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.