INTEGRATION OF INSTRUMENTATION AND PROCESSING SOFTWARE OF A LASER SPECKLE CONTRAST IMAGING SYSTEM

Laser speckle contrast imaging (LSCI) has the potential to be a powerful tool in medicine, but more research in the field is required so it can be used properly. To help in the progression of Michigan Tech's research in the field, a graphical user interface (GUI) was designed in Matlab to control the instrumentation of the experiments as well as process the raw speckle images into contrast images while they are being acquired. The design of the system was successful and is currently being used by Michigan Tech's Biomedical Engineering department. This thesis describes the development of the LSCI GUI as well as offering a full introduction into the history, theory and applications of LSCI.

Feasibility study and flow scheduling optimization of a multi-product pipeline system

The first goal of this study is to analyse a real-world multiproduct onshore pipeline system in order to verify its hydraulic configuration and operational feasibility by constructing a simulation model step by step from its elementary building blocks that permits to copy the operation of the real system as precisely as possible. The second goal is to develop this simulation model into a user-friendly tool that one could use to find an “optimal” or “best” product batch schedule for a one year time period. Such a batch schedule could change dynamically as perturbations occur during operation that influence the behaviour of the entire system. The result of the simulation, the ‘best’ batch schedule is the one that minimizes the operational costs in the system. The costs involved in the simulation are inventory costs, interface costs, pumping costs, and penalty costs assigned to any unforeseen situations. The key factor to determine the performance of the simulation model is the way time is represented. In our model an event based discrete time representation is selected as most appropriate for our purposes. This means that the time horizon is divided into intervals of unequal lengths based on events that change the state of the system. These events are the arrival/departure of the tanker ships, the openings and closures of loading/unloading valves of storage tanks at both terminals, and the arrivals/departures of trains/trucks at the Delivery Terminal. In the feasibility study we analyse the system’s operational performance with different Head Terminal storage capacity configurations. For these alternative configurations we evaluated the effect of different tanker ship delay magnitudes on the number of critical events and product interfaces generated, on the duration of pipeline stoppages, the satisfaction of the product demand and on the operative costs. Based on the results and the bottlenecks identified, we propose modifications in the original setup.

Off-line and On-line Affective Recognition of a Computer User through A Biosignal Processing Approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment (“relaxation” vs. “stress”) are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the “relaxation” vs. “stress” states.

System-on-a-Chip (SoC) based Hardware Acceleration in Register Transfer Level (RTL) Design

Today, modern System-on-a-Chip (SoC) systems have grown rapidly due to the increased processing power, while maintaining the size of the hardware circuit. The number of transistors on a chip continues to increase, but current SoC designs may not be able to exploit the potential performance, especially with energy consumption and chip area becoming two major concerns. Traditional SoC designs usually separate software and hardware. Thus, the process of improving the system performance is a complicated task for both software and hardware designers. The aim of this research is to develop hardware acceleration workflow for software applications. Thus, system performance can be improved with constraints of energy consumption and on-chip resource costs. The characteristics of software applications can be identified by using profiling tools. Hardware acceleration can have significant performance improvement for highly mathematical calculations or repeated functions. The performance of SoC systems can then be improved, if the hardware acceleration method is used to accelerate the element that incurs performance overheads. The concepts mentioned in this study can be easily applied to a variety of sophisticated software applications. The contributions of SoC-based hardware acceleration in the hardware-software co-design platform include the following: (1) Software profiling methods are applied to H.264 Coder-Decoder (CODEC) core. The hotspot function of aimed application is identified by using critical attributes such as cycles per loop, loop rounds, etc. (2) Hardware acceleration method based on Field-Programmable Gate Array (FPGA) is used to resolve system bottlenecks and improve system performance. The identified hotspot function is then converted to a hardware accelerator and mapped onto the hardware platform. Two types of hardware acceleration methods – central bus design and co-processor design, are implemented for comparison in the proposed architecture. (3) System specifications, such as performance, energy consumption, and resource costs, are measured and analyzed. The trade-off of these three factors is compared and balanced. Different hardware accelerators are implemented and evaluated based on system requirements. 4) The system verification platform is designed based on Integrated Circuit (IC) workflow. Hardware optimization techniques are used for higher performance and less resource costs. Experimental results show that the proposed hardware acceleration workflow for software applications is an efficient technique. The system can reach 2.8X performance improvements and save 31.84% energy consumption by applying the Bus-IP design. The Co-processor design can have 7.9X performance and save 75.85% energy consumption.

Gravimetric and density profiling using the combination of surface acoustic waves and neutron reflectivity

A new approach is described herein, where neutron reflectivity measurements that probe changes in the density profile of thin films as they absorb material from the gas phase have been combined with a Love wave based gravimetric assay that measures the mass of absorbed material. This combination of techniques not only determines the spatial distribution of absorbed molecules, but also reveals the amount of void space within the thin film (a quantity that can be difficult to assess using neutron reflectivity measurements alone). The uptake of organic solvent vapours into spun cast films of polystyrene has been used as a model system with a view to this method having the potential for extension to the study of other systems. These could include, for example, humidity sensors, hydrogel swelling, biomolecule adsorption or transformations of electroactive and chemically reactive thin films. This is the first ever demonstration of combined neutron reflectivity and Love wave-based gravimetry and the experimental caveats, limitations and scope of the method are explored and discussed in detail.

The user participation on facebook pages of the three Portuguese tv news channels – RTP3, SIC NOTÍCIAS, AND TVI24

In recent decades, the national and international media contexts, in particular television media, significantly changed. The role that social networks, in particular Facebook, have taken as a content diffusion platform is unquestionable. Nowadays, traditional media (radio, newspaper, television) use the Web’s potential to distribute news content (Canelas, 2011). Currently, all TV news channels in Portugal have a website or a page on social networks. TV stations have increased communication channels with the public on digital platforms and study strategies that promote the participation and interaction with the news content (Cazajeira, 2015). The TV / Internet convergence will not only reach the content, but also the consumer, who becomes an interactive and participative audience. This reality imposes on journalism a continuous and updated news production system, dependent on a user being permanently connected to the Internet (Cazajeira, 2015). In fact, a report launched by an autonomous institution that has the function of supervising and regulating the media Portugal (ERC, 2015), confirms the relevance that social media has assumed in the publication and consumption of news. Social networks are recognised as one of the most important means for news media consultation, right after television, and the practice of sharing news is very common among consumers of online news in Portugal. Furthermore, when compared to other countries analysed by Reuters Institute (Newman, Levy, & Nielsen, 2015), Portuguese consumers are those who make the most comments to online news, preferring social networks to news sites. Considering the importance of new online platforms for journalism, this study aims to present a quantitative analysis of user participation on the Facebook pages of the three Portuguese TV news channels, specifically RTP3, SIC Notícias and TVI24, between 8 and 14 February 2016. To track this participation, the following parameters were used: the "like" button as a way to study the demonstration of publication interest; "sharing" of a particular element, be it a photo, a video or a text, on the user Timeline, the Timeline of a friend or by private message. This monitoring is important to understand the dissemination of news content; and the comments area. The number of comments will help understand the dynamics and the discussion that the publication has on the public. The results of 1063 posts indicate that of the analysed parameters - "Like", "Comment", and "Share" – the one with the greatest power of participation among the users of the pages of the three Portuguese TV news channels is the "Like" system, followed by "Share" and then "Comment". The theme that generates the most user participation with "Likes" and “Comments” parameters are "Science and Technology", “Education” and “Humorous/Satirical/Unusual”. Finally, the publications available end of the night (10pm-1am) has better participation rates.

Improving surgery operations by means of cloud systems and distributed user interfaces

Surgical interventions are usually performed in an operation room; however, access to the information by the medical team members during the intervention is limited. While in conversations with the medical staff, we observed that they attach significant importance to the improvement of the information and communication direct access by queries during the process in real time. It is due to the fact that the procedure is rather slow and there is lack of interaction with the systems in the operation room. These systems can be integrated on the Cloud adding new functionalities to the existing systems the medical expedients are processed. Therefore, such a communication system needs to be built upon the information and interaction access specifically designed and developed to aid the medical specialists. Copyright 2014 ACM.

Improving user-insurance communication on accident reports

This paper presents an easy to use methodology and system for insurance companies targeting at managing traffic accidents reports process. The main objective is to facilitate and accelerate the process of creating and finalizing the necessary accident reports in cases without mortal victims involved. The diverse entities participating in the process from the moment an accident occurs until the related final actions needed are included. Nowadays, this market is limited to the consulting platforms offered by the insurance companies. Copyright 2014 ACM.

I-Smart Date Entry User Guide, May 2015

I-SMART is an Internet-based client management system that allows the State of Iowa and its licensed substance abuse treatment providers to administer, manage and provide cost efficient and quality substance abuse assessment and treatment services. Implementation of the I-SMART System is a key product in meeting the federal government requirements for National Outcome Monitoring System (NOMS).

MARS (Media Asset Retrieval System): Puget Sound’s Digital Archive

The MARS (Media Asset Retrieval System) Project is the collaborative effort of public broadcasters,libraries and schools in the Puget Sound region to create a digital online resource that provides access to content produced by public broadcasters via the public libraries. Convergence ConsortiumThe Convergence Consortium is a model for community collaboration, including organizations such as public broadcasters, libraries, museums, and schools in the Puget Sound region to assess the needs of their constituents and pool resources to develop solutions to meet those needs. Specifically, the archives of public broadcasters have been identified as significant resources for the local communities and nationally. These resources can be accessed on the broadcasters websites, and through libraries and used by schools, and integrated with text and photographic archives from other partners.MARS’ goalCreate an online resource that provides effective access to the content produced locally by KCTS (Seattle PBS affiliate) and KUOW (Seattle NPR affiliate). The broadcasts will be made searchable using the CPB Metadata Element Set (under development) and controlled vocabularies (to be developed). This will ensure a user friendly search and navigation mechanism and user satisfaction.Furthermore, the resource can search the local public library’s catalog concurrently and provide the user with relevant TV material, radio material, and books on a given subject.The ultimate goal is to produce a model that can be used in cities around the country.The current phase of the project assesses the community’s need, analyzes the current operational systems, and makes recommendations for the design of the resource.Deliverables• Literature review of the issues surrounding the organization, description and representation of media assets• Needs assessment report of internal and external stakeholders• Profile of the systems in the area of managing and organizing media assetsfor public broadcasting nationwideActivities• Analysis of information seeking behavior• Analysis of collaboration within the respective organizations• Analysis of the scope and context of the proposed system• Examining the availability of information resources and exchangeof resources among users

Machine learning techniques for implicit interaction using mobile sensors

Interactions in mobile devices normally happen in an explicit manner, which means that they are initiated by the users. Yet, users are typically unaware that they also interact implicitly with their devices. For instance, our hand pose changes naturally when we type text messages. Whilst the touchscreen captures finger touches, hand movements during this interaction however are unused. If this implicit hand movement is observed, it can be used as additional information to support or to enhance the users’ text entry experience. This thesis investigates how implicit sensing can be used to improve existing, standard interaction technique qualities. In particular, this thesis looks into enhancing front-of-device interaction through back-of-device and hand movement implicit sensing. We propose the investigation through machine learning techniques. We look into problems on how sensor data via implicit sensing can be used to predict a certain aspect of an interaction. For instance, one of the questions that this thesis attempts to answer is whether hand movement during a touch targeting task correlates with the touch position. This is a complex relationship to understand but can be best explained through machine learning. Using machine learning as a tool, such correlation can be measured, quantified, understood and used to make predictions on future touch position. Furthermore, this thesis also evaluates the predictive power of the sensor data. We show this through a number of studies. In Chapter 5 we show that probabilistic modelling of sensor inputs and recorded touch locations can be used to predict the general area of future touches on touchscreen. In Chapter 7, using SVM classifiers, we show that data from implicit sensing from general mobile interactions is user-specific. This can be used to identify users implicitly. In Chapter 6, we also show that touch interaction errors can be detected from sensor data. In our experiment, we show that there are sufficient distinguishable patterns between normal interaction signals and signals that are strongly correlated with interaction error. In all studies, we show that performance gain can be achieved by combining sensor inputs.

Query routing in cooperative semi-structured peer-to-peer information retrieval networks

Conventional web search engines are centralised in that a single entity crawls and indexes the documents selected for future retrieval, and the relevance models used to determine which documents are relevant to a given user query. As a result, these search engines suffer from several technical drawbacks such as handling scale, timeliness and reliability, in addition to ethical concerns such as commercial manipulation and information censorship. Alleviating the need to rely entirely on a single entity, Peer-to-Peer (P2P) Information Retrieval (IR) has been proposed as a solution, as it distributes the functional components of a web search engine – from crawling and indexing documents, to query processing – across the network of users (or, peers) who use the search engine. This strategy for constructing an IR system poses several efficiency and effectiveness challenges which have been identified in past work. Accordingly, this thesis makes several contributions towards advancing the state of the art in P2P-IR effectiveness by improving the query processing and relevance scoring aspects of a P2P web search. Federated search systems are a form of distributed information retrieval model that route the user’s information need, formulated as a query, to distributed resources and merge the retrieved result lists into a final list. P2P-IR networks are one form of federated search in routing queries and merging result among participating peers. The query is propagated through disseminated nodes to hit the peers that are most likely to contain relevant documents, then the retrieved result lists are merged at different points along the path from the relevant peers to the query initializer (or namely, customer). However, query routing in P2P-IR networks is considered as one of the major challenges and critical part in P2P-IR networks; as the relevant peers might be lost in low-quality peer selection while executing the query routing, and inevitably lead to less effective retrieval results. This motivates this thesis to study and propose query routing techniques to improve retrieval quality in such networks. Cluster-based semi-structured P2P-IR networks exploit the cluster hypothesis to organise the peers into similar semantic clusters where each such semantic cluster is managed by super-peers. In this thesis, I construct three semi-structured P2P-IR models and examine their retrieval effectiveness. I also leverage the cluster centroids at the super-peer level as content representations gathered from cooperative peers to propose a query routing approach called Inverted PeerCluster Index (IPI) that simulates the conventional inverted index of the centralised corpus to organise the statistics of peers’ terms. The results show a competitive retrieval quality in comparison to baseline approaches. Furthermore, I study the applicability of using the conventional Information Retrieval models as peer selection approaches where each peer can be considered as a big document of documents. The experimental evaluation shows comparative and significant results and explains that document retrieval methods are very effective for peer selection that brings back the analogy between documents and peers. Additionally, Learning to Rank (LtR) algorithms are exploited to build a learned classifier for peer ranking at the super-peer level. The experiments show significant results with state-of-the-art resource selection methods and competitive results to corresponding classification-based approaches. Finally, I propose reputation-based query routing approaches that exploit the idea of providing feedback on a specific item in the social community networks and manage it for future decision-making. The system monitors users’ behaviours when they click or download documents from the final ranked list as implicit feedback and mines the given information to build a reputation-based data structure. The data structure is used to score peers and then rank them for query routing. I conduct a set of experiments to cover various scenarios including noisy feedback information (i.e, providing positive feedback on non-relevant documents) to examine the robustness of reputation-based approaches. The empirical evaluation shows significant results in almost all measurement metrics with approximate improvement more than 56% compared to baseline approaches. Thus, based on the results, if one were to choose one technique, reputation-based approaches are clearly the natural choices which also can be deployed on any P2P network.

Age and Gender Identification using Stacking for Classification⋆ Notebook for PAN at CLEF 2016

This paper presents our approach of identifying the profile of an unknown user based on the activities of known users. The aim of author profiling task of PAN@CLEF 2016 is cross-genre identification of the gender and age of an unknown user. This means training the system using the behavior of different users from one social media platform and identifying the profile of other user on some different platform. Instead of using single classifier to build the system we used a combination of different classifiers, also known as stacking. This approach allowed us explore the strength of all the classifiers and minimize the bias or error enforced by a single classifier.

Integrating an Emotion Recognition Model for the Flobi System

This thesis investigates if emotional states of users interacting with a virtual robot can be recognized reliably and if specific interaction strategy can change the users’ emotional state and affect users’ risk decision. For this investigation, the OpenFace [1] emotion recognition model was intended to be integrated into the Flobi [2] system, to allow the agent to be aware of the current emotional state of the user and to react appropriately. There was an open source ROS [3] bridge available online to integrate OpenFace to the Flobi simulation but it was not consistent with some other projects in Flobi distribution. Then due to technical reasons DeepFace was selected. In a human-agent interaction, the system is compared to a system without using emotion recognition. Evaluation could happen at different levels: evaluation of emotion recognition model, evaluation of the interaction strategy, and evaluation of effect of interaction on user decision. The results showed that the happy emotion induction was 58% and fear emotion induction 77% successful. Risk decision results show that: in happy induction after interaction 16.6% of participants switched to a lower risk decision and 75% of them did not change their decision and the remaining switched to a higher risk decision. In fear inducted participants 33.3% decreased risk 66.6 % did not change their decision The emotion recognition accuracy was and had bias to. The sensitivity and specificity is calculated for each emotion class. The emotion recognition model classifies happy emotions as neutral in most of the time.

Graph-based User Scheduling for MIMO LEO-based Satellite Communication Systems

The study of the user scheduling problem in a Low Earth Orbit (LEO) Multi-User MIMO system is the objective of this thesis. With the application of cutting-edge digital beamforming algorithms, a LEO satellite with an antenna array and a large number of antenna elements can provide service to many user terminals (UTs) in full frequency reuse (FFR) schemes. Since the number of UTs on-ground are many more than the transmit antennas on the satellite, user scheduling is necessary. Scheduling can be accomplished by grouping users into different clusters: users within the same cluster are multiplexed and served together via Space Division Multiple Access (SDMA), i.e., digital beamforming or Multi-User MIMO techniques; the different clusters of users are then served on different time slots via Time Division Multiple Access (TDMA). The design of an optimal user grouping strategy is known to be an NP-complete problem which can be solved only through exhaustive search. In this thesis, we provide a graph-based user scheduling and feed space beamforming architecture for the downlink with the aim of reducing user inter-beam interference. The main idea is based on clustering users whose pairwise great-circle distance is as large as possible. First, we create a graph where the users represent the vertices, whereas an edge in the graph between 2 users exists if their great-circle distance is above a certain threshold. In the second step, we develop a low complex greedy user clustering technique and we iteratively search for the maximum clique in the graph, i.e., the largest fully connected subgraph in the graph. Finally, by using the 3 aforementioned power normalization techniques, a Minimum Mean Square Error (MMSE) beamforming matrix is deployed on a cluster basis. The suggested scheduling system is compared with a position-based scheduler, which generates a beam lattice on the ground and randomly selects one user per beam to form a cluster.