The consistency of estimator under fixed design regression model with NQD errors

In this article, basing on NQD samples, we investigate the fixed design nonparametric regression model, where the errors are pairwise NQD random errors, with fixed design points, and an unknown function. Nonparametric weighted estimator will be introduced and its consistency is studied. As special case, the consistency result for weighted kernel estimators of the model is obtained. This extends the earlier work on independent random and dependent random errors to NQD case

Electrically Tunable Energy Bandgap in Dual-Gated Ultra-Thin Black Phosphorus Field Effect Transistors

The energy bandgap is an intrinsic character of semiconductors, which largely determines their properties. The ability to continuously and reversibly tune the bandgap of a single device during real time operation is of great importance not only to device physics but also to technological applications. Here we demonstrate a widely tunable bandgap of few-layer black phosphorus (BP) by the application of vertical electric field in dual-gated BP field-effect transistors. A total bandgap reduction of 124 meV is observed when the electrical displacement field is increased from 0.10V/nm to 0.83V/nm. Our results suggest appealing potential for few-layer BP as a tunable bandgap material in infrared optoelectronics, thermoelectric power generation and thermal imaging.Comment: 5 pages, 4 figure

CardioCam: Leveraging Camera on Mobile Devices to Verify Users While Their Heart is Pumping

With the increasing prevalence of mobile and IoT devices (e.g., smartphones, tablets, smart-home appliances), massive private and sensitive information are stored on these devices. To prevent unauthorized access on these devices, existing user verification solutions either rely on the complexity of user-defined secrets (e.g., password) or resort to specialized biometric sensors (e.g., fingerprint reader), but the users may still suffer from various attacks, such as password theft, shoulder surfing, smudge, and forged biometrics attacks. In this paper, we propose, CardioCam, a low-cost, general, hard-to-forge user verification system leveraging the unique cardiac biometrics extracted from the readily available built-in cameras in mobile and IoT devices. We demonstrate that the unique cardiac features can be extracted from the cardiac motion patterns in fingertips, by pressing on the built-in camera. To mitigate the impacts of various ambient lighting conditions and human movements under practical scenarios, CardioCam develops a gradient-based technique to optimize the camera configuration, and dynamically selects the most sensitive pixels in a camera frame to extract reliable cardiac motion patterns. Furthermore, the morphological characteristic analysis is deployed to derive user-specific cardiac features, and a feature transformation scheme grounded on Principle Component Analysis (PCA) is developed to enhance the robustness of cardiac biometrics for effective user verification. With the prototyped system, extensive experiments involving 25 subjects are conducted to demonstrate that CardioCam can achieve effective and reliable user verification with over 99% average true positive rate (TPR) while maintaining the false positive rate (FPR) as low as 4%