New Algorithm Helps Autonomous Vehicles Find Themselves, Summer or Winter
06-24-21
Without GPS, autonomous systems get lost easily. Now a new algorithm developed at Caltech allows autonomous systems to recognize where they are simply by looking at the terrain around them—and for the first time, the technology works regardless of seasonal changes to that terrain. The general process, known as visual terrain-relative navigation (VTRN), was first developed in the 1960s. By comparing nearby terrain to high-resolution satellite images, autonomous systems can locate themselves. The problem is that, in order for it to work, the current generation of VTRN requires that the terrain it is looking at closely matches the images in its database. To overcome this challenge, Anthony Fragoso, Lecturer in Aerospace; Staff Scientist, Connor Lee, Graduate student in Aerospace, Austin McCoy, Undergraduate, and Soon-Jo Chung, Bren Professor of Aerospace and Control and Dynamical Systems and research scientist at JPL, turned to deep learning and artificial intelligence (AI) to remove seasonal content that hinders current VTRN systems. [Caltech story]
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GALCIT
MCE
CMS
Soon-Jo Chung
Anthony Fragoso
Connor Lee
Austin McCoy
A Swiss Army Knife for Genomic Data
04-05-21
A good way to find out what a cell is doing—whether it is growing out of control as in cancers, or is under the control of an invading virus, or is simply going about the routine business of a healthy cell—is to look at its gene expression. Lior Pachter, Bren Professor of Computational Biology and Computing and Mathematical Sciences, has developed a complex software tool that enables the processing of large sets of genomic data in about 30 minutes, using the computing power of an average laptop. Like a Swiss Army knife, the tool can be used in myriad ways for different biological needs, and will help ensure the reproducibility of scientific studies. "The interdisciplinarity of our team was crucial to conceiving of and executing this project," says Pachter. "There are people in the lab who are computer scientists, biologists, engineers. Sina Booeshaghi is in the mechanical engineering department and brings the perspective of his design background and engineering." [Caltech story]
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MCE
CMS
Lior Pachter
Sina Booeshaghi
Astronomers Image Magnetic Fields at the Edge of M87's Black Hole
03-24-21
The Event Horizon Telescope (EHT) collaboration, which produced the first-ever image of a black hole, revealed a new view of the massive object at the center of the M87 galaxy: a picture of its polarized light. This is the first time astronomers have been able to measure polarization, a signature of magnetic fields, this close to the edge of a black hole. "We are now able to see a different dimension of the light circling the M87 black hole," says Katie Bouman, Assistant Professor of Computing and Mathematical Sciences, Electrical Engineering and Astronomy, Rosenberg Scholar, and co-coordinator of the EHT Imaging Working Group. "The image we reconstructed earlier showed us how bright the light was around the black hole shadow. This image is telling us about the direction of that light." [Caltech story]
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EE
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CMS
Katie Bouman
Professor Bouman Featured in Inverse Magazine
03-10-21
Katie Bouman, Assistant Professor of Computing and Mathematical Sciences, Electrical Engineering and Astronomy; Rosenberg Scholar, was featured in Inverse Magazine as one of the astronomers who captured the first image of a black hole. In 2019, Bouman and a group of more than 200 astronomers from all over the world managed the inconceivable: They captured the first image of a black hole, rendering the invisible visible. "Ideally, to see a black hole, we would need a telescope the size of the entire Earth," says Bouman. "We had to come up with a computational telescope that size." [Inverse article]
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CMS
Katie Bouman
Paul Rothemund Places Molecule-Scale Devices in Precise Orientation
02-22-21
Paul Rothemund, Research Professor of Bioengineering, Computing and Mathematical Sciences, and Computation and Neural Systems, has developed a technique that allows him to precisely place microscopic devices formed from folded DNA molecules in not only a specific location but also in a specific orientation. This method for precisely placing and orienting DNA-based molecular devices may make it possible to use these molecular devices to power new kinds of chips that integrate molecular biosensors with optics and electronics for applications such as DNA sequencing or measuring the concentrations of thousands of proteins at once. [Caltech story]
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CMS
Paul Rothemund
KNI
CNS
AI-Driven COVID-19 Model Outperforms Competitors
11-30-20
While existing models to predict the spread of a disease already exist, few, if any, incorporate artificial intelligence (AI). Yaser Abu-Mostafa, Professor of Electrical Engineering and Computer Science, is using a new model for predicting COVID-19's impact using AI and it dramatically outperforms other models, so much so that it has attracted the interest of public health officials across the country. "AI is a powerful tool, so it only makes sense to apply it to one of the most urgent problems the world faces," says Yaser Abu-Mostafa. [Caltech story]
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CMS
Yaser Abu-Mostafa
CNS
Robotics Engineers Take on COVID-19
11-18-20
Methods that were originally created to help robots to walk and autonomous cars to drive safely can also help epidemiologists predict the spread of the COVID-19 pandemic. Aaron Ames, Bren Professor of Mechanical and Civil Engineering and Control and Dynamical Systems, and colleagues took these tools and applied them to the development of an epidemiological methodology that accounts for human interventions (like mask mandates and stay-at-home orders). By utilizing the U.S. COVID-19 data from March through May, they were able to predict the infection wave during the summer to high accuracy. "This is the greatest health challenge to face our society in a generation at least. We all need to pitch in and help in any way we can," Ames says. [Caltech story]
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MCE
CMS
IST
Aaron Ames
CDS
Andrew Singletary
