7, 199206 (2016). 1, begins with an array of distinct spectral encoding elements that are designed through approximate electromagnetic transmission models. J. Med. Optica 5, 960966 (2018). 4)41,56,57. Deep learning-enabled point-of-care sensing using multiplexed paper-based sensors. However, like any emerging technology, there exist inherent challenges that must be understood and addressed. (4) The optimal subset of encoders is used to form metapixels for a hyperspectral image sensor realized via the machine learning-inspired design. Rapid, low-cost detection of Zika virus using programmable biomolecular components. Furthermore, neural networks provide a major advantage in computational prediction speed compared with other traditional signal recovery approaches based on, for example, compressive sampling and iterative signal reconstruction, and can be readily integrated into common processors on mobile phones and tablet PCs, paving the way for cost-effective mobile and powerful sensing and diagnostic systems10,11,12. Remote sensing tasks belong to data intensive applications as well. A primer on deep learning in genomics. 1, to re-engineer poorly performing toehold switches using the knowledge gained from the iterative analysis of the RNA features using the trained neural network model (Fig. Spin-enhanced nanodiamond biosensing for ultrasensitive diagnostics. Earth Obs. A serological assay to detect SARS-CoV-2 seroconversion in humans. Therefore, it becomes a central challenge to ensure that the training datasets are not biased or severely contaminated by noise sources characteristic of only the training set. An initial design, located in the centre of the diagram, is produced through standard engineering practices or given a randomized initialization in terms of its transduction elements or sensing components (that is, features), which are denoted here by various shapes and colours. In this Perspective, we discuss computational sensing with a focus on intelligent sensor system design. Jiang, T., Li, C., He, Q. Wick, R. R., Judd, L. M. & Holt, K. E. Performance of neural network basecalling tools for Oxford nanopore sequencing. 9, 11721181 (2017). These sensing data, matched with a gold standard clinical diagnosis, are then used to train a neural network-based diagnostic algorithm, which infers a Lyme positive or Lyme negative diagnosis from the multiplexed IgM and IgG antibody measurements (2), after which the optimal antigen panel, a subset of the full antigen measurement features, is determined through a feature analysis method called sequential forward selection with the area under the receiver operating characteristic (ROC) curve as the objective function to be optimized (3). Sarwar, T., Cheekati, S., Chung, K. & Ku, P.-C. On-chip optical spectrometer based on GaN wavelength-selective nanostructural absorbers. Such an approach, outlined in Fig. Nature 587, 588593 (2020). The refractive index sensitivity is shown as a demonstration of the computation-enabled sensing scheme (right). Signal Process. An overview of this broad concept is presented in Fig. Genet. Recent notable examples of this emerging opportunity have been reported for personal gait identification through the use of distributed contact electrodes in floor mats36 (Fig. We will next discuss some of the emerging examples of machine learning-enabled sensor designs in point-of-care diagnostics and synthetic biology fields. (3) A cost function \({J}({y}_{{i}},{{y}}_{{i}}^\prime )\) is used to evaluate the learned model, using the ground-truth sensing information, yi, with the added purpose of scrutinizing the ensemble of transduction elements or features. The proposed methodology is based on remote sensing data . The authors declare no competing interests. Pardee, K. et al. The Special Issue "Remote Sensing Applications in Vegetation Classification" is an overview of the applications of remote sensing data with different resolutions for the identification of vegetation at different levels of detail. Ultrasensitive hyperspectral imaging and biodetection enabled by dielectric metasurfaces. . To obtain Min, F., Hu, Q. 9, 4405 (2018). & Jensen, K. F. Toward machine learning-enhanced high-throughput experimentation. Plasma Sci. MathSciNet Wang, Z. et al. Machine Learning for Remote Sensing Tutorial | Machine Learning for Commun. 5a)42. Zhou, G., Qi, Y., Lim, Z. H. & Zhou, G. Single-pixel MEMS spectrometer based on compressive sensing. Machine learning algorithms have shown a surprisingly successful development within the last years. Automated, cost-effective optical system for accelerated antimicrobial susceptibility testing (AST) using deep learning. Eraslan, G., Avsec, ., Gagneur, J. J. R. Stat. Nat. Sequence-to-function deep learning frameworks for engineered riboregulators. b, STORM, an optimization pipeline devised to re-engineer poorly performing toehold switches using deep learning. For predicting and mapping soil salinity, several statistical models from classical artificial neural networks (ANN) and deep learning (DL) were applied in the past few. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. For instance, wearable devices (such as the activity monitor shown in Fig. The deep learning model is in turn evaluated to reveal biological insights about key RNA components that yield the desired function. Such scenarios would lead to overfitting, where learned sensing algorithms fail to generalize, sometimes catastrophically, upon the introduction of sensing inputs that deviate only slightly from what have already been explicitly learned81. Rivenson, Y. et al. This process, known as feature selection, can thus be thought of as a way to objectively determine an elite democracy of measurement features47. Furthermore, large-scale manufacturability of sensing technologies can also benefit from feature selection and the computational sensing workflow. The results show that SVM has the best F1 score of 0.99 and estimated major crop areas have 95.9% agreement with the ground surveyed crop area, and the classification accuracy is evaluated by estimated crop areas. Z.B., C.B., A.M.M. Am. Golestani, N. & Moghaddam, M. Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks. Hayasaka, T. et al. Tibshirani, R. Regression shrinkage and selection via the lasso. 14, 1650029 (2016). 92, 15181524 (2020). Edgar, M. P., Gibson, G. M. & Padgett, M. J. Automated screening of sickle cells using a smartphone-based microscope and deep learning. b, Smart triboelectric flooring system for user recognition, made from a polyethylene terephthalate (PET) film friction layer, silver electrode layer, and a polyvinyl chloride (PVC) base layer. & Theis, F. J. 58, e00310e00320 (2020). RBS, ribosome-binding site. Learning-based feature selection algorithms such as the least absolute shrinkage operator73 along with genetic algorithms and wrapper methods based on standard statistical tests, support vector machines, and neural networks have been utilized in previous works to select an optimal subset of spectral bands for efficient and application-specific sensing74,75,76,77,78. Joung, H.-A. volume3,pages 556565 (2021)Cite this article. IEEE J. Sel. The field of spectral sensing provides a rich set of examples of how this compressive signal recovery framework can transform traditional grating and line-scan CCD (charge-coupled device)-based spectrometer designs into much more compact computational spectroscopy tools. npj Digit. Cho, S.-Y. 14, 800812 (2015). Sci. Despite this progress in the field of sensing at large, there is an important opportunity that has not yet been extensively explored: computation and machine learning methods can fundamentally change the hardware designs of traditional sensors and can be used to holistically design intelligent sensor systems. As a concrete example of this opportunity, we believe that hyperspectral image sensors would significantly benefit from the machine learning-enabled intelligent sensor design framework depicted in Fig. Opin. For example, distributed sensing networks for environmental monitoring could autonomously decide where to sample and how to sense (which modality to use). Machine learning methods for remote sensing applications: an overview For example, support vector machine-based algorithms have increasingly been employed for sensing-related analysis such as material characterization, hyperspectral geological and environmental mapping, and cross-reactive sensor arrays forming (for example) an electronic/optoelectronic nose for the identification of trace amounts of explosives and toxins, as well as for diagnostics and genomics applications including pattern recognition of biological pathways for disease prediction1,2,3,4. Nanoeng. Machine learning methods for remote sensing applications: an overview Here, a traditional training procedure can be used to optimize the model weights starting from an initialization (that is, at time t =0) in order to best predict the ON and OFF signals resulting from a given fixed sequence (top). Point-of-care serodiagnostic test for early-stage Lyme disease using a multiplexed paper-based immunoassay and machine learning. Cao, B. et al. A novel hybrid feature selection strategy in quantitative analysis of laser-induced breakdown spectroscopy. Aydogan Ozcan. PubMedGoogle Scholar. 20, 129 (2019). 7, 66 (2018). Feature selection methods would therefore serve as a useful tool for machine learning-based engineering of next-generation computational spectral sensor systems that iteratively converge on application-specific sensing tasks. (2) These data, Xi, are then used to train a learning model outputting sensing results as predictions, \({y}_{i}^\prime\), illustrated here by a diagram of circular nodes and interconnections. In this study, the major DL concepts pertinent to remote-sensing are introduced, and more than 200 publications in this field, most of which were published during the last two years, are reviewed and analyzed. This paper provides an overview of methods and applications for remote sensing data analysis, as well as a discussion of the most recent methodological breakthroughs in machine learning. Photon. Figure adapted with permission from: a, ref. (1) Encoded spectral information resulting from known incident spectra, Si() is acquired and (2) used to train a reconstruction model (for example, a neural network) that outputs a predicted spectrum, S. With such noisy and sparse sensing landscapes, computational methods have evolved to help us garner meaningful information from raw sensor data. 1, with the spectral encoding elements selected on the basis of their importance in a learning-based spectral reconstruction model (Fig. Single-shot on-chip spectral sensors based on photonic crystal slabs. and A.O. 12, 659670 (2018). Figure adapted with permission from: a, ref. GigaScience 7, giy037 (2018). Correspondence to Single-nanowire spectrometers. Get what matters in translational research, free to your inbox weekly. J. Clin. ACS Photon. We anticipate that these new computational sensors enabled by machine learning will foster a plethora of new applications by enabling unique sensing capabilities in different areas including environmental monitoring, medical diagnostics, the internet of things (IoT), autonomous vehicles and security. A. et al. Infect. After synthesis, the toehold switches are characterized via a pooled sequential assay and analysed using various learning models (that is, multilayer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN)) (right) to predict their functionality in terms of ON/OFF signals from expression of a targeted gene. Chem. Finding hidden signals in chemical sensors using deep learning. 2b,c) engineered on the basis of their approximate behaviour as optical filters29,30. 3a could be reconfigured on the basis of deviations from the ground-truth frequency and the origin of a sensed vibration. the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Similarly, wearable sensors could greatly benefit from reconfigurable computational sensing designs as a means to optimize signal acquisition for different body types, health states, motion artefacts/activity states and misalignments46,80. The future scope of machine learning-based classification is very high with the evolution in artificial intelligence and computer vision for pattern recognition. & Hecht, B. Plasmonic nanoantenna design and fabrication based on evolutionary optimization. For example, base-calling algorithms that utilize neural networks have been implemented to reduce the error rate when inferring base sequences from the often noisy signals generated by nanopore sequencing hardware69,70. In the field of biomedical sensing, for instance, where the cost per test can be high, sensing outcomes can depend on a number of factors such as the shelf-life of reagents, ambient conditions (temperature, humidity and so on) and cross contamination, among others. A feature selection method combined with ridge regression and recursive feature elimination in quantitative analysis of laser induced breakdown spectroscopy. Google Scholar. Commun. Deep learning-based classification of hyperspectral data. The choice of remote sensing data with the classification approach plays a major role, several recommendations for the combination are already mentioned in Table 8. A computational algorithm is then utilized to iteratively converge to a given set or subset of features/parameters along with a corresponding statistical inference model that most accurately yield this target (Fig. A genetic algorithm-based framework for wavelength selection on sample categorization. The human immune response is also highly dynamic, evolving over time, and is further complicated by variations across populations and pathogen strains. A Review on the Application of Machine Learning Methods in Tropical Grcs, Z. et al. 3, 18 (2020). Discussion of some of these challenges may lead the reader to believe that a properly executed learning-based computational sensing approach is prohibitively time and resource intensive for the design phase of a given intelligent sensing system. Appl. This application-specific workflow, termed sequence-based toehold optimization and redesign model (STORM), therefore presents a powerful computational tool for the genomic sensing community to use to design and optimize toehold switches, and has in fact already been employed to engineer highly relevant SARS-CoV-2 viral RNA sensors43. Cands, E. J., Romberg, J. K. & Tao, T. Stable signal recovery from incomplete and inaccurate measurements. Evaluation of SARS-CoV-2 serology assays reveals a range of test performance.
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