Integrated silicon nitride time-bin entanglement circuits.

Time-bin entangled photons allow robust entanglement distribution over quantum networks. Integrated photonic circuits positioned at the nodes of a quantum network can perform the important functions of generating highly entangled photons and precisely manipulating their quantum state. In this Letter, we demonstrate time-bin entangled photon generation, noise suppression, wavelength division, and entanglement analysis on a single photonic chip utilizing low-loss double-stripe silicon nitride waveguide structures.

An Unsupervised Deep Hyperspectral Anomaly Detector.

Hyperspectral image (HSI) based detection has attracted considerable attention recently in agriculture, environmental protection and military applications as different wavelengths of light can be advantageously used to discriminate different types of objects. Unfortunately, estimating the background distribution and the detection of interesting local objects is not straightforward, and anomaly detectors may give false alarms. In this paper, a Deep Belief Network (DBN) based anomaly detector is proposed.

Automated quality control of forced oscillation measurements: respiratory artifact detection with advanced feature extraction.

The forced oscillation technique (FOT) can provide unique and clinically relevant lung function information with little cooperation with subjects. However, FOT has higher variability than spirometry, possibly because strategies for quality control and reducing artifacts in FOT measurements have yet to be standardized or validated. Many quality control procedures rely on either simple statistical filters or subjective evaluation by a human operator.

Freezing of Gait Detection in Parkinson's Disease: A Subject-Independent Detector Using Anomaly Scores.

Freezing of gait (FoG) is common in Parkinsonian gait and strongly relates to falls. Current clinical FoG assessments are patients' self-report diaries and experts' manual video analysis. Both are subjective and yield moderate reliability.

Multiwavelength stabilization control of a thermo-optic system with adaptive reconfiguration.

Accurate temperature control is crucial for the reliable operation of photonic integrated circuits in the presence of internal thermal crosstalk or external thermal disturbance. We propose an adaptive multiple-input and multiple-output (MIMO) control scheme to stabilize the operation wavelength of on-chip wavelength demultiplexers that have many applications in photonic-chip-based optical signal processing. Using the MIMO control scheme, the wavelength drift is reduced from 0.

Respiratory Artefact Removal in Forced Oscillation Measurements: A Machine Learning Approach.

Goal: Respiratory artefact removal for the forced oscillation technique can be treated as an anomaly detection problem. Manual removal is currently considered the gold standard, but this approach is laborious and subjective. Most existing automated techniques used simple statistics and/or rejected anomalous data points.

A Hybrid CMOS-Memristor Neuromorphic Synapse.

Although data processing technology continues to advance at an astonishing rate, computers with brain-like processing capabilities still elude us. It is envisioned that such computers may be achieved by the fusion of neuroscience and nano-electronics to realize a brain-inspired platform. This paper proposes a high-performance nano-scale Complementary Metal Oxide Semiconductor (CMOS)-memristive circuit, which mimics a number of essential learning properties of biological synapses.

Adapting content-based image retrieval techniques for the semantic annotation of medical images.

The automatic annotation of medical images is a prerequisite for building comprehensive semantic archives that can be used to enhance evidence-based diagnosis, physician education, and biomedical research. Annotation also has important applications in the automatic generation of structured radiology reports. Much of the prior research work has focused on annotating images with properties such as the modality of the image, or the biological system or body region being imaged.

An online algorithm for least-square spectral analysis: Applied to time-frequency analysis of heart rate.

We propose a novel online algorithm for computing least-square based periodograms, otherwise known as the Lomb-Scargle Periodogram. Our spectral analysis technique has been shown to be superior to traditional discrete Fourier transform (DFT) based methods, and we introduce an algorithm which has O(N) time complexity per input sample. The technique is suitable for real-time embedded implementations and its utility is demonstrated through an application to the high resolution time-frequency domain analysis of heart rate variability (HRV).

Unsupervised discrimination of motor unit action potentials using spectrograms.

Single motor unit activity study is a major research interest because changes of MUAP morphology, MU activation, and MU recruitment provide the most informative part in diagnosis and treatment of neuromuscular disorders. Intramuscular recordings often provide a more than one motor unit activities, thus MUAP discrimination is a crucial task to study single unit activities. Most neurology laboratories worldwide still need specialists who spend hours to classify MUAPs.

Electrode contact impedance sensitivity to variations in geometry.

Electrode contact impedance is a crucial factor in physiological measurements and can be an accuracy-limiting factor when performing electroencephalography and electrical impedance tomography. In this work, standard flat electrodes and micromachined multipoint spiked electrodes are characterized with a finite-element method electromagnetic solver and the dependence of the contact impedance on geometrical factors is explored. It is found that flat electrodes are sensitive to changes in the outer skin layer properties related to hydration and thickness, while spike electrodes are not.