Multiple source direction-of-arrival estimation applicable under near-, far-, and mixed-field scenarios.

Traditionally, direction-of-arrival (DOA) estimations under near- and far-field scenarios are treated as independent tasks based on the corresponding acoustic model, hence necessitating a proper soundfield detector as an upstream processing tool, whereas there may not be a distinct boundary between different soundfield types, especially the mixed-field scenarios where both near- and far-field sources coexist simultaneously. To handle this issue, this article investigates a multisource DOA estimator that equally localizes multiple near-, far-, and mixed-field sources, not requiring any specialized adjustments. We (i) define a signal-invariant multichannel feature denoted generalized relative harmonic coefficients in the spherical harmonics domain; (ii) derive the analytical expression of this feature and summarize its unique properties, exhibiting consistence for both near- and far-field sources; (iii) estimate source elevation and azimuth using the magnitude and phase parts of this feature, respectively; (iv) detect single-source dominated periods from the mixed measurements based on an investigated distance measure; and (v) count the number of sources and localize their DOAs by clustering the single-source dominated estimates. Extensive experimental results, in both simulated and real-life environments, finally confirm the effectiveness of the proposed algorithm under diverse acoustic scenarios, and a superiority over baseline approaches in localizing mixed-field sources.

Closed-form multiple source direction-of-arrival estimator under reverberant environments.

Accurate direction-of-arrival (DOA) estimation of multiple sources, simultaneously active in a reverberant environment, remains a challenge, as the multi-path acoustic reflections and overlapped periods dramatically distort the direct-path wave propagation. This article proposes a prominent solution localizing multiple sources in a reverberant environment using closed-form estimates, circumventing any exhaustive search over the two-dimensional directional space. Apart from a low complexity cost, the algorithm has robustness to reverberant, inactive, and overlapped periods and an ease of operation in practice, achieving sufficient accuracy compared to state-of-the-art approaches. Specifically, this algorithm localizes an unknown number of sources through four steps: (i) decomposing the frequency domain signals on a spherical array to the spherical harmonics domain; (ii) extracting the first-order relative harmonic coefficients as the input features; (iii) achieving direct-path dominance detection and localization using closed-form estimation; and (iv) estimating the number of sources and their DOAs based on those pass the direct-path detection. Experimental results, using extensive simulated and real-life recordings, confirm the algorithm with a significantly reduced computational complexity, while preserving competitive localization accuracy as compared to the baseline approaches. Additional tests confirm this low-complexity algorithm even with a potential capacity for online DOA tracking of multiple moving sources.

Colorless to Multicolored, Fast Switching, and Highly Stable Electrochromic Devices Based on Thermally Cross-Linking Copolymer.

Transparent-to-colored electrochromic devices exhibit promising application prospects and have gained popularity. Herein, two triphenylamine derivatives TPA-OCH3 and TPA-CN with styryl moieties and different donor or acceptor units were designed and synthesized to further prepare solvent-resistant thermally cross-linking polymer P(TPA-OCH3) and P(TPA-CN) without any additional initiator. P(TPA-OCH3) and P(TPA-CN) possess two pairs of redox peaks, and P(TPA-OCH3) shows a lower onset oxidation potential compared to P(TPA-CN) because of the pendent donor unit. Correspondingly, both polymers exhibit multicolored changes from the neutral colorless state to noticeable oxidized colors under different potentials. Furthermore, the thermally cross-linking copolymer P(TPA-OCH3-co-TPA-CN) was obtained by TPA-OCH3 and TPA-CN (the molar ratio is 2:1) and presents outstanding electrochromism with four color changes (colorless-orange-blue-purple) due to the multistep redox process of TPA-OCH3 and TPA-CN units. It is more intriguing that the electrochromic device based on the copolymer films possesses a high optical contrast of 57.8% at 680 nm, fast switching time (0.52 and 0.66 s), and robust cyclic stability over 30 000 cycles with very little decay. Therefore, the thermally cross-linking copolymer is a promising candidate material for high-performance transmittive electrochromic devices, such as smart windows, sunglasses, and E-papers.

An improved non-Cartesian partially parallel imaging by exploiting artificial sparsity.

PURPOSE: To improve the performance of non-Cartesian partially parallel imaging (PPI) by exploiting artificial sparsity, the generalized autocalibrating partially parallel acquisitions (GRAPPA) operator for wider band lines (GROWL) is taken as a specific example for explanation. THEORY: This work is based on the GRAPPA-like PPI having an improved performance when the to-be-reconstructed image is sparse in the image domain. METHODS: A systematic scheme is proposed to artificially generate the sparse image for non-Cartesian trajectory. Using GROWL as a specific non-Cartesian PPI method, artificial sparsity-enhanced GROWL (ARTS-GROWL) is used to demonstrate the efficiency of the proposed scheme. The ARTS-GROWL consists of three steps: 1) generating synthetic k-space data corresponding to an image with smaller support, that is, artificial sparsity; 2) applying GROWL to the synthetic k-space data from previous step; and 3) recovering the final image from the reconstruction with the processed data. RESULTS: For simulation and in vivo data, the experiments demonstrate that the proposed ARTS-GROWL significantly reduces the reconstruction errors compared with the conventional GROWL technique for the tested acceleration factors. CONCLUSION: Taking ARTS-GROWL, for instance, experimental results indicate that artificial sparsity improved the signal-to-noise ratio and normalized root-mean-square error of non-Cartesian PPI. Magn Reson Med 78:271-279, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Passive shimming of a superconducting magnet using the L1-norm regularized least square algorithm.

The uniformity of the static magnetic field B0 is of prime importance for an MRI system. The passive shimming technique is usually applied to improve the uniformity of the static field by optimizing the layout of a series of steel shims. The steel pieces are fixed in the drawers in the inner bore of the superconducting magnet, and produce a magnetizing field in the imaging region to compensate for the inhomogeneity of the B0 field. In practice, the total mass of steel used for shimming should be minimized, in addition to the field uniformity requirement. This is because the presence of steel shims may introduce a thermal stability problem. The passive shimming procedure is typically realized using the linear programming (LP) method. The LP approach however, is generally slow and also has difficulty balancing the field quality and the total amount of steel for shimming. In this paper, we have developed a new algorithm that is better able to balance the dual constraints of field uniformity and the total mass of the shims. The least square method is used to minimize the magnetic field inhomogeneity over the imaging surface with the total mass of steel being controlled by an L1-norm based constraint. The proposed algorithm has been tested with practical field data, and the results show that, with similar computational cost and mass of shim material, the new algorithm achieves superior field uniformity (43% better for the test case) compared with the conventional linear programming approach.