In recent years, various applications based on propagation characteristics have been developed. They generally utilize the locality of the fading characteristics of multipath environments. On the other hand, if a received signal at a remote location can be estimated beyond the correlation distance of the multipath fading environment, a wide variety of new applications can be possible. In this paper, we attempt to estimate a received signal at a remote location using the MUSIC method and the least squares method. Based on the plane wave assumption for each arriving wave, multipath environment is analyzed through estimation of the directions of arrival by the MUISC method and the complex amplitudes of the received signals by the least squares method, respectively. We present evaluation results on the estimation performance of the method by computer simulations.

In this paper, a linear optimization of the dual-tree complex wavelet transform (DTCWT) based on the least squares method is proposed. The proposed method can design efficient DTCWTs by improving the design degrees of freedom and solving the least square solution iteratively. Because the resulting DTCWTs have good approximation accuracy of the half sample delay condition and the stopband attenuation, they provide precise shift-invariance and directionality. Finally, the proposed DTCWTs are evaluated by applying to non-linear approximation and image denoising, and showed their effectiveness, compared with the conventional DTCWTs.

This paper presents an ultra-wideband amplifier embedded with band-pass filter design. The scattering parameters of a frequency-domain GaAs field effect transistor are converted into z-domain representations by employing the weighted linear least squares method. A least squares scheme is employed to obtain characteristic impedances of transmission line elements that form the amplifier having a flat gain in the passband and good fall-off selectivity in the stopband. Experimental results illustrate the validity of the proposed design method.

A method for extracting a sound signal from a particular area that is surrounded by multiple ambient noise sources is proposed. This method performs several fixed beamformings on a pair of small microphone arrays separated from each other to estimate the signal and noise power spectra. Noise suppression is achieved by applying spectrum emphasis to the output of fixed beamforming in the frequency domain, which is derived from the estimated power spectra. In experiments performed in a room with reverberation, this method succeeded in suppressing the ambient noise, giving an SNR improvement of more than 10 dB, which is better than the performance of the conventional fixed and adaptive beamforming methods using a large-aperture microphone array. We also confirmed that this method keeps its performance even if the noise source location changes continuously or abruptly.

Using the recursive generalized eigendecomposition method, we develop a recursive form solution to the data least squares (DLS) problem in which the error is assumed to lie in the data matrix only. We apply it to a linear channel equalizer. Simulations shows that the DLS-based equalizer outperforms the ordinary least squares-based one in a channel equalization problem.

Recently, DOAS (differential optical absorption spectroscopy) has been used for nondestructive air monitoring, in which the LS (least squares) method is used to calculate trace gas concentrations due to its computational simplicity. This paper applies the ICA (independent component analysis) method to the DOAS system of air monitoring, since the LS method is insufficient to recover the desired spectra perfectly due to sparsity characteristic. If the sparsity of reference spectra in the DOAS system imposes the assumption of independence, the ICA algorithm can be used. The proposed method is used to regress the observed spectrum on the estimates of the reference spectra. The ICA algorithm can be seen as a preprocessing method where the ICs of the references are used as the input in the regression. The performance of the proposed method is evaluated in simulation studies using synthetic data.

In this paper, we propose a new algorithm for calculating the exact poles of the admittance matrix of RLCG interconnects. After choosing dominant poles and corresponding residues, each element of the exact admittance matrix is approximated by partial fraction. A procedure to obtain the residues that guarantee the passivity is also provided, based on experimental studies. In the procedure the residues are calculated by using the least squares method so that the partial fraction matches each element of the exact admittance matrix in the frequency-domain. From the partial fraction representation, the asymptotic equivalent circuit models which can be easily simulated with SPICE are synthesized. It is shown that an efficient model-order reduction is possible for short-length interconnects.

In this paper, we present a new numerical method for the complex approximation of FIR digital filters. Our objective is to design FIR filters with equiripple magnitude and phase errors. The proposed method solves the least squares (LS) problem iteratively. At each iteration, the desired response is updated so as to have an equiripple error. The proposed methods do not require any time-consuming optimization procedure such as the quasi-Newton methods and converge to equiripple solutions quickly. We show some examples to illustrate the advantages of our proposed methods.

A non-invasive method is proposed to estimate the location of intracranial vascular disease using several sensors placed on the forehead. The advantage of this method over earlier measurements with a single ocular sensor is the abilty to localize the region of abnormal vascular tissue. A weighted least mean square procedure is applied to estimating the time difference between the sensor outputs using the phase distribution in the cross-spectrum. It is possible to estimate time differences shorter than sampling period. Computer simulation and clinical experiments demonstrate that a distance difference of around 20 times shorter than the wavelength can be obtained.