Abstract: A method of operating an inertial sensor module includes receiving a stream of inertial sensor data representing activity of a user of an electronic device and generating a plurality of wavelet sub-bands by performing a wavelet transform on the inertial sensor data. The method includes identifying a wavelet sub-band of highest energy from the plurality of wavelet sub-bands, generating augmented inertial sensor data by combining the wavelet sub-band of highest energy to the inertial sensor data, and identifying a first transition in the activity of the user based on the augmented inertial sensor data.

Abstract: A method of operating a PMUT electro-acoustical transducer, the method comprising: applying over an excitation interval to the transducer an excitation signal which is configured to emit corresponding ultrasound pulses towards a surrounding space, acquiring at a receiver reflected ultrasound pulses as reflected in said surrounding space, generating a reference echo signal, performing a cross-correlation of said acquired received ultrasound pulses with said reference echo signal, performing a measurement based on the cross-correlation results, in particular a measurement of the time of flight of the ultrasound pulses, wherein said reference echo is obtained by finding an oscillation frequency of the transmitter on the basis of a transmitter ringdown signal, finding an oscillation frequency of the receiver on the basis of a receiver ringdown signal, performing a frequency tuning respectively on the transmitter and the receiver on the basis of said respective oscillation frequencies, then sweeping an input frequ

Abstract: A method of performing an electro-thermo simulation includes defining a non-linear heat diffusion problem for at least a portion of a semiconductor device to be modeled, performing a finite volume discretization of the non-linear heat diffusion problem, reformulating a non-linear term of the discretized non-linear heat diffusion problem to decrease dimensions thereof, performing a hyper reduction of the reformulated non-linear term, and recovering the non-linear heat diffusion problem for the portion of the semiconductor device, and manufacturing the modeled semiconductor device.

Abstract: A method, comprising: providing an ANN processing stage having a plurality of processing layers with respective parameters including at least one set of weight parameters, at least one input, resp. output, activation parameter and at least one activation function parameter; setting to an integer value a dimensional parameter of a lattice having a plurality of lattice points and identified by a set of basis vectors; selecting a set of weight parameters of a respective processing layer; vectorizing the selected set of weight parameters producing a set of weight vectors arranged as items of a matrix of weight vectors; normalizing the matrix of weight vectors; applying lattice vector quantization, LVQ, processing to the matrix of normalized weight vectors, producing a codebook of codewords; indexing by encoding codewords of the codebook as a function of the lattice, producing respective tuples of indices.

Abstract: A device includes image generation circuitry and a convolutional neural network. The image generation circuitry, in operation, generates a binned representation of a wafer defect map (WDM). The convolutional-neural-network, in operation, generates and outputs an indication of a root cause of a defect associated with the WDM based on the binned representation of the WDM and a data-driven model associating WDMs with classes of a defined set of classes of wafer defects.

Abstract: A device includes image generation circuitry and a convolutional neural network. The image generation circuitry, in operation, generates a binned representation of a wafer defect map (WDM). The convolutional-neural-network, in operation, generates and outputs an indication of a root cause of a defect associated with the WDM based on the binned representation of the WDM and a data-driven model associating WDMs with classes of a defined set of classes of wafer defects.

Abstract: A heart-rate associated with a heartbeat signal is determined. A transform is selected based on the determined heart-rate associated with the heartbeat signal and a reference heart-rate associated with a dictionary of a sparse approximation model. The transform is selected independent of other factors associated with generation of the heartbeat signal. The selected transform is applied to the dictionary of the sparse approximation model, generating an adjusted dictionary of the sparse approximation model. Anomalous heartbeats in the heartbeat signal are detected using the adjusted dictionary of the sparse approximation model.

Abstract: A heartrate monitor detects heartbeats in a test signal. A local heartrate and an energy of acceleration are associated with the detected heartbeats. Detected heartbeats are included or excluded from a test set of heartbeats based on the local heartrate and energy of acceleration associated with the respective heartbeats. Anomalous heartbeats in the test set of heartbeats are detected using a sparse approximation model. The heartrate monitor may detect heartbeats in a training heartbeat signal. A reference heart rate and an energy of acceleration are associated with detected beats of the training heartbeat signal and selectively included in a set of training data based on the heart rate and energy of acceleration associated with the detected beat in the training heartbeat signal. A dictionary of the sparse representation model may be generated using the set of training data.

Abstract: A heart-rate associated with a heartbeat signal is determined. A transform is selected based on the determined heart-rate associated with the heartbeat signal and a reference heart-rate associated with a dictionary of a sparse approximation model. The transform is selected independent of other factors associated with generation of the heartbeat signal. The selected transform is applied to the dictionary of the sparse approximation model, generating an adjusted dictionary of the sparse approximation model. Anomalous heartbeats in the heartbeat signal are detected using the adjusted dictionary of the sparse approximation model.

Abstract: A heartrate monitor detects heartbeats in a test signal. A local heartrate and an energy of acceleration are associated with the detected heartbeats. Detected heartbeats are included or excluded from a test set of heartbeats based on the local heartrate and energy of acceleration associated with the respective heartbeats. Anomalous heartbeats in the test set of heartbeats are detected using a sparse approximation model. The heartrate monitor may detect heartbeats in a training heartbeat signal. A reference heart rate and an energy of acceleration are associated with detected beats of the training heartbeat signal and selectively included in a set of training data based on the heart rate and energy of acceleration associated with the detected beat in the training heartbeat signal. A dictionary of the sparse representation model may be generated using the set of training data.