Question 38 on Communication from GATE (Graduate Aptitude Test in Engineering) 2012 Electronics and Communication Engineering paper. Q38. A binary symmetric channel (BSC) has a transition probability of 1/8. If the binary transmit symbol X is such that P(X=0)=9/10, then the probability of error for an optimum receiver will be (A) 7/80 (B) 63/80 (C)…
Question 52 on electromagnetics from GATE (Graduate Aptitude Test in Engineering) 2012 Electronics and Communication Engineering paper. An infinitely long uniform solid wire of radius carries a uniform dc current of density . Q52. The magnetic field at a distance from the center of the wire is proportional to (A) for and for (B) for and for (C) for and for (D) for and for Solution…
Question 15 on communication from GATE (Graduate Aptitude Test in Engineering) 2012 Electronics and Communication Engineering paper. Q15. A source alphabet consists of N symbols with the probability of the first two symbols being the same. A source encoder increases the probability of the first symbol by a small amount and decreases that of the…
From the previous post on OFDM (here), we have understood that an OFDM waveform is made of sum of multiple sinusoidals (also called subcarriers) each modulated independently. In this post, let us try to understand the estimation of frequency offset in a typical OFDM receiver (using the short preamble specified per IEEE 802.11a specification as…
I have written another article in DSPDesginLine.com. This article can be treated as the third post in the series aimed at understanding Shannon’s capacity equation. For the first two posts in the series are: 1. Understanding Shannon’s capacity equation 2. Bounds on Communication based on Shannon’s capacity The article summarizes the symbol error rate derivations…
Mr. Raghuram contacted me and informed about Happy Schools Blog. He writes about Graduate School Admission in U.S., Job opportunities for students, University Selection based on his personal experience. He recently published few articles which might of interest to some of our readers. Here are the URL for few articles:
In a classification problem, the output (also called the label or class) takes a small number of discrete values rather than continuous values. For a simple binary classification problem, where output takes only two discrete values : 0 or 1, the sigmoid function can be used to transform the output of a linear regression model…
As on 21st October 2007, we moved from blogspot domain at to the self hosted domain at https://dsplog.com. Over the last two years, the blog has grown quite a bit, and am reasonably happy with the progress. Let us look back at the positives and negatives over the last year.
Question 7 on digital from GATE (Graduate Aptitude Test in Engineering) 2012 Electronics and Communication Engineering paper. Q7. The output Y of a 2-bit comparator is logic 1 whenever the 2 bit input A is greater than 2 bit input B. The number of combinations for which output is logic 1 is (A) 4 (B)…
Thanks to the keen observation by Mr. Phan Minh Hoang, I was notified that the Matlab/Octave scripts provided along with the topic raised cosine filtering was not behaving properly. Reason: I was not taking care of the division by zero when creating the raised cosine filter taps. 🙁
Question 46 on math from GATE (Graduate Aptitude Test in Engineering) 2012 Electronics and Communication Engineering paper. Q46. The maximum value of in the interval [1, 6] is (A) 21 (B) 25 (C) 41 (D) 46 Solution Let us start by finding the critical points of the function . The first derivative is, . Solving by…
The definition of Toeplitz matrix from [1] is: A matrix is said to be Toeplitz if the elements are determined completely by the difference .
Mr. Lealem Tamirat, in a comment on BER for BPSK in Rayleigh channel, wondered about the performance of an OFDM modulated system in a frequency selective Rayeligh fading channel. My response was that, Though the total channel is a frequency selective channel, the channel experienced by each subcarrier in an OFDM system is a flat…
The post covers various neural network based word embedding models. Starting from the Neural Probabilistic Language Model from Bengio et al 2003, then reduction of complexity using Hierarchical softmax and Noise Contrastive Estimation. Further works like CBoW, GlOVe, Skip Gram and Negative Sampling which helped to train on much higher data.