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Satyam Agarwal
เข้าร่วมเมื่อ 26 มี.ค. 2020
Demonstrating BPSK demodulation using Machine Learning.
Demonstrating BPSK demodulation using Machine Learning.
Paper - A. Ahmad, S. Agarwal, S. Darshi and S. Chakravarty, "DeepDeMod: BPSK Demodulation Using Deep Learning Over Software-Defined Radio," in IEEE Access, vol. 10, pp. 115833-115848, 2022
Paper - A. Ahmad, S. Agarwal, S. Darshi and S. Chakravarty, "DeepDeMod: BPSK Demodulation Using Deep Learning Over Software-Defined Radio," in IEEE Access, vol. 10, pp. 115833-115848, 2022
มุมมอง: 107
วีดีโอ
Demonstrating BPSK demodulation using Machine Learning - COMSNETS 2023
มุมมอง 144ปีที่แล้ว
Demonstrating BPSK demodulation using Machine Learning. Paper - A. Ahmad, S. Agarwal, S. Darshi and S. Chakravarty, "DeepDeMod: BPSK Demodulation Using Deep Learning Over Software-Defined Radio," in IEEE Access, vol. 10, pp. 115833-115848, 2022
Lecture 20: Detection of Random Signals with unknown Parameters
มุมมอง 3414 ปีที่แล้ว
Lecture 20: Detection of Random Signals with unknown Parameters
Lecture 19: Detection of Deterministic Signals with Unknown Parameters
มุมมอง 4774 ปีที่แล้ว
Lecture 19: Detection of Deterministic Signals with Unknown Parameters
Lecture 18: Generalised Likelihood Ratio Test
มุมมอง 4.1K4 ปีที่แล้ว
Lecture 18: Generalised Likelihood Ratio Test
Lecture 17: Bayesian Approach to Composite Hypothesis Testing
มุมมอง 8494 ปีที่แล้ว
Lecture 17: Bayesian Approach to Composite Hypothesis Testing
Lecture 16: Composite Hypothesis Testing - Introduction
มุมมอง 1.4K4 ปีที่แล้ว
Lecture 16: Composite Hypothesis Testing - Introduction
Lecture 15: Random Signal Detection (Contd.)
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Lecture 15: Random Signal Detection (Contd.)
Lecture 7: Bayesian Detection - Binary Hypothesis Testing
มุมมอง 1.7K4 ปีที่แล้ว
Lecture 7: Bayesian Detection - Binary Hypothesis Testing
Lecture 6: Classical Detection Theory: NP Theorem Examples
มุมมอง 5284 ปีที่แล้ว
Lecture 6: Classical Detection Theory: NP Theorem Examples
Lecture 5 - Classical Detection Theory - Examples
มุมมอง 6494 ปีที่แล้ว
Lecture 5 - Classical Detection Theory - Examples
Lecture 4: Neyman-Pearson Theorem -Examples
มุมมอง 1.2K4 ปีที่แล้ว
Lecture 4: Neyman-Pearson Theorem -Examples
Lecture 3: Neymen-Pearson Theorem - Detection Theory
มุมมอง 2.1K4 ปีที่แล้ว
Lecture 3: Neymen-Pearson Theorem - Detection Theory
Lecture 1: Introduction to Detection Theory
มุมมอง 2.6K4 ปีที่แล้ว
Lecture 1: Introduction to Detection Theory
Thank you 😁
Is there any video series for estimation theory?
Thanks For sharing
are you accounting for inband noise? The BERs seem like it might not account for this.
I don't know how to thank you?! I lost hope in studies as i have different background and yet landed in learning Statistics.....indeed this helped a lot!
can you please provide slides of course
thank you very much this helped me pass my exam
Sir, all your lectures on detection theory are awesome. Can you please upload lectures on estimation theory also.
the expression written at 8:02 should be the reciprocal after dividing and bringing it over to the other side.
Its a very clear explanation
In 22:54 to 23:10, are these formulas for conditional expectation and conditional variance, or is it just an arbitrary variable you assigned to the terms you circled?
Great work sir !!
Xi*=(1/(u*-Lmdi*))-alpha.i*
Sir there is a little mistake Minimum value of function at -1 is 2.
Correct
Sir Please give the information about the Reference books.
fundaments of statistical signal estimation by S m kay
@@satyamagarwal6706 thank You Sir.
Thank you sir, for sharing the knowledge , highly helpful
Sir I want to simulate in MATLAB the scenario of s[n] having zero mean with Cs covariance and w[n] being WGN but i am facing little bit problem. If you can describe a little how to implement in MATLAB to get Pd vs SNR plot for ESTIMATOR CORRELATOR it would be great help.
How will we calculate the mean and variance when we have only the first observation a single value.
Thankyou Sir