Stmicroelectronics S.r.l.

Cisco Technology, Inc.

Giacomo Boracchi

Giuseppe Desoli

Pasqualina Fragneto

Beatrice Rossi

Diego Carrera

Daniele Zambon

Stefano Piciaccia

Alberto Arnaldo Tanzi

Prasenjeet Acharjee

Luca Magri

Enrico Sozio

Pietro Invernizzi

Cesare Alippi

Antonino Maria Rizzo

Annalisa Aurigemma

Paolo Giugni

Elena Tagliabue

Mauro Fumagalli

Lidia Moioli

In some aspects, the techniques described herein relate to a method that includes: obtaining optical channel spectrum data that includes amplified spontaneous emission data and channel data associated with optical signals propagated through an optical fiber; fitting an amplified spontaneous emission trend to the amplified spontaneous emission data; fitting a channel trend to the channel data; jointly optimizing the amplified spontaneous emission trend and the channel trend to determine an optimized channel trend; and determining an anomaly in the channel data based upon the optimized channel trend.

Automatic trend identification and anomaly detection in optical channel monitoring spectrum

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.

Wafer manufacturing system, device and method

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.

Method for the detecting electrocardiogram anomalies and corresponding system

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.

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Buccinasco, Italy

Giacomo Boracchi