The discipline of Vibrometery is wide and has many applications, vibrations are present in any mechanical system that involves moving components. So far, the main method to measure these vibrations has been the traditional accelerometer sensor, although it has its limitations and challenges.
Laser Doppler Vibrometers (LDV) were developed in order to address some of these limitations and they offer a non-contact measurement of vibrations by leveraging the Doppler shift effect.
With rapidly improving display technologies used in the home, cinema needs to remain competitive in terms of achieving the highest image quality on screen. While increasing the quantity of pixels is currently rolled out, so far nothing has been proposed to improve the quality of those pixels. Emerging prototype cinema displays are currently being show-cased which brings significant improvement over traditional cinema experience. However new display technology with different pixel quality require modification to the traditional cinema workflow.
The fellowship mainly investigates an analysis of the state-of-the-art approaches, design and implementation of cutting-edge deep neural network models to be used on a mobile platform. It explored ways to optimize the deployment of these machine-learning models for prediction tasks on the mobile devices which requires energy efficiency and accuracy.
Within the aerospace sector, aftermarket services account for over 50% of revenue generated by aero engine manufacturers. Central to this is the ability to inspect and repair high unit cost components. Many processes are manual but given the ever-increasing quality, cost and delivery requirements, and the safety critical nature of these rotating parts, there is a strong drive towards process automation.
Global service providers in highly regulated financial sectors must accommodate an ever-changing, sometimes competing, landscape of regulatory and business concerns. This project seeks to define a technology infrastructure design that supports current and anticipated data privacy and data residency concerns, making it possible to keep data within borders while still facilitating collaboration across those borders. Consumers are increasingly aware of the collection of their private data, but are often unaware of cross-border movement of their data.
Addictive Tech Corp is a fast-growing ad-tech company. They use real-time advertisement bidding software which is massive and sophisticated. The actual dynamics involved in any given bid are complex and hard to predict. This makes writing test logic for such a system cumbersome and catching all corner cases next to impossible. Because of the scale of operations, understanding the environment in which the bidding software operates is difficult. This is problematic as such software needs to be highly optimized to be competitive.
The wide adoption and development of wireless sensing technologies for the monitoring and autonomous identification of financial activities have affected financial institutions in the past decade. However, wider utilization of RFID technologies in the banking sector has introduced challenges regarding the security and privacy of sensitive financial data. The proposed innovations and technological developments will revolutionize the banking sector by increasing efficiency, decreasing cost and provide secure and privacy sensitive financial transactions.
This project proposes a novel planning algorithm based on merging a sampling-based global planner with an inverse kinematics-based controller, defined local planner. The idea is to run off-line the global planner before the motion starts. Then, during the motion, the local planner is executed and simultaneously at lower frequency the global one is performed to capture eventual environmental changes. In this way, the advantages of both the planners are collected. Indeed, the local one ensures the feasibility of trajectories and whereas the global one ensures of avoiding the local minima.
With the trend of increasing technological complexity, software content and mechatronic implementation, there are increasing risks from systematic failures and random hardware failures. ISO 26262 ( Road vehicles Functional safety ) is the most important standard concerning functional safety in the automotive industry. The standard was published at the end of 2011. Even small modifications of a system after it has been certified necessitate a re-certification.
This project aims at applying the technique of Model Predictive Control (MPC) to control the thermostatic loads in HVAC systems in the context of smart buildings. The main objective of this project is to verify the capability of MPC-base control schemes developed in academic research projects with a real system operated by Fusion in terms of energy efficiency improvement and operational cost reduction. In the first phase, the model of a candidate building will be established and validated for the controller design.