Mixed analogue–digital artificial-neural-network architecture with on-chip learning

In astronomy, the light emitted by an object travels through the vacuum of space and then the turbulent atmosphere before arriving at a ground based telescope. By passing through the atmosphere a series of turbulent layers modify the light's wave-front in such a way that Adaptive Optics reconstruction techniques are needed to improve the image quality. A novel reconstruction technique based in Artificial Neural Networks (ANN) is proposed. The network is designed to use the local tilts of the wave-front measured by a Shack Hartmann Wave-front Sensor (SHWFS) as inputs and estimate the turbulence in terms of Zernike coefficients. The ANN used is a Multi-Layer Perceptron (MLP) trained with simulated data with one turbulent layer changing in altitude. The reconstructor was tested using three different atmospheric profiles and compared with two existing reconstruction techniques: Least Squares type Matrix Vector Multiplication (LS) and Learn and Apply (L + A).

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“…These include digital [32–34], analog [35,36], hybrid [37,38], FPGA based [39–41], and (non-electronic) optical implementations [42–44]. …”

Section: Future Implementation: Hardware Neural Networkmentioning

confidence: 99%

An ANN-Based Smart Tomographic Reconstructor in a Dynamic Environment

Juez

Lasheras

Roqueñí

et al. 2012

Sensors

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“…Digitization is the trends of times. Many studies have been carried out to digitize analog parts [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

A VLSI Design for Scalable High-Speed Digital Winner-Take-All Circuit

Yoon¹

2015

JSTS:Journal of Semiconductor Technology and Science

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“…This are some issues that have been studied and address and explored in many researches. These include digital (Bermak and Martinez, 2003;Kung, 1992;Lenne, 1995), analog (Brown et al, 2004;Mead, 1989), hybrid (Lehman et al, 1996;Schmid et al, 2004), FPGA based (Nedjah and Mourelle, 2007;Rak et al, 2009;Schrauwen and D'Haene, 2005), and (non-electronic) optical implementations (Moerland, 2007;Tokes et al, 2000). In spite of all the difficults to implement ANN in hardware a lot of research have been done.…”

Section: Introductionmentioning

confidence: 99%