This paper shows that, provided circuits contain no zero-delay loops, a tight relationship, full abstraction, exists between a natural event-based operational semantics for circuits and a natural denotational semantics for circuits based on causal functions on value timelines. The paper also discusses what goes wrong if zero-delay loops are allowed, and illustrates the application of this semantic relationship to modeling questions.

This study investigates the applicability of node.js for developing web services.Node.js is a software platform for developing event-driven networking applicationsusing JavaScript. Moreover, the language JavaScript is discussed regardingfeatures that facilitate development of event-driven software.Node.js’s selling point is to be a solution to the problem of massive amount ofconcurrent network connections. In addition, it tries to avoid scalability issuesthat may appear in large web applications. To verify and investigate if this holds,an evaluation of the platform was conducted by developing an HTTP boot serverfor Motorola Mobility. The boot server, named Wellington, is used to manageconfiguration and distribution of set-top box software.Furthermore, an investigation and comparison between event based and threadedconcurrency models has been made. Lastly, the maturity of node.js and its ecosystemof libraries and frameworks are discussed.In conclusion, node.js is an interesting piece of technology and it was suitableas development platform for Wellington. JavaScript is a powerful language andworks well to write event-driven server-side software. When learning to buildnetworking applications, node.js is a good start to do so using an event-drivenparadigm.

Wireless networked control systems use shared wireless links to communicate between sensors and controllers, and require a channel access policy to arbitrate access to the links. Existing multiple access protocols perform this role in an agnostic manner, by remaining insular to the applications that run over the network. This approach does not give satisfactory control performance guarantees. To enable the use of wireless networks in emerging industrial applications, we must be able to systematically design wireless networked control systems that provide guaranteed performances in resource-constrained networks. In this thesis, we advocate the use of state-based channel access policies. A state-based policy uses the state of the controlled plant to influence access to the network. The state contains information about not only the plant, but also the network, due to the feedback in the system. Thus, by using the state to decide when and how frequently to transmit, a control system can adapt its contribution to the network traffic, and enable the network to adapt access to the plant state. We show that such an approach can provide better performance than existing methods. We examine two different state-based approaches that are distributed and easy to implement on wireless devices: event-based scheduling and adaptive prioritization. Our first approach uses events to reduce the traffic in the network. We use a state-based scheduler in every plant sensor to generate non-coordinated channel access requests by selecting a few critical data packets, or events, for transmission. The network uses a contention resolution mechanism to deal with simultaneous channel access requests. We present three main contributions for this formulation. The first contribution is a structural analysis of stochastic event-based systems, where we identify a dual predictor architecture that results in separation in design of the state-based scheduler, observer and controller. The second contribution is a Markov model that describes the interactions in a network of event-based systems. The third contribution is an analysis of the stability of event-based systems, leading to a stabilizing design of event-based policies. Our second approach uses state-based priorities to determine access to the network. We use a dominance protocol to evaluate priorities in a contention-based setting, and characterize the resulting control performance. An implementation and evaluation of this channel access mechanism on sensor nodes is also presented. The thesis finally examines the general networked control problem of jointly optimizing measurement and control policies, when a nonlinear measurement policy is used to perform quantization, event-triggering or companding. This contribution focuses on some of the fundamental aspects of analyzing and synthesizing control systems with state-based measurement policies in a more generalized setting. We comment on the dual effect, certainty equivalence and separation properties for this problem. In particular, we show that it is optimal to apply separation and certainty equivalence to a design problem that permits a dynamic choice of the measurement and control policies. / <p>QC 20140408</p>

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Through innovations in sensors and storage, event-based data collection has increased significantly in recent years. Personal security cameras allow users to collect event data in their homes, Google Maps allows users to view places they have been throughout the day, and web browsers collect a history of the sites users visit. Despite the innovations in event-based data collection, the Graphical User Interfaces (GUIs) designed for these systems have not changed for years. Many applications stick to a linear timeline to display the events, leading to large downtime gaps when sparse events occur throughout the day. However, linear timelines create a natural sense of temporal perception since users can easily tell what time of day certain events occur. Our project seeks to find a balance between the natural temporal perception that linear timelines offer and improving the usability of visualizing event-based data using timelines. Through our design, users will be able to leverage the simple temporal perception that a linear timeline provides and avoid unnecessary searching to locate specific events. By seamlessly integrating a linear timeline with density visualizations and a timeline of events, we aim to allow users to search and view events easily. To evaluate our interface, we conducted two usability studies, first with specialists who view security camera information often and then with all individuals. Our findings inform the design of future event-based timeline visualizations. / Master of Science / Through innovations in sensors and storage, event-based data collection has increased significantly in recent years. Personal security cameras allow users to collect event data in their homes, Google Maps allows users to view places they have been throughout the day, and web browsers collect a history of the sites users visit. Despite the innovations in event-based data collection, the Graphical User Interfaces (GUIs) designed for these systems have not changed for years. Many applications stick to a linear timeline to display the events, leading to large downtime gaps when sparse events occur throughout the day. However, linear timelines create a natural sense of temporal perception since users can easily tell what time of day certain events occur. Our project seeks to find a balance between the natural temporal perception that linear timelines offer and improving the usability of visualizing event-based data using timelines. Through our design, users will be able to leverage the simple temporal perception that a linear timeline provides and avoid unnecessary searching to locate specific events. By seamlessly integrating a linear timeline with density visualizations and a timeline of events, we aim to allow users to search and view events easily. To evaluate our interface, we conducted two usability studies, first with specialists who view security camera information often and then with all individuals. Our findings inform the design of future event-based timeline visualizations.

We consider sensor data scheduling for remote state estimation. Due to constrained communication energy and bandwidth, a sensor needs to decide whether it should send the measurement to a remote estimator for further processing. We propose an event-based sensor data scheduler for linear systems and derive the corresponding minimum squared error estimator. By selecting an appropriate eventtriggering threshold, we illustrate how to achieve a desired balance between the sensor-to-estimator communication rate and the estimation quality. Simulation examples are provided to demonstrate the theory. / <p>QC 20130318</p>

The growing popularity of mobile platforms is changing the Internet user’s computing experience. Current studies suggest that the traditional ubiquitous computing landscape is shifting towards more enhanced and broader mobile computing platform consists of large number of heterogeneous devices. Smartphones and tablets begin to replace the desktop as the primary means of interacting with IT resources. While mobile devices facilitate in consuming web resources in the form of web services, the growing demand for consuming services on mobile device is introducing a complex ecosystem in the mobile environment. This research addresses the communication challenges involved in mobile distributed networks and proposes an event-driven communication approach for information dissemination. This research investigates different communication techniques such as synchronous and asynchronous polling and long-polling, server-side push as mechanisms between client-server interactions and the latest web technologies namely HTML5 standard WebSocket as communication protocol within a publish/subscribe paradigm. Finally, this research introduces and evaluates a framework that is hybrid of REST and event-based publish/subscribe for operating in the mobile environment.

Prospective memory is colloquially known as “remembering to remember” and refers to forming an intention in the present time to fulfill at some point in the future. It has previously been studied within the context of executive functioning (i.e., purposive and goal directed behavior) and impulsive behaviors (e.g., gambling, risk seeking) within clinical populations. This study sought to further elucidate the relationships of impulsivity and executive functions on prospective memory in a non-clinical population. One hundred and nine undergraduates completed the UPPS-P impulsivity self-report questionnaire, three cognitive tasks measuring components of executive function, (i.e., planning, inhibition, and switching), and a Complex Prospective Memory Task that included Time- and Event-based cues. The UPPS-P and executive function tasks did not significantly predict the Complex Prospective Memory Task. However, executive function was found to be a significant predictor above and beyond that of impulsivity for a component of the Time-based prospective memory task. Implications of the results and future directions are discussed.

Research findings relating to anomalous equity returns should ideally be repeatable by others. Usually, only a small subset of the decisions made in a particular backtest workflow are released, which limits reproducability. Data collection and cleaning, parameter setting, algorithm development and report generation are often done with manual point-and-click tools which do not log user actions. This problem is compounded by the fact that the trial-and-error approach of researchers increases the probability of backtest overfitting. Borrowing practices from the reproducible research community, we introduce a set of scripts that completely automate a portfolio-based, event-driven backtest. Based on free, open source tools, these scripts can completely capture the decisions made by a researcher, resulting in a distributable code package that allows easy reproduction of results.

Our study investigates how training with sparse simulated data versus sparse real data affects image reconstruction. We compared on several criteria such as number of events, speed and high dynamic range, HDR. The results indicate that the difference between simulated data and real data is not large. Training with real data performed often better, but only by 2%. The findings confirm what earlier studies have shown; training with simulated data generalises well, even when training on sparse datasets as this study shows. / Vår studie undersöker hur träning med gles simulerad data och gles verklig data från en eventkamera, påverkar bildrekonstruktion. Vi tränade två modeller, en med simulerad data och en med verklig för att sedan jämföra dessa på ett flertal kriterier som antal event, hastighet och high dynamic range, HDR. Resultaten visar att skillnaden mellan att träna med simulerad data och verklig data inte är stor. Modellen tränad med verklig data presterade bättre i de flesta fall, men den genomsnittliga skillnaden mellan resultaten är bara 2%. Resultaten bekräftar vad tidigare studier har visat; träning med simulerad data generaliserar bra, och som denna studie visar även vid träning på glesa datamängder.