NonDestructive Testing (NDT) is a key discipline in major industrial sectors to ensure quality and safety. Several methods are regularly employed in areas ranging from x-ray or ultrasound testing of metallic or composite components in the automotive and aerospace industries, to the inspection of petrochemical ducts using eddy currents or acoustical emissions. The present proposal combines different NDT-related subjects under the oN DuTy!
The goal of this project is to explore the use of log analytics and machine/deep learning techniques to improve Ubisoft operational intelligence. Logs contain a wealth of information, but often hindered by the lack of best practices, tools, and processes. Despite the importance of logging, the area has not evolved much over the years. At Ubisoft, logs are used extensively for various system diagnosis tasks. The analysis of logs, however, is usually performed manually, limiting the full potential of the information contained in logs.
Decreasing operational costs is a key criterion for organizations that manage compute clusters, such as Amazon, Microsoft, Google, Alibaba, etc. One way to decrease costs it to improve resource utilization in the cluster [13, 14]. Yet, high resource utilization can negatively affect workload performance and thus user satisfaction. Performance degradation happens when workloads running on the same machine compete for shared resources, e.g., a workload that consumes a large portion of memory delays execution of other, memory-intensive workloads.
Head-mounted displays (HMDs) allow a convenient delivery of visualized data to the user. HMDs in the form of glasses and goggles (otherwise known as smart glasses and goggles), such as Vuzix Blade and Epson Moverio [1-3], have been introduced but the public acceptance of these devices have been rather lackluster. Part of the sluggish acceptance may be attributed to the still-high device costs (>$1000) and a large form-factor, owning largely to the fact that these devices utilize unique and sophisticated optics on dedicated and non-retrofittable platforms.
This multidisciplinary project will investigate the social, data, and technical issues surrounding design and implementation of a blockchain-based solution for the management of consent for the use of individuals omics data in medical research. Blockchains are distributed ledgers in which confirmed and validated blocks are organized in an append-only chain using cryptographic links.
Radio frequency integrated circuit power amplifiers (RFIC PAs) operating at microwave frequencies (e.g. 5 to 6 GHz) and at millimeter-wave (mm-wave) frequencies (e.g. 60 GHz) are electronic components used in the front-end modules (close to the antenna) of mobile communication equipment such as cellular handsets. Envelope detectors constitute a critical component in a newly proposed dynamic biasing technique for RFIC PAs based on positive envelope feedback, for power efficiency improvement and distortion reduction.
Leading aircraft manufacturers have gradually turned their attention to the development of all-electric prototype airplanes, some of which have been flown recently. Such is the case of the Airbus E-fan aircraft that in 2015 has crossed the English Channel, completing the 74 kilometer flight from Lydd, England to Calais, France, in about 37 minutes. With battery performance constantly improving, its advantage over kerosene fuel becomes more and more evident, especially in terms of being CO2 emission free.
Image denoising is a fundamental process in most of computer vision systems, imaging systems, and photography productions. Recently, with the power of deep neural networks, image denoising has been pushed towards new boundaries. However, neural network image denoisers are constrained by the accuracy of the noise model used to train them. Training on a poor noise model results in poor generalization performance on real-world images.
In this project we address the problem of power consumption for wireless sensor nodes. This is where among different components of a sensor, RF transceivers consume a significant amount of power e.g. approximately 80%. Hence the main objective is this project is to tackle the power consumption problem at the RF transmitter, where we aim to reduce the power consumption to micro-watts of power, with minimal sacrifice in achievable data rate and by keeping the connectivity range within an acceptable radius.
The recent advances in machine learning based on deep neural networks, coupled with the availability of phenomenal storage capacity, are transforming the industrial landscape. However, these novel machine learning approaches are known to be data hungry, as they need to tune a huge number of parameters in order to perform well. As more and more AI based applications are being deployed to learn from personal data, privacy concerns are rising, and more specifically on sensible domains like medicine, finance or mobile related data.