Abstract: Systems, apparatuses and methods may provide for technology that assigns confidence levels to data bins containing similarity data and length of stay data, wherein the similarity data and the length of stay data correspond to a plurality of previous admissions. Additionally, the confidence levels may be weighted based on a distribution metric that assigns higher weights to denser regions. A length of stay of a target admission may be predicted based on the weighted confidence levels.

Abstract: A disclosed example method to predict an injury for a target player on a target date includes determining a first probability of injury of the target player based on probabilities of injuries of second players having similarities with the target player; determining a second probability of injury of the target player based on injuries of the target player; determining a third probability of injury of the target player based on the first probability of injury of the target player and the second probability of injury of the target player; and generating, by executing an instruction with the processor, a report of a predicted probability of injury of the target player for the target date based on the third probability of injury of the target player.

Abstract: Methods, systems, and storage media for robust monitoring a gauge are disclosed herein. In an embodiment, a digital image of a gauge may be received to identify an analog value indicator of the gauge and to form an indicator representation corresponding to the analog value indicator. Robust optical character recognition and image filter may provide enhanced gauge reading and/or monitoring. Other embodiments may be disclosed and/or claimed.

Abstract: Technologies for efficient identity recognition based on skin features include a compute device. The compute device includes an image capture device and an image acquisition module to obtain, with the image capture device, an image that depicts the skin of the person. The compute device also includes a skin feature determination module to identify pixels in the obtained image that are associated with the skin of the person, and determine one or more features of the skin based on the identified pixels. Additionally, the compute device includes an identity determination module to generate a feature vector that includes the determined features of the skin, and analyze the feature vector with reference data to determine an identity of the person. Other embodiments are described and claimed.

Abstract: The present disclosure is directed to a system for determining sensor condition. A sensor signal generated by a sensor to communicate the current condition of an aspect being monitored by the sensor may also be employed to determine the condition of the sensor itself. For example, a device capable of determining if the sensor condition is normal or malfunctioning (e.g., erratic, stuck, etc.) may comprise a monitoring module (MM) to receive the sensor signal. The MM may comprise a sensor condition determination module (SCDM) to determine sensor condition. The SCDM may include a feature extraction engine to determine various characteristics of (e.g., to “extract features” from) the sensor signal and a model to determine sensor condition based on the extracted features. The model may include a support vector machine (SVM) taught to determine sensor condition utilizing sampled sensor signals correlated with annotations of sensor condition.

Abstract: In various embodiments, an angular gauge reporting system (“AGRS”) may determine one or more values from an image of an angular gauge. The AGRS may receive one or more images of the gauge and develop an angular map to determine values indicated by the gauge. The AGRS may identify numbers in the image to generate the angular map. The AGRS may determine a center for the angular gauge. The AGRS may determine numerical values by processing capture images of the angular gauge though angular or linear interpolation of values. By generating the angular map prior to later determination of values, the AGRS may provide for determination of numerical values without requiring repetition of actions which may be computationally complex or resource intensive. Other embodiments may be described and/or claimed.

Abstract: Described is an apparatus comprising: an ensemble of sensors including a moisture sensor to sense moisture content of hair, the moisture sensor positioned in a first housing to be in contact with the hair; and a processor to receive data associated with sensed moisture content and to transmit information related to the received data to another device, the processor positioned in a second housing different from the first housing.

Abstract: Methods, systems, and storage media for monitoring a gauge are disclosed herein. In an embodiment, an image processing module may receive a digital image of a gauge to identify an analog value indicator of the gauge and to form an indicator representation corresponding to the analog value indicator. A geometric analysis module may determine from the indicator representation a geometric characteristic corresponding to the analog value indicator and an indicated gauge value. Other embodiments may be disclosed and/or claimed.

Abstract: This disclosure is directed to a system for analytic model development. In general, an analytic system may be able to formulate a model of a target system based on user interaction and data received from the system, and to perform real time activities based on the model. An analytics system may comprise at least a segment recipe module (SRM), a user interface module (UIM) and an automated analytics module (AAM). The SRM may include at least one segment recipe for use in configuring the UIM and AAM. For example, the UIM may be configured to present plain language prompts to a user. At least one of the segment recipe or data input by the user in response to the prompts may be used to configure the AAM to generate the model. The AAM may also perform real time activities that generate notifications, etc. based on the model.

Abstract: Described is an apparatus which comprises: one or more sensors for coupling to a power source and for sensing electrical parameters of the power source, wherein the power source is operable to provide power to a system having one or more sub-systems; and a processor to analyze the sensed electrical parameters and to detect and identify one or more events associated with the system and the one or more sub-systems.

Abstract: The present disclosure is directed to a system for determining sensor condition. A sensor signal generated by a sensor to communicate the current condition of an aspect being monitored by the sensor may also be employed to determine the condition of the sensor itself. For example, a device capable of determining if the sensor condition is normal or malfunctioning (e.g., erratic, stuck, etc.) may comprise a monitoring module (MM) to receive the sensor signal. The MM may comprise a sensor condition determination module (SCDM) to determine sensor condition. The SCDM may include a feature extraction engine to determine various characteristics of (e.g., to “extract features” from) the sensor signal and a model to determine sensor condition based on the extracted features. The model may include a support vector machine (SVM) taught to determine sensor condition utilizing sampled sensor signals correlated with annotations of sensor condition.

Abstract: The present disclosure is directed to a system for determining scaling in a boiler. At least one sensor may monitor a boiler during operation and provide sensor data to a boiler monitoring module including a boiler scaling determination module that may determine an amount of scaling in the boiler. Example sensor data may comprise power input, a temperature of liquid in the boiler and an air temperature within an enclosure housing the boiler. The boiler monitoring module may determine thermal energy transfer to the boiler based on the liquid and enclosure temperatures. A machine learning engine may determine a rate of thermal energy transfer to the liquid in view of the power input, the rate of thermal energy transfer being evaluated by the machine learning engine to identify delay in the rate of thermal energy transfer that quantifies an amount of scaling in the boiler.

Abstract: There is described a video based monitoring system and method. The system comprises an image acquisition module, a movement detection module, and a stationary object detection module. The movement detection module is adapted for detecting the presence of a moving object in a region of interest of said captured video image. The stationary object detection module is adapted for detecting the presence of a stationary object in said region of interest and operable when said movement detection module fails to detect a moving object in a region of interest of a current frame of the captured image. The stationary object detection module includes a pixel-by-pixel comparison module adapted to determine the number of pixels in the region of interest in the current frame whose pixel values match with that of corresponding pixels in an immediately preceding frame.