When you think of high-throughput ptychographic cytometry (wait, you do think about high throughput ptychographic cytometry, right?) does it bring to mind something you can hack together from an old Blu-ray player, an Arduino, and, er, some blood? Apparently so for [Shaowei Jiang] and some of his buddies in this ACS Sensors Article.
For those of you who haven’t had a paper accepted by the American Chemical Society, we should probably clarify things a bit. Ptychography is a computational method of microscopic imaging, and cytometry has to do with measuring the characteristics of cells. Obviously.
Anyway, if you shoot a laser through a sample, it diffracts. If you then move the sample slightly, the diffraction pattern shifts. If you capture the diffraction pattern in each position with a CCD sensor, you can reconstruct the shape of the sample using breathtaking amounts of math.
One hitch – the CCD sensor needs a bunch of tiny lenses, and by tiny we mean six to eight microns. Red blood cells are just that size, and they’re lens shaped. So the researcher puts a drop of their own blood on the surface of the CCD and covers it with a bit of polyvinyl film, leaving a bit of CCD bloodless for reference. There’s an absolutely wild video of it in action here.
Java versions 15, 16, 17, and 18 (and maybe some older versions) have a big problem, ECDSA signature verification is totally broken. The story is a prime example of the dangers of unintended consequences, the pitfall of rolling your own crypto, and why to build a test suite for important code. In Java 15, the ECDSA verification code was re-written, moving the code from C++ to a Java-native implementation. The new code misses an important check, that the initialization and proof values are both non-zero.
Part Racing Drone, Part RC Airplane, Part Rocket…all Menace. How else could you describe a quadcopter that shoots off at high speed and is designed for taking down other small quadcopters? The Interceptor Drone by [Aleksey] borrows elements from all of the aforementioned disciplines of flying things.
Built with standard racing drone parts, [Aleksey] assures that no prohibited parts are used in its construction. Instead, the Interceptor Drone relies on a very powerful motors and a light weight frame to keep the power to weight ratio in the “rocketing into the sky” category.
But what Interceptor Drone would be complete without a way to take its target out of the sky? This is where the biggest divergences begin. The motors are all oriented to point away from the center-line of the craft. Upon command, these motors actually detach from the frame, each spreading out and deploying the corner of a net that’s designed to entangle the rotors of the target, causing its battle with gravity to come to a grinding halt.
How does the Interceptor Drone survive the attack? Without its motors, the core of the quadcopter falls to the earth. Arresting the fall is a parachute much like those used in model rocketry. An audio beacon sounds the alarm to help somebody to find it — a move taken straight from the RC aircraft hobby.
There’s certainly a lot of room to discuss legalities in localities, but regardless of opinion about the craft’s intended use, the system looks very slick, and there are some great hacks baked right in. Don’t want to build a drone-killing-drone? Maybe all you need is a pumpkin and good (bad?) timing.
Humans weren’t made to sit in front of a computer all day, yet for many of us that’s how we spend a large part of our lives. Of course we all know that it’s important to get up and move around every now and then to stretch our muscles and get our blood flowing, but it’s easy to forget if you’re working towards a deadline. [Victor Sonck] thought he needed some reminders — as well as some not-so-gentle nudging — to get into the habit of doing a quick workout a few times a day.
To this end, he designed a piece of software that would lock his computer’s screen and only unlock it if he performed five push-ups. Locking the screen on his Linux box was as easy as sending a command through the network, but recognizing push-ups was a harder task for which [Victor] decided to employ machine learning. A Raspberry Pi with a webcam attached could do the trick, but the limited processing power of the Pi’s CPU might prove insufficient for processing lots of raw image data.
[Victor] therefore decided on using a Luxonis OAK-1, which is a 4K camera with a built-in machine-learning processor. It can run various kinds of image recognition systems including Blazepose, a pre-trained model that can recognize a person’s pose from an image. The OAK-1 uses this to send out a set of coordinates that describe the position of a person’s head, torso and limbs to the Raspberry Pi through a USB interface. A second machine-learning model running on the Pi then analyzes this dataset to recognize push-ups.