Silicon Laboratories Inc.

Sigmatel, Inc.

Other

Freescale Semiconductor,inc.

Nitero Pty Limited

Bandspeed, Inc.

Antonio Torrini

John Y Morton

Sauli Johannes Lehtimaki

Yan Zhou

Hendricus De Ruijter

Javier Elenes

David S Trager

Joel Kauppo

Konstantin Shkolnyy

Sebastian Ahmed

William Hong

Josef Zeevi

Jean Charles Pina

Marc Kevin Jordan

Glenn Reinhardt

Ngoc Vinh Vu

Daniele Limoni

David Cureton Baker

Ramin Khoini-Poorfard

Efstratios Skafidas

Ben William Jones

Romesh Mangho Jessani

Neil Patrick Kelly

Eric Crawford

William John Rinderknecht

Mika Tapio Länsirinne

Mark William Benson

Michael Luther

Mike Gibson

Nestor Fesas

John Jack Morton

Blaine Kohl

Darren Rae Di Cera

Andrew Wheeler

Robert Koelling

Michael Casey Luther

Ben Jones

An apparatus includes a radio-frequency (RF) receiver for receiving an RF signal using a plurality of antennas. The RF receiver includes a demodulator to provide a switch signal to cause the RF receiver to use an antenna in the plurality of antennas. The RF receiver further includes a carrier frequency offset (CFO) correction circuit that estimates and removes a carrier frequency offset.

Apparatus for receiver with carrier frequency offset correction using frequency information and associated methods

In an embodiment, an apparatus includes: a sensor to sense real world information; a digitizer coupled to the sensor to digitize the real world information into digitized information; a signal processor coupled to the digitizer to process the digitized information into an image; a discriminator coupled to the signal processor to determine, based at least in part on the image, whether the real world information comprises an anomaly, where the discriminator is trained via a generative adversarial network; and a controller coupled to the discriminator.

Detecting anomalous events in a discriminator of an embedded device

A wireless locator that facilitates determining a location of a nearby wireless asset including an antenna array with multiple antennas, at least one wireless transceiver that receives a location signal from the nearby wireless asset and that takes multiple samples from the location signal including a set of samples for each antenna, and a processor that compresses the samples to generate location information associated with the nearby wireless asset. The wireless locator may be part of a wireless location system including multiple wireless locators distributed in the area and a central processing system. Various compression methods are disclosed, including averaging of the samples, bit reduction of the samples, converting the samples to corresponding phase values, and combining corresponding samples of multiple sample supplemental sets. Combinations of the various compression methods are also disclosed.

Compression of location information during backhaul communications of a wireless location system

In one embodiment, an apparatus includes: a sensor to sense real world information; a digitizer coupled to the sensor to digitize the real world information into digitized information; a signal processor coupled to the digitizer to process the digitized information into a spectrogram; a neural engine coupled to the signal processor, the neural engine comprising an autoencoder to compress the spectrogram into a compressed spectrogram; and a wireless circuit coupled to the neural engine to send the compressed spectrogram to a remote destination, to enable the remote destination to process the compressed spectrogram.

Compressing information in an end node using an autoencoder neural network

An apparatus includes a radio-frequency (RF) receiver for receiving an RF signal using a plurality of antennas. The RF receiver includes a demodulator to provide a switch signal to cause the RF receiver to use an antenna in the plurality of antennas. The RF receiver further includes a carrier frequency offset (CFO) correction circuit that uses an estimation of the carrier frequency offset and an estimation of phase differences to remove the carrier frequency offset.

Apparatus for receiver with carrier frequency offset correction using phase and frequency information and associated methods

A system and method of keyword spotting using two neural networks is disclosed. The system is in sleep mode most of the time, and wakes up periodically. Upon waking, a limited duration of audio is examined. This may be performed using an auxiliary neural network. If any audio activity is detected in this duration, the system fully wakes and examines a longer duration of audio for keywords. The keyword spotting is also performed by the main neural network, which may be a convolutional neural network (CNN).

Keyword spotting using machine learning

A system and method for detecting a multipath environment is disclosed. A first pseudospectrum based on azimuth angle and elevation angle is created. The result of this first pseudospectrum are used to create a second pseudospectrum based on polarization and field ratio. The sharpness of the results for these two pseudospectrums is determined and may be used to detect whether a multipath environment exists. If a multipath environment is believed to exist, the results from this device are ignored in determining the spatial position of the object.

AoX multipath detection

A system and method of determining the angle of arrival or departure using a neural network is disclosed. The system collects a plurality of I and Q samples as a packet containing a constant tone extension is being received. The I and Q samples are used to form I and Q arrays, which are used as the input to the neural network. The neural network produces a first output representative of the azimuth angle and a second output representative of the elevation angle. In certain embodiments, the neural network is capable of detecting a plurality of angles, where, for each angle, there are three outputs, a first output representative of the azimuth angle, a second output representative of the elevation angle and a third output representative of the relative amplitude. In some embodiments, the neural network is configured to determine the carrier frequency offset of an incoming signal as well.

Angle of arrival using machine learning

A system and method of recording and transmitting compressed audio signals over a network is disclosed. The end node device first converts the audio signal to a spectrogram, which is commonly used by machine learning algorithms to perform speech recognition. The end node device then compresses the spectrogram prior to transmission. In certain embodiments, the compression is performed using Discrete Cosine Transforms (DCT). Furthermore, in some embodiments, the DCT is performed on the difference between two columns of the spectrogram. Further, in some embodiments, a function that replaces values below a predetermined threshold with zeroes in the Encoded Spectrogram is utilized. These functions may be performed in hardware or software.

End node spectrogram compression for machine learning speech recognition

In one embodiment, an apparatus includes: an antenna switch to receive a first radio frequency (RF) signal from a first antenna and a second RF signal from a second antenna and controllable to output a selected one of the first and second RF signals; an RF circuit to receive and process the first and second RF signals; at least one mixer to downconvert the first and second RF signals to first and second baseband signals; and an antenna diversity control circuit to receive sub-symbol portions of a first plurality of symbols of the first baseband signal and sub-symbol portions of a second plurality of symbols of the second baseband signal, and control the antenna switch to output one of the first and second RF signals, based at least in part on one or more of the sub-symbol portions of the first and second pluralities of symbols.

System, apparatus and method for performing antenna diversity selection based on multi-symbol correlations

A system and method for detecting and compensating for carrier frequency offset is disclosed. This system compensates for CFO and calculates a corrected phase. This corrected phase may be used by, for example, an AoX algorithm, such as MUSIC, to more accurately determine the angle of arrival or angle of departure of a signal. In certain embodiments, the system oversamples the incoming signal to create a plurality of samples. The system then determines the phase of each of the plurality of samples and calculates the carrier frequency based on the time derivative of the phase. In certain embodiments, a particular portion of an incoming packet is used to determine the carrier frequency offset. In other embodiments, the system calculates the carrier frequency offset throughout an entirety of the incoming packet. Once the carrier frequency offset is determined, it can be used to adjust the received signals. These adjusted signals are then used to determine the angle of arrival or angle of departure.

Angle of arrival carrier frequency offset correction

Growing community of inventors

Austin, TX, United States of America

Antonio Torrini