Publications

As abstract representations of relational data, graphs and networks find wide use in a variety of fields, particularly when working in non- Euclidean spaces. Yet for graphs to be truly useful in in the context of signal processing, one ultimately must have access to flexible and tractable statistical models. One...

A crucial element of large web companies is their ability to collect and analyze massive amounts of data. Tuple store databases are a key enabling technology employed by many of these companies (e.g., Google Big Table and Amazon Dynamo). Tuple stores are highly scalable and run on commodity clusters, but...

Efficient operation of RF power amplifiers requires compensation strategies to mitigate nonlinear behavior. As bandwidth increases, memory effects become more pronounced, and Volterra series based compensation becomes onerous due to the exponential growth in the number of necessary coefficients. Behavioral models such as Wiener-Hammerstein systems with a parallel feedforward or...

We present a framework for optimizing the distributed performance of sparse matrix computations. These computations are optimally parallelized by distributing their operations across processors in a subtly uneven balance. Because the optimal balance point depends on the non-zero patterns in the data, the algorithm, and the underlying hardware architecture, it...

Rapid prototyping of advanced signal processing algorithms is critical to developing new radars. Signal processing engineers usually use high level languages like MATLAB, IDL, or Python to develop advanced algorithms and to determine the optimal parameters for these algorithms. Many of these algorithms have very long execution times due to...

Fusion of 3D laser radar (LIDAR) imagery and aerial optical imagery is an efficient method for constructing 3D virtual reality models. One difficult aspect of creating such models is registering the optical image with the LIDAR point cloud, which is characterized as a camera pose estimation problem. We propose a...

Systems currently being developed to operate across wide bandwidths with high sensitivity requirements are limited by the inherent dynamic range of a receiver's analog and mixed-signal components. To increase a receiver's overall linearity, we have developed a digital NonLinear EQualization (NLEQ) processor which is capable of extending a receiver's dynamic...

In this paper, we explore the ease of tackling a communication-intensive parallel computing task - namely, the 2D fast Fourier transform (FFT). We start with a simple serial Matlab code, explore in detail a ID parallel FFT, and illustrate how it can be extended to multidimensional FFTs.

Automatic recognition systems are commonly used in speech processing to classify observed utterances by the speaker's identity, dialect, and language. These problems often require high processing throughput, especially in applications involving multiple concurrent incoming speech streams, such as in datacenter-level processing. Recent advances in processor technology allow multiple processors to...

PVTOL provides an object-oriented C++ API that hides the complexity of multicore architectures within a PGAS programming model, improving programmer productivity. Tasks and conduits enable data flow patterns such as pipelining and round-robining. Hierarchical maps concisely describe how to allocate hierarchical arrays across processor and memory hierarchies and provide a...