Infineon Technologies Ag

Andrea Heinz

Cecilia Carbonelli

Avik Santra

Raghavendran Vagarappan Ulaganathan

Jianyu Zhao

Sebastian Schober

Manuel Carro Dominguez

In an embodiment, a method includes: obtaining one or more radar measurement frames, each one of the one or more radar measurement frames including respective data samples acquired by a radar sensor monitoring a scene; for each one of the one or more radar measurement frames, determining a respective 2-D angular intensity map of the scene based on the respective radar measurement frame; and performing a people counting operation based on the one or more 2-D angular intensity maps determined for the one or more radar measurement frames to determine a people count for the scene.

People counting based on radar measurement

In accordance with an embodiment, a system includes: an audio signal pre-processor; a pressure signal pre-processor; an audio signal feature processor; a pressure signal feature processor; a feature combining processor; and a classification processor configured for classifying the external impact on a window or access opening of an enclosed structure by classifying an audio feature and pressure feature vector in order to produce a classification output; wherein the classification processor is configured for executing a first machine learning algorithm, wherein the audio feature and pressure feature vector is fed to an input layer of the first machine learning algorithm, and wherein the classification output is based on an output of the first machine learning algorithm.

Classification system and method for classifying an external impact on a window or on an access opening of an enclosed structure

A gas sensing device includes one or more chemo-resistive gas sensors; one or more heat sources, wherein the gas sensors are heated according to one or more first temperature profiles during the recovery phases and according to one or more second temperature profiles during the sense phases; a preprocessing processor for generating preprocessed signal samples; a feature extraction processor for extracting one or more feature values from the received preprocessed signal samples; and a gas concentration processor for creating a sensing result, wherein the gas concentration processor includes a classification processor for outputting a class decision value, wherein the classification processor is configured for outputting a confidence value, wherein the classification processor includes a first trained model based algorithm processor, wherein the gas concentration processor comprises a quantification processor for creating an estimation value, and wherein the quantification processor comprises a second trained model based algorithm processor.

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Munich, Germany

Andrea Heinz