mmWave Zero Order Resonant Antenna with Patch-Like Radiation Fed by a Butler Matrix for Passive Beamforming.

A small zero-order resonant antenna based on the composite right-left-handed (CRLH) principle is designed and fabricated without metallic vias at 30 GHz to have patch-like radiation. The mirror images of two CRLH structures are connected to design the antenna without via holes. The equivalent circuit, parameter extraction, and dispersion diagram are studied to analyze the characteristics of the CRLH antenna. The antenna was fabricated and experimentally verified. The measured realized gain of the antenna is 5.35 dBi at 30 GHz. The designed antenna is free of spurious resonance over a band width of 10 GHz. A passive beamforming array is designed using the proposed CRLH antenna and the Butler matrix. A substrate integrated waveguide is used to implement the Butler matrix. The CRLH antennas are connected to four outputs of a 4×4 Butler matrix. The scanning angles are 12∘, -68∘, 64∘, and -11∘ for excitations from port 1 to port 4 of the 4×4 Butler matrix feeding the CRLH antenna.

mm-Wave Radar-Based Vital Signs Monitoring and Arrhythmia Detection Using Machine Learning.

A non-contact, non-invasive monitoring system to measure and estimate the heart and breathing rate of humans using a frequency-modulated continuous wave (FMCW) mm-wave radar at 77 GHz is presented. A novel diagnostic system is proposed which extracts heartbeat phase signals from the FMCW radar (reconstructed using Fourier series analysis) to test a three-layer artificial neural network model to predict the presence of arrhythmia in individuals. The effect of person orientation, distance of measurement and movement was analyzed with respect to a reference device based on statistical measures that include number of outliers, mean, mean squared error (MSE), mean absolute error (MAE), median absolute error (medAE), skewness, standard deviation (SD) and R-squared values. The individual oriented in front of the radar outperformed almost all other orientations for most distances with an expected d = 90 cm and d = 120 cm. Furthermore, it was found that the heart rate that was measured while walking and the breathing rate which was measured for a motionless individual generated results with the lowest SD and MSE. An artificial neural network (ANN) was trained using the MIT-BIH database with a training accuracy of 93.9 % and an R2 value = 0.876. The diagnostic tool was tested on 15 subjects and achieved a mean test accuracy of 75%.

Machine Learning Models for Enhanced Estimation of Soil Moisture Using Wideband Radar Sensor.

In this paper, machine learning models for an effective estimation of soil moisture content using a microwave short-range and wideband radar sensor are proposed. The soil moisture is measured as the volumetric water content using a short-range off-the-shelf radar sensor operating at 3-10 GHz. The radar captures the reflected signals that are post processed to determine the soil moisture which is mapped to the input features extracted from the reflected signals for the training of the machine learning models. In addition, the results are compared and analyzed with a contact-based Vernier soil sensor. Different machine learning models trained using neural network, support vector machine, linear regression and k-nearest neighbor are evaluated and presented in this work. The efficiency of the model is computed using root mean square error, co-efficient of determination and mean absolute error. The RMSE and MAE values of KNN, SVM and Linear Regression are 11.51 and 9.27, 15.20 and 12.74, 3.94 and 3.54, respectively. It is observed that the neural network gives the best results with an R2 value of 0.9894. This research work has been carried out with an intention to develop cost-effective solutions for common users such as agriculturists to monitor the soil moisture conditions with improved accuracy.

Pangolin-Inspired Stretchable, Microwave-Invisible Metascale.

Microwave-invisible devices are emerging as a valuable technology in various applications, including soft robotics, shape-morphing structures, and textural camouflages, especially in electronic countermeasures. Unfortunately, conventional microwave-absorbing metastructures and bulk absorbers are stretching confined, limiting their application in deformable or special-shaped targets. To overcome such limitations, a conceptually novel soft-rigid-connection strategy, inspired by the pangolin, is proposed. Pangolin-inspired metascale (PIMS), which is a kind of stretchable metamaterial consisting of an electromagnetic dissipative scale (EMD-scale) and elastomer, is rationally designed. Such a device exhibits robust microwave-absorbing capacity under the interference of 50% stretching. Besides, profiting from the covering effect and size-confined effect of EMD-scale, the out-of-plane indentation failure force of PIMS is at least 5 times larger than conventional device. As a proof of concept, the proposed device is conformally pasted on nondevelopable surfaces. For a spherical dome surface, the maximum radar cross-section (RCS) reduction of PIMS is 6.3 dB larger than that of a conventional device, while for a saddle surface, the bandwidth of 10 dB RCS reduction exhibits an increase of 83%. In short, this work provides a conceptually novel platform to develop stretchable, nondevelopable surface conformable functional devices.