Multiband Convolutional Riemannian Network with Band-wise Riemannian Triplet Loss for Motor Imagery Classification.

This paper presents a novel motor imagery classification algorithm that uses an overlapping multiscale multiband convolutional Riemannian network with band-wise Riemannian triplet loss to improve classification performance. Despite the superior performance of the Riemannian approach over the common spatial pattern filter approach, deep learning methods that generalize the Riemannian approach have received less attention. The proposed algorithm develops a state-of-the-art multiband Riemannian network that reduces the potential overfitting problem of Riemannian networks, a drawback of Riemannian networks due to their inherent large feature dimension from covariance matrix, by using fewer subbands with discriminative frequency diversity, by inserting convolutional layers before computing the subband covariance matrix, and by regularizing subband networks with Riemannian triplet loss.

Multi-Band CNN With Band-Dependent Kernels and Amalgamated Cross Entropy Loss for Motor Imagery Classification.

In this paper, we present a novel MI classification method based on multi-band convolutional neural network (CNN) with band-dependent kernel sizes, named MBK-CNN, to improve classification performance, by resolving the subject dependency issue of the widely used CNN-based approaches due to the kernel size optimization problem. The proposed structure exploits the frequency diversity of the EEG signals and simultaneously resolves the subject dependent kernel size issue. EEG signal is decomposed into overlapping multi-band and passed through multiple CNNs (termed 'branch-CNNs') with different kernel sizes to generate frequency dependent features, which are combined by a simple weighted sum.

A cascade of preconditioned conjugate gradient networks for accelerated magnetic resonance imaging.

Background And Objective: Recent unfolding based compressed sensing magnetic resonance imaging (CS-MRI) methods only reinterpret conventional CS-MRI optimization algorithms and, consequently, inherit the weaknesses of the alternating optimization strategy. In order to avoid the structural complexity of the alternating optimization strategy and achieve better reconstruction performance, we propose to directly optimize the ℓ regularized convex optimization problem using a deep learning approach.

Method: In order to achieve direct optimization, a system of equations solving the ℓ regularized optimization problem is constructed from the optimality conditions of a novel primal-dual form proposed for the effective training of the sparsifying transform.

Design Method for a Wideband Non-Uniformly Spaced Linear Array Using the Modified Reinforcement Learning Algorithm.

In this paper, we present a design method for a wideband non-uniformly spaced linear array (NUSLA), with both symmetric and asymmetric geometries, using the modified reinforcement learning algorithm (MORELA). We designed a cost function that provided freedom to the beam pattern by setting limits only on the beam width (BW) and side-lobe level (SLL) in order to satisfy the desired BW and SLL in the wide band. We added the scan angle condition to the cost function to design the scanned beam pattern, as the ability to scan a beam in the desired direction is important in various applications.

Multi-Target Localization Based on Unidentified Multiple RSS/AOA Measurements in Wireless Sensor Networks.

All existing hybrid target localization algorithms using received signal strength (RSS) and angle of arrival (AOA) measurements in wireless sensor networks, to the best of our knowledge, assume a single target such that even in the presence of multiple targets, the target localization problem is translated to multiple single-target localization problems by assuming that multiple measurements in a node are identified with their originated targets. Herein, we first consider the problem of multi-target localization when each anchor node contains multiple RSS and AOA measurement sets of unidentified origin. We propose a computationally efficient method to cluster RSS/AOA measurement sets that originate from the same target and apply the existing single-target linear hybrid localization algorithm to estimate multiple target positions.

Target Localization with Unknown Transmit Power and Path-Loss Exponent Using a Kalman Filter in WSNs.

We present a novel hybrid localization algorithm for wireless sensor networks in the absence of knowledge regarding the transmit power and path-loss exponent. Transmit power and the path-loss exponent are critical parameters for target localization algorithms in wireless sensor networks, which help extract target position information from the received signal strength. In the absence of information on transmit power and path-loss exponent, it is critical to estimate them for reliable deployment of conventional target localization algorithms.

Hybrid RSS/AOA Localization using Approximated Weighted Least Square in Wireless Sensor Networks.

We present a target localization method using an approximated error covariance matrix based weighted least squares (WLS) solution, which integrates received signal strength (RSS) and angle of arrival (AOA) data for wireless sensor networks. We approximated linear WLS errors via second-order Taylor approximation, and further approximated the error covariance matrix using a least-squares solution and the variance in measurement noise over the sensor nodes. The algorithm does not require any prior knowledge of the true target position or noise variance.

Selective Feature Generation Method Based on Time Domain Parameters and Correlation Coefficients for Filter-Bank-CSP BCI Systems.

This paper presents a novel motor imagery (MI) classification algorithm using filter-bank common spatial pattern (FBCSP) features based on MI-relevant channel selection. In contrast to existing channel selection methods based on global CSP features, the proposed algorithm utilizes the Fisher ratio of time domain parameters (TDPs) and correlation coefficients: the channel with the highest Fisher ratio of TDPs, named principle channel, is selected and a supporting channel set for the principle channel that consists of highly correlated channels to the principle channel is generated. The proposed algorithm using the FBCSP features generated from the supporting channel set for the principle channel significantly improved the classification performance.

Frequency-Optimized Local Region Common Spatial Pattern Approach for Motor Imagery Classification.

This paper presents a novel feature extraction approach for motor imagery classification overcoming the weakness of conventional common spatial pattern (CSP) methods, especially for small sample settings. We consider local CSPs generated from individual channels and their neighbors (termed "local regions") rather than a global CSP generated from all channels. The novelty is to select a few good local regions using interquartile range (IQR) or an "above the mean" rule based on variance ratio dispersion score (VRDS) and inter-class feature distance (ICFD); instead of computationally expensive cross-validation method.

Target Localization Using Double-Sided Bistatic Range Measurements in Distributed MIMO Radar Systems.

We develop a novel approach improving existing target localization algorithms for distributed multiple-input multiple-output (MIMO) radars based on bistatic range measurements (BRMs). In the proposed algorithms, we estimate the target position with auxiliary parameters consisting of both the target-transmitter distances and the target-receiver distances (hence, "double-sided") in contrast to the existing BRM methods. Furthermore, we apply the double-sided approach to multistage BRM methods.

Complex artifact suppression using vestigial sideband filter in Fourier-domain optical coherence tomography.

In order to achieve computationally efficient mirror image rejection during the off-pivot, full-range approach in spectral-domain optical coherence tomography, we used a vestigial sideband (VSB) filter in place of a Hilbert transform. The appropriate choice of the VSB filter parameters enabled almost complete removal of one sideband with much reduced computational load. To determine the optimal filter parameters, we acquired images of the infrared card and analyzed the mirror suppression ratio of the card surface.

Transmitter IQ mismatch compensation in coherent optical OFDM systems using pilot signals.

Transmitter in-phase/quadrature (IQ) mismatch in coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems is difficult to mitigate at the receiver using conventional time domain methods such as the Gram-Schmidt orthogonalization procedure, particularly in the presence of channel distortion. In this paper, we present a scheme that mitigates both transmitter IQ mismatch and channel distortion. We propose a pilot structure to estimate both channel and IQ mismatch, and develop a minimum mean square error compensation method.