As consumer preferences shift from shopping at brick and mortar stores to on-line shopping, there is an increased need for warehouses to use automated systems that fill orders quickly and accurately. This research project will design a warehouse robot that is adaptable to different platform systems and shelving configurations, providing a lighter, faster, and more cost-effective warehousing system.
Currently, the automotive industry is going through a very significant transformation---one that is blending cars with modern IT, involving technologies such as: multiple CPUs for in-car computing, ad-hoc networking and Internet connectivity, computer vision and sensing technologies, entertainment and artificial intelligence for automated driving and real-world congestion control. Connected and intelligence vehicles are also raising cyber-security concerns.
Machine learning techniques have been applied to the financial industry for some time. They have allowed large utilities and generators to better forecast their needs, and the prices they will pay, leading to a generally more efficient grid. However, very little research has been done that could benefit power marketers, who do not have a load to serve or a generating facility to manage. The application of machine learning techniques has yielded great results in the financial industry.
The objective of the proposed research project is to automate the redaction of financial portfolios reports. The generated reports should inform the reader about which factors influenced the portfolio’s returns, to what degree, and how far these factors deviate from the norm.
In today’s candidate-driven market, talent recruitment represents a major challenge for many companies. Younger millennials have different expectations of their work environment than previous generations. They are heavy users of technology in almost all their activities, including job search. They are also used to multiple social medias and expect faster feedback. In this changing environment, traditional job web sites do not answer the challenges facing today’s talent recruitment. It becomes obvious that we need a new approach to tackle this problem.
Against a backdrop of alarming privacy breaches at social media, banking, and government websites, the European Union will see the General Data Protection Regulation (GDPR) come into force as of May 25 2018. GDPR will drastically increase fines for data loss and breach, introduces new requirements to obtain individual consent for data use, and defines new responsibilities for data processors and controllers.
In this project, we are interested in device-free methods that passively sense, monitor, and track peoples indoor presence, location, and movement using off-the-shelf Wi-Fi-enabled devices. We use information extracted from the physical layer of wireless links to detect and interpret human presence, location, and physical activities. The current design and implementation of Wi-Fi-based systems exhibit some temporal inconsistencies and limitations due to the complexity of the wireless signal propagation in indoor environment and the challenging nature of human's behavior itself.
According to the World Petroleum Council (WPC), the average age of employees in Oil and Gas companies is 50 years, and it is estimated that in the next 5 years 40-60% of them will retire. The consequence is an age-related crisis in the sector given that, in many cases, the knowledge accumulated goes with the retiring gray-beards.
The rapid development in the areas of statistics and machine learning demonstrate unprecedented performance in making cognitive business decisions. Quartic.ai aims to use state-of-the-art machine learning technology to help manufacturers assess and maintain the quality of their industrial units, which suffer damage due to continuous usage and normal wear and tear. Such damage needs to be detected early to prevent further losses. The data in this domain are recorded using sensors at various stages in the process flow.
For an Autonomous Vehicle (AV) to make decisions and drive independently on urban streets, the problem at hand can be broken down into many phases, two of which are perception and prediction. Perception refers to the process of extracting valuable information from the environment using data collected by sensors such as LIDAR and camera. This includes detection of cars, ped estrians, lanes among many objects. Prediction refers to the process of tracking all the known objects and predicting the possible future actions so as to enable the autonomous vehicle to make informed decisions.