Heterogeneous multicore systems for signal processing

This thesis explores the capabilities of heterogeneous multi-core systems, based on multiple Graphics Processing Units (GPUs) in a standard desktop framework. Multi-GPU accelerated desk side computers are an appealing alternative to other high performance computing (HPC) systems: being composed of commodity hardware components fabricated in large quantities, their price-performance ratio is unparalleled in the world of high performance computing. Essentially bringing “supercomputing to the masses”, this opens up new possibilities for application fields where investing in HPC resources had been considered unfeasible before. One of these is the field of bioelectrical imaging, a class of medical imaging technologies that occupy a low-cost niche next to million-dollar systems like functional Magnetic Resonance Imaging (fMRI). In the scope of this work, several computational challenges encountered in bioelectrical imaging are tackled with this new kind of computing resource, striving to help these methods approach their true potential. Specifically, the following main contributions were made: Firstly, a novel dual-GPU implementation of parallel triangular matrix inversion (TMI) is presented, addressing an crucial kernel in computation of multi-mesh head models of encephalographic (EEG) source localization. This includes not only a highly efficient implementation of the routine itself achieving excellent speedups versus an optimized CPU implementation, but also a novel GPU-friendly compressed storage scheme for triangular matrices. Secondly, a scalable multi-GPU solver for non-hermitian linear systems was implemented. It is integrated into a simulation environment for electrical impedance tomography (EIT) that requires frequent solution of complex systems with millions of unknowns, a task that this solution can perform within seconds. In terms of computational throughput, it outperforms not only an highly optimized multi-CPU reference, but related GPU-based work as well. Finally, a GPU-accelerated graphical EEG real-time source localization software was implemented. Thanks to acceleration, it can meet real-time requirements in unpreceeded anatomical detail running more complex localization algorithms. Additionally, a novel implementation to extract anatomical priors from static Magnetic Resonance (MR) scansions has been included.

LCA-GIS Integrated approach for a Sustainable Land Use Under Energy Crops

In genere, negli studi di vocazionalità delle colture, vengono presi in considerazione solo variabili ambientali pedo-climatiche. La coltivazione di una coltura comporta anche un impatto ambientale derivante dalle pratiche agronomiche ed il territorio può essere più o meno sensibile a questi impatti in base alla sua vulnerabilità. In questo studio si vuole sviluppare una metodologia per relazionare spazialmente l’impatto delle colture con le caratteristiche sito specifiche del territorio in modo da considerare anche questo aspetto nell’allocazione negli studi di vocazionalità. LCA è stato utilizzato per quantificare diversi impatti di alcune colture erbacee alimentari e da energia, relazionati a mappe di vulnerabilità costruite con l’utilizzo di GIS, attraverso il calcolo di coefficienti di rischio di allocazione per ogni combinazione coltura-area vulnerabile. Le colture energetiche sono state considerate come un uso alternativo del suolo per diminuire l’impatto ambientale. Il caso studio ha mostrato che l’allocazione delle colture può essere diversa in base al tipo e al numero di impatti considerati. Il risultato sono delle mappe in cui sono riportate le distribuzioni ottimali delle colture al fine di minimizzare gli impatti, rispetto a mais e grano, due colture alimentari importanti nell’area di studio. Le colture con l’impatto più alto dovrebbero essere coltivate nelle aree a vulnerabilità bassa, e viceversa. Se il rischio ambientale è la priorità, mais, colza, grano, girasole, e sorgo da fibra dovrebbero essere coltivate solo nelle aree a vulnerabilità bassa o moderata, mentre, le colture energetiche erbacee perenni, come il panico, potrebbero essere coltivate anche nelle aree a vulnerabilità alta, rappresentando cosi una opportunità per aumentare la sostenibilità di uso del suolo rurale. Lo strumento LCA-GIS inoltre, integrato con mappe di uso attuale del suolo, può aiutare a valutarne il suo grado di sostenibilità ambientale.

Cooperative Wireless Systems

This Ph.D. dissertation reports on the work performed at the Wireless Communication Laboratory - University of Bologna and National Research Council - as well as, for six months, at the Fraunhofer Institute for Integrated Circuit (IIS) in Nürnberg. The work of this thesis is in the area of wireless communications, especially with regards to cooperative communications aspects in narrow-band and ultra-wideband systems, cooperative links characterization, network geometry, power allocation techniques,and synchronization between nodes. The underpinning of this work is devoted to developing a general framework for design and analysis of wireless cooperative communication systems, which depends on propagation environment, transmission technique, diversity method, power allocation for various scenarios and relay positions. The optimal power allocation for minimizing the bit error probability at the destination is derived. In addition, a syncronization algorithm for master-slave communications is proposed with the aim of jointly compensate the clock drift and offset of wireless nodes composing the network.

Life Cycle Assessment of Peach Nectar: a comparative analysis between conventional and bioenergy-from-waste integrated food chains

Modern food systems are characterized by a high energy intensity as well as by the production of large amounts of waste, residuals and food losses. This inefficiency presents major consequences, in terms of GHG emissions, waste disposal, and natural resource depletion. The research hypothesis is that residual biomass material could contribute to the energetic needs of food systems, if recovered as an integrated renewable energy source (RES), leading to a sensitive reduction of the impacts of food systems, primarily in terms of fossil fuel consumption and GHG emissions. In order to assess these effects, a comparative life cycle assessment (LCA) has been conducted to compare two different food systems: a fossil fuel-based system and an integrated system with the use of residual as RES for self-consumption. The food product under analysis has been the peach nectar, from cultivation to end-of-life. The aim of this LCA is twofold. On one hand, it allows an evaluation of the energy inefficiencies related to agro-food waste. On the other hand, it illustrates how the integration of bioenergy into food systems could effectively contribute to reduce this inefficiency. Data about inputs and waste generated has been collected mainly through literature review and databases. Energy balance, GHG emissions (Global Warming Potential) and waste generation have been analyzed in order to identify the relative requirements and contribution of the different segments. An evaluation of the energy “loss” through the different categories of waste allowed to provide details about the consequences associated with its management and/or disposal. Results should provide an insight of the impacts associated with inefficiencies within food systems. The comparison provides a measure of the potential reuse of wasted biomass and the amount of energy recoverable, that could represent a first step for the formulation of specific policies on the integration of bioenergies for self-consumption.

Hybrid portable systems for bio-nanosensors

The promising development in the routine nanofabrication and the increasing knowledge of the working principles of new classes of highly sensitive, label-free and possibly cost-effective bio-nanosensors for the detection of molecules in liquid environment, has rapidly increased the possibility to develop portable sensor devices that could have a great impact on many application fields, such as health-care, environment and food production, thanks to the intrinsic ability of these biosensors to detect, monitor and study events at the nanoscale. Moreover, there is a growing demand for low-cost, compact readout structures able to perform accurate preliminary tests on biosensors and/or to perform routine tests with respect to experimental conditions avoiding skilled personnel and bulky laboratory instruments. This thesis focuses on analysing, designing and testing novel implementation of bio-nanosensors in layered hybrid systems where microfluidic devices and microelectronic systems are fused in compact printed circuit board (PCB) technology. In particular the manuscript presents hybrid systems in two validating cases using nanopore and nanowire technology, demonstrating new features not covered by state of the art technologies and based on the use of two custom integrated circuits (ICs). As far as the nanopores interface system is concerned, an automatic setup has been developed for the concurrent formation of bilayer lipid membranes combined with a custom parallel readout electronic system creating a complete portable platform for nanopores or ion channels studies. On the other hand, referring to the nanowire readout hybrid interface, two systems enabling to perform parallel, real-time, complex impedance measurements based on lock-in technique, as well as impedance spectroscopy measurements have been developed. This feature enable to experimentally investigate the possibility to enrich informations on the bio-nanosensors concurrently acquiring impedance magnitude and phase thus investigating capacitive contributions of bioanalytical interactions on biosensor surface.

Theoretical and Experimental Analysis of micro-CHP Energy Systems

In the framework of the micro-CHP (Combined Heat and Power) energy systems and the Distributed Generation (GD) concept, an Integrated Energy System (IES) able to meet the energy and thermal requirements of specific users, using different types of fuel to feed several micro-CHP energy sources, with the integration of electric generators of renewable energy sources (RES), electrical and thermal storage systems and the control system was conceived and built. A 5 kWel Polymer Electrolyte Membrane Fuel Cell (PEMFC) has been studied. Using experimental data obtained from various measurement campaign, the electrical and CHP PEMFC system performance have been determinate. The analysis of the effect of the water management of the anodic exhaust at variable FC loads has been carried out, and the purge process programming logic was optimized, leading also to the determination of the optimal flooding times by varying the AC FC power delivered by the cell. Furthermore, the degradation mechanisms of the PEMFC system, in particular due to the flooding of the anodic side, have been assessed using an algorithm that considers the FC like a black box, and it is able to determine the amount of not-reacted H2 and, therefore, the causes which produce that. Using experimental data that cover a two-year time span, the ageing suffered by the FC system has been tested and analyzed.

A farm-level programming model to compare the atmospheric impact of conventional and organic farming

A model is developed to represent the activity of a farm using the method of linear programming. Two are the main components of the model, the balance of soil fertility and the livestock nutrition. According to the first, the farm is supposed to have a total requirement of nitrogen, which is to be accomplished either through internal sources (manure) or through external sources (fertilisers). The second component describes the animal husbandry as having a nutritional requirement which must be satisfied through the internal production of arable crops or the acquisition of feed from the market. The farmer is supposed to maximise total net income from the agricultural and the zoo-technical activities by choosing one rotation among those available for climate and acclivity. The perspective of the analysis is one of a short period: the structure of the farm is supposed to be fixed without possibility to change the allocation of permanent crops and the amount of animal husbandry. The model is integrated with an environmental module that describes the role of the farm within the carbon-nitrogen cycle. On the one hand the farm allows storing carbon through the photosynthesis of the plants and the accumulation of carbon in the soil; on the other some activities of the farm emit greenhouse gases into the atmosphere. The model is tested for some representative farms of the Emilia-Romagna region, showing to be capable to give different results for conventional and organic farming and providing first results concerning the different atmospheric impact. Relevant data about the representative farms and the feasible rotations are extracted from the FADN database, with an integration of the coefficients from the literature.

Parallel Architectures for Many-Core Systems-On-Chip in Deep Sub-Micron Technology

Despite the several issues faced in the past, the evolutionary trend of silicon has kept its constant pace. Today an ever increasing number of cores is integrated onto the same die. Unfortunately, the extraordinary performance achievable by the many-core paradigm is limited by several factors. Memory bandwidth limitation, combined with inefficient synchronization mechanisms, can severely overcome the potential computation capabilities. Moreover, the huge HW/SW design space requires accurate and flexible tools to perform architectural explorations and validation of design choices. In this thesis we focus on the aforementioned aspects: a flexible and accurate Virtual Platform has been developed, targeting a reference many-core architecture. Such tool has been used to perform architectural explorations, focusing on instruction caching architecture and hybrid HW/SW synchronization mechanism. Beside architectural implications, another issue of embedded systems is considered: energy efficiency. Near Threshold Computing is a key research area in the Ultra-Low-Power domain, as it promises a tenfold improvement in energy efficiency compared to super-threshold operation and it mitigates thermal bottlenecks. The physical implications of modern deep sub-micron technology are severely limiting performance and reliability of modern designs. Reliability becomes a major obstacle when operating in NTC, especially memory operation becomes unreliable and can compromise system correctness. In the present work a novel hybrid memory architecture is devised to overcome reliability issues and at the same time improve energy efficiency by means of aggressive voltage scaling when allowed by workload requirements. Variability is another great drawback of near-threshold operation. The greatly increased sensitivity to threshold voltage variations in today a major concern for electronic devices. We introduce a variation-tolerant extension of the baseline many-core architecture. By means of micro-architectural knobs and a lightweight runtime control unit, the baseline architecture becomes dynamically tolerant to variations.

Nano-Power Integrated Circuits for Energy Harvesting

The energy harvesting research field has grown considerably in the last decade due to increasing interests in energy autonomous sensing systems, which require smart and efficient interfaces for extracting power from energy source and power management (PM) circuits. This thesis investigates the design trade-offs for minimizing the intrinsic power of PM circuits, in order to allow operation with very weak energy sources. For validation purposes, three different integrated power converter and PM circuits for energy harvesting applications are presented. They have been designed for nano-power operations and single-source converters can operate with input power lower than 1 μW. The first IC is a buck-boost converter for piezoelectric transducers (PZ) implementing Synchronous Electrical Charge Extraction (SECE), a non-linear energy extraction technique. Moreover, Residual Charge Inversion technique is exploited for extracting energy from PZ with weak and irregular excitations (i.e. lower voltage), and the implemented PM policy, named Two-Way Energy Storage, considerably reduces the start-up time of the converter, improving the overall conversion efficiency. The second proposed IC is a general-purpose buck-boost converter for low-voltage DC energy sources, up to 2.5 V. An ultra-low-power MPPT circuit has been designed in order to track variations of source power. Furthermore, a capacitive boost circuit has been included, allowing the converter start-up from a source voltage VDC0 = 223 mV. A nano-power programmable linear regulator is also included in order to provide a stable voltage to the load. The third IC implements an heterogeneous multisource buck-boost converter. It provides up to 9 independent input channels, of which 5 are specific for PZ (with SECE) and 4 for DC energy sources with MPPT. The inductor is shared among channels and an arbiter, designed with asynchronous logic to reduce the energy consumption, avoids simultaneous access to the buck-boost core, with a dynamic schedule based on source priority.

Interoperability Challenges in Internet of Things Systems: a Service-Oriented Computing Approach

Internet of Things systems are pervasive systems evolved from cyber-physical to large-scale systems. Due to the number of technologies involved, software development involves several integration challenges. Among them, the ones preventing proper integration are those related to the system heterogeneity, and thus addressing interoperability issues. From a software engineering perspective, developers mostly experience the lack of interoperability in the two phases of software development: programming and deployment. On the one hand, modern software tends to be distributed in several components, each adopting its most-appropriate technology stack, pushing programmers to code in a protocol- and data-agnostic way. On the other hand, each software component should run in the most appropriate execution environment and, as a result, system architects strive to automate the deployment in distributed infrastructures. This dissertation aims to improve the development process by introducing proper tools to handle certain aspects of the system heterogeneity. Our effort focuses on three of these aspects and, for each one of those, we propose a tool addressing the underlying challenge. The first tool aims to handle heterogeneity at the transport and application protocol level, the second to manage different data formats, while the third to obtain optimal deployment. To realize the tools, we adopted a linguistic approach, i.e.\ we provided specific linguistic abstractions that help developers to increase the expressive power of the programming language they use, writing better solutions in more straightforward ways. To validate the approach, we implemented use cases to show that the tools can be used in practice and that they help to achieve the expected level of interoperability. In conclusion, to move a step towards the realization of an integrated Internet of Things ecosystem, we target programmers and architects and propose them to use the presented tools to ease the software development process.

Smart Farming in Italian agriculture: essays on adoption and diffusion dynamics shaping the agricultural digital transition

Smart Farming Technologies (SFT) is a term used to define the set of digital technologies able not only to control and manage the farm system, but also to connect it to the many disruptive digital applications posed at multiple links along the value chain. The adoption of SFT has been so far limited, with significant differences at country-levels and among different types of farms and farmers. The objective of this thesis is to analyze what factors contributes to shape the agricultural digital transition and to assess its potential impacts in the Italian agri-food system. Specifically, this overall research objective is approached under three different perspectives. Firstly, we carry out a review of the literature that focuses on the determinants of adoption of farm-level Management Information Systems (MIS), namely the most adopted smart farming solutions in Italy. Secondly, we run an empirical analysis on what factors are currently shaping the adoption of SFT in Italy. In doing so, we focus on the multi-process and multi-faceted aspects of the adoption, by overcoming the one-off binary approach often used to study adoption decisions. Finally, we adopt a forward-looking perspective to investigate what the socio-ethical implications of a diffused use of SFT might be. On the one hand, our results indicate that bigger, more structured farms with higher levels of commercial integration along the agri-food supply chain are those more likely to be early adopters. On the other hand, they highlight the need for the institutional and organizational environment around farms to more effectively support farmers in the digital transition. Moreover, the role of several other actors and actions are discussed and analyzed, by highlighting the key role of specific agri-food stakeholders and ad-hoc policies, with the aim to propose a clearer path towards an efficient, fair and inclusive digitalization of the agrifood sector.

Edge and Big Data technologies for Industry 4.0 to create an integrated pre-sale and after-sale environment

The fourth industrial revolution, also known as Industry 4.0, has rapidly gained traction in businesses across Europe and the world, becoming a central theme in small, medium, and large enterprises alike. This new paradigm shifts the focus from locally-based and barely automated firms to a globally interconnected industrial sector, stimulating economic growth and productivity, and supporting the upskilling and reskilling of employees. However, despite the maturity and scalability of information and cloud technologies, the support systems already present in the machine field are often outdated and lack the necessary security, access control, and advanced communication capabilities. This dissertation proposes architectures and technologies designed to bridge the gap between Operational and Information Technology, in a manner that is non-disruptive, efficient, and scalable. The proposal presents cloud-enabled data-gathering architectures that make use of the newest IT and networking technologies to achieve the desired quality of service and non-functional properties. By harnessing industrial and business data, processes can be optimized even before product sale, while the integrated environment enhances data exchange for post-sale support. The architectures have been tested and have shown encouraging performance results, providing a promising solution for companies looking to embrace Industry 4.0, enhance their operational capabilities, and prepare themselves for the upcoming fifth human-centric revolution.

Early-stage deformation localisation in thrust systems: new perspectives from carbonates in the Italian Southern Alps and Oman Mountains

This thesis investigates mechanisms and boundary conditions that steer the early localisation of deformation and strain in carbonate multilayers involved in thrust systems, under shallow and mid-crustal conditions. Much is already understood about deformation localisation, but some key points remain loosely constrained. They encompass i) the understanding of which structural domains can preserve evidence of early stages of tectonic shortening, ii) the recognition of which mechanisms assist deformation during these stages and iii) the identification of parameters that actually steer the beginning of localisation. To clarify these points, the thesis presents the results of an integrated, multiscale and multi-technique structural study that relied on field and laboratory data to analyse the structural, architectural, mineralogical and geochemical features that govern deformation during compressional tectonics. By focusing on two case studies, the Eastern Southern Alps (northern Italy), where deformation is mainly brittle, and the Oman Mountains (northeastern Oman), where ductile deformation dominates, the thesis shows that the deformation localisation is steered by several mechanisms that mutually interact at different stages during compression. At shallow crustal conditions, derived conceptual and numerical models show that both inherited (e.g., stratigraphic) and acquired (e.g., structural) features play a key role in steering deformation and differentiating the seismic behaviour of the multilayer succession. At the same time, at deeper crustal conditions, strain localises in narrow domains in which fluids, temperature, shear strain and pressure act together during the development of the internal fabric and the chemical composition of mylonitic shear zones, in which localisation took place under high-pressure (HP) and low-temperature (LT) conditions. In particular, results indicate that those shear zones acted as “sheltering structural capsules” in which peculiar processes can happen and where the results of these processes can be successively preserved even over hundreds of millions of years.

Network assimilation methodology and sectorization of water distribution systems by graph mining and optimization algorithm

Water Distribution Networks (WDNs) play a vital importance rule in communities, ensuring well-being band supporting economic growth and productivity. The need for greater investment requires design choices will impact on the efficiency of management in the coming decades. This thesis proposes an algorithmic approach to address two related problems:(i) identify the fundamental asset of large WDNs in terms of main infrastructure;(ii) sectorize large WDNs into isolated sectors in order to respect the minimum service to be guaranteed to users. Two methodologies have been developed to meet these objectives and subsequently they were integrated to guarantee an overall process which allows to optimize the sectorized configuration of WDN taking into account the needs to integrated in a global vision the two problems (i) and (ii). With regards to the problem (i), the methodology developed introduces the concept of primary network to give an answer with a dual approach, of connecting main nodes of WDN in terms of hydraulic infrastructures (reservoirs, tanks, pumps stations) and identifying hypothetical paths with the minimal energy losses. This primary network thus identified can be used as an initial basis to design the sectors. The sectorization problem (ii) has been faced using optimization techniques by the development of a new dedicated Tabu Search algorithm able to deal with real case studies of WDNs. For this reason, three new large WDNs models have been developed in order to test the capabilities of the algorithm on different and complex real cases. The developed methodology also allows to automatically identify the deficient parts of the primary network and dynamically includes new edges in order to support a sectorized configuration of the WDN. The application of the overall algorithm to the new real case studies and to others from literature has given applicable solutions even in specific complex situations.

Modelling, simulations and monitoring for sustainable plant and animal production systems through precision technique

Protected crop production is a modern and innovative approach to cultivating plants in a controlled environment to optimize growth, yield, and quality. This method involves using structures such as greenhouses or tunnels to create a sheltered environment. These productive solutions are characterized by a careful regulation of variables like temperature, humidity, light, and ventilation, which collectively contribute to creating an optimal microclimate for plant growth. Heating, cooling, and ventilation systems are used to maintain optimal conditions for plant growth, regardless of external weather fluctuations. Protected crop production plays a crucial role in addressing challenges posed by climate variability, population growth, and food security. Similarly, animal husbandry involves providing adequate nutrition, housing, medical care and environmental conditions to ensure animal welfare. Then, sustainability is a critical consideration in all forms of agriculture, including protected crop and animal production. Sustainability in animal production refers to the practice of producing animal products in a way that minimizes negative impacts on the environment, promotes animal welfare, and ensures the long-term viability of the industry. Then, the research activities performed during the PhD can be inserted exactly in the field of Precision Agriculture and Livestock farming. Here the focus is on the computational fluid dynamic (CFD) approach and environmental assessment applied to improve yield, resource efficiency, environmental sustainability, and cost savings. It represents a significant shift from traditional farming methods to a more technology-driven, data-driven, and environmentally conscious approach to crop and animal production. On one side, CFD is powerful and precise techniques of computer modeling and simulation of airflows and thermo-hygrometric parameters, that has been applied to optimize the growth environment of crops and the efficiency of ventilation in pig barns. On the other side, the sustainability aspect has been investigated and researched in terms of Life Cycle Assessment analyses.