Tag Archives: software

Electrical and computer engineering Professor Barry Van Veen wears an electrode net used to monitor brain activity via EEG signals. His research could help untangle what happens in the brain during sleep and dreaming.

Photo Credit: Nick Berard/UW-Madison

Software lets designers exploit the extremely high resolution of 3-D printers.

Designing the microstructure of printed objects

Software lets designers exploit the extremely high resolution of 3-D printers.

By Larry Hardesty


CAMBRIDGE, Mass. – Today’s 3-D printers have a resolution of 600 dots per inch, which means that they could pack a billion tiny cubes of different materials into a volume that measures just 1.67 cubic inches.

Such precise control of printed objects’ microstructure gives designers commensurate control of the objects’ physical properties — such as their density or strength, or the way they deform when subjected to stresses. But evaluating the physical effects of every possible combination of even just two materials, for an object consisting of tens of billions of cubes, would be prohibitively time consuming.

So researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a new design system that catalogues the physical properties of a huge number of tiny cube clusters. These clusters can then serve as building blocks for larger printable objects. The system thus takes advantage of physical measurements at the microscopic scale, while enabling computationally efficient evaluation of macroscopic designs.

“Conventionally, people design 3-D prints manually,” says Bo Zhu, a postdoc at CSAIL and first author on the paper. “But when you want to have some higher-level goal — for example, you want to design a chair with maximum stiffness or design some functional soft [robotic] gripper — then intuition or experience is maybe not enough. Topology optimization, which is the focus of our paper, incorporates the physics and simulation in the design loop. The problem for current topology optimization is that there is a gap between the hardware capabilities and the software. Our algorithm fills that gap.”

Zhu and his MIT colleagues presented their work this week at Siggraph, the premier graphics conference. Joining Zhu on the paper are Wojciech Matusik, an associate professor of electrical engineering and computer science; Mélina Skouras, a postdoc in Matusik’s group; and Desai Chen, a graduate student in electrical engineering and computer science.

Points in space

The MIT researchers begin by defining a space of physical properties, in which any given microstructure will assume a particular location. For instance, there are three standard measures of a material’s stiffness: One describes its deformation in the direction of an applied force, or how far it can be compressed or stretched; one describes its deformation in directions perpendicular to an applied force, or how much its sides bulge when it’s squeezed or contract when it’s stretched; and the third measures its response to shear, or a force that causes different layers of the material to shift relative to each other.

Those three measures define a three-dimensional space, and any particular combination of them defines a point in that space.

In the jargon of 3-D printing, the microscopic cubes from which an object is assembled are called voxels, for volumetric pixels; they’re the three-dimensional analogue of pixels in a digital image. The building blocks from which Zhu and his colleagues assemble larger printable objects are clusters of voxels.

In their experiments, the researchers considered clusters of three different sizes — 16, 32, and 64 voxels to a face. For a given set of printable materials, they randomly generate clusters that combine those materials in different ways: a square of material A at the cluster’s center, a border of vacant voxels around that square, material B at the corners, or the like. The clusters must be printable, however; it wouldn’t be possible to print a cluster that, say, included a cube of vacant voxels with a smaller cube of material floating at its center.

For each new cluster, the researchers evaluate its physical properties using physics simulations, which assign it a particular point in the space of properties.

Gradually, the researchers’ algorithm explores the entire space of properties, through both random generation of new clusters and the principled modification of clusters whose properties are known. The end result is a cloud of points that defines the space of printable clusters.

Establishing boundaries

The next step is to calculate a function called the level set, which describes the shape of the point cloud. This enables the researchers’ system to mathematically determine whether a cluster with a particular combination of properties is printable or not.

The final step is the optimization of the object to be printed, using software custom-developed by the researchers. That process will result in specifications of material properties for tens or even hundreds of thousands of printable clusters. The researchers’ database of evaluated clusters may not contain exact matches for any of those specifications, but it will contain clusters that are extremely good approximations.

The MIT researchers’ work was supported by the U.S. Defense Advanced Research Projects Agency’s SIMPLEX program.

Source: MIT News Office

Fake diploma scandal: Why we need to seriously address it?

By Syed Faisal ur Rahman


 

Recent scandal related to a Pakistani software company Axact’s alleged involvement in selling fake degrees has shocked the whole country especially IT industry, media related circles and academia. The story published on 17th May 25, 2015; in The New York Times written by Declan Walsh was not just another exposé about a criminal activity happening somewhere.

The story basically jolted the foundations of our developing IT industry which relies heavily on outsourcing. It also raised questions about the standards of academic integrity and how as a society we give importance to it. I am not interested in passing judgments over Axact’s credibility or their involvement in the alleged scam but my focus is on highlighting the importance of solving it with utmost seriousness and transparency.

We are a small economy of the size of roughly 232 billion dollars which is lesser than many countries with less than half of our population. We are stuck in over a decade long warfare and our industry has faced the worst of it. In the past few years our Software and other IT related industries have provided some hope for our aspiring entrepreneurs to achieve their dreams and show the world that they are more than suspected terrorists.

Scandals like the diploma scandal, if not handled seriously will cast doubts over the credibility and ethics culture in our IT industry which will eventually result in the loss of international clientage confidence. Our aspiring young engineers and technologists are now making some serious contributions in mobile applications, game development, e-commerce, cloud computing and many other related areas. It will be unfair for them if our government simply tries to put the issue under the carpet using delaying tactics and leave the question mark on our industry’s credibility unaddressed.

The bigger issue in my view however is related to academic integrity and how we see it as a society. Few years ago, the issue of fake MNA/MPA degrees has damaged the reputation of our education sector all over the world. As a result, students and professionals who want to go abroad, now go through some serious scrutiny process which is really embarrassing and time consuming. It becomes more painful when we see that people from various other countries do not need to go through such painstaking process. If, in any way, comes out that our government officials are involved in any capacity in covering up the issue then whatever credibility is left of our academic sector will suffer too.

Also, we should keep our eyes open to see if the issue is being used for some other motives. The recent statement by one of our federal ministers linking Axact issue with absence of cyber crime law should also be seen with a great concern. Mixing two different issues like the proposed controversial cyber crime bill and this diploma scam will worsen the situation and can create more panic in our local IT industry.

The need is to investigate and prosecute the issue with highest professional standards and transparency so that we can prove to our-selves (not just the world) that we believe in fair play especially when it comes to the most respected field of education.

At the same time, I will urge Axact and its affiliate institution BOL that if they feel that they have been falsely targeted as a result of some conspiracy then they should file a lawsuit against The New York Times instead of using social media to clear their image.

 


The article is also available on Daily Times website with slight editing.

 

Software that knows the risks

Planning algorithms evaluate probability of success, suggest low-risk alternatives.

By Larry Hardesty


CAMBRIDGE, Mass. – Imagine that you could tell your phone that you want to drive from your house in Boston to a hotel in upstate New York, that you want to stop for lunch at an Applebee’s at about 12:30, and that you don’t want the trip to take more than four hours. Then imagine that your phone tells you that you have only a 66 percent chance of meeting those criteria — but that if you can wait until 1:00 for lunch, or if you’re willing to eat at TGI Friday’s instead, it can get that probability up to 99 percent.

That kind of application is the goal of Brian Williams’ group at MIT’s Computer Science and Artificial Intelligence Laboratory — although the same underlying framework has led to software that both NASA and the Woods Hole Oceanographic Institution have used to plan missions.

At the annual meeting of the Association for the Advancement of Artificial Intelligence (AAAI) this month, researchers in Williams’ group will present algorithms that represent significant steps toward what Williams describes as “a better Siri” — the user-assistance application found in Apple products. But they would be just as useful for any planning task — say, scheduling flights or bus routes.

Together with Williams, Peng Yu and Cheng Fang, who are graduate students in MIT’s Department of Aeronautics and Astronautics, have developed software that allows a planner to specify constraints — say, buses along a certain route should reach their destination at 10-minute intervals — and reliability thresholds, such as that the buses should be on time at least 90 percent of the time. Then, on the basis of probabilistic models — which reveal data such as that travel time along this mile of road fluctuates between two and 10 minutes — the system determines whether a solution exists: For example, perhaps the buses’ departures should be staggered by six minutes at some times of day, 12 minutes at others.

If, however, a solution doesn’t exist, the software doesn’t give up. Instead, it suggests ways in which the planner might relax the problem constraints: Could the buses reach their destinations at 12-minute intervals? If the planner rejects the proposed amendment, the software offers an alternative: Could you add a bus to the route?

Short tails

One aspect of the software that distinguishes it from previous planning systems is that it assesses risk. “It’s always hard working directly with probabilities, because they always add complexity to your computations,” Fang says. “So we added this idea of risk allocation. We say, ‘What’s your budget of risk for this entire mission? Let’s divide that up and use it as a resource.’”

The time it takes to traverse any mile of a bus route, for instance, can be represented by a probability distribution — a bell curve, plotting time against probability. Keeping track of all those probabilities and compounding them for every mile of the route would yield a huge computation. But if the system knows in advance that the planner can tolerate a certain amount of failure, it can, in effect, assign that failure to the lowest-probability outcomes in the distributions, lopping off their tails. That makes them much easier to deal with mathematically.

At AAAI, Williams and another of his students, Andrew Wang, have a paper describing how to evaluate those assignments efficiently, in order to find quick solutions to soluble planning problems. But the paper with Yu and Fang — which appears at the same conference session — concentrates on identifying those constraints that prevent a problem’s solution.

There’s the rub

Both procedures are rooted in graph theory. In this context, a graph is a data representation that consists of nodes, usually depicted as circles, and edges, usually depicted as line segments connecting the nodes. Any scheduling problem can be represented as a graph. Nodes represent events, and the edges indicate the sequence in which events must occur. Each edge also has an associated weight, indicating the cost of progressing from one event to the next — the time it takes a bus to travel between stops, for instance.

Yu, Williams, and Fang’s algorithm first represents a problem as a graph, then begins adding edges that represent the constraints imposed by the planner. If the problem is soluble, the weights of the edges representing constraints will everywhere be greater than the weights representing the costs of transitions between events. Existing algorithms, however, can quickly home in on loops in the graph where the weights are imbalanced. The MIT researchers’ system then calculates the lowest-cost way of rebalancing the loop, which it presents to the planner as a modification of the problem’s initial constraints.

Source: MIT News Office

Amazon's delivery drones. Credit: Amaon

Making drones more customizable

Airware’s operating system makes drones simple to build and modify for multiple applications.

By Rob Matheson 

A first-ever standard “operating system” for drones, developed by a startup with MIT roots, could soon help manufacturers easily design and customize unmanned aerial vehicles (UAVs) for multiple applications.

Today, hundreds of companies worldwide are making drones for infrastructure inspection, crop- and livestock-monitoring, and search-and-rescue missions, among other things. But these are built for a single mission, so modifying them for other uses means going back to the drawing board, which can be very expensive.

Now Airware, founded by MIT alumnus Jonathan Downey ’06, has developed a platform — hardware, software, and cloud services — that lets manufacturers pick and choose various components and application-specific software to add to commercial drones for multiple purposes.

The key component is the startup’s Linux-based autopilot device, a small red box that is installed into all of a client’s drones. “This is responsible for flying the vehicle in a safe, reliable manner, and acts as hub for the components, so it can collect all that data and display that info to a user,” says Downey, Airware’s CEO, who researched and built drones throughout his time at MIT.

To customize the drones, customers use software to select third-party drone vehicles and components — such as sensors, cameras, actuators, and communication devices — configure settings, and apply their configuration to a fleet. Other software helps them plan and monitor missions in real time (and make midflight adjustments), and collects and displays data. Airware then pushes all data to the cloud, where it’s aggregated and analyzed, and available to designated users.

If a company decides to use a surveillance drone for crop management, for instance, it can easily add software that stitches together different images to determine which areas of a field are overwatered or underwatered. “They don’t have to know the flight algorithms, or underlying hardware, they just need to connect their software or piece of hardware to the platform,” Downey says. “The entire industry can leverage that.”

Clients have trialed Airware’s platform over the past year — including researchers at MIT, who are demonstrating delivery of vaccines in Africa. Delta Drone in France is using the platform for open-air mining operations, search-and-rescue missions, and agricultural applications. Another UAV maker, Cyber Technology in Australia, is using the platform for drones responding to car crashes and other disasters, and inspecting offshore oilrigs.

Now, with its most recent $25 million funding round, Airware plans to launch the platform for general adoption later this year, viewing companies that monitor crops and infrastructure — with drones that require specific cameras and sensors — as potential early customers.

A company from scratch

Airware’s roots date to 2005, when Downey, who studied electrical engineering and computer science, organized an MIT student team — including Airware’s chief technology officer, Buddy Michini ’07, SM ’09, PhD ’13 — to build drones for an intercollegiate competition.

At the time, drones were primarily used for military surveillance, powered by a “black box” that could essentially fly the drones and control the camera. There were also a handful of open-source projects — made by hobbyists — that let people modify drones, but the code was unreliable when tweaked. “If you wanted to do anything novel, your hands were tied,” Downey says.

The group’s decision: build a drone from scratch. But their advisor, Jonathan How, a professor of aeronautics and astronautics who directs of the Aerospace Controls Laboratory, told them that required too much time, and would cost them the competition.

“We said, ‘You’re right, but we’re MIT students, and we’d feel better getting last place and learning a lot doing it than winning the competition by repackaging a black-box solution,’” Downey says.

Sure enough, the team earned second-to-last place. “But we learned that black-box solution didn’t work if you’re trying to address new applications, and the open-source wasn’t reliable even though you could change the software,” Downey says.

A five-year stretch at Boeing — as an engineer for the U.S. military’s A160 Hummingbird UAV and as a commercial pilot — put Downey in contact with drone manufacturers, who, he found, were still using black boxes or open-source designs.

“They were basically facing the same challenges we faced as undergrads at MIT,” Downey says. Thus Airware was born in 2010 — first run only by Downey, then with Michini and a team of Boeing engineers — to make a military-grade “black box” system, but whose capabilities could be tweaked and extended.

Early prototypes were trialed by How’s group at MIT, before Airware entered two California incubators, Lemnos Labs and Y-Combinator, in 2013. Since then, they’ve raised $40 million from investors and expanded their team from five to more than 50 employees. “The last 18 months has been a rapid rise,” Downey says.

Not much of the early MIT drone designs made it into the final Airware platform. “But building that early drone at MIT, and having the idea to leverage an enterprise-grade platform that you can extend the capabilities of, very directly became what Airware is today,” Downey says.

“The DOS for drones”

Today, Downey says, the development of a standard operating system for drones is analogous to Intel processors and Microsoft’s DOS paving the way for personal computers in the 1980s. Before those components became available, hobbyists built computers using software that didn’t work with different computers. At the same time, powerful mainframes were only available to a select few — and still suffered software-incompatibility issues.

Then came Intel’s processors and DOS. Suddenly, engineers could build computers around the standard processor and create software on the operating system, without needing to know details of the underlying hardware.

“We’re doing the same thing for the drone space,” Downey says. “There are 600 companies building differing versions of drone hardware. We think they need the Intel processor of the drones, if you will, and that operating system-level software component, too — like the DOS for drones.”

The benefits are far-reaching, Downey says: “Drone companies, for instance, want to build drones and tailor them for different applications without having to build everything from scratch,” he says.

But companies developing cameras, sensors, and communication links for drones also stand to benefit, he adds, as their components will only need to be compatible with a single platform.

Additionally, it could help the Federal Aviation Administration (FAA) better assess the reliability of drones; Congress recently tasked the agency with compiling UAV rules and regulations by 2015. This could also help promote commercial drone use in the United States, which lags behind other countries around the world, primarily in Europe, Downey says.

“Rather than see a world where there’s 500 drones flying overhead, and every drone has different software and electronics, it’s good for the FAA if all of them had reliable and common hardware and software,” he says. “We think it’s valuable for everybody.”

Source: MIT News