Jul 27th, 2009| 07:37 am | Posted by hlee

I wish I could chase all rabbits. Another rabbit I missed came to a realization by a friend, who was sure that I already knew this “call for papers” notice for the special issue of the signal processing magazine (SPM). Although those due dates were mistaken (the white paper due was several months back), my friend thought it would be useful for me and my group just in case I didn’t know about it. Yes, I was very delighted such things were on going. No doubt that I was disappointed when the white paper due was long gone. Continue reading ‘Another exciting news (with no use)’ »

Dec 7th, 2008| 11:23 pm | Posted by hlee

MADS stands for **“Missing in ADS.”** Every astronomer, I believe, knows what ADS is. As we have [EotW] series and used to have [ArXiv] series, creating a new series for semi-periodic postings under the well known name ADS seems interesting. Continue reading ‘[MADS] HMM’ »

Oct 9th, 2008| 04:28 pm | Posted by hlee

Without signal processing courses, the following equation should be awfully familiar to astronomers of photometry and handling data:

$$c_k=\int_\Lambda l(\lambda) r(\lambda) f_k(\lambda) \alpha(\lambda) d\lambda +n_k$$

Terms are in order, camera response (c_k), light source (l), spectral radiance by l (r), filter (f), sensitivity (α), and noise (n_k), where Λ indicates the range of the spectrum in which the camera is sensitive.

Or simplified to $$c_k=\int_\Lambda \phi_k (\lambda) r(\lambda) d\lambda +n_k$$

where φ denotes the combined illuminant and the spectral sensitivity of the k-th channel, which goes by augmented spectral sensitivity. Well, we can skip spectral radiance r, though. Unfortunately, the sensitivity α has multiple layers, not a simple closed function of λ in astronomical photometry.

Or $$c_k=\Theta r +n$$

Inverting Θ and finding a reconstruction operator such that r=inv(Θ)c_k leads spectral reconstruction although Θ is, in general, not a square matrix. Otherwise, approach from indirect reconstruction. Continue reading ‘[tutorial] multispectral imaging, a case study’ »

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matrix,

Mona Lisa,

multispectral,

noise,

signal processing,

signal processing magazine,

Tutorial Category:

Algorithms,

arXiv,

Cross-Cultural,

Data Processing,

Fitting,

Imaging,

Methods,

Quotes,

Spectral,

Stat,

Uncertainty |

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Sep 10th, 2008| 10:15 am | Posted by hlee

Physicists believe that the Gaussian law has been proved in mathematics while mathematicians think that it was experimentally established in physics — **Henri Poincare**

Continue reading ‘Why Gaussianity?’ »

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CLT,

Gaussianity,

Henry Poincare,

IEEE,

normal,

signal processing,

signal processing magazine,

Why Category:

arXiv,

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Frequentist,

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Mar 14th, 2008| 03:44 pm | Posted by hlee

Warning! The list is long this week but diverse. Some are of CHASC’s obvious interest. Continue reading ‘[ArXiv] 2nd week, Mar. 2008’ »

Tags:

ANN,

autocorrelation,

Classification,

cross-correlation,

Estimation,

Fisher information,

lensing,

LF,

Model Selection,

Pareto,

signal processing,

tessellation Category:

arXiv,

MCMC |

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Jan 30th, 2008| 02:33 am | Posted by hlee

Astronomers have developed their ways of processing signals almost independent to but sometimes collaboratively with engineers, although the fundamental of signal processing is same: extracting information. Doubtlessly, these two parallel roads of astronomers’ and engineers’ have been pointing opposite directions: one toward the sky and the other to the earth. Nevertheless, without an intensive argument, we could say that somewhat statistics has played the medium of signal processing for both scientists and engineers. This particular issue of IEEE signal processing magazine may shed lights for astronomers interested in signal processing and statistics outside the astronomical society.

IEEE Signal Processing Magazine Jul. 2007 Vol 24 Issue 4: Bootstrap methods in signal processing

This link will show the table of contents and provide links to articles; however, the access to papers requires IEEE Xplore subscription via libraries or individual IEEE memberships). Here, I’d like to attempt to introduce some articles and tutorials.

Continue reading ‘Signal Processing and Bootstrap’ »

Tags:

bootstrap,

compressive sensing,

confidence interval,

GLM,

IEEE,

jacknife,

machine learning,

multitaper estimate,

particle filter,

signal processing,

statistical inference,

Tutorial,

wavelet Category:

Algorithms,

arXiv,

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MC,

MCMC,

Methods,

Misc,

Spectral,

Stat,

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