Op Amp Challenge: An Op-Amp Buck Regulator

May 1, 2023 by Jenny List 10 Comments

Switching regulators have delivered such convenience and efficiency compared to their linear siblings, that it’s now becoming rare to see an old-style three-terminal regulator. Modern designs have integrated to such an extent that for many of us the inner workings remain something of a mystery. It’s still possible to make switching regulators from first principles though, which is what [Aaron Lager] has done by designing a buck regulator from a quad op-amp IC,

It’s an entry in our Op Amp Challenge and it appears to be a work in progress, but the design is solid enough. We’re no fans of the schematic style of representing an op-amp chip as a rectangle rather than individual op-amps, but it’s simply a PWM generator with a final op-amp used as a driver for the usual diode-inductor-capacitor network. We’re guessing that the op-amp driver won’t make this the most powerful of switchers, but in this case that’s hardly the point. Build this if you’re interested in taking an op-amp out of its normal sphere, or if you’re interested in the workings of a buck converter.

Need more in the way of switching regulators from first principles? We’ve got you covered, with the ultimate regulator kit of parts, the Fairchild UA723.

My Great-Great-Grandad, The Engineer Who Invented A Coffee Pot

May 1, 2023 by Jenny List 34 Comments

In the study of genealogy it’s common to find people who will go to great lengths involving tenuous cross-links to establish royalty or famous figures such as George Washington or William Shakespeare in their family tree. There’s no royal blood and little in the way of fame to be found in my family tree, but I do have someone I find extremely interesting. One of my great-great-grandfathers was a Scottish engineer called James R Napier, and though his Wikipedia entry hasn’t caught up with this contribution to 1840s technology, he was the inventor of the vacuum coffee pot.

He was born in Glasgow in 1821 and was the son of a successful shipbuilder, Robert Napier, into whose business he followed once he’d received his education. He’s probably most well known today for his work in nautical engineering and for inventing Napier’s Diagram, a method for computing magnetic deviance on compass readings, but he was also a prolific engineer and author whose name crops up in fields as diverse as air engines, weights and measures, drying timber, and even the analysis of some dodgy wine. The coffee percolator was something of a side project for him, and for us it’s one of those pieces of family lore that’s been passed down the generations. It seems he was pretty proud of it, though he never took the trouble to patent it and and thus it was left to others to profit from that particular invention.

Vacuum Coffee Pots: Impressive, But Slooow

Just what is a vacuum coffee pot, and what makes it special? The answer lies in the temperature at which it infuses the coffee. We take for granted our fancy coffee machinery here in the 21st century, but a century and a half ago the making of coffee was a much simpler and less exact process. Making coffee by simply boiling grounds in water can burn it, imparting bitter flavours, and thus at the time a machine that could make a better cup was seen as of some importance. Continue reading “My Great-Great-Grandad, The Engineer Who Invented A Coffee Pot” →

An electronic neuron implemented on a purple neuron-shaped PCB

Hackaday Prize 2023: Explore The Basics Of Neuroscience With This Electronic Neuron

May 1, 2023 by Robin Kearey 2 Comments

Brains are the most complex systems in the universe, but their basic building blocks are surprisingly simple — the complexity arises from billions of neurons, axons and synapses working together. Simulating an entire brain therefore requires vast computing resources, but if it’s just a few cells you’re interested in, you don’t need much: a handful of op-amps and transistors will do the job, as [Sebastian Billaudelle] has demonstrated. He has designed an electronic neuron called Lu.i that does everything a real neuron does, in a convenient package suitable for educational use.

[Sebastian]’s neuron implements what’s known as the leaky integrate-and-fire model, first proposed by [Louis Lapicque] as a simple model for a neuron’s behavior. Basically, the neuron acts as an integrator that stores all incoming charge in a capacitor and generates a spiky output signal once its voltage reaches a certain threshold level. The capacitor is slowly discharged however, which means the neuron will only “fire” when it gets a strong enough input signal.

A couple of MCP6004 op-amps implement this model, with an LM339 comparator acting as the threshold detector. The neuron’s inputs are generated by electronic synapses made from logic-level MOSFETS. These circuits route signals between different neurons and can be manually set to either source or sink current, thereby increasing or decreasing the neuron’s voltage level.

All of this is built onto a neat purple PCB in the shape of a nerve cell, with external connections on the tips of its dendrites. The neuron’s internal state is made visible by an LED bar graph, giving the user an immediate feel for what’s going on inside the network. Multiple neurons can be connected together to form reasonably complex networks that can implement things like oscillators or logic functions, examples of which are shown on the project’s GitHub page.

The Lu.i project is a great way to teach the basics of neuroscience, turning dry differential equations into a neat display of signals racing around a network. Neurons are fascinating things that we’re learning more about every day, enabling things like brain-computer interfaces and neuromorphic computing.

Your Guide To Using Amazon’s Sidewalk Network For The Internet Of Things

May 1, 2023 by Lewin Day 61 Comments

As the Internet of Things became a mainstream reality, it raised an interesting point about connectivity. We quickly learned it wasn’t ideal to have every light bulb, toaster, and kettle buzzing away on our main WiFi networks. Nor was it practical to sign up for a cellular data plan for every tracker tag or remote sensor we wanted to use.

To solve this issue, various tech companies have developed their own low-power mesh networking solutions. Amazon’s Sidewalk network is one of the widest spread in the US. Now, it’s opening it up for wider use beyond its own products, and you can get in on the action.

Continue reading “Your Guide To Using Amazon’s Sidewalk Network For The Internet Of Things” →

Patent Spat Leaves DJI Owing Textron $279M

May 1, 2023 by Navarre Bartz 46 Comments

Patents are the murky waters where technical jargon and legalese meet, and in this vast grey area of interpretation, DJI now owes Textron $279M.

At issue in the case were two patents issued to Textron (#8,014,909 and #9,162,752) regarding aircraft control systems for relative positioning to other vehicles and automatic hovering. The jury found that Textron’s intellectual property (IP) had been infringed and that damages amounted to $279M. DJI asserts that Textron’s patents are not valid and will appeal the decision. Appeals in patent trials are handled by the Federal Circuit and can be kicked up to the US Supreme Court, so don’t expect a final decision in the case anytime soon.

We’re not lawyers, so we won’t comment on the merits of the case, but, while it was a jury trial, it was one of many cases decided in the court of Judge Alan Albright, who has been the focus of scrutiny despite efforts to assign fewer cases to his docket amid wider efforts to stymie venue shopping in patent cases. Despite these efforts, the Western District of Texas is such a popular venue for patent cases that Berkeley offers a CEU on going to trial in Waco.

If you’re curious about more IP shenanigans, checkout the Honda mass takedown, the legality of making something similar, or why E3D patents some of their work.

Hinges Live Inside 3D Prints

May 1, 2023 by Bryan Cockfield 5 Comments

Since desktop 3D printers have become more common, we’ve seen dramatic shifts in all kinds of areas such as rapid prototyping, antique restoration, mass production of consumer goods, or even household repairs that might not have been possible otherwise. There are a lot of unique manufacturing methods that can be explored in depth with a 3D printer as well, and [Slant 3D] demonstrates how one such method known as the living hinge can be created with this revolutionary new tool.

Living hinges, unlike a metal hinge you might pick up at a hardware store, are integrated into the design of the part and made of the same material. Typically found in plastic containers, they allow for flexibility while keeping parts count and cost low. The major downside is that they create stresses in the materials when used, so their lifespan is finite. But there are a number of ways to extend their life, albeit with a few trade-offs.

The first note is to make sure that you’re using the right kind of plastic, but after that’s taken care of [Slant 3D] builds a few flexible parts starting with longer circular-shaped living hinge which allows greater range of motion and distributes the forces across a wider area, at a cost of greater used space and increased complexity. A few other types of living hinges are shown to use less space in some areas, but make the hinges only suitable for use in other narrower applications.

One of the more interesting living hinges he demonstrates is one that’s more commonly seen in woodworking, known there as a kerf bend. By removing strips of material from a sheet, the entire sheet can be rotated around the cuts. In woodworking this is often done by subtracting material with a CNC machine or a laser cutter, but in 3D printing the voids can simply be designed into the part.

Continue reading “Hinges Live Inside 3D Prints” →

Liquid Neural Networks Do More With Less

April 30, 2023 by Joseph Long 14 Comments

[Ramin Hasani] and colleague [Mathias Lechner] have been working with a new type of Artificial Neural Network called Liquid Neural Networks, and presented some of the exciting results at a recent TEDxMIT.

Liquid neural networks are inspired by biological neurons to implement algorithms that remain adaptable even after training. [Hasani] demonstrates a machine vision system that steers a car to perform lane keeping with the use of a liquid neural network. The system performs quite well using only 19 neurons, which is profoundly fewer than the typically large model intelligence systems we’ve come to expect. Furthermore, an attention map helps us visualize that the system seems to attend to particular aspects of the visual field quite similar to a human driver’s behavior.

Mathias Lechner and Ramin Hasani — [Mathias Lechner] and [Ramin Hasani]

The typical scaling law of neural networks suggests that accuracy is improved with larger models, which is to say, more neurons. Liquid neural networks may break this law to show that scale is not the whole story. A smaller model can be computed more efficiently. Also, a compact model can improve accountability since decision activity is more readily located within the network. Surprisingly though, liquid neural network performance can also improve generalization, robustness, and fairness.

A liquid neural network can implement synaptic weights using nonlinear probabilities instead of simple scalar values. The synaptic connections and response times can adapt based on sensory inputs to more flexibly react to perturbations in the natural environment.

We should probably expect to see the operational gap between biological neural networks and artificial neural networks continue to close and blur. We’ve previously presented on wetware examples of building neural networks with actual neurons and ever advancing brain-computer interfaces.

Continue reading “Liquid Neural Networks Do More With Less” →