A Modular Logic Analyzer For FPGAs

When working on a project, it’s incredibly helpful to be able to visualize the various signals in play. This is important when attempting to determine if what is supposed to be happening is actually happening. However, logic analyzers can be expensive, so a group from [Bruce Land]’s ECE5760 class developed their own hardware solution instead.

The primary idea behind the project is modularity. The basic building blocks of the logic analyser are coded in Verilog. They’re designed so that the number of channels and added functions can be mixed and match to suit the given purpose and the capabilities of the target FPGA platform. The team’s logic analyzer is also capable of decoding SPI and I2C in hardware, and has a graphical user interface running on an attached laptop for visualizing signals.

It’s a tidy build, and an excellent project to learn the fundamentals of both FPGA programming and the various communications protocols involved. [Bruce Land]’s classes are a hotbed of FPGA projects, from pokerbots to NES chiptune emulators. Video after the break.

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PokerBot Uses FPGA For Card Calculating Horsepower

Played against humans, Poker is a game as much about reading your opponent as it is about the cards you’re dealt. That doesn’t mean there aren’t certain mathematical ways to aid your decision making based on probabilities. In this vein, a group of students from Cornell’s ECE 5760 class built a pokerbot on an FPGA.

The bot uses the principle of Monte Carlo simulation to calculate the probabilities of an individual winning a hand of Limit Texas Hold’em. Calculating the entire set of possible hands is impractical, so in a Monte Carlo simulation a sample is calculated instead. By accelerating these calculations on an FPGA, the pokerbot is able to calculate 300,000 possible hands in just 150 ms, and present a probability of winning to the human player. This same calculation method is then used to make decisions for the computer players in the game, too.

The team report that the FPGA’s processing power brought a 10x speed up compared to their C++ program running on an Intel i7-6700HQ. The strong statistical calculations help to make the computer players engaging and realistic to play against.

It’s another great example of a project from Bruce Land’s classes, which are somewhat of a hotbed of development each year. Video after the break.

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Tweetbot Expresses Twitter Emotions

When reading textual communications, it can be difficult to accurately acertain emotional intent. Individual humans can be better or worse at this, with sometimes hilarious results when it goes wrong. Regardless, there’s nothing a human can do that a machine won’t eventually do better. For just this purpose, Tweetbot is here to emotionally react to Twitter so you don’t have to.

The ‘bot receives tweets over a bluetooth link, handled by a PIC32, which also displays them on a small TFT screen. The PIC then analyses the tweet for emotional content before sending the result to a second PIC32, which displays emotes on a second TFT screen, creating the robot’s face. Varying LEDs are also flashed depending on the emotion detected – green for positive emotions, yellow for sadness, and red for anger.

The final bot is capable of demonstrating 8 unique emotional states, far exceeding the typical Facebook commenter who can only express unbridled outrage. With the ‘bot packing displays, multiple microcontrollers, and even motor drives, we imagine the team learned a great deal in the development of the project.

The project was the product of [Bruce Land]’s ECE 4760 course, which has shown us plenty of great hacks in the past – Bike Sonar being one of our favorites. Video after the break.

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FPGA Makes Digital Analog Computer

When you think of analog computing, it’s possible you don’t typically think of FPGAs. Sure, a few FPGAs will have specialized analog blocks, but usually they are digital devices. [Bruce Land] — a name well-known to Hackaday — has a post about building a digital differential analyzer using an FPGA and it is essentially an analog computer simulated on the digital fabric of an FPGA.

Whereas traditional analog computers use operational amplifiers to do mathematical integration, on the FPGA [Land] uses digital summers The devices simulate a system of differential equations, which can be nonlinear.

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This Machine Teaches Sign Language

Sign language can like any language be difficult to learn if you’re not immersed in it, or at least learning from someone who is fluent. It’s not easy to know when you’re making minor mistakes or missing nuances. It’s a medium with its own unique issues when learning, so if you want to learn and don’t have access to someone who knows the language you might want to reach for the next best thing: a machine that can teach you.

This project comes from three of [Bruce Land]’s senior electrical and computer engineering students, [Alicia], [Raul], and [Kerry], as part of their final design class at Cornell University. Someone who wishes to learn the sign language alphabet slips on a glove outfitted with position sensors for each finger. A computer inside the device shows each letter’s proper sign on a screen, and then checks the sensors from the glove to ensure that the hand is in the proper position. Two letters include making a gesture as well, and the device is able to track this by use of a gyroscope and compass to ensure that the letter has been properly signed. It appears to only cover the alphabet and not a wider vocabulary, but as a proof of concept it is very effective.

The students show that it is entirely possible to learn the alphabet reliably using the machine as a teaching tool. This type of technology could be useful for other applications as well, such as gesture recognition for a human interface device. If you want to see more of these interesting and well-referenced senior design builds we’ve featured quite a few, from polygraph machines to a sonar system for a bicycle.

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PIC-Powered Game Console Is Blocky Goodness

Picking up new skills in the electronics field is often best served by the classic mantra – “learn by doing”. [Juan] and [Leo] did just that, deciding to build a handheld game console for a University project, and delivering the PGC-32.

Built as a final project for the Digital Systems Design course at Cornell University, the PGC-32 takes on a daunting chunk of functionality, and pulls it off in time to get the grades. The team coded a basic block-based game for the hardware, and control is switchable between the analog stick and a built-in accelerometer. Gameplay is displayed on a 320 x 280 color TFT display. Learning to code a basic game is useful, as it teaches student engineers to consider important concepts like timing, race conditions, interrupts, and display routines.

As a university project, it is well documented and the team step through each detail in their code with explanations as to how and why things are done. The internals are particularly neat, too, with a tidy PCB layout and 3D printed case holding everything together.

We’ve seen plenty of work from Cornell’s courses before, too – like this sleep quality monitor.  Video after the break.

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Smart Station Runs Entertainment, Is Entertainment

It’s that special time of year—time for the parade of student projects from [Bruce Land]’s embedded microcontroller design course at Cornell. [Timothy], [Dhruv], and [Shaurya] are all into remote sensing and control applications, so they built a smart station that combines audiovisual entertainment with environmental sensing.

As with the other projects in this course, the smart station is built on a PIC32 dev board. It does Bluetooth audio playback via RN-52 module and has a beat-matching light show in the form of a NeoPixel ring mounted atop the 3D-printed enclosure. But those blinkenlights aren’t just there to party. They also provide visual feedback about the environment, which comes from user-adjustable high and low trigger values for the mic, an accelerometer, a temperature and humidity sensor, and a luminosity sensor.

The group wanted to add an ultrasonic wake-up feature, but it refused to work with the 3.3V from the PIC. The NeoPixel ring wanted 5V too, but isn’t as picky. It looks to be plenty bright at 3.3V. Another challenge came from combining I²C, UART, analog inputs, and digital outputs. They had to go to the chip’s errata to verify it, but it’s there: whenever I²C1 is enabled, the first two analog pins are compromised, and there’s no official solution. The team got around it by using a single analog pin and a multiplexer. You can check out those blinkenlights after the break.

Maybe you prefer working in wood. If so, you might like this hexagonal take on audio-visualization.

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