Virtual Labs

a: Maximize the output power of the beamformer b: Minimize the output power of the beamformer while maintaining unity response in the desired direction c: Equalize the output power in all directions d: Enhance signals from all directions equally

What is application of Autoregressive process?

a: Texture modelling of visual content b: Speech processing c: Models for future sample predictions d: All of above

What is the purpose of the Least Mean Squares (LMS) algorithm in the context of beamforming?

a: To adaptively adjust the beamforming weights to minimize the mean square error b: To maximize the signal-to-noise ratio (SNR) c: To calculate the covariance matrix of the input data d: To ensure equal signal power from all directions

The steering vector a(θ) in beamforming is used to:

a: Minimize the output power b: Adjust the step size parameter c: Measure the error signal d: Define the desired signal direction

AR process with low order results in:

a: We will obtain a smoothed spectrum b: There is a risk of introducing spurious low-level peaks in spectrum c: Both of above d: None of above

LMS equalizer minimizes __________

a: Computational complexity b: Cost c: Mean square error d: Power density of output signal

Major application of adaptive filtering is:

a: System Identification b: Prediction c: Channel equalization d: All of above

For N symbol inputs, LMS algorithm requires ______ operations per iteration.

a: 2N b: N + 1 c: 2N + 1 d: N²

In the context of adaptive prediction, which matrix is updated recursively in the RLS algorithm?

a: Identity matrix b: Correlation matrix c: Inverse correlation matrix d: Gain matrix

Which of the following correctly describes the cost function minimized by the RLS algorithm?

a: The sum of squared errors b: The product of squared errors c: The weighted sum of squared errors with an exponential weighting factor d: The absolute sum of errors

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