Empire Technology Development LLC

Other

Hifi Engineering Inc.

Innovative Alert Systems Inc.

Resource Energy Solutions Inc.

Henry Leung

Xiaoxiang Liu

Siyue Chen

Trent Marx

Hengky Chang

Gary William Reid

Seyed Ehsan Jalilian

Dongliang Huang

King Fai Ma

Ha Nguyen

Gary W Reid

There is provided a method of estimating flowrate in a pipeline based on acoustic behaviour of the pipe. First acoustic data is measured from the pipeline. A flowrate of the fluid in the pipeline is then estimated. The estimation is based on the first acoustic data and based on a correlation established between second acoustic data and corresponding flowrate data from an experimental pipeline. The correlation is established by a machine learning process (which may include the use of an artificial neural network, such as an autoencoder). The second acoustic data and corresponding flowrate data are used as inputs to the machine learning process.

Method of estimating flowrate in a pipeline

Nano scale collagen particles can be obtained from an embrittling and attrition process that reduces the size of collagen particles to the nano scale. These nano scale collagen particles have many favorable properties such as providing beneficial and enhanced properties for cell seeding and wound healing. The nano scale collagen particles can be included in biocompatible (e.g., biostable or biodegradable) compositions and are useful for wound treatment and management, as well as in cell cultures and tissue engineering implants.

Nanoscale collagen particles and membranes

A system for event detection and reporting has primary sensors for producing raw data from observing the proximate area, one or more processors for the primary sensors, for processing the raw data to produce output, a centralized controller to which each of the processors is connected, for receiving the output, a security network for communicating between the sensors, the controller, and one or more remote terminals, the security network having an alarm, and a plurality of auxiliary sensors for providing secondary sensor information to the one or more processors wherein the controller provides an alarm to the remote terminals through the security network if an event has occurred. A method of event detection has the steps of receiving a control command, alarm message and sensor information, determining if an event has occurred, and sending alarm messages and filtered sensor information to remote terminals if an event has occurred.

System and method for event monitoring and detection

Technologies are generally described to provide multi-objective dynamic resource optimization for self-organizing networks. In some examples, a system employing dynamic resource optimization may allocate network resources to a group of subscribers to enable granular policy management per cell, per subscriber type, per service, and/or per user resource. The dynamic resource optimization may be based on optimization/improvement of a throughput objective and a quality of experience (QoE) objective. Users maybe segmented into different groups according to subscriber types with each group having a priority level assigned. Resources may be allocated first to a highest or relatively higher priority group of users, and remaining resources may be consecutively allocated to lower priority groups. The QoE objective may be quantitatively determined by mapping real time quality of service (QoS) parameters associated with the subscriber group. A set of group policy parameters may also be applied to weight the throughput and QoE objectives.

Dynamic frequency and power resource allocation with granular policy management

Methods and systems are described for improved drilling operations through the use of real-time drilling data to predict bit wear, lithology, pore pressure, a rotating friction coefficient, permeability, and cost in real-time and to adjust drilling parameters in real-time based on the predictions. The real-time lithology prediction is made by processing the real-time drilling data through a multilayer neural network. The real-time bit wear prediction is made by using the real-time drilling data to predict a bit efficiency factor and to detect changes in the bit efficiency factor over time. These predictions may be used to adjust drilling parameters in the drilling operation in real-time, subject to override by the operator. The methods and systems may also include determining various downhole hydraulics parameters and a rotary friction factor. Historical data may be used in combination with real-time data to provide expert system assistance and to identify safety concerns.

Methods and systems for improved drilling operations using real-time and historical drilling data

Technologies are presented for implementing a compressive-sensing-based data collection system in a cloud environment. In some examples, high-dimensional sensor data may be compressed using sparsity transforms and compressive sampling. The resulting low-dimensional data messages may be steered through a switch network to a cloud service manager, which then reconstructs the compressed messages for subsequent analysis, reporting, and/or comparable actions.

Real-time compressive data collection for cloud monitoring

Technologies are generally described for a broadband passive sensing and tracking system that may employ a number of passive receivers that each have the capability of sensing electromagnetic waves (e.g., Radio Frequency 'RF' signals) from surrounding broadcast sources. Each passive receiver may be adapted to sense through one or more antennas. Multiple receivers at different positions may be utilized to form a broadband sensing network adapted to perform collaborative tracking of a scene of interest. According to some examples, a beam-forming algorithm may be applied over the broadband sensing network utilizing an antenna array formed by the passive receivers to localize and track objects.

Broadband passive tracking for augmented reality

Examples of encoding and decoding a watermark for a digital image and to using the watermark for authenticating the digital image are disclosed. The examples may include embedding a chaotic watermark in a digital image and using parameters associated with source and capture information for the digital image to both generate the chaotic watermark and to authenticate the digital image.

Chaotic watermarking for a digital image

Technologies described herein generally provide for an improved augmented reality system for providing augmented reality images comprising a pre-operative image superimposed on a patient image. The accuracy of registering the pre-operative image on the patient image, and hence the quality of the augmented reality image, may be impacted by the periodic movement of an organ. Registration of the pre-operative image on the patient image can be improved by accounting for motion of the organ. That is, the organ motion, which can be described by a dynamical model, can be used to correct registration errors that do not match the dynamical model. The technologies may generate a sequence of 3-D patient images in real-time for guided surgery.

Four dimensional image registration using dynamical model for augmented reality in medical applications

Technologies are generally described herein for generating a higher resolution still frame. Some example technologies may configure a video camera at a first configuration, which the video camera to capture video at a first pixel offset. The technologies may capture a first frame of al field-of-view through the video camera configured at the first configuration. The first flame may contain the field-of-view captured at the first pixel offset. The technologies may adjust the video camera from the first configuration to a second configuration, which adapts the video camera to capture the video at a second pixel offset, the adjustment using a hardware mechanism. The technologies may capture a second frame of the field-of-view through the video camera configured at the second configuration. The second frame may contain the field-of-view captured at the second pixel offset.

Higher resolution still image generation from lower resolution video camera frames

Technologies are presented for anomaly detection in a cloud environment using a sparsity measure. In some examples, cloud metric data may be gathered and processed into a dictionary base. Linear transform coefficients for a test sample may then be calculated from the dictionary base using l-norm minimization. A sparsity measure may then be computed from the linear transform coefficients. If the sparsity measure does not exceed a predefined threshold, the test sample may be determined as anomalous.

Anomaly detection for cloud monitoring

Technologies are generally described for providing a robust object recognition scheme based on dynamic modeling. Correlations in fine scale temporal structure of cellular regions may be employed to group the regions together into higher-order entities. The entities represent a rich structure and may be used to code high level objects. Object recognition may be formatted as elastic graph matching.

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Calgary, Canada

Henry Leung