Circuits I
Goomey https://youtu.be/D7JAHcKMhCs
https://youtu.be/xyMH8wKK-Ag?list=PLWUmK-m4olhEHVXnyW8HLs22vHJTT-vSf
Circuits II
https://youtu.be/tZBMfDvWF4U?list=PLWUmK-m4olhHrR1_jbTyzAJJcF0aRTdUX
http://spicesim.blogspot.com/p/measuring-ac-impedance.html
Thursday, May 12, 2016
S-Domain
Once the equation is boiled down, the final two steps to do is make the numerator a polynomial (no s's with negative exponents--including #/s). And second, have the denominator's highest s-term have no constant out in front of it.
After that when finding the roots (zeros or poles, which all mean the same thing), and you're left with a complex form of something like s = α + jβ, take the (+) & (-) expression. The other expression isn't needed; in the end it equals 2||A1||cos(βt + θ)e^(-αt). And if you're lucky, you can sometimes check that time constant with regular AC analysis methods.
After that when finding the roots (zeros or poles, which all mean the same thing), and you're left with a complex form of something like s = α + jβ, take the (+) & (-) expression. The other expression isn't needed; in the end it equals 2||A1||cos(βt + θ)e^(-αt). And if you're lucky, you can sometimes check that time constant with regular AC analysis methods.
Tuesday, January 26, 2016
algorithm predict human behavior or yellow journalism
from
https://robindoherty.com/2016/01/06/nothing-to-hide.html
from
http://qz.com/527008/an-algorithm-can-predict-human-behavior-better-than-humans/
Deep Feature Synthesis:Towards Automating Data Science Endeavors
http://groups.csail.mit.edu/EVO-DesignOpt/groupWebSite/uploads/Site/DSAA_DSM_2015.pdf
https://robindoherty.com/2016/01/06/nothing-to-hide.html
from
http://qz.com/527008/an-algorithm-can-predict-human-behavior-better-than-humans/
Deep Feature Synthesis:Towards Automating Data Science Endeavors
http://groups.csail.mit.edu/EVO-DesignOpt/groupWebSite/uploads/Site/DSAA_DSM_2015.pdf
- must have significant data
- data across multiple websites
- cross-company database
- "3 datasets from different domains"--conclusion
- of course we have a pattern of what we buy
- we buy at certain frequencies
- we buy for certain prices, depending on our preferences/available income/etc
- preferences are roughly either luxurious or economical
- "These questions can be turned into features by following relationships, aggregating values, and calculating new features."--calculating = estimating (in the end that's all)
Saturday, January 16, 2016
Soldering
Electronics Soldering.pdf
Soldering Guide.pdf
The best way to learn soldering is by doing it on projects. Make a goal to do 10 projects in the summer. Now all you need to do is compile a list of projects. Here's what I found interesting:
DO: http://elecprojects.blogspot.com/ categorize some of these and gather supplies for 1
DC power supply (converts AC to DC)Variable Voltage Power Supply- an inverter is the opposite
- 555 Timer IC Testing Circuit
- Polarity cum Continuity Tester (already in DMM)
- Ultrasonic Rangefinder using 8051 (make sure parts are all less than $15)
- FM Bugger Circuit
- Supplies
- 3V battery
- Trimmer cap (max of 50pF)
- $2 approx.
- 2n2222 transistor
- http://electronics.stackexchange.com/questions/66971/how-the-transistor-npn-2n2222-works
- http://electronics.stackexchange.com/questions/153425/help-between-2n2222-and-2n2222a
- http://www.edaboard.com/thread18748.html (deciding between 2n2222 or 2n2222A, where both have the same price ~ no brainer to go with the new)
- Mic
Dipoleantenna- wire
- Wire inductor (DIY)
- http://electronics.stackexchange.com/questions/145298/20awg-wire-inductor-and-8a-possible
- http://electronics.stackexchange.com/questions/217419/spaced-air-core-inductor-wire-size-effect
- http://electronics.stackexchange.com/questions/246662/will-an-inductor-wound-using-wire-of-0-6438-mm-22-awg-be-close-enough-to-0-508
- http://www.arrl.org/files/file/Technology/tis/info/pdf/9708033.pdf
- Other spy bugs
- FM
- 200a meters
- 200b meters but what about this
- 300 meters
- 400 meters (1/4 mile)
- Non-radio??
- Temperature Controlled DC Fan using Microcontroller
- Car Battery Charger Circuit
- Mobile Jammer Circuit
- TV Remote Control Jammer
- Password Based Door Lock System using 8051 Microcontroller
- One Transistor Electronic Code Lock System
- Motion Detector Circuit
- Battery Level Indicator
- Sun Tracking Solar Panel
- Metal Detector Circuit
- Car Parking Guard Circuit Using Infrared Sensor
- Reverse Parking Sensor Circuit
- 8 Channel Quiz Buzzer Circuit using Microcontroller
- Automatic Plant Irrigation System
- Wire Loop Breaking Alarm
- Cell Phone Detector
- Transistor Intercom Circuit
- Interfacing GPS with 8051 Microcontroller
- Auto Turnoff Soldering Iron Circuit
- Pull Pin Security Alarm System
- Metal Detector Robotic Vehicle
- Wireless Switch Circuit using CD4027
- Automatic Street Light Controller Circuit Using Relays and LDR
- 12v DC to 220v AC Converter Circuit
- 12V to 24V DC Converter Circuit
- Audio Equalizer Circuit
- Toy Organ using 555 Timer IC
- FM Radio Circuit
- FM Remote Encoder/Decoder Circuit
- Biometric Attendance System
- J?
Air Flow Detector Circuit - Stun Gun Circuit
- Battery Charger Circuit Using SCR
- Don't know what kind of batteries this can charge
- aside, F1 = fuse
- How would it know what appliances to control?
Mains Operated LED Light Circuit
RFID based Attendance System
http://www.instructables.com/id/Upcycle-your-old-PC-fans-into-mini-wind-generators/
http://www.instructables.com/id/Make-a-portable-handy-lie-detector-in-Altoid-tin/
Sources
http://www.electronicshub.org/electronics-projects-ideas/
https://learn.sparkfun.com/tutorials/how-to-read-a-schematic
Sunday, December 27, 2015
A.I. tracks
What are the different tracks that can end in, "A.I. gain[ing] the ability to improve itself, and in short order exceeds the intellectual potential of the human brain."
http://www.newyorker.com/magazine/2015/11/23/doomsday-invention-artificial-intelligence-nick-bostrom
The people who say that artificial intelligence is not a problem tend to work in artificial intelligence. [...] Oren Etzioni, the C.E.O. of the Allen Institute for Artificial Intelligence, in Seattle, referred to the fear of machine intelligence as a “Frankenstein complex.” Another leading researcher declared, “I don’t worry about that for the same reason I don’t worry about overpopulation on Mars.” Jaron Lanier, a Microsoft researcher and tech commentator, told me that even framing the differing views as a debate was a mistake. “This is not an honest conversation,” he said. “People think it is about technology, but it is really about religion, people turning to metaphysics to cope with the human condition. They have a way of dramatizing their beliefs with an end-of-days scenario—and one does not want to criticize other people’s religions.” [A digit-al god.]
Eventually, the researchers started to question the goal of building a mind altogether. Why not try instead to divide the problem into pieces? They began to limit their interests to specific cognitive functions: vision, say, or speech.
In the history of computer science, no programmer has created code that can substantially improve itself. [But it doesn't need to. Any minute measure could be replicated, improved. Unless Khatchadourian (the author of this) means that no program has been created that does anything related to 'intelligence.' Can a program edit itself? Do they merely boil down to 1s & 0s, nothing more? An example, unrelated to the last strand of thought, comes to me as voice recognition software. I'm told that it can adjust to your tendencies, but this is not intelligence in the self-autonomous realm. That is just taking a multiple of different programs and cherry picking; starting at a sub-root of the tree, {{cont. taking in reverse order}} jumping onto different limbs, then different branches, multiple trunks, stems, sub-stems, and finally (at computer computational speed), the preferred route. Just nodes on nodes.]
The book begins with an “unfinished” fable about a flock of sparrows that decide to raise an owl to protect and advise them. They go looking for an owl egg to steal and bring back to their tree, but, because they believe their search will be so difficult, they postpone studying how to domesticate owls until they succeed. Bostrom concludes, “It is not known how the story ends.”
“Artificial intelligence already outperforms human intelligence in many domains.” The examples range from chess to Scrabble.
One program from 1981, called Eurisko, was designed to teach itself a naval role-playing game. After playing ten thousand matches, it arrived at a morally grotesque strategy: to field thousands of small, immobile ships, the vast majority of which were intended as cannon fodder. In a national tournament, Eurisko demolished its human opponents, who insisted that the game’s rules be changed. The following year, Eurisko won again—by forcing its damaged ships to sink themselves.
Given even the most benign objective—to win a game—such a system, Bostrom argues, might develop “instrumental goals”: gather resources, or invent technology [...].
The brain of the village idiot and the brain of a scientific genius are almost identical.
wtf is this saying:
A respected minority of A.I. researchers began to wonder: If increasingly powerful hardware could facilitate the deep-learning revolution, would it make other long-shelved A.I. principles viable? “Suppose the brain is just a million different evolutionarily developed hacks:
[...] deep learning. Perhaps the most interesting acquisition is a British company called DeepMind, started in 2011 to build a general artificial intelligence. Its founders had made an early bet on deep learning, and sought to combine it with other A.I. mechanisms in a cohesive architecture. In 2013, they published the results of a test in which their system played seven classic Atari games, with no instruction other than to improve its score. For many people in A.I., the importance of the results was immediately evident. I.B.M.’s chess program had defeated Garry Kasparov, but it could not beat a three-year-old at tic-tac-toe. In six games, DeepMind’s system outperformed all previous algorithms; in three it was superhuman. In a boxing game, it learned to pin down its opponent and subdue him with a barrage of punches.
DeepMind’s system still fails hopelessly at tasks that require long-range planning, knowledge about the world, or the ability to defer rewards
http://www.newyorker.com/magazine/2015/11/23/doomsday-invention-artificial-intelligence-nick-bostrom
The people who say that artificial intelligence is not a problem tend to work in artificial intelligence. [...] Oren Etzioni, the C.E.O. of the Allen Institute for Artificial Intelligence, in Seattle, referred to the fear of machine intelligence as a “Frankenstein complex.” Another leading researcher declared, “I don’t worry about that for the same reason I don’t worry about overpopulation on Mars.” Jaron Lanier, a Microsoft researcher and tech commentator, told me that even framing the differing views as a debate was a mistake. “This is not an honest conversation,” he said. “People think it is about technology, but it is really about religion, people turning to metaphysics to cope with the human condition. They have a way of dramatizing their beliefs with an end-of-days scenario—and one does not want to criticize other people’s religions.” [A digit-al god.]
Eventually, the researchers started to question the goal of building a mind altogether. Why not try instead to divide the problem into pieces? They began to limit their interests to specific cognitive functions: vision, say, or speech.
In the history of computer science, no programmer has created code that can substantially improve itself. [But it doesn't need to. Any minute measure could be replicated, improved. Unless Khatchadourian (the author of this) means that no program has been created that does anything related to 'intelligence.' Can a program edit itself? Do they merely boil down to 1s & 0s, nothing more? An example, unrelated to the last strand of thought, comes to me as voice recognition software. I'm told that it can adjust to your tendencies, but this is not intelligence in the self-autonomous realm. That is just taking a multiple of different programs and cherry picking; starting at a sub-root of the tree, {{cont. taking in reverse order}} jumping onto different limbs, then different branches, multiple trunks, stems, sub-stems, and finally (at computer computational speed), the preferred route. Just nodes on nodes.]
The book begins with an “unfinished” fable about a flock of sparrows that decide to raise an owl to protect and advise them. They go looking for an owl egg to steal and bring back to their tree, but, because they believe their search will be so difficult, they postpone studying how to domesticate owls until they succeed. Bostrom concludes, “It is not known how the story ends.”
“Artificial intelligence already outperforms human intelligence in many domains.” The examples range from chess to Scrabble.
One program from 1981, called Eurisko, was designed to teach itself a naval role-playing game. After playing ten thousand matches, it arrived at a morally grotesque strategy: to field thousands of small, immobile ships, the vast majority of which were intended as cannon fodder. In a national tournament, Eurisko demolished its human opponents, who insisted that the game’s rules be changed. The following year, Eurisko won again—by forcing its damaged ships to sink themselves.
Given even the most benign objective—to win a game—such a system, Bostrom argues, might develop “instrumental goals”: gather resources, or invent technology [...].
The brain of the village idiot and the brain of a scientific genius are almost identical.
wtf is this saying:
A respected minority of A.I. researchers began to wonder: If increasingly powerful hardware could facilitate the deep-learning revolution, would it make other long-shelved A.I. principles viable? “Suppose the brain is just a million different evolutionarily developed hacks:
[...] deep learning. Perhaps the most interesting acquisition is a British company called DeepMind, started in 2011 to build a general artificial intelligence. Its founders had made an early bet on deep learning, and sought to combine it with other A.I. mechanisms in a cohesive architecture. In 2013, they published the results of a test in which their system played seven classic Atari games, with no instruction other than to improve its score. For many people in A.I., the importance of the results was immediately evident. I.B.M.’s chess program had defeated Garry Kasparov, but it could not beat a three-year-old at tic-tac-toe. In six games, DeepMind’s system outperformed all previous algorithms; in three it was superhuman. In a boxing game, it learned to pin down its opponent and subdue him with a barrage of punches.
DeepMind’s system still fails hopelessly at tasks that require long-range planning, knowledge about the world, or the ability to defer rewards
Thursday, June 11, 2015
Friday, May 22, 2015
STEM Courses
- Read the section before coming to class, it would help. In class, ask lots of Qs.
- Keep on trying with hard problems: the answer will come sooner or later. Don't give up. Eventually the answers will come to you; if not, ask. Expect that you will get stuck on problems and need extra help.
- Maintain a list of hard problems in a separate or in the back of the notebook. Practice doing these problems over and over until you can write down the complete correct solution without any help.
- Strike middle ground between conceptualizing and practicing. They both draw upon each other to efficiency.
- Review by explaining challenging types of problems. (1) Get a blank sheet of paper (2) Explain the technique or concept from sheer recollection.
General pointers
- two to four hours on each homework assignment
- memorize formulas or definitions immediately
- prepare for exams by working on new problems
- focus on why when reviewing assessments
Supplements
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