A smart wireless inertial measurement unit system: simplifying & encouraging usage of WIMU technology

Wireless Inertial Measurement Units (WIMUs) combine motion sensing, processing & communications functionsin a single device. Data gathered using these sensors has the potential to be converted into high quality motion data. By outfitting a subject with multiple WIMUs full motion data can begathered. With a potential cost of ownership several orders of magnitude less than traditional camera based motion capture, WIMU systems have potential to be crucially important in supplementing or replacing traditional motion capture and opening up entirely new application areas and potential markets particularly in the rehabilitative, sports & at-home healthcarespaces. Currently WIMUs are underutilized in these areas. A major barrier to adoption is perceived complexity. Sample rates, sensor types & dynamic sensor ranges may need to be adjusted on multiple axes for each device depending on the scenario. As such we present an advanced WIMU in conjunction with a Smart WIMU system to simplify this aspect with 3 usage modes: Manual, Intelligent and Autonomous. Attendees will be able to compare the 3 different modes and see the effects of good andbad set-ups on the quality of data gathered in real time.

Low power predictable memory and processing architectures

Great demand in power optimized devices shows promising economic potential and draws lots of attention in industry and research area. Due to the continuously shrinking CMOS process, not only dynamic power but also static power has emerged as a big concern in power reduction. Other than power optimization, average-case power estimation is quite significant for power budget allocation but also challenging in terms of time and effort. In this thesis, we will introduce a methodology to support modular quantitative analysis in order to estimate average power of circuits, on the basis of two concepts named Random Bag Preserving and Linear Compositionality. It can shorten simulation time and sustain high accuracy, resulting in increasing the feasibility of power estimation of big systems. For power saving, firstly, we take advantages of the low power characteristic of adiabatic logic and asynchronous logic to achieve ultra-low dynamic and static power. We will propose two memory cells, which could run in adiabatic and non-adiabatic mode. About 90% dynamic power can be saved in adiabatic mode when compared to other up-to-date designs. About 90% leakage power is saved. Secondly, a novel logic, named Asynchronous Charge Sharing Logic (ACSL), will be introduced. The realization of completion detection is simplified considerably. Not just the power reduction improvement, ACSL brings another promising feature in average power estimation called data-independency where this characteristic would make power estimation effortless and be meaningful for modular quantitative average case analysis. Finally, a new asynchronous Arithmetic Logic Unit (ALU) with a ripple carry adder implemented using the logically reversible/bidirectional characteristic exhibiting ultra-low power dissipation with sub-threshold region operating point will be presented. The proposed adder is able to operate multi-functionally.

Pregnancy-specific glycoprotein function, conservation and receptor investigation

Pregnancy-specific glycoproteins (PSGs) are highly glycosylated secreted proteins encoded by multi-gene families in some placental mammals. They are carcinoembryonic antigen (CEA) family and immunoglobulin (Ig) superfamily members. PSGs are immunomodulatory, and have been demonstrated to possess antiplatelet and pro-angiogenic properties. Low serum levels of these proteins have been correlated with adverse pregnancy outcomes. Objectives: Main research goals of this thesis were: 1). To attempt to replicate previously reported cytokine responses to PSG-treatment of immune cells and subsequently to investigate functionally important amino acids within PSG1. 2). To determine whether candidate receptor, integrin αvβ3, was a binding partner for PSG1 and to investigate whether PSG1 possessed functionality in a leukocyte-endothelial interaction assay. 3). To determine whether proteins generated from recently identified putative PSG genes in the horse shared functional properties with PSGs from other species. Outcomes: 1). Sequential domain deletion of PSG1 as well as mutation of conserved residues within the PSG1 Ndomain did not affect PSG1-induced TGF-β1. The investigated response was subsequently found to be the result of latent TGF-β1 contaminating the recombinant protein. Protein further purified by SEC to remove this showed no induction of TGF-β1. The most N-terminal glycosylation site was demonstrated to have an important role in PSG N domain secretion. PSG1 attenuated LPS-induced IL-6 and TNF-α. Investigations into signalling underpinning this proved inconclusive. 2). Integrin αvβ3 was identified as a novel PSG1 receptor mediating an as yet unknown function. Preliminary investigations into a role for PSGs as inhibitors of leukocyte endothelial interactions showed no effect by PSG1. 3). Horse PSG protein, CEACAM49, was shown to be similarly contaminated by latent TGF-β1 particle and once removed did not demonstrate TGF-β1 release. Interestingly horse PSG did show anti-platelet properties through inhibition of the plateletfibrinogen interaction as previously published for mouse and human PSGs.

The use of fault detection and diagnostics to reduce air handling unit energy consumption

The contribution of buildings towards total worldwide energy consumption in developed countries is between 20% and 40%. Heating Ventilation and Air Conditioning (HVAC), and more specifically Air Handling Units (AHUs) energy consumption accounts on average for 40% of a typical medical device manufacturing or pharmaceutical facility’s energy consumption. Studies have indicated that 20 – 30% energy savings are achievable by recommissioning HVAC systems, and more specifically AHU operations, to rectify faulty operation. Automated Fault Detection and Diagnosis (AFDD) is a process concerned with potentially partially or fully automating the commissioning process through the detection of faults. An expert system is a knowledge-based system, which employs Artificial Intelligence (AI) methods to replicate the knowledge of a human subject matter expert, in a particular field, such as engineering, medicine, finance and marketing, to name a few. This thesis details the research and development work undertaken in the development and testing of a new AFDD expert system for AHUs which can be installed in minimal set up time on a large cross section of AHU types in a building management system vendor neutral manner. Both simulated and extensive field testing was undertaken against a widely available and industry known expert set of rules known as the Air Handling Unit Performance Assessment Rules (APAR) (and a later more developed version known as APAR_extended) in order to prove its effectiveness. Specifically, in tests against a dataset of 52 simulated faults, this new AFDD expert system identified all 52 derived issues whereas the APAR ruleset identified just 10. In tests using actual field data from 5 operating AHUs in 4 manufacturing facilities, the newly developed AFDD expert system for AHUs was shown to identify four individual fault case categories that the APAR method did not, as well as showing improvements made in the area of fault diagnosis.