Hematite (Fe2O3) is one of the best candidates for photoelectrochemical water splitting due to its abundance and suitable bandgap. However, its efficiency is mostly impeded due to the intrinsically low conductivity and poor light absorption. In this study, we targeted this intrinsic behavior to investigate the thermodynamic stability, photoconductivity and optical properties of rhodium doped hematite using density functional theory. The calculated formation energy of pristine and rhodium doped hematite was − 4.47 eV and − 5.34 eV respectively, suggesting that the doped material is thermodynamically more stable. The DFT results established that the bandgap of doped hematite narrowed down to the lower edge (1.61 eV) in the visible region which enhanced the optical absorption and photoconductivity of the material. Moreover, doped hematite has the ability to absorb a broad spectrum (250–800) nm. The enhanced optical absorption boosted the photocurrent and incident photon to current efficiency. The calculated results also showed that the incorporation of rhodium in hematite induced a redshift in optical properties.

Abstract:The aim of this study was implementation of a wireless patch system that can predict a patient's body temperature. The proposed patch can predict the body temperature from the skin temperature and sweat rate by using a modified Pennes bio-heat transfer equation. The proposed patch was small and lights enough to be attached to the patient's skin, and a small skin temperature transducer was built-in the patch. Further, the sweat rate was measured by using humidity sensors while the sweat was evaporating. The proposed patch was compared with commercial body temperature measuring device, and the results were found to be correlated.

With widely deployed smart meters, non-intrusive energy measurements have become feasible, which may benefit people by furnishing a better understanding of appliance-level energy consumption. This work is a step forward in using graph signal processing for non-intrusive load monitoring (NILM) by proposing two novel techniques: the spectral cluster mean (SC-M) and spectral cluster eigenvector (SC-EV) methods. These methods use spectral clustering for extracting individual appliance energy usage from the aggregate energy profile of the building. After clustering the data, different strategies are employed to identify each cluster and thus the state of each device. The SC-M method identifies the cluster by comparing its mean with the devices’ pre-defined profiles. The SC-EV method employs an eigenvector resultant to locate the event and then recognize the device using its profile. An ideal dataset and a real-world REFIT dataset are used to test the performance of these two techniques. The f-measure score and disaggregation accuracy of the proposed techniques demonstrate that these two techniques are competitive and viable, with advantages of low complexity, high accuracy, no training data requirement, and fast processing time. Therefore, the proposed techniques are suitable candidates for NILM.

Rehabilitation in the form of locomotion assistance and gait training through robotic exoskeletons requires both precision and accuracy to achieve effective results. The essential challenge is to ensure robust tracking of the reference signal, i.e., of the gait or locomotion. This paper presents the design of model-based (MB) and model-free (MF) robust control strategies to achieve desired performance and robustness in terms of transient behavior and steady-state/tracking error, implementable to the locomotion assistance and gait training by exoskeletons. The dynamic responses of the exoskeleton system were investigated with both the control strategies. The study was carried out with a variety of reference signals and performance was evaluated to identify the best suited approach for rehabilitation exoskeletons. In case of the model-based control, a mathematical model of the system was developed using a bond graph modeling technique and a lead compensated H-infinity reference gain controller was designed to ensure robust tracking performance. In the model-free control strategy, however, the system function is approximated using radial basis function neural networks (RBFNNs) and an adaptive proportional-derivative RBFNN controller was designed to achieve the desired results with minimum tracking error. Both strategies make the system robust and stable. However, the MF control strategy is faster for all reference inputs as compared to the MB control strategy i.e., faster to approach the peak value and settle, and rapidly approaches the zero steady-state/tracking error. The rise time in the case of a sinusoidal input for model-free control is 0.4 s faster than the rise time in model-based control. Similarly, the settling time is 3.9 s faster in the case of model-free control, which is a prominent difference and can provide better rehabilitation results.

Cardiac rate analysis is constituted as one of the most essential parts of physiological patient monitoring. In this research paper, it has been investigated that electrical impedance plethysmography is the most conducive technique for this purpose. It is perceptible that the artery closest to the epidermis will generate a precise reading and for that purpose, we have designed an efficient non-invasive model based on a tetraelectrode setup placed in contact with the stratum corneum of the skin. By varying electrode size and the distance between the electrodes we can efficiently judge the point where the electric field reaches the artery. The simulation of this model is carried out by Comsol Multiphysics.