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 - 1.5 THz) device characterization. Using this methodology, we measure the signal transmission through the TABG and find the experimental results align with simulation and theory provides a rejection of approximately 20 dB across the stop-band.

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. We observe three dominate modes within the measured frequency range; the predicted LC resonator mode at 0.510 THz, a higher-order LC resonator mode at 1.03 THz, and a higher-order dipole mode at 1.85 THz. The LC resonator mode is investigated using a modified version of the standard lumped element model which incorporates the transmission line between adjacent meta-atoms using ABCD matrices.

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. ANSYS HFSS was used to provide a full-wave analysis and characterize the effective parameters of the TLPF with a designed cutoff-frequency around 0.6 THz. Using 7 sections of right-angle CPS bending with total length 1.4 mm, we demonstrate experimentally an elliptic-function TLPF characterized by a low-ripple at passband, a roll-off transition with zero transmission near the cutoff frequency and a wide stopband with -60 dB rejection.

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.g., MS Kinect and Asus Xtion PRO). The proposed methods are based on extracting medically -justified features to compose a simple descriptor. Thereafter, a probabilistic normalcy model is trained on normal motion patterns. For abnormality detection, a test sequence is classified as either normal or abnormal based on its likelihood, which is calculated from the trained normalcy model. For motion quality assessment, a linear regression model is built using the proposed descriptor in order to quantitatively assess the motion quality. The proposed methods were evaluated on four common daily actions-sit to stand, stand to sit, flat walk, and gait on stairs-from two datasets, a publicly released dataset and our dataset that was collected in a clinic from 32 patients suffering from different neuromusculoskeletal disorders and 11 healthy individuals. Experimental results demonstrate promising results, which is a step toward having convenient in-home automatic health care services.

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., MS Kinect, Asus Xtion PRO). We use Gait Energy Image (GEI) as our main feature that can be computed from different views. Our method depends on computing GEI, learning the representative features from the GEI using convolutional autoencoder, and using anomaly detection method for detecting abnormal gait. We applied the proposed method on two different public datasets that include normal and abnormal gait from different views. Experimental results show that our method achieves high accuracy in detecting different gait disorders from different views, which makes it general to be applied to home environment and adds a step towards convenient in-home automatic health care services.

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. Test sequences are evaluated based on their fitness to the normal motion models, with a threshold over the likelihood, to assess abnormality. Results show that the descriptor is able to detect abnormality with accuracy ranging from 0.97 to 1 for the various motions.

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. In computational docking, a large number of binding poses are evaluated and ranked using a scoring function. The scoring function is a mathematical predictive model that produces a score that represents the binding free energy, and hence the stability, of the resulting complex molecule. Generally, such a function should produce a set of plausible ligands ranked according to their binding stability along with their binding poses. In more practical terms, an effective scoring function should produce promising drug candidates which can then be synthesized and physically screened using high throughput screening process. Therefore, the key to computer-aided drug design is the design of an efficient highly accurate scoring function (using ML techniques). METHODS: The methods presented in this paper are specifically based on ML techniques. Despite many traditional techniques have been proposed, the performance was generally poor. Only in the last few years started the application of the ML technology in the design of scoring functions; and the results have been very promising. MATERIAL: The ML-based techniques are based on various molecular features extracted from the abundance of protein-ligand information in the public molecular databases, e.g., protein data bank bind (PDBbind). RESULTS: In this paper, we present this paradigm shift elaborating on the main constituent elements of the ML approach to molecular docking along with the state-of-the-art research in this area. For instance, the best random forest (RF)-based scoring function on PDBbind v2007 achieves a Pearson correlation coefficient between the predicted and experimentally determined binding affinities of 0.803 while the best conventional scoring function achieves 0.644. The best RF-based ranking power ranks the ligands correctly based on their experimentally determined binding affinities with accuracy 62.5% and identifies the top binding ligand with accuracy 78.1%. CONCLUSIONS: We conclude with open questions and potential future research directions that can be pursued in smart computational docking; using molecular features of different nature (geometrical, energy terms, pharmacophore), advanced ML techniques (e.g., deep learning), combining more than one ML models.