A system for receiving multipath signals is disclosed. The system includes an equalizer that includes an input for a received data signal, wherein the received data comprises a first multipath component and a second multipath component. The equalizer further includes a channel impulse response estimator coupled to the input configured to determine one or more channel impulse response (CIR) estimates for the first multipath component and the second multipath component. The equalizer further includes a statistical estimation module coupled to the channel impulse response estimator configured to estimate a state of the first multipath component and the second multipath component based on the one or more channel impulse response estimates. The equalizer further includes a detector coupled to the statistical estimation module configured to detect data from the received data signal based on an estimated future state of the first multipath component and the second multipath component.

The disclosure relates to technology for load balancing link utilization of a networking device based on fractal analysis. In one embodiment, link utilization of switches, routers, etc. in a data center is balanced based on a fractal model of the link utilization. Techniques disclosed herein are proactive. For example, instead of reacting to link congestion, the technique predicts future link utilization based on fractal analysis. Then, packet flows (or flowlets) may be assigned to links based on the predicted future link utilization. Hence, congestion on links may be reduced or prevented.

In a method for accurately estimating gait characteristics of a user, first parameters indicative of user movement, including a GNSS-derived speed and step count, are monitored. Values of the first parameters are processed to determine values of second parameters indicative of movement of the user. The processing includes using values of at least one monitored parameter to generate one or more inputs to an estimator (e.g., Kalman filter) having the second parameters as estimator states. At least two of the second parameters are collectively indicative of a mapping between step frequency and step length of the user. A graphical user interface may display values of at least one of the second parameters, and/or at least one parameter derived from one or more of the second parameters.

In a method for accurately estimating gait characteristics of a user, first parameters indicative of user movement, including a GNSS-derived speed and step count, are monitored. Values of the first parameters are processed to determine values of second parameters indicative of movement of the user. The processing includes using values of at least one monitored parameter to generate one or more inputs to an estimator (e.g., Kalman filter) having the second parameters as estimator states. At least two of the second parameters are collectively indicative of a mapping between step frequency and step length of the user. A graphical user interface may display values of at least one of the second parameters, and/or at least one parameter derived from one or more of the second parameters.

In a method for accurately estimating gait characteristics of a user, first parameters indicative of user movement, including a GNSS-derived speed and step count, are monitored. Values of the first parameters are processed to determine values of second parameters indicative of movement of the user. The processing includes using values of at least one monitored parameter to generate one or more inputs to an estimator (e.g., Kalman filter) having the second parameters as estimator states. At least two of the second parameters are collectively indicative of a mapping between step frequency and step length of the user. A graphical user interface may display values of at least one of the second parameters, and/or at least one parameter derived from one or more of the second parameters.

In a method for accurately estimating gait characteristics of a user, first parameters indicative of user movement, including a GNSS-derived speed and step count, are monitored. Values of the first parameters are processed to determine values of second parameters indicative of movement of the user. The processing includes using values of at least one monitored parameter to generate one or more inputs to an estimator (e.g., Kalman filter) having the second parameters as estimator states. At least two of the second parameters are collectively indicative of a mapping between step frequency and step length of the user. A graphical user interface may display values of at least one of the second parameters, and/or at least one parameter derived from one or more of the second parameters.

In a method for accurately estimating gait characteristics of a user, first parameters indicative of user movement, including a GNSS-derived speed and step count, are monitored. Values of the first parameters are processed to determine values of second parameters indicative of movement of the user. The processing includes applying, as inputs to an estimator (e.g., Kalman filter) having the second parameters as estimator states, values of at least one of the first parameters and/or values of at least one parameter derived from one or more of the first parameters. At least two of the second parameters are collectively indicative of a mapping between step frequency and step length of the user. A graphical user interface may display values of at least one of the second parameters, and/or at least one parameter derived from one or more of the second parameters.

The disclosure relates to technology for load balancing link utilization of a networking device based on fractal analysis. In one embodiment, link utilization of switches, routers, etc. in a data center is balanced based on a fractal model of the link utilization. Techniques disclosed herein are proactive. For example, instead of reacting to link congestion, the technique predicts future link utilization based on fractal analysis. Then, packet flows (or flowlets) may be assigned to links based on the predicted future link utilization. Hence, congestion on links may be reduced or prevented.

In a method for accurately estimating gait characteristics of a user, first parameters indicative of user movement, including a GNSS-derived speed and step count, are monitored. Values of the first parameters are processed to determine values of second parameters indicative of movement of the user. The processing includes applying, as inputs to an estimator (e.g., Kalman filter) having the second parameters as estimator states, values of at least one of the first parameters and/or values of at least one parameter derived from one or more of the first parameters. At least two of the second parameters are collectively indicative of a mapping between step frequency and step length of the user. A graphical user interface may display values of at least one of the second parameters, and/or at least one parameter derived from one or more of the second parameters.

A radio network node serves a first cell in a radio communications network, and is configured to measure a received total power value at the radio network node in the first cell, compute a factor indicating a load in the first cell, estimate a noise floor level in the first cell, and compute a utilization probability value of the load in the first cell and a neighbor cell interference value simultaneously in a non-linear interference model. This is based on the measured received total power value, the computed factor, and the estimated noise floor level in the first cell. The neighbor cell interference value is an interference from at least one second cell affecting said first cell, and the utilization probability value of the load in the first cell and/or the neighbor cell interference value is to be used for managing radio resources in the radio communications network.