Tag Archives: Computing/Software

Rohde & Schwarz Presents: Smart Jammer / DRFM Testing – Test and Measurement Solutions for the Next Level

Post Syndicated from IEEE Spectrum Recent Content full text original https://spectrum.ieee.org/webinar/rohde-schwarz-presents-smart-jammer-drfm-testing-test-and-measurement-solutions-for-the-next-level

The webinar introduces the concept of Digital RF Memory Jammers, describes their technology and the respective test and measurement challenges and solutions from Rohde & Schwarz.

The DRFM jammer has become a highly complex key element of the EA suite. It has evolved from a simple repeater with some fading capabilities to a complex electronic attack asset. Some of the more critical tests are verifying proper operation and timing of the deception techniques on the system level, qualifying the individual components, submodules and modules at the RF/IF level, and last but not least making sure that clock jitter and power integrity are addressed early at the design stage. For all these requirements, Rohde & Schwarz offers cutting edge test and measurement solutions. The webinar introduces the concept of Digital RF Memory Jammers, describes their technology and the respective Test and Measurement challenges and solutions from Rohde & Schwarz.

Please note: By downloading a webinar, you’re contact information will be shared with the sponsoring company, Rohde & Schwarz GmbH & Co.KG and the Rohde & Schwarz entity or subsidiary company mentioned in the imprint of www.rohde-schwarz.com, and you may be contacted by them directly via email or phone for marketing or advertising purposes.

Download for FREE: EMI step-by-step guide from Rohde & Schwarz

Post Syndicated from IEEE Spectrum Recent Content full text original https://spectrum.ieee.org/whitepaper/download-for-free-emi-stepbystep-guide-from-rohde-schwarz

Be able to discover & analyze EMI in a more systematic & methodical approach to solve your problems.

In our free step-by-step guide, we break down the whole EMI design test process into “Locate”, “Capture”, and “Analyze”. Download & learn more.


Early Warning System Predicts Risk of Online Students Dropping Out

Post Syndicated from Michelle Hampson original https://spectrum.ieee.org/tech-talk/computing/software/a-predictive-modeling-tool-for-identifying-postsecondary-students-at-risk-of-dropping-out

With the new system, every student is scored based on how likely they are to finish their courses

Journal Watch report logo, link to report landing page

It’s easy enough for students to sign up for online university courses, but getting them to finish is much harder. Dropout rates for online courses can be as high as 80 percent. Researchers have tried to help by developing early warning systems that predict which students are more likely to drop out. Administrators could then use these predictions to target at-risk students with extra retention efforts. And as these early warning systems become more sophisticated, they also reveal which variables are most closely correlated with dropout risk.

In a paper published 16 April in IEEE Transactions on Learning Technologies, a team of researchers in Spain describe an early warning system that uses machine learning to provide tailored predictions for both new and recurrent students. The system, called SPA (a Spanish acronym for Dropout Prevention System), was developed using data from more than 11,000 students who were enrolled in online programs at Madrid Open University (UDIMA) over the course of five years.

Amateurs’ Al Tells Real Rembrandts From Fakes

Post Syndicated from Mark Anderson original https://spectrum.ieee.org/tech-talk/computing/software/the-rembrandt-school-of-ai-an-algorithm-that-detects-art-forgery

In their spare time, a Massachusetts couple programmed a system that they say accurately identifies Rembrandts 90 percent of the time

A new AI algorithm may crack previously inaccessible image-recognition and analysis problems—especially those stymied by AI training sets that are too small, or whose individual sample images are too big and full of high-resolution detail that AI algorithms cannot process. Already, the new algorithm can detect forgeries of one famous artist’s work, and its creators are actively searching for other areas where it could potentially improve our ability to transform small data sets into ones large enough to train an AI neural network.

According to two amateur AI researchers, whose study is now under peer review at IEEE Transactions on Neural Networks and Learning Systems, the concept of entropy, borrowed from thermodynamics and information theory, may help AI systems uncover fake works of art.

In physical systems such as boiling pots of water and black holes, entropy concerns the amount of disorder contained within a given volume. In an image file, entropy is defined as the amount of useful, nonredundant information the file contains.

For Better AI, Turn Up the Contrast on Reality

Post Syndicated from Robert W. Lucky original https://spectrum.ieee.org/computing/software/for-better-ai-turn-up-the-contrast-on-reality

Humans are sometimes criticized for seeing the world in black and white, but maybe AI should learn the same trick

Many years ago, I was touring a working orchard with a friend. His son, who was the orchard’s manager, was describing his work. His father and I, being engineers, got into a discussion about how a robot might be instructed to pick the fruit.

The son stopped and stared at us in consternation. “What are you guys talking about!? It’s simple—you see it, you pick it.”

Not so simple: It’s only now, decades later, that commercial fruit-picking robots are on the radar. There are many everyday tasks that seem trivial yet are difficult to describe and structure for automation. Humans have the advantage of common-sense reasoning, which is much more deep and profound than most people would believe.

In my February column, I wrote about our success in creating computer programs that can master games like chess and poker. By their descriptions, these games are extraordinarily simple—a small number of immutable rules involving a few elements, whether they be chess pieces or playing cards. But there is a paradox, because underneath this simplicity is an enormous complexity. Nonetheless, that complexity is precisely defined, and that’s what we engineers are good at.

However, life is fuzzy and often ill defined. (If only real-life tasks could be modeled as board games, we’d be in business.) I love the idea of fuzzy logic, but on reflection, I actually do want my computer to be precise.

But maybe there could be some mechanism that would take fuzziness as an input and hand off well-defined output to a computational unit. This unit could then bring to bear the kind of techniques we’ve used to master games.

In real life we have such mechanisms. Consider American football, for example. There are rules about what happens following a forward pass that depend on whether it is caught or not caught. But “catch” is a fuzzy concept. So we have a device that digitizes the analog “catch.” It is called a referee. In law we have the equivalent in the courts, where judges and juries use various subjective standards such as “reasonableness” to determine whether or not an action falls on one side of the law or the other. And if we don’t like the court’s digitization, we treat it as an analog result and send it through another court.

As a manager, I was the digital arbiter on many personnel decisions—who got raises and in what dollar amounts, who got promoted, who got fired, and so forth. People always asked me what criteria I used for these decisions. What is the algorithm you use? they wanted to know. In truth, I wanted an algorithm too. There was a lot of fuzziness involved.

Besides being fuzzy, much of life is influenced by luck. The best team doesn’t always win, and the best person doesn’t always get the promotion. Life isn’t always fair, but this does shake up the pieces, so I’m not sure if this is a bug or a feature. Many board games, like Monopoly, do combine luck with skill. Maybe the fuzziness converter could add a bit of randomness.

But I’m just dreaming about this. Whatever real-life task we’re trying to automate, someone will ask why it’s taking so long. It’s simple, they’ll say. But it really isn’t.

This article appears in the May 2019 print issue as “AI’s Achilles’ Heel: Ambiguity.”