This paper studies on a fast approach for the eigenproblems of correlation matrices used in direction-of-arrival (DOA) estimation algorithms, especially for the case that the number of arriving waves is a few. The eigenvalues and the corresponding eigenvectors can be obtained in a very short time by the algebraic solvent of up to quartic polynomials. We also confirm that the present approach does not make the accuracy worse when it is implemented by finite word-length processors like digital signal processor (DSP) or field programmable gate array (FPGA).

This paper presents a weighted spatial filter (WSF) design method based on direction of arrival (DOA) estimates for a novel array configuration called a sum and difference composite co-array. A sum and difference composite co-array is basically a combination of sum and difference co-arrays. Our configuration can realize higher degrees of freedom (DOF) with the sum co-array part at a calculation cost lower than those of the other sparse arrays. To further enhance the robustness of our proposed sum and difference composite co-array we design an optimal beam pattern by WSF based on the information of estimated DOAs. Performance of the proposed system and the DOA estimation accuracy of close-impinging waves are evaluated through computer simulations.

This paper proposes a 2.6 GHz low cost DBF array antenna system and reports its evaluation based on our experimental results. The proposed system is partially constructed by digital devices for the simplification of hardware, and employs some techniques for improving the resolution. The system is evaluated through the DOA estimation by the MUSIC algorithm inside a radio anechoic chamber. As a result, we found that the proposed system estimates the DOA with the highest accuracy at which MUSIC algorithm can perform. Moreover, this paper discusses the estimation errors. We also found that the estimation error is particularly affected from the inaccurate element interval.

This paper presents a novel concept of a Two-Dimensional (2-D) Finite-Difference Time-Domain (FDTD) formulation for the numerical analysis of electromagnetic fields. FDTD method proposed by Yee is widely used for such analysis, although it has an inherent problem that there exist half-cell-length and half-time-step distances between electric and magnetic field components. To dissolve such distances, we begin with the finite-difference approximation of the wave equation, not Maxwell's equations. Employing several approximation techniques, we develop a novel algorithm which can condense all field components to equidistant discrete nodes. The proposed algorithm is evaluated in comparison with several conventional algorithms by computer simulations.

A method of radar-based contactless vital-sign sensing and electrocardiogram (ECG) signal reconstruction using deep learning is proposed. A radar system is an effective tool for contactless vital-sign sensing because it can measure a small displacement of the body surface without contact. However, most of the conventional methods have limited evaluation indices and measurement conditions. A method of measuring body-surface-displacement signals by using frequency-modulated continuous-wave (FMCW) radar and reconstructing ECG signals using a convolutional neural network (CNN) is proposed. This study conducted two experiments. First, we trained a model using the data obtained from six subjects breathing in a seated condition. Second, we added sine wave noise to the data and trained the model again. The proposed model is evaluated with a correlation coefficient between the reconstructed and actual ECG signal. The results of first experiment show that their ECG signals are successfully reconstructed by using the proposed method. That of second experiment show that the proposed method can reconstruct signal waveforms even in an environment with low signal-to-noise ratio (SNR).

This paper presents a fast algorithm for calculating box splines sampled at regular intervals. This algorithm is based on the representation by directional summations, while splines are often represented by convolutions. The summation-based representation leads less computational complexity: the proposed algorithm requires fewer additions and much fewer multiplications than the algorithm based on convolutions. The proposed algorithm is evaluated in the sense of the number of additions and multiplications for three- and four-directional box splines to see how much those operations are reduced.

We have developed a novel array configuration based on the combination of sum and difference co-arrays. There have been many studies on array antenna configurations that enhance the degree of freedom (DOF) of an array, but the maximum DOF of the difference co-array configuration is often limited. With our proposed array configuration, called “sum and difference composite co-array”, we aim to further enhance the DOF by combining the concept of sum co-array and difference co-array. The performance of the proposed array configuration is evaluated through computer simulated beamforming*.

Undersampling (or bandpass sampling) phase modulated signals directly at high frequency band, the harmful effects of the aperture jitter characteristics of ADCs (Analog-to-Digital converters) and sampling clock instability of the system can not be ignored. In communication systems the sampling jitter brings additional phase noise to the constellation pattern besides thermal noise, thus the BER (bit error rate) performance will be degraded. This paper examines the relationship between the input frequency to ADC and the sampling jitter in digital IF (Intermediate Frequency) downconversion receivers with undersampling scheme. This paper presents the measurement results with a real hardware prototype system as well as the computer simulation results with a theoretically modeled IF sampling receiver. We evaluated EVM (Error Vector Magnitude) in various clock jitter configurations with commonly used and reasonable cost ADCs of which sampling rates was 40 MHz. According to the results, the IF input frequencies of QPSK (16 QAM) signals were limited below around 290 (210) MHz for wireless LAN standard, and 730 (450) MHz for W-CDMA standard, respectively, in our best configuration.

This paper presents a high resolution Direction-Of-Arrival (DOA) estimation method using unwrapped phase information of MUSIC-based noise subspace. Superresolution DOA estimation methods such as MUSIC, Root-MUSIC and ESPRIT methods are paid great attention because of their brilliant properties in estimating DOAs of incident signals. Those methods achieve high accuracy in estimating DOAs in a good propagation environment, but would fail to estimate DOAs in severe environments like low Signal-to-Noise Ratio (SNR), small number of snapshots, or when incident waves are coming from close angles. In MUSIC method, its spectrum is calculated based on the absolute value of the inner product between array response and noise eigenvectors, means that MUSIC employs only the amplitude characteristics and does not use any phase characteristics. Recalling that phase characteristics plays an important role in signal and image processing, we expect that DOA estimation accuracy could be further improved using phase information in addition to MUSIC spectrum. This paper develops a procedure to obtain an accurate spectrum for DOA estimation using unwrapped and differentiated phase information of MUSIC-based noise subspace. Performance of the proposed method is evaluated through computer simulation in comparison with some conventional estimation methods.

This paper proposes a fast method for the calculation of exponential B-splines sampled at regular intervals. This algorithm is based on a combination of FIR and IIR filters which enables a fast decomposition and reconstruction of a signal. When complex values are selected for the parameters of the exponentials, complex trigonometric functions are obtained. Only the real part of these functions are used for the interpolation of real signals, leading less bandlimited signals when they are compared with the polynomial B-spline counterparts. These characteristics were verified with 1-D and 2-D examples. This paper also discusses the effectiveness of exponential B-splines, when they are applied to image processing.

We propose an iterative single-image haze-removal method that first divides images with haze into regions in which haze-removal processing is difficult and then estimates the ambient light. The existing method has a problem wherein it often overestimates the amount of haze in regions where there is a large distance between the location the photograph was taken and the subject of the photograph; this problem prevents the ambient light from being estimated accurately. In particular, it is often difficult to accurately estimate the ambient light of images containing white and sky regions. Processing those regions in the same way as other regions has detrimental results, such as darkness or unnecessary color change. The proposed method divides such regions in advance into multiple small regions, and then, the ambient light is estimated from the small regions in which haze removal is easy to process. We evaluated the proposed method through some simulations, and found that the method achieves better haze reduction accuracy even than the state-of-the art methods based on deep learning.

DOA (Direction Of Arrival) estimation is a useful technique in various positioning applications including the DOA-based adaptive array antenna system. This paper presents a practical implementation of FPGA (Field Programmable Gate Array) based fast DOA estimator for wireless cellular basestation. This system incorporates spectral unitary MUSIC (MUltiple SIgnal Classification) algorithm, which is one of the representative super resolution DOA estimation techniques. This paper proposes a way of digital signal processor design suitable for FPGA and its real hardware implementation. In this system, all digital signal processing procedures are computed by the only fixed-point operation with finite word-length for fast processing and low power consumption. The performance will be assessed by hardware level simulations and experiments in a radio anechoic chamber.

This letter presents an efficient multichannel low-IF reception scheme that improves digital communication quality in the sense of BER performance. Created by simply adding cosine rolloff filters to the conventional multichannel receiver, the proposed receiver achieves much higher accuracy than the conventional one.

We study the correlation matrix element properties in array signal processing and apply them to a Direction-Of-Arrival (DOA) estimation problem of coherent or highly-correlated sources for a Uniform Linear Array (ULA). The proposed algorithm is generally based on the relation between the elements of the array correlation matrix and does not need an eigendecomposition, iteration, or angular peak-search. The performance of the proposed method was evaluated through a computer simulation.

In this paper, we propose a method for predicting radio wave propagation using a correlation graph convolutional neural network (C-Graph CNN). We examine what kind of parameters are suitable to be used as system parameters in C-Graph CNN. Performance of the proposed method is evaluated by the path loss estimation accuracy and the computational cost through simulation.

This letter presents an efficient Signed-Power-of-Two (SPT) term allocation for filter coefficients in order to improve the BER characteristics of digital communication systems. The performance of the present allocation is evaluated by BER characteristics through digital modulation simulations and FPGA-based digital implementation.

This paper presents a simple but high resolution DOA estimation method using second-order differential of MUSIC spectrum. MUSIC method is paid attention as one of "Superresolution" DOA estimation methods because of their brilliant characteristics, however MUSIC also has the problem of estimation accuracy in severe environments like low SNR, small number of snapshots, or incident waves from closely-spaced angles. Especially the case of two or more incident waves from closely-spaced angles, MUSIC often fails in making spectrum peaks that leads inaccurate DOA estimation. We pay attention to the fact that the second-order differential of MUSIC spectrum makes negative peaks around the original DOAs even when MUSIC spectrum does not make peaks there. We try to estimate DOAs not by MUSIC spectrum but by the second-order differential of the MUSIC spectrum, and to find its peaks for being estimated DOAs. The performance of the present method is evaluated in compared with MUSIC and Root-MUSIC methods through computer simulations and experiments.

In this paper, we propose a method for video reflection removal using a video restoration framework with enhanced deformable networks (EDVR). We examine the effect of each module in EDVR on video reflection removal and modify the models using 3D convolutions. The performance of each modified model is evaluated in terms of the RMSE between the structural similarity (SSIM) and the smoothed SSIM representing temporal consistency.

This paper proposes two accurate source-number estimation methods for array antennas and multi-input multi-output radar. Direction of arrival (DOA) estimation is important in high-speed wireless communication and radar imaging. Most representative DOA estimation methods require the source-number information in advance and often fail to estimate DOAs in severe environments such as those having low signal-to-noise ratio or large transmission-power difference. Received signals are often bandlimited or narrowband signals, so the proposed methods first involves denoising preprocessing by removing undesired components then comparing the original and denoised signal information. The performances of the proposed methods were evaluated through computer simulations.

This paper proposes a novel fast algorithm for the decomposition and reconstruction of two-dimensional (2-D) signals by box splines. The authors have already proposed an algorithm to calculate the discrete box splines which enables the fast reconstruction of 2-D signals (images) from box spline coefficients. The problem still remains in the decomposition process to derive the box spline coefficients from an input image. This paper first investigates the decomposition algorithm which consists of the truncated geometric series of the inverse filter and the steepest descent method with momentum (SDM). The reconstruction process is also developed to correspond to the enlargement of images. The proposed algorithm is tested for the expansion of several natural images. As a result, the peak signal-to-noise ratio (PSNR) of the reconstructed images became more than 50 dB, which can be considered as enough high level. Moreover, the property of box splines are discussed in comparison with 2-D (the tensor product of) B-splines.