Remote electricity actuation and monitoring mote

This work presents the design and evaluation of the REAM (Remote Electricity Actuation and Monitoring) node based around the modular Tyndall Mote platform. The REAM node enables the user to remotely actuate power to a mains power extension board while sampling the current, voltage, power and power factor of the attached load. The node contains a current transformer interfaced to an Energy Metering IC which continuously samples current and voltage. These values are periodically read from the part by a PIC24 microcontroller, which calculates the RMS current and voltage, power factor and overall power. The resultant values can then be queried wirelessly employing the Tyndall 802.15.4 compliant wireless module.

Biological wastes as plant growth substrate amendments

Globally, agriculture is being intensified with mechanization and increased use of synthetic fertilizers and pesticides. There has been a scaling up of production to satisfy the demands of supermarket distribution. Problems associated with intensification of production, trade globalisation and a larger market demand for greater volumes of fresh produce, include consumers' concern about pesticide residues and leaching of nutrients and pesticides into the environment, as well as increases in the transmission of human food-poisoning pathogens on raw vegetables and in fruit juices. The first part of this research was concerned with the evaluation of a biological control strategy for soil-borne pathogens, these are difficult to eliminate and the chemicals of which the most effective fumigants e.g. methyl bromide, are being withdrawn form use. Chitin-containing crustaceans shellfish waste was investigated as a selective growth substrate amendment in the field, in glasshouse and in storage trials against Sclerotinia disease of Helianthus tuberosus, Phytophthora fragariae disease of Fragaria vesca and Fusarium disease of Dianthus. Results showed that addition to shellfish waste stimulated substrate microbial populations and lytic activity and induced plant defense proteins, namely chitinases and cellulases. Protective effects were seen in all crop models but the results indicate that further trials are required to confirm long-term efficacy. The second part of the research investigated the persistence of enteric bacteria in raw salad vegetables using model food poisoning isolates. In clinical investigations plants are sampled for bacterial contamination but no attempt is made to differentiate between epiphytes and endophytes. Results here indicate that the mode isolates persist endophytically thereby escaping conventional chlorine washes and they may also induce host defenses, which results in their suppression and in negative results in conventional plate count screening. Finally a discussion of criteria that should be considered for a HACCP plan for safe raw salad vegetable production is presented.

ALD: adaptive layer distribution for scalable video

Recent years have witnessed a rapid growth in the demand for streaming video over the Internet and mobile networks, exposes challenges in coping with heterogeneous devices and varying network throughput. Adaptive schemes, such as scalable video coding, are an attractive solution but fare badly in the presence of packet losses. Techniques that use description-based streaming models, such as multiple description coding (MDC), are more suitable for lossy networks, and can mitigate the effects of packet loss by increasing the error resilience of the encoded stream, but with an increased transmission byte cost. In this paper, we present our adaptive scalable streaming technique adaptive layer distribution (ALD). ALD is a novel scalable media delivery technique that optimises the tradeoff between streaming bandwidth and error resiliency. ALD is based on the principle of layer distribution, in which the critical stream data are spread amongst all packets, thus lessening the impact on quality due to network losses. Additionally, ALD provides a parameterised mechanism for dynamic adaptation of the resiliency of the scalable video. The Subjective testing results illustrate that our techniques and models were able to provide levels of consistent high-quality viewing, with lower transmission cost, relative to MDC, irrespective of clip type. This highlights the benefits of selective packetisation in addition to intuitive encoding and transmission.

ALD: adaptive layer distribution for scalable video

Bandwidth constriction and datagram loss are prominent issues that affect the perceived quality of streaming video over lossy networks, such as wireless. The use of layered video coding seems attractive as a means to alleviate these issues, but its adoption has been held back in large part by the inherent priority assigned to the critical lower layers and the consequences for quality that result from their loss. The proposed use of forward error correction (FEC) as a solution only further burdens the bandwidth availability and can negate the perceived benefits of increased stream quality. In this paper, we propose Adaptive Layer Distribution (ALD) as a novel scalable media delivery technique that optimises the tradeoff between the streaming bandwidth and error resiliency. ALD is based on the principle of layer distribution, in which the critical stream data is spread amongst all datagrams thus lessening the impact on quality due to network losses. Additionally, ALD provides a parameterised mechanism for dynamic adaptation of the scalable video, while providing increased resilience to the highest quality layers. Our experimental results show that ALD improves the perceived quality and also reduces the bandwidth demand by up to 36% in comparison to the well-known Multiple Description Coding (MDC) technique.

Artificial neural network application in short-term prediction in an oscillating water column

Oscillating Water Column (OWC) is one type of promising wave energy devices due to its obvious advantage over many other wave energy converters: no moving component in sea water. Two types of OWCs (bottom-fixed and floating) have been widely investigated, and the bottom-fixed OWCs have been very successful in several practical applications. Recently, the proposal of massive wave energy production and the availability of wave energy have pushed OWC applications from near-shore to deeper water regions where floating OWCs are a better choice. For an OWC under sea waves, the air flow driving air turbine to generate electricity is a random process. In such a working condition, single design/operation point is nonexistent. To improve energy extraction, and to optimise the performance of the device, a system capable of controlling the air turbine rotation speed is desirable. To achieve that, this paper presents a short-term prediction of the random, process by an artificial neural network (ANN), which can provide near-future information for the control system. In this research, ANN is explored and tuned for a better prediction of the airflow (as well as the device motions for a wide application). It is found that, by carefully constructing ANN platform and optimizing the relevant parameters, ANN is capable of predicting the random process a few steps ahead of the real, time with a good accuracy. More importantly, the tuned ANN works for a large range of different types of random, process.

Selection of a network of large lakes and reservoirs suitable for global environmental change analysis using Earth Observation

The GloboLakes project, a global observatory of lake responses to environmental change, aims to exploit current satellite missions and long remote-sensing archives to synoptically study multiple lake ecosystems, assess their current condition, reconstruct past trends to system trajectories, and assess lake sensitivity to multiple drivers of change. Here we describe the selection protocol for including lakes in the global observatory based upon remote-sensing techniques and an initial pool of the largest 3721 lakes and reservoirs in the world, as listed in the Global Lakes and Wetlands Database. An 18-year-long archive of satellite data was used to create spatial and temporal filters for the identification of waterbodies that are appropriate for remote-sensing methods. Further criteria were applied and tested to ensure the candidate sites span a wide range of ecological settings and characteristics; a total 960 lakes, lagoons, and reservoirs were selected. The methodology proposed here is applicable to new generation satellites, such as the European Space Agency Sentinel-series.

Measuring QoS in an aeronautical opportunistic network architecture with limited access to a satellite communications backhaul

We measure quality of service (QoS) in a wireless network architecture of transoceanic aircraft. A distinguishing characteristic of the network scheme we analyze is that it mixes the concept of Delay Tolerant Networking (DTN) through the exploitation of opportunistic contacts, together with direct satellite access in a limited number of the nodes. We provide a graph sparsification technique for deriving a network model that satisfies the key properties of a real aeronautical opportunistic network while enabling scalable simulation. This reduced model allows us to analyze the impact regarding QoS of introducing Internet-like traffic in the form of outgoing data from passengers. Promoting QoS in DTNs is usually really challenging due to their long delays and scarce resources. The availability of satellite communication links offers a chance to provide an improved degree of service regarding a pure opportunistic approach, and therefore it needs to be properly measured and quantified. Our analysis focuses on several QoS indicators such as delivery time, delivery ratio, and bandwidth allocation fairness. Obtained results show significant improvements in all metric indicators regarding QoS, not usually achievable on the field of DTNs.