Infineon Technologies Ag

Intel Corporation

Intel Mobile Communications Gmbh

Cecilia Carbonelli

Stefan Fechtel

Stefan Franz

Sabine Roessel

Wolfgang Furtner

Renato Bessegato

Michael Horvat

Prashanth Makaram

Andrea Heinz

Jianyu Zhao

Sebastian Schober

Hyung-Nam Choi

Bernhard Raaf

Honglei Miao

Michael Faerber

Ulrich Krumbein

Ingolf Karls

Kilian Roth

Johannes Manz

Maria Fresia

Alexander Zoepfl

Caterina Travan

Waldemar Marsetz

Alexandra Marina Roth

Alessandra Fusco

Andrea Hollenbach

Manuel Carro Dominguez

Alexander Jesipow

Simon Mittermaier

Luca Iten

Manz Johannes

In accordance with an embodiment, a sensing device for sensing an environmental parameter includes a measurement module configured for providing a sequence of measurement values in dependence on the environmental parameter; a communication module configured for communicating with a further sensing device; and a function analysis module coupled to the measurement module and the communication module. The function analysis module configured for using a neural network for determining a first temporal feature on the basis of the sequence of measurement values, and determining, on the basis of the first temporal feature and on the basis of a second temporal feature provided by the further sensing device, information about a functional state of the measurement module.

Sensing device for sensing an environmental parameter and method for determining information about a functional state of a sensing device

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.

Gas sensing device for sensing one or more gases in a mixture of gases

A method for determining a calibrated measurement value for a concentration of the target gas comprises obtaining a measurement signal based on the concentration of the target gas. The method further comprises determining the calibrated measurement value based on the measurement signal and based on a calibration model. The calibration model is based on calibration data of a plurality of test sensor units having the same type as the sensor unit.

Gas sensing device and method for determining a calibrated measurement value for a concentration of a target gas

A gas sensing device includes chemo-resistive gas sensors; heating elements for heating each of the gas sensors; an information extraction block for receiving signal samples and for generating representations for the received signal samples; and a decision making block configured for receiving the representations, wherein the decision making block comprises a weighting block and a trained model based algorithm stage, wherein the weighting block receives feature samples of the representations and applies time-variant weighting functions to the feature samples of the respective representation in order to calculate a weighted representation including weighted feature samples.

Gas sensing device and method for operating a gas sensing device

A gas sensing device includes gas sensors for generating signal samples corresponding to a concentration of a gas; a heat source for heating the gas sensors according to a first temperature profile during recovery phases and according to a second temperature profile during sense phases, a preprocessing processor for preprocessing the received signal samples; a feature extraction processor for extracting feature values from the preprocessed signal samples; a humidity processor for estimating a humidity value of the mixture of gases, including a first trained model based algorithm processor, and wherein the humidity value is based on an output of the first algorithm processor; a gas concentration processor for creating sensing results, wherein the gas concentration processor comprises a second trained model based algorithm processor, wherein the sensing results are based on output values of the second algorithm processor, and wherein the sensing results depend on the humidity value.

A gas sensing device includes one or more chemo-resistive gas sensors; one or more heating elements for heating each of the gas sensors; a preprocessing block for filtering signal samples in order to generate filtered signal samples for each of the gas sensors; an information extraction block for generating representations for the filtered signal samples for each of the gas sensors based on dynamic characteristics of the received filtered signal samples of the respective gas sensor; and a decision making block for receiving the representations, wherein the decision making block includes a trained model based algorithm stage having an input layer and an output layer, wherein the decision making block includes trained models, wherein the decision making block creates sensing results based on output values of the output layer of the algorithm stage, and wherein the output values are created by using the trained models.

An apparatus for event detection is provided. The apparatus comprises an error signal determiner for determining an error signal portion depending on a pressure signal. The error signal determiner determines a predicted signal portion depending on a first signal portion of the pressure signal. The error signal determiner also determines the error signal portion depending on the predicted signal portion and depending on a second signal portion of the pressure signal, wherein the second signal portion of the pressure signal succeeds the first signal portion of the pressure signal in time. The apparatus also comprises a classifier for determining, depending on the error signal portion, whether an event of a group of one or more events has occurred or whether no event of the group has occurred.

Apparatus and method for event classification based on barometric pressure sensor data

A gas sensing device includes a photoacoustic spectrometry device, including a radiator for emitting light, a gas detection chamber for exposing a mixture of gases to the light, a microphone for detecting sound in the detection chamber, which is caused by exposing the mixture of gases to the light, and wherein the photoacoustic spectrometry device generates signal samples corresponding to a concentration of the gas in the mixture of gases based on the sound detected by the microphone, and a computing device for receiving the signal samples. The computing device includes a feature extraction block including a trained model algorithm block.

Gas sensing device for sensing a gas in a mixture of gases and method for operating a gas sensing device

In accordance with an embodiment, a method of measuring a gas concentration includes modulating an infrared light source according to a frequency-hopped sequence or according to a pulse sequence, receiving a microphone signal from an output of a microphone acoustically coupled to a gas exposed to infrared light produced by the infrared light source; bandpass filtering the microphone signal using a bandpass filter; and estimating the gas concentration from the filtered microphone signal.

System and method for estimating a gas concentration

A method of gas sensing includes obtaining a first signal by a gas sensor, generating one or more second signals according to the first signal, and calculating a first weight value corresponding to the first signal. The first signal indicates a response of the gas sensor to a concentration of a target gas at the gas sensor. The method of gas sensing further includes calculating one or more second weight values corresponding to each of the one or more second signals and calculating an estimated concentration of the target gas at the gas sensor according to the first weight value and the one or more second weight values.

Sensing systems and methods for the estimation of analyte concentration

A channel estimation coefficients generator () for generating channel estimation coefficients for channel estimation filtering includes: a parameter acquisition unit () configured to acquire a first set of input parameters () and to acquire a second set of input parameters (), wherein a time variability of the first set of input parameters () is smaller than a time variability of the second set of input parameters (); a first channel estimation coefficients generator () configured to generate a prototype set of channel estimation coefficients () based on the first set of input parameters (); and a second channel estimation coefficients generator () configured to generate a refined set of channel estimation coefficients () based on the prototype set of channel estimation coefficients () and based on the second set of input parameters ().

Device and method for generating channel estimation coefficients for channel estimation filtering

Technology for downlink signaling between an enhanced Node B (eNB) and user equipment (UE) with a selected cyclic prefix (CP) for the UE is disclosed. In an example, a user equipment (UE) can include circuitry configured to: estimate a delay spread for a corresponding channel between the eNB and the UE; communicate the estimated delay spread from the UE to the eNB to enable the eNB to determine a selected cyclic prefix (CP) length for downlink signaling; receive the selected CP length from the eNB for a transmission time interval (TTI); and process received downlink data for the TTI using the selected CP length.

Downlink signaling for UE specific cyclic prefix transmission

Technology for a user equipment (UE) operable to perform carrier measurements in a wireless network is disclosed. The UE can receive, from a network element, switching parameters that indicate possible switching occasions for the UE. The UE can be configured to switch a radio frequency (RF) to another carrier frequency during the possible switching occasions in order to start performing carrier measurements or stop performing carrier measurements. The UE can send, to the network element, a switching notification that indicates an upcoming switching occasion that is selected by the UE. The switching notification can be sent in conjunction with a measurement value that indicates one or more of: selected carriers for which the UE is to start performing carrier measurements after an occurrence of the switching occasion or selected carriers for which the UE is to stop performing carrier measurements after the occurrence of the switching occasion.

Growing community of inventors

Munich, Germany

Cecilia Carbonelli