Obtaining the desired signals in wireless sensor networks can be challenging due to various constraints on sensor
placement or deployment. Retrieving the information accurately from sensors placed at non-uniform locations, is a
problem of sensor communication and signal interpolation. In this research, the optimal recovery (OR) method, a
deterministic framework that can use a priori bandwidth or spectral shape information, is used to interpolate from the
given non-uniformly spaced samples. The error to be minimized is the maximum possible norm difference over a set of
feasible signals. In the OR problem formulation and solution, the role of worst case feasible signals can be recognized
but these signals are very difficult to find analytically. Computer simulations of feasible signals can help to produce
estimates of the theoretical minimal worst-case error bounds. In this paper, monitoring of OR error bounds serves to
assess sensor deployment configuration quality and to optimize the placement of additional sensors. Starting from an
initial configuration of sensors, optimal deployment of additional sensors is clearly a more powerful option than random
deployment of such sensors. These two approaches are compared and contrasted to show the improvement that is
possible using the OR framework for one-dimensional and two-dimensional signals.
In this paper, we propose spatio-spectral processing techniques for the detection of dust storms and automatically
finding its transport direction in 5-band NOAA-AVHRR imagery. Previous methods that use simple band math
analysis have produced promising results but have drawbacks in producing consistent results when low signal
to noise ratio (SNR) images are used. Moreover, in seeking to automate the dust storm detection, the presence
of clouds in the vicinity of the dust storm creates a challenge in being able to distinguish these two types of
image texture. This paper not only addresses the detection of the dust storm in the imagery, it also attempts
to find the transport direction and the location of the sources of the dust storm. We propose a spatio-spectral
processing approach with two components: visualization and automation. Both approaches are based on digital
image processing techniques including directional analysis and filtering. The visualization technique is intended
to enhance the image in order to locate the dust sources. The automation technique is proposed to detect the
transport direction of the dust storm. These techniques can be used in a system to provide timely warnings of
dust storms or hazard assessments for transportation, aviation, environmental safety, and public health.
In this paper we investigate the use of the Affine Scaling Transformation (AST) family of algorithms in solving the
sparse signal recovery problem of harmonic retrieval for the DFT-grid frequencies case. We present the problem in the
more general Compressive Sampling/Sensing (CS) framework where any set of incomplete, linearly independent
measurements can be used to recover or approximate a sparse signal. The compressive sampling problem has been
approached mostly as a problem of l1 norm minimization, which can be solved via an associated linear programming
problem. More recently, attention has shifted to the random linear projection measurements case. For the harmonic
retrieval problem, we focus on linear measurements in the form of: consecutively located time samples, randomly
located time samples, and (Gaussian) random linear projections. We use the AST family of algorithms which is
applicable to the more general problem of minimization of the lp p-norm-like diversity measure that includes the
numerosity (p=0), and the l1 norm (p=1). Of particular interest in this paper is to experimentally find a relationship
between the minimum number M of measurements needed for perfect recovery and the number of components K of the
sparse signal, which is N samples long. Of further interest is the number of AST iterations required to converge to its
solution for various values of the parameter p. In addition, we quantify the reconstruction error to assess the closeness
of the AST solution to the original signal. Results show that the AST for p=1 requires 3-5 times more iterations to
converge to its solution than AST for p=0. The minimum number of data measurements needed for perfect recovery is
approximately the same on the average for all values of p, however, there is an increasing spread as p is reduced from
p=1 to p=0. Finally, we briefly contrast the AST results with those obtained using another l1 minimization algorithm
solver.
Image interpolation is a standard feature in digital image editing software, digital camera systems and printers. Classical methods for resizing produce blurred images with unacceptable quality. Bamberger Pyramids and filter banks have been successfully used for texture and image analysis. They provide excellent multiresolution and directional selectivity. In this paper we present an edge-directed image interpolation algorithm which takes advantage of the simultaneous spatial-directional edge localization at the subband level. The proposed algorithm outperform classical schemes like bilinear and bicubic schemes from the visual and numerical point of views.
The lossless and near-lossless image compression standard, JPEG-LS, while offering state-of-the-art compression performance with low complexity, fails to be idempotent in near-lossless mode (i.e., images degrade upon successive compression/decompressions). This paper identifies the cause and presents two solutions. First it presents a modification to the compressor and decompressor that maintains or improves the error bounds and achieves idempotence. Second it describes a preprocessor that acts upon any image and returns one upon which JPEG-LS does perform idempotently at the expense of doubling the guaranteed error bound on a small subset of pixels (typically below 0.5%).
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