News & Events

82 Talks were found.

  •  TALK   Recursive Sparse Recovery and Applications in Dynamic Imaging
    Date & Time: Friday, June 3, 2011; 11:00 AM
    Speaker: Prof. Namrata Vaswani, Iowa State University
    MERL Host: Petros Boufounos
    • In this talk, I will discuss our recent work on Recursive Sparse Recovery (RecSparsRec) and show how it provides novel solutions to two very different problems in dynamic imaging. RecSparsRec refers to recursive approaches to causally recover a time sequence of signals/images from a greatly reduced number of measurements (compared to existing approaches), by utilizing their sparsity.

      The motivating application for RecSparsRec is fast recursive dynamic magnetic resonance imaging (MRI) for real-time applications like MRI-guided surgery. MRI is a technique for cross-sectional imaging that acquires Fourier projections of the cross-section to be reconstructed, one-at-a-time. Thus, the ability to accurately reconstruct using fewer measurements directly translates into reduced scan times. This, along with online (causal) and fast (recursive) reconstruction algorithms, can enable real-time imaging of fast changing physiological phenomena, and thus make real-time MRI feasible. Cross-sectional images of the brain, heart, or other organs are known to be wavelet sparse. Our recent work was the first to observe that, in a time sequence, their sparsity pattern changes quite slowly. Using this fact, we were able to reformulate the RecSparsRec problem as one of sparse reconstruction with partially known support. We introduced a simple, but very powerful, approach called!
      Modified-CS that achieves provably exact reconstruction (in the noise-free case) and whose error is provably stable over time (in the noisy case), with using much fewer measurements than existing work. Our preliminary experiments indicate that Modified-CS needs roughly 5-times fewer measurements than existing MR scanner technology and 1.5-times fewer than existing research literature.

      I will briefly also discuss our ongoing work on the difficult video analysis problem of separating foreground moving objects from a background scene that is itself is changing and dong this in real-time. This can be posed as a recursive robust principal components analysis (PCA) problem in the presence of correlated sparse outliers or equivalently, as a problem of recursive sparse recovery in the presence of very large, but ``low rank" noise (noise with a low rank covariance matrix).
  •  TALK   Resource Block Embedding: Towards High Throughput Broadband Multimedia Wireless Networks
    Date & Time: Thursday, June 2, 2011; 12:00 PM
    Speaker: Ramesh Annavajjala, MERL
    MERL Host: Philip Orlik
    • For orthogonal frequency-division multiplexing (OFDM) based wireless systems, a resource block (RB) in a two-dimensional time-frequency plane is defined as a data block spanned by a number of consecutive OFDM symbols over a number of consecutive subcarriers. Traditionally, RBs contain modulation symbols for data transmission and pilot symbols for channel estimation.

      In this talk, I present a novel approach to RB designs for OFDM systems with multiple antennas at the transmitter and the receiver (i.e., MIMO-OFDM). The proposed approach, termed resource block embedding, does not require explicit pilot symbols to estimate the channel at the receiver, and hence reduces the channel estimation overhead significantly. I describe, in detail, the encoding and decoding algorithms for our proposed embedded resource blocks (ERB) for single-user single-antenna transmission, two transmitter antenna Alamouti code, four transmitter antenna stacked Alamouti code, and multi-stream spatial multiplexing. I also outline construction of ERBs for multi-user MIMO systems.

      This is a joint work with Phil Orlik and Jin Zhang.