Ever wish you could guess more precisely? Or maybe just make your guesses look confusingly legitimate? Guesstimate could help.
It uses Monte Carlo simulations to add some legitimacy to the ranges given to it. For example, if you say the cost of lumber for your next project could be between 2 and 8 dollars a piece, you don’t typically mean that it’s equally likely to be any of those numbers. Most people mean that the boards are most likely to be around 3-5 dollars and everything lower or higher is less probable. Using different shaped distributions, Guesstimate can help include this discrepancy of thought into your pseudo-calculations.
It’s a neat bit of code with a nice interface. There is a commercial side to the project for those who want to collaborate openly or pay someone to host it privately. It has a few neat example models for those interested.
Does anyone use anything like this in their daily lives? Is there another similar project out there? This kind of thing is pretty cool!
Artist [Petros Vrellis] has done something that we’ve never seen before: his piece “A New Way to Knit” lives up to its name. What he’s done is to take the traditional circular loom, some black thread, and toss some computing at it. And then he loops the string around and around and around.
The end result of following the computer’s instructions is a greyscale portrait. Where few black strings overlap, it’s light, and where more overlap, it’s darker. That’s the whole gimmick, but the effect is awesome. As you zoom in and out, it goes from a recognizable face to a tangle of wires and back. Check out his video embedded below.
Continue reading “Computer-Designed Portraits, Knit By Hand!”
If you want to factor a number, one way to do it is Shor’s algorithm. That’s a quantum algorithm and finds prime factors of integers. That’s interesting because prime factorization is a big deal of creating or breaking most modern encryption techniques.
Back in 2001, a group at IBM factored 15 (the smallest number that the algorithm can factor) using a 7 qubit system that uses nuclear magnetic resonance. Later, other groups duplicated the feat using photonic qubits. Typical implementations take 12 qubits. However, recent work at MIT and the University of Innsbruck can do the same trick with 5 atoms caught in an ion trap. The researchers believe their implementation will easily scale to larger numbers.
Each qubit is an atom and LASER pulses perform the logic operations. By removing an electron to make each atom positively charged, an electric field can exactly hold the positively charged ions in position only microns apart.
We’ve covered quantum computing before. We’ve even talked about the effect of practical quantum computing on encryption. You might also want to read more about the algorithm involved.
Photo credit: Jose-Luis Olivares/MIT
What’s to be gained from reverse engineering a four-decade-old video game? As it turns out, quite a lot, and as you’ll learn from [Norbert]’s recent talk at the ViennaJS meetup, it’s not just about bringing a classic back to life.
Continue reading “Forty-Year-Old Arcade Game Reveals Secrets of Robot Path Planning”
Cheap, brushless motors may be the workhorses behind our RC planes and quadcopters these days, but we’ve never seen them in any application that requires low-speed precision. Why? Sadly, cheap brushless motors simply aren’t mechanically well-constructed enough to offer precise position control because they exhibit cogging torque, an unexpected motor characteristic that causes slight variations in the output torque that depend rotor position. Undaunted, [Matthew Piccoli] and the folks at UPenn’s ModLab have developed two approaches to compensate and minimize torque-ripple, essentially giving a cheap BLDC Motor comparable performance to it’s pricier cousins. What’s more, they’ve proven their algorithm works in hardware by building a doodling direct-drive robotic arm from brushless motors that can trace trajectories.
Cogging torque is a function of position. [Matthew’s] algorithm works by measuring the applied voltage (or current) needed to servo the rotor to each measurable encoder position in a full revolution. Cogging torque is directional, so this “motor fingerprint” needs to be taken in both directions. With these measured voltages (or currents) logged for all measurable positions, compensating for the cogging torque is just a matter of subtracting off that measured value at any given position while driving the motor. [Matthew] has graciously taken the trouble of detailing the subtleties in his paper (PDF), where he’s actually developed an additional acceleration-based method.
Hobby BLDC motors abound these days, and you might even have a few spares tucked away on the shelf. This algorithm, when applied on the motor controller electronics, can give us the chance to revisit those projects that mandate precise motor control with high torque–something we could only dream about if we could afford a few Maxon motors. If you’re new to BLDC Motor Control theory, check out a few projects of the past to get yourself up-and-running.
Continue reading “Anti-Cogging Algorithm Brings Out the Best in your Hobby Brushless Motors”
Sorting. It’s a classic problem that’s been studied for decades, and it’s a great first step towards “thinking algorithmically.” Over the years, a handful of sorting algorithms have emerged, each characterizable by it’s asymptotic order, a measure of how much longer an algorithm takes as the problem size gets bigger. While all sorting algorithms take longer to complete the more elements that must be sorted, some are slower than others.
For a sorter like bubble sort, the time grows quadradically longer for a linear increase in the number of inputs; it’s of order
O(N²).With a faster sorter like merge-sort, which is
O(N*log(N)), the time required grows far less quickly as the problem size gets bigger. Since sorting is a bit old-hat among many folks here, and since
O(N*log(N)) seems to be the generally-accepted baseline for top speed with a single core, I thought I’d pop the question: can we go faster?
In short — yes, we can! In fact, I’ll claim that we can sort in linear time, i.e a running time of
O(N). There’s a catch, though: to achieve linear time, we’ll need to build some custom hardware to help us out. In this post, I’ll unfold the problem of sorting in parallel, and then I”ll take us through a linear-time solution that we can synthesize at home on an FPGA.
Need to cut to the chase? Check out the full solution implemented in SystemVerilog on GitHub. I’ve wrapped it inside an SPI communication layer so that we can play with it using an everyday microcontroller.
To understand how it works, join us as we embark on an adventure in designing algorithms for hardware. If you’re used to thinking of programming in a stepwise fashion for a CPU, it’s time to get out your thinking cap!
Continue reading “Sort Faster with FPGAs”
A team of Cornell students recently built a prototype electronic glove that can detect sign language and speak the characters out loud. The glove is designed to work with a variety of hand sizes, but currently only fits on the right hand.
The glove uses several different sensors to detect hand motion and position. Perhaps the most obvious are the flex sensors that cover each finger. These sensors can detect how each finger is bent by changing the resistance according to the degree of the bend. The glove also contains an MPU-6050 3-axis accelerometer and gyroscope. This sensor can detect the hand’s orientation as well as rotational movement.
While the more high-tech sensors are used to detect most characters, there are a few letters that are similar enough to trick the system. Specifically, they had trouble with the letters R, U, and V. To get around this, the students strategically placed copper tape in several locations on the fingers. When two pieces of tape come together, it closes a circuit and acts as a momentary switch.
The sensor data is collected by an ATmega1284p microcontroller and is then compiled into a packet. This packet gets sent to a PC which then does the heavy processing. The system uses a machine learning algorithm. The user can train the it by gesturing for each letter of the alphabet multiple times. The system will collect all of this data and store it into a data set that can then be used for detection.
This is a great project to take on. If you need more inspiration there’s a lot to be found, including another Cornell project that speaks the letters you sign, as well as this one which straps all needed parts to your forearm.
Continue reading “Electronic Glove Detects Sign Language”