Abstract: A method, non-transitory computer readable medium, and system for multiple-input multiple-output for blind identification that includes receiving an input signal, originated as an output signal of a transmitter, at a receiver. A signal processing module can obtain the input signal from the receiver. The signal processing module can use a finite impulse response filter and one or more matrices derived from the input signal to minimize a cost function and obtain a parameter matrix. The parameter matrix can then be used to estimate the output signal by generating one or more Toeplitz matrices using the parameter matrix.

Abstract: A system, method, and non-transitory computer readable medium that perform blind signal estimation for single-input multiple-output systems. The method can include receiving, by the two or more receiver antennas of the receiver, an observed signal comprising the input signal and an additive noise term. The method can then form a data matrix using the observed signals from the two or more receiver antennas. The method can also include computing a singular value decomposition of the data matrix. The singular value decomposition can then be used to generate a parameter matrix. The method can then form a Toeplitz signal matrix using the parameter matrix. The method can estimating the input signal using the Toeplitz signal matrix.

Abstract: A device, method, and non-transitory computer readable medium that for two-dimensional blind single-input multiple-output channel identification for image restoration. The method includes receiving, by a receiver having independent channels, a two-dimensional image data matrix then transforming the received two-dimensional image data matrix to a one-dimensional image vector. Channel parameters can then be estimated using the one-dimensional image vector. The method can then construct a restored image using the estimated channel parameters and the two-dimensional image data matrix.

Abstract: A system, computer-readable storage medium and method of reflection seismic survey in a wireless seismic network within a survey area is described. The method includes detecting, in each of a plurality of wireless seismic sensor nodes, seismic reflection signals from a seismic energy source; recording, in each of a plurality of wireless geophones, detected seismic signals; transmitting, by the geophones, the recorded seismic signals as digital data, using a combination of Orthogonal Frequency-Division Multiple Access (OFDMA) and Time Division Multiple Access (TDMA), to a central data receiving device; changing the seismic energy source location for seismic reflection; and repeating the detecting, recording and transmitting a number of times for each change in seismic energy source.

Abstract: An apparatus, method, and non-transitory computer readable medium that can mitigate wireless channel impairments in seismic data transmission using deep neural networks is disclosed. The apparatus includes a receiving circuitry to receive seismic data and a processing circuitry. The processing circuitry is configured to apply a blind system identification process to the seismic data to estimate a channel impulse response of the seismic data, apply an optimum equalization process to obtain estimated seismic data based on the channel impulse response, process the estimated seismic data to generate processed seismic data, classify the processed seismic data into a first group of seismic data each of which has a signal-to-noise ratio (SNR) less than an SNR threshold and a second group of seismic data each of which has an SNR no less than the SNR threshold, and enhance the SNR of each of the first group of seismic signals.

Abstract: A method and apparatus for equalization in a communication system. The method includes receiving an input of a block of symbols, filtering the input in the frequency domain, calculating an error factor in the time domain based on the filtered input, updating tap weights of the equalizer in the frequency domain using circular correlation based on the error factor and the conjugate of the input in the frequency domain, and outputting the filtered input.

Abstract: A method and apparatus for equalization in a communication system. The method includes receiving an input of a block of symbols, filtering the input in the frequency domain, calculating an error factor in the time domain based on the filtered input, updating tap weights of the equalizer in the frequency domain using circular correlation based on the error factor and the conjugate of the input in the frequency domain, and outputting the filtered input.

Abstract: A method and apparatus for equalization in a communication system. The method includes receiving an input of a block of symbols, filtering the input in the frequency domain, calculating an error factor in the time domain based on the filtered input, updating tap weights of the equalizer in the frequency domain using circular correlation based on the error factor and the conjugate of the input in the frequency domain, and outputting the filtered input.

Abstract: A method and apparatus for equalization in a communication system. The method includes receiving an input of a block of symbols, filtering the input in the frequency domain, calculating an error factor in the time domain based on the filtered input, updating tap weights of the equalizer in the frequency domain using circular correlation based on the error factor and the conjugate of the input in the frequency domain, and outputting the filtered input.

Abstract: A method and apparatus for equalization in a communication system. The method includes receiving an input of a block of symbols, filtering the input in the frequency domain, calculating an error factor in the time domain based on the filtered input, updating tap weights of the equalizer in the frequency domain using circular correlation based on the error factor and the conjugate of the input in the frequency domain, and outputting the filtered input.

Abstract: The adaptive filter for sparse system identification is an adaptive filter that uses an algorithm in the feedback loop that is designed to provide better performance when the unknown system model is sparse, i.e., when the filter has only a few non-zero coefficients, such as digital TV transmission channels and echo paths. The algorithm is a least mean square algorithm with filter coefficients updated at each iteration, as well as a step size that is also updated at each iteration. The adaptive filter may be implemented on a digital signal processor (DSP), an application-specific integrated circuit (ASIC), or by field-programmable gate arrays (FPGAs).

Abstract: A method and apparatus for equalization in a communication system. The method includes receiving an input of a block of symbols, filtering the input in the frequency domain, calculating an error factor in the time domain based on the filtered input, updating tap weights of the equalizer in the frequency domain using circular correlation based on the error factor and the conjugate of the input in the frequency domain, and outputting the filtered input.

Abstract: A system and method for linearizing a power amplifier using digital predistortion technique is provided including processing circuitry, the processing circuitry configured to apply a digital predistortion function based on a weighted static polynomial function, a weighted dynamic polynomial function and a threshold parameter which splits the nonlinear transfer characteristics of the device under test into a region where the static nonlinearity predominates and a region where the dynamic distortions predominate.

Abstract: Methods and apparatuses are provided for channel equalization in a communication system. The method includes initializing, using processing circuitry, filter coefficients of an adaptive decision feedback equalizer randomly in a predetermined search space. Further, the method includes updating, using the processing circuitry, the filter coefficients. The filter coefficients are updated using a least mean square recursion when the filter coefficients are stagnant. The filter coefficients are updated using a particle swarm optimization procedure when the filter coefficients are not stagnant. Further, the updating step is repeated until a predetermined stopping criteria is met. Further, the method includes, filtering, using the processing circuitry, a received signal using the filter coefficients.

Abstract: Methods and apparatuses are provided for channel equalization in a communication system. The method includes initializing, using processing circuitry, filter coefficients of an adaptive decision feedback equalizer randomly in a predetermined search space. Further, the method includes updating, using the processing circuitry, the filter coefficients. The filter coefficients are updated using a least mean square recursion when the filter coefficients are stagnant. The filter coefficients are updated using a particle swarm optimization procedure when the filter coefficients are not stagnant. Further, the updating step is repeated until a predetermined stopping criteria is met. Further, the method includes, filtering, using the processing circuitry, a received signal using the filter coefficients.

Abstract: A system and method for linearizing a power amplifier using digital predistortion technique is provided including processing circuitry, the processing circuitry configured to apply a digital predistortion function based on a weighted static polynomial function, a weighted dynamic polynomial function and a threshold parameter which splits the nonlinear transfer characteristics of the device under test into a region where the static nonlinearity predominates and a region where the dynamic distortions predominate.

Abstract: A system and method for linearizing a power amplifier using digital predistortion technique is provided including processing circuitry, the processing circuitry configured to apply a digital predistortion function based on a weighted static polynomial function, a weighted dynamic polynomial function and a threshold parameter which splits the nonlinear transfer characteristics of the device under test into a region where the static nonlinearity predominates and a region where the dynamic distortions predominate.

Abstract: A system and method for linearizing a power amplifier using digital predistortion technique is provided including processing circuitry, the processing circuitry configured to apply a digital predistortion function based on a weighted static polynomial function, a weighted dynamic polynomial function and a threshold parameter which splits the nonlinear transfer characteristics of the device under test into a region where the static nonlinearity predominates and a region where the dynamic distortions predominate.

Abstract: The memory polynomial based digital predistorter utilizes a cluster-based particle swarm optimization (PSO) technique and has an embedded model-size estimator. The PSO technique and model-size estimator efficiently find the dimensions and accurately estimate the coefficients of the memory polynomial based digital predistorter for improved linearity of a power amplifier.

Abstract: The adaptive filter for sparse system identification is an adaptive filter that uses an algorithm in the feedback loop that is designed to provide better performance when the unknown system model is sparse, i.e., when the filter has only a few non-zero coefficients, such as digital TV transmission channels and echo paths. The algorithm is a least mean square algorithm with filter coefficients updated at each iteration, as well as a step size that is also updated at each iteration. The adaptive filter may be implemented on a digital signal processor (DSP), an application-specific integrated circuit (ASIC), or by field-programmable gate arrays (FPGAs).