TSP Volume 67 Issue 21

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

November, 2019

TSP Volume 67 Issue 21

In this paper, we design and implement a new on-line portfolio selection strategy based on reversion mechanism and weighted on-line learning. Our strategy, called “Gaussian Weighting Reversion” (GWR), improves the reversion estimator to form optimal portfolios and effectively overcomes the shortcomings of existing on-line portfolio selection strategies.

Recently, a novel method for developing filtering algorithms, based on the interconnection of two Bayesian filters and called double Bayesian filtering, has been proposed. In this manuscript we show that the same conceptual approach can be exploited to devise a new smoothing method, called double Bayesian smoothing.

In this work, we propose a non-parametric sequential hypothesis test based on random distortion testing (RDT). RDT addresses the problem of testing whether or not a random signal, Ξ , observed in independent and identically distributed (i.i.d) additive noise deviates by more than a specified tolerance, τ , from a fixed model, ξ0 .

SPS on Twitter

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar