Abstract: This document presents methods by which system parameters are tracked online. For adjusting the values of the system parameters, the presented methods take advantage of the data that become available during the system's operation. The values of the parameters can be used for estimating the system state and controlling the system. This document includes methods comprising constructing a cost function based on the data collected during the system operation as a function system parameters and running a memetic algorithm that reduces the cost function value. A memetic algorithm combines an evolutionary or a population-based algorithm with a procedure for local improvement (local search, refinements) of candidate solutions. This document includes methods by which the rotor speed, the rotor flux, and the stator currents of the induction motor can be estimated from electrical measurements while covariance matrices of noise are adjusted online.

Abstract: This document presents methods by which system parameters are tracked online. For adjusting the values of the system parameters, the presented methods take advantage of the data that become available during the system's operation. The values of the parameters can be used for estimating the system state and controlling the system. This document includes methods comprising constructing a cost function based on the data collected during the system operation as a function system parameters and running a memetic algorithm that reduces the cost function value. A memetic algorithm combines an evolutionary or a population-based algorithm with a procedure for local improvement (local search, refinements) of candidate solutions. This document includes methods by which the rotor speed, the rotor flux, and the stator currents of the induction motor can be estimated from electrical measurements while covariance matrices of noise are adjusted online.

Abstract: Provided are methods by which multiple devices self-form a communication network and self-coordinate their access to communication media. In these methods, an individual node maintains its knowledge of other nodes in its vicinity and knowledge of those nodes' scheduled activities to an adequate degree of details. Such knowledge can be obtained by receiving control signals from nodes that are within the range of wireless communication. Examples of such knowledge include the identities of the nodes in the vicinity, the nodes' scheduled times of control message transmission and/or reception, the nodes' scheduled times of application data transmission and/or reception, etc. Such knowledge can be embodied in the form of a memory module of a computing device.

Abstract: Provided are methods by which multiple devices self-form a wireless communication network and self-coordinate their access to communication media. In these methods, an individual node maintains its knowledge of other nodes in its vicinity and knowledge of those nodes' scheduled activities to an adequate degree of details. Such knowledge can be obtained by receiving control signals from nodes that are within the range of wireless communication. Examples of such knowledge include the identities of the nodes in the vicinity, the nodes' scheduled times of control message transmission and/or reception, the nodes' scheduled times of application data transmission and/or reception, etc. Such knowledge can be embodied in the form of a memory module of a computing device.

Abstract: Methods and apparatus for recovering data from communication signals apply estimated distribution algorithms. Estimated probability distributions may be constructed to avoid deterministic values. Non-converging data elements may be resolved by performing selective local searches. Embodiments recover data from space-time-frequency block coded signals. A data receiver comprises two or more antennas and signal detectors for detecting signals received at the antennas. A data detection module constructs a fitness function based on the received set of signals and generates additional possible solution sets by an iterative process in which the fitness of possible data sequences are evaluated using the fitness function.

Abstract: Methods and apparatus for recovering source data from noisy encoded signals apply population-based probabilistic learning algorithms. Non-converging data elements may be resolved by selective local searches. Initial populations are constructed from the data contents of the message bit positions of the received sequence, which resulted from encoding by a systematic code and channel distortion and noise.

Abstract: Methods are described for near optimal Antenna and/or sensor selection via population-based probabilistic evolutionary algorithms such as estimation of distribution algorithm (EDA) and bio-inspired Optimization (BIO). The aspects of the invention includes a method for joint transmit and receive antenna selection using EDA; and an enhanced EDA, which uses cyclic shift register and biased estimation of distribution; and methods for joint transmit and receive antenna selection using improved population-based optimization. The proposed EDA-based and bio-inspired selection methods results in performances that are close to the ESA (exhaustive search algorithm) and yet impose mush less computational burden than ESA. Another advantage of our methods is that they can be easily implemented on parallel processors.

Abstract: Methods and apparatus for recovering data from communication signals apply estimated distribution algorithms. Estimated probability distributions may be constructed to avoid deterministic values. Non-converging data elements may be resolved by performing selective local searches. Embodiments recover data from space-time-frequency block coded signals.