Demonstration of an Integrated terahertz band-stop filter using an apodized Bragg grating.

This paper presents the demonstration of an on-chip integrated terahertz (THz) apodized Bragg grating (TABG) which functions as band-stop filter with a center frequency of 0.8 THz and a bandwidth of 200 GHz. For experimentation, we integrate the TABG into our THz system-on-chip to enable wideband (DC - 1.

Characterization of a split-ring-resonator-loaded transmission line at terahertz frequencies.

Recently we demonstrated the fabrication and testing of a variety of RF-engineered passive transmission-line-based components designed for operation at terahertz frequencies and fabricated on thin (1 μm) silicon-nitride membranes. In this work we measure the transmission response of a coplanar-strip transmission line loaded with split-ring resonators up to 2.5 THz.

Terahertz low-pass filter based on cascaded resonators formed by CPS bending on a thin membrane.

A membrane-based coplanar-stripline (CPS) transmission-line platform has recently enabled implementation of diverse THz system-on-chip (TSoC) components. In this paper, we demonstrate an elliptic-function THz low-pass filter (TLPF) using cascaded λ/4 resonators between the right-angle bending of a CPS transmission line defined on a 1 μm-thin membrane. We investigated the effect of bending the CPS transmission line with different angles that introduces a frequency response similar to a simple LC low-pass filter (LPF) and facilitates the design of a desired roll-off performance using traditional methods.

Unsupervised GEI-Based Gait Disorders Detection From Different Views.

Early detection of gait disorders may provide a safer living for elderly people. In this paper, we propose an automatic method for detecting gait disorders using RGB or RGBD camera (e.g.

Efficient and Robust Skeleton-Based Quality Assessment and Abnormality Detection in Human Action Performance.

Elderly people can be provided with safer and more independent living by the early detection of abnormalities in their performing actions and the frequent assessment of the quality of their motion. Low-cost depth sensing is one of the emerging technologies that can be used for unobtrusive and inexpensive motion abnormality detection and quality assessment. In this study, we develop and evaluate vision-based methods to detect and assess neuromusculoskeletal disorders manifested in common daily activities using three-dimensional skeletal data provided by the SDK of a depth camera (e.

A general descriptor for detecting abnormal action performance from skeletal data.

We propose an action-independent descriptor for detecting abnormality in motion, based on medically-inspired skeletal features. The descriptor is tested on four actions/motions captured using a single depth camera: sit-to-stand, stand-to-sit, flat-walk, and climbing-stairs. For each action, a Gaussian Mixture Model (GMM) trained on normal motions is built using the action-independent feature descriptor.

Machine learning in computational docking.

Objective: The objective of this paper is to highlight the state-of-the-art machine learning (ML) techniques in computational docking. The use of smart computational methods in the life cycle of drug design is relatively a recent development that has gained much popularity and interest over the last few years. Central to this methodology is the notion of computational docking which is the process of predicting the best pose (orientation + conformation) of a small molecule (drug candidate) when bound to a target larger receptor molecule (protein) in order to form a stable complex molecule.