Performance of a Differential GPS in Dynamic Mode Under Sitka Spruce Canopies

Quantified, performance indicators for dynamic mode Differential GPS (DGPS) were collected at 31 sites established under 3 canopy classes (none, pre-first thinning and mature) of Sitka spruce stands in Ireland with varying altitude (100-500 m) and aspect. Canopy cover was quantified using total obstruction, size of largest opening and fragmentation of sky view. At sites with no canopy above 2 m, a mean precision of 1.5 m was recorded. Where canopy was present, precision ranged between 2.6 m and 2.8 m. The results indicated that in dynamic mode, differences in DGPS performance between canopy cover types were limited to a presence/absence effect. oss of 3-dimensional operation (i.e. only 3 satellites in view) occurred more frequently than loss of differential correction signal and thus had a greater impact on recorded precision over the duration of the experiment. It was concluded from the data collected that the most versatile approach when using dynamic DGPS is to collect more (potentially poorer quality) data, rather than to apply a filter at the signal acquisition stage. The observations made, and the conclusions drawn in this paper are relevant to the forest industry in the selection and operation of DGPS equipment for dynamic tasks where ~ 2 m precision is required

Exploring quality-aware architectural transformations at run-time: the ENIA case

Adapting software systems at run-time is a key issue, especially when these systems consist of components used as intermediary for human-computer interaction. In this sense, model transformation techniques have a widespread acceptance as a mechanism for adapting and evolving the software architecture of such systems. However, existing model transformations often focus on functional requirements, and quality attributes are only manually considered after the transformations are done. This paper aims to improve the quality of adaptations and evolutions in component-based software systems by taking into account quality attributes within the model transformation process. To this end, we present a quality-aware transformation process using software architecture metrics to select among many alternative model transformations. Such metrics evaluate the quality attributes of an architecture. We validate the presented quality-aware transformation process in ENIA, a geographic information system whose user interfaces are based on coarsegrained components and need to be adapted at run-time

A Coherence Maximisation Process For Solving Normative Inconsistencies

Norms can be used in multi-agent systems for defining patterns of behaviour in terms of permissions, prohibitions and obligations that are addressed to agents playing a specific role. Agents may play different roles during their execution and they may even play different roles simultaneously. As a consequence, agents may be affected by inconsistent norms; e.g., an agent may be simultaneously obliged and forbidden to reach a given state of affairs. Dealing with this type of inconsistency is one of the main challenges of normative reasoning. Existing approaches tackle this problem by using a static and predefined order that determines which norm should prevail in the case where two norms are inconsistent. One main drawback of these proposals is that they allow only pairwise comparison of norms; it is not clear how agents may use the predefined order to select a subset of norms to abide by from a set of norms containing multiple inconsistencies. Furthermore, in dynamic and non-deterministic environments it can be difficult or even impossible to specify an order that resolves inconsistencies satisfactorily in all potential situations. In response to these two problems, we propose a mechanism with which an agent can dynamically compute a preference order over subsets of its competing norms by considering the coherence of its cognitive and normative elements. Our approach allows flexible resolution of normative inconsistencies, tailored to the current circumstances of the agent. Moreover, our solution can be used to determine norm prevalence among a set of norms containing multiple inconsistencies

The era of nano-bionic: 2D materials for wearable and implantable body sensors

Nano-bionics have the potential of revolutionizing modern medicine. Among nano-bionic devices, body sensors allow to monitor in real-time the health of patients, to achieve personalized medicine, and even to restore or enhance human functions. The advent of two-dimensional (2D) materials is facilitating the manufacturing of miniaturized and ultrathin bioelectronics, that can be easily integrated in the human body. Their unique electronic properties allow to efficiently transduce physical and chemical stimuli into electric current. Their flexibility and nanometric thickness facilitate the adaption and adhesion to human body. The low opacity permits to obtain transparent devices. The good cellular adhesion and reduced cytotoxicity are advantageous for the integration of the devices in vivo. Herein we review the latest and more significant examples of 2D material-based sensors for health monitoring, describing their architectures, sensing mechanisms, advantages and, as well, the challenges and drawbacks that hampers their translation into commercial clinical devices

GPS, LiDAR and VNIR data to monitor the spatial behavior of grazing sheep

Traditional knowledge about the behavior of grazing livestock is about to disappear. Shepherds well know that sheep behavior follows non-random patterns. As a novel alternative to seeking behavioral patterns, this study quantified the grazing activities of two sheep flocks of Churra breed (both in the same area but separated by 10 years) based on Global Position System (GPS) monitoring and remote monitoring sensing techniques. In the first monitoring period (2009-10), geolocations were recorded every 5 min (4, 240 records), while in the second one (2018-20), records were taken every 30 min (7, 636 records). The data were clustered based on the day/night and the activity (resting, moving, or grazing). An airborne LiDAR dataset was used to study the slope, aspect, and vegetation height. Four visible-infrared orthophotographs were mosaicked and classified to obtain the land use/land cover (LU/LC) map. Then, GPS locations were overlain on the terrain features, and a Chi-square test evaluated the relationships between locations and terrain features. Three spatial statistics (directional distribution, Kernel density, and Hot Spot analysis) were also calculated. Results in both monitoring periods suggested that the spatial distribution of free-grazing ewes was non-random. The flocks showed strong preferences for grazing areas with gentle north-facing slopes, where the herbaceous layer formed by pasture predominates. The geostatistical analyses of the sheep locations corroborated those preferences. Geotechnologies have emerged as a potent tool to demonstrate the influence of environmental and terrain attributes on the non-random spatial behavior of grazing sheep. © 2022 Malque Publishing. All rights reserved

Multi-user norm consensus

Many agents act in environments with multiple human users, from care robots to smart assistants. When interacting in multi-user environments it is paramount that these agents act as all users expect. However, it is not always possible to have well-defined collective preferences, nor to easily infer them from individual preferences. This is especially true in fast changing environments, like a device placed in a public space where users can enter and exit freely. In response, this paper proposes a model to represent individual preferences about the behaviour of an agent and a mechanism to find multi-user consensuses over these preferences. Norms can then be generated to ensure that when the agent follows them it will act according to the preferences of all users. We formalise what a consensus norm is and what properties the set of consensus norms should satisfy (i.e. generate the minimum number of norms while maximising the coverage of user preferences). We provide an optimisation approach to find this set of norms and show that our approach satisfies the aforementioned properties

Predicting Privacy Preferences for Smart Devices as Norms

Smart devices, such as smart speakers, are becoming ubiquitous, and users expect these devices to act in accordance with their preferences. In particular, since these devices gather and manage personal data, users expect them to adhere to their privacy preferences. However, the current approach of gathering these preferences consists in asking the users directly, which usually triggers automatic responses failing to capture their true preferences. In response, in this paper we present a collaborative filtering approach to predict user preferences as norms. These preference predictions can be readily adopted or can serve to assist users in determining their own preferences. Using a dataset of privacy preferences of smart assistant users, we test the accuracy of our predictions

Percolation properties of the 2D Heisenberg model

We analyze the percolation properties of certain clusters defined on configurations of the 2--dimensional Heisenberg model. We find that, given any direction \vec{n} in O(3) space, the spins almost perpendicular to \vec{n} form a percolating cluster. This result gives indications of how the model can avoid a previously conjectured Kosterlitz-Thouless phase transition at finite temperature T.Comment: 4 pages, 3 eps figures. Revised version (more clear abstract, some new references

A Self-Organising Multi-Agent System For Decentralised Forensic Investigations

As network-based threats continue to evolve more rapidly, detecting and responding to intrusion attempts in real-time requires an increasingly automated and intelligent response. This paper provides an agent-based framework for the analysis of cyber events within networks of varying sizes to detect complex multi-stage attacks. Agents are used as intelligent systems to explore domain specific and situational information showing the benefit of adaptive technologies that proactively analyse security events in real time. We introduce several algorithms to encapsulate and manage the traditional detection technologies and provide agent-based performance introspection as a mechanism to identify poorly performing systems. Our evaluation shows that the algorithms can reduce the amount of processing needed to analyse a security event by over 50% and improve the detection rate by up to 20% by introducing corrective systems to reduce false alarm rates in error-prone environments

Multi-Agent Systems for Scalable Internet of Things Security

Providing effective and scalable real-time security to Inter- net of Things devices can be a challenging task given the limited computational capacity of the devices and the amount of network traffic that can be viewed at any given time. Multi- Agent Systems have proven to be a valuable tool within the areas of cyber security, distributed networks and legacy systems because of their scalable and flexible architecture. In this paper we present a novel implementation of a Completely Decentralised Multi-Agent System for use within, or to support, Internet of Things networks through the distributed processing of security events to offload the computational cost of data processing from Internet of Things devices. The concepts of conditions and effects are introduced to allow agents to describe digital evidence found in an abstract language instead of sharing individual pieces of data to mitigate concerns of data leakage in extended networks. Emphasis is placed upon the scalable architecture design al- lowing domain experts to independently create agents specific to a particular technology or application process which will automatically work with other existing agents without further configuration