Selection of a new hardware and software platform for railway interlocking

The interlocking system is one of the main actors for safe railway transportation. In most cases, the whole system is supplied by a single vendor. The recent regulations from the European Union direct for an “open” architecture to invite new game changers and reduce life-cycle costs. The objective of the thesis is to propose an alternative platform that could replace a legacy interlocking system. In the thesis, various commercial off-the-shelf hardware and software products are studied which could be assembled to compose an alternative interlocking platform. The platform must be open enough to adapt to any changes in the constituent elements and abide by the proposed baselines of new standardization initiatives, such as ERTMS, EULYNX, and RCA. In this thesis, a comparative study is performed between these products based on hardware capacity, architecture, communication protocols, programming tools, security, railway certifications, life-cycle issues, etc

Infrastructures and Algorithms for Testable and Dependable Systems-on-a-Chip

Every new node of semiconductor technologies provides further miniaturization and higher performances, increasing the number of advanced functions that electronic products can offer. Silicon area is now so cheap that industries can integrate in a single chip usually referred to as System-on-Chip (SoC), all the components and functions that historically were placed on a hardware board. Although adding such advanced functionality can benefit users, the manufacturing process is becoming finer and denser, making chips more susceptible to defects. Today’s very deep-submicron semiconductor technologies (0.13 micron and below) have reached susceptibility levels that put conventional semiconductor manufacturing at an impasse. Being able to rapidly develop, manufacture, test, diagnose and verify such complex new chips and products is crucial for the continued success of our economy at-large. This trend is expected to continue at least for the next ten years making possible the design and production of 100 million transistor chips. To speed up the research, the National Technology Roadmap for Semiconductors identified in 1997 a number of major hurdles to be overcome. Some of these hurdles are related to test and dependability. Test is one of the most critical tasks in the semiconductor production process where Integrated Circuits (ICs) are tested several times starting from the wafer probing to the end of production test. Test is not only necessary to assure fault free devices but it also plays a key role in analyzing defects in the manufacturing process. This last point has high relevance since increasing time-to-market pressure on semiconductor fabrication often forces foundries to start volume production on a given semiconductor technology node before reaching the defect densities, and hence yield levels, traditionally obtained at that stage. The feedback derived from test is the only way to analyze and isolate many of the defects in today’s processes and to increase process’s yield. With the increasing need of high quality electronic products, at each new physical assembly level, such as board and system assembly, test is used for debugging, diagnosing and repairing the sub-assemblies in their new environment. Similarly, the increasing reliability, availability and serviceability requirements, lead the users of high-end products performing periodic tests in the field throughout the full life cycle. To allow advancements in each one of the above scaling trends, fundamental changes are expected to emerge in different Integrated Circuits (ICs) realization disciplines such as IC design, packaging and silicon process. These changes have a direct impact on test methods, tools and equipment. Conventional test equipment and methodologies will be inadequate to assure high quality levels. On chip specialized block dedicated to test, usually referred to as Infrastructure IP (Intellectual Property), need to be developed and included in the new complex designs to assure that new chips will be adequately tested, diagnosed, measured, debugged and even sometimes repaired. In this thesis, some of the scaling trends in designing new complex SoCs will be analyzed one at a time, observing their implications on test and identifying the key hurdles/challenges to be addressed. The goal of the remaining of the thesis is the presentation of possible solutions. It is not sufficient to address just one of the challenges; all must be met at the same time to fulfill the market requirements

Development of a Reconfigurable Multi-Faceted Communications Device Using Partial Dynamic Reconfiguration

Supporting a variety of communication protocols has typically required extensive hard- ware and Input/Output (I/O) interfaces targeting each protocol specifically. Recent designs in the past ten years have created more dynamic approaches by using Field Programmable Gate Arrays (FPGAs) and embedded hardware to implement or simulate previous hardware I/O designs. With the constant increase in FPGA and embedded technologies the capabilities of dynamic implementations have expanded. This report addresses the design of an up-to-date reconfigurable multi-faceted embedded device targeting recent technological advances in FPGAs

System-on-Chip Design and Test with Embedded Debug Capabilities

In this project, I started with a System-on-Chip platform with embedded test structures. The baseline platform consisted of a Leon2 CPU, AMBA on-chip bus, and an Advanced Encryption Standard decryption module. The basic objective of this thesis was to use the embedded reconfigurable logic blocks for post-silicon debug and verification. The System-on-Chip platform was designed at the register transistor level and implemented in a 180-nm IBM process. Test logic instrumentation was done with DAFCA (Design Automation for Flexible Chip Architecture) Inc. pre-silicon tools. The design was then synthesized using the Synopsys Design Compiler and placed and routed using Cadence SOC Encounter. Total transistor count is about 3 million, including 1400K transistors for the debug module serving as on chip logic analyzer. Core size of the design is about 4.8mm x 4.8mm and the system is working at 151MHz. Design verification was done with Cadence NCSim. The controllability and observability of internal signals of the design is greatly increased with the help of pre-silicon tools which helps locate bugs and later fix them with the help of post-silicon tools. This helps prevent re-spins on several occasions thus saving millions of dollars. Post-silicon tools have been used to program assertions and triggers and inject numerous personalities into the reconfigurable fabric which has greatly increased the versatility of the circuit

Intrinsically Evolvable Artificial Neural Networks

Dedicated hardware implementations of neural networks promise to provide faster, lower power operation when compared to software implementations executing on processors. Unfortunately, most custom hardware implementations do not support intrinsic training of these networks on-chip. The training is typically done using offline software simulations and the obtained network is synthesized and targeted to the hardware offline. The FPGA design presented here facilitates on-chip intrinsic training of artificial neural networks. Block-based neural networks (BbNN), the type of artificial neural networks implemented here, are grid-based networks neuron blocks. These networks are trained using genetic algorithms to simultaneously optimize the network structure and the internal synaptic parameters. The design supports online structure and parameter updates, and is an intrinsically evolvable BbNN platform supporting functional-level hardware evolution. Functional-level evolvable hardware (EHW) uses evolutionary algorithms to evolve interconnections and internal parameters of functional modules in reconfigurable computing systems such as FPGAs. Functional modules can be any hardware modules such as multipliers, adders, and trigonometric functions. In the implementation presented, the functional module is a neuron block. The designed platform is suitable for applications in dynamic environments, and can be adapted and retrained online. The online training capability has been demonstrated using a case study. A performance characterization model for RC implementations of BbNNs has also been presented

Enhancing Real-time Embedded Image Processing Robustness on Reconfigurable Devices for Critical Applications

Nowadays, image processing is increasingly used in several application fields, such as biomedical, aerospace, or automotive. Within these fields, image processing is used to serve both non-critical and critical tasks. As example, in automotive, cameras are becoming key sensors in increasing car safety, driving assistance and driving comfort. They have been employed for infotainment (non-critical), as well as for some driver assistance tasks (critical), such as Forward Collision Avoidance, Intelligent Speed Control, or Pedestrian Detection. The complexity of these algorithms brings a challenge in real-time image processing systems, requiring high computing capacity, usually not available in processors for embedded systems. Hardware acceleration is therefore crucial, and devices such as Field Programmable Gate Arrays (FPGAs) best fit the growing demand of computational capabilities. These devices can assist embedded processors by significantly speeding-up computationally intensive software algorithms. Moreover, critical applications introduce strict requirements not only from the real-time constraints, but also from the device reliability and algorithm robustness points of view. Technology scaling is highlighting reliability problems related to aging phenomena, and to the increasing sensitivity of digital devices to external radiation events that can cause transient or even permanent faults. These faults can lead to wrong information processed or, in the worst case, to a dangerous system failure. In this context, the reconfigurable nature of FPGA devices can be exploited to increase the system reliability and robustness by leveraging Dynamic Partial Reconfiguration features. The research work presented in this thesis focuses on the development of techniques for implementing efficient and robust real-time embedded image processing hardware accelerators and systems for mission-critical applications. Three main challenges have been faced and will be discussed, along with proposed solutions, throughout the thesis: (i) achieving real-time performances, (ii) enhancing algorithm robustness, and (iii) increasing overall system's dependability. In order to ensure real-time performances, efficient FPGA-based hardware accelerators implementing selected image processing algorithms have been developed. Functionalities offered by the target technology, and algorithm's characteristics have been constantly taken into account while designing such accelerators, in order to efficiently tailor algorithm's operations to available hardware resources. On the other hand, the key idea for increasing image processing algorithms' robustness is to introduce self-adaptivity features at algorithm level, in order to maintain constant, or improve, the quality of results for a wide range of input conditions, that are not always fully predictable at design-time (e.g., noise level variations). This has been accomplished by measuring at run-time some characteristics of the input images, and then tuning the algorithm parameters based on such estimations. Dynamic reconfiguration features of modern reconfigurable FPGA have been extensively exploited in order to integrate run-time adaptivity into the designed hardware accelerators. Tools and methodologies have been also developed in order to increase the overall system dependability during reconfiguration processes, thus providing safe run-time adaptation mechanisms. In addition, taking into account the target technology and the environments in which the developed hardware accelerators and systems may be employed, dependability issues have been analyzed, leading to the development of a platform for quickly assessing the reliability and characterizing the behavior of hardware accelerators implemented on reconfigurable FPGAs when they are affected by such faults

Reinforced silica-carbon nanotube monolithic aerogels synthesised by rapid controlled gelation

This work introduces a new synthesis procedure for obtaining homogeneous silica hybrid aerogels with carbon nanotube contents up to 2.50 wt.%. The inclusion of nanotubes in the highly porous silica matrix was performed by a two-step sol–gel process, resulting in samples with densities below 80 mg/cm3. The structural analyses (N2 physisorption and SEM) revealed the hierarchical structure of the porous matrix formed by nanoparticles arranged in clusters of 100 and 300 nm in size, specific surface areas around 600 m2/g and porous volumes above 4.0 cm3/g. In addition, a relevant increase on the mechanical performance was found, and an increment of 50% for the compressive strength and 90% for the maximum deformation were measured by uniaxial compression. This reinforcement was possible thanks to the outstanding dispersion of the CNT within the silica matrix and the formation of Si–O–C bridges between nanotubes and silica matrix, as suggested by FTIR. Therefore, the original synthesis procedure introduced in this work allows the fabrication of highly porous hybrid materials loaded with carbon nanotubes homogeneously distributed in the space, which remain available for a variety of technological applications

Digitalization of Offshore Wind Farm Systems

Master's thesis in Offshore Technology: Industrial asset managementThis thesis investigates how new digital technologies and digitalization can help further evolve the offshore wind industry using the Industry 4.0 concept as a basis and explores how technologies within this concept can contribute to an offshore wind farm that overcomes some of these challenges. The study focuses on an offshore wind farm from a systems perspective, including respective modules, and where the Industry 4.0 technologies can be applied. Following this is the establishment of a systematic digitalization framework and a proposal on how to cope with increased volumes of data, connectivity, and complexity.publishedVersio