Applications of Temporal Graph Metrics to Real-World Networks

Real world networks exhibit rich temporal information: friends are added and removed over time in online social networks; the seasons dictate the predator-prey relationship in food webs; and the propagation of a virus depends on the network of human contacts throughout the day. Recent studies have demonstrated that static network analysis is perhaps unsuitable in the study of real world network since static paths ignore time order, which, in turn, results in static shortest paths overestimating available links and underestimating their true corresponding lengths. Temporal extensions to centrality and efficiency metrics based on temporal shortest paths have also been proposed. Firstly, we analyse the roles of key individuals of a corporate network ranked according to temporal centrality within the context of a bankruptcy scandal; secondly, we present how such temporal metrics can be used to study the robustness of temporal networks in presence of random errors and intelligent attacks; thirdly, we study containment schemes for mobile phone malware which can spread via short range radio, similar to biological viruses; finally, we study how the temporal network structure of human interactions can be exploited to effectively immunise human populations. Through these applications we demonstrate that temporal metrics provide a more accurate and effective analysis of real-world networks compared to their static counterparts.Comment: 25 page

Development of Capacitive Imaging Technology for Measuring Skin Hydration and Other Skin Properties

In this thesis, capacitive imaging systems are assessed for their suitability in skin research studies, as multi-purpose and portable laboratory equipment. The water content of the human skin, the status of the skin barrier, its permeability by solvents, and the skin texture are crucial pieces of information in pharmaceutical and cosmetic industries for the development of skin treatment products. Normally, multiple high-end scientific instruments with expensive dedicated analysis software are employed to measure the above skin properties. The aim of this work is to demonstrate how fingerprint sensors, originally designed for biometric security, can be exploited to achieve reliable skin hydration readings and analyse multiple other skin properties while maintaining low cost and portability. To begin with, the anatomy of human skin is summarised alongside the functional properties of each skin layer. The skin hydration instruments study the outermost layer of skin and its appendages, so their thickness, biology, functions, hydration levels and water holding capabilities are presented in the literature review in order to understand the target measurands. Since capacitive imaging, rather than single sensor, probes are employed in this work, the skin texture and its importance in cosmetic science are also studied as a part of the target measurand. In order to understand how this technology fits in the current skin research instrument market, well established measurement apparatuses are presented. These include opto-thermal transient emission radiometry and confocal Raman microspectroscopy for skin hydration and solvent permeation measurements as well as depth profiling. Then, electrical hygrometry and the dynamic vapour sortpion measurement principles are outlined, which focus on water diffusion and sorption measurements correspondingly. Since the skin texture will also be studied in this work, dermatoscopy is also summarised. A literature review on the non-invasive electrical-based measurement method is achieved, alongside the stratum corneum and viable skin capacitance and conductance as functions of sampling frequency. The latter allows to establish the criteria for the suitability of electrical based apparatuses in skin hydration measurements. More specifically, it is concluded that the measurement depth of the instrument should not be reaching viable skin and that the sampling frequency should be constant and below 100kHz for capacitive measurements. The presentation of existing electrical based skin hydration probes in the market demonstrates the current development stage of this technology, and it enables the expression of the research aim and its objectives for this work. In order to improve trust in the use of capacitive imaging technology for measuring skin hydration, apart from visualisation, established electrical based skin hydration probes are examined and compared with a capacitive imaging sensor. The criteria for this comparison derive from the literature review, i.e. the sampling frequency and the penetration depth of the electric field. The sampling frequency is measured directly on the hardware using an oscilloscope, while the measurement depth is estimated using an electrostatic model. The development of this model for different sensor geometries is presented and it is evaluated against different models as well as experimental results in the literature. It is concluded that low cost instruments tend to have high measurement depth that makes them unsuitable for stratum corneum hydration measurements. Higher end instruments, although they are using high sampling frequency, have safe penetration depth but low measurement sensitivity. The capacitive imaging sensor shown acceptable penetration depth, on the high end of the expected range, and good measurement sensitivity due to the miniaturisation of the technology. A common disadvantage of most of these instruments is that the readouts are provided in arbitrary units, so experimental results cannot be compared directly with the literature when different scientific equipment has been used. To overcome this disadvantage, and based on the previous analysis of capacitive measurement principle, a system calibration is proposed to convert system capacitance or arbitrary units to dielectric permittivity units, a property of the sample measurand. This allows the calculation of hydration and solvent percentage concentration within the sample and so direct comparison with a wider range of reported results in the literature. Furthermore, image analysis techniques are applied on the dielectric permittivity images to allow targeting and relocating skin regions of interest, as well as excluding pixels with bad sample contact that distort the results. Next, the measurement reliability of the capacitive imaging arrays is examined through in-vivo and in-vitro experiments as well as side-by-side comparative measurements with single sensor skin hydration probes. The advantages of the developed calibration method and image analysis tools are demonstrated via the introduction of new system applications in the skin research, including skin damage characterisation via occlusion, skin solvent penetration and water desorption in hair samples experiments. It has to be mentioned that a small number of subjects is used in these experiments and the results are compared with the literature, so the statistical significance is not clearly examined. Next, advanced image processing techniques are adapted and applied on the capacitive skin images to expand further the application of this technology. More specifically, the skin micro-relief aspects of interest in cosmetic industry are summarised, and algorithmic approaches for measuring the micro-relief orientation and intensity as well as the automatic skin grids account are reviewed and experimentally evaluated. The main research aim and its objective have been achieved, with their methodologies clearly presented the their implementations evaluated with experimental results. However, vulnerabilities of this technology have also been exposed and suggestions for further improvement are provided in the conclusions

Experimental and numerical characterization of a gravitational electromagnetic energy harvester

In this paper, the dynamic experimental identification of an inductive energy harvester for the conversion of vibration energy into electric power is presented. Recent advances and requirements in structural monitoring and vehicle diagnostic allow defining Autonomous Internet of Things (AIoT) systems that combine wireless sensor nodes with energy harvester devices properly designed considering the specific duty cycle. The proposed generator was based on an asymmetrical magnetic suspension and was addressed to structural monitoring applications on vehicles. The design of the interfaces of the electric, magnetic, and structural coupled systems forming the harvester are described including dynamic modeling and simulation. Finally, the results of laboratory tests were compared with the harvester dynamic response calculated through numerical simulations, and a good correspondence was obtained

Low-resource Multi-task Audio Sensing for Mobile and Embedded Devices via Shared Deep Neural Network Representations

Continuous audio analysis from embedded and mobile devices is an increasingly important application domain. More and more, appliances like the Amazon Echo, along with smartphones and watches, and even research prototypes seek to perform multiple discriminative tasks simultaneously from ambient audio; for example, monitoring background sound classes (e.g., music or conversation), recognizing certain keywords (‘Hey Siri’ or ‘Alexa’), or identifying the user and her emotion from speech. The use of deep learning algorithms typically provides state-of-the-art model performances for such general audio tasks. However, the large computational demands of deep learning models are at odds with the limited processing, energy and memory resources of mobile, embedded and IoT devices. In this paper, we propose and evaluate a novel deep learning modeling and optimization framework that speci cally targets this category of embedded audio sensing tasks. Although the supported tasks are simpler than the task of speech recognition, this framework aims at maintaining accuracies in predictions while minimizing the overall processor resource footprint. The proposed model is grounded in multi-task learning principles to train shared deep layers and exploits, as input layer, only statistical summaries of audio lter banks to further lower computations. We nd that for embedded audio sensing tasks our framework is able to maintain similar accuracies, which are observed in comparable deep architectures that use single-task learning and typically more complex input layers. Most importantly, on an average, this approach provides almost a 2.1⇥ reduction in runtime, energy, and memory for four separate audio sensing tasks, assuming a variety of task combinations.Microsoft Researc

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery

This paper presents a method for mapping the nitrogen (N) status in a maize field using hyperspectral remote sensing imagery. An airborne survey was conducted with an AISA Eagle hyperspectral sensor over an experimental farm where maize (Zea mays L.) was grown with two N fertilization levels (0 and 100 kg N ha-1) in four replicates. Leaf and canopy field data were collected during the flight. The nitrogen (N) status has been estimated in this work based on the Nitrogen Nutrition Index (NNI) defined as the ratio between the leaf actual N concentration (%Na) of the crop and the minimum N content required for the maximum biomass production (critical N concentration (%Nc)) calculated through the dry mass at the time of the flight (Wflight). The inputs required to calculate the NNI (i.e. %Na and Wflight) have been estimated through regression analyses between field data and remotely sensed vegetation indices. MCARI/MTVI2 (Modified Chlorophyll Absorption Ratio Index / Modified Triangular Vegetation Index 2) showed the best performances in estimating the %Na (R2 = 0.59) and MTVI2 in estimating the Wflight (R2 = 0.80). The %Na and the Wflight were then mapped and used to compute the NNI map over the entire field. The NNI map agreed with the NNI estimated using field data through traditional destructive measurements (R2 = 0.70) confirming the potential of using remotely sensed indices to assess the crop N condition. Finally, a method to derive a pixel based variable rate N fertilization map was proposed as the difference between the actual N content and the optimal N content. We think that the proposed operational methodology is promising for precision farming since it represents an innovative attempt to derive from an aerial hyperspectral image a variable rate N fertilization map based on the actual crop N status.JRC.H.4-Monitoring Agricultural Resource