You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
We derive Concentration of Measure (CoM) inequalities for randomized Toeplitz matrices. These inequalities show that the norm of a high-dimensional signal mapped by a Toeplitz matrix to a low-dimensional space concentrates around its mean with a tail probability bound that decays exponentially in the dimension of the range space divided by a quantity which is a function of the signal. For the class of sparse signals, the introduced quantity is bounded by the sparsity level of the signal. However, we observe that this bound is highly pessimistic for most sparse signals and we show that if a random distribution is imposed on the non-zero entries of the signal, the typical value of the quantity is bounded by a term that scales logarithmically in the ambient dimension. As an application of the CoM inequalities, we consider Compressive Binary Detection (CBD).

Cited Abstract(s):
[1]: In this paper, we derive concentration of measure inequalities for compressive Toeplitz matrices (having fewer rows than columns) with entries drawn from an independent and identically distributed (i.i.d.) Gaussian random sequence. These inequalities show that the norm of a vector mapped by a Toeplitz matrix to a lower dimensional space concentrates around its mean with a tail probability bound that decays exponentially in the dimension of the range space divided by a factor that is a function of the sample covariance of the vector. Motivated by the emerging field of Compressive Sensing (CS), we apply these inequalities to problems involving the analysis of high-dimensional systems from convolution-based compressive measurements. We discuss applications such as system identification, namely the estimation of the impulse response of a system, in cases where one can assume that the impulse response is high-dimensional, but sparse. We also consider the problem of detecting a change in the dynamic behavior of a system, where the change itself can be modeled by a system with a sparse impulse response.

[2]: In a typical communications problem, Toeplitz matrices Φ arise when modeling the task of determining an unknown impulse response a from a given probe signal φ. When a is sparse, then whenever Φ formed from the probe signal φ satisfy the Restricted Isometry Property (RIP), a can be robustly recovered from its measurements via l1-minimization. In this paper, we derived the RIP for compressive Toeplitz matrices whose number of rows of the matrices J is much smaller than the number of columns N . We show that J should scale like J ∼ S log(N), where S is the sparsity of the impulse response. While this is marginally worse than the state-of-the-art scaling currently achieved in the literature, the novelty of this work comes from making the relation between the Toeplitz matrix of interest to a block diagonal matrix. The proof of the RIP then follows from using recent results on the concentration of measure inequalities of block diagonal matrices, together with a standard covering-and-counting argument.

[3]: Recovering or estimating the initial state of a high-dimensional system can require a potentially large number of measurements. In this paper, we explain how this burden can be significantly reduced for certain linear systems when randomized measurement operators are employed. Our work builds upon recent results from the field of Compressive Sensing (CS), in which a high-dimensional signal containing few nonzero entries can be efficiently recovered from a small number of random measurements. In particular, we develop concentration of measure bounds for the observability matrix and explain circumstances under which this matrix can satisfy the Restricted Isometry Property (RIP), which is central to much analysis in CS. We also illustrate our results with a simple case study of a diffusion system. Aside from permitting recovery of sparse initial states, our analysis has potential applications in solving inference problems such as detection and classification of more general initial states.

[4]: Theoretical analysis of randomized, compressive operators often depends on a concentration of measure inequality for the operator in question. Typically, such inequalities quantify the likelihood that a random matrix will preserve the norm of a signal after multiplication. Concentration of measure results are well established for unstructured compressive matrices, populated with independent and identically distributed (i.i.d.) random entries. Many real-world acquisition systems, however, are subject to architectural constraints that make such matrices impractical. In this paper we derive concentration of measure bounds for two types of block diagonal compressive matrices, one in which the blocks along the main diagonal are random and independent, and one in which the blocks are random but equal. For both types of matrices, we show that the likelihood of norm preservation depends on certain properties of the signal being measured, but that for the best case signals, both types of block diagonal matrices can offer concentration performance on par with their unstructured, i.i.d. counterparts. We support our theoretical results with illustrative simulations as well as analytical and empirical investigations of several signal classes that are highly amenable to measurement using block diagonal matrices. We also discuss applications of these results in ensuring stable embeddings for various signal families and in establishing performance guarantees for solving various signal processing tasks (such as detection and classification) directly in the compressed domain.
There also exist CoM analyses for other types of structured matrices. For example, [4] derived concentration bounds for two types of block diagonal compressive matrices, one in which the blocks along the diagonal are random and independent, and one in which the blocks are random but equal. Shortly after our own development of CoM inequalities for compressive Toeplitz matrices (a preliminary version of Theorem appeared in [1] ), Yap and Rozell [2] showed that similar inequalities can be derived by extending the CoM results for block diagonal matrices. Our Theorem and the associated discussion, however, is unique to this paper. We subsequently extended these CoM results for block diagonal matrices to the observability matrices that arise in the analysis of linear dynamical systems [3] .