For each question in this section, please provide where you got your information via a URL in the [source URL] placeholder.
What maximum bit size does an Arduino Uno use and what's the largest number it can represent? (3 pts)
This is vauge, but I assume you mean what the largest interger value possible on Ardino is, which would probably be an unsigned Long
- 2 Bytes (16 bits)
- 65535
- http://arduino.cc/en/Reference/UnsignedInt
- AnalogRead returns an int for 0 to 1024
- 2^8
- http://arduino.cc/en/Reference/analogRead
- around 10000 times a second
- http://arduino.cc/en/Reference/analogRead
When working with "Big Data" on your own laptop, what memory barriers might you run into? Explain your answer. [5 pts]
Assuming my laptop is now the limiter: One I have already run into is my C drive has less than a gig of space on it. Many programs let you decide where you would like to install, but use a temp file buffer on C regardless. This leads to me constantly fighting with my Windows installation to free up a couple of gigs of space when need be. Another problem I might run into is the need to store massive arrays/buffers/strings in memory. I could easiy exceed the 4GB RAM allocation if I'm not careful.
Already fought with this quite a bit. I am trying to write a script that stores as many values as possible in concated strings/ a string array before the very, very slow operation of opening the SD card and writing to it. This leads to needing to figure out the max string/array I can store before needing to reset. I also have a hunch that my code currently has a memeory leak, I need to learn a bit about garbage collection in Aurdino.
For the next part of the assignment, you will look at your sample data.
Is it possible to get high enough fidelity data from our Sound Sensor such that one can recognize musical frequencies?
What would you define as your 'signal' you are looking for in your data? That is, what might constitute as finding something of interest to your question? (5 pts)
The "Signal" I am lookin for is a sequence of voltages that looks like a sine wave when plotted against time. This needs to be high enough fidelity that I can run it through FFT or another wave-collapse function to analyze the sample against known musical frequencies
Insert a small sample of your data where you notice something that might be a signal and explain why you think so. (5 pts)
42,9,0 43,9,1 44,9,2 44,9,3 44,9,4 44,9,5 44,9,6 44,9,7 44,9,8 44,9,9 43,9,10 43,9,11 42,9,12 41,9,13 41,9,14 40,9,15 40,9,16 40,9,17 42,9,18 42,9,19 43,9,20 44,9,21 44,9,22 44,9,23 44,9,24 43,9,25 43,9,26 42,9,27 42,9,28 40,9,29 40,9,30 41,9,31 40,9,32 40,9,33 40,9,34 39,9,35 38,9,36 39,9,37 38,9,38 38,9,39 38,9,40 39,9,41 38,9,42 37,9,43 37,9,44 36,9,45 35,9,46 34,9,47 33,9,48 32,9,49 31,9,50 32,9,51 32,9,52 32,9,53 34,9,54 35,9,55 36,9,56 37,9,57 37,9,58 36,9,59 35,9,60 33,9,61 33,9,62 30,9,63 28,9,64 28,9,65 27,9,66 28,9,67 28,9,68 28,9,69 29,9,70 29,9,71 29,9,72 29,9,73 29,9,74 28,9,75 27,9,76 27,9,77 26,9,78 25,9,79 25,9,80 24,9,81 24,9,82 24,9,83 25,9,84 25,9,85 25,9,86 26,9,87 28,9,88 28,9,89 28,9,90 28,9,91 28,9,92 27,9,93 26,9,94 25,9,95 23,9,96 22,9,97 20,9,98 20,9,99 19,9,100 20,9,101 20,9,102 21,9,103 23,9,104 23,9,105 23,9,106 24,9,107 24,9,108 23,9,109 21,9,110 21,9,111 20,9,112 19,9,113 18,9,114 19,9,115 20,9,116 21,9,117 22,9,118 22,9,119 22,9,120 22,9,121 22,9,122 22,9,123 21,9,124 20,9,125 20,9,126 20,9,127 19,9,128 19,9,129 19,9,130 18,9,131 17,9,132 16,9,133 16,9,134 17,9,135 16,9,136 17,9,137 18,9,138 19,9,139 19,9,140 20,9,141 20,9,142 20,9,143 20,9,144 19,9,145 19,9,146 18,9,147 17,9,148 16,9,149 17,9,150 17,9,151 18,9,152 18,9,153 18,9,154 18,9,155 18,9,156 18,9,157 19,9,158 17,9,159 17,9,160 17,9,161 17,9,162 16,9,163 16,9,164 16,9,165 15,9,166 15,9,167 14,9,168 15,9,169 14,9,170 14,9,171 15,9,172 15,9,173 17,9,174 18,9,175 19,9,176 20,9,177 20,9,178 19,9,179 18,9,180 18,9,181 16,9,182 14,9,183 14,9,184 13,9,185 14,9,186 14,9,187 14,9,188 16,9,189 16,9,190 16,9,191 17,9,192 17,9,193 16,9,194 16,9,195 14,9,196 13,9,197 13,9,198 12,9,199 13,9,200 13,9,201 14,9,202 14,9,203 15,9,204 16,9,205 16,9,206 17,9,207 17,9,208 18,9,209 19,9,210 18,9,211 18,9,212 17,9,213 16,9,214 15,9,215 14,9,216 12,9,217 11,9,218 11,9,219 12,9,220 13,9,221 14,9,222 15,9,223 16,9,224 16,9,225 16,9,226 17,9,227 16,9,228 15,9,229 14,9,230 14,9,231 12,9,232 12,9,233 12,9,234 12,9,235 14,9,236 15,9,237 16,9,238 16,9,239 17,9,240 17,9,241 18,9,242 16,9,243 16,9,244 16,9,245 14,9,246 14,9,247 14,9,248 14,9,249 13,9,250 13,9,251 12,9,252 13,9,253 13,9,254 12,9,255 13,9,256 14,9,257 14,9,258 15,9,259 16,9,260 16,9,261 16,9,262 16,9,263 15,9,264 15,9,265 14,9,266 14,9,267 13,9,268 13,9,269 13,9,270 15,9,271 15,9,272 16,9,273 17,9,274 16,9,275 16,9,276 16,9,277 15,9,278 14,9,279 14,9,280 14,9,281 14,9,282 13,9,283 14,9,284 13,9,285 13,9,286 13,9,287 13,9,288 13,9,289 13,9,290 14,9,291 14,9,292 15,9,293 15,9,294 16,9,295 16,9,296 17,9,297 17,9,298 17,9,299 16,9,300 15,9,301 14,9,302 13,9,303 13,9,304 13,9,305 13,9,306 14,9,307 15,9,308 15,9,309 16,9,310 16,9,311 16,9,312 16,9,313 15,9,314 14,9,315 13,9,316 12,9,317 12,9,318 11,9,319 11,9,320 12,9,321 12,9,322 13,9,323 14,9,324 15,9,325 16,9,326 16,9,327 17,9,328 17,9,329 17,9,330 17,9,331 16,9,332 16,9,333 14,9,334 14,9,335 12,9,336 12,9,337 11,9,338 11,9,339 12,9,340 13,9,341 13,9,342 14,9,343 15,9,344 16,9,345 16,9,346 16,9,347 15,9,348 15,9,349 13,9,350 13,9,351 11,9,352 11,9,353 11,9,354 12,9,355 13,9,356 15,9,357 16,9,358 16,9,359 18,9,360 17,9,361 17,9,362 16,9,363 15,9,364 14,9,365 14,9,366 13,9,367 13,9,368 12,9,369 13,9,370 11,9,371 12,9,372 12,9,373 13,9,374 13,9,375 13,9,376 13,9,377 14,9,378 14,9,379 14,9,380 15,9,381 15,9,382 14,9,383 14,9,384 14,9,385 13,9,386 13,9,387 13,9,388 13,9,389 14,9,390 15,9,391 15,9,392 16,9,393 15,9,394 16,9,395 15,9,396 15,9,397 14,9,398 13,9,399 13,9,400 12,9,401 12,9,402 12,9,403 12,9,404 13,9,405 13,9,406 13,9,407 13,9,408 13,9,409 14,9,410 13,9,411 14,9,412 14,9,413 15,9,414 15,9,415 16,9,416 17,9,417 16,9,418 16,9,419 15,9,420 14,9,421 14,9,422 13,9,423 13,9,424 13,9,425 13,9,426 14,9,427 15,9,428 15,9,429 16,9,430 16,9,431 16,9,432 16,9,433 15,9,434 13,9,435 12,9,436 12,9,437 10,9,438 11,9,439 11,9,440 12,9,441 13,9,442 14,9,443 16,9,444 16,9,445 17,9,446 16,9,447 17,9,448 17,9,449 16,9,450 16,9,451 16,9,452 15,9,453 14,9,454 13,9,455 13,9,456 12,9,457 12,9,458 12,9,459 12,9,460 13,9,461 14,9,462 15,9,463 16,9,464 16,9,465 16,9,466 15,9,467 14,9,468 14,9,469 12,9,470 11,9,471 11,9,472 11,9,473 11,9,474 13,9,475 14,9,476 16,9,477 17,9,478 18,9,479 18,9,480 17,9,481 17,9,482 15,9,483 15,9,484 13,9,485 13,9,486 13,9,487 12,9,488 11,9,489 12,9,490 13,9,491 13,9,492 14,9,493 13,9,494 14,9,495 15,9,496 14,9,497 15,9,498 14,9,499
the first few hundred values oscollate back and forth, as you would expect from a sine wave (or musical note)
Provide a data snippet of some noise you've encountered in your data collection, then try to explain it. (5 pts)
-322,0-322,0216,0 354,0 355,0 218,0 281,0 354,0 206,0 217,0 256,0 262,0 225,0 246,0 258,0 173,0 206,0 101,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 -322,0216,0 354,0 355,0 218,0 281,0 354,0 206,0 217,0 256,0 262,0 225,0 246,0 258,0 173,0 206,0 101,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 216,0 354,0 355,0 218,0 281,0 354,0 206,0 217,0 256,0 262,0 225,0 246,0 258,0 173,0 206,0 101,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 354,0 355,0 218,0 281,0 354,0 206,0 217,0 256,0 262,0 225,0 246,0 258,0 173,0 206,0 101,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 355,0 218,0 281,0 354,0 206,0 217,0 256,0 262,0 225,0 246,0 258,0 173,0 206,0 101,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 218,0 281,0 354,0 206,0 217,0 256,0 262,0 225,0 246,0 258,0 173,0 206,0 101,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 281,0 354,0 206,0 217,0 256,0 262,0 225,0 246,0 258,0 173,0 206,0 101,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 354,0 206,0 217,0 256,0 262,0 225,0 246,0 258,0 173,0 206,0 101,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 206,0 217,0 256,0 262,0 225,0 246,0 258,0 173,0 206,0 101,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 217,0 256,0 262,0 225,0 246,0 258,0 173,0 206,0 101,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 256,0 262,0 225,0 246,0 258,0 173,0 206,0 101,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 262,0 225,0 246,0 258,0 173,0 206,0 101,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 225,0 246,0 258,0 173,0 206,0 101,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 246,0 258,0 173,0 206,0 101,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 258,0 173,0 206,0 101,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 173,0 206,0 101,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 206,0 101,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 101,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 144,0 138,0 135,0 115,0 153,0 115,0 53,0 138,0 135,0 115,0 153,0 115,0 53,0 135,0 115,0 153,0 115,0 53,0 115,0 153,0 115,0 53,0 153,0 115,0 53,0 115,0 53,0 53,0 116,0 82,0 82,0 49,0 69,0 70,0 64,0 69,0 68,0 76,0 65,0 62,0 49,0 44,0 39,0 47,0 47,0 47,0 39,0 46,0 56,0 27,0 29,0 37,0 29,0 28,0 32,0 25,0 28,0 23,0 13,0 28,0 28,0 33,0 53,0 34,0 55,0 57,0 52,0 25,0 16,0 -8,0 -14,0 28,0 28,0 10,0 13,0 -2,0 27,0 15,0 -3,0 12,0 11,0 25,0 22,0 12,0 0,0 15,0 5,0 20,0 6,0 -4,0 9,0 10,0 13,0 -9,0 7,0 3,0 2,0 1,0 9,0 4,0 -3,0 -12,0 -1,0 -1,0 6,0 3,0 -33,0 354,0 -322,0-322,0354,0
My code is set up to listen for a "loud (intial)" noise, and then sample at close to the maximum rate for around a second afteword. This was likely from when I made a loud sound while not playing a note, leading to the next second fo "garbage" being collected. Plus, this was when my code was overflowing. a bunch of these values arn't even sane.
How might you go about getting rid of the noise? (Don't worry, we'll learn more about this, but try to think of a first method) (5 pt)
I can safely throw out all data that doesn't have the sine wave pattern I am looking for. The easiest way to do this may be to conduct anlysis against all data I have, and then throw out all values that are "too far" from a goal value.
For your research question, do you think your biggest challenge is in the sampling (i.e., getting valid data) or the analysis (i.e., cleaning noise out of your data)? Explain (4 pts)
Gathering the data seems like it will be the larger challange. I spent ~5 hours fighting with aurdinos memory restrictions, but now that I have data it, easiy seperated by sample index, it seems like it will be somewhat straight forward to throw out nonsense data.
In thinking ahead about the big project, what additional Arduino tools may be helpful for getting higher fidelity data? Do a bit of your own research here.
"Higher Fidelity" data is a big vauge. I assume you mean for sound/light! Sparkfun seems ot have a ton of cool addons. We could pick up a more sensitive microphone [https://www.sparkfun.com/products/12656]. Personally I think the biometrics look like a ton of fun [https://www.sparkfun.com/categories/146]. I have a vision for an "accidently interactive" display in the ATLAS lobby, and I think we could do some fun things with biometrics.
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent