We report the design and characterization of a disposable light shielded paper–plastic microfluidic device that can detect nanomolar levels of H₂O₂ using a smartphone camera and a light sealed accessory.

As an integral part of the electromagnetic system, antennas are becoming more advanced and versatile than ever before, thus making it necessary to adopt new techniques to enhance their performance. Machine Learning (ML), a branch of artificial intelligence, is a method of data analysis that automates analytical model building with minimal human intervention. The potential for ML to solve unpredictable and non-linear complex challenges is attracting researchers in the field of electromagnetics (EM), especially in antenna and antenna-based systems. Numerous antenna simulations, synthesis, and pattern recognition of radiations as well as non-linear inverse scattering-based object identifications are now leveraging ML techniques. Although the accuracy of ML algorithms depends on the availability of sufficient data and expert handling of the model and hyperparameters, it is gradually becoming the desired solution when researchers are aiming for a cost-effective solution without excessive time consumption. In this context, this paper aims to present an overview of machine learning, and its applications in Electromagnetics, including communication, radar, and sensing. It extensively discusses recent research progress in the development and use of intelligent algorithms for antenna design, synthesis and analysis, electromagnetic inverse scattering, synthetic aperture radar target recognition, and fault detection systems. It also provides limitations of this emerging field of study. The unique aspect of this work is that it surveys the state-of the art and recent advances in ML techniques as applied to EM.

We report a machine learning approach to accurately correlate the impedance variations in zinc oxide/multi walled carbon nanotube nanocomposite (F-MWCNT/ZnO-NFs) to NH₄⁺ ions concentrations. Impedance response of F-MWCNT/ZnO-NFs nanocomposites with varying ZnO:MWCNT compositions were evaluated for its sensitivity and selectivity to NH₄⁺ ions in the presence of structurally similar analytes. A decision-making model was built, trained and tested using important features of the impedance response of F-MWCNT/ZnO-NF to varying NH₄⁺ concentrations. Different algorithms such as kNN, random forest, neural network, Naïve Bayes and logistic regression are compared and discussed. ML analysis have led to identify the most prominent features of an impedance spectrum that can be used as the ML predictors to estimate the real concentration of NH₄⁺ ion levels. The proposed NH₄⁺ sensor along with the decision-making model can identify and operate at specific operating frequencies to continuously collect the most relevant information from a system.

We report a smart irrigation system that allows selective irrigation of localized dry spots in an agricultural field. The proposed irrigation system uses a quadcopter drone equipped with a Thermal Infrared (TIR) camera and a GPS module to generate georeferenced thermal images that indicate the area and location of the dry spots in a survey area. Drones navigate and acquire aerial thermal images, which are then processed by an onboard edge intelligence module along with the flight data (GPS coordinates, altitude, and drone direction). Smart sprinklers deployed on the field are able to wirelessly receive the coordinates of dry spots so they can be irrigated selectively. A terrestrial edge unit generates an irrigation pattern for the smart sprinklers using a pre-trained machine learning (ML) model to generate an irrigation pattern by varying head rotation angle (θ) and the water flow control valve rotation angle(∅) of the smart sprinkler.

We report dielectrophoretic (DEP) assembly of biological cells and microparticles using platinum-black electrodeposited conductive textile fiber. The three-dimensional conductive structures with high aspect ratios were found to facilitate high electric field regions, as revealed by scanning electron microscope characterization. The effective conducting area (Aeff) and its stability of thread electrodes were estimated using electrochemical methods. Potential of platinum black electrodeposited thread as 3-D electrodes for creating high gradient electrical field for dielectrophoretic assembly of microspheres and Saccharomyces cerevisiae (yeast cells) into 1D and two-dimensional structures over long ranges under the application of low voltages (4–10 Vpp) has been demonstrated. The formation of highly ordered pearl chains of microparticles using thread electrodes when subjected to dielectrophoresis (DEP) has been discussed in detail.

An intelligent sensing framework using Machine Learning (ML) and Deep Learning (DL) architectures to precisely quantify dielectrophoretic force invoked on microparticles in a textile electrode-based DEP sensing device is reported. The prediction accuracy and generalization ability of the framework was validated using experimental results. Images of pearl chain alignment at varying input voltages were used to build deep regression models using modified ML and CNN architectures that can correlate pearl chain alignment patterns of Saccharomyces cerevisiae(yeast) cells and polystyrene microbeads to DEP force. Various ML models such as K-Nearest Neighbor, Support Vector Machine, Random Forest, Neural Networks, and Linear Regression along with DL models such as Convolutional Neural Network (CNN) architectures of AlexNet, ResNet-50, MobileNetV2, and GoogLeNet have been analyzed in order to build an effective regression framework to estimate the force induced on yeast cells and microbeads. The efficiencies of the models were evaluated using Mean Absolute Error, Mean Absolute Relative, Mean Squared Error, R-squared, and Root Mean Square Error (RMSE) as evaluation metrics. ResNet-50 with RMSPROP gave the best performance, with a validation RMSE of 0.0918 on yeast cells while AlexNet with ADAM optimizer gave the best performance, with a validation RMSE of 0.1745 on microbeads. This provides a baseline for further studies in the application of deep learning in DEP aided Lab-on-Chip devices.