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.

Abstract: A gas sensing device for sensing a target gas in a gas mixture comprises: a measurement module configured for obtaining a sampled measurement signal, the sampled measurement signal being responsive to a concentration of the target gas in the gas mixture; a compensation module configured for compensating the sampled measurement signal using an offset information to obtain a compensated measurement signal; a processing module configured for detecting a sample of the sampled measurement signal, which sample fulfils a set of one or more criteria, and wherein a first criterion of the set of criteria is fulfilled if, according to an evaluation of the sampled measurement signal, the sample is associated with a concentration of the target gas below a threshold, and updating the offset information based on a sample which fulfills the set of criteria.

Abstract: 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 m

Abstract: 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.

Abstract: A device for determining a concentration of a target gas in a mobile application comprises a measurement module for obtaining measurement information about a concentration measurement of a target gas; a communication module for communicating with at least one further device via a direct wireless communication path, wherein the communication module receives information about a further concentration measurement of the target gas from the further device; and a calibration module for using calibration information for determining a calibrated measurement value on the basis of the measurement information, and for revising the calibration information by using the received information about the further concentration measurement.

Abstract: A gas sensing device includes chemo-resistive gas sensors, wherein each of the gas sensors generates signals corresponding to concentrations of gases in a mixture of gases; a heating arrangement for heating gas sensors according to a periodic temperature profile; a preprocessing processor for receiving the signals from each of the gas sensors and for preprocessing the received signals to generate a preprocessed signal sample for each of the gas sensors for each period of the periodic temperature profile; a feature extraction processor configured for receiving the preprocessed signal samples and for extracting for each of the periods a set of feature values from the preprocessed signal samples received for the respective period; and a gas concentration processor for receiving sets of feature values.

Abstract: 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.

Abstract: A gas sensing device includes one or more chemo-resistive gas sensors; one or more heat sources; a preprocessing processor; a feature extraction processor; a discriminative embedding network processor for receiving sets of feature values and for creating for each of the sets of feature values a set of embedded feature values; a classification processor for receiving the sets of embedded feature values and for creating a classification value for each set of the embedded feature values, wherein the classification value indicates a class of a mixture of gases; and a quantification processor for receiving the sets of embedded feature values and the classification values, wherein the quantification processor is creates, for each of the gases, a sensing result for each of the sets of embedded feature values.

Abstract: 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.

Abstract: A sensing device for sensing a target gas includes a measurement module for providing measurement information about a measurement of the concentration. The sensing device further includes a signal calibration module for using a machine learning model for determining, on the basis of the measurement information, a calibrated measurement value of the concentration. The signal calibration module determines a feedback feature using the calibrated measurement value. The signal calibration module uses the machine learning model for determining a subsequent calibrated measurement value on the basis of subsequent measurement information about a subsequent measurement of the concentration and on the basis of the feedback feature.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 m

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method of gas sensing performed by a processor electrically coupled to a gas sensor includes obtaining, by the gas sensor, a first electrical signal indicating a response of the gas sensor to a concentration of a target gas at the gas sensor, generating one or more second electrical signals according to the first electrical signal, determining a first condition in which the concentration at the gas sensor is increasing using the first electrical signal and the one or more electrical second signals, determining a second condition occurring after the first condition in which the concentration at the gas sensor is substantially constant using the first electrical signal and the one or more second electrical signals, and calculating a concentration value of the target gas according to the first electrical signal and the one or more second electrical signals. The calculating is performed while the concentration is substantially constant.

Abstract: 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.