I am preparing an interview which may require some knowledges on DSP. Is there any questions, fundamental concepts, and theories that I would not want miss? Thanks a lot for the suggestions.
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4$\begingroup$ What kind of DSP engineer ? $\endgroup$– Paul RDec 13, 2011 at 22:05
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3$\begingroup$ My stance on this would be that if you don't know the questions to ask, then you're not going to know if you get a made-up answer. Do you have other personnel on staff who are more experienced in signal processing? $\endgroup$– Jason RDec 13, 2011 at 22:08
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9$\begingroup$ This is like asking "What songs should I know as a musician?" or "What theorems should you know as a mathematician?". The answer is always "It depends. What exactly are you interested in?" So you'll need to answer PaulR first. Moreover, this is a blanket, 'list all possible interview questions you can think of' type of question and I fail to see how this can be useful. Perhaps if you had a specific interview question that you faced and stumbled on it, then you could ask us how to answer that. I would urge people to consider whether this question is appropriate for the site $\endgroup$– Lorem IpsumDec 13, 2011 at 23:23
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2$\begingroup$ I think I misread the question originally; I thought it sounded like you were the interviewer, not the interviewee. I don't think it's really clear from your question, but I'm guessing you're the one seeking the position. I concur with yoda's suggestions above. $\endgroup$– Jason RDec 14, 2011 at 2:22
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2 Answers
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When I interview people, my questions depend a lot on the CV they've submitted.
If they're fresh from university, I am much more interested in whether they have ever heard of some of the basic image processing concepts: What is their definition of noise and background? What does a Gaussian filter do? And of course, I want to know if they know how to program as opposed to hacking together a last-minute solution for homework.
If they claim to be more experienced, I want to know how well they know what they're talking about. What's their opinion on supervised vs. unsupervised segmentation? What are good tracking algorithms, and why? What informs their choice of a filtering algorithm? How do they deal with stochastic data? And of course, do they know how to program?
Finally, I'm always happy to find out that they did some research into the topic ahead of time, even if they just read my papers and totally misunderstood them. I can always teach them knowledge, but it is hard to teach effort.
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1$\begingroup$ ...so...what is this supervised vs unsupervised segmentation you speak of? I have never heard of anything like this in the DSP context... :-) $\endgroup$– SpaceyJan 4, 2012 at 6:52
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$\begingroup$ @Mohammad: supervised methods in segmentation involve a learning step to teach the software the defining characteristics of the features that should be segmented. For an explanation of supervised and unsupervised spot detection algorithms, see e.g. this article $\endgroup$– JonasJan 4, 2012 at 13:04
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$\begingroup$ I see - I took a machine learning class not too long ago, where we learnt about k-means clustering, k-center, etc. They called those 'unsupervised learning' methods. So I was wondering if you were referring to the same 'class' of things here. $\endgroup$– SpaceyJan 4, 2012 at 15:22
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1$\begingroup$ @Mohammad: Unsupervised means that you manually set one or several thresholds and the software takes it from there, while supervised means that you have to train the software on a bunch of examples. Thus, supervised vs unsupervised in image analysis is the same as in clustering. The algorithms may even be the same. $\endgroup$– JonasJan 4, 2012 at 15:27
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I would flip though a couple of your DSP textbooks to see what sub-topics need jogging in your memory. That helps prevent blank looks while you try to dredge up the words for some filter (etc.) you last used long ago. Perhaps also brush up on your complex math familiarity.
This is actually more important in DSP, as many of basic DSP skills are based, not on just the latest coding methodology or language fad, but on much older (more mature?) algorithms and mathematics.