Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Machine-learning reprogrammable metasurface imager.

ABSTRACT: Conventional microwave imagers usually require either time-consuming data acquisition, or complicated reconstruction algorithms for data post-processing, making them largely ineffective for complex in-situ sensing and monitoring. Here, we experimentally report a real-time digital-metasurface imager that can be trained in-situ to generate the radiation patterns required by machine-learning optimized measurement modes. This imager is electronically reprogrammed in real time to access the optimized solution for an entire data set, realizing storage and transfer of full-resolution raw data in dynamically varying scenes. High-accuracy image coding and recognition are demonstrated in situ for various image sets, including hand-written digits and through-wall body gestures, using a single physical hardware imager, reprogrammed in real time. Our electronically controlled metasurface imager opens new venues for intelligent surveillance, fast data acquisition and processing, imaging at various frequencies, and beyond.

SUBMITTER: Li L

PROVIDER: S-EPMC6403242 | biostudies-other | 2019 Mar

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Publications

Machine-learning reprogrammable metasurface imager.

Li Lianlin L Ruan Hengxin H Liu Che C Li Ying Y Shuang Ya Y Alù Andrea A Qiu Cheng-Wei CW Cui Tie Jun TJ

Nature communications 20190306 1

Conventional microwave imagers usually require either time-consuming data acquisition, or complicated reconstruction algorithms for data post-processing, making them largely ineffective for complex in-situ sensing and monitoring. Here, we experimentally report a real-time digital-metasurface imager that can be trained in-situ to generate the radiation patterns required by machine-learning optimized measurement modes. This imager is electronically reprogrammed in real time to access the optimized ...[more]

PMID: 30842417

Similar Datasets

Intelligent metasurface imager and recognizer.

Project description:There is an increasing need to remotely monitor people in daily life using radio-frequency probe signals. However, conventional systems can hardly be deployed in real-world settings since they typically require objects to either deliberately cooperate or carry a wireless active device or identification tag. To accomplish complicated successive tasks using a single device in real time, we propose the simultaneous use of a smart metasurface imager and recognizer, empowered by a network of artificial neural networks (ANNs) for adaptively controlling data flow. Here, three ANNs are employed in an integrated hierarchy, transforming measured microwave data into images of the whole human body, classifying specifically designated spots (hand and chest) within the whole image, and recognizing human hand signs instantly at a Wi-Fi frequency of 2.4 GHz. Instantaneous in situ full-scene imaging and adaptive recognition of hand signs and vital signs of multiple non-cooperative people were experimentally demonstrated. We also show that the proposed intelligent metasurface system works well even when it is passively excited by stray Wi-Fi signals that ubiquitously exist in our daily lives. The reported strategy could open up a new avenue for future smart cities, smart homes, human-device interaction interfaces, health monitoring, and safety screening free of visual privacy issues.

| S-EPMC6804847 | biostudies-literature

Real-time machine learning-enhanced hyperspectro-polarimetric imaging via an encoding metasurface.

Project description:Light fields carry a wealth of information, including intensity, spectrum, and polarization. However, standard cameras capture only the intensity, disregarding other valuable information. While hyperspectral and polarimetric imaging systems capture spectral and polarization information, respectively, in addition to intensity, they are often bulky, slow, and costly. Here, we have developed an encoding metasurface paired with a neural network enabling a normal camera to acquire hyperspectro-polarimetric images from a single snapshot. Our experimental results demonstrate that this metasurface-enhanced camera can accurately resolve full-Stokes polarization across a broad spectral range (700 to 1150 nanometer) from a single snapshot, achieving a spectral sensitivity as high as 0.23 nanometer. In addition, our system captures full-Stokes hyperspectro-polarimetric video in real time at a rate of 28 frames per second, primarily limited by the camera's readout rate. Our encoding metasurface offers a compact, fast, and cost-effective solution for multidimensional imaging that effectively uses information within light fields.

| S-EPMC11373597 | biostudies-literature

Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.

2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress

Identifying transcription factor-DNA interactions using machine learning

Project description:We focused on building models that incorporated transcription factor (TF)-DNA interaction data for 12 members of the Auxin Response Factor (ARF) family from soybean as assessed by DNA Affinity Purification and sequencing (DAP-seq).

2023-06-01 | GSE193400 | GEO

Machine learning for discovery: deciphering RNA splicing logic

Project description:Machine learning methods, particularly neural networks trained on large datasets, are transforming how scientists approach scientific discovery and experimental design. However, current state-of-the-art neural networks are limited by their uninterpretability: despite providing accurate predictions, they cannot describe how they arrived at their predictions. Here, using an ``interpretable-by-design'' approach, we present a neural network model that provides insights into RNA splicing, a fundamental process in the transfer of genomic information into functional biochemical products. Although we designed our model to emphasize interpretability, its predictive accuracy is on par with state-of-the-art models. To demonstrate the model's interpretability, we introduce a visualization that, for any given exon, allows us to trace and quantify the entire decision process from input sequence to output splicing prediction. Importantly, the model revealed novel components of the splicing logic, which we experimentally validated. This study highlights how interpretable machine learning can advance scientific discovery.

2022-10-01 | GSE200096 | GEO

Machine Learning-based Peptide ID Rescoring

Project description:Machine Learning-based Mascot Rescoring to Reduce False Positives in Tumor Immunopeptidomics using Mass Spectrometry

2024-08-25 | PXD044852 | JPOST Repository

Machine learning driven biomarker discovery for the discrimination between allergic and irritant contact dermatitis

Project description:Allergic and irritant contact dermatitis induces different immunological cascades, involving a plethora of immune cells as well as keratinocytes. 96 patients were investigated using mRNA microarray experiments, of which 88 passed our QC criteria. Patients were topically exposed to allergens (nickel (Ni), epoxy resin (EP) and methylchloroisothiazolinone (CM)), irritants (sodium lauryl sulfate (SL) and nonanoic acid (NO)) for 48 hours or were left untreated.

2020-09-01 | E-MTAB-9501 | biostudies-arrayexpress

Machine learning in human olfactory research.

Project description:The complexity of the human sense of smell is increasingly reflected in complex and high-dimensional data, which opens opportunities for data driven approaches that complement hypothesis driven research. Contemporary developments in computational and data science, with its currently most popular implementation as machine learning, facilitate complex data driven research approaches. The use of machine-learning in human olfactory research included major approaches comprising (i) the study of the physiology of pattern-based odor detection and recognition processes, (ii) pattern recognition in olfactory phenotypes, (iii) the development of complex disease biomarkers including olfactory features, (iv) odor prediction from physicochemical properties of volatile molecules and (v) knowledge discovery in publicly available big databases. A limited set of unsupervised and supervised machine-learned methods has been used in these projects, however, the increasing use of contemporary methods of computational science is reflected in a growing number of reports employing machine learning for human olfactory research. This review provides key concepts of machine learning and summarizes current applications on human olfactory data.

| S-EPMC6295796 | biostudies-other

High-security learning-based optical encryption assisted by disordered metasurface.

Project description:Artificial intelligence has gained significant attention for exploiting optical scattering for optical encryption. Conventional scattering media are inevitably influenced by instability or perturbations, and hence unsuitable for long-term scenarios. Additionally, the plaintext can be easily compromised due to the single channel within the medium and one-to-one mapping between input and output. To mitigate these issues, a stable spin-multiplexing disordered metasurface (DM) with numerous polarized transmission channels serves as the scattering medium, and a double-secure procedure with superposition of plaintext and security key achieves two-to-one mapping between input and output. In attack analysis, when the ciphertext, security key, and incident polarization are all correct, the plaintext can be decrypted. This system demonstrates excellent decryption efficiency over extended periods in noisy environments. The DM, functioning as an ultra-stable and active speckle generator, coupled with the double-secure approach, creates a highly secure speckle-based cryptosystem with immense potentials for practical applications.

| S-EPMC10960874 | biostudies-literature

Circulating miRNAs and Machine Learning for Lateralizing Primary Aldosteronism

Project description:Primary aldosteronism (PA) is the most prevalent cause of secondary hypertension. Its two main clinical forms are unilateral adenoma (UPA) and bilateral hyperplasia (BAH), which require markedly different medical treatments, so differentiating between the two is of utmost clinical importance. The current gold standard method for this is adrenal vein sampling (AVS), the application of which is hindered by limited availability and high skill requirements. Our goal was to identify circulating microRNAs – or their combinations – which enable differentiation between the two most prevalent aetiologies of PA from a peripheral blood sample. MicroRNA specific sequencing was performed on an Illumina platform, using EDTA coagulated blood samples taken during AVS, from 18 patients (10 uni-, and 8 bilateral). First, plasma samples from both adrenal veins were evaluated. Bioinformatical analysis applying the DeSeq2 algorithm was used to evaluate the differences in expression; and a neural network model, tasked to identify the most fit individual and groups of microRNAs for differentiation was used. The microRNAs comprising the five best performing models were then validated using reverse transcription real-time PCR.

2024-10-31 | GSE264578 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data