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Wolper, J
Idaho State University
Pocatello, Idaho
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1 - 10 of 37
reviews
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 The seven tools of causal inference, with reflections on machine learning
Pearl J. Communications of the ACM 62(3): 54-60, 2019. Type: Article, Reviews: (1 of 3)
Social media tells us that data science, machine learning (ML), and artificial intelligence (AI) are big. The author of this article, whose title evokes a well-known self-help book [1], has made important contributions to these areas. ...
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Mar 21 2019 |
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Metric sensing and control of a quadrotor using a homography-based visual inertial fusion method
Li P., Garratt M., Lambert A., Lin S. Robotics and Autonomous Systems 761-14, 2016. Type: Article
Is a locust a computer?...
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Jul 20 2016 |
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Toward cluster-based weighted compressive data aggregation in wireless sensor networks
Abbasi-Daresari S., Abouei J. Ad Hoc Networks 36, Part 1368-385, 2016. Type: Article
It seems surprising that so many users of remote sensing (including wireless networks) have yet to hear the 10-year-old message of compressive sensing: data from nature is “sparse,” so a small number of properly sel...
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Jun 27 2016 |
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Adaptive compressive sensing based sample scheduling mechanism for wireless sensor networks
Hao J., Zhang B., Jiao Z., Mao S. Pervasive and Mobile Computing 22(C): 113-125, 2015. Type: Article
Compressed sensing (CS) has matured rapidly and should continue to do so for some time. In CS, a so-called sparse signal is accurately observed using a very small number of samples. Early applications used a fixed sampling scheme, but ...
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May 9 2016 |
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neCODEC: nearline data compression for scientific applications
Tian Y., Xu C., Yu W., Vetter J., Klasky S., Liu H., Biaz S. Cluster Computing 17(2): 475-486, 2014. Type: Article
Data scientists and practitioners of high-performance computing, the target audience for this paper, know well that massively parallel systems generate massive amounts of data, which must be stored and transmitted as well as processed....
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Aug 17 2015 |
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Similar sensing matrix pursuit: an efficient reconstruction algorithm to cope with deterministic sensing matrix
Liu J., Mallick M., Han C., Yao X., Lian F. Signal Processing 95101-110, 2014. Type: Article
Compressed sensing (CS) is a new set of techniques that promise good or even exact reconstruction of signals based on a very small number of measurements. When x is a large, sparse vector (that is, x
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Aug 22 2014 |
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Distributed data aggregation for sparse recovery in wireless sensor networks
Li S., Qi H. DCOSS 2013 (Proceedings of the 2013 IEEE International Conference on Distributed Computing in Sensor Systems,May 20-23, 2013) 62-69, 2013. Type: Proceedings
We are standing on the threshold of an era of widely distributed but limited sensors: low-power, low-speed devices that collect data about their environment, both physical and virtual. The multihop wireless sensor network (WSN) is a wi...
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Jan 3 2014 |
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A mathematical introduction to compressive sensing
Foucart S., Rauhut H., Birkhäuser Basel, New York, NY, 2013. 605 pp. Type: Book (978-0-817649-47-0)
Compressed (or compressive) sensing tries to compress data at its source, rather than after collection. The grossly overdetermined system that ensues would seem to make reconstruction impossible, but when the data is sparse (in an exte...
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Nov 15 2013 |
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Blind compressed sensing
Gleichman S., Eldar Y. IEEE Transactions on Information Theory 57(10): 6958-6975, 2011. Type: Article
One of the big issues in applying compressed sensing to reconstruct a sparse signal is the need to know which basis was used to determine sparsity. The authors of this paper consider the blind case, in which the basis is unknown, and d...
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Mar 21 2013 |
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Hard thresholding pursuit: an algorithm for compressive sensing
Foucart S. SIAM Journal on Numerical Analysis 49(6): 2543-2563, 2011. Type: Article
Compressive sensing (CS) is an exciting new model for signal processing. The author presents a new reconstruction algorithm, which is the main thrust of current CS research....
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Feb 15 2013 |
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