DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

Switching Patterns and Steady-State Analysis of Grid-Connected and Stand-Alone Single-Stage Boost-Inverters for PV Applications

Renewable or sustainable energy (SE) sources have attracted the attention of many countries because the power generated is environmentally friendly, and the sources are not subject to the instability of price and availability. This dissertation presents new trends in the DC-AC converters (inverters) used in renewable energy sources, particularly for photovoltaic (PV) energy systems. A review of the existing technologies is performed for both single-phase and three-phase systems, and the pros and cons of the best candidates are investigated. In many modern energy conversion systems, a DC voltage, which is provided from a SE source or energy storage device, must be boosted and converted to an AC voltage with a fixed amplitude and frequency. A novel switching pattern based on the concept of the conventional space-vector pulse-width-modulated (SVPWM) technique is developed for single-stage, boost-inverters using the topology of current source inverters (CSI). The six main switching states, and two zeros, with three switches conducting at any given instant in conventional SVPWM techniques are modified herein into three charging states and six discharging states with only two switches conducting at any given instant. The charging states are necessary in order to boost the DC input voltage. It is demonstrated that the CSI topology in conjunction with the developed switching pattern is capable of providing the required residential AC voltage from a low DC voltage of one PV panel at its rated power for both linear and nonlinear loads. In a micro-grid, the active and reactive power control and consequently voltage regulation is one of the main requirements. Therefore, the capability of the single-stage boost-inverter in controlling the active power and providing the reactive power is investigated. It is demonstrated that the injected active and reactive power can be independently controlled through two modulation indices introduced in the proposed switching algorithm. The system is capable of injecting a desirable level of reactive power, while the maximum power point tracking (MPPT) dictates the desirable active power. The developed switching pattern is experimentally verified through a laboratory scaled three-phase 200W boost-inverter for both grid-connected and stand-alone cases and the results are presented.

An Assessment of Novel Biodegradable Magnesium Alloys for Endovascular Biomaterial Applications

Magnesium alloys have been widely explored as potential biomaterials, but several limitations to using these materials have prevented their widespread use, such as uncontrollable degradation kinetics which alter their mechanical properties. In an attempt to further the applicability of magnesium and its alloys for biomedical purposes, two novel magnesium alloys Mg-Zn-Cu and Mg-Zn-Se were developed with the expectation of improving upon the unfavorable qualities shown by similar magnesium based materials that have previously been explored. The overall performance of these novel magnesium alloys has been assessesed in three distinct phases of research: 1) analysing the mechanical properties of the as-cast magnesium alloys, 2) evaluating the biocompatibility of the as-cast magnesium alloys through the use of in-vitro cellular studies, and 3) profiling the degradation kinetics of the as-cast magnesium alloys through the use of electrochemical potentiodynamic polarization techqnique as well as gravimetric weight-loss methods. As compared to currently available shape memory alloys and degradable as-cast alloys, these experimental alloys possess superior as-cast mechanical properties with elongation at failure values of 12% and 13% for the Mg-Zn-Se and Mg-Zn-Se alloys, respectively. This is substantially higher than other as-cast magnesium alloys that have elongation at failure values that range from 7-10%. Biocompatibility tests revealed that both the Mg-Zn-Se and Mg-Zn-Cu alloys exhibit low cytotoxicity levels which are suitable for biomaterial applications. Gravimetric and electrochemical testing was indicative of the weight loss and initial corrosion behavior of the alloys once immersed within a simulated body fluid. The development of these novel as-cast magnesium alloys provide an advancement to the field of degradable metallic materials, while experimental results indicate their potential as cost-effective medical devices.

Bionano Electronics: Magneto-Electric Nanoparticles for Drug Delivery, Brain Stimulation and Imaging Applications

Nanoparticles are often considered as efficient drug delivery vehicles for precisely dispensing the therapeutic payloads specifically to the diseased sites in the patient’s body, thereby minimizing the toxic side effects of the payloads on the healthy tissue. However, the fundamental physics that underlies the nanoparticles’ intrinsic interaction with the surrounding cells is inadequately elucidated. The ability of the nanoparticles to precisely control the release of its payloads externally (on-demand) without depending on the physiological conditions of the target sites has the potential to enable patient- and disease-specific nanomedicine, also known as Personalized NanoMedicine (PNM). In this dissertation, magneto-electric nanoparticles (MENs) were utilized for the first time to enable important functions, such as (i) field-controlled high-efficacy dissipation-free targeted drug delivery system and on-demand release at the sub-cellular level, (ii) non-invasive energy-efficient stimulation of deep brain tissue at body temperature, and (iii) a high-sensitivity contrasting agent to map the neuronal activity in the brain non-invasively. First, this dissertation specifically focuses on using MENs as energy-efficient and dissipation-free field-controlled nano-vehicle for targeted delivery and on-demand release of a anti-cancer Paclitaxel (Taxol) drug and a anti-HIV AZT 5’-triphosphate (AZTTP) drug from 30-nm MENs (CoFe2O4-BaTiO3) by applying low-energy DC and low-frequency (below 1000 Hz) AC fields to separate the functions of delivery and release, respectively. Second, this dissertation focuses on the use of MENs to non-invasively stimulate the deep brain neuronal activity via application of a low energy and low frequency external magnetic field to activate intrinsic electric dipoles at the cellular level through numerical simulations. Third, this dissertation describes the use of MENs to track the neuronal activities in the brain (non-invasively) using a magnetic resonance and a magnetic nanoparticle imaging by monitoring the changes in the magnetization of the MENs surrounding the neuronal tissue under different states. The potential therapeutic and diagnostic impact of this innovative and novel study is highly significant not only in HIV-AIDS, Cancer, Parkinson’s and Alzheimer’s disease but also in many CNS and other diseases, where the ability to remotely control targeted drug delivery/release, and diagnostics is the key.

A service-oriented user interaction analysis framework supporting adaptive applications

A comprehensive user model, built by monitoring a user's current use of applications, can be an excellent starting point for building adaptive user-centred applications. The BaranC framework monitors all user interaction with a digital device (e.g. smartphone), and also collects all available context data (such as from sensors in the digital device itself, in a smart watch, or in smart appliances) in order to build a full model of user application behaviour. The model built from the collected data, called the UDI (User Digital Imprint), is further augmented by analysis services, for example, a service to produce activity profiles from smartphone sensor data. The enhanced UDI model can then be the basis for building an appropriate adaptive application that is user-centred as it is based on an individual user model. As BaranC supports continuous user monitoring, an application can be dynamically adaptive in real-time to the current context (e.g. time, location or activity). Furthermore, since BaranC is continuously augmenting the user model with more monitored data, over time the user model changes, and the adaptive application can adapt gradually over time to changing user behaviour patterns. BaranC has been implemented as a service-oriented framework where the collection of data for the UDI and all sharing of the UDI data are kept strictly under the user's control. In addition, being service-oriented allows (with the user's permission) its monitoring and analysis services to be easily used by 3rd parties in order to provide 3rd party adaptive assistant services. An example 3rd party service demonstrator, built on top of BaranC, proactively assists a user by dynamic predication, based on the current context, what apps and contacts the user is likely to need. BaranC introduces an innovative user-controlled unified service model of monitoring and use of personal digital activity data in order to provide adaptive user-centred applications. This aims to improve on the current situation where the diversity of adaptive applications results in a proliferation of applications monitoring and using personal data, resulting in a lack of clarity, a dispersal of data, and a diminution of user control.

Gesture based IoT light control for smart clothing

In this paper, a smart wireless wristband is proposed. The potential of innovative gesture based interactivity with connected lighting solutions is reviewed. The solution is intended to offer numerous benefits, in terms of ease of use, and enhanced dynamic interactive functionality. A comparative analysis will be carried out between this work and existing solutions. The evolution of lighting and gesture controls will be discussed and an overview of alternative applications will be provided, as part of the critical analysis.

Heterogeneous Catalysts: Design, Applications and Research Insights

The cyclization of pseudoionone yields a mixture of alpha-ionone, beta-ionone and gamma-ionone. By careful control of reagent and reaction conditions, either the alpha- and beta- isomer can be favoured. The alpha-ionone has violet odour and is widely used in perfumery and flavours. beta-Ionone is the main precursor of Vitamin A and beta-carotene. Traditionally, strong homogeneous catalysts, like sulphuric acid and phosphoric acid have been used. These problems can be overcome by the use of solid acid catalysts. This work reports the cyclization of pseudoionone over USY zeolites, at 80ºC. USY It is observed that the initial activity increases with the Si/Al ratio of zeolite until a maximum, which is obtained with USY3. With higher Si/Al ratio, a decrease in the catalytic activity is observed. Selectivity to ionone isomers is around 42 %, at 75% of pseudoionone conversion, after 24 h of reaction. USY3 zeolite was reused four times with the same catalyst sample in the same condicions. It was observed a stabilization of the catalytic activity, after the second use.

Integrated control of two tortricid (Lepidoptera) pests in apple orchards with sex pheromones and insecticides.

The apple is attacked by a significant number of insect pests in Brazilian commercial orchards, including Bonagota salubricola and Grapholita molesta (Lepidoptera: Tortricidae). Sexual disruption of B. salubricola and G. molesta was evaluated in apple orchard using the flowable pheromone formulations, SPLAT Grafo+Bona (SG+B), SPLAT Attract and Kill Grafo+Bona (SAKG+B), and compared with the standard insecticides used for management in the Integrated Apple Production (IAP) system. Both formulations were applied at a rate of 1kg/ha on October 10, 2005 and December 13, 2005 using 300 and 1000 point sources/ha of SG+B and SAKG+B, respectively in experimental units of 7 ha. Adult male captures of B. salubricola and G. molesta were evaluated weekly in Delta traps with specific synthetic sex pheromone from October 10, 2005 to February 14, 2006. Damage to fruits was evaluated on November 21 and December 21, 2005, and January 25 and February 14, 2006. In the SPLAT treated experimental units a significant reduction was observed in the number of B. salubricola and G. molesta males caught in Delta traps compared to the experimental unit IAP. Damage by B. salubricola at harvest ranged from 1.63 to 4.75% with no differences between treatments, while damage by G. molesta was near zero in all experimental units. Mating disruption using SG+B and SAKG+B was sufficient to control B. salubricola and G. molesta with results equivalent to IAP guidelines. This technology is promising for management of both pests in Brazilian apple orchards with immediate reduction of 43% in the number of insecticide applications.

Applications of metrological techniques for clinical implementation of dosimetry and radiobiology in molecular radiotherapy

Molecular radiotherapy (MRT) is a fast developing and promising treatment for metastasised neuroendocrine tumours. Efficacy of MRT is based on the capability to selectively "deliver" radiation to tumour cells, minimizing administered dose to normal tissues. Outcome of MRT depends on the individual patient characteristics. For that reason, personalized treatment planning is important to improve outcomes of therapy. Dosimetry plays a key role in this setting, as it is the main physical quantity related to radiation effects on cells. Dosimetry in MRT consists in a complex series of procedures ranging from imaging quantification to dose calculation. This doctoral thesis focused on several aspects concerning the clinical implementation of absorbed dose calculations in MRT. Accuracy of SPECT/CT quantification was assessed in order to determine the optimal reconstruction parameters. A model of PVE correction was developed in order to improve the activity quantification in small volume, such us lesions in clinical patterns. Advanced dosimetric methods were compared with the aim of defining the most accurate modality, applicable in clinical routine. Also, for the first time on a large number of clinical cases, the overall uncertainty of tumour dose calculation was assessed. As part of the MRTDosimetry project, protocols for calibration of SPECT/CT systems and implementation of dosimetry were drawn up in order to provide standard guidelines to the clinics offering MRT. To estimate the risk of experiencing radio-toxicity side effects and the chance of inducing damage on neoplastic cells is crucial for patient selection and treatment planning. In this thesis, the NTCP and TCP models were derived based on clinical data as help to clinicians to decide the pharmaceutical dosage in relation to the therapy control and the limitation of damage to healthy tissues. Moreover, a model for tumour response prediction based on Machine Learning analysis was developed.

Resonant metamaterials for the control of Rayleigh waves

Since their emergence, locally resonant metamaterials have found several applications for the control of surface waves, from micrometer-sized electronic devices to meter-sized seismic barriers. The interaction between Rayleigh-type surface waves and resonant metamaterials has been investigated through the realization of locally resonant metasurfaces, thin elastic interfaces constituted by a cluster of resonant inclusions or oscillators embedded near the surface of an elastic waveguide. When such resonant metasurfaces are embedded in an elastic homogeneous half-space, they can filter out the propagation of Rayleigh waves, creating low-frequency bandgaps at selected frequencies. In the civil engineering context, heavy resonating masses are needed to extend the bandgap frequency width of locally resonant devices, a requirement that limits their practical implementations. In this dissertation, the wave attenuation capabilities of locally resonant metasurfaces have been enriched by proposing (i) tunable metasurfaces to open large frequency bandgaps with small effective inertia, and by developing (ii) an analytical framework aimed at studying the propagation of Rayleigh waves propagation in deep resonant waveguides. In more detail, inertial amplified resonators are exploited to design advanced metasurfaces with a prescribed static and a tunable dynamic response. The modular design of the tunable metasurfaces allows to shift and enlarge low-frequency spectral bandgaps without modifying the total inertia of the metasurface. Besides, an original dispersion law is derived to study the dispersive properties of Rayleigh waves propagating in thick resonant layers made of sub-wavelength resonators. Accordingly, a deep resonant wave barrier of mechanical resonators embedded inside the soil is designed to impede the propagation of seismic surface waves. Numerical models are developed to confirm the analytical dispersion predictions of the tunable metasurface and resonant layer. Finally, a medium-size scale resonant wave barrier is designed according to the soil stratigraphy of a real geophysical scenario to attenuate ground-borne vibration.

Employment of Real-Time/FPGA Architectures for Test and Control of Automotive Engines

Nowadays the production of increasingly complex and electrified vehicles requires the implementation of new control and monitoring systems. This reason, together with the tendency of moving rapidly from the test bench to the vehicle, leads to a landscape that requires the development of embedded hardware and software to face the application effectively and efficiently. The development of application-based software on real-time/FPGA hardware could be a good answer for these challenges: FPGA grants parallel low-level and high-speed calculation/timing, while the Real-Time processor can handle high-level calculation layers, logging and communication functions with determinism. Thanks to the software flexibility and small dimensions, these architectures can find a perfect collocation as engine RCP (Rapid Control Prototyping) units and as smart data logger/analyser, both for test bench and on vehicle application. Efforts have been done for building a base architecture with common functionalities capable of easily hosting application-specific control code. Several case studies originating in this scenario will be shown; dedicated solutions for protype applications have been developed exploiting a real-time/FPGA architecture as ECU (Engine Control Unit) and custom RCP functionalities, such as water injection and testing hydraulic brake control.

Advanced Luminescent Materials for Technological Applications

This thesis focusses on the study of several luminescent materials and investigates some related technological applications. It is made of six chapters. Chapter 1 introduces a brief history, basic principles and applications of photoluminescence. Chapter 2 presents the photophysical properties of five benzoheterodiazole dyes. These molecules were incorporated in PMMA- and PDMS-based LSC-PV devices to determine the emission quantum yields, transmission, re-absorption and IPCE properties. DFT calculations were performed to investigate the structures and energy levels of these dyes. Chapter 3 concerns the preparation of a luminescent film to calibrate an ESA satellite that will monitor the fluorescence of terrestrial vegetation. ZnPc was selected as suitable dye to make the film. Ferrocene was selected as quencher to control the emission intensity. An industrial printing technology was used to produce large-area calibration sheets coated with green pigment that simulates the NIR reflectance of green plants in which the ZnPc is embedded. Photophysical properties of a series of alkynyl gold NHC complexes containing naphthalimide chromophore were studied in Chapter 4. All the compounds were studied in solution and solid state. Further investigations were carried out by incorporating these compounds in PMMA matrix to make films. XRD and DFT calculations were made to determine the structures and energy levels of the complexes. In chapter 5 we studied the photophysical properties of star-shaped molecular systems which can operate as molecular motors when attached onto surface, along with those of their related ligands/moieties in tetrahydrofuran solution. The photophysical properties of these molecular systems can show if they are suitable to operate as light-triggered molecular machines. Finally, chapter 6 concerns the photoluminescence behavior of three NHC half-sandwich Ir/Rh metal complexes. The photophysical properties of these compounds were examined in CH2Cl2 solutions and PMMA films. These complexes may prove potential candidates for organic phosphorescent materials.

Modeling of Electric Hybrid Vehicles based on Innovative Energy Storage Technologies for Super Sports Cars Applications

Today, the contribution of the transportation sector on greenhouse gases is evident. The fast consumption of fossil fuels and its impact on the environment has given a strong impetus to the development of vehicles with better fuel economy. Hybrid electric vehicles fit into this context with different targets, starting from the reduction of emissions and fuel consumption, but also for performance and comfort enhancement. Vehicles exist with various missions; super sport cars usually aim to reach peak performance and to guarantee a great driving experience to the driver, but great attention must also be paid to fuel consumption. According to the vehicle mission, hybrid vehicles can differ in the powertrain configuration and the choice of the energy storage system. Lamborghini has recently invested in the development of hybrid super sport cars, due to performance and comfort reasons, with the possibility to reduce fuel consumption. This research activity has been conducted as a joint collaboration between the University of Bologna and the sportscar manufacturer, to analyze the impact of innovative energy storage solutions on the hybrid vehicle performance. Capacitors have been studied and modeled to analyze the pros and cons of such solution with respect to batteries. To this aim, a full simulation environment has been developed and validated to provide a concept design tool capable of precise results and able to foresee the longitudinal performance on regulated emission cycles and real driving conditions, with a focus on fuel consumption. In addition, the target of the research activity is to deepen the study of hybrid electric super sports cars in the concept development phase, focusing on defining the control strategies and the energy storage system’s technology that best suits the needs of the vehicles. This dissertation covers the key steps that have been carried out in the research project.

Development of a Predictive Control Function for a Plug-in Hybrid Electric Vehicle with Electrically-heated Catalyst

This master thesis work is focused on the development of a predictive EHC control function for a diesel plug-in hybrid electric vehicle equipped with a EURO 7 compliant exhaust aftertreatment system (EATS), with the purpose of showing the advantages provided by the implementation of a predictive control strategy with respect to a rule-based one. A preliminary step will be the definition of an accurate powertrain and EATS physical model, starting from already existing and validated applications. Then, a rule-based control strategy managing the torque split between the electric motor (EM) and the internal combustion engine (ICE) will be developed and calibrated, with the main target of limiting tailpipe NOx emission by taking into account EM and ICE operating conditions together with EATS conversion efficiency. The information available from vehicle connectivity will be used to reconstruct the future driving scenario, also referred to as electronic horizon (eHorizon), and in particular to predict ICE first start. Based on this knowledge, an EATS pre-heating phase can be planned to avoid low pollutant conversion efficiencies, thus preventing high NOx emission due to engine cold start. Consequently, the final NOx emission over the complete driving cycle will be strongly reduced, allowing to comply with the limits potentially set by the incoming EURO 7 regulation. Moreover, given the same NOx emission target, the gain achieved thanks to the implementation of an EHC predictive control function will allow to consider a simplified EATS layout, thus reducing the related manufacturing cost. The promising results achieved in terms of NOx emission reduction show the effectiveness of the application of a predictive control strategy focused on EATS thermal management and highlight the potential of a complete integration and parallel development of involved vehicle physical systems, control software and connectivity data management.

Modelling, control and testing of internal combustion engines to minimize consumption and pollutant emissions

The objective of the PhD thesis was to research technologies and strategies to reduce fuel consumption and pollutants emission produced by internal combustion engines. In order to meet this objective my activity was focused on the research of advanced controls based on cylinder pressure feedback. These types of control strategies were studied because they present promising results in terms of engine efficiency enhancement. In the PhD dissertation two study cases are presented. The first case is relative to a control strategy to be used at the test bench for the optimisation of the spark advance calibration of motorcycle Engine. The second case is relative to a control strategy to be used directly on board of mining engines with the objective or reducing the engine consumption and correct ageing effects. In both cases the strategies proved to be effective but their implementation required the use of specific toolchains for the measure of the cylinder pressure feedback that for a matter of cost makes feasible the strategy use only for applications: • At test bench • In small-markets like large off-road engines The major bottleneck that prevents the implementation of these strategies on mass production is the cost of cylinder pressure sensor. In order to tackle this issue, during the PhD research, the development of a low-cost sensor for the estimation of cylinder pressure was studied. The prototype was a piezo-electric washer designed to replace the standard spark-plug washer or high-pressure fuel injectors gasket. From the data analysis emerged the possibility to use the piezo-electric prototype signal to evaluate with accuracy several combustion metrics compatible for the implementation of advanced control strategies in on-board applications. Overall, the research shows that advanced combustion controls are feasible and beneficial, not only at the test bench or on stationary engines, but also in mass-produced engines.