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In most naturally occurring superconductors, electrons with opposite spins are paired up to form Cooper pairs. This includes both conventional $s$-wave superconductors such as aluminum as well as high-$T_c$, $d$-wave superconductors. Materials with intrinsic $p$-wave superconductivity, hosting Cooper pairs made of equal-spin electrons, have not been conclusively identified, nor synthesized, despite promising progress. Instead, engineered platforms where $s$-wave superconductors are brought into contact with magnetic materials have shown convincing signatures of equal-spin pairing. Here, we directly measure equal-spin pairing, proximity-induced from an $s$-wave superconductor into a semiconducting nanowire with strong spin-orbit interaction. We demonstrate such pairing using spin-selective quantum dots by showing that breaking a Cooper pair can result in two electrons with equal spin polarization. Our results demonstrate controllable detection of singlet and triplet pairing in the proximitized nanowire. Achieving such triplet pairing in a sequence of quantum dots will be required for realizing an artificial Kitaev chain.

Summarization: Smart mobile devices have displaced personal computers in many daily applications such as internet browsing and email. However, for content creation, users still need to use a large display, keyboard and mouse. Many initiatives are currently working on enabling I/O functionality for content creation and peripheral access, and on preserving the grab-and-go experience where the mobile device is not tethered to the docking station but merely placed in proximity of it and the traffic is carried over Wi-Fi. Maintaining the Quality of Service (QoS) and Experience (QoE) of low-latency, high fidelity video (for example the desktop view of a smart device) when transmitted over a Wi-Fi link in heavily loaded environments has been proven problematic. In this work, we propose for the first time in the relevant literature to the best of our knowledge, a highly accurate video traffic model that is capable of predicting the volume of video traffic generated by an average user's computer during a day. Our modeling techniques are tested on real user-generated screen mirroring traffic from a large shared cube space similar to an enterprise environment, and can be easily used as source traffic generators in order to facilitate the study of H.264 transmission performance over wireless networks. Presented on: Computer Communications

Data centers are the backbone of computing, storing, processing and allowing access to large amounts of data. They consume a huge amount of energy, as of 2018, each year consuming approximately 200 TWh and this amount is only going to increase. A lot of work has been done to increase the energy efficiency of data centers. Research on reducing the power consumption of data centers has examined different levels of operations. Some of this research has proposed algorithms in lower levels such as at the device level and some in higher levels such as servers, cooling systems and workload managers. But there is no work that has considered inefficiencies in power distribution to servers (server power loss). This thesis proposes an algorithm that minimizes server power loss and then compares the resulting algorithms to those that do not consider server power loss to determine whether the resulting algorithms are consistent or not. It will be shown that the power savings of such an algorithm can be quite significant and as a result it is crucial to include such considerations when performing workload assignment. Finally, some opportunities to continue this work in the future will be discussed. Thesis Master of Applied Science (MASc)

Data centres are an integral part of today's technology. With the growing demand for data centers to meet computational needs, there is pressure to decrease data center-related costs. By reducing the amount of power needed to cool servers, the overall power consumption can be decreased. Efficient cooling of data centres involves meeting temperature constraints while minimizing power consumption. By exploring the opportunities that may be available through controlling multiple cooling units, we can avoid issues such as overcooling (some parts of the data center being cooled more than necessary) or warm air recirculation (return of exhausted hot air to inlets of servers). Currently, in data centres with more than one cooling unit, each of the cooling units is controlled independently. This mode of operation results in each cooling unit needing to be set for the worst case, which results in over cooling and is not energy efficient. Coordinating cooling units has the potential to decrease the power consumption of a data centre by eliminating this over cooling. Furthermore, coordinating with workload management may help mitigate cooling unit power consumption. This research is concerned with exploring what is feasible within the options discussed above. It contains two main parts. In the first part, we present an algorithm to minimize power consumption of cooling units while keeping all the server cores below a temperature threshold. In the second part of this thesis, we derived a data-driven model for server outlet temperature. Thesis Master of Science (MSc)