Abstract:
A method for configuring an artificial neuron includes receiving a set of input spike trains comprising asynchronous pulse modulation coding representations. The method also includes generating output spikes representing a similarity between the set of input spike trains and a spatial-temporal filter.
Abstract:
Methods and apparatus for piecewise linear neuron modeling and implementing artificial neurons in an artificial nervous system based on linearized neuron models. One example method for operating an artificial neuron generally includes determining that a first state of the artificial neuron is within a first region; determining a second state of the artificial neuron based at least in part on a first set of linear equations, wherein the first set of linear equations is based at least in part on a first set of parameters corresponding to the first region; determining that the second state of the artificial neuron is within a second region; and determining a third state of the artificial neuron based at least in part on a second set of linear equations, wherein the second set of linear equations is based at least in part on a second set of parameters corresponding to the second region.
Abstract:
Methods and apparatus for piecewise linear neuron modeling and implementing one or more artificial neurons in an artificial nervous system based on one or more linearized neuron models. One example method (for implementing a combination of a plurality of neuron models in a system of neural processing units) generally includes loading parameters for a first neuron model selected from the plurality of neuron models into a first neural processing unit, determining a first state of the first neural processing unit based at least in part on the parameters for the first neuron model, and determining a second state of the first neural processing unit based at least in part on the parameters for the first neuron model and on the first state. This method may also include updating the plurality of neuron models (e.g., by adding, deleting, or adjusting parameters for the first neuron model or another neuron model).
Abstract:
A mobile device or access terminal of a wireless wide area network (WWAN) communication system is provisioned for Multi-Mode System Selection (MMSS) wherein an MMSS System Priority List (MSPL) is used with respect to the underlying system selection priority list (e.g., Private Land Mobile Network (PLMN) list). Relating a current location to one or more entries in an MMSS Location Associated Priority List (MLPLs) enables scaling a range of entries in the PLMN list, indicating whether the MSPL apply to the entire list of PLMNs stored in an access terminal or to some subset of the PLMN List. Similarly, the present innovation addresses whether the MSPL applies to the entire Preferred Roaming List (PRL) or some subset of a geo-spatial location (GEO) area.
Abstract:
A method of processing asynchronous event-driven input samples of a continuous time signal, includes calculating a convolutional output directly from the event-driven input samples. The convolutional output is based on an asynchronous pulse modulated (APM) encoding pulse. The method further includes interpolating output between events.
Abstract:
A method of signal processing includes comparing an input signal with one or more positive threshold values and one or more negative threshold values. The method also includes generating an output signal based on the comparison of the input signal with the positive threshold(s) and negative threshold(s). The method further includes feeding the output signal back into a decaying reconstruction filter to create a reconstructed signal and combining the reconstructed signal with the input signal.
Abstract:
Multi-mode system selection (MMSS) enables a mobile station (MS) to prioritize MS preference for selecting particular radio air-interfaces (AI) across multiple standards (e.g., 3GPP, 3GPP2, WiMAX). 3GPP2 is developing a scheme MMSS-3GPP2 which is usually referred to as simply 'MMSS.' Other schemes exist e.g., proprietary ones (e.g., internal ePRL), an MMSS-3GPP based on the PLMN with Access Technologies of non-3GPP systems. MMSS OTASP messages and parameters are being defined in 3GPP2 to allow the carriers to provision MMSS parameters to the mobile device. With MMSS, the mobile can select and hence acquire cdma2000 and non-cdma2000 systems (e.g., LTE, WiMAX) based on carrier's preferences.
Abstract:
A method of processing asynchronous event-driven input samples of a continuous time signal includes calculating a short-time Fourier transform (STFT) output based on the event-driven input samples. The STFT output may be calculated by expressing an encoding pulse and an STFT window function as a sum of complex weighted causal complex exponentials. The method further includes interpolating output between events.
Abstract:
A method and apparatus for over-the-air provisioning of authentication credentials at an access device via a first access system, wherein the authentication credentials are for a second access system lacking an over-the-air provisioning procedure. For example, the second access system may be a 3GPP system using AKA authentication methods. The first access system may be CDMA, using an OTASP or IOTA procedure. Provisioning the authentication credentials may include provisioning any of a 3GPP AKA authentication root key (K), AKA authentication related parameters, an AKA authentication algorithm to be used in the 3GPP authentication, or authentication algorithm customization parameters.
Abstract:
Methods and apparatus for piecewise linear neuron modeling and implementing one or more artificial neurons in an artificial nervous system based on one or more linearized neuron models. One example method (for implementing a combination of a plurality of neuron models in a system of neural processing units) generally includes loading parameters for a first neuron model selected from the plurality of neuron models into a first neural processing unit, determining a first state of the first neural processing unit based at least in part on the parameters for the first neuron model, and determining a second state of the first neural processing unit based at least in part on the parameters for the first neuron model and on the first state. This method may also include updating the plurality of neuron models (e.g., by adding, deleting, or adjusting parameters for the first neuron model or another neuron model).