The unified approach that we address to solve such a problem is based on the recently proposed concept of descriptive experiment design regularization (DEDR) [7, 8].
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.
In [7], an approach was proposed to treat the uncertain RS imaging problems that unifies the MR spectral estimation strategy with the worst case statistical performance (WCSP) optimization-based convex regularization resulting in the descriptive experiment design regularization (DEDR) method.
First, we have analytically established that to alleviate the problem ill-posedness and reduce the overall computational load of the large-scale image enhancement/reconstruction tasks at the algorithmic processing level, some special form of descriptive experiment design projection-type numerical regularization must be employed.
We commence with the descriptive experiment design regularization (DEDR) approach for solving the image enhancement inverse problem based on the ℓ2 -type squared error norm minimization strategy robust against the problem model uncertainties in the sense of the worst case statistical performance optimization.
The studies were marked as "inconclusive" if the presented evidence was judged as insufficient based on inadequacies, such as small sample sizes, lack of comparison groups, use of purely descriptive statistics, short experiment timeframes, and unreliable statistical evidence.
We used a combination of implanted substrates, controlled exposure experiments and descriptive surveys to examine dispersal and colonization behaviour of macroinvertebrates in a developing stream in the presence/absence of decaying salmon flesh.
The coherence of the visual stimulus also exerted a strong effect on choices, as evidenced by the well-formed psychometric functions in Figure 2. In the Introduction we posed four questions about neural processing in LIP to be addressed in these experiments, two descriptive and two mechanistic.
In the rest of this section, we describe in detail the results of our experiments through descriptive statistics computed from the logs of the interactions.
