Uncertain Sampling with Certain Priors

Sampling has always been at the heart of signal processing providing a bridge between the analogue world and discrete representations of it, as our ability to process data in continuous space is quite limited. Furthermore, sampling plays a key part in understanding how to efficiently capture, store and process signals. Shannon's sampling theorem states that if the original signal is known to have a limited bandwidth, we can retrieve the signal from uniformly-spaced samples, provided that the sampling rate is greater than twice the highest frequency in the signal. Here, we see two key attributes: prior knowledge on the original signal (limited bandwidth) and a constrained sampling setup (uniform samples at a particular rate). In this thesis, we make weaker assumptions on the sampling setup by assuming that some information, such as the sample positions, is lost. We show that under proper prior knowledge, we can reconstruct the signal from its samples uniquely or up to some equivalence class. We start by the problem of linear sampling of discrete signals, where the sample values are known, but their order is lost. In general, the original signal is impossible to retrieve from the samples, but we show that by taking out symmetry from the sampling vectors, we can reconstruct the signal uniquely. We provide an efficient algorithm to find the sample orders and thus reconstruct the original signal. We also study the problem of reconstructing a continuous signal from samples taken at unknown locations. The lost sample locations take away any hope of uniquely retrieving the signal without prior knowledge. We show that this problem is equivalent to reconstructing a composite of functions from uniformly spaced samples. Then we provide an efficient algorithm that can recover bandlimited signals warped by a linear function uniquely given enough sampling frequency. We then investigate a problem, dubbed shape from bandwidth, where we have uniform samples from a picture (projection) of an unknown surface that is painted with an unknown texture. The goal is to reconstruct the shape of the surface from these samples. We show that having prior knowledge of the texture bandwidth provides us with enough information to reconstruct the surface from its picture. We provide reconstruction algorithms for both orthogonal and central projections and provide equivalence classes of solutions in each case. Next, in two consecutive chapters, using techniques from geometrical signal processing, we offer new designs for 3-D barcodes, whose information can be retrieved from a single projection using penetrating waves from an unknown direction. Because of the unknown scan direction, the correct correspondence of the samples to the information bits in the barcodes is lost. In this case, we use the known shape of the barcode as prior knowledge to estimate the unknown scan direction from the samples, and then transform the reconstruction as a linear problem that can be solved efficiently. Finally, we cover the theory of coordinate difference matrices (CDMs): matrices that store mutual differences between coordinates of points (sensors, microphones, molecules, etc.) in space. We show how we can leverage specific properties of these matrices, such as their low rank, as prior knowledge in order to reconstruct the position of the points in space using CDMs. We use our reconstruction algorithm to solve many real-life signal processing problems.