Enabling computationally intensive bioinformatics applications on the Grid platform

Bioinformatics is a recent and emerging discipline which aims at studying biological problems through computational approaches. Most branches of bioinformatics such as Genomics, Proteomics and Molecular Dynamics are particularly computationally intensive, requiring huge amount of computational resources for running algorithms of everincreasing complexity over data of everincreasing size. In the search for computational power, the EGEE Grid platform, world's largest community of interconnected clusters load balanced as a whole, seems particularly promising and is considered the new hope for satisfying the everincreasing computational requirements of bioinformatics, as well as physics and other computational sciences. The EGEE platform, however, is rather new and not yet free of problems. In addition, specific requirements of bioinformatics need to be addressed in order to use this new platform effectively for bioinformatics tasks. In my three years' Ph.D. work I addressed numerous aspects of this Grid platform, with particular attention to those needed by the bioinformatics domain. I hence created three major frameworks, Vnas, GridDBManager and SETest, plus an additional smaller standalone solution, to enhance the support for bioinformatics applications in the Grid environment and to reduce the effort needed to create new applications, additionally addressing numerous existing Grid issues and performing a series of optimizations. The Vnas framework is an advanced system for the submission and monitoring of Grid jobs that provides an abstraction with reliability over the Grid platform. In addition, Vnas greatly simplifies the development of new Grid applications by providing a callback system to simplify the creation of arbitrarily complex multistage computational pipelines and provides an abstracted virtual sandbox which bypasses Grid limitations. Vnas also reduces the usage of Grid bandwidth and storage resources by transparently detecting equality of virtual sandbox files based on content, across different submissions, even when performed by different users. BGBlast, evolution of the earlier project GridBlast, now provides a Grid Database Manager (GridDBManager) component for managing and automatically updating biological flatfile databases in the Grid environment. GridDBManager sports very novel features such as an adaptive replication algorithm that constantly optimizes the number of replicas of the managed databases in the Grid environment, balancing between response times (performances) and storage costs according to a programmed cost formula. GridDBManager also provides a very optimized automated management for older versions of the databases based on reverse delta files, which reduces the storage costs required to keep such older versions available in the Grid environment by two orders of magnitude. The SETest framework provides a way to the user to test and regressiontest Python applications completely scattered with side effects (this is a common case with Grid computational pipelines), which could not easily be tested using the more standard methods of unit testing or test cases. The technique is based on a new concept of datasets containing invocations and results of filtered calls. The framework hence significantly accelerates the development of new applications and computational pipelines for the Grid environment, and the efforts required for maintenance. An analysis of the impact of these solutions will be provided in this thesis. This Ph.D. work originated various publications in journals and conference proceedings as reported in the Appendix. Also, I orally presented my work at numerous international conferences related to Grid and bioinformatics.

Management and routing algorithms for ad-hoc and sensor networks

Large scale wireless adhoc networks of computers, sensors, PDAs etc. (i.e. nodes) are revolutionizing connectivity and leading to a paradigm shift from centralized systems to highly distributed and dynamic environments. An example of adhoc networks are sensor networks, which are usually composed by small units able to sense and transmit to a sink elementary data which are successively processed by an external machine. Recent improvements in the memory and computational power of sensors, together with the reduction of energy consumptions, are rapidly changing the potential of such systems, moving the attention towards datacentric sensor networks. A plethora of routing and data management algorithms have been proposed for the network path discovery ranging from broadcasting/floodingbased approaches to those using global positioning systems (GPS). We studied WGrid, a novel decentralized infrastructure that organizes wireless devices in an adhoc manner, where each node has one or more virtual coordinates through which both message routing and data management occur without reliance on either flooding/broadcasting operations or GPS. The resulting adhoc network does not suffer from the deadend problem, which happens in geographicbased routing when a node is unable to locate a neighbor closer to the destination than itself. WGrid allow multidimensional data management capability since nodes' virtual coordinates can act as a distributed database without needing neither special implementation or reorganization. Any kind of data (both single and multidimensional) can be distributed, stored and managed. We will show how a location service can be easily implemented so that any search is reduced to a simple query, like for any other data type. WGrid has then been extended by adopting a replication methodology. We called the resulting algorithm WRGrid. Just like WGrid, WRGrid acts as a distributed database without needing neither special implementation nor reorganization and any kind of data can be distributed, stored and managed. We have evaluated the benefits of replication on data management, finding out, from experimental results, that it can halve the average number of hops in the network. The direct consequence of this fact are a significant improvement on energy consumption and a workload balancing among sensors (number of messages routed by each node). Finally, thanks to the replications, whose number can be arbitrarily chosen, the resulting sensor network can face sensors disconnections/connections, due to failures of sensors, without data loss. Another extension to {WGrid} is {W*Grid} which extends it by strongly improving network recovery performance from link and/or device failures that may happen due to crashes or battery exhaustion of devices or to temporary obstacles. W*Grid guarantees, by construction, at least two disjoint paths between each couple of nodes. This implies that the recovery in W*Grid occurs without broadcasting transmissions and guaranteeing robustness while drastically reducing the energy consumption. An extensive number of simulations shows the efficiency, robustness and traffic road of resulting networks under several scenarios of device density and of number of coordinates. Performance analysis have been compared to existent algorithms in order to validate the results.

An experimental and theoretical approach to correct for the scattered radiation in an X-ray computer tomography system for industrial applications

The main problem connected to cone beam computed tomography (CT) systems for industrial applications employing 450 kV X-ray tubes is the high amount of scattered radiation which is added to the primary radiation (signal). This stray radiation leads to a significant degradation of the image quality. A better understanding of the scattering and methods to reduce its effects are therefore necessary to improve the image quality. Several studies have been carried out in the medical field at lower energies, whereas studies in industrial CT, especially for energies up to 450 kV, are lacking. Moreover, the studies reported in literature do not consider the scattered radiation generated by the CT system structure and the walls of the X-ray room (environmental scatter). In order to investigate the scattering on CT projections a GEANT4-based Monte Carlo (MC) model was developed. The model, which has been validated against experimental data, has enabled the calculation of the scattering including the environmental scatter, the optimization of an anti-scatter grid suitable for the CT system, and the optimization of the hardware components of the CT system. The investigation of multiple scattering in the CT projections showed that its contribution is 2.3 times the one of primary radiation for certain objects. The results of the environmental scatter showed that it is the major component of the scattering for aluminum box objects of front size 70 x 70 mm2 and that it strongly depends on the thickness of the object and therefore on the projection. For that reason, its correction is one of the key factors for achieving high quality images. The anti-scatter grid optimized by means of the developed MC model was found to reduce the scatter-toprimary ratio in the reconstructed images by 20 %. The object and environmental scatter calculated by means of the simulation were used to improve the scatter correction algorithm which could be patented by Empa. The results showed that the cupping effect in the corrected image is strongly reduced. The developed CT simulation is a powerful tool to optimize the design of the CT system and to evaluate the contribution of the scattered radiation to the image. Besides, it has offered a basis for a new scatter correction approach by which it has been possible to achieve images with the same spatial resolution as state-of-the-art well collimated fan-beam CT with a gain in the reconstruction time of a factor 10. This result has a high economic impact in non-destructive testing and evaluation, and reverse engineering.

A tuple space implementation for large-scale infrastructures

Coordinating activities in a distributed system is an open research topic. Several models have been proposed to achieve this purpose such as message passing, publish/subscribe, workflows or tuple spaces. We have focused on the latter model, trying to overcome some of its disadvantages. In particular we have applied spatial database techniques to tuple spaces in order to increase their performance when handling a large number of tuples. Moreover, we have studied how structured peer to peer approaches can be applied to better distribute tuples on large networks. Using some of these result, we have developed a tuple space implementation for the Globus Toolkit that can be used by Grid applications as a coordination service. The development of such a service has been quite challenging due to the limitations imposed by XML serialization that have heavily influenced its design. Nevertheless, we were able to complete its implementation and use it to implement two different types of test applications: a completely parallelizable one and a plasma simulation that is not completely parallelizable. Using this last application we have compared the performance of our service against MPI. Finally, we have developed and tested a simple workflow in order to show the versatility of our service.

Design of wireless sensor networks for fluid dynamic applications

In fluid dynamics research, pressure measurements are of great importance to define the flow field acting on aerodynamic surfaces. In fact the experimental approach is fundamental to avoid the complexity of the mathematical models for predicting the fluid phenomena. It’s important to note that, using in-situ sensor to monitor pressure on large domains with highly unsteady flows, several problems are encountered working with the classical techniques due to the transducer cost, the intrusiveness, the time response and the operating range. An interesting approach for satisfying the previously reported sensor requirements is to implement a sensor network capable of acquiring pressure data on aerodynamic surface using a wireless communication system able to collect the pressure data with the lowest environmental–invasion level possible. In this thesis a wireless sensor network for fluid fields pressure has been designed, built and tested. To develop the system, a capacitive pressure sensor, based on polymeric membrane, and read out circuitry, based on microcontroller, have been designed, built and tested. The wireless communication has been performed using the Zensys Z-WAVE platform, and network and data management have been implemented. Finally, the full embedded system with antenna has been created. As a proof of concept, the monitoring of pressure on the top of the mainsail in a sailboat has been chosen as working example.

A skin surface characterization system based on capacitive image analysis

During the last few years, several methods have been proposed in order to study and to evaluate characteristic properties of the human skin by using non-invasive approaches. Mostly, these methods cover aspects related to either dermatology, to analyze skin physiology and to evaluate the effectiveness of medical treatments in skin diseases, or dermocosmetics and cosmetic science to evaluate, for example, the effectiveness of anti-aging treatments. To these purposes a routine approach must be followed. Although very accurate and high resolution measurements can be achieved by using conventional methods, such as optical or mechanical profilometry for example, their use is quite limited primarily to the high cost of the instrumentation required, which in turn is usually cumbersome, highlighting some of the limitations for a routine based analysis. This thesis aims to investigate the feasibility of a noninvasive skin characterization system based on the analysis of capacitive images of the skin surface. The system relies on a CMOS portable capacitive device which gives 50 micron/pixel resolution capacitance map of the skin micro-relief. In order to extract characteristic features of the skin topography, image analysis techniques, such as watershed segmentation and wavelet analysis, have been used to detect the main structures of interest: wrinkles and plateau of the typical micro-relief pattern. In order to validate the method, the features extracted from a dataset of skin capacitive images acquired during dermatological examinations of a healthy group of volunteers have been compared with the age of the subjects involved, showing good correlation with the skin ageing effect. Detailed analysis of the output of the capacitive sensor compared with optical profilometry of silicone replica of the same skin area has revealed potentiality and some limitations of this technology. Also, applications to follow-up studies, as needed to objectively evaluate the effectiveness of treatments in a routine manner, are discussed.

A multilevel converter structure for grid-connected PV plants

A new conversion structure for three-phase grid-connected photovoltaic (PV) generation plants is presented and discussed in this Thesis. The conversion scheme is based on two insulated PV arrays, each one feeding the dc bus of a standard 2-level three-phase voltage source inverter (VSI). Inverters are connected to the grid by a traditional three-phase transformer having open-end windings at inverters side and either star or delta connection at the grid side. The resulting conversion structure is able to perform as a multilevel VSI, equivalent to a 3-level inverter, doubling the power capability of a single VSI with given voltage and current ratings. Different modulation schemes able to generate proper multilevel voltage waveforms have been discussed and compared. They include known algorithms, some their developments, and new original approaches. The goal was to share the grid power with a given ratio between the two VSI within each cycle period of the PWM, being the PWM pattern suitable for the implementation in industrial DSPs. It has been shown that an extension of the modulation methods for standard two-level inverter can provide a elegant solution for dual two-level inverter. An original control method has been introduced to regulate the dc-link voltages of each VSI, according to the voltage reference given by a single MPPT controller. A particular MPPT algorithm has been successfully tested, based on the comparison of the operating points of the two PV arrays. The small deliberately introduced difference between two operating dc voltages leads towards the MPP in a fast and accurate manner. Either simulation or experimental tests, or even both, always accompanied theoretical developments. For the simulation, the Simulink tool of Matlab has been adopted, whereas the experiments have been carried out by a full-scale low-voltage prototype of the whole PV generation system. All the research work was done at the Lab of the Department of Electrical Engineering, University of Bologna.

LBT AO system: tests and calibration

An Adaptive Optic (AO) system is a fundamental requirement of 8m-class telescopes. We know that in order to obtain the maximum possible resolution allowed by these telescopes we need to correct the atmospheric turbulence. Thanks to adaptive optic systems we are able to use all the effective potential of these instruments, drawing all the information from the universe sources as best as possible. In an AO system there are two main components: the wavefront sensor (WFS) that is able to measure the aberrations on the incoming wavefront in the telescope, and the deformable mirror (DM) that is able to assume a shape opposite to the one measured by the sensor. The two subsystem are connected by the reconstructor (REC). In order to do this, the REC requires a “common language" between these two main AO components. It means that it needs a mapping between the sensor-space and the mirror-space, called an interaction matrix (IM). Therefore, in order to operate correctly, an AO system has a main requirement: the measure of an IM in order to obtain a calibration of the whole AO system. The IM measurement is a 'mile stone' for an AO system and must be done regardless of the telescope size or class. Usually, this calibration step is done adding to the telescope system an auxiliary artificial source of light (i.e a fiber) that illuminates both the deformable mirror and the sensor, permitting the calibration of the AO system. For large telescope (more than 8m, like Extremely Large Telescopes, ELTs) the fiber based IM measurement requires challenging optical setups that in some cases are also impractical to build. In these cases, new techniques to measure the IM are needed. In this PhD work we want to check the possibility of a different method of calibration that can be applied directly on sky, at the telescope, without any auxiliary source. Such a technique can be used to calibrate AO system on a telescope of any size. We want to test the new calibration technique, called “sinusoidal modulation technique”, on the Large Binocular Telescope (LBT) AO system, which is already a complete AO system with the two main components: a secondary deformable mirror with by 672 actuators, and a pyramid wavefront sensor. My first phase of PhD work was helping to implement the WFS board (containing the pyramid sensor and all the auxiliary optical components) working both optical alignments and tests of some optical components. Thanks to the “solar tower” facility of the Astrophysical Observatory of Arcetri (Firenze), we have been able to reproduce an environment very similar to the telescope one, testing the main LBT AO components: the pyramid sensor and the secondary deformable mirror. Thanks to this the second phase of my PhD thesis: the measure of IM applying the sinusoidal modulation technique. At first we have measured the IM using a fiber auxiliary source to calibrate the system, without any kind of disturbance injected. After that, we have tried to use this calibration technique in order to measure the IM directly “on sky”, so adding an atmospheric disturbance to the AO system. The results obtained in this PhD work measuring the IM directly in the Arcetri solar tower system are crucial for the future development: the possibility of the acquisition of IM directly on sky means that we are able to calibrate an AO system also for extremely large telescope class where classic IM measurements technique are problematic and, sometimes, impossible. Finally we have not to forget the reason why we need this: the main aim is to observe the universe. Thanks to these new big class of telescopes and only using their full capabilities, we will be able to increase our knowledge of the universe objects observed, because we will be able to resolve more detailed characteristics, discovering, analyzing and understanding the behavior of the universe components.

Development of SuperEnsemble Techniques for the Mediterranean Ocean Forecasting System

This research activity studied how the uncertainties are concerned and interrelated through the multi-model approach, since it seems to be the bigger challenge of ocean and weather forecasting. Moreover, we tried to reduce model error throughout the superensemble approach. In order to provide this aim, we created different dataset and by means of proper algorithms we obtained the superensamble estimate. We studied the sensitivity of this algorithm in function of its characteristics parameters. Clearly, it is not possible to evaluate a reasonable estimation of the error neglecting the importance of the grid size of ocean model, for the large amount of all the sub grid-phenomena embedded in space discretizations that can be only roughly parametrized instead of an explicit evaluation. For this reason we also developed a high resolution model, in order to calculate for the first time the impact of grid resolution on model error.

Technologies for Ambient intelligence: from Smart Objects to Sensor Networks

n the last few years, the vision of our connected and intelligent information society has evolved to embrace novel technological and research trends. The diffusion of ubiquitous mobile connectivity and advanced handheld portable devices, amplified the importance of the Internet as the communication backbone for the fruition of services and data. The diffusion of mobile and pervasive computing devices, featuring advanced sensing technologies and processing capabilities, triggered the adoption of innovative interaction paradigms: touch responsive surfaces, tangible interfaces and gesture or voice recognition are finally entering our homes and workplaces. We are experiencing the proliferation of smart objects and sensor networks, embedded in our daily living and interconnected through the Internet. This ubiquitous network of always available interconnected devices is enabling new applications and services, ranging from enhancements to home and office environments, to remote healthcare assistance and the birth of a smart environment. This work will present some evolutions in the hardware and software development of embedded systems and sensor networks. Different hardware solutions will be introduced, ranging from smart objects for interaction to advanced inertial sensor nodes for motion tracking, focusing on system-level design. They will be accompanied by the study of innovative data processing algorithms developed and optimized to run on-board of the embedded devices. Gesture recognition, orientation estimation and data reconstruction techniques for sensor networks will be introduced and implemented, with the goal to maximize the tradeoff between performance and energy efficiency. Experimental results will provide an evaluation of the accuracy of the presented methods and validate the efficiency of the proposed embedded systems.

Grid Connected Doubly Fed Induction Generator Based Wind Turbine under LVRT

This project concentrates on the Low Voltage Ride Through (LVRT) capability of Doubly Fed Induction Generator (DFIG) wind turbine. The main attention in the project is, therefore, drawn to the control of the DFIG wind turbine and of its power converter and to the ability to protect itself without disconnection during grid faults. It provides also an overview on the interaction between variable speed DFIG wind turbines and the power system subjected to disturbances, such as short circuit faults. The dynamic model of DFIG wind turbine includes models for both mechanical components as well as for all electrical components, controllers and for the protection device of DFIG necessary during grid faults. The viewpoint of this project is to carry out different simulations to provide insight and understanding of the grid fault impact on both DFIG wind turbines and on the power system itself. The dynamic behavior of DFIG wind turbines during grid faults is simulated and assessed by using a transmission power system generic model developed and delivered by Transmission System Operator in the power system simulation toolbox Digsilent, Matlab/Simulink and PLECS.

The APSEL4D Monolithic Active Pixel Sensor and its Usage in a Single Electron Interference Experiment

We have realized a Data Acquisition chain for the use and characterization of APSEL4D, a 32 x 128 Monolithic Active Pixel Sensor, developed as a prototype for frontier experiments in high energy particle physics. In particular a transition board was realized for the conversion between the chip and the FPGA voltage levels and for the signal quality enhancing. A Xilinx Spartan-3 FPGA was used for real time data processing, for the chip control and the communication with a Personal Computer through a 2.0 USB port. For this purpose a firmware code, developed in VHDL language, was written. Finally a Graphical User Interface for the online system monitoring, hit display and chip control, based on windows and widgets, was realized developing a C++ code and using Qt and Qwt dedicated libraries. APSEL4D and the full acquisition chain were characterized for the first time with the electron beam of the transmission electron microscope and with 55Fe and 90Sr radioactive sources. In addition, a beam test was performed at the T9 station of the CERN PS, where hadrons of momentum of 12 GeV/c are available. The very high time resolution of APSEL4D (up to 2.5 Mfps, but used at 6 kfps) was fundamental in realizing a single electron Young experiment using nanometric double slits obtained by a FIB technique. On high statistical samples, it was possible to observe the interference and diffractions of single isolated electrons traveling inside a transmission electron microscope. For the first time, the information on the distribution of the arrival time of the single electrons has been extracted.

Power Converters and Electric Drives for Smart Grid Applications

The present dissertation aims to explore, theoretically and experimentally, the problems and the potential advantages of different types of power converters for “Smart Grid” applications, with particular emphasis on multi-level architectures, which are attracting a rising interest even for industrial requests. The models of the main multilevel architectures (Diode-Clamped and Cascaded) are shown. The best suited modulation strategies to function as a network interface are identified. In particular, the close correlation between PWM (Pulse Width Modulation) approach and SVM (Space Vector Modulation) approach is highlighted. An innovative multilevel topology called MMC (Modular Multilevel Converter) is investigated, and the single-phase, three-phase and "back to back" configurations are analyzed. Specific control techniques that can manage, in an appropriate way, the charge level of the numerous capacitors and handle the power flow in a flexible way are defined and experimentally validated. Another converter that is attracting interest in “Power Conditioning Systems” field is the “Matrix Converter”. Even in this architecture, the output voltage is multilevel. It offers an high quality input current, a bidirectional power flow and has the possibility to control the input power factor (i.e. possibility to participate to active and reactive power regulations). The implemented control system, that allows fast data acquisition for diagnostic purposes, is described and experimentally verified.

Development of a microgrid with renewable energy sources and electrochemical storage system integration

Beside the traditional paradigm of "centralized" power generation, a new concept of "distributed" generation is emerging, in which the same user becomes pro-sumer. During this transition, the Energy Storage Systems (ESS) can provide multiple services and features, which are necessary for a higher quality of the electrical system and for the optimization of non-programmable Renewable Energy Source (RES) power plants. A ESS prototype was designed, developed and integrated into a renewable energy production system in order to create a smart microgrid and consequently manage in an efficient and intelligent way the energy flow as a function of the power demand. The produced energy can be introduced into the grid, supplied to the load directly or stored in batteries. The microgrid is composed by a 7 kW wind turbine (WT) and a 17 kW photovoltaic (PV) plant are part of. The load is given by electrical utilities of a cheese factory. The ESS is composed by the following two subsystems, a Battery Energy Storage System (BESS) and a Power Control System (PCS). With the aim of sizing the ESS, a Remote Grid Analyzer (RGA) was designed, realized and connected to the wind turbine, photovoltaic plant and the switchboard. Afterwards, different electrochemical storage technologies were studied, and taking into account the load requirements present in the cheese factory, the most suitable solution was identified in the high temperatures salt Na-NiCl2 battery technology. The data acquisition from all electrical utilities provided a detailed load analysis, indicating the optimal storage size equal to a 30 kW battery system. Moreover a container was designed and realized to locate the BESS and PCS, meeting all the requirements and safety conditions. Furthermore, a smart control system was implemented in order to handle the different applications of the ESS, such as peak shaving or load levelling.

Motor control system in Parkinson’s disease: a modeling approach

Parkinson’s disease is a neurodegenerative disorder due to the death of the dopaminergic neurons of the substantia nigra of the basal ganglia. The process that leads to these neural alterations is still unknown. Parkinson’s disease affects most of all the motor sphere, with a wide array of impairment such as bradykinesia, akinesia, tremor, postural instability and singular phenomena such as freezing of gait. Moreover, in the last few years the fact that the degeneration in the basal ganglia circuitry induces not only motor but also cognitive alterations, not necessarily implicating dementia, and that dopamine loss induces also further implications due to dopamine-driven synaptic plasticity got more attention. At the present moment, no neuroprotective treatment is available, and even if dopamine-replacement therapies as well as electrical deep brain stimulation are able to improve the life conditions of the patients, they often present side effects on the long term, and cannot recover the neural loss, which instead continues to advance. In the present thesis both motor and cognitive aspects of Parkinson’s disease and basal ganglia circuitry were investigated, at first focusing on Parkinson’s disease sensory and balance issues by means of a new instrumented method based on inertial sensor to provide further information about postural control and postural strategies used to attain balance, then applying this newly developed approach to assess balance control in mild and severe patients, both ON and OFF levodopa replacement. Given the inability of levodopa to recover balance issues and the new physiological findings than underline the importance in Parkinson’s disease of non-dopaminergic neurotransmitters, it was therefore developed an original computational model focusing on acetylcholine, the most promising neurotransmitter according to physiology, and its role in synaptic plasticity. The rationale of this thesis is that a multidisciplinary approach could gain insight into Parkinson’s disease features still unresolved.