Abstract: Apparatus and method to facilitate automatic detection of a device state are disclosed herein. Selectively constraining a sensor based data set associated with one or more states of a device, wherein selectively constraining the sensor based data set includes analyzing a distribution of the sensor based data set to determine whether to constrain the sensor based data set, the sensor based data set including a first class and a second class of data values. Determining a threshold associated with the sensor based data set by selecting the threshold based on a variance between the first and second classes of the sensor based data set, wherein selecting the threshold includes using a constrained sensor based data set when the sensor based data set is determined to be constrained, and wherein the threshold indicates the data values associated with the first and second classes.

Abstract: Apparatus and method to facilitate automatic detection of a device state are disclosed herein. Selectively constraining a sensor based data set associated with one or more states of a device, wherein selectively constraining the sensor based data set includes analyzing a distribution of the sensor based data set to determine whether to constrain the sensor based data set, the sensor based data set including a first class and a second class of data values. Determining a threshold associated with the sensor based data set by selecting the threshold based on a variance between the first and second classes of the sensor based data set, wherein selecting the threshold includes using a constrained sensor based data set when the sensor based data set is determined to be constrained, and wherein the threshold indicates the data values associated with the first and second classes.

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: Apparatus and method to facilitate automatic detection of a device state are disclosed herein. Selectively constraining a sensor based data set associated with one or more states of a device, wherein selectively constraining the sensor based data set includes analyzing a distribution of the sensor based data set to determine whether to constrain the sensor based data set, the sensor based data set including a first class and a second class of data values. Determining a threshold associated with the sensor based data set by selecting the threshold based on a variance between the first and second classes of the sensor based data set, wherein selecting the threshold includes using a constrained sensor based data set when the sensor based data set is determined to be constrained, and wherein the threshold indicates the data values associated with the first and second classes.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store data. The processor is to: store a first dataset on the memory; identify a plurality of bin sizes for compressing the first dataset; compute a plurality of performance costs associated with the plurality of bin sizes; identify a minimum performance cost of the plurality of performance costs; identify an optimal bin size based on the particular bin size associated with the minimum performance cost; partition the first dataset into a plurality of bins based on the optimal bin size; identify a plurality of bin counts associated with the plurality of bins; generate a second dataset based on the plurality of bin counts, wherein the second dataset is smaller than the first dataset; and store the second dataset on the memory, wherein the second dataset is stored using less memory space than the first dataset.

Abstract: Techniques for monitoring the health of a device comprising a motor or generator are described. An apparatus to monitor the health of the device includes a plurality of sensors to detect root-mean-square (RMS) current values for a plurality of stator windings of the device. The apparatus also includes a processor to receive the RMS current values from the plurality of sensors and compute a fault indicator based on the RMS current values. The fault indicator represents a level of current imbalance between the plurality of stator windings due to winding faults.

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: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store data. The processor is to: store a first dataset on the memory; identify a plurality of bin sizes for compressing the first dataset; compute a plurality of performance costs associated with the plurality of bin sizes; identify a minimum performance cost of the plurality of performance costs; identify an optimal bin size based on the particular bin size associated with the minimum performance cost; partition the first dataset into a plurality of bins based on the optimal bin size; identify a plurality of bin counts associated with the plurality of bins; generate a second dataset based on the plurality of bin counts, wherein the second dataset is smaller than the first dataset; and store the second dataset on the memory, wherein the second dataset is stored using less memory space than the first dataset.

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: Techniques for monitoring the health of a device comprising a motor or generator are described. An apparatus to monitor the health of the device includes a plurality of sensors to detect root-mean-square (RMS) current values for a plurality of stator windings of the device. The apparatus also includes a processor to receive the RMS current values from the plurality of sensors and compute a fault indicator based on the RMS current values. The fault indicator represents a level of current imbalance between the plurality of stator windings due to winding faults.

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.