Abstract: Capturing, presenting parameters related to and affecting physical systems to generate a digital twin that helps in operating, maintaining, monitoring, upgrading systems to deliver the desired operational results, such as for distributed antenna systems and in-building systems. Systems, methods are for seamless measurement collection, analysis, and integration with design software to create and maintaining exact as-built digital twins and capturing deviation of deployed systems from designs. Facilitate determining positioning using distributed antenna system with service availability monitoring. Positioning methods include network-based methods, handset-assisted methods in addition to a monitoring system to report any service outage and possible location information loss. Combined monitoring system that monitors antenna output power for mobile coverage and service availability helps also in monitoring the availability of the localization system and dynamic update of lookup information.

Abstract: A microgrid (MG) controller can control a plurality of distributed generators (DGs) of photovoltaic (PV) arrays. The MG controller can include a power sharing unit configured to share power proportionally in a single current controlled mode (CCM) between the plurality of DGs based on a power capacity for each DG. The power sharing unit can include a robust current droop controller (RCDC) configured to provide proportional active current shared between the plurality of DGs. The power sharing unit can also include a quasi-proportional resonant (QPR) regulator coupled to the RCDC within a unified architecture. The QPR regulator can be configured to regulate a reference current signal.

Abstract: A device (e.g., an image sensor, camera, etc.) may identify a camera lens and color filter array (CFA) sensor used to capture an image, and may determine filter parameters (e.g., a convolutional operator) based on the identified camera lens and CFA sensor. For example, a set of kernels (e.g., including a set of horizontal filters and a set of vertical filters) may be determined based on properties of a given lens and/or q-channel CFA sensor. Each kernel or filter may correspond to a row of a convolutional operator (e.g., of a restoration bit matrix) used by an image signal processor (ISP) of the device for non-linear filtering of the captured image. The corresponding outputs from the horizontal and vertical filters (e.g., two outputs of the horizontal and vertical filters corresponding to an input channel associated with the CFA sensor) may then be combined using a non-linear classification operation.

Abstract: A device (e.g., an image sensor, camera, etc.) may identify a camera lens and color filter array (CFA) sensor used to capture an image, and may determine filter parameters (e.g., a convolutional operator) based on the identified camera lens and CFA sensor. For example, a set of kernels (e.g., including a set of horizontal filters and a set of vertical filters) may be determined based on properties of a given lens and/or q-channel CFA sensor. Each kernel or filter may correspond to a row of a convolutional operator (e.g., of a restoration bit matrix) used by an image signal processor (ISP) of the device for non-linear filtering of the captured image. The corresponding outputs from the horizontal and vertical filters (e.g., two outputs of the horizontal and vertical filters corresponding to an input channel associated with the CFA sensor) may then be combined using a non-linear classification operation.

Abstract: Systems and methods are disclosed for processing spectral imaging (SI) data. A training operation estimates reconstruction matrices based on a spectral mosaic of an SI sensor, generates directionally interpolated maximum a-priori (MAP) estimations of image data based on the estimated reconstruction matrices. The training operation may determine filter coefficients for each of a number of cross-band interpolation filters based at least in part on the MAP estimations, and may determine edge classification factors based at least in part on the determined filter coefficients. The training operation may configure a cross-band interpolation circuit based at least in part on the determined filter coefficients and the determined edge classification factors. The configured cross-band interpolation circuit captures mosaic data using the SI sensor, and recovers full-resolution spectral data from the captured mosaic data.

Abstract: Systems and methods are disclosed for processing spectral imaging (SI) data. A training operation estimates reconstruction matrices based on a spectral mosaic of an SI sensor, generates directionally interpolated maximum a-priori (MAP) estimations of image data based on the estimated reconstruction matrices. The training operation may determine filter coefficients for each of a number of cross-band interpolation filters based at least in part on the MAP estimations, and may determine edge classification factors based at least in part on the determined filter coefficients. The training operation may configure a cross-band interpolation circuit based at least in part on the determined filter coefficients and the determined edge classification factors. The configured cross-band interpolation circuit captures mosaic data using the SI sensor, and recovers full-resolution spectral data from the captured mosaic data.

Abstract: A computer system for improving the presentation of social media data from multiple social network feeds is provided. The computer system may include aggregating social media content received from the multiple social network feeds. The computer system may also include generating filtered data by eliminating repetitive data from among the received aggregated social media content. The computer system may further include analyzing the filtered data for determining at least one data category and presenting a digest of social media content based on the determined at least one data category.

Abstract: A method for improving the presentation of social media data from multiple social network feeds is provided. The method may include aggregating social media content received from the multiple social network feeds. The method may also include generating filtered data by eliminating repetitive data from among the received aggregated social media content. The method may further include analyzing the filtered data for determining at least one data category and presenting a digest of social media content based on the determined at least one data category.

Abstract: A method for improving the presentation of social media data from multiple social network feeds is provided. The method may include aggregating social media content received from the multiple social network feeds. The method may also include generating filtered data by eliminating repetitive data from among the received aggregated social media content. The method may further include analyzing the filtered data for determining at least one data category and presenting a digest of social media content based on the determined at least one data category.

Abstract: A computer system for improving the presentation of social media data from multiple social network feeds is provided. The computer system may include aggregating social media content received from the multiple social network feeds. The computer system may also include generating filtered data by eliminating repetitive data from among the received aggregated social media content. The computer system may further include analyzing the filtered data for determining at least one data category and presenting a digest of social media content based on the determined at least one data category.

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 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 method and apparatus for secure data entry by sensing biometric information and determining a desired data based on the biometric information. The method can include sensing a biometric characteristic of an object, generating an expression by associating the biometric characteristic with enrollment data, the expression identifying at least a portion of a specific finger unique to a individual user of the multipurpose input device, determining positional information corresponding to a source location of the biometric characteristic, and generating a token representing a desired data entry based on the expression and the positional information. The positional information or the expression can indicate a subset of a set of potential data entry values and the combination of the positional information and the expression can uniquely define the token representing a desired data entry of the subset of potential data entry values.