How To Make Bisected Pine Cones Look Great, Step-by-Step

February 10, 2019 by Donald Papp 8 Comments

[Black Beard Projects] sealed some pine cones in colored resin, then cut them in half and polished them up. The results look great, but what’s really good about this project is that it clearly demonstrates the necessary steps and techniques from beginning to end. He even employs some homemade equipment, to boot.

Briefly, the process is to first bake the pine cones to remove any moisture. Then they get coated in a heat-activated resin for stabilizing, which is a process that infuses and pre-seals the pine cones for better casting results. The prepped pine cones go into molds, clear resin is mixed with coloring and poured in. The resin cures inside a pressure chamber, which helps ensure that it gets into every nook and cranny while also causing any small air bubbles introduced during mixing and pouring to shrink so small that they can’t really be seen. After that is cutting, then sanding and polishing. It’s an excellent overview of the entire process.

The video (which is embedded below) also has an outstanding depth of information in the details section. Not only is there an overview of the process and links to related information, but there’s a complete time-coded index to every action taken in the entire video. Now that’s some attention to detail.

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Spectrometer Is Inexpensive And Capable

February 9, 2019 by Brian McEvoy 14 Comments

We know the effect of passing white light through a prism and seeing the color spectrum that comes out of the other side. It will not be noticeable to the naked eye, but that rainbow does not fully span the range of [Roy G. Biv]. There are narrowly absent colors which blur together, and those missing portions are a fingerprint of the matter the white light is passing through or bouncing off. Those with a keen eye will recognize that we are talking about spectrophotometry which is identifying those fingerprints and determining what is being observed and how much is under observation. The device which does this is called a spectrometer and [Justin Atkin] invites us along for his build. Video can also be seen below.

Along with the build, we learn how spectrophotometry works, starting with how photons are generated and why gaps appear in the color spectrum. It is all about electrons, which some of our seasoned spectrometer users already know. The build uses a wooden NanoDrop style case cut on a laser engraver. It needs some improvements which are mentioned and shown in the video so you will want to have some aluminum tape on hand. The rest of the bill of materials is covered including “Black 2.0” which claims to be the “mattest, flattest, black acrylic paint.” Maybe that will come in handy for other optical projects. It might be wise to buy first surface mirrors cut to size, but you can always make bespoke mirrors with carefully chosen tools.

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AI On Raspberry Pi With The Intel Neural Compute Stick

January 31, 2019 by Sean Boyce 58 Comments

I’ve always been fascinated by AI and machine learning. Google TensorFlow offers tutorials and has been on my ‘to-learn’ list since it was first released, although I always seem to neglect it in favor of the shiniest new embedded platform.

Last July, I took note when Intel released the Neural Compute Stick. It looked like an oversized USB stick, and acted as an accelerator for local AI applications, especially machine vision. I thought it was a pretty neat idea: it allowed me to test out AI applications on embedded systems at a power cost of about 1W. It requires pre-trained models, but there are enough of them available now to do some interesting things.

You can add a few of them in a hub for parallel tasks. Image credit Intel Corporation.

I wasn’t convinced I would get great performance out of it, and forgot about it until last November when they released an improved version. Unambiguously named the ‘Neural Compute Stick 2’ (NCS2), it was reasonably priced and promised a 6-8x performance increase over the last model, so I decided to give it a try to see how well it worked.

I took a few days off work around Christmas to set up Intel’s OpenVino Toolkit on my laptop. The installation script provided by Intel wasn’t particularly user-friendly, but it worked well enough and included several example applications I could use to test performance. I found that face detection was possible with my webcam in near real-time (something like 19 FPS), and pose detection at about 3 FPS. So in accordance with the holiday spirit, it knows when I am sleeping, and knows when I’m awake.

That was promising, but the NCS2 was marketed as allowing AI processing on edge computing devices. I set about installing it on the Raspberry Pi 3 Model B+ and compiling the application samples to see if it worked better than previous methods. This turned out to be more difficult than I expected, and the main goal of this article is to share the process I followed and save some of you a little frustration.

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How To Make Your Own Springs For Extruded Rail T-Nuts

January 30, 2019 by Sonya Vasquez 24 Comments

Open-Source Extruded Profile systems are a mature breed these days. With Openbuilds, Makerslide, and Openbeam, we’ve got plenty of systems to choose from; and Amazon and Alibaba are coming in strong with lots of generic interchangeable parts. These open-source framing systems have borrowed tricks from some decades-old industry players like Rexroth and 80/20. But from all they’ve gleaned, there’s still one trick they haven’t snagged yet: affordable springloaded T-nuts.

I’ve discussed a few tricks when working with these systems before, and Roger Cheng came up with a 3D printed technique for working with T-nuts. But today I’ll take another step and show you how to make our own springs for VSlot rail nuts.

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Plastics: Acrylic

January 16, 2019 by Dan Maloney 32 Comments

If anything ends up on the beds of hobbyist-grade laser cutters more often than birch plywood, it’s probably sheets of acrylic. There’s something strangely satisfying about watching a laser beam trace over a sheet of the crystal-clear stuff, vaporizing a hairs-breadth line while it goes, and (hopefully) leaving a flame-polished cut in its wake.

Acrylic, more properly known as poly(methyl methacrylate) or PMMA, is a wonder material that helped win a war before being developed for peacetime use. It has some interesting chemistry and properties that position it well for use in the home shop as everything from simple enclosures to laser-cut parts like gears and sprockets.

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Project Shows How To Use Machine Learning To Detect Pedestrians

January 14, 2019 by Donald Papp 18 Comments

Most people are familiar with the idea that machine learning can be used to detect things like objects or people, but for anyone who’s not clear on how that process actually works should check out [Kurokesu]’s example project for detecting pedestrians. It goes into detail on exactly what software is used, how it is configured, and how to train with a dataset.

The application uses a USB camera and the back end work is done with Darknet, which is an open source framework for neural networks. Running on that framework is the YOLO (You Only Look Once) real-time object detection system. To get useful results, the system must be trained on large amounts of sample data. [Kurokesu] explains that while pre-trained networks can be used, it is still necessary to fine-tune the system by adding a dataset which more closely models the intended application. Training is itself a bit of a balancing act. A system that has been overly trained on a model dataset (or trained on too small of a dataset) will suffer from overfitting, a condition in which the system ends up being too picky and unable to usefully generalize. In terms of pedestrian detection, this results in false negatives — pedestrians that don’t get flagged because the system has too strict of an idea about what a pedestrian should look like.

[Kurokesu]’s walkthrough on pedestrian detection is great, but for those interested in taking a step further back and rolling their own projects, this fork of Darknet contains YOLO for Linux and Windows and includes practical notes and guides on installing, using, and training from a more general perspective. Interested in learning more about machine learning basics? Don’t forget Google has a free online crash course to get you up to speed.

Making Your Breadboard Projects A Little More Permanent

January 6, 2019 by Lewin Day 30 Comments

Many a budding electronics maker got their start not with a soldering iron, but with the humble breadboard. With its push connections, the breadboard enables electronics experimentation without requiring the specialised skill of soldering or any dangerous hot tools. What it lacks is a certain robustness that can make all but the simplest projects rather difficult to execute. [Runtime Micro] have shared a few tips on making things just a little more robust, however.

Applied correctly, these techniques have the added bonus of making a project neat, tidy, and easy to troubleshoot.

The fundamental principle behind this process is replacing point-to-point jumper wires with custom cables, made using 0.1″ pitch headers and wire-wrapping techniques. Other techniques include pinning down components with Blu-tack, and selecting components with the appropriate wire diameter to avoid them falling out of the breadboard’s spring clip contacts. There are also useful tips on using foam tape for appropriate strain relief.

While breadboards aren’t really suitable for projects dealing with high frequencies and can rapidly become unmanageable, these basic techniques should improve a project’s chance of success. These simple ways of improving connection quality and reducing the likelihood of things falling apart are likely to reduce frustration immensely.

However, once a maker has a taste for corralling electrons to do their bidding, soldering should be the first lesson on the agenda.

[Thanks to stockvu for the tip!]