Abstract: A determination of a position of a user equipment, includes: obtaining measurements of signals transmitted by a plurality of satellites; determining one or more residuals based on the measurements for the plurality of satellites; determining one or more corrected residuals based on one or more relationships between the measurements for the plurality of satellites in the current position fix, or between the measurements for the plurality of satellites in the current position fix and measurements for the plurality of satellites in one or more previous position fixes for the plurality of satellites; and determining the position of the user equipment based on the one or more corrected residuals and the measurements for the plurality of satellites.

Abstract: Disclosed are techniques for wireless positioning. In some aspects, a user equipment (UE) may obtain one or more global navigation satellite system (GNSS) measurements. The UE may obtain map data. The UE may obtain a most likely position of the UE based on one or more prediction algorithms and one or more neural network (NN) models applied to the one or more GNSS measurements and the map data.

Abstract: A method of determining a position of a device includes obtaining an initial position of the device without using Global Navigation Satellite System (GNSS) satellites. GNSS measurements are taken of radio frequency (RF) signals transmitted by the GNSS satellites. Initial residuals are determined based, at least in part, on GNSS measured distances determined from the at least a portion of the GNSS measurements and expected distances determined from the initial position. Errors of the GNSS measurements based on the RF signals are estimated. An optimization is performed using some of the estimated errors to produce a modified set of residuals, wherein the optimization is further based on H, wherein H represents a matrix with trigonometric functions of a geometry of the GNSS satellites. A cost minimization method of the modified set of residuals and actual geometry of the GNSS satellites (H) to determine an improved position of the device.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for cache aware dynamic module selection for a computation model. An example method generally includes generating at least one output, in a first inference round, using a first subset of modules of a computational model loaded in a cache memory from another memory, evaluating modules of the computational model to use for a second inference round, using a function that biases evaluation of the first subset of modules of the computational model already in the cache, and performing the second inference round with a second subset of modules of the computational module, based on the evaluation.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for biometric authentication using an anti-spoofing protection model refined using online data. The method generally includes receiving a biometric data input for a user. Features for the received biometric data input are extracted through a first machine learning model. It is determined, using the extracted features for the received biometric data input and a second machine learning model, whether the received biometric data input for the user is authentic or inauthentic. It is determined whether to add the extracted features for the received biometric data input, labeled with an indication of whether the received biometric data input is authentic or inauthentic, to a finetuning data set. The second machine learning model is adjusted based on the finetuning data set.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for biometric authentication using an anti-spoofing protection model refined using online data. The method generally includes receiving a biometric data input for a user. Features for the received biometric data input are extracted through a first machine learning model. It is determined, using the extracted features for the received biometric data input and a second machine learning model, whether the received biometric data input for the user is authentic or inauthentic. It is determined whether to add the extracted features for the received biometric data input, labeled with an indication of whether the received biometric data input is authentic or inauthentic, to a finetuning data set. The second machine learning model is adjusted based on the finetuning data set.

Abstract: In some implementations, a global navigation satellite system (GNSS) device may determine its approximate location, and, for each pseudorange measurement of a plurality of pseudorange measurements performed by the GNSS device: determine a location of a respective satellite vehicle (SV) that transmits a respective GNSS signal of which the pseudorange measurement is performed, and determine a respective residual grid, where the respective residual grid is based on respective information from the pseudorange measurement and the location of the respective SV, and the respective residual grid is indicative of possible locations of the GNSS device within a geographical region including the approximate location of the GNSS device. The GNSS device may aggregate the residual grids corresponding to at least a portion of the plurality of pseudorange measurements and may determine a location estimate of the GNSS device based on the aggregation of the residual grids.

Abstract: A method of determining a position of a device includes obtaining an initial position of the device without using Global Navigation Satellite System (GNSS) satellites. GNSS measurements are taken of radio frequency (RF) signals transmitted by the GNSS satellites. Initial residuals are determined based, at least in part, on GNSS measured distances determined from the at least a portion of the GNSS measurements and expected distances determined from the initial position. Errors of the GNSS measurements based on the RF signals are estimated. An optimization is performed using some of the estimated errors to produce a modified set of residuals, wherein the optimization is further based on H, wherein H represents a matrix with trigonometric functions of a geometry of the GNSS satellites. A cost minimization method of the modified set of residuals and actual geometry of the GNSS satellites (H) to determine an improved position of the device.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for biometric authentication using an anti-spoofing protection model refined using online data. The method generally includes receiving a biometric data input for a user. Features for the received biometric data input are extracted through a first machine learning model. It is determined, using the extracted features for the received biometric data input and a second machine learning model, whether the received biometric data input for the user is authentic or inauthentic. It is determined whether to add the extracted features for the received biometric data input, labeled with an indication of whether the received biometric data input is authentic or inauthentic, to a finetuning data set. The second machine learning model is adjusted based on the finetuning data set.

Abstract: Certain aspects of the present disclosure provide techniques and apparatus for biometric authentication using neural-network-based anti-spoofing protection mechanisms. An example method generally includes receiving an image of a biometric data source for a user; extracting, through a first artificial neural network, features for at least the received image; combining the extracted features for the at least the received image and a combined feature representation of a plurality of enrollment biometric data source images; determining, using the combined extracted features for the at least the received image and the combined feature representation as input into a second artificial neural network, whether the received image of the biometric data source for the user is from a real biometric data source or a copy of the real biometric data source; and taking one or more actions to allow or deny the user access to a protected resource based on the determination.