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Added by
Amina Chebira
Publication date
2010
fields
Informatics Engineering
and research's language is
English
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In this paper we characterize and construct novel oversampled filter banks implementing fusion frames. A fusion frame is a sequence of orthogonal projection operators whose sum can be inverted in a numerically stable way. When properly designed, fusion frames can provide redundant encodings of signals which are optimally robust against certain types of noise and erasures. However, up to this point, few implementable constructions of such frames were known; we show how to construct them using oversampled filter banks. In this work, we first provide polyphase domain characterizations of filter bank fusion frames. We then use these characterizations to construct filter bank fusion fra
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Data is said to follow the transform (or analysis) sparsity model if it becomes sparse when acted on by a linear operator called a sparsifying transform. Several algorithms have been designed to learn such a transform directly from data, and data-adaptive sparsifying transforms have demonstrated excellent performance in signal restoration tasks. Sparsifying transforms are typically learned using small sub-regions of data called patches, but these algorithms often ignore redundant information shared between neighboring patches. We show that many existing transform and analysis sparse representations can be viewed as filter banks, thus linking the local properties of patch-based model to the global properties of a convolutional model. We propose a new transform learning framework where the sparsifying transform is an undecimated perfect reconstruction filter bank. Unlike previous transform learning algorithms, the filter length can be chosen independently of the number of filter bank channels. Numerical results indicate filter bank sparsifying transforms outperform existing patch-based transform learning for image denoising while benefiting from additional flexibility in the design process.
We draw a random subset of $k$ rows from a frame with $n$ rows (vectors) and $m$ columns (dimensions), where $k$ and $m$ are proportional to $n$. For a variety of important deterministic equiangular tight frames (ETFs) and tight non-ETF frames, we consider the distribution of singular values of the $k$-subset matrix. We observe that for large $n$ they can be precisely described by a known probability distribution -- Wachters MANOVA spectral distribution, a phenomenon that was previously known only for two types of random frames. In terms of convergence to this limit, the $k$-subset matrix from all these frames is shown to be empirically indistinguishable from the classical MANOVA (Jacobi) random matrix ensemble. Thus empirically the MANOVA ensemble offers a universal description of the spectra of randomly selected $k$-subframes, even those taken from deterministic frames. The same universality phenomena is shown to hold for notable random frames as well. This description enables exact calculations of properties of solutions for systems of linear equations based on a random choice of $k$ frame vectors out of $n$ possible vectors, and has a variety of implications for erasure coding, compressed sensing, and sparse recovery. When the aspect ratio $m/n$ is small, the MANOVA spectrum tends to the well known Marcenko-Pastur distribution of the singular values of a Gaussian matrix, in agreement with previous work on highly redundant frames. Our results are empirical, but they are exhaustive, precise and fully reproducible.
We have designed, fabricated, and measured a 5-channel prototype spectrometer pixel operating in the WR10 band to demonstrate a novel moderate-resolution (R=f/{Delta}f~100), multi-pixel, broadband, spectrometer concept for mm and submm-wave astronomy. Our design implements a transmission line filter bank using waveguide resonant cavities as a series of narrow-band filters, each coupled to an aluminum kinetic inductance detector (KID). This technology has the potential to perform the next generation of spectroscopic observations needed to drastically improve our understanding of the epoch of reionization (EoR), star formation, and large-scale structure of the universe. We present our design concept, results from measurements on our prototype device, and the latest progress on our efforts to develop a 4-pixel demonstrator instrument operating in the 130-250 GHz band.
Fusion frame theory is an emerging mathematical theory that provides a natural framework for performing hierarchical data processing. A fusion frame is a frame-like collection of subspaces in a Hilbert space, thereby generalizing the concept of a frame for signal representation. In this paper, we study the existence and construction of fusion frames. We first present a complete characterization of a special class of fusion frames, called Parseval fusion frames. The value of Parseval fusion frames is that the inverse fusion frame operator is equal to the identity and therefore signal reconstruction can be performed with minimal complexity. We then introduce two general methods -- the spatial complement and the Naimark complement -- for constructing a new fusion frame from a given fusion frame. We then establish existence conditions for fusion frames with desired properties. In particular, we address the following question: Given $M, N, m in NN$ and ${lambda_j}_{j=1}^M$, does there exist a fusion frame in $RR^M$ with $N$ subspaces of dimension $m$ for which ${lambda_j}_{j=1}^M$ are the eigenvalues of the associated fusion frame operator? We address this problem by providing an algorithm which computes such a fusion frame for almost any collection of parameters $M, N, m in NN$ and ${lambda_j}_{j=1}^M$. Moreover, we show how this procedure can be applied, if subspaces are to be added to a given fusion frame to force it to become Parseval.
Physical layer security has been considered as an important security approach in wireless communications to protect legitimate transmission from passive eavesdroppers. This paper investigates the physical layer security of a wireless multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) communication system in the presence of a multiple-antenna eavesdropper. We first propose a transmit-filter-assisted secure MIMO-OFDM system which can destroy the orthogonality of eavesdroppers signals. Our proposed transmit filter can disturb the reception of eavesdropper while maintaining the quality of legitimate transmission. Then, we propose another artificial noise (AN)-assisted secure MIMO-OFDM system to further improve the security of the legitimate transmission. The time-domain AN signal is designed to disturb the reception of eavesdropper while the legitimate transmission will not be affected. Simulation results are presented to demonstrate the security performance of the proposed transmit filter design and AN-assisted scheme in the MIMO-OFDM system.
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