It was around the year 1590 when mankind figured out how to use optical lenses to bring into sight things smaller than the natural eye can observe. With the invention of the microscope, a new and unexplored world was discovered. It will likely be of great surprise to the reader that scientists of the time did not believe that within this new microscopic realm lay the source of sickness and disease. Most would still hold on to a belief of what was known as Miasma theory, which dates back to the Roman Empire. This theory states that the source of disease was contaminated air through decomposing organic materials. It wouldn’t be until the 1850’s that a man by the name of Louis Pasteur, from whom we get “pasteurization”, would promote Germ Theory into the spotlight of the sciences.

Pasteur, considered by many as the father of microbiology, would go on to assist fellow biologist Charles Chameberland in the invention of the aptly named Pasteur Chamberland filter — a porcelain filter with a pore size between 100 and 1000 nanometers. This was small enough to filter out the microscopic bacteria and cells known at that time from a liquid suspension, leaving behind a supply of uncontaminated water. But like so many other early scientific instrumentation inventions it would lead to the discovery of something unexpected. In this case, a world far smaller than 100 nanometers… and add yet another dimension to the ever-shrinking world of the microscopic.

This is when we began to learn about viruses.

Discovery of the Virus and the Vaccine

The word “virus” stems from the Latin phrase “slimy fluid“. In 1898, a man by the name of Martinus Beijerinck passed a solution containing a still unknown infectious agent that targeted tobacco plants through a Pasteur Chamberland filter. The purified solution was applied to a healthy tobacco plant and to his great surprise the plant became infected. He concluded that the infectious agent was unfilterable, and took an even further leap to describe the infectious agent as a “living liquid”.

In that same year, a pair of German scientists, Friedrich Loeffler and Paul Frosch, performed the same experiment which returned the same results with what we now know as Foot-and-Mouth Disease (FMDV) in livestock. They, however, did not agree with Beijerinck’s conclusion of the infectious agent being a living liquid. But instead believed it to be a particulate that was smaller than the porcelain filter pore size. They pushed forward with their belief by heating the filter element to “destroy the agent’s infectivity”, though it was not clear to them as to how heat destroyed it. Nevertheless, they were successful in creating an FMDV vaccine for cows and sheep from the infectious solution that was passed through the heated filter, which put more precedence on understanding a virus as an ultrascopic living particulate, and not a living liquid.

It should be noted that the smallpox vaccine was widely used at this time, though no one had any understanding of how it worked or what it even was. Indeed, the term vaccine is derived from the Latin word vacca, which translates directly to cow.  This odd relationship owes its history to the somewhat accidental discovery that milkmaids who often contracted a very mild disease called cowpox did not get infected with the much more severe smallpox disease. In 1796, a cruel experiment was performed by a man named Edward Jenner. He took puss from a cowpox blister and purposely infected a young boy. Once the boy recovered, he repeated the process with smallpox and found that the boy did not get the disease. Jenner’s vaccination technique would not only go on to save millions of lives from smallpox; it would be used to vaccinate people from several other diseases, including polio and yellow fever.

The Birth of Molecular Biology

The next few decades would identify hundreds of viruses of all shapes and sizes, and along with them various types of vaccines. But it was the study of exactly what these viruses were and how the vaccines actually worked that would give rise to a revolutionary new science — molecular biology.

New technologies such as electron microscopy, along with other advances in scientific understandings would show that viruses are some of the smallest lifeforms on earth. It could even be argued that they’re not alive at all… an debate that carries on to this day. Our own [Dan Maloney] has written several articles on the details of how viruses and our immune system work at a molecular level. And the clever ways we try to stop viruses. However, there is still much to be learned.

Understanding viruses at the molecular level presents a very real modern day challenge. Despite the full power, wealth, and knowledge of our modern civilization, a tiny packet of RNA enclosed in a fatty drop continues to wreak havoc on our world. The COVID-19 virus has in some shape, form, or fashion effected every single human being on earth. Those viruses once invisible to us now stand before our very eyes in full view, and yet we have suffered terrible losses to this one. Our best tool is a breakthrough barely 30 years old — our ability to tailor messenger RNA (mRNA) a targeted purpose — has very quickly led to a viable vaccine. There is no doubt in my mind that eventually this virus will succumb to the might of human ingenuity that has been unlocked by more than a century of cumulative scientific knowledge.

3D printers now come in all shapes and sizes, and use a range of technologies to take a raw material and turn it into a solid object. We’re most familiar with Additive Manufacturing – where the object is created layer by layer. This approach is quite useful, but has a down side of being time consuming. Two professors at the University of Michigan have figured out a way to speed this process up, big time.

They start off with a VAT additive printing approach. These work by using an ultraviolet laser to harden or cure specific areas in a vat of resin, layer by layer, until the object is complete. The resin is then drained revealing your 3D printed object. Traditionally, VAT printing has been limited to small objects because the resin needs to have a relatively low viscosity.

The clever professors at U-M were able to get around this problem by adding a second laser that keeps the resin in a liquid state. By combining a curing laser with an ‘uncuring’ laser, they’re able to use resins that are more viscous, allowing them to print more durable parts. And do so about 100 times faster than traditional printers!

Thanks to [Baldpower] for the tip!

Anyone who’s ever written more than a dozen or so lines of code knows that debugging is a part of life in our world. Anyone who’s written code for microcontrollers knows that physical debugging is a part of our life as well. Atmel processors use a serial communications protocol called debugWire, which is a simpler version of JTAG and allows full read/write access to all registers and allows one to single step, break, etc. [Nerd Ralph], a prominent fixture here at Hackaday has dug into the AVR debugWire protocol and enlightened us with some valuable information.

While the protocol side of debugWire is a mostly-solved problem, the physical layer was giving him trouble. He started with a diode, and then went through a couple resistors and other components to interface with the debugWire pin on the AVR microcontroller, doing most of the troubleshooting work so now you don’t have to. He notes that interface components might need to be tailored to specific USB-TTL adapters, so keep that in mind if you care to delve into working with debugWire yourself.

We’re no strangers to debugging techniques here at Hackaday. As always, be sure to let us know if you run across any new techniques or try anything new yourself!

Designing a bi-pedal robot is a relatively straight forward task, given the array of tools that we now have at our disposal. There are many open source examples out there for anyone to get started. Designing one that doesn’t fall over a lot… well that’s not so simple. This is because when we walk our center of balance is constantly shifting, so during our adolescence we learn to shift our body weight around to maintain a stable center of balance. By the time we hit our mid-teens most of us have mastered the art of walking, and can maintain stability even through intense movements such as seen in many sports.

The question is of course, how does one convey this type of learning into a bi-pedal robot? It’s not easy to say the least. Take a look at what the robotics team over at Guangdong University of Technology’s School of Automation in China are doing. They’ve strapped a pair of ducted fan jet engines to the feet of a bi-pedal setup. What this does is allow the robot to maintain its center of balance over a large distance. Generally we see bi-pedal robots “tip toe over egg shells” because they need to keep the center of balance as stable as possible. By applying a thrusting force that comes out of the foot; they’re able to maintain center of gravity even though the robot is extended well beyond its normal range of motion.

Be sure to check out the video below for an excellent demonstration. Sometimes Hollywood does hackers a great service by giving us some inspiration!

[Thanks Itay for the tip!]

How does one go about programming a drone to fly itself through the real world to a location without crashing into something? This is a tough problem, made even tougher if you’re pushing speeds higher and high. But any article with “MIT” implies the problems being engineered are not trivial.

The folks over at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have put their considerable skill set to work in tackling this problem. And what they’ve come up with is (not surprisingly) quite clever: they’re embracing uncertainty.

Why Is Autonomous Navigation So Hard?

Suppose we task ourselves with building a robot that can insert a key into the ignition switch of a motor vehicle and start the engine, and could do so in roughly the same time-frame that a human could do — let’s say 10 seconds. It may not be an easy robot to create, but we can all agree that it is very doable. With foreknowledge of the coordinate information of the vehicle’s ignition switch relative to our robotic arm, we can place the key in the switch with 100% accuracy. But what if we wanted our robot to succeed in any car with a standard ignition switch?

Now the location of the ignition switch will vary slightly (and not so slightly) for each model of car. That means we’re going to have to deal with this in real time and develop our coordinate system on the fly. This would not be too much of an issue if we could slow down a little. But keeping the process limited to 10 seconds is extremely difficult, perhaps impossible. At some point, the amount of environment information and computation becomes so large that the task becomes digitally unwieldy.

This problem is analogous to autonomous navigation. The environment is always changing, so we need sensors to constantly monitor the state of the drone and its immediate surroundings. If the obstacles become too great, it  creates another problem that lies in computational abilities… there is just too much information to process. The only solution is to slow the drone down. NanoMap is a new modeling method that breaks the artificial speed limit normally imposed with on-the-fly environment mapping.

NanoMap

All autonomous drones have a speed limit, which is dependent on the amount of obstacles it must avoid. If the forward speed is too great or the amount of obstacles are too man it becomes impossible for the drone to keep track of them all. Once this line is breached, a probability of a collision is incurred. Traditionally, if you can’t reduce obstacles you must slow the drone down to await the calculations.

MIT CSAIL’s idea is to stop trying to keep track of every single obstacle. It accepts the fact that it cannot know exactly where it is… that there is a fundamental uncertainty of position that exists for the drone in space over a period of time. NanoMap accounts for this uncertainty and attempts to keep it as low as possible. This allows a drone to operate at a much higher speed in an obstacle-rich environment while keeping the probability of a collision relatively low.

Understanding Uncertainty Is The Key

NanoMap uses forward-looking depth sensors that put together an idea of its immediate environment, creating a local 3D data structure. It then uses an algorithm to search that structure. It searches back in time to find a view from its past that resembles its current view. Basically, it gathers just enough information to know that it’s in a “certain area”, and then plans its flight path accordingly. It doesn’t attempt to calculate its exact location and orientation as other models do. It only gets the data it needs not to run into something, and isn’t concerned with exact position and location. They’re calling this idea “pose uncertainty”.

Be sure to check out the white paper for full details, but we suggest blocking out some spare time. It’s a lot to wrap your brain around. If you can do that, the determined hacker or maker can give this a try themselves; the research team’s incredible work is open-source. Let us know in the comments if you plan to use this new and exciting technology in your next autonomous project!

“If I have seen further than others, it is by standing upon the shoulders of giants.” This famous quote by Isaac Newton points to an axiom that lies at the heart of The Sciences — knowledge precedes knowledge.

What we know today is entirely based upon what we learned in the past. This general pattern is echoed throughout recorded history by the revelation of one scientific mystery leading to other mysteries… other more compounding questions. In the vast majority of cases these mysteries and other questions are sprung from the source of an experiment with an unexpected outcome sparking the question: “why the hell did it do that?” This leads to more experiments which creates even more questions and next thing you know we go from moving around on horse-drawn carriages to landing drones on Mars in a few generations.

The observant of you will have noticed that I preceded a statement above with “the vast majority of cases.” Apart from particle physics, almost all scientific discovery throughout recorded history has been made via experiment and observation. There are a few, however, that have been discovered hidden within the confines of an equation, only later to be confirmed with observation. One such discovery is the Black Hole, and how it was stumbled upon on a dusty chalkboard in the early 1900s will be the focal point of today’s article.

Only A Mathematical Hypothesis

This was what Einstein had to say on the subject of Black Holes:

The essential results of this investigation is a clear understanding as to why ‘Schwarzschild singularities’ do not exist in physical reality.

He believed them to be only a “mathematical hypothesis.” It was not one of his finest papers, that’s for certain. Those of you who are learned of his views on quantum theory will not be surprised that he took such a formal vantage point. But before we get into that, let us backtrack to a point where it all began.

Einstein’s general relativity was a giant leap forward in our understanding of gravity. Newton’s theory worked, but it was unable to explain how the force originated and it failed under a handful of observable events — such as the perihelion orbit of Mercury. Einstein realized that gravity had the peculiar property of disappearing under its own effect. After looking at free-fall very carefully, he was able to explain the origin of gravity by giving space geometric properties. The presence of mass changes the geometric properties of the space around it. We sense this change as gravity.

General relativity explained mercury’s odd orbit and was all but confirmed during a 1919 solar eclipse when the bending of starlight (as it passed the curved space around the sun) was measured by an amount precisely predicted by the theory. Einstein and his theory shot to stardom, but it would not last long. For in the summer of 1916, a physicists managed to use general relativity’s intimidating field equations to calculate the curved space around a star to an exact solution. And what he found hidden deep within the complex math would send the whole of the theory crashing in on itself. This man’s name was Karl Schwarzschild.

The Schwarzschild Singularity

General Relativity was still hot off the press when Karl Schwarzschild was able to use it to find a solution that would calculate the space-time curvature around a single stationary point. The small amounts of angular momentum possessed by planets and stars would not hinder his equation’s ability to be applied to them. The Swartzschild Metric was used to calculate the sun’s space-time curvature and was confirmed during the 1919 solar eclipse. While the mathematics of the equation are complicated and well beyond the scope of this article, we can still use it to understand some basic properties of space-time around a massive object. Or more importantly, to see what happens to them under certain ‘extreme’ conditions.

The two variables we’re to be concerned with are “r” and “r_s”.

• r = coordinate of a sphere around a massive point.
• r_s = scalar factor of r with respect to the point’s mass, and is equal to 2GM/c²

The variable rs is also known as the Schwarzschild Radius, and is quite interesting when you apply some pressure to it. We have two things going on here: mass and radius.

If you keep cranking up the mass while dialing down the radius, at some point the space-time curvature becomes so great that light will not be able to leave the point. This occurs when rs = r.

If you have and rs is equal to r, you have a NaN.

The technical term for this is known mathematically as a singularity. What you know as the “singularity” at the center of a Black Hole stems from this relatively simple math.

Once the Schwarzschild radius is equal to the radius of the point in question, a Black Hole is formed. Theoretically, any massive object can become a Black Hole. The Schwarzschild radius of the Earth is about 9mm. So if you compress the radius of the earth to smaller than 9mm, the space-time curvature would become so great that no light would be emitted from it, as it would curve back in on itself. This is where the idea and name of the Black Hole originated — extreme space-time curvature as the result of a singularity within the Schwarzschild metric.

Meet Jocelyn Bell Burnell

At this point in time of the early 1900s, it was not believed that instances of Schwarzschild singularities could actually exist in nature. Singularities within equations are common, and this was purely a mathematical problem that Einstein had yet to solve. But as time passed without a solution, general relativity began to take on the reputation as an incomplete theory of gravity. It would stay this way for several decades, and sadly Einstein would not live to see its vindication. It wasn’t until 1967 when a graduate student made a discovery that would change everything.

Jocelyn Bell Burnell was a graduate student at Cambridge pursuing her Doctorate in radio astronomy. She and fellow students hand built a 4-acre radio telescope over the course of two years. When it came time to turn it on, what she found would launch her name into the annals of astronomical history. What showed up as regular, concise “beeps” on her graphical printer turned out to be the discovery of the first pulsar. Pulsars are neutron stars — stars so large that their collapse compromises the atoms themselves, forcing electrons into protons, leaving nothing but a big ball of neutrons left over. The mass remains the same, however, making neutron stars some of the most dense objects in the universe. With the discovery of the pulsar, the idea of Black Holes being real objects began to take foot in the halls of universities and observatories around the world. The hunt was on.

Black Holes Are Real, But Many Questions Remain

From this point forward, evidence of Black Holes began to pop up everywhere. They are believed to be at the heart of spiral galaxies, such as our own. A few years ago, the first gravitational waves were detected — emitted from the collision of two Black Holes. This year, the Event Horizons Telescope is hoping to produce an actual image of an event horizon, a non-physical location defined by the Schwarzschild radius.

There are still many questions about what goes on beyond that radius, however. Time plays a big role in this as time in and near a Black Hole is heavily dilated from our vantage point. But to the observer in the Black Hole, everything seems normal (all things considered). To us, a Black Hole appears virtually frozen in time — seconds passing within it are perceived as billions of years outside of it . There is also the question of mass. Black Holes are akin to giant galactic vacuum cleaners. What happens to all the mass that a Black Hole acquires?

The most interesting question is that of a more fundamental one. How does a mathematical singularity exist in our reality? How does one of the most massive objects in space become an “infinitely small point”… a point where nature figuratively divides itself by zero. Black Holes exist at the edge of our reality and outside the boundaries of our ability to logically describe. They are where physics ends, and speculation begins. So let’s speculate away in the comments! Free t-shirt to the first person who calculates the Schwarzschild radius of a Raspberry Pi!

We’re not sure which is more fun – putting together a little RC truck with parts laying around on your workbench, or driving it around through a Linux terminal. We’ll take the easy road and say they’re both equally fun. [technodict] had some spare time on his hands and decided to build such a truck.

He started off with a great little chassis that can act as the base for many projects. Powering the four motors is a cheap little dual H bridge motor driver and a couple rechargeable batteries. But the neatest part of this build is that it’s controlled using a little bit of python and driven directly from a terminal, made possible by the Raspberry Pi Zero of course.

With Raspberry Pi Zero now having built in WiFi and Bluetooth – we should see a lot more projects popping up with one at its heart. Be sure to visit [technodict’s] blog for full source and details. And let us know how you could use that little chassis for your next mobile project!