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

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

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

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-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 Identifies Signatures of Macrophage Reactivity and Tolerance

Project description:A continuum of macrophage polarization states is essential tissue homeostasis. We used machine learning approaches to identify universally relevant definition of macrophage polarization states and create a predictive framework for developing macrophage-targeted precision diagnostics and therapeutics. CCDC88A was identified as a key gene in the continuum state clusters that is essential for the tolerant polarization state.

2022-05-20 | GSE203423 | GEO

Quantifying Biopolymer Sequence Recognition using Biophysically Informed Machine Learning

Project description:Quantifying sequence-specific protein-ligand interactions is critical for understanding and exploiting numerous cellular processes, including gene expression regulation and signal transduction. Given their importance, next-generation sequencing (NGS) based assays that characterize such recognition with high-throughput are increasingly being used to profile a range of protein classes and interactions. However, these methods do not measure the biophysical parameters that have long been used to uncover the quantitative rules underlying sequence recognition. We developed a highly flexible machine learning framework, called ProBound, to quantify sequence recognition in terms of biophysical parameters based on NGS data. ProBound quantifies transcription factor (TF) behavior with models that accurately predict binding affinity over a range exceeding that of previous resources, captures the impact of DNA modifications and conformational flexibility of multi-TF complexes, and infers specificity directly from \textit{in vivo} data such as ChIP-seq without peak calling. When coupled with a new assay called Kd-seq, it quantifies the absolute affinity of protein-ligand interactions. Its applicability extends beyond thermodynamic equilibrium binding, to the kinetics of kinase-substrate interactions. Altogether, ProBound provides a versatile algorithmic framework for understanding sequence recognition in a wide variety of biological contexts.

2021-06-02 | GSE175942 | GEO

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

Thalamocortical dysrhythmia detected by machine learning.

Project description:Thalamocortical dysrhythmia (TCD) is a model proposed to explain divergent neurological disorders. It is characterized by a common oscillatory pattern in which resting-state alpha activity is replaced by cross-frequency coupling of low- and high-frequency oscillations. We undertook a data-driven approach using support vector machine learning for analyzing resting-state electroencephalography oscillatory patterns in patients with Parkinson's disease, neuropathic pain, tinnitus, and depression. We show a spectrally equivalent but spatially distinct form of TCD that depends on the specific disorder. However, we also identify brain areas that are common to the pathology of Parkinson's disease, pain, tinnitus, and depression. This study therefore supports the validity of TCD as an oscillatory mechanism underlying diverse neurological disorders.

| S-EPMC5856824 | biostudies-other

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