[PDF] 2015 3rd International Workshop On Compressed Sensing Theory And Its Applications To Radar Sonar And Remote Sensing Cosera eBook
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The aim of CoSeRa is to bring experts of Compressive Sensing (CS) and radar sonar signal processing and remote sensing together to explore the state of the art in development of CS techniques for different Radar SAR Sonar IR applications and to turn out its advantages or possible drawbacks compared to classical solutions
The aim of CoSeRa is to bring experts of Compressive Sensing (CS)and radar sonar EO IR signal processing and remote sensing together to explore the state of the art in development of CS techniques for different areas of applications and to turn out its advantages or possible drawbacks compared to classical solutions
Specialized resource providing detailed coverage of recent advances in theory and applications of sparse arrays Sparse Arrays for Radar, Sonar, and Communications discusses various design approaches of sparse arrays, including those seeking to increase the corresponding one-dimensional and two-dimensional virtual array apertures, as well as others that configure the arrays based on solutions of constrained minimization problems. The latter includes statistical bounds and signal-to-interference and noise ratio; in this respect, the book utilizes the recent strides made in convex optimizations and machine learning for sparse array configurability in both fixed and dynamic environments. Similar ideas are presented for sparse array-waveform design. The book also discusses the role of sparse arrays in improving target detection and resolution in radar, improving channel capacity in massive MIMO, and improving underwater target localization in sonar. It covers different sparse array topologies, and provides various approaches that deliver the optimum and semi-optimum sparse array transceivers. . Edited by a world-leading expert in Radar and Signal Processing and contributed to by world-class researchers in their respective fields, Sparse Arrays for Radar, Sonar, and Communications covers topics including: Utilizing sparse arrays in emerging technologies and showing their offerings in various sensing and communications applications Applying sparse arrays to different environments and obtain superior performances over conventional uniform arrays Solving the localization, beamforming, and direction-finding problems using non-uniform array structures for narrowband and wideband signals Designing sparse array structures for both stationary and moving platforms that produce physical and synthesized array apertures. Using deep neural networks that learn the underlying complex nonlinear model and output the sparse array configuration using representations of the input data spatio-temporal observations Solving for optimum sparse array configurations and beamforming coefficients in sensing using iterative convex optimization methods Providing complete coverage of the recent considerable progress in sparse array design and configurations, Sparse Arrays for Radar, Sonar, and Communications is an essential resource on the subject for graduate students and engineers pursuing research and applications in the broad areas of active/passive sensing and communications.
Miguel Heredia Conde aims at finding novel ways to fit the valuable mathematical results of the Compressive Sensing (CS) theory to the specific case of the Photonic Mixer Device (PMD).To this end, methods are presented that take profit of the sparsity of the signals gathered by PMD sensors. In his research, the author reveals that CS enables outstanding tradeoffs between sensing effort and depth error reduction or resolution enhancement.
Future remote sensing systems will make extensive use of Compressive Sensing (CS) as it becomes more integrated into the system design with increased high resolution sensor developments and the rising earth observation data generated each year. Written by leading experts in the field Compressive Sensing of Earth Observations provides a comprehensive and balanced coverage of the theory and applications of CS in all aspects of earth observations. This work covers a myriad of practical aspects such as the use of CS in detection of human vital signs in a cluttered environment and the corresponding modeling of rib-cage breathing. Readers are also presented with three different applications of CS to the ISAR imaging problem, which includes image reconstruction from compressed data, resolution enhancement, and image reconstruction from incomplete data.
This book constitutes the proceedings of the 13th International Symposium on Bioinformatics Research and Applications, ISBRA 2017, held in Honolulu, HI, USA, in May/June 2017. The 27 full papers presented together with 18 short papers and 24 invited abstracts were carefully reviewed and selected from 131 submissions. They cover topics such as: biomarker discovery; biomedical databases and data integration; biomedical text mining and ortologies; biomolecular imaging; comparative genomics; computational genetic epidemiology; computational proteomics; data mining and visualization; gene expression analysis; genome analysis; high-performance bio-computing; metagenomics; molecular evolution; molecular modelling and simulation; next-generation sequencing data analysis; pattern discovery and classification; population genetics; software tools and applications; structural biology; and systems biology.
The adaptive configuration of nodes in a sensor network has the potential to improve sequential estimation performance by intelligently allocating limited sensor network resources. In addition, the use of heterogeneous sensing nodes provides a diversity of information that also enhances estimation performance. This work reviews cognitive systems and presents a cognitive fusion framework for sequential state estimation using adaptive configuration of heterogeneous sensing nodes and heterogeneous data fusion. This work also provides an application of cognitive fusion to the sequential estimation problem of target tracking using foveal and radar sensors.