Abstract: The present disclosure addresses unresolved problem of monopoly in content authoring, creation of dependency loop by providing a single, scalable, one-stop knowledge platform offering high degree of customization by multiple users and leveraging emerging technologies for rapid end-to-end game or learning solutions development. Embodiment of the present disclosure provides methods and systems for providing customized game-based learning and assessment framework. The present disclosure provides a mechanism to configure custom content into a readymade learning and assessment platform. The framework proposed in the present disclosure is custom built to support scalable architecture of a modular digital learning platform, and its functionalities. The system of the present disclosure allows content creators to author custom content to create immersive learning experience that could range from Spatial three-dimensional (3D) technologies, Augmented Reality (AR) and Virtual Reality (VR) or games at one place.

Abstract: Present disclosure relates to method and synchronization system for synchronizing plurality of events associated with one or more processes in assembly line for tracing entity. Initially, information related to plurality of events associated with one or more processes from one or more devices is received, where each of the plurality of events comprises respective first timestamp. Upon receiving, the first timestamp between each of the plurality of events is synchronized by performing first level and second level synchronization. The synchronization converts first timestamp into second timestamp with respect to common reference timestamp. Further, the synchronization system may identify one or more defects based on quality value assigned to entity. Thus, the present disclosure helps the synchronization system to efficiently trace entity when a defect is identified and replaces/repairs the entity.

Abstract: The present disclosure addresses unresolved problem of monopoly in content authoring, creation of dependency loop by providing a single, scalable, one-stop knowledge platform offering high degree of customization by multiple users and leveraging emerging technologies for rapid end-to-end game or learning solutions development. Embodiment of the present disclosure provides methods and systems for providing customized game-based learning and assessment framework. The present disclosure provides a mechanism to configure custom content into a readymade learning and assessment platform. The framework proposed in the present disclosure is custom built to support scalable architecture of a modular digital learning platform, and its functionalities. The system of the present disclosure allows content creators to author custom content to create immersive learning experience that could range from Spatial three-dimensional (3D) technologies, Augmented Reality (AR) and Virtual Reality (VR) or games at one place.

Abstract: Techniques for efficiently and accurately computing log-likelihood ratio (LLRs) for code bits are described. A set of code bits may be mapped to a modulation symbol in a signal constellation. Different code bits in the set may be associated with different LLR functions. A receiver obtains received symbols for a transmission sent via a communication channel. The receiver derives LLRs for code bits based on the received symbols and piecewise linear approximation of at least one LLR function. The piecewise linear approximation of each LLR function may comprise one or more linear functions for one or more ranges of input values. The receiver may select one of the linear functions for each code bit based on a corresponding received symbol component value. The receiver may then derive an LLR for each code bit based on the linear function selected for that first code bit.

Abstract: Techniques for performing equalization at a receiver are described. In an aspect, equalization is performed by sub-sampling an over-sampled input signal to obtain multiple sub-sampled signals. An over-sampled channel impulse response estimate is derived and sub-sampled to obtain multiple sub-sampled channel impulse response estimates. At least one set of equalizer coefficients is derived based on at least one sub-sampled channel impulse response estimate. At least one sub-sampled signal is filtered with the at least one set of equalizer coefficients to obtain at least one output signal. One sub-sampled signal (e.g., with largest energy) may be selected and equalized based on a set of equalizer coefficients derived from an associated sub-sampled channel impulse response estimate. Alternatively, the multiple sub-sampled signals may be equalized based on multiple sets of equalizer coefficients, which may be derived separately or jointly.

Abstract: This disclosure describes equalization techniques for spread spectrum wireless communication. The techniques may involve estimating a channel impulse response, estimating channel variance, and selecting filter coefficients for an equalizer based on the estimated channel impulse response and the estimated channel variance. Moreover, in accordance with this disclosure, the channel variance estimation involves estimation of two or more co-variances for different received samples. Importantly, the equalizer is “fractionally spaced,” which means that the equalizer defines fractional filtering coefficients (filter taps), unlike conventional equalizers that presume that filter coefficients are defined at integer chip spacing. The techniques can allow the equalizer to account for antenna diversity, such as receive diversity, transmit diversity, or possibly both.

Abstract: Techniques for performing equalization at a receiver are described. In an aspect, equalization is performed by sub-sampling an over-sampled input signal to obtain multiple sub-sampled signals. An over-sampled channel impulse response estimate is derived and sub-sampled to obtain multiple sub-sampled channel impulse response estimates. At least one set of equalizer coefficients is derived based on at least one sub-sampled channel impulse response estimate. At least one sub-sampled signal is filtered with the at least one set of equalizer coefficients to obtain at least one output signal. One sub-sampled signal (e.g., with largest energy) may be selected and equalized based on a set of equalizer coefficients derived from an associated sub-sampled channel impulse response estimate. Alternatively, the multiple sub-sampled signals may be equalized based on multiple sets of equalizer coefficients, which may be derived separately or jointly.

Abstract: Techniques for performing equalization at a receiver are described. In an aspect, equalization is performed by sub-sampling an over-sampled input signal to obtain multiple sub-sampled signals. An over-sampled channel impulse response estimate is derived and sub-sampled to obtain multiple sub-sampled channel impulse response estimates. At least one set of equalizer coefficients is derived based on at least one sub-sampled channel impulse response estimate. At least one sub-sampled signal is filtered with the at least one set of equalizer coefficients to obtain at least one output signal. One sub-sampled signal (e.g., with largest energy) may be selected and equalized based on a set of equalizer coefficients derived from an associated sub-sampled channel impulse response estimate. Alternatively, the multiple sub-sampled signals may be equalized based on multiple sets of equalizer coefficients, which may be derived separately or jointly.

Abstract: Techniques for efficiently and accurately computing log-likelihood ratio (LLRs) for code bits are described. A set of code bits may be mapped to a modulation symbol in a signal constellation. Different code bits in the set may be associated with different LLR functions. A receiver obtains received symbols for a transmission sent via a communication channel. The receiver derives LLRs for code bits based on the received symbols and piecewise linear approximation of at least one LLR function. The piecewise linear approximation of each LLR function may comprise one or more linear functions for one or more ranges of input values. The receiver may select one of the linear functions for each code bit based on a corresponding received symbol component value. The receiver may then derive an LLR for each code bit based on the linear function selected for that first code bit.

Abstract: This disclosure describes equalization techniques for spread spectrum wireless communication. The techniques may involve estimating a channel impulse response, estimating channel variance, and selecting filter coefficients for an equalizer based on the estimated channel impulse response and the estimated channel variance. Moreover, in accordance with this disclosure, the channel variance estimation involves estimation of two or more co-variances for different received samples. Importantly, the equalizer is “fractionally spaced,” which means that the equalizer defines fractional filtering coefficients (filter taps), unlike conventional equalizers that presume that filter coefficients are defined at integer chip spacing. The techniques can allow the equalizer to account for antenna diversity, such as receive diversity, transmit diversity, or possibly both.

Abstract: Techniques for performing equalization at a receiver are described. In an aspect, equalization is performed by sub-sampling an over-sampled input signal to obtain multiple sub-sampled signals. An over-sampled channel impulse response estimate is derived and sub-sampled to obtain multiple sub-sampled channel impulse response estimates. At least one set of equalizer coefficients is derived based on at least one sub-sampled channel impulse response estimate. At least one sub-sampled signal is filtered with the at least one set of equalizer coefficients to obtain at least one output signal. One sub-sampled signal (e.g., with largest energy) may be selected and equalized based on a set of equalizer coefficients derived from an associated sub-sampled channel impulse response estimate. Alternatively, the multiple sub-sampled signals may be equalized based on multiple sets of equalizer coefficients, which may be derived separately or jointly.

Abstract: Techniques for incorporating non-pilot symbols along with pilot symbols to improve the estimate of the characteristics (e.g., amplitude and phase) of a communication link. A pilot filter weighs samples corresponding to pilot and non-pilot symbol by different sets of coefficients, which have values determined by and/or corresponding to the confidence in the detected sample. Samples corresponding to pilot symbols are typically associated with higher degree of confidence and are weighted more (e.g., with weights of 1.0). Samples corresponding to non-pilot symbols are typically associated with lower confidence and are weighted with values that may be variable and dependent on the degree of confidence in the samples (e.g., with weights ranging from 0.0 up to 1.0). The weights are updated based on a particular estimator such as a MAP (Maximum a Posteriori) estimator, a MLE (Maximum Likelihood Estimator), or some other estimator.

Abstract: Techniques to improve the performance of a Turbo decoder when scale information for the bits in a code segment to be decoded is not known. A number of hypotheses are formed for the code segment, with each hypothesis corresponding to a particular set of one or more values for a set of one or more parameters used for decoding the code segment. For the MAP decoding scheme, these parameters may be for the sequence of scaling factors used to scale the bits prior to decoding and/or a scale used to evaluate a (e.g., min*) function for the MAP decoding. The code segment is decoded based on the MAP decoding scheme and in accordance with each hypothesis. The quality of the decoded result for each hypothesis is determined based on one or more performance metrics. The decoded bits for the best hypothesis are provided as the Turbo decoder output.

Abstract: Techniques for performing finger merge protection (FMP) using polling. Whenever a command to adjust the timing of a designated finger is received, other fingers “on the same channel” (i.e., those for which merge protection is sought) are polled for permission to apply the command. The designated finger sets a poll request and provides other pertinent information to an FMP storage unit. Thereafter, each of the other fingers on the same channel determines whether or not applying the command would result in that finger merging with the designated finger. Merging may be detected if the difference between the timing (or offsets) of the two fingers is less than a specified offset spacing. Any finger on the same channel may disallow the command if merging is detected. The designated finger would then adjust its timing if the command is not disallowed by any of the fingers on the same channel.

Abstract: Techniques to improve the performance of a Turbo decoder when scale information for the bits in a code segment to be decoded is not known. A number of hypotheses are formed for the code segment, with each hypothesis corresponding to a particular set of one or more values for a set of one or more parameters used for decoding the code segment. For the MAP decoding scheme, these parameters may be for the sequence of scaling factors used to scale the bits prior to decoding and/or a scale used to evaluate a (e.g., min*) function for the MAP decoding. The code segment is decoded based on the MAP decoding scheme and in accordance with each hypothesis. The quality of the decoded result for each hypothesis is determined based on one or more performance metrics. The decoded bits for the best hypothesis are provided as the Turbo decoder output.

Abstract: Techniques for incorporating non-pilot symbols along with pilot symbols to improve the estimate of the characteristics (e.g., amplitude and phase) of a communication link. A pilot filter weighs samples corresponding to pilot and non-pilot symbol by different sets of coefficients, which have values determined by and/or corresponding to the confidence in the detected sample. Samples corresponding to pilot symbols are typically associated with higher degree of confidence and are weighted more (e.g., with weights of 1.0). Samples corresponding to non-pilot symbols are typically associated with lower confidence and are weighted with values that may be variable and dependent on the degree of confidence in the samples (e.g., with weights ranging from 0.0 up to 1.0). The weights are updated based on a particular estimator such as a MAP (Maximum a Posteriori) estimator, a MLE (Maximum Likelihood Estimator), or some other estimator.