Matthews Cotton (nephewaunt59)

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that can deliver a high information transfer rate (ITR) usually require subject's calibration data to learn the class- and subject-specific model parameters (e.g. the spatial filters and SSVEP templates). Normally, the amount of the calibration data for learning is proportional to the number of classes (or visual stimuli), which could be huge and consequently lead to a time-consuming calibration. This study presents a transfer learning scheme to substantially reduce the calibration effort. Inspired by the parameter-based and instance-based transfer learning techniques, we propose a subject transfer based canonical correlation analysis (stCCA) method which utilizes the knowledge within subject and between subjects, thus requiring few calibration data from a new subject. The evaluation study on two SSVEP datasets (from Tsinghua and UCSD) shows that the stCCA method performs well with only a small amount of calibration data, providing an ITR at 198.18±59.12 (bits/min) with 9 calibration trials in the Tsinghua dataset and 111.04±57.24 (bits/min) with 3 trials in the UCSD dataset. Such performances are comparable to those from using the multi-stimulus CCA (msCCA) and the ensemble task-related component analysis (eTRCA) methods with the minimally required calibration data (i.e., at least 40 trials in the Tsinghua dataset and at least 12 trials in the UCSD dataset), respectively. Inter- and intra-subject transfer helps the recognition method achieve high ITR with extremely little calibration effort. The proposed approach saves much calibration effort without sacrificing the ITR, which would be significant for practical SSVEP-based BCIs. The proposed approach saves much calibration effort without sacrificing the ITR, which would be significant for practical SSVEP-based BCIs.We report on the solution of the heat equation, which describes the electrocaloric effect in a ferroelectric layer. The dependence of the heat flux at the layer interface and the thermodynamic efficiency on the shape and frequency of the applied pulse is investigated. According to the calculations, the dependence of the efficiency on the frequency for a rectangular and sinusoidal pulse has a markedly different character. The maximum efficiency at 0.74 is achieved by a sinusoidal impulse at a frequency of 77.6 mHz. The new approach allows for better optimization of the cooling system. To model a heat switch, it is proposed to use a change in the Biot number over time. Theoretical calculations are compared with experimental results for a 0.5-mm-thick barium titanate plate. The maximum heat flux that can be created for a given plate under an electric field of 2 MV/m with an ideal heat switch turned out to be equal 120.72 W/m2.Unlike many other states across America that struggled to get enough diagnostic tests for coronavirus 2019 disease (COVID-19) this past spring, New Mexico was able to not only meet the demand for testing symptomatic patients, but was able to begin expanding its screening to asymptomatic individuals. How did this largely rural and relatively low-income state-among the bottom five states in population density [1] and median income per capita [2] -stay on top of testing when larger and wealthier states fell behind? The answer lies in both centralization and diversification.More than 30% of the world's population is overweight or obese. That is double the percentage in 1980, and it is getting worse [1].That excess weight has been linked to numerous health conditions, notably type 2 (adult-onset) diabetes, the prevalence of which has also nearly doubled since 1980 [2]. Eating less and exercising more is good advice, but it doesn't work for everyone. Other options such as gastric bypass surgeries and systemic weight-loss drugs are also not suitable for everyone, an