Very Long Baseline Interferometry in Italy Wide-field VLBI imaging and astrometry and prospects for an Italian VLBI network including the Sardinia Radio Telescope

In this thesis the use of widefield imaging techniques and VLBI observations with a limited number of antennas are explored. I present techniques to efficiently and accurately image extremely large UV datasets. Very large VLBI datasets must be reduced into multiple, smaller datasets if today’s imaging algorithms are to be used to image them. I present a procedure for accurately shifting the phase centre of a visibility dataset. This procedure has been thoroughly tested and found to be almost two orders of magnitude more accurate than existing techniques. Errors have been found at the level of one part in 1.1 million. These are unlikely to be measurable except in the very largest UV datasets. Results of a four-station VLBI observation of a field containing multiple sources are presented. A 13 gigapixel image was constructed to search for sources across the entire primary beam of the array by generating over 700 smaller UV datasets. The source 1320+299A was detected and its astrometric position with respect to the calibrator J1329+3154 is presented. Various techniques for phase calibration and imaging across this field are explored including using the detected source as an in-beam calibrator and peeling of distant confusing sources from VLBI visibility datasets. A range of issues pertaining to wide-field VLBI have been explored including; parameterising the wide-field performance of VLBI arrays; estimating the sensitivity across the primary beam both for homogeneous and heterogeneous arrays; applying techniques such as mosaicing and primary beam correction to VLBI observations; quantifying the effects of time-average and bandwidth smearing; and calibration and imaging of wide-field VLBI datasets. The performance of a computer cluster at the Istituto di Radioastronomia in Bologna has been characterised with regard to its ability to correlate using the DiFX software correlator. Using existing software it was possible to characterise the network speed particularly for MPI applications. The capabilities of the DiFX software correlator, running on this cluster, were measured for a range of observation parameters and were shown to be commensurate with the generic performance parameters measured. The feasibility of an Italian VLBI array has been explored, with discussion of the infrastructure required, the performance of such an array, possible collaborations, and science which could be achieved. Results from a 22 GHz calibrator survey are also presented. 21 out of 33 sources were detected on a single baseline between two Italian antennas (Medicina to Noto). The results and discussions presented in this thesis suggest that wide-field VLBI is a technique whose time has finally come. Prospects for exciting new science are discussed in the final chapter.

Sistemi riconfigurabili a basso consumo per applicazioni di monitoraggio distribuito

The term Ambient Intelligence (AmI) refers to a vision on the future of the information society where smart, electronic environment are sensitive and responsive to the presence of people and their activities (Context awareness). In an ambient intelligence world, devices work in concert to support people in carrying out their everyday life activities, tasks and rituals in an easy, natural way using information and intelligence that is hidden in the network connecting these devices. This promotes the creation of pervasive environments improving the quality of life of the occupants and enhancing the human experience. AmI stems from the convergence of three key technologies: ubiquitous computing, ubiquitous communication and natural interfaces. Ambient intelligent systems are heterogeneous and require an excellent cooperation between several hardware/software technologies and disciplines, including signal processing, networking and protocols, embedded systems, information management, and distributed algorithms. Since a large amount of fixed and mobile sensors embedded is deployed into the environment, the Wireless Sensor Networks is one of the most relevant enabling technologies for AmI. WSN are complex systems made up of a number of sensor nodes which can be deployed in a target area to sense physical phenomena and communicate with other nodes and base stations. These simple devices typically embed a low power computational unit (microcontrollers, FPGAs etc.), a wireless communication unit, one or more sensors and a some form of energy supply (either batteries or energy scavenger modules). WNS promises of revolutionizing the interactions between the real physical worlds and human beings. Low-cost, low-computational power, low energy consumption and small size are characteristics that must be taken into consideration when designing and dealing with WSNs. To fully exploit the potential of distributed sensing approaches, a set of challengesmust be addressed. Sensor nodes are inherently resource-constrained systems with very low power consumption and small size requirements which enables than to reduce the interference on the physical phenomena sensed and to allow easy and low-cost deployment. They have limited processing speed,storage capacity and communication bandwidth that must be efficiently used to increase the degree of local ”understanding” of the observed phenomena. A particular case of sensor nodes are video sensors. This topic holds strong interest for a wide range of contexts such as military, security, robotics and most recently consumer applications. Vision sensors are extremely effective for medium to long-range sensing because vision provides rich information to human operators. However, image sensors generate a huge amount of data, whichmust be heavily processed before it is transmitted due to the scarce bandwidth capability of radio interfaces. In particular, in video-surveillance, it has been shown that source-side compression is mandatory due to limited bandwidth and delay constraints. Moreover, there is an ample opportunity for performing higher-level processing functions, such as object recognition that has the potential to drastically reduce the required bandwidth (e.g. by transmitting compressed images only when something ‘interesting‘ is detected). The energy cost of image processing must however be carefully minimized. Imaging could play and plays an important role in sensing devices for ambient intelligence. Computer vision can for instance be used for recognising persons and objects and recognising behaviour such as illness and rioting. Having a wireless camera as a camera mote opens the way for distributed scene analysis. More eyes see more than one and a camera system that can observe a scene from multiple directions would be able to overcome occlusion problems and could describe objects in their true 3D appearance. In real-time, these approaches are a recently opened field of research. In this thesis we pay attention to the realities of hardware/software technologies and the design needed to realize systems for distributed monitoring, attempting to propose solutions on open issues and filling the gap between AmI scenarios and hardware reality. The physical implementation of an individual wireless node is constrained by three important metrics which are outlined below. Despite that the design of the sensor network and its sensor nodes is strictly application dependent, a number of constraints should almost always be considered. Among them: • Small form factor to reduce nodes intrusiveness. • Low power consumption to reduce battery size and to extend nodes lifetime. • Low cost for a widespread diffusion. These limitations typically result in the adoption of low power, low cost devices such as low powermicrocontrollers with few kilobytes of RAMand tenth of kilobytes of program memory with whomonly simple data processing algorithms can be implemented. However the overall computational power of the WNS can be very large since the network presents a high degree of parallelism that can be exploited through the adoption of ad-hoc techniques. Furthermore through the fusion of information from the dense mesh of sensors even complex phenomena can be monitored. In this dissertation we present our results in building several AmI applications suitable for a WSN implementation. The work can be divided into two main areas:Low Power Video Sensor Node and Video Processing Alghoritm and Multimodal Surveillance . Low Power Video Sensor Nodes and Video Processing Alghoritms In comparison to scalar sensors, such as temperature, pressure, humidity, velocity, and acceleration sensors, vision sensors generate much higher bandwidth data due to the two-dimensional nature of their pixel array. We have tackled all the constraints listed above and have proposed solutions to overcome the current WSNlimits for Video sensor node. We have designed and developed wireless video sensor nodes focusing on the small size and the flexibility of reuse in different applications. The video nodes target a different design point: the portability (on-board power supply, wireless communication), a scanty power budget (500mW),while still providing a prominent level of intelligence, namely sophisticated classification algorithmand high level of reconfigurability. We developed two different video sensor node: The device architecture of the first one is based on a low-cost low-power FPGA+microcontroller system-on-chip. The second one is based on ARM9 processor. Both systems designed within the above mentioned power envelope could operate in a continuous fashion with Li-Polymer battery pack and solar panel. Novel low power low cost video sensor nodes which, in contrast to sensors that just watch the world, are capable of comprehending the perceived information in order to interpret it locally, are presented. Featuring such intelligence, these nodes would be able to cope with such tasks as recognition of unattended bags in airports, persons carrying potentially dangerous objects, etc.,which normally require a human operator. Vision algorithms for object detection, acquisition like human detection with Support Vector Machine (SVM) classification and abandoned/removed object detection are implemented, described and illustrated on real world data. Multimodal surveillance: In several setup the use of wired video cameras may not be possible. For this reason building an energy efficient wireless vision network for monitoring and surveillance is one of the major efforts in the sensor network community. Energy efficiency for wireless smart camera networks is one of the major efforts in distributed monitoring and surveillance community. For this reason, building an energy efficient wireless vision network for monitoring and surveillance is one of the major efforts in the sensor network community. The Pyroelectric Infra-Red (PIR) sensors have been used to extend the lifetime of a solar-powered video sensor node by providing an energy level dependent trigger to the video camera and the wireless module. Such approach has shown to be able to extend node lifetime and possibly result in continuous operation of the node.Being low-cost, passive (thus low-power) and presenting a limited form factor, PIR sensors are well suited for WSN applications. Moreover techniques to have aggressive power management policies are essential for achieving long-termoperating on standalone distributed cameras needed to improve the power consumption. We have used an adaptive controller like Model Predictive Control (MPC) to help the system to improve the performances outperforming naive power management policies.

Self-Assembled Monolayers (SAMs) in Organic Field-Effect Transistors

Organic printed electronics is attracting an ever-growing interest in the last decades because of its impressive breakthroughs concerning the chemical design of π-conjugated materials and their processing. This has an impact on novel applications, such as flexible-large-area displays, low- cost printable circuits, plastic solar cells and lab-on-a-chip devices. The organic field-effect transistor (OFET) relies on a thin film of organic semiconductor that bridges source and drain electrodes. Since its first discovery in the 80s, intensive research activities were deployed in order to control the chemico-physical properties of these electronic devices and consequently their charge. Self-assembled monolayers (SAMs) are a versatile tool for tuning the properties of metallic, semi-conducting, and insulating surfaces. Within this context, OFETs represent reliable instruments for measuring the electrical properties of the SAMs in a Metal/SAM/OS junction. Our experimental approach, named Charge Injection Organic-Gauge (CIOG), uses OTFT in a charge-injection controlled regime. The CIOG sensitivity has been extensively demonstrated on different homologous self-assembling molecules that differ in either chain length or in anchor/terminal group. One of the latest applications of organic electronics is the so-called “bio-electronics” that makes use of electronic devices to encompass interests of the medical science, such as biosensors, biotransducers etc… As a result, thee second part of this thesis deals with the realization of an electronic transducer based on an Organic Field-Effect Transistor operating in aqueous media. Here, the conventional bottom gate/bottom contact configuration is replaced by top gate architecture with the electrolyte that ensures electrical contact between the top gold electrode and the semiconductor layer. This configuration is named Electrolyte-Gated Field-Effect Transistor (EGOFET). The functionalization of the top electrode is the sensing core of the device allowing the detection of dopamine as well as of protein biomarkers with ultra-low sensitivity.

Methodologies for Synthesizable Programmable Devices based on Multi-Stage Switching Networks

Nowadays the rise of non-recurring engineering (NRE) costs associated with complexity is becoming a major factor in SoC design, limiting both scaling opportunities and the flexibility advantages offered by the integration of complex computational units. The introduction of embedded programmable elements can represent an appealing solution, able both to guarantee the desired flexibility and upgradabilty and to widen the SoC market. In particular embedded FPGA (eFPGA) cores can provide bit-level optimization for those applications which benefits from synthesis, paying on the other side in terms of performance penalties and area overhead with respect to standard cell ASIC implementations. In this scenario this thesis proposes a design methodology for a synthesizable programmable device designed to be embedded in a SoC. A soft-core embedded FPGA (eFPGA) is hence presented and analyzed in terms of the opportunities given by a fully synthesizable approach, following an implementation flow based on Standard-Cell methodology. A key point of the proposed eFPGA template is that it adopts a Multi-Stage Switching Network (MSSN) as the foundation of the programmable interconnects, since it can be efficiently synthesized and optimized through a standard cell based implementation flow, ensuring at the same time an intrinsic congestion-free network topology. The evaluation of the flexibility potentialities of the eFPGA has been performed using different technology libraries (STMicroelectronics CMOS 65nm and BCD9s 0.11μm) through a design space exploration in terms of area-speed-leakage tradeoffs, enabled by the full synthesizability of the template. Since the most relevant disadvantage of the adopted soft approach, compared to a hardcore, is represented by a performance overhead increase, the eFPGA analysis has been made targeting small area budgets. The generation of the configuration bitstream has been obtained thanks to the implementation of a custom CAD flow environment, and has allowed functional verification and performance evaluation through an application-aware analysis.