computer engineering


Cybersecurity tool uses machine learning, honeypots to stop attacks

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In recent months, the FBI issued a high-impact cybersecurity warning in response to increasing attacks on government targets. Government officials have warned major cities such hacks are a disturbing trend likely to continue. Purdue University researchers may help stop some of those threats with a tool designed to alert organizations to cyberattacks. The system is called LIDAR – which stands for lifelong, intelligent, diverse, agile and robust. "The name for this architecture for network security really defines its significant attributes," said Aly El Gamal, an assistant professor of electrical and computer engineering in Purdue's College of Engineering.


NSF awards UIC $1.5M for new data science institute

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Newswise -- A multi-disciplinary team of University of Illinois at Chicago researchers received a three-year, $1.5 million grant from the National Science Foundation to form a new data science institute. The UIC Foundations of Data Science Institute is intended to establish a place on campus that will focus on the theory of data science. The institute will concentrate on three themes: the representation and structure of data, machine learning and complexity, and robustness and privacy. These themes will link theory with the application of data science to create new ways to apply data to research. The institute will further develop the data science curriculum at UIC, promote interdisciplinary collaborations on and off-campus, and train the next generation of data scientists.


Doctoral student develops algorithm to improve clarity of partial MRI scans

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Having an MRI scan can be an unpleasant experience. The procedure often involves a patient lying down inside a large tube for at least 30 minutes and being instructed to remain motionless as magnetic and radio waves create detailed pictures of their organs. Many find the machine's loud, clanging noises unnerving and are left feeling panicky and claustrophobic. An approach created by Puyang Wang, a doctoral degree candidate in electrical and computer engineering at Johns Hopkins University's Whiting School of Engineering, could help provide some relief. The solution, an algorithm that speeds up MRI data acquisition and results in clearer images, was among the projects recognized at the fastMRI competition held earlier this month by Facebook AI and New York University's Langone Health.


Final lecture in AI Seminar Series explores how machines might learn as humans do

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The third annual Modern Artificial Intelligence (AI) seminar series at NYU Tandon, bringing together students and experts to discuss recent advances in the field, wrapped up on December 6 with a presentation by Raia Hadsell, Head of Robotics Research at DeepMind. In the final presentation of the series, sponsored by the Department of Electrical and Computer Engineering and organized by Professor Anna Choromanska, Hadsell explored ways in which human learning can inform machine learning systems to develop highly sophisticated AI to solve complex real-world tasks. The Fall roster kicked off in early October with a lecture by Facebook AI Research's Leon Bottou. The researcher, who harbors the long-term ambition of replicating human-level intelligence, examined causal inference, or finding the relationship between existing facts and objects. Next, on November 14, Francis Bach, researcher at Institut National de Recherche en Informatique et en Automatique (INRIA) in France, spoke about a new generation of "distributed optimization" schemes that are critically needed to scale algorithms to massive data.


Synthesizing an artificial synapse for artificial intelligence

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In reality, the opposite is true: a human brain - which today is still more proficient than CPUs at cognitive tasks like pattern recognition - needs only 20 watts of power to complete a task, while a supercomputer requires more than 50,000 times that amount of energy. For that reason, researchers are turning to neuromorphic computer and artificial neural networks that work more like the human brain. However, with current technology, it is both challenging and expensive to replicate the spatio-temporal processes native to the brain, like short-term and long-term memory, in artificial spiking neural networks (SNN). Feng Xiong, PhD, assistant professor of electrical and computer engineering at the University of Pittsburgh's Swanson School of Engineering, received a $500,000 CAREER Award from the National Science Foundation (NSF) for his work developing the missing element, a dynamic synapse, that will dramatically improve energy efficiency, bandwidth and cognitive capabilities of SNNs. "When the human brain sees rain and then feels wetness, or sees fire and feels heat, the brain's synapses link the two ideas, so in the future, it will associate rain with wetness and fire with warmth. The two ideas are strongly linked in the brain," explains Xiong.


Artificial Intelligence Converts 2D Images Into 3D Using Deep Learning [Video]

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An illustration representing Deep-Z, an artificial intelligence-based framework that can digitally refocus a 2D fluorescence microscope image (at bottom) to produce 3D slices (at left). A University of California, Los Angeles research team has devised a technique that extends the capabilities of fluorescence microscopy, which allows scientists to precisely label parts of living cells and tissue with dyes that glow under special lighting. The researchers use artificial intelligence to turn two-dimensional images into stacks of virtual three-dimensional slices showing activity inside organisms. In a study published in Nature Methods on November 4, 2019, the scientists also reported that their framework, called "Deep-Z," was able to fix errors or aberrations in images, such as when a sample is tilted or curved. Further, they demonstrated that the system could take 2D images from one type of microscope and virtually create 3D images of the sample as if they were obtained by another, more advanced microscope.


Researchers convert 2-D images into 3-D using deep learning

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A UCLA research team has devised a technique that extends the capabilities of fluorescence microscopy, which allows scientists to precisely label parts of living cells and tissue with dyes that glow under special lighting. The researchers use artificial intelligence to turn two-dimensional images into stacks of virtual three-dimensional slices showing activity inside organisms. In a study published in Nature Methods, the scientists also reported that their framework, called "Deep-Z," was able to fix errors or aberrations in images, such as when a sample is tilted or curved. Further, they demonstrated that the system could take 2-D images from one type of microscope and virtually create 3-D images of the sample as if they were obtained by another, more advanced microscope. "This is a very powerful new method that is enabled by deep learning to perform 3-D imaging of live specimens, with the least exposure to light, which can be toxic to samples," said senior author Aydogan Ozcan, UCLA chancellor's professor of electrical and computer engineering and associate director of the California NanoSystems Institute at UCLA.


Machine learning finds new metamaterial designs for energy harvesting

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Electrical engineers at Duke University have harnessed the power of machine learning to design dielectric (non-metal) metamaterials that absorb and emit specific frequencies of terahertz radiation. The design technique changed what could have been more than 2000 years of calculation into 23 hours, clearing the way for the design of new, sustainable types of thermal energy harvesters and lighting. The study was published online on September 16 in the journal Optics Express. Metamaterials are synthetic materials composed of many individual engineered features, which together produce properties not found in nature through their structure rather than their chemistry. In this case, the terahertz metamaterial is built up from a two-by-two grid of silicon cylinders resembling a short, square Lego.


Amman AI Bootcamp

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Reem Mahmoud is the Co-founder and Education Lead at Zaka. Reem's passion for Machine Intelligence and education is the main driver for her role at Zaka. She is currently pursuing a Ph.D. degree at the American University of Beirut (AUB) in Electrical & Computer Engineering where her research is in the area of Machine Intelligence with a focus on learning from limited data in the IoT and sensing applications. Her research interests also include digital signal processing, optimization methods, and computer vision. Reem graduated with a B.S. in Electrical Engineering with high distinction from Alfaisal University in Riyadh, Saudi Arabia in 2015 and received her M.E. in Electrical & Computer Engineering from AUB in 2017 where her thesis was about designing accurate personalized user models from sensing data.


5G wireless to connect robots on the ground to AI in the cloud

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A research team at the NYU Tandon School of Engineering, with the support of the National Science Foundation's National Robotics Initiative 2.0, is building the foundations of a wireless system that takes advantage of superfast fifth-generation (5G) wireless communications to outsource a mobile robots' artificial intelligence (AI) functions to the edge cloud--the server in the cloud closest to the robot. The collaborators, all of whom are members of the faculty of NYU Tandon's renowned NYU WIRELESS center for telecommunications research, will design manipulation and locomotion algorithms that address some important technical hurdles to making 5G networks a viable bridge between robot and server. Shifting AI capabilities from the robot to a remote server offers tantalizing operational benefits, such as allowing robots to perceive the environment, perform complex operations, and make decisions autonomously, all without incurring major energy and weight costs from onboard computational and power-generation equipment. Comprising Ludovic Righetti, professor in the Departments of Electrical and Computer Engineering and Mechanical and Aerospace Engineering; and Siddharth Garg, Sundeep Rangan and Elza Erkip, professors in the Department of Electrical and Computer Engineering, the team will focus on solving issues of reliability, safety of robotic operation under communication degradation, and scalability to multi-robot systems. The collaboration brings expertise in robotics (Righetti), computer architecture and computation (Garg), wireless networks (Rangan and Erkip), and information theory (Erkip).