Investigation into the use of satellite remote sensing data products as part of a multi-modal marine environmental monitoring network

In this paper it is investigated how conventional in-situ sensor networks can be complemented by the satellite data streams available through numerous platforms orbiting the earth and the combined analyses products available through services such as MyOcean. Despite the numerous benefits associated with the use of satellite remote sensing data products, there are a number of limitations with their use in coastal zones. Here the ability of these data sources to provide contextual awareness, redundancy and increased efficiency to an in-situ sensor network is investigated. The potential use of a variety of chlorophyll and SST data products as additional data sources in the SmartBay monitoring network in Galway Bay, Ireland is analysed. The ultimate goal is to investigate the ability of these products to create a smarter marine monitoring network with increased efficiency. Overall it was found that while care needs to be taken in choosing these products, there was extremely promising performance from a number of these products that would be suitable in the context of a number of applications especially in relation to SST. It was more difficult to come to conclusive results for the chlorophyll analysis

Dynamic monitoring of Android malware behavior: a DNS-based approach

The increasing technological revolution of the mobile smart devices fosters their wide use. Since mobile users rely on unofficial or thirdparty repositories in order to freely install paid applications, lots of security and privacy issues are generated. Thus, at the same time that Android phones become very popular and growing rapidly their market share, so it is the number of malicious applications targeting them. Yet, current mobile malware detection and analysis technologies are very limited and ineffective. Due to the particular traits of mobile devices such as the power consumption constraints that make unaffordable to run traditional PC detection engines on the device; therefore mobile security faces new challenges, especially on dynamic runtime malware detection. This approach is import because many instructions or infections could happen after an application is installed or executed. On the one hand, recent studies have shown that the network-based analysis, where applications could be also analyzed by observing the network traffic they generate, enabling us to detect malicious activities occurring on the smart device. On the other hand, the aggressors rely on DNS to provide adjustable and resilient communication between compromised client machines and malicious infrastructure. So, having rich DNS traffic information is very important to identify malevolent behavior, then using DNS for malware detection is a logical step in the dynamic analysis because malicious URLs are common and the present danger for cybersecurity. Therefore, the main goal of this thesis is to combine and correlate two approaches: top-down detection by identifying malware domains using DNS traces at the network level, and bottom-up detection at the device level using the dynamic analysis in order to capture the URLs requested on a number of applications to pinpoint the malware. For malware detection and visualization, we propose a system which is based on dynamic analysis of API calls. Thiscan help Android malware analysts in visually inspecting what the application under study does, easily identifying such malicious functions. Moreover, we have also developed a framework that automates the dynamic DNS analysis of Android malware where the captured URLs at the smartphone under scrutiny are sent to a remote server where they are: collected, identified within the DNS server records, mapped the extracted DNS records into this server in order to classify them either as benign or malicious domain. The classification is done through the usage of machine learning. Besides, the malicious URLs found are used in order to track and pinpoint other infected smart devices, not currently under monitoring

Satellite measurement of ocean turbulence

Turbulence and mixing in the surface layer of the ocean is a significant element in the combined ocean-atmosphere system, and plays a considerable role in the transfer of heat, gas and momentum across the air-sea boundary. Furthermore, improving knowledge of the evolution of energy within the ocean system, both globally and locally, holds importance for improving our understanding of the dynamics of the ocean at large- and small-scales. As such, insight into turbulence and turbulent flows at the ocean surface is becoming increasingly important for its role in ocean-atmosphere exchange and, from a wider perspective, climate change.A research project was initiated to understand the role that spacecraft remote-sensing may play in improving observation of “turbulence” (in a broad sense) in the ocean, and for identifying how steps towards such observation may be made. An initial, exploratory study identified the potential benefit of Synthetic Aperture Radar in “bridging the gap” between in-situ and remote observations o

Advanced Engineering Laboratory project summaries : 1995-1996

The Advanced Engineering Laboratory of the Woods Hole Oceanographic Institution is a development laboratory within the Applied Ocean Physics and Engineering Department. Its function is the development of oceanographic instrumentation to test developing theories in oceanography and to enhance current research projects in other disciplines within the community. This report summarizes recent and ongoing projects performed by members of this laboratory

Understanding the performance of interactive applications

Many if not most computer systems are used by human users. The performance of such interactive systems ultimately affects those users. Thus, when measuring, understanding, and improving system performance, it makes sense to consider the human user's perspective. Essentially, the performance of interactive applications is determined by the perceptible lag in handling user requests. So, when characterizing the runtime of an interactive application we need a new approach that focuses on the perceptible lags rather than on overall and general performance characteristics. Such a new characterization approach should enable a new way to profile and improve the performance of interactive applications. Imagine a way that would seek out these perceptible lags and then investigate the causes of these lags. Performance analysts could simply optimize responsible parts of the software, thus eliminating perceptible lag for interactive applications. Unfortunately, existing profiling approaches either incur significant overhead that makes them impractical for an interactive scenario, or they lack the ability to provide insight into the causes of long latencies. An effective approach for interactive applications has to fulfill several requirements such as an accurate view of the causes of performance problems and insignificant perturbation of the interactive application. We propose a new profiling approach that helps developers to understand and improve the perceptible performance of interactive applications and satisfies the above needs

Data collection system: Earth Resources Technology Satellite-1

Subjects covered at the meeting concerned results on the overall data collection system including sensors, interface hardware, power supplies, environmental enclosures, data transmission, processing and distribution, maintenance and integration in resources management systems

Alternate Means of Digital Design Communication

This thesis reconceptualises communication in digital design as an integrated social and technical process. The friction in the communicative processes pertaining to digital design can be traced to the fact that current research and practice emphasise technical concerns at the expense of social aspects of design communication. With the advent of BIM (Building Information Modelling), a code model of communication (machine-to-machine) is inadequately applied to design communication. This imbalance is addressed in this thesis by using inferential models of communication to capture and frame the psychological and social aspects behind the communicative contracts between people. Three critical aspects of the communicative act have been analysed, namely (1) data representation, (2) data classification and (3) data transaction, with the help of a new digital design communication platform, Speckle, which was developed during this research project for this purpose. By virtue of an applied living laboratory context, Speckle facilitated both qualitative and quantitative comparisons against existing methodologies with data from real-world settings. Regarding data representation (1), this research finds that the communicative performance of a low-level composable object model is better than that of a complete and universal one as it enables a more dynamic process of ontological revision. This implies that current practice and research operates at an inappropriate level of abstraction. On data classification (2), this thesis shows that a curatorial object-based data sharing methodology, as opposed to the current file-based approaches, leads to increased relevancy and a reduction in noise (information without intent, or meaning). Finally, on data transaction (3), the analysis shows that an object-based data sharing methodology is technically better suited to enable communicative contracts between stakeholders. It allows for faster and more meaningful change-dependent transactions, as well as allow for the emergence of traceable communicative networks outside of the predefined exchanges of current practices

Enabling peer-to-peer remote experimentation in distributed online remote laboratories

Remote Access Laboratories (RALs) are online platforms that allow human user interaction with physical instruments over the Internet. Usually RALs follow a client-server paradigm. Dedicated providers create and maintain experiments and corresponding educational content. In contrast, this dissertation focuses on a Peer-to-Peer (P2P) service model for RALs where users are encouraged to host experiments at their location. This approach can be seen as an example of an Internet of Things (IoT) system. A set of smart devices work together providing a cyber-physical interface for users to run experiments remotely via the Internet. The majority of traditional RAL learning activities focus on undergraduate education where hands-on experience such as building experiments, is not a major focus. In contrast this work is motivated by the need to improve Science, Technology, Engineering and Mathematics (STEM) education for school-aged children. Here physically constructing experiments forms a substantial part of the learning experience. In the proposed approach, experiments can be designed with relatively simple components such as LEGO Mindstorms or Arduinos. The user interface can be programed using SNAP!, a graphical programming tool. While the motivation for the work is educational in nature, this thesis focuses on the technical details of experiment control in an opportunistic distributed environment. P2P RAL aims to enable any two random participants in the system - one in the role of maker creating and hosting an experiment and one in the role of learner using the experiment - to establish a communication session during which the learner runs the remote experiment through the Internet without requiring a centralized experiment or service provider. The makers need to have support to create the experiment according to a common web based programing interface. Thus, the P2P approach of RALs requires an architecture that provides a set of heterogeneous tools which can be used by makers to create a wide variety of experiments. The core contribution of this dissertation is an automaton-based model (twin finite state automata) of the controller units and the controller interface of an experiment. This enables the creation of experiments based on a common platform, both in terms of software and hardware. This architecture enables further development of algorithms for evaluating and supporting the performance of users which is demonstrated through a number of algorithms. It can also ensure the safety of instruments with intelligent tools. The proposed network architecture for P2P RALs is designed to minimise latency to improve user satisfaction and learning experience. As experiment availability is limited for this approach of RALs, novel scheduling strategies are proposed. Each of these contributions has been validated through either simulations, e.g. in case of network architecture and scheduling, or test-bed implementations, in case of the intelligent tools. Three example experiments are discussed along with users' feedback on their experience of creating an experiment and using others’ experimental setup. The focus of the thesis is mainly on the design and hosting of experiments and ensuring user accessibility to them. The main contributions of this thesis are in regards to machine learning and data mining techniques applied to IoT systems in order to realize the P2P RALs system. This research has shown that a P2P architecture of RALs can provide a wide variety of experimental setups in a modular environment with high scalability. It can potentially enhance the user-learning experience while aiding the makers of experiments. It presents new aspects of learning analytics mechanisms to monitor and support users while running experiments, thus lending itself to further research. The proposed mathematical models are also applicable to other Internet of Things applications