Abstract: An Artificial Neural Network (110) includes a hidden layer (209) of distance metric computer nodes (210, 214, 218) that evaluate distances of a input vector from metric space centers, an additional layer of adaptable infinite logic aggregators (236, 240, 244) that combine the per-unit distance output values by the distance metric computer nodes (210, 214, 218) using adaptable infinite logic. In certain embodiments the adaptable infinite logic aggregators include veracity signal pre-processors (602, 702) that can be configured to make inferences in a continuum from positive to negative including no inference from each distance and infinite logic connective signal processors (604, 702) that can implement a continuum of functions covering the range of fuzzy logic union operators, fuzzy logic intersection operators, and all linear and nonlinear averaging operators between them. Control parameters (e.g.

Abstract: Automatic use (102) of a disjoint probabilistic analysis of captured temporally parsed data (101) regarding at least a first and a second item serves to facilitate disambiguating state information as pertains to the first item from information as pertains to the second item. This can also comprise, for example, using a joint probability as pertains to the temporally parsed data for the first item and the temporally parsed data for the second item, by using, for example, a Bayesian-based probabilistic analysis of the temporally parsed data.

Abstract: A method (1500) and apparatus (700, 2300, 2400) for aggregating two or more input signals with a versatile reconfigurable signal aggregator. The aggregator (700, 2300, 2400) is reconfigured by adjusting a control signal ?, 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 (700, 2300, 2400) allows systems in which the aggregator (700, 2300, 2400) is incorporated to be highly adaptable, and thereby fosters improved machine learning.

Abstract: A risk management system (100) for first responders (102) equips first responders with physiological and environmental sensors (104) 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 (202, 704) that may include noise filters, (710) derivative filters (706), integrators (708) and derived signal generators (712) that use stored data on first responders, before being fed into a pattern recognition system (402, 900) 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.

Abstract: 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.

Abstract: A recursive lambda rule engine (114, 302) includes a first multiplier (204) that sequentially multiplies each of series of inputs by a nonlinearity determining parameter and supplies results to a second multiplier (214) that multiplies the output of the first multiplier (204) by a previous output of the engine (114, 302). A three input adder (220, 228) sequentially sums the output of the second multiplier (214), inputs from the series of inputs, and the previous output of the engine (114, 302). A shift register (244) is used to feedback the output of the engine (114, 302) to the three input adder (220, 228) and second multiplier (214). A MUX (234) is used to route an initial value through the shift register (244) for the first cycle of operation.

Abstract: A pattern recognition system (100, 900, 1202, 1300) includes a configurable distance metric evaluator (112, 600, 1204). The configurable distance metric evaluator (112, 600, 1204) is adaptable, via a configuration parameter to better match distributions of feature vectors within classifications and clusters and moreover to better match boundaries between feature vector subspaces associated with different classifications or clusters, and therefore provides for reduced pattern recognition errors.

Abstract: Software (100, 600, 1000, 1100) for automatically designing and optimizing signal processing networks (e.g., 200, 700, 800, 900) is provided. The software use genetic programming e.g., gene expression programming in combination with numerical optimization, e.g., a hybrid differential evolution/genetic algorithm numerical optimization to design and optimize signal processing networks.

Abstract: A hardware sorter comprises a comparator matrix (104) for checking if each number in an unsorted array input (102) is at least equal to each other number, a set of column summers (108) for counting the number of numbers that each number is at least equal to, a decoder array (112) for decoding the count, a matrix of partial row summers (116) for locating ties, A set of shift registers (130) and shift controllers (128) for shifting output (114) of the decoder array (112) to separate ties. The shifted output can be encoded row-by-row to create a permutation array (134) that determines a sort, and is used as select inputs for a set of multiplexers (136), or can be applied to switch inputs (1104) of a crossbar switch (1102).

Abstract: Signal processing networks (700, 800, 1008, 1010, 1012) that include a configurable infinite logic aggregator (100) 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 (500) are provided. A method of designing such networks that includes a genetic programming program (1802) e.g., a gene expression programming program (1600), for designing the network topology, in combination with a numerical optimization (1804), e.g., a hybrid genetic algorithm/differential evolution numerical optimization (1700) for setting control signal values of the network and optionally other numerical parameters is provided.

Abstract: An nonlinear digital signal processing filter (100, 200, 1100, 1308, 1310, 1312, 1346, 1604) maintains a magnitude ordering for successive windows of signal samples. A set of filter density generator values [f1, f2, f3 . . . fj . . . fndensities] 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 ?. 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.

Abstract: A method, apparatus, and electronic device for generating outphased signals are disclosed. The method may include determining bitmap representations of two binary circles, storing the bitmap representations of the two binary circles, generating two constant-amplitude signals from an original signal using bitmap overlapping of the stored bitmap representations where the vector sum of the two constant-amplitude signals equals the original signal, and outputting the two constant-amplitude signals for use in an electronic device.

Abstract: Temporally parsed data regarding at least a first item is captured (101). This temporally parsed data comprises data that corresponds to substantially simultaneous sequential samples of the first item with respect to at least a first and a second different points of view. Conditional probabilistic analysis of at least some of this temporally parsed data is then automatically used (102) to disambiguate state information as pertains to this first item. This conditional probabilistic analysis comprises analysis of at least some of the temporally parsed data as corresponds in a given sample to both the first point of reference and the second point of reference.

Abstract: 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.

Abstract: A method for detecting heart rate, the method including the steps of: receiving (100) a heart activity signal at an input of a Q-filter processor that includes at least a first and a second Q-filter; removing noise (300) from the heart activity signal using the Q-filter processor to generate a filtered heart activity signal; detecting (400) a heart activity pattern, that includes at least a first S1 or QRS at a first time and a second S1 or QRS at a second time, from the filtered heart activity signal; and determining (500) a heart rate value based on the time interval between the first S1 and the second S1 or between the first QRS and the second QRS.

Abstract: 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.

Abstract: 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 ?-Measure.

Abstract: 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 ?-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.

Abstract: 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 ?-Measure.

Abstract: A first language to second language translator (11) converts text comprising a message in a first language into text representing that message in a second language. A text to speech transducer (12) then synthesizes an audiblized version of the text in the second language. An animated display driver (14) creates a corresponding display (15) of an image (16) that includes a mouth (17) 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.