Machine learning and computation-enabled intelligent sensor design

Ballard, Zachary S.; Brown, Calvin; Madni, A.M.; Ozcan, Aydogan

doi:10.1038/s42256-021-00360-9

Cited by 141 publications

(82 citation statements)

References 81 publications

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“…For example, [64,56] optimized only M Θ1 of (4.1) with F Θ2 fixed and G Θ3 being an identity operator (with z = u); [33,23,84] optimized (4.1) for both M Θ1 and F Θ2 while G Θ3 is taken as an identity operator; [42,41,80,30] optimized F Θ2 and G Θ3 of (4.1) with M Θ1 fixed. In the rising field of intelligent sensor design [6], all three operators are considered except that M Θ1 is often relatively simple. In another emerging field known as the "deep optics" [79], the operator M Θ1 represents an optical module (a physical layer) while G Θ3 • F Θ2 is the digital image processing module.…”

Section: Task-driven Computational Imagingmentioning

confidence: 99%

A Note on Machine Learning Approach for Computational Imaging

Dong¹

2022

Preprint

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Computational imaging has been playing a vital role in the development of natural sciences. Advances in sensory, information, and computer technologies have further extended the scope of influence of imaging, making digital images an essential component of our daily lives. For the past three decades, we have witnessed phenomenal developments of mathematical and machine learning methods in computational imaging. In this note, we will review some of the recent developments of the machine learning approach for computational imaging and discuss its differences and relations to the mathematical approach. We will demonstrate how we may combine the wisdom from both approaches, discuss the merits and potentials of such a combination and present some of the new computational and theoretical challenges it brings about.

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Section: Task-driven Computational Imagingmentioning

confidence: 99%

A Note on Machine Learning Approach for Computational Imaging

Dong¹

2022

Preprint

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“…ML-enabled intelligent sensor design is the use of inverse design and related machine learning techniques to optimally design data acquisition hardware with respect to a user-defined cost function or design constraint. Here is a general outline of the process (motivated by Ballard et al [552]):…”

Section: Si-based Materials Acceleration Platforms and Open-ended Sea...mentioning

confidence: 99%

Simulation Intelligence: Towards a New Generation of Scientific Methods

Lavin¹,

Zenil²,

Krakauer³

et al. 2021

Preprint

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The original "Seven Motifs" set forth a roadmap of essential methods for the field of scientific computing, where a motif is an algorithmic method that captures a pattern of computation and data movement. 1 We present the Nine Motifs of Simulation Intelligence, a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence. We call this merger simulation intelligence (SI), for short. We argue the motifs of simulation intelligence are interconnected and interdependent, much like the components within the layers of an operating system. Using this metaphor, we explore the nature of each layer of the simulation intelligence "operating system" stack (SI-stack) and the motifs therein:1. Multi-physics and multi-scale modeling 2. Surrogate modeling and emulation 3. Simulation-based inference 4. Causal modeling and inference 5. Agent-based modeling 6. Probabilistic programming 7. Differentiable programming 8. Open-ended optimization Machine programmingWe believe coordinated efforts between motifs offers immense opportunity to accelerate scientific discovery, from solving inverse problems in synthetic biology and climate science, to directing nuclear energy experiments and predicting emergent behavior in socioeconomic settings. We elaborate on each layer of the SI-stack, detailing the state-of-art methods, presenting examples to highlight challenges and opportunities, and advocating for specific ways to advance the motifs and the synergies from their combinations. Advancing and integrating these technologies can enable a robust and efficient hypothesis-simulation-analysis type of scientific method, which we introduce with several use-cases for human-machine teaming and automated science.

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“…These analytical equipment have a high capital cost making them unattractive to SME Egyptian cotton lint producers that already suffer from high production costs [3]. However, combining affordable, easy to use, low-cost sensors with data-driven modelling will enable the development of intelligent sensors [14] that can replace high-cost analytical equipment for grading Egyptian fibre cotton.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Image Classification for Grading Egyptian Cotton Lint

Fisher¹,

Rady²,

El-Banna³

et al. 2021

Preprint

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Egyptian cotton is one of the most important commodities to the Egyptian economy and is renowned globally for its quality, which is currently graded by manual inspection. This has several drawbacks including significant labour requirement, low inspection efficiency, and influence from inspection conditions such as light and human subjectivity. This current work uses a low-cost colour vision system, combined with machine learning to predict the cotton lint grade of the cultivars Giza 86, 97, 90, 94 and 96. Unsupervised and supervised machine learning approaches were explored and compared. Three different supervised learning algorithms were evaluated: linear discriminant analysis, decision trees and ensemble modelling. The highest accuracy models (77.3-98.2%) used an ensemble modelling technique to classify samples within the Egyptian cotton grades: Fully Good, Good, Fully Good Fair, Good Fair and Fully Fair. The unsupervised learning technique k-means showed that human error is more likely to occur when classifying lint belonging to the higher quality grades and underlined the need for an intelligent system to replace manual inspection.

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Machine learning and computation-enabled intelligent sensor design

Cited by 141 publications

References 81 publications

A Note on Machine Learning Approach for Computational Imaging

A Note on Machine Learning Approach for Computational Imaging

Simulation Intelligence: Towards a New Generation of Scientific Methods

Intelligent Image Classification for Grading Egyptian Cotton Lint

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