A new aggregated Hardware/Software (HW/SW) codesign approach to optimization of the digital signal processing techniques for enhanced imaging with real-world uncertain remote sensing (RS) data based on the concept of descriptive experiment design regularization (DEDR) is addressed. We consider the applications of the developed approach to typical single-look synthetic aperture radar (SAR) imaging systems operating in the real-world uncertain RS scenarios. The software design is aimed at the algorithmic-level decrease of the computational load of the large-scale SAR image enhancement tasks. The innovative algorithmic idea is to incorporate into the DEDR-optimized fixed-point iterative reconstruction/enhancement procedure the convex convergence enforcement regularization via constructing the proper multilevel projections onto convex sets (POCS) in the solution domain. The hardware design is performed via systolic array computing based on a Xilinx Field Programmable Gate Array (FPGA) XC4VSX35-10ff668 and is aimed at implementing the unified DEDR-POCS image enhancement/reconstruction procedures in a computationally efficient multi-level parallel fashion that meets the (near) real-time image processing requirements. Finally, we comment on the simulation results indicative of the significantly increased performance efficiency both in resolution enhancement and in computational complexity reduction metrics gained with the proposed aggregated HW/SW co-design approach.
In this paper, we address a new approach for high-resolution reconstruction and enhancement of remote sensing (RS) imagery in near-real computational time based on the aggregated hardware/software (HW/SW) co-design paradigm. The software design is aimed at the algorithmic-level decrease of the computational load of the large-scale RS image enhancement tasks via incorporating into the fixed-point iterative reconstruction/enhancement procedures the convex convergence enforcement regularization by constructing the proper projectors onto convex sets (POCS) in the solution domain. The established POCS-regularized iterative techniques are performed separately along the range and azimuth directions over the RS scene frame making an optimal use of the sparseness properties of the employed sensor system modulation format. The hardware design is oriented on employing the Xilinx Field Programmable Gate Array XC4VSX35-10ff668 and performing the image enhancement/reconstruction tasks in a computationally efficient parallel fashion that meets the near-real time imaging system requirements. Finally, we report some simulation results and discuss the implementation performance issues related to enhancement of the real-world RS imagery indicative of the significantly increased performance efficiency gained with the developed approach.
The paper studies the behaviour of three different R/S Statistic algorithms for Hurst-index estimation in self-similar and long-range dependent discrete time series. The accuracy of the algorithms under convergence and aggregation in time is obtained and compared. The results show that static blocks implementations of the R/S Statistic present better accuracy than those based on a dynamic blocks implementation. The accuracy is obtained with the use of synthetic fGn and FARIMA(0, d, 0) self-similar traces. The behavior of the algorithms for well-known LAN traces is also accomplished. Identification of current software tools and fields of science using the dynamic blocks approach is also accomplished.
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