This entry has a "fast" flavor and is sometimes peripherically related to CS:Automatic Code Generation for Real-Time Convex Optimization by Jacob Mattingley and Stephen Boyd. The abstract reads:
This chapter concerns the use of convex optimization in real-time embedded systems, in areas such as signal processing, automatic control, real-time estimation, real-time resource allocation and decision making, and fast automated trading. By ‘embedded’ we mean that the optimization algorithm is part of a larger, fully automated system, that executes automatically with newly arriving data or changing conditions, and without any human intervention or action. By ‘real-time’ we mean that the optimization algorithm executes much faster than a typical or generic method with a human in the loop, in times measured in milliseconds or microseconds for small and medium size problems, and (a few) seconds for larger problems. In real-time embedded convex optimization the same optimization problem is solved many times, with different data, often with a hard real-time deadline. In this chapter we propose an automatic code generation system for real-time embedded convex optimization. Such a system scans a description of the problem family, and performs much of the analysis and optimization of the algorithm, such as choosing variable orderings used with sparse factorizations and determining storage structures, at code generation time. Compiling the generated source code yields an extremely efficient custom solver for the problem family. We describe a preliminary implementation, built on the Python-based modeling framework CVXMOD, and give some timing results for several examples.
OFDM Channel Estimation Based on Adaptive Thresholding for Sparse Signal Detection by Mahdi Soltanolkotabi, Arash Amini and Farokh Marvasti. The abstract reads:
Wireless OFDMchannels can be approximated by a time varying filter with sparse time domain taps. Recent achievements in sparse signal processing such as compressed sensing have facilitated the use of sparsity in estimation, which improves the performance significantly. The problem of these sparse-based methods is the need for a stable transformation matrix which is not fulfilled in the current transmission setups. To assist the analog filtering at the receiver, the transmitter leaves some of the subcarriers at both edges of the bandwidth unused which results in an ill-conditioned DFT submatrix. To overcome this difficulty we propose Adaptive Thresholding for Sparse Signal Detection (ATSSD). Simulation results confirm that the proposed method works well in time-invariant and specially time-varying channels where other methods may not work as well.
From the text, one can read:
As a result, linear-programming-based algorithms used in compressed sensing, similar to the ones introduced in [?] can be applied to OFDM channel estimation. However, the authors of [?] did not consider zero-padding at the endpoints of the bandwidth in their scenario, which is an essential part of current OFDMstandards. This assumption, causes the matrix Fp,CP to contradict the Restricted Isometric Property (RIP) defined in [?] and thus the use of Compressive Sensing (CS) algorithms as described in [?], unpractical.I am not quite sure about this and this is not the first time that such wording is being used. The Restricted Isometry Property is just a sufficient condition. Not fulfilling the RIP, doesn't mean that CS algorithms cannot work.
and On the rate distortion function of Bernoulli Gaussian sequences by Cheng Chang. The abstract reads:
In this paper, we study the rate distortion function of the i.i.d sequence of multiplications of a Bernoulli p random variable and a gaussian random variable = N(0, 1). We use a new technique in the derivation of the lower bound in which we establish the duality between channel coding and lossy source coding in the strong sense. We improve the lower bound on the rate distortion function over the best known lower bound by p log_2 (1/p) if distortion D is small. This has some interesting implications on sparse signals where p is small since the known gap between the lower and upper bound is H(p). This improvement in the lower bound shows that the lower and upper bounds are almost identical for sparse signals with small distortion because lim p 0 plog_2 (1/p)/ H(p) = 1.
Finally, there is a webinar today about jacket:
Jacket: Accelerating MATLAB using CUDA-Enabled GPUs, February 5, 2009, 11am PST / 2pm EST. Are you looking for ways to improve your productivity by accelerating MATLAB functions? Now you can with the unprecedented performance of GPU computing. By attending this webinar, you will learn:
- What is GPU computing
- What is NVIDIA CUDA parallel computing architecture
- What is the Jacket engine for MATLAB from AccelerEyes
- How to get 10x to 50x speed-up for several MATLAB functions
Time: 11:00am PST / 2:00pm EST
Duration: 45 Minute Presentation, 15 Minute Q&A
Register Here
Presented By: Sumit Gupta, Ph.D., Sr Product Manager of Tesla GPU Computing at NVIDIA and John Melonakos, Ph.D., CEO at AccelerEyes LLC
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