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: Examples described herein provide for a memory and at least one processor coupled to the memory. The at least one processor indicates a prediction of a performance goal failure based on performance monitoring of the at least one processor. The performance goal can be based on a service level agreement (SLA). The performance monitoring can be related to core activity or inactivity. A trained machine learning (ML) model can be used to infer performance goal failure based on performance monitoring of the at least one processor. The ML model can be trained using a simulation of traffic to use a compact set of performance monitoring indicators. Mitigation efforts can take place to avoid violation of the SLA.

Abstract: Various systems and methods for implementing context-based digital signal processing are described herein. An object detection system includes a processor to: access sensor data from a first sensor and a second sensor integrated in a vehicle; access an operating context of the vehicle; assign a first weight to a first object detection result from sensor data of the first sensor, the first weight adjusted based on the operating context; assign a second weight to a second object detection result from sensor data of the second sensor, the second weight adjusted based on the operating context; and perform a combined object detection technique by combining the first object detection result weighted by the first weight and the second object detection result weighted by the second weight.

Abstract: Methods, systems and apparatuses may provide for technology that conducts an automated vision analysis of image data associated with an interior of a vehicle cabin, determines a state of a child restraint system (CRS) based on the automated vision analysis, and generates an alert if the state of the CRS does not satisfy one or more safety constraints. In one example, the technology identifies the safety constraint(s) based on a geographic location of the vehicle cabin.

Abstract: Machine vision processing includes capturing 3D spatial data representing a field of view and including ranging measurements to various points within the field of view, applying a segmentation algorithm to the 3D spatial data to produce a segmentation assessment indicating a presence of individual objects within the field of view, wherein the segmentation algorithm is based on at least one adjustable parameter, and adjusting a value of the at least one adjustable parameter based on the ranging measurements. The segmentation assessment is based on application of the segmentation algorithm to the 3D spatial data, with different values of the at least one adjustable parameter value corresponding to different values of the ranging measurements of the various points within the field of view.

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: Spatial data may be divided along an axis of the second dimension into a first data segment and a second data segment, such that the first data segment is limited to data points of the spatial data with second dimension coordinates within a first range and the second data segment is limited to data points of the spatial data with second dimension coordinates within a second range. A first processing element may execute an object detection process on the first data segment to generate a first list of objects within the first data segment. A second processing element may execute the object detection process on the second data segment to generate a second list of objects within the second data segment. A first set of objects detected in the first data segment may be combined with a second set of objects detected in the second data segment.

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 performing sensor fusion include a compute device. The compute device includes circuitry configured to obtain detection data indicative of objects detected by each of multiple sensors of a host system. The detection data includes camera detection data indicative of a two or three dimensional image of detected objects and lidar detection data indicative of depths of detected objects. The circuitry is also configured to merge the detection data from the multiple sensors to define final bounding shapes for the objects.

Abstract: Methods, apparatus, systems, and articles of manufacture for monitoring robot health in manufacturing environments are described herein. An example system, to monitor health of a robot in a semiconductor wafer manufacturing facility, includes a sensor coupled to the robot. The sensor is to obtain a vibration signal representative of vibration of the robot. The example system also includes a health monitor extract a feature from the vibration signal, compare the feature to a threshold, and, in response to determining the feature satisfies the threshold, transmit an alert.

Abstract: Apparatus and methods to dynamically adjust an analytics threshold are disclosed. An example method includes analyzing a set of probabilities of injury to determine minimum and maximum probability in the set based on a request for an injury risk for a target player; determining possible thresholds to divide the probabilities of injury between the minimum and maximum probability; converting the probabilities of injury into percentages of injury based on at least one of the possible thresholds; distributing the percentages of injury into a plurality of bands based on the possible threshold(s); comparing the percentages of injury in each of the plurality of bands according to a criterion; and when the criterion is satisfied, updating a target threshold to classify the injury risk for the target player based on at least one of the possible thresholds dividing the percentages of injury in the plurality of bands and outputting the injury risk for the target player.

Abstract: Various systems and methods for implementing context-based digital signal processing are described herein. An object detection system includes a processor to: access sensor data from a first sensor and a second sensor integrated in a vehicle; access an operating context of the vehicle; assign a first weight to a first object detection result from sensor data of the first sensor, the first weight adjusted based on the operating context; assign a second weight to a second object detection result from sensor data of the second sensor, the second weight adjusted based on the operating context; and perform a combined object detection technique by combining the first object detection result weighted by the first weight and the second object detection result weighted by the second weight.

Abstract: Various systems and methods for implementing LiDAR-based object detection and classification are described herein. An object detection system includes a feature extraction and object identification (FEOI) circuit to: receive segmented data of an environment around the object detection system, the segmented data obtained using a light imaging detection and ranging (LiDAR) system, oriented with respect to a direction of travel; compute spatial and structural parameters of a segment of the segmented data; and use the spatial and structural parameters with a machine learning model to obtain a classification of the segment.

Abstract: Machine vision processing includes capturing 3D spatial data representing a field of view and including ranging measurements to various points within the field of view, applying a segmentation algorithm to the 3D spatial data to produce a segmentation assessment indicating a presence of individual objects within the field of view, wherein the segmentation algorithm is based on at least one adjustable parameter, and adjusting a value of the at least one adjustable parameter based on the ranging measurements. The segmentation assessment is based on application of the segmentation algorithm to the 3D spatial data, with different values of the at least one adjustable parameter value corresponding to different values of the ranging measurements of the various points within the field of view.

Abstract: Technologies for autonomous vehicle driving quality of service (QoS) determination and communication include an advanced vehicle with a vehicle computing device. The computing device determines multiple route segments of one or more routes to a destination. The computing device determines, for each route segment, one or more autonomous driving factors that are each indicative of an autonomy level achievable by the advanced vehicle for the associated route segment. Factors may include map availability, weather conditions, road conditions, or other factors. The computing device determines, for each route segment, an autonomous QoS score based on the autonomous driving factors. The computing device may rank multiple routes to the destination based on the autonomous QoS scores associated with the route segments of those routes. The computing device may display a proposed route to a user of the advanced vehicle and receive a selection from the user. Other embodiments are described and claimed.

Abstract: Apparatuses, methods and storage medium associated with monitoring or assisting in monitoring of an equipment process are disclosed herein. In embodiments, an apparatus may comprise an analyzer to: receive a plurality of simulation results of a plurality of control limit and alert zone combinations for potential use with a control chart to monitor the equipment process, and calculate a plurality of performance metrics for each of the plurality of control limit and alert zone combinations, using the plurality of simulation results. The apparatus may further select an optimal combination of control limits and alert zones, based at least in part on the plurality of performance metrics, and configure an equipment process monitor with the selected optimal combination of control limits and alert zones for use with a control chart to monitor the equipment process. Other embodiments may be described 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: 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: 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.