Design synthesis for dynamically reconfigurable logic systems

Dynamic reconfiguration of logic circuits has been a research problem for over four decades. While applications using logic reconfiguration in practical scenarios have been demonstrated, the design of these systems has proved to be a difficult process demanding the skills of an experienced reconfigurable logic design expert. This thesis proposes an automatic synthesis method which relieves designers of some of the difficulties associated with designing partially dynamically reconfigurable systems. A new design abstraction model for reconfigurable systems is proposed in order to support design exploration using the presented method. Given an input behavioural model, a technology server and a set of design constraints, the method will generate a reconfigurable design solution in the form of a 3D floorplan and a configuration schedule. The approach makes use of genetic algorithms. It facilitates global optimisation to accommodate multiple design objectives common in reconfigurable system design, while making realistic estimates of configuration overheads and of the potential for resource sharing between configurations. A set of custom evolutionary operators has been developed to cope with a multiple-objective search space. Furthermore, the application of a simulation technique verifying the lll results of such an automatic exploration is outlined in the thesis. The qualities of the proposed method are evaluated using a set of benchmark designs taking data from a real reconfigurable logic technology. Finally, some extensions to the proposed method and possible research directions are discussed

Generalised correlation higher order neural networks, neural network operation and Levenberg-Marquardt training on field programmable gate arrays

Higher Order Neural Networks (HONNs) were introduced in the late 80's as a solution to the increasing complexity within Neural Networks (NNs). Similar to NNs HONNs excel at performing pattern recognition, classification, optimisation particularly for non-linear systems in varied applications such as communication channel equalisation, real time intelligent control, and intrusion detection. This research introduced new HONNs called the Generalised Correlation Higher Order Neural Networks which as an extension to the ordinary first order NNs and HONNs, based on interlinked arrays of correlators with known relationships, they provide the NN with a more extensive view by introducing interactions between the data as an input to the NN model. All studies included two data sets to generalise the applicability of the findings. The research investigated the performance of HONNs in the estimation of short term returns of two financial data sets, the FTSE 100 and NASDAQ. The new models were compared against several financial models and ordinary NNs. Two new HONNs, the Correlation HONN (C-HONN) and the Horizontal HONN (Horiz-HONN) outperformed all other models tested in terms of the Akaike Information Criterion (AIC). The new work also investigated HONNs for camera calibration and image mapping. HONNs were compared against NNs and standard analytical methods in terms of mapping performance for three cases; 3D-to-2D mapping, a hybrid model combining HONNs with an analytical model, and 2D-to-3D inverse mapping. This study considered 2 types of data, planar data and co-planar (cube) data. To our knowledge this is the first study comparing HONNs against NNs and analytical models for camera calibration. HONNs were able to transform the reference grid onto the correct camera coordinate and vice versa, an aspect that the standard analytical model fails to perform with the type of data used. HONN 3D-to-2D mapping had calibration error lower than the parametric model by up to 24% for plane data and 43% for cube data. The hybrid model also had lower calibration error than the parametric model by 12% for plane data and 34% for cube data. However, the hybrid model did not outperform the fully non-parametric models. Using HONNs for inverse mapping from 2D-to-3D outperformed NNs by up to 47% in the case of cube data mapping. This thesis is also concerned with the operation and training of NNs in limited precision specifically on Field Programmable Gate Arrays (FPGAs). Our findings demonstrate the feasibility of on-line, real-time, low-latency training on limited precision electronic hardware such as Digital Signal Processors (DSPs) and FPGAs. This thesis also investigated the e�ffects of limited precision on the Back Propagation (BP) and Levenberg-Marquardt (LM) optimisation algorithms. Two new HONNs are compared against NNs for estimating the discrete XOR function and an optical waveguide sidewall roughness dataset in order to find the Minimum Precision for Lowest Error (MPLE) at which the training and operation are still possible. The new findings show that compared to NNs, HONNs require more precision to reach a similar performance level, and that the 2nd order LM algorithm requires at least 24 bits of precision. The final investigation implemented and demonstrated the LM algorithm on Field Programmable Gate Arrays (FPGAs) for the first time in our knowledge. It was used to train a Neural Network, and the estimation of camera calibration parameters. The LM algorithm approximated NN to model the XOR function in only 13 iterations from zero initial conditions with a speed-up in excess of 3 x 10^6 compared to an implementation in software. Camera calibration was also demonstrated on FPGAs; compared to the software implementation, the FPGA implementation led to an increase in the mean squared error and standard deviation of only 17.94% and 8.04% respectively, but the FPGA increased the calibration speed by a factor of 1:41 x 106

Intelligent Simulation Modeling of a Flexible Manufacturing System with Automated Guided Vehicles

Although simulation is a very flexible and cost effective problem solving technique, it has been traditionally limited to building models which are merely descriptive of the system under study. Relatively new approaches combine improvement heuristics and artificial intelligence with simulation to provide prescriptive power in simulation modeling. This study demonstrates the synergy obtained by bringing together the "learning automata theory" and simulation analysis. Intelligent objects are embedded in the simulation model of a Flexible Manufacturing System (FMS), in which Automated Guided Vehicles (AGVs) serve as the material handling system between four unique workcenters. The objective of the study is to find satisfactory AGV routing patterns along available paths to minimize the mean time spent by different kinds of parts in the system. System parameters such as different part routing and processing time requirements, arrivals distribution, number of palettes, available paths between workcenters, number and speed of AGVs can be defined by the user. The network of learning automata acts as the decision maker driving the simulation, and the FMS model acts as the training environment for the automata network; providing realistic, yet cost-effective and risk-free feedback. Object oriented design and implementation of the simulation model with a process oriented world view, graphical animation and visually interactive simulation (using GUI objects such as windows, menus, dialog boxes; mouse sensitive dynamic automaton trace charts and dynamic graphical statistical monitoring) are other issues dealt with in the study

Execution Time of Optimal Controls in Hard Real Time, a Minimal Execution Time Solution for Nonlinear SDRE

Many engineering fields, such as automotive, aerospace, and the emerging challenges towards industry 4.0, have to deal with Real-Time (RT) or Hard Real Time (HRT) systems, where temporal constraints must be fulfilled, to avoid critical behaviours or unacceptable system failures. For this reason, estimation of code's Worst-Case Execution Time (WCET) has received lots attention because in RT systems a fundamental requirement is to guarantee at least a temporal upper bound of the code execution for avoiding any drawbacks. However, until now there is no approved method to compute extremely tight WCET. Nowadays, indeed, HRT requirements are solved via hardware, using multi-cores embedded boards that allow the computation of the deterministic Execution Time (ET). The availability of these embedded architectures has encouraged the designers to look towards more computationally demanding optimal control techniques for RT scenarios, and to compare and analyze performances also evaluating a tight WCET. However, this area still lacks deep investigations. This paper has the intent of analysing results regarding the choice between three of the most established optimal controls (LQR, MPC, SDRE), providing the first link between WCET analysis and control algorithms performances. Moreover, this work shows how it is also possible to obtain a minimal ET solution for the nonlinear SDRE controller. The results might be useful for future implementations and for coping with Industry 4.0 emerging challenges. Furthermore, this approach can be useful in control system engineering field, especially in the design stage for RT or HRT systems, where temporal bounds have to be fulfilled jointly with all the other application's specifications

The Mars Gravity Biosatellite as an innovative partial gravity research platform

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.Includes bibliographical references (leaves 269-275).The Mars Gravity Biosatellite is an unprecedented independent spaceflight platform for gravitational biology research. With a projected first launch after 2010, the low Earth orbit satellite will support a cohort of fifteen 14.5- to 25.5-week-old female BALB/cByJ mice for up to five weeks. During this time, the spacecraft will rotate at a rate of 31.6 rpm to generate Mars-equivalent artificial gravity of magnitude 0.38-g. Reentry capability will permit the return of live specimens to the Earth's surface at the culmination of the study. The proposed first mission aims to explore the physiological impacts on mice of 0.38-g. On board the Mars Gravity Biosatellite, a video acquisition and digitisation system will enhance in-flight collection of data on sensorimotor adaptation. As part of this thesis, a rotational ground control system has been designed and constructed at MIT. The apparatus incorporates a video processing module similar to that baselined for the mission. It also features the first custom-designed gondola centrifuge that accommodates up to four singlyhoused rodents in flight-equivalent habitat modules. At a rotation rate of 31.6 rpm, the centripetal acceleration experienced by each animal is less than 1.07-g. The 0.34 m radius of rotation is equivalent to that of the orbital vehicle. A behavioural study with four BALB/cByJ mice explores the effects of chronic rotation alone and confirms that they can be quantified and therefore decoupled from the anticipated on-orbit effects of rotation-induced Mars-equivalent gravity. The results provide justification for the scientific validity of the Mars Gravity Biosatellite as a rotating spaceflight platform. In addition, details are presented on the design, implementation, test and operation of a two-mouse closed-loop environmental control and life support system (ECLSS). The ground-based assembly is colocated with the centrifuge, and the entire apparatus is enclosed within a sealed zero-pressure urethane/polyethylene membrane. It incorporates scaled-down versions of a subset of flight-equivalent atmospheric reconditioning subassemblies together with sensors, actuators and a computer to perform autonomous feedback-driven supervisory control.(cont.) Data is presented that validates a system that includes oxygen replenishment, carbon dioxide scrubbing via reaction with lithium hydroxide, ammonia removal using acidtreated activated charcoal, and humidity control with a custom-designed condensing heat exchanger. Results of a multi-week test represent an experimental proof-of-concept for the Mars Gravity Biosatellite's ECLSS strategy, showing good control of environmental parameters within specified ranges. The work presented in this thesis offers four primary contributions to aerospace biomedical engineering and rodent behavioural science: 1. Preliminary design and operations plans for the Mars Gravity payload. This thesis claims specific contributions in the areas of electronics, instrumentation, software and systems-level design of the payload module. 2. The first direct measurement of the influence of chronic rotation on mice in flight-like habitats at 31.6 rpm. The first in-centrifuge use of video-based behavioural analysis. 3. Proof-of-concept justification for the Mars Gravity Biosatellite ECLSS strategy. 4. The conception, design, implementation and operation of the first integrated ground test apparatus to combine chronic rotation capability with an ECLSS testbed.by Thaddeus R. F. Fulford-Jones.Ph.D

Collaborative fixture design and analysis system with robustness for machining parts

Ph.DDOCTOR OF PHILOSOPH

DTT - Divertor Tokamak Test facility - Interim Design Report

The “Divertor Tokamak Test facility, DTT” is a milestone along the international program aimed at demonstrating – in the second half of this century – the feasibility of obtaining to commercial electricity from controlled thermonuclear fusion. DTT is a Tokamak conceived and designed in Italy with a broad international vision. The construction will be carried out in the ENEA Frascati site, mainly supported by national funds, complemented by EUROfusion and European incentive schemes for innovative investments. The project team includes more than 180 high-standard researchers from ENEA, CREATE, CNR, INFN, RFX and various universities. The volume, entitled DTT Interim Design Report (“Green Book” from the colour of the cover), briefly describes the status of the project, the planning of the design future activities and its organizational structure. The publication of the Green Book also provides an occasion for thorough discussions in the fusion community and a broad international collaboration on the DTT challenge