Cattle-powered nodes experience in a heterogeneous network for localization of herds

A heterogeneous network, mainly based on nodes that use harvested energy to self-energize is presented and its use demonstrated. The network, mostly kinetically powered, has been used for the localization of herds in grazing areas under extreme climate conditions. The network consists of secondary and primary nodes. The former, powered by a kinetic generator, take advantage of animal movements to broadcast a unique identifier. The latter are battery-powered and gather secondarynode transmitted information to provide it, along with position and time data, to a final base station in charge of the animal monitoring. Because a limited human interaction is desirable, the aim of this network is to reduce the battery count of the system

Application of rasch model on resilience in higher education: an examination of validity and reliability of Malaysian academician happiness index (MAHI)

This preliminary study was conducted to examine and verify the validity and reliability of the instrument on the Malaysian Academician Happiness Index (MAHI) on resilience. MAHI could be seen as a tool to measure the level of happiness and stress of academicians before determining how resilient the academicians were. Resilience can be defined as a mental ability of a person to recover quickly from illness or depression. MAHI instrument consisted of 66 items. The instrument was distributed to 40 academicians from three groups of universities which were the Focus University, Comprehensive University and Research University is using a survey technique. The instrument was developed to measure three main constructs which were the organization, individual and social that would affect the happiness and stress levels of academicians. This preliminary study employed the Rasch Measurement Model uses Winsteps software version 3.69.1.11. to examine the validity and reliability of the items. The results of the analysis of the MAHI instrument showed that the item reliability was 0.87, person reliability was 0.83 and value of Alpha Cronbach was 0.84. Meanwhile, misfit analysis showed that only there was one item with 1.46 logit that could be considered for dropping or needed improvement. Therefore, it highlighted that most of the items met the constructs’ need and can be used as a measurement indicator of MAHI. The implication of this instrument can help Malaysian academicians to be more resilient in facing challenges in the future

Energy harvesting applied to smart shoes

The appeal of energy harvesting systems lies in the possibility of capturing free energy that would be dissipated and is therefore obtainable without costs. Today, advanced techniques and devices exist for capturing from the environment, storing, and managing quotas of natural energy, which are made available in the form of electrical energy. At the same time, the most recent microprocessors grant an extremely high power efficiency, which permits their operation with minimal power consumption. As a consequence, low-consuming devices can be power supplied by using energy harvesting systems. If this concept is applied to wearable electronics, the most efficient choice is that of exploiting the energy released when the users walk, by developing systems that are embedded in the shoe sole. At each step, the force exerted on the device can be transformed into a relatively high amount of electrical energy, for example by using piezoelectric elements and electromagnetic induction systems. The paper describes the design of four different solutions for smart shoes that make use of energy harvesting apparatuses for the power supply of sensors and complex monitoring systems, for example aimed at GPS localization. An initial comparative assessment of the four architectures is reported, by weighing production costs, ease of manufacture and energy harvesting performance

Tecnologias IoT para pastoreio e controlo de postura animal

The unwanted and adverse weeds that are constantly growing in vineyards, force wine producers to repeatedly remove them through the use of mechanical and chemical methods. These methods include machinery such as plows and brushcutters, and chemicals as herbicides to remove and prevent the growth of weeds both in the inter-row and under-vine areas. Nonetheless, such methods are considered very aggressive for vines, and, in the second case, harmful for the public health, since chemicals may remain in the environment and hence contaminate water lines. Moreover, such processes have to be repeated over the year, making it extremely expensive and toilsome. Using animals, usually ovines, is an ancient practice used around the world. Animals, grazing in vineyards, feed from the unwanted weeds and fertilize the soil, in an inexpensive, ecological and sustainable way. However, sheep may be dangerous to vines since they tend to feed on grapes and on the lower branches of the vines, which causes enormous production losses. To overcome that issue, sheep were traditionally used to weed vineyards only before the beginning of the growth cycle of grapevines, thus still requiring the use of mechanical and/or chemical methods during the remainder of the production cycle. To mitigate the problems above, a new technological solution was investigated under the scope of the SheepIT project and developed in the scope of this thesis. The system monitors sheep during grazing periods on vineyards and implements a posture control mechanism to instruct them to feed only from the undesired weeds. This mechanism is based on an IoT architecture, being designed to be compact and energy efficient, allowing it to be carried by sheep while attaining an autonomy of weeks. In this context, the thesis herein sustained states that it is possible to design an IoT-based system capable of monitoring and conditioning sheep’s posture, enabling a safe weeding process in vineyards. Moreover, we support such thesis in three main pillars that match the main contributions of this work and that are duly explored and validated, namely: the IoT architecture design and required communications, a posture control mechanism and the support for a low-cost and low-power localization mechanism. The system architecture is validated mainly in simulation context while the posture control mechanism is validated both in simulations and field experiments. Furthermore, we demonstrate the feasibility of the system and the contribution of this work towards the first commercial version of the system.O constante crescimento de ervas infestantes obriga os produtores a manter um processo contínuo de remoção das mesmas com recurso a mecanismos mecânicos e/ou químicos. Entre os mais populares, destacam-se o uso de arados e roçadores no primeiro grupo, e o uso de herbicidas no segundo grupo. No entanto, estes mecanismos são considerados agressivos para as videiras, assim como no segundo caso perigosos para a saúde pública, visto que os químicos podem permanecer no ambiente, contaminando frutos e linhas de água. Adicionalmente, estes processos são caros e exigem mão de obra que escasseia nos dias de hoje, agravado pela necessidade destes processos necessitarem de serem repetidos mais do que uma vez ao longo do ano. O uso de animais, particularmente ovelhas, para controlar o crescimento de infestantes é uma prática ancestral usada em todo o mundo. As ovelhas, enquanto pastam, controlam o crescimento das ervas infestantes, ao mesmo tempo que fertilizam o solo de forma gratuita, ecológica e sustentável. Não obstante, este método foi sendo abandonado visto que os animais também se alimentam da rama, rebentos e frutos da videira, provocando naturais estragos e prejuízos produtivos. Para mitigar este problema, uma nova solução baseada em tecnologias de Internet das Coisas é proposta no âmbito do projeto SheepIT, cuja espinha dorsal foi construída no âmbito desta tese. O sistema monitoriza as ovelhas enquanto estas pastoreiam nas vinhas, e implementam um mecanismo de controlo de postura que condiciona o seu comportamento de forma a que se alimentem apenas das ervas infestantes. O sistema foi incorporado numa infraestrutura de Internet das Coisas com comunicações sem fios de baixo consumo para recolha de dados e que permite semanas de autonomia, mantendo os dispositivos com um tamanho adequado aos animais. Neste contexto, a tese suportada neste trabalho defende que é possível projetar uma sistema baseado em tecnologias de Internet das Coisas, capaz de monitorizar e condicionar a postura de ovelhas, permitindo que estas pastem em vinhas sem comprometer as videiras e as uvas. A tese é suportada em três pilares fundamentais que se refletem nos principais contributos do trabalho, particularmente: a arquitetura do sistema e respetivo sistema de comunicações; o mecanismo de controlo de postura; e o suporte para implementação de um sistema de localização de baixo custo e baixo consumo energético. A arquitetura é validada em contexto de simulação, e o mecanismo de controlo de postura em contexto de simulação e de experiências em campo. É também demonstrado o funcionamento do sistema e o contributo deste trabalho para a conceção da primeira versão comercial do sistema.Programa Doutoral em Informátic

Efficient Range-Free Monte-Carlo-Localization for Mobile Wireless Sensor Networks

Das Hauptproblem von Lokalisierungsalgorithmen für WSNs basierend auf Ankerknoten ist die Abhängigkeit von diesen. Mobilität im Netzwerk kann zu Topologien führen, in denen einzelne Knoten oder ganze Teile des Netzwerks temporär von allen Ankerknoten isoliert werden. In diesen Fällen ist keine weitere Lokalisierung möglich. Dies wirkt sich primär auf den Lokalisierungsfehler aus, der in diesen Fällen stark ansteigt. Des weiteren haben Betreiber von Sensornetzwerken Interesse daran, die Anzahl der kosten- und wartungsintensiveren Ankerknoten auf ein Minimum zu reduzieren. Dies verstärkt zusätzlich das Problem von nicht verfügbaren Ankerknoten während des Netzwerkbetriebs. In dieser Arbeit werden zunächst die Vor- und Nachteile der beiden großen Hauptkategorien von Lokalisierungsalgorithmen (range-based und range-free Verfahren) diskutiert und eine Studie eines oft für range-based Lokalisierung genutzten Distanzbestimmungsverfahren mit Hilfe des RSSI vorgestellt. Danach werden zwei neue Varianten für ein bekanntes range-free Lokalisierungsverfahren mit Namen MCL eingeführt. Beide haben zum Ziel das Problem der temporär nicht verfügbaren Ankerknoten zu lösen, bedienen sich dabei aber unterschiedlicher Mittel. SA-MCL nutzt ein dead reckoning Verfahren, um die Positionsschätzung vom letzten bekannten Standort weiter zu führen. Dies geschieht mit Hilfe von zusätzlichen Sensorinformationen, die von einem elektronischen Kompass und einem Beschleunigungsmesser zur Verfügung gestellt werden. PO-MCL hingegen nutzt das Mobilitätsverhalten von einigen Anwendungen in Sensornetzwerken aus, bei denen sich alle Knoten primär auf einer festen Anzahl von Pfaden bewegen, um den Lokalisierungsprozess zu verbessern. Beide Methoden werden durch detaillierte Netzwerksimulationen evaluiert. Im Fall von SA-MCL wird außerdem eine Implementierung auf echter Hardware vorgestellt und eine Feldstudie in einem mobilen Sensornetzwerk durchgeführt. Aus den Ergebnissen ist zu sehen, dass der Lokalisierungsfehler in Situationen mit niedriger Ankerknotendichte im Fall von SA-MCL um bis zu 60% reduziert werden kann, beziehungsweise um bis zu 50% im Fall von PO-MCL.

Implementation of machine learning for the evaluation of mastitis and antimicrobial resistance in dairy cows

Bovine mastitis is one of the biggest concerns in the dairy industry, where it affects sustainable milk production, farm economy and animal health. Most of the mastitis pathogens are bacterial in origin and accurate diagnosis of them enables understanding the epidemiology, outbreak prevention and rapid cure of the disease. This thesis aimed to provide a diagnostic solution that couples Matrix-Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF) mass spectroscopy coupled with machine learning (ML), for detecting bovine mastitis pathogens at the subspecies level based on their phenotypic characters. In Chapter 3, MALDI-TOF coupled with ML was performed to discriminate bovine mastitis-causing Streptococcus uberis based on transmission routes; contagious and environmental. S. uberis isolates collected from dairy farms across England and Wales were compared within and between farms. The findings of this chapter suggested that the proposed methodology has the potential of successful classification at the farm level. In Chapter 4, MALDI-TOF coupled with ML was performed to show proteomic differences between bovine mastitis-causing Escherichia coli isolates with different clinical outcomes (clinical and subclinical) and disease phenotype (persistent and non-persistent). The findings of this chapter showed that phenotypic differences can be detected by the proposed methodology even for genotypically identical isolates. In Chapter 5, MALDI-TOF coupled with ML was performed to differentiate benzylpenicillin signatures of bovine mastitis-causing Staphylococcus aureus isolates. The findings of this chapter presented that the proposed methodology enables fast, affordable and effective diag-nostic solution for targeting resistant bacteria in dairy cows. Having shown this methodology successfully worked for differentiating benzylpenicillin resistant and susceptible S. aureus isolates in Chapter 5, the same technique was applied to other mastitis agents Enterococcus faecalis and Enterococcus faecium and for profiling other antimicrobials besides benzylpenicillin in Chapter 6. The findings of this chapter demonstrated that MALDI-TOF coupled with ML allows monitoring the disease epidemiology and provides suggestions for adjusting farm management strategies. Taken together, this thesis highlights that MALDI-TOF coupled with ML is capable of dis-criminating bovine mastitis pathogens at subspecies level based on transmission route, clinical outcome and antimicrobial resistance profile, which could be used as a diagnostic tool for bo-vine mastitis at dairy farms

CMOS indoor light energy harvesting system for wireless sensing applications

Dissertação para obtenção do Grau de Doutor em Engenharia Electrotécnica e de ComputadoresThis research thesis presents a micro-power light energy harvesting system for indoor environments. Light energy is collected by amorphous silicon photovoltaic (a-Si:H PV) cells, processed by a switched-capacitor (SC) voltage doubler circuit with maximum power point tracking (MPPT), and finally stored in a large capacitor. The MPPT Fractional Open Circuit Voltage (VOC) technique is implemented by an asynchronous state machine (ASM) that creates and, dynamically, adjusts the clock frequency of the step-up SC circuit, matching the input impedance of the SC circuit to the maximum power point (MPP) condition of the PV cells. The ASM has a separate local power supply to make it robust against load variations. In order to reduce the area occupied by the SC circuit, while maintaining an acceptable efficiency value, the SC circuit uses MOSFET capacitors with a charge reusing scheme for the bottom plate parasitic capacitors. The circuit occupies an area of 0.31 mm2 in a 130 nm CMOS technology. The system was designed in order to work under realistic indoor light intensities. Experimental results show that the proposed system, using PV cells with an area of 14 cm2, is capable of starting-up from a 0 V condition, with an irradiance of only 0.32 W/m2. After starting-up, the system requires an irradiance of only 0.18 W/m2 (18 mW/cm2) to remain in operation. The ASM circuit can operate correctly using a local power supply voltage of 453 mV, dissipating only 0.085 mW. These values are, to the best of the authors’ knowledge, the lowest reported in the literature. The maximum efficiency of the SC converter is 70.3% for an input power of 48 mW, which is comparable with reported values from circuits operating at similar power levels.Portuguese Foundation for Science and Technology (FCT/MCTES), under project PEst-OE/EEI/UI0066/2011, and to the CTS multiannual funding, through the PIDDAC Program funds. I am also very grateful for the grant SFRH/PROTEC/67683/2010, financially supported by the IPL – Instituto Politécnico de Lisboa

Implementation of machine learning for the evaluation of mastitis and antimicrobial resistance in dairy cows

Bovine mastitis is one of the biggest concerns in the dairy industry, where it affects sustainable milk production, farm economy and animal health. Most of the mastitis pathogens are bacterial in origin and accurate diagnosis of them enables understanding the epidemiology, outbreak prevention and rapid cure of the disease. This thesis aimed to provide a diagnostic solution that couples Matrix-Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF) mass spectroscopy coupled with machine learning (ML), for detecting bovine mastitis pathogens at the subspecies level based on their phenotypic characters. In Chapter 3, MALDI-TOF coupled with ML was performed to discriminate bovine mastitis-causing Streptococcus uberis based on transmission routes; contagious and environmental. S. uberis isolates collected from dairy farms across England and Wales were compared within and between farms. The findings of this chapter suggested that the proposed methodology has the potential of successful classification at the farm level. In Chapter 4, MALDI-TOF coupled with ML was performed to show proteomic differences between bovine mastitis-causing Escherichia coli isolates with different clinical outcomes (clinical and subclinical) and disease phenotype (persistent and non-persistent). The findings of this chapter showed that phenotypic differences can be detected by the proposed methodology even for genotypically identical isolates. In Chapter 5, MALDI-TOF coupled with ML was performed to differentiate benzylpenicillin signatures of bovine mastitis-causing Staphylococcus aureus isolates. The findings of this chapter presented that the proposed methodology enables fast, affordable and effective diag-nostic solution for targeting resistant bacteria in dairy cows. Having shown this methodology successfully worked for differentiating benzylpenicillin resistant and susceptible S. aureus isolates in Chapter 5, the same technique was applied to other mastitis agents Enterococcus faecalis and Enterococcus faecium and for profiling other antimicrobials besides benzylpenicillin in Chapter 6. The findings of this chapter demonstrated that MALDI-TOF coupled with ML allows monitoring the disease epidemiology and provides suggestions for adjusting farm management strategies. Taken together, this thesis highlights that MALDI-TOF coupled with ML is capable of dis-criminating bovine mastitis pathogens at subspecies level based on transmission route, clinical outcome and antimicrobial resistance profile, which could be used as a diagnostic tool for bo-vine mastitis at dairy farms

Internet of Things Strategic Research Roadmap

Internet of Things (IoT) is an integrated part of Future Internet including existing and evolving Internet and network developments and could be conceptually defined as a dynamic global network infrastructure with self configuring capabilities based on standard and interoperable communication protocols where physical and virtual “things” have identities, physical attributes, and virtual personalities, use intelligent interfaces, and are seamlessly integrated into the information network