Master Thesis Vs. Lectures-Feb 4

Well It seems quite a time, I had updated my last post on this Blog.

But now with solid grounds, I had started my M.Sc thesis in Cetecom telecommunication Lab in Germany.

I will write and share my whole experience throughout my Journey to solve the Problem of Non-linearity. How much it would improve empower Society by conducting research.

Few observations From transitioning from Lecture based to Independent based Learning:

 1) Materials need to be explored on self-study basis.

2) Reasoning skills play a major role to understand the problem and to look for the solution on the right direction.

3)Consumes time though it will be an enjoyable run.

4)Focusing on the smaller details will create a situation to handle the problem very efficiently.

On side with Academic Lectures:

A)Material will  be consolidated so less time to consume.

B) Need to think how Prof might have solved the problem.

C) “Exam prospective result is just credits but in thesis the goal makes us to push the Human race forward”…

Dresden Microelectronics Academy 2013-Inaugural

I’ve always wonder ‘Ain’t that cool to take a break for Summer school’.  Apparently, last week I got a opportunity to visit TU-Dresden(Technische Universitat), Germany. Microelectronics was always a fascinating thing which no one could less estimate because ‘Every chip counts for the computation we do with our computer’. The every nm(nanometre) of it’s size measures the capability of computation. As, We know breaking Moore’s law will be the next big thing in the silicon sector I just wanted ‘How industry experts try to solve Manufacturing and Moore’s law problem’.

Aside:though the event was held last week I din’t find time, So my write-up won’t be chronological & it is non-linear narrative !

Tada…. Now I am here in TU-Dresden!!

Day-1 (Inaugural and Meet up)

Impressions from the first Inaugural speech by Prof. Johann Bartha(TU-Dresden)


the cloud nine moment was when professor introduced different people form various University … Gladly I was the Only one from Saarland University.

Day went well with loads of talk and finally the awe moment ,, Yep,  the Music & sight seeing of City-Dresden.

Few clicks ….


Footnote on Hard working von Quora!

The best advice I’ve ever heard to a question like this was something like “No problem, don’t motivate yourself, stay a loser” (in Russian, not sure how good the translation is).

Asking advise from other people doesn’t work.


Browsing Internet and Quora doesn’t work. You just sit down and study for 12 hours per day. Or you don’t.  

This might sound harsh, but that’s pretty much what I started to do when I was in 8th grade, what I kept doing for the next ten years and more-or-less what I keep doing nowadays. And you know, I love it when people say “It’s easy for you, you’re so smart”. It isn’t easy, nowhere near that, but it’s worth the effort.

German ‘Punctuality’ -A view

Leaving other Stereotypes. Germans have the great culture of Discipline being‘PUNCTUAL’ it’s a timely gift possessed by the entire nation .

Few occasions to cite:

1. Business meetings- They would like to start the meetings on time irrespective of people. They don’t start early nor late but They start ‘On-time’.

2. It holds true for Family gathering apart , the Transit in Germany will  be consistent and on time 

                 [Image :Train to arrive the Bahnhof(station) on-time]

The Local ‘DB’(Deutsche Bahn) arrives on time . Despite it’s timing native Germans say they are inconsistent but compared to US standards they are quiet consistent.

Though,there exists many characters to define a German quality but personally ‘Punctuality’ defines there discipline , nature of work. work life balance etc..,

 ‘Time management’- Would be the take home lesson for everyone from Deutschland!

Walsh haldmard CDMA systems

Walsh codes (a.k.a Walsh-Hadamard codes) are a set of perfectly orthogonal codes used to separate users on the downlink (DL) channel in CDMA systems (for example in CDMA2000, UMTS cellular standards etc.). Code orthogonality between two codes W_i and W_j is defined as 

These codes can be generated iteratively. Here is a simple illustration of a binary Walsh code tree.

As you might notice, any two codes in the same level or codes in different branches (at different levels) are perfectly orthogonal (called OVSF codes – Orthogonal Variable Spreading Factor Codes). The different levels of the tree correspond to different spreading factors (SF) for the code (which translates to different data rates – higher SF->higher data rates). 

The code tree itself is easy to construct when you observe the pattern. If the code at a given level n is x (with SF 2^(n-1)), the codes generated at the next level n+1 are [x x] and [x -x] (with SF 2^n). You may also want to look into the construction of theWalsh matrix.

An important requirement to keep in mind for the orthogonality condition to work is that the codes must be synchronized in time and this is easier to achieve on the DL where a single base station is orthogonalizing multiple users in its cell area.

Aside: Walsh codes are also used for M-ary orthogonal modulation on the uplink (UL) channel for each user.

Communication Multiple Access

Ever wonder what the latest technologies on Mobile communications like CDMA, GSM, 3G, 4G, etc. mean?

They are different types of cellphone technology.

  • 1G was analog voice. Huge brick phones.
  • 2G was digital = voice + text. Nice small phones with long battery life but no internet.
  • 3G = voice + reasonably good data. Most smartphones & todays phones.
  • 4G = broadband wireless data, with voice as VoIP. Really fast data, and smartphones in the future.

They are different technologies:

  • CDMA is a standard used in USA and a few other places for 2G & 3G eg by Verison
  • GSM was a rival standard for 2G, that is used in most of the world
  • WCDMA and HSPA are the follow-on technology for 3G
    (but in America some people still called it GSM to emphasize its roots and just to confuse people)
  • LTE is the 4G technology used everywhere

The Problem set ‘problem’

Below write would be best ‘Study hack’:

ImageProblem sets defy many of the strategies we use to tame academic work. When you’re given a reading assignment, for example, you can estimate, within 10 – 20 minutes, how long it will take you to complete. You can then break up that work into reasonable chunks and get it done. No problem.

Problem sets offer no such consistency. A given problem might take you ten minutes. On the other hand, it might devour an entire day and still yield no progress. This inconsistency is the bane of students, like Jake, stuck in technical classes.

How do you solve hard problem sets in such a way that they can be integrated into a structured, low-stress study schedule? In this post I will present a four step process. It’s a mixture of the results of my research for this book as well as personal experience, having fought these beasts over the past seven years.

A Four Step Process for Solving Hard Problem Sets

The motivating idea behind this strategy is simple: your brain can only work productively on a hard problem for 1 -3 hours before needing to reboot. To reboot your brain, so more productive work can be accomplished, requires a significant break. Preferably overnight.

Here’s a four step strategy built around this idea. It mimics the work schedule of the typical high-scoring technical student.

Step 1: Pick Off the Simple, Prime the Hard

Your first block of work should occur early in the week. Set aside 2 – 3 hours, in the morning. Make this the first thing you do that day (when your energy is at its highest). Your goal is two-fold. First, you want to solve easy problems. Your strong focus will help you avoid stupid mistakes. Second, you want to tackle at least two hard problems. You probably won’t solve them. This is why they are hard. But you can do something almost as important: prime them.

To prime a hard problem is to discover exactly why you can’t solve it. Pick an obvious approach — even if you suspect it won’t work — and start working through the problem until you get stuck. Identify why you are stuck. Ask what you need to figure out to make progress. What is it that makes this hard? Then take a break…

Step 2: Think in the Shower

For the next 2 – 3 days, think about how to get around the obstacles you discovered while priming. Don’t do this formally, in the library, with books around you. Instead, do this while walking around campus. While waiting for class to start. In the shower. I used to solve myAlgorithms take home exam problems, for example, while jogging.

This is when breakthroughs occur. If you end up with a great insight, take 20 minutes, next time you can spare it, to sit down and write it down formally. If needed, prime a new hard problem so you can keep making progress as your wander campus throughout the week.

If you encounter ambiguities in the problem description that are giving your trouble, send concise questions to your TA requesting clarification. You don’t want these details to slow down progress any longer than they need to. (You might end up e-mailing your TA many times early in the week. This is okay so long as the questions are specific and concise. Don’t wait until office hours. By then, it’s too late.)

Step 3: Meet with your Problem Partner

A team effort is crucial for problem sets. But it has to be the right effort. Don’t meet with a large group. These are rarely efficient. Most of the time is spent griping about the class. Usually, there is one kid in the group who actually did the work, and, in the end, everyone copies off of him. Avoid this. The “smart kid” is often wrong, and likes the group because it boosts his self-esteem. Not to mention that your lack of understanding will come back to tag you on the exam.

The other extreme is to work alone. I see this a lot at MIT. Too many movies like Good Will Hunting got people thinking that to be smart at math means you should be able to stare at a problem for 5 – 10 seconds and then instantly solve it. Sorry. Doesn’t work that way. I walk past real geniuses every day — people, for example, who are my age and are also tenured professors — and guess what: it takes them a long time to solve hard problems; and they work with other people. The ideal configuration for a problem set is a single partner who is at roughly your ability and is willing to meet earlier in the week.

Meet with this partner for 2 – 3 hours to discuss progress made so far. Check your answers on the easy problems. Trade insights on the hard problems. Make new, collaborative attacks on those that still resist solving.

Step 4: Finalize the Problem Set at Office Hours

Show up early to office hours. Arrive understanding exactly why you are stuck on the small number of problems (hopefully) on which you are still stuck. Translate this into a small number of highly specific questions. Ask the TA these questions right after he or she arrives. The key here — and I base this on my own TA experience — is to avoidsimplying saying: “I don’t know how to do this problem, help!” That’s frustrating. Instead, you need targeted information that shows the effort you’ve expended. For example: “I’ve been trying approach XX, it’s promising, but I keep getting stuck with YY, can you point me in the right direction?”

Bring your laptop to office hours and work on finalizing these problems right there. If small questions or ambiguities pop up as you make progress, the TA can be asked on the spot. Aim to leave office hours with a completed problem set. Notice, this is much different from most students who arrive at office hours with very little done. You are arriving with most of the work done, and are just filling in the details.

In Conclusion

Repeated fresh attacks are how hard problems are solved in the real world. Problem sets teach you this skill. The issue, however, is that professors often forget to convey this strategy to their students, many of whom still believe that the high school style, big pushtactic for finishing work is still applicable. So keep this advice in mind. Until you’ve approached a problem fresh, 3 – 4 times, you haven’t really yet tried to solve it.