problems; i) tracking of an artificial signal and a Henon chaotic signal, ii) estimation of filter parameters using a system identification model. Experimental results are comparatively presented to test accuracy, performance and logic occupation. The results show that our proposed hardware implementation not only conserves the capabilities of software versions of the LST-AF algorithm but also achieves a better performance than them.

Absorption, scattering, and color distortion are three major issues in underwater optical imaging. Light rays traveling through water are scattered and absorbed according to their wavelength. Scattering is caused by large suspended particles that degrade underwater optical images. Color distortion occurs because different wavelengths are attenuated to different degrees in water; consequently, images of ambient underwater environments are dominated by a bluish tone. In the present paper, we propose a novel underwater imaging model that compensates for the attenuation discrepancy along the propagation path. In addition, we develop a fast weighted guided normalized convolution domain filtering algorithm for enhancing underwater optical images. The enhanced images are characterized by a reduced noise level, better exposure in dark regions, and improved global contrast, by which the finest details and edges are enhanced significantly.

This paper proposes a method of watermarking for digital audio signals based on adaptive phase modulation. Audio signals are usually non-stationary, i.e., their own characteristics are time-variant. The features for watermarking are usually not selected by combining the principle of variability, which affects the performance of the whole watermarking system. The proposed method embeds a watermark into an audio signal by adaptively modulating its phase with the watermark using IIR all-pass filters. The frequency location of the pole-zero of an IIR all-pass filter that characterizes the transfer function of the filter is adapted on the basis of signal power distribution on sub-bands in a magnitude spectrum domain. The pole-zero locations are adapted so that the phase modulation produces slight distortion in watermarked signals to achieve the best sound quality. The experimental results show that the proposed method could embed inaudible watermarks into various kinds of audio signals and correctly detect watermarks without the aid of original signals. A reasonable trade-off between inaudibility and robustness could be obtained by balancing the phase modulation scheme. The proposed method can embed a watermark into audio signals up to 100 bits per second with 99% accuracy and 6 bits per second with 94.3% accuracy in the cases of no attack and attacks, respectively.

In this letter, an effective acoustic feedback cancellation algorithm is proposed based on the normalized sub-band adaptive filter (NSAF). To improve the confliction between fast convergence rate and low misalignment in the NSAF algorithm, a variable step size is designed to automatically vary according to the update state of the filter. The update state of the filter is adaptively detected via the normalized distance between the long term average and the short term average of the tap-weight vector. Simulation results demonstrate that the proposed algorithm has superior performance in terms of convergence rate and misalignment.

A tri-band Multiple-input-multiple-output (MIMO) antenna system for LTE 700, LTE2500 and GSM 1800/1900 mobile handset application is presented. The whole system consists of four identical 3-D IFAs (inverted F antenna) folded on FR4 cuboids. Without any special designed decoupling structures, the measured isolation among antenna elements is higher than 20dB in the low and upper bands, even in the middle band, the isolation is higher than 13.7dB. Reflection coefficient, correlation coefficient, gain and radiation pattern are also presented. Acceptable agreement between the antenna models with and without plastic housing, battery and LCD screen demonstrate that the proposed antenna is a competitive candidate for mobile handsets.

This letter describes a speech enhancement algorithm for stereo signals corrupted by diffuse noise. It estimates the noise signal and also a beamformed target signal based on blind target signal cancelation derived from sparsity minimization. Enhanced target speech is obtained by Wiener filtering using both the signals. Experimental results demonstrate the effectiveness of the proposed method.

Internet of Things (IoT) devices, which have different characteristics in mobility and communication patterns from traditional mobile devices such as cellular phones, have come into existence as a new type of mobile devices. A strict mobility management scheme for providing highly mobile devices with seamless access is over-engineered for IoT devices' mobility management. We revisit current mobility management schemes for wireless mobile networks based on identifier/locator separation. In this paper, we focus on IoT communication patterns, and propose a new routing-based mobility scheme for them. Our scheme adopts routing information aggregation scheme using the Bloom Filter as a data structure to store routing information. We clarify the effectiveness of our scheme in IoT environments with a large number of IoT devices, and discuss its deployment issues.

The role of an optical low-pass filter (OLPF) in a digital still camera is to remove the high spatial frequencies that cause aliasing, thereby enhancing the image quality. However, this also causes some loss of detail. Yet, when an image is captured without the OLPF, moiré generally appears in the high spatial frequency region of the image. Accordingly, this paper presents a moiré reduction method that allows omission of the OLPF. Since most digital still cameras use a CCD or a CMOS with a Bayer pattern, moiré patterns and color artifacts are simultaneously induced by aliasing at high spatial frequencies. Therefore, in this study, moiré reduction is performed in both the luminance channel to remove the moiré patterns and the color channel to reduce color smearing. To detect the moiré patterns, the spatial frequency response (SFR) of the camera is first analyzed. The moiré regions are identified using patterns related to the SFR of the camera and then analyzed in the frequency domain. The moiré patterns are reduced by removing their frequency components, represented by the inflection point between the high-frequency and DC components in the moiré region. To reduce the color smearing, color changing regions are detected using the color variation ratios for the RGB channels and then corrected by multiplying with the average surrounding colors. Experiments confirm that the proposed method is able to reduce the moiré in both the luminance and color channels, while also preserving the detail.

A novel approach for the multiple-model multi-sensor Bernoulli filter (MM-MSBF) based on the theory of finite set statistics (FISST) is proposed for a single maneuvering target tracking in the presence of detection uncertainty and clutter. First, the FISST is used to derive the multi-sensor likelihood function of MSBF, and then combining the MSBF filter with the interacting multiple models (IMM) algorithm to track the maneuvering target. Moreover, the sequential Monte Carlo (SMC) method is used to implement the MM-MSBF algorithm. Eventually, the simulation results are provided to demonstrate the effectiveness of the proposed filter.

In this paper, we propose a method for designing finite impulse response (FIR) filters with canonic signed digit (CSD) coefficients using particle swarm optimization (PSO). In such a design problem, a large number of local minimums appear in an evaluation function for the optimization. An updating procedure of PSO tends to stagnate around such local minimums and thus indicates a premature convergence property. Therefore, a new framework for avoiding such a situation is proposed, in which the evaluation function is modified around the stagnation point. Several design examples are shown to present the effectiveness of the proposed method.

This paper introduces a fast image denoising algorithm by estimating noise parameters without prior information about the noise. Under the assumption that additive noise has a Gaussian distribution, the noise parameters were estimated from an observed degraded image, and were used to define the constraints of a noise detection process that was coupled with a Markov random field (MRF). In addition, an adaptive modified weighted Gaussian filter with variable window sizes defined by the constraints on noise detection was used to control the degree of smoothness of the reconstructed image. Experimental results demonstrate the capability of the proposed algorithm.

The conventional non-negative matrix factorization (NMF)-based speech enhancement is accomplished by updating iteratively with the prior knowledge of the clean speech and noise spectra bases. With the probabilistic estimation of whether the speech is present or not in a certain frame, this letter proposes a speech enhancement algorithm incorporating the speech presence probability (SPP) obtained via noise estimation to the NMF process. To take advantage of both the NMF-based and statistical model-based approaches, the final enhanced speech is achieved by applying a statistical model-based filter to the output of the SPP weighted NMF. Objective evaluations using perceptual evaluation of speech quality (PESQ) on TIMIT with 20 noise types at various signal-to-noise ratio (SNR) levels demonstrate the superiority of the proposed algorithm over the conventional NMF and statistical model-based baselines.

A compact dual-band MIMO (Multiple-Input-Multiple-Output) antenna system for LTE33 band, LTE 2300 and ISM 2.4GHz applications is presented. The whole system consists of four identical folded 3-D IFAs (inverted F antenna) mounted on nonmetallic cuboids. By using the radiation pattern diversity of the antenna elements, higher than 15dB isolation among the antenna elements and low correlation coefficient (CC<0.3) are achieved without any specially designed decoupling structures. Its gain and radiation pattern are elucidated.

A Bayer-like White-RGB (W-RGB) color filter array (CFA) was invented for overcoming the weaknesses of commonly used RGB based Bayer CFA. In order to reproduce full-color images from the Bayer-like W-RGB CFA, a demosaicing or a CFA interpolation process which estimates missing color channels of raw mosaiced images from CFA is an essential process for single sensor digital cameras having CFA. In the case of Bayer CFA, numerous demosaicing methods which have remarkable performance were already proposed. In order to take advantage of both remarkable performance of demosaicing method for Bayer CFA and the characteristic of high-sensitive Bayer-like W-RGB CFA, a new method of transforming Bayer-like W-RGB to Bayer pattern is required. Therefore, in this letter, we present a new method of transforming Bayer-like W-RGB pattern to Bayer pattern. The proposed method mainly uses the color difference assumption between different channels which can be applied to practical consumer digital cameras.

This study improves the compression efficiency of Lee's colorization-based coding framework by introducing a novel colorization matrix construction and an adaptive color conversion. Colorization-based coding methods reconstruct color components in the decoder by colorization, which adds color to a base component (a grayscale image) using scant color information. The colorization process can be expressed as a linear combination of a few column vectors of a colorization matrix. Thus it is important for colorization-based coding to make a colorization matrix whose column vectors effectively approximate color components. To make a colorization matrix, Lee's colorization-based coding framework first obtains a base and color components by RGB-YCbCr color conversion, and then performs a segmentation method on the base component. Finally, the entries of a colorization matrix are created using the segmentation results. To improve compression efficiency on this framework, we construct a colorization matrix based on a correlation of base-color components. Furthermore, we embed an edge-preserving smoothing filtering process into the colorization matrix to reduce artifacts. To achieve more improvement, our method uses adaptive color conversion instead of RGB-YCbCr color conversion. Our proposed color conversion maximizes the sum of the local variance of a base component, which resulted in increment of the difference of intensities at region boundaries. Since segmentation methods partition images based on the difference, our adaptive color conversion leads to better segmentation results. Experiments showed that our method has higher compression efficiency compared with the conventional method.

The advanced front-end (AFE) for automatic speech recognition (ASR) was standardized by the European Telecommunications Standards Institute (ETSI). The AFE provides speech enhancement realized by an iterative Wiener filter (IWF) in which a smoothed FFT spectrum over adjacent frames is used to design the filter. We have previously proposed robust time-varying complex Auto-Regressive (TV-CAR) speech analysis for an analytic signal and evaluated the performance of speech processing such as F0 estimation and speech enhancement. TV-CAR analysis can estimate more accurate spectrum than FFT, especially in low frequencies because of the nature of the analytic signal. In addition, TV-CAR can estimate more accurate speech spectrum against additive noise. In this paper, a time-invariant version of wide-band TV-CAR analysis is introduced to the IWF in the AFE and is evaluated using the CENSREC-2 database and its baseline script.

This paper proposes the MIMO MC-CDMA channel estimation method for the various mobile environments. The distinctive feature of the proposed method is possible to robustly estimate with respect to the mobile velocity using the Kalman filter with the colored driving source. Effectiveness of the proposed method are shown by computer simulations.

Heterogeneous networks (HetNet) with different radio access technologies have been deployed to support a range of communication services. To manage these HetNets efficiently, some interworking solutions such as MIH (media independent handover), ANQP (access network query protocol) or ANDSF (access network discovery and selection function) have been studied. Recently, the millimeter-wave (mm-wave) based HetNet has been explored to provide multi-gigabits-per-second data rates over short distances in the 60GHz frequency band for 5G wireless networks. WiGig (Wireless Gigabit Alliance) is one of the available radio access technologies using mm-wave. However, the conventional interworking solutions are not sufficient for the implementation of LTE (Long Term Evolution)/WiGig HetNets. Since the coverage area of WiGig is very small due to the high propagation loss of the mm-wave band signal, it is difficult for UEs to perform cell discovery and handover if using conventional LTE/WLAN (wireless local area networks) interworking solutions, which cannot support specific techniques of WiGig well, such as beamforming and new media access methods. To solve these problems and find solutions for LTE/WiGig interworking, RAN (radio access network)-level tightly coupled interworking architecture will be a promising solution. As a RAN-level tightly coupled interworking solution, this paper proposes to design a LTE/WiGig protocol adaptor above the protocol stacks of WiGig to process and transfer control signaling and user data traffic. The proposed extended control plane can assist UEs to discover and access mm-wave BSs successfully and support LTE macro cells to jointly control the radio resources of both LTE and WiGig, so as to improve spectrum efficiency. The effectiveness of the proposal is evaluated. Simulation results show that LTE/WiGig HetNets with the proposed interworking solution can decrease inter-cell handover and improve user throughput significantly. Moreover, the downlink backhaul throughput and energy efficiency of mm-wave HetNets are evaluated and compared with that of 3.5GHz LTE HetNets. Results indicate that 60GHz mm-wave HetNets have better energy efficiency but with much heavier backhaul overhead.

In this letter, a new subband adaptive filter (SAF) which is robust against impulsive noise in system identification is presented. To address the vulnerability of adaptive filters based on the L2-norm optimization criterion to impulsive noise, the robust SAF (R-SAF) comes from the L1-norm optimization criterion with a constraint on the energy of the weight update. Minimizing L1-norm of the a posteriori error in each subband with a constraint on minimum disturbance gives rise to robustness against impulsive noise and the capable convergence performance. Simulation results clearly demonstrate that the proposal, R-SAF, outperforms the classical adaptive filtering algorithms when impulsive noise as well as background noise exist.