Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 l1-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.

Abstract: Technologies are generally described for aligning objects in augmented reality. In some examples, a processor may be adapted to receive detected image data and virtual object data. In some examples, the processor may further be adapted to generate and apply weights to log-likelihood functions at intensity and feature levels based on the virtual object data and detected image data. In some examples, the processor may further be adapted to add the weighted log-likelihood function at intensity level to the weighted log-likelihood function at feature level to produce a cost function. In some examples, the processor may further be adapted to determine transformation parameters based on the cost function that may be used to align the detected image data with virtual object data.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Technologies are described herein for generating a diagnostic three dimensional image for a patient. Some example technologies may obtain a sequence of multiple images of the patient using an imaging modality device. The technologies may estimate a registration vector for each image based on a motion function and an image transformation function. Each image may be defined by a measurement noise added to the image transformation function operating on the registration vector with respect to a reference image. The registration vector may be a function of a breathing motion of a prior registration vector added to a transition noise value. The technologies may estimate motion parameters based on the registration vector. The technologies may iteratively refine the registration vector and the motion parameters. The technologies may generate the diagnostic three dimensional image of the patient using the registration vector for each image and the motion parameters.

Abstract: 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.

Abstract: 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 l1-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.

Abstract: 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.

Abstract: 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.

Abstract: Technologies are described herein for generating a diagnostic three dimensional image for a patient. Some example technologies may obtain a sequence of multiple images of the patient using an imaging modality device. The technologies may estimate a registration vector for each image based on a motion function and an image transformation function. Each image may be defined by a measurement noise added to the image transformation function operating on the registration vector with respect to a reference image. The registration vector may be a function of a breathing motion of a prior registration vector added to a transition noise value. The technologies may estimate motion parameters based on the registration vector. The technologies may iteratively refine the registration vector and the motion parameters. The technologies may generate the diagnostic three dimensional image of the patient using the registration vector for each image and the motion parameters.

Abstract: Technologies are generally described for tracking an object. In some examples, a system may comprise a mobile phone and an augmented reality device. The mobile phone may be effective to receive a transmitted wave and to receive a reflected wave reflected off of an object. The mobile phone may be configured to determine a difference between the transmitted and the reflected wave, and generate first tracking data based on the determined difference. The augmented reality device may be adapted to receive the first tracking data and determine second tracking data regarding the location of the object based on the first tracking data. An image may be generated on a display based on the determined first and second tracking data.

Abstract: Technologies are generally described for aligning objects in augmented reality. In some examples, a processor may be adapted to receive detected image data and virtual object data. In some examples, the processor may further be adapted to generate and apply weights to log-likelihood functions at intensity and feature levels based on the virtual object data and detected image data. In some examples, the processor may further be adapted to add the weighted log-likelihood function at intensity level to the weighted log-likelihood function at feature level to produce a cost function. In some examples, the processor may further be adapted to determine transformation parameters based on the cost function that may be used to align the detected image data with virtual object data.

Abstract: Technologies are generally described for interpretation of constrained objects in augmented reality. An example system may comprise a processor, a memory arranged in communication with the processor, and a display arranged in communication with the processor. An example system may further comprise a sensor arranged in communication with the processor. The sensor may be effective to detect measurement data regarding a constrained object. The sensor may be configured to send the measurement data to the processor. The processor may be effective to receive the measurement data, determine a model for the object, and process the measurement data to produce weighted measurement data. The processor may also be effective to apply a filter to the model and to the weighted measurement data to produce position information regarding the object, which may be utilized to generate an image based on the position information. The display may be effective to display the image.

Abstract: 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.