Intel Corporation

Varvara Kollia

Esha John

Oguz Hasan Elibol

Ramune Nagisetty

Noureddine Tayebi

Ryan M Field

Gaudencio Hernandez Sosa

Pradyumna S Singh

Technologies for detecting minority events are disclosed. By performing a guided hierarchical classification algorithm with a decision tree structure and grouping the minority class(es) in with some of the majority classes, large majority classes may be separated from a minority class without requiring good detection of the minority events by themselves. The decision tree structure may be used only for the purpose of identifying if the data sample in question is a member of a minority class. If it is determined that it is not, a primary classification algorithm may be used. With this approach, the guided hierarchical classification algorithm need not perform as well as the primary classification algorithm for the majority events, but may provide improved detection for minority events.

Technologies for detection of minority events

Embodiments herein relate to gas identification with a gas identification apparatus having a plurality of metal oxide semiconductor (MOS) sensors. In various embodiments, a gas identification apparatus may include a set of heterogeneous MOS sensors to provide different response patterns for the presence of different gases and an identification engine coupled with the sensors, and having a plurality of regression models and one or more artificial neural networks, to analyze a response pattern to identify presence of a gas, based at least in part on a plurality of property measurements of the MOS sensors when exhibiting the response pattern, and using one or more of the plurality of regression models and the one or more artificial neural networks. Other embodiments may be described and/or claimed.

Gas identification apparatus and machine learning method

An analog frontend (AFE) interface is dynamically programmable based on a determined operating state. The AFE includes hardware to interface with multiple different sensors. The AFE includes analog processing hardware that can select input data from one of the multiple sensors. The analog processing hardware is coupled to a processor that computes features from the sensor, where the features represent selected operating condition information of the AFE for the sensor. The processor is to determine one of multiple discrete operating states of the AFE for the sensor based on the computed features and dynamically adjust operation of the AFE to interface with the sensor based on the determined operating state. Dynamically adjusting the operation of the AFE includes controlling a configuration of the AFE that controls how the AFE receives the input sensor data from the sensor.

Low-power context-aware control for analog frontend

In one embodiment, a receiver includes: a data path having a first slicer to receive and sample an incoming analog signal and to determine a bit level for the incoming analog signal, the first slicer to provide a bit decision to a consuming logic; an analysis path having a second slicer to receive and sample the incoming analog signal and to determine a second bit level for the incoming analog signal; and a controller coupled to receive an output of the first slicer and an output of the second slicer to determine a bit error rate for the data path based on the first and second slicer outputs. Other embodiments are described and claimed.

Growing community of inventors

Santa Clara, CA, United States of America

Varvara Kollia