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anitayjime
AIESEC UWA Innovation Award Application, July 2012
AIESEC UWA Innovation Award Application, July 2012
lebedevadawg
This paper describes the concept of adaptive noise cancelling. The noise cancellation using the Recursive Least Squares (RLS) to remove the noise from an input signal. The RLS adaptive filter uses the reference signal on the Input port and the desired signal on the desired port to automatically match the filter response in the Noise Filter block. The filtered noise should be completely subtracted from the "noisy signal” of the input Sine wave & noise input signal, and the "Error Signal" should contain only the original signal. Finally, the functions of field programmable gate array based system structure for adaptive noise canceller based on RLS algorithm are synthesized, simulated, and implemented on Xilinx XC3s200 field programmable gate array using Xilinx ISE tool.
FPGA IMPLEMENTATION OF NOISE CANCELLATION USING ADAPTIVE ALGORITHMS
FPGA IMPLEMENTATION OF NOISE CANCELLATION USING ADAPTIVE ALGORITHMS
Editor IJMTER
trabajo practico n°4
trabajo practico n°4
anitayjime
This is the presentation I gave at AGI-12 (also called the Winter Intelligence 2012 conferece) in Oxford, UK, on Dec.11, 2012. There is an AGI-12 proceedings paper that accompanies this talk. I will make that available on my publications page at http://randalkoene.com and I will put both together on the http://carboncopies.org page about this event. The video (recorded by Adam Ford) should also appear soon. Abstract. Feasible and practical routes to Artificial General Intelligence involve short-cuts tailored to environments and challenges. A prime example of a system with built-in short-cuts is the human brain. Deriving from the brain the functioning system that implements intelligence and generality at the level of neurophysiology is interesting for many reasons, but also poses a set of specific challenges. Representations and models demand that we pick a constrained set of signals and behaviors of interest. The systematic and iterative process of model building involves what is known as System Identification, which is made feasible by decomposing the overall problem into a collection of smaller System Identification problems. There is a roadmap to tackle that includes structural scanning (a way to obtain the “connectome”) as well as new tools for functional recording. We examine the scale of the endeavor, and the many challenges that remain, as we consider specific approaches to System Identification in neural circuitry.
Toward Tractable AGI: Challenges for System Identification in Neural Circuitry
Toward Tractable AGI: Challenges for System Identification in Neural Circuitry
Randal Koene
this ppts deal with adaptive noise cancellation using normalized least mean fourth algorithm and mean square comparison for both normalized least mean square algorithm and least mean fourth algorithm with gaussian, binary and unifrom signals as inputs.
ADAPTIVE NOISE CANCELLATION
ADAPTIVE NOISE CANCELLATION
SREENIVASA ARUN KUMAR
Structural Equation Modeling-
SEM
SEM
Mohsen Sharifirad
Which estimation method is optimal for structural equation modeling (SEM) of Likert scale data? Conventional SEM assumes continuous measurement, and some SEM estimators assume a multivariate normal distribution, but Likert scale data are ordinal and do not necessarily resemble a discretized normal distribution. When treated as continuous, these data may yet be skewed due to item difficulty, choice of population, or various response biases. One can fit an SEM to a matrix of polychoric correlations, which estimate latent, continuous constructs underlying ordinally measured variables, but polychoric correlations also assume these latent factors are normally distributed. To what extent are these methods robust with continuous versus ordinal data and with varying degrees of skewness and kurtosis? To answer, I simulated 10,000 samples of multivariate normal data, each consisting of 500 observations of five strongly correlated variables. I transformed each consecutive sample to an incrementally greater degree to increase skew and kurtosis from approximately normal levels to extremes beyond six and 30, respectively. I then performed five confirmatory factor analyses on each sample using five different estimators: maximum likelihood (ML), weighted least squares (WLS), diagonally weighted least squares (DWLS), unweighted least squares (ULS), and generalized least squares (GLS). I compared results for continuous and discretized (ordinal) data, including loadings, error variances, fit statistics, and standard errors. I also noted frequencies of failures, which complicated calculation of polychoric correlations, and particularly plagued the WLS estimator. WLS estimation produced relatively biased loadings and error variance estimates. GLS also underestimated error variances. Neither estimator exhibited any unique advantage to offset these disadvantages. ML estimated parameters more accurately, but some fit statistics appeared biased by it, especially in the context of extreme nonnormality. Specifically, the chi squared goodness-of-fit test statistic and the root mean square error of approximation (RMSEA) began higher with ML-estimated SEMs of approximately normal data, and worsened sharply with greater nonnormality. The Tucker Lewis Index (TLI) and standardized root mean square residual (SRMR) also worsened more moderately with nonnormality when using ML estimation. GLS-estimated fit statistics shared ML’s sensitivity to nonnormality, and were even worse for the TLI and SRMR. Results generally favored ULS and DWLS estimators, which produced accurate parameter estimates, good and robust fit statistics, and small standard errors (SEs) for loadings. DWLS tended to produce smaller SEs than ULS when skewness was below three, but ULS SEs were more robust to nonnormality and smaller with extremely nonnormal data. ML SEs were larger for loadings, but smaller for error variance estimates, and fairly robust to nonnormality...
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Estimators for structural equation models of Likert scale data
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AIESEC UWA Innovation Award Application, July 2012
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lebedevadawg
This paper describes the concept of adaptive noise cancelling. The noise cancellation using the Recursive Least Squares (RLS) to remove the noise from an input signal. The RLS adaptive filter uses the reference signal on the Input port and the desired signal on the desired port to automatically match the filter response in the Noise Filter block. The filtered noise should be completely subtracted from the "noisy signal” of the input Sine wave & noise input signal, and the "Error Signal" should contain only the original signal. Finally, the functions of field programmable gate array based system structure for adaptive noise canceller based on RLS algorithm are synthesized, simulated, and implemented on Xilinx XC3s200 field programmable gate array using Xilinx ISE tool.
FPGA IMPLEMENTATION OF NOISE CANCELLATION USING ADAPTIVE ALGORITHMS
FPGA IMPLEMENTATION OF NOISE CANCELLATION USING ADAPTIVE ALGORITHMS
Editor IJMTER
trabajo practico n°4
trabajo practico n°4
anitayjime
This is the presentation I gave at AGI-12 (also called the Winter Intelligence 2012 conferece) in Oxford, UK, on Dec.11, 2012. There is an AGI-12 proceedings paper that accompanies this talk. I will make that available on my publications page at http://randalkoene.com and I will put both together on the http://carboncopies.org page about this event. The video (recorded by Adam Ford) should also appear soon. Abstract. Feasible and practical routes to Artificial General Intelligence involve short-cuts tailored to environments and challenges. A prime example of a system with built-in short-cuts is the human brain. Deriving from the brain the functioning system that implements intelligence and generality at the level of neurophysiology is interesting for many reasons, but also poses a set of specific challenges. Representations and models demand that we pick a constrained set of signals and behaviors of interest. The systematic and iterative process of model building involves what is known as System Identification, which is made feasible by decomposing the overall problem into a collection of smaller System Identification problems. There is a roadmap to tackle that includes structural scanning (a way to obtain the “connectome”) as well as new tools for functional recording. We examine the scale of the endeavor, and the many challenges that remain, as we consider specific approaches to System Identification in neural circuitry.
Toward Tractable AGI: Challenges for System Identification in Neural Circuitry
Toward Tractable AGI: Challenges for System Identification in Neural Circuitry
Randal Koene
this ppts deal with adaptive noise cancellation using normalized least mean fourth algorithm and mean square comparison for both normalized least mean square algorithm and least mean fourth algorithm with gaussian, binary and unifrom signals as inputs.
ADAPTIVE NOISE CANCELLATION
ADAPTIVE NOISE CANCELLATION
SREENIVASA ARUN KUMAR
Structural Equation Modeling-
SEM
SEM
Mohsen Sharifirad
Which estimation method is optimal for structural equation modeling (SEM) of Likert scale data? Conventional SEM assumes continuous measurement, and some SEM estimators assume a multivariate normal distribution, but Likert scale data are ordinal and do not necessarily resemble a discretized normal distribution. When treated as continuous, these data may yet be skewed due to item difficulty, choice of population, or various response biases. One can fit an SEM to a matrix of polychoric correlations, which estimate latent, continuous constructs underlying ordinally measured variables, but polychoric correlations also assume these latent factors are normally distributed. To what extent are these methods robust with continuous versus ordinal data and with varying degrees of skewness and kurtosis? To answer, I simulated 10,000 samples of multivariate normal data, each consisting of 500 observations of five strongly correlated variables. I transformed each consecutive sample to an incrementally greater degree to increase skew and kurtosis from approximately normal levels to extremes beyond six and 30, respectively. I then performed five confirmatory factor analyses on each sample using five different estimators: maximum likelihood (ML), weighted least squares (WLS), diagonally weighted least squares (DWLS), unweighted least squares (ULS), and generalized least squares (GLS). I compared results for continuous and discretized (ordinal) data, including loadings, error variances, fit statistics, and standard errors. I also noted frequencies of failures, which complicated calculation of polychoric correlations, and particularly plagued the WLS estimator. WLS estimation produced relatively biased loadings and error variance estimates. GLS also underestimated error variances. Neither estimator exhibited any unique advantage to offset these disadvantages. ML estimated parameters more accurately, but some fit statistics appeared biased by it, especially in the context of extreme nonnormality. Specifically, the chi squared goodness-of-fit test statistic and the root mean square error of approximation (RMSEA) began higher with ML-estimated SEMs of approximately normal data, and worsened sharply with greater nonnormality. The Tucker Lewis Index (TLI) and standardized root mean square residual (SRMR) also worsened more moderately with nonnormality when using ML estimation. GLS-estimated fit statistics shared ML’s sensitivity to nonnormality, and were even worse for the TLI and SRMR. Results generally favored ULS and DWLS estimators, which produced accurate parameter estimates, good and robust fit statistics, and small standard errors (SEs) for loadings. DWLS tended to produce smaller SEs than ULS when skewness was below three, but ULS SEs were more robust to nonnormality and smaller with extremely nonnormal data. ML SEs were larger for loadings, but smaller for error variance estimates, and fairly robust to nonnormality...
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FPGA IMPLEMENTATION OF NOISE CANCELLATION USING ADAPTIVE ALGORITHMS
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Toward Tractable AGI: Challenges for System Identification in Neural Circuitry
Toward Tractable AGI: Challenges for System Identification in Neural Circuitry
ADAPTIVE NOISE CANCELLATION
ADAPTIVE NOISE CANCELLATION
SEM
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Álbum de fotografías