Controlled Growth of Semiconducting ZnO Nanorods for Piezoelectric Energy Harvesting-Based Nanogenerators.

Zinc oxide (ZnO) nanorods have attracted considerable attention in recent years owing to their piezoelectric properties and potential applications in energy harvesting, sensing, and nanogenerators. Piezoelectric energy harvesting-based nanogenerators have emerged as promising new devices capable of converting mechanical energy into electric energy via nanoscale characterizations such as piezoresponse force microscopy (PFM). This technique was used to study the piezoresponse generated when an electric field was applied to the nanorods using a PFM probe. However, this work focuses on intensive studies that have been reported on the synthesis of ZnO nanostructures with controlled morphologies and their subsequent influence on piezoelectric nanogenerators. It is important to note that the diatomic nature of zinc oxide as a potential solid semiconductor and its electromechanical influence are the two main phenomena that drive the mechanism of any piezoelectric device. The results of our findings confirm that the performance of piezoelectric devices can be significantly improved by controlling the morphology and initial growth conditions of ZnO nanorods, particularly in terms of the magnitude of the piezoelectric coefficient factor (d33). Moreover, from this review, a proposed facile synthesis of ZnO nanorods, suitably produced to improve coupling and switchable polarization in piezoelectric devices, has been reported.

Higher Order Feature Extraction and Selection for Robust Human Gesture Recognition using CSI of COTS Wi-Fi Devices.

Device-free human gesture recognition (HGR) using commercial off the shelf (COTS) Wi-Fi devices has gained attention with recent advances in wireless technology. HGR recognizes the human activity performed, by capturing the reflections of Wi-Fi signals from moving humans and storing them as raw channel state information (CSI) traces. Existing work on HGR applies noise reduction and transformation to pre-process the raw CSI traces. However, these methods fail to capture the non-Gaussian information in the raw CSI data due to its limitation to deal with linear signal representation alone. The proposed higher order statistics-based recognition (HOS-Re) model extracts higher order statistical (HOS) features from raw CSI traces and selects a robust feature subset for the recognition task. HOS-Re addresses the limitations in the existing methods, by extracting third order cumulant features that maximizes the recognition accuracy. Subsequently, feature selection methods derived from information theory construct a robust and highly informative feature subset, fed as input to the multilevel support vector machine (SVM) classifier in order to measure the performance. The proposed methodology is validated using a public database SignFi, consisting of 276 gestures with 8280 gesture instances, out of which 5520 are from the laboratory and 2760 from the home environment using a 10 × 5 cross-validation. HOS-Re achieved an average recognition accuracy of 97.84%, 98.26% and 96.34% for the lab, home and lab + home environment respectively. The average recognition accuracy for 150 sign gestures with 7500 instances, collected from five different users was 96.23% in the laboratory environment.