公开(公告)号：US20250124740A1

公开(公告)日：2025-04-17

申请号：US18914761

申请日：2024-10-14

Applicant: Infineon Technologies AG

Inventor： Sarah Seifi ,  Cecilia Carbonelli ,  Simon Mittermaier ,  Tobias Sukianto

IPC: G06V40/20 ,  G06V10/764

Abstract: In accordance with an embodiment, a method includes using a machine-learning model to infer from at least one feature vector, a gesture class prediction associated with a gesture; and determining at least one feature relevance vector for the at least one feature vector, where each of the at least one feature relevance vector includes feature relevance values, and each of the feature relevance values are indicative of a dependency of the gesture class prediction on respective one or more feature values of the at least one feature vector.

公开(公告)号：US20240159724A1

公开(公告)日：2024-05-16

申请号：US18506533

申请日：2023-11-10

Applicant: Infineon Technologies AG

Inventor： Tobias Sukianto ,  Sebastian Schober ,  Cecilia Carbonelli ,  Simon Mittermaier

IPC: G01N33/00 ,  G06N3/0499 ,  G06N3/096

CPC classification number: G01N33/0062 ,  G06N3/0499 ,  G06N3/096

Abstract: A gas sensing device for sensing a target gas in a gas mixture, including a measurement module configured for obtaining a measurement signal, the measurement signal being responsive to a concentration of the target gas in the gas mixture, and a processing module configured for determining, for each of a sequence of samples of the measurement signal, a set of features, the features representing respective characteristics of the measurement signal, and using a neural network for determining an estimation of the concentration of the target gas based on the sets of features determined for the samples of the sequence, where the neural network comprises an attention layer to weight respective contributions of the samples to the estimation.