Browse
Submit Data
Databases
API
Help

Dataset Information

19 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Machine learning assisted metamaterial-based reconfigurable antenna for low-cost portable electronic devices.

ABSTRACT: Antenna design has evolved from bulkier to small portable designs but there is a need for smarter antenna design using machine learning algorithms that can meet today's high growing demand for smart and fast devices. Here in this research, main focus is on developing smart antenna design using machine learning applicable in 5G mobile applications and portable Wi-Fi, Wi-MAX, and WLAN applications. Our design is based on the metamaterial concept where the patch is truncated and etched with a split ring resonator (SRR). The high gain requirement is met by adding metamaterial superstrates having thin wires (TW) and SRRs. The reconfigurability is achieved by adding three PIN diode switches. Multiple designs have been observed by adding superstrate layers ranging from one layer to four layers with interchanging TWs and SRRs. The TW metamaterial superstrate design with two layers is giving the best performance in gain, bandwidth, and the number of bands. The design is optimized by changing the path's physical parameters. To shrink simulation time, Extra Tree Regression based machine learning model is used to learn the behavior of the antenna and predict the reflectance value for a wide range of frequencies. Experimental results prove that the use of the Extra Tree Regression based model for simulation of antenna design can cut the simulation time, resource requirements by 80%.

SUBMITTER: Patel SK

PROVIDER: S-EPMC9296536 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Design and Validation of a Portable Machine Learning-Based Electronic Nose.

Project description:Volatile organic compounds (VOCs) are chemicals emitted by various groups, such as foods, bacteria, and plants. While there are specific pathways and biological features significantly related to such VOCs, detection of these is achieved mostly by human odor testing or high-end methods such as gas chromatography-mass spectrometry that can analyze the gaseous component. However, odor characterization can be quite helpful in the rapid classification of some samples in sufficient concentrations. Lower-cost metal-oxide gas sensors have the potential to allow the same type of detection with less training required. Here, we report a portable, battery-powered electronic nose system that utilizes multiple metal-oxide gas sensors and machine learning algorithms to detect and classify VOCs. An in-house circuit was designed with ten metal-oxide sensors and voltage dividers; an STM32 microcontroller was used for data acquisition with 12-bit analog-to-digital conversion. For classification of target samples, a supervised machine learning algorithm such as support vector machine (SVM) was applied to classify the VOCs based on the measurement results. The coefficient of variation (standard deviation divided by mean) of 8 of the 10 sensors stayed below 10%, indicating the excellent repeatability of these sensors. As a proof of concept, four different types of wine samples and three different oil samples were classified, and the training model reported 100% and 98% accuracy based on the confusion matrix analysis, respectively. When the trained model was challenged against new sets of data, sensitivity and specificity of 98.5% and 98.6% were achieved for the wine test and 96.3% and 93.3% for the oil test, respectively, when the SVM classifier was used. These results suggest that the metal-oxide sensors are suitable for usage in food authentication applications.

| S-EPMC8201040 | biostudies-literature

Machine-Learning-Assisted Analysis of Colorimetric Assays on Paper Analytical Devices.

Project description:Paper-based analytical devices (PADs) employing colorimetric detection and smartphone images have gained wider acceptance in a variety of measurement applications. PADs are primarily meant to be used in field settings where assay and imaging conditions greatly vary, resulting in less accurate results. Recently, machine-learning (ML)-assisted models have been used in image analysis. We evaluated a combination of four ML models-logistic regression, support vector machine (SVM), random forest, and artificial neural network (ANN)-as well as three image color spaces, RGB, HSV, and LAB, for their ability to accurately predict analyte concentrations. We used images of PADs taken at varying lighting conditions, with different cameras and users for food color and enzyme inhibition assays to create training and test datasets. The prediction accuracy was higher for food color than enzyme inhibition assays in most of the ML models and color space combinations. All models better predicted coarse-level classifications than fine-grained concentration classes. ML models using the sample color along with a reference color increased the models' ability to predict the result in which the reference color may have partially factored out the variation in ambient assay and imaging conditions. The best concentration class prediction accuracy obtained for food color was 0.966 when using the ANN model and LAB color space. The accuracy for enzyme inhibition assay was 0.908 when using the SVM model and LAB color space. Appropriate models and color space combinations can be useful to analyze large numbers of samples on PADs as a powerful low-cost quick field-testing tool.

| S-EPMC8675014 | biostudies-literature

Simulation-assisted machine learning.

Project description:MotivationIn a predictive modeling setting, if sufficient details of the system behavior are known, one can build and use a simulation for making predictions. When sufficient system details are not known, one typically turns to machine learning, which builds a black-box model of the system using a large dataset of input sample features and outputs. We consider a setting which is between these two extremes: some details of the system mechanics are known but not enough for creating simulations that can be used to make high quality predictions. In this context we propose using approximate simulations to build a kernel for use in kernelized machine learning methods, such as support vector machines. The results of multiple simulations (under various uncertainty scenarios) are used to compute similarity measures between every pair of samples: sample pairs are given a high similarity score if they behave similarly under a wide range of simulation parameters. These similarity values, rather than the original high dimensional feature data, are used to build the kernel.ResultsWe demonstrate and explore the simulation-based kernel (SimKern) concept using four synthetic complex systems-three biologically inspired models and one network flow optimization model. We show that, when the number of training samples is small compared to the number of features, the SimKern approach dominates over no-prior-knowledge methods. This approach should be applicable in all disciplines where predictive models are sought and informative yet approximate simulations are available.Availability and implementationThe Python SimKern software, the demonstration models (in MATLAB, R), and the datasets are available at https://github.com/davidcraft/SimKern.Supplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC6792064 | biostudies-literature

Quantum machine learning for electronic structure calculations.

Project description:Considering recent advancements and successes in the development of efficient quantum algorithms for electronic structure calculations-alongside impressive results using machine learning techniques for computation-hybridizing quantum computing with machine learning for the intent of performing electronic structure calculations is a natural progression. Here we report a hybrid quantum algorithm employing a restricted Boltzmann machine to obtain accurate molecular potential energy surfaces. By exploiting a quantum algorithm to help optimize the underlying objective function, we obtained an efficient procedure for the calculation of the electronic ground state energy for a small molecule system. Our approach achieves high accuracy for the ground state energy for H2, LiH, H2O at a specific location on its potential energy surface with a finite basis set. With the future availability of larger-scale quantum computers, quantum machine learning techniques are set to become powerful tools to obtain accurate values for electronic structures.

| S-EPMC6180079 | biostudies-other

Development of a Low-Cost Portable Electronic Nose for Cigarette Brands Identification.

Project description:In China, the government and the cigarette industry yearly lose millions in sales and tax revenue because of imitation cigarettes. Usually, visual observation is not enough to identify counterfeiting. An auxiliary analytical method is needed for cigarette brands identification. To this end, we developed a portable, low-cost electronic nose (e-nose) system for brand recognition of cigarettes. A gas sampling device was designed to reduce the influence caused by humidity fluctuation and the volatile organic compounds (VOCs) in the environment. To ensure the uniformity of airflow distribution, the structure of the sensing chamber was optimized by computational fluid dynamics (CFD) simulations. The e-nose system is compact, portable, and lightweight with only 15 cm in side length and the cost of the whole device is less than $100. Results from the machine learning algorithm showed that there were significant differences between 5 kinds of cigarettes we tested. Random Forest (RF) has the best performance with accuracy of 91.67% and K Nearest Neighbor (KNN) has the accuracy of 86.98%, which indicated that the e-nose was able to discriminate samples. We believe this portable, cheap, reliable e-nose system could be used as an auxiliary screen technique for counterfeit cigarettes.

| S-EPMC7435456 | biostudies-literature

Near-zero metamaterial inspired UHF antenna for nanosatellite communication system.

Project description:Epsilon-and-mu-near-zero (EMNZ) metamaterial structure inspired UHF antenna for nanosatellite has been proposed in this paper. The antenna consists of 3?×?2-unit cell array on the ground plane and a meander line radiating patch. Coaxial probe feeding technique has been obtained to excite the antenna. The meander line enables the antenna to resonate at lower UHF band and the metamaterial array is used to make the resonant frequency stable by reducing the coupling effect with metallic nanosatellite structure. The metamaterial structure exhibits EMNZ characteristics from 385?MHz to 488.5?MHz, which facilitates stable resonant frequency and higher antenna efficiency when embedded with nanosatellite structure. The proposed EMNZ inspired antenna has achieved measured impedance bandwidth (S11?<?-10?dB) of 14.92?MHz (391?MHz-405.92?MHz). The perceptible novelty of this paper is the development of EMNZ metamaterial that significantly improves the UHF antenna's operating frequency stability as well as efficiency for low earth orbit nanosatellite communications.

| S-EPMC6401189 | biostudies-literature

A Gravity-Triggered Liquid Metal Patch Antenna with Reconfigurable Frequency.

Project description:In this paper, a gravity-triggered liquid metal microstrip patch antenna with reconfigurable frequency is proposed with experimental verification. In this work, the substrate of the antenna is quickly obtained through three-dimensional (3D) printing technology. Non-toxic EGaIn alloy is filled into the resin substrate as a radiation patch, and the NaOH solution is used to remove the oxide film of EGaIn. In this configuration, the liquid metal inside the antenna can be flexibly flowed and deformed with different rotation angles due to the gravity to realize different working states. To validate the conception, the reflection coefficients and radiation patterns of the prototyped antenna are then measured, from which it can be observed that the measured results closely follow the simulations. The antenna can obtain a wide operating bandwidth of 3.69-4.95 GHz, which coverage over a range of frequencies suitable for various channels of the 5th generation (5G) mobile networks. The principle of gravitational driving can be applied to the design of reconfigurable antennas for other types of liquid metals.

| S-EPMC8234692 | biostudies-literature

Material-assisted metamaterial: a new dimension to create functional metamaterial.

Project description:A high Q-value reflective type metasurface consisting of 1D Au nanorods, a SiO2 spacer and a Au back reflector is demonstrated. It is shown that the sideband of the resonant mode can be suppressed as the resonant wavelength close to the phonon absorption of SiO2. By combining both designed structured resonance and inherent property of the based materials, a low angle-dependent metasurface with a Q-value of 40 has been demonstrated. The proposed structure will be useful for high sensitivity sensing and narrow band thermal emitter.

| S-EPMC5292717 | biostudies-literature

Integrating a Low-Cost Electronic Nose and Machine Learning Modelling to Assess Coffee Aroma Profile and Intensity.

Project description:Aroma is one of the main attributes that consumers consider when appreciating and selecting a coffee; hence it is considered an important quality trait. However, the most common methods to assess aroma are based on expensive equipment or human senses through sensory evaluation, which is time-consuming and requires highly trained assessors to avoid subjectivity. Therefore, this study aimed to estimate the coffee intensity and aromas using a low-cost and portable electronic nose (e-nose) and machine learning modeling. For this purpose, triplicates of nine commercial coffee samples with different intensity levels were used for this study. Two machine learning models were developed based on artificial neural networks using the data from the e-nose as inputs to (i) classify the samples into low, medium, and high-intensity (Model 1) and (ii) to predict the relative abundance of 45 different aromas (Model 2). Results showed that it is possible to estimate the intensity of coffees with high accuracy (98%; Model 1), as well as to predict the specific aromas obtaining a high correlation coefficient (R = 0.99), and no under- or over-fitting of the models were detected. The proposed contactless, nondestructive, rapid, reliable, and low-cost method showed to be effective in evaluating volatile compounds in coffee, which is a potential technique to be applied within all stages of the production process to detect any undesirable characteristics on-time and ensure high-quality products.

| S-EPMC7998415 | biostudies-literature

Toward Joint Acquisition-Annotation of Images with Egocentric Devices for a Lower-Cost Machine Learning Application to Apple Detection.

Project description:Since most computer vision approaches are now driven by machine learning, the current bottleneck is the annotation of images. This time-consuming task is usually performed manually after the acquisition of images. In this article, we assess the value of various egocentric vision approaches in regard to performing joint acquisition and automatic image annotation rather than the conventional two-step process of acquisition followed by manual annotation. This approach is illustrated with apple detection in challenging field conditions. We demonstrate the possibility of high performance in automatic apple segmentation (Dice 0.85), apple counting (88 percent of probability of good detection, and 0.09 true-negative rate), and apple localization (a shift error of fewer than 3 pixels) with eye-tracking systems. This is obtained by simply applying the areas of interest captured by the egocentric devices to standard, non-supervised image segmentation. We especially stress the importance in terms of time of using such eye-tracking devices on head-mounted systems to jointly perform image acquisition and automatic annotation. A gain of time of over 10-fold by comparison with classical image acquisition followed by manual image annotation is demonstrated.

| S-EPMC7435909 | biostudies-literature

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