Soft-Decision-Driven Sparse Channel Estimation and Turbo Equalization for MIMO Underwater Acoustic Communications

Multi-input multi-output (MIMO) detection based on turbo principle has been shown to provide a great enhancement in the throughput and reliability of underwater acoustic (UWA) communication systems. Benefits of the iterative detection in MIMO systems, however, can be obtained only when a high quality channel estimation is ensured. In this paper, we develop a new soft-decision-driven sparse channel estimation and turbo equalization scheme in the triply selective MIMO UWA. First, the Homotopy recursive least square dichotomous coordinate descent (Homotopy RLS-DCD) adaptive algorithm, recently proposed for sparse single-input single-output system identification, is extended to adaptively estimate rapid time-varying MIMO sparse channels. Next, the more reliable a posteriori soft-decision symbols, instead of the hard decision symbols or the a priori soft-decision symbols, at the equalizer output, are not only feedback to the Homotopy RLS-DCD-based channel estimator but also to the minimum mean-square-error (MMSE) equalizer. As the turbo iterations progress, the accuracy of channel estimation and the quality of the MMSE equalizer are improved gradually, leading to the enhancement in the turbo equalization performance. This also allows the reduction in pilot overhead. The proposed receiver has been tested by using the data collected from the SHLake2013 experiment. The performance of the receiver is evaluated for various modulation schemes, channel estimators, and MIMO sizes. Experimental results demonstrate that the proposed a posteriori soft-decision-driven sparse channel estimation based on the Homotopy RLS-DCD algorithm and turbo equalization offer considerable improvement in system performance over other turbo equalization schemes

Convolutional Neural Network Optimization and Parallel Compressive Sensing Algorithms for Accelerated MRI Reconstruction

Magnetic resonance imaging (MRI) is a noninvasive imaging modality that produces high-quality images. One of the biggest challenges in MRI is the lengthy scan procedure which could lead to motion artifact and patient discomfort. Due to the physical and physiological limits, undersampling the signals in the k-space signals has been used to shorten the scan time. However, the undersampling of k-space data results in undersampling artifacts that require advanced reconstruction algorithms to compensate for the missed signals. Many reconstruction algorithms have been proposed to address this problem. Linear interpolations in parallel imaging (PI) techniques usually suffer from high noise-like interpolation artifacts, and compressive sensing (CS) reconstructions are usually blurred in high-order undersampling factors. In this study, we first introduce a hybrid CS-PI algorithm and show it outperforms CS or PI individually in image reconstructions using actual data from MR-guided radiotherapy. Nevertheless, PI, CS, and hybrid CS-PI highly depend on the number of ACS in the center of the k-space and require a particular sampling strategy. Deep learning models can solve these problems with lower scan and reconstruction time with fewer interpolation artifacts and blurriness. In deep learning-based MRI reconstruction methods, the network’s architecture plays a crucial role in the quality of the reconstructed image. According to the large number of architectures that can be considered for these models, manually designing architectures and testing all the possible solutions are not practical. We introduce a new evolutionary-based search strategy to design a deep network for MR reconstruction automatically. We use different numerical metrics to compare the results of the optimized model with the ad-hoc model and three different published methods. The results showed that the proposed algorithm could automatically design a network that is not limited to only one particular sampling strategy and outperforms three related published models

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion