Motorola Corporation

Motorola Mobility LLC

Magdi A Mohamed

Weimin Xiao

Irfan Nasir

Tom Mathew

Mark A Tarlton

Chi Zhou

Faisal Ishtiaq

George Thomas Valliath

Renxiang Li

William L Olson

Eric Rolse Buhrke

Jon Lindsay Schindler

Carl M Danielsen

Mohamed I Ahmed

Amir S Ibrahim

A method and system for collaborative communications is described. In one embodiment, a central virtual reality communications environment is created. A plurality of client communication devices are connected to the central virtual reality communications environment. Each one of the connected plurality of client communication devices are represented as an avatar present in the central virtual reality communications environment. An uploaded data object is received from any one of the connected plurality of client communication devices. Finally, the data object is displayed in the central virtual reality communications environment to the connected plurality of client communication devices.

Method and system for distributed collaborative communications

A signal processing method and power amplifier device are disclosed. The method may include receiving a signal to be transmitted, decomposing an original signal into a plurality of smaller constant-amplitude signals, wherein a vector sum each of the smaller constant-amplitude signals equals the original signal, amplifying the smaller constant-amplitude signals by an amplification factor using a plurality of amplifiers, wherein one or more of the plurality of amplifiers are enabled based on the amplitude of the original signal, combining the amplified smaller constant-amplitude signals into the original signal, the original signal being amplified by the amplification factor, wherein the amplified original signal is transmitted.

Signal processing method and power amplifier device

A recursive lambda rule engine () includes a first multiplier () that sequentially multiplies each of series of inputs by a nonlinearity determining parameter and supplies results to a second multiplier () that multiplies the output of the first multiplier () by a previous output of the engine (). A three input adder () sequentially sums the output of the second multiplier (), inputs from the series of inputs, and the previous output of the engine (). A shift register () is used to feedback the output of the engine () to the three input adder () and second multiplier (). A MUX () is used to route an initial value through the shift register () for the first cycle of operation.

Hardware arithmetic engine for lambda rule computations

A risk management system () for first responders () equips first responders with physiological and environmental sensors () in order to track the stress conditions and stress responses of the first responders. Data from the sensors is digitized and filtered and processed through a feature extraction system () that may include noise filters, () derivative filters (), integrators () and derived signal generators () that use stored data on first responders, before being fed into a pattern recognition system () that assesses the risk state of the first responder. Data from multiple first responders in a team can be aggregated to obtain team risk assessments.

Intelligent risk management system for first responders

A method () and apparatus () for aggregating two or more input signals with a versatile reconfigurable signal aggregator. The aggregator () is reconfigured by adjusting a control signal &#x3bb;, and can emulate a range of union type signal aggregators, a range of intersection type signal aggregators, and a continuum of functions between the two, including a signal averager. The versatility of the aggregator () allows systems in which the aggregator () is incorporated to be highly adaptable, and thereby fosters improved machine learning.

Method and apparatus for nonlinear signal and data aggregation

Signal processing networks () that include a configurable infinite logic aggregator () that can be configured as an infinite logic AND gate and infinite logic OR gate or as other gates along a continuum of function between the two by adjusting control signal magnitudes and a configurable infinite logic signal inverter () are provided. A method of designing such networks that includes a genetic programming program () e.g., a gene expression programming program (), for designing the network topology, in combination with a numerical optimization (), e.g., a hybrid genetic algorithm/differential evolution numerical optimization () for setting control signal values of the network and optionally other numerical parameters is provided.

Configurable infinite logic signal processing network and genetic computing method of designing the same

Computer programs () and a programmed computer () for automatically generating computer programs (i.e. sequences of instructions) are provided. The computer programs () use Hidden Markov Models () to generate sequences of program tokens, e.g., Gene Expression Programming chromosomes (). Parameters of the Hidden Markov Models () are numerically optimized, for example, by Differential Evolution with a goal of increasing the fitness of automatically generated programs.

Gene expression programming based on Hidden Markov Models

An nonlinear digital signal processing filter () maintains a magnitude ordering for successive windows of signal samples. A set of filter density generator values [f, f, f. . . f. . . f] are used according to the ordering in a recursion relation that computes successive values of a set function over the set of filter density generator values. The recursion relation involves an adjustable nonlinearity defining parameter &#x3bb;. The values are normalized by dividing by a largest of the values, and differences between successive values are taken. An inner product between each window of signal values (used in order according to magnitude) and the adaptive differences is a filtered signal sample.

Fast Q-filter

A Q-Filter is a reconfigurable technique that performs a continuum of linear and nonlinear filtering operations. It is modeled by unique mathematical structure, utilizing a function called the Q-Measure, defined using a set of adjustable kernel parameters to enable efficient hardware and software implementations of a variety of useful, new and conventional, filtering operations. The Q-Measure is based on an extension of the well-known Sugeno &#x3bb;-Measure. In order to optimize the Q-Filter kernel parameters, the value of an error function is minimized. The error function is based on difference between the filtered signal and target signal, with the target signal being a desired result of filtering.

Method for optimization of Q-Filter kernel parameters

A multi-sensor system includes a first analog sensor sub-system, a second analog sensor sub-system, and a system for synchronizing the outputs of the first and second analog sub-systems. Each analog sensor sub-system includes a sensor that produces an analog output. Each sensor is coupled to analog circuitry that processes the output from the sensor. The system for synchronizing the outputs of the first and second analog sensor sub-systems simultaneously inserts a marker into the outputs of the first and second analog sensors. Then, the outputs of the analog circuitry of the first and second analog sub-systems are synchronized based upon the marker. The marker signal may be produced using a Barker sequence signal generator.

System for synchronization of multi-sensor data

In a parallel computation of a Hough transform of an array of input data values, the transform space of the Hough transform is partitioned dynamically or statically into a number of sub-spaces. Each sub-space of the transform is stored in a sub-space of memory locations. Data values from the array of input data values are passed to a plurality of processors, each processor associated dynamically or statically with a sub-space of memory locations. Each processor, acting in parallel with the other processors, updates constituent elements of the Hough transform stored in the associated sub-space memory locations dependent upon the input data value.

Method and system for parallel processing of Hough transform computations

A Q-Filter is a reconfigurable technique that performs a continuum of linear and nonlinear filtering operations. It is modeled by unique mathematical structure, utilizing a function called the Q-Measure, defined using a set of adjustable kernel parameters to enable efficient hardware and software implementations of a variety of useful, new and conventional, filtering operations. The Q-Measure is is based on an extension of the well-known Sugeno &#x3bb;-Measure.

Method and apparatus for digital signal filtering

A first language to second language translator () converts text comprising a message in a first language into text representing that message in a second language. A text to speech transducer () then synthesizes an audiblized version of the text in the second language. An animated display driver () creates a corresponding display () of an image () that includes a mouth () that moves appropriately and synchronously with respect to the audiblized second language. In other embodiments, other facial features can be animated as well. Such animation can be random and unrelated to the appearance of the original speaker. Or, such animation can accurately represent at least some alterations to facial features of the original speaker while speaking. In some embodiments, the facial features can be altered by correlating specific detected alterations with specific corresponding substantive content in the original message and the translated text.

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Schaumburg, IL, United States of America

Magdi A Mohamed