31 research outputs found

    Analog Photonics Computing for Information Processing, Inference and Optimisation

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    This review presents an overview of the current state-of-the-art in photonics computing, which leverages photons, photons coupled with matter, and optics-related technologies for effective and efficient computational purposes. It covers the history and development of photonics computing and modern analogue computing platforms and architectures, focusing on optimization tasks and neural network implementations. The authors examine special-purpose optimizers, mathematical descriptions of photonics optimizers, and their various interconnections. Disparate applications are discussed, including direct encoding, logistics, finance, phase retrieval, machine learning, neural networks, probabilistic graphical models, and image processing, among many others. The main directions of technological advancement and associated challenges in photonics computing are explored, along with an assessment of its efficiency. Finally, the paper discusses prospects and the field of optical quantum computing, providing insights into the potential applications of this technology.Comment: Invited submission by Journal of Advanced Quantum Technologies; accepted version 5/06/202

    Modeling and Verification for a Scalable Neuromorphic Substrate

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    Mixed-signal accelerated neuromorphic hardware is a class of devices that implements physical models of neural networks in dedicated analog and digital circuits. These devices offer the advantages of high acceleration and energy efficiency for the emulation of spiking neural networks but pose constraints in form of device variability and of limited connectivity and bandwidth. We address these constraints using two complementary approaches: At the network level, the influence of multiple distortion mechanisms on two benchmark models is analyzed and compensation methods are developed that counteract the resulting effects. The compensation methods are validated using a simulation of the BrainScaleS neuromorphic hardware system. At the single neuron level, calibration procedures are presented that counteract device variability for a new analog implementation of an adaptive exponential integrate-and-fire neuron model in a 65 nm process. The functionality of the neuron circuit together with these calibration methods is verified in detailed transistor-level simulations before production. The versatility of the circuit design that includes novel multi-compartment and plateau-potential features is demonstrated in use cases inspired by biology and machine learning

    Pipe circularity reformation via line heating

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    Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2001.Includes bibliographical references (leaves 119-120).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Fabrication of pipes requires the use of several manufacturing processes, such as bending, welding, drilling and wringing. However, in most cases the circular ends deviate from true circles and need reformation to be welded to angles. Currently the reformation is conducted by hammering and relies on the intuition of skilled workers. This reforming process is not only expensive but also generates unhealthy loud noise. The objective of this research is to develop an automatic system of circularizing the ends of a deformed pipe by laser line heating. The overall problem is defined as follows: Given the design of a metal pipe, measure the shape of the cross sections of both ends and a branch end of the manufactured pipe and determine the heating paths together with the heating conditions to reform the manufactured pipe to within acceptable tolerances with respect to the designed pipe using the line heating method. The line heating conditions to be applied to the pipe have to be determined in real time to make the process ecient. A Neural Network is created for this purpose and the database used to run it is generated using a simplified thermo-mechanical model of the pipe validated by a Finite Element Model (FEM).by Rodrigo V. Andrade.S.M

    Computational framework for real-time diagnostics and prognostics of aircraft actuation systems

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    Prognostics and Health Management (PHM) are emerging approaches to product life cycle that will maintain system safety and improve reliability, while reducing operating and maintenance costs. This is particularly relevant for aerospace systems, where high levels of integrity and high performances are required at the same time. We propose a novel strategy for the nearly real-time Fault Detection and Identification (FDI) of a dynamical assembly, and for the estimation of Remaining Useful Life (RUL) of the system. The availability of a timely estimate of the health status of the system will allow for an informed adaptive planning of maintenance and a dynamical reconfiguration of the mission profile, reducing operating costs and improving reliability. This work addresses the three phases of the prognostic flow - namely (1) signal acquisition, (2) Fault Detection and Identification, and (3) Remaining Useful Life estimation - and introduces a computationally efficient procedure suitable for real-time, on-board execution. To achieve this goal, we propose to combine information from physical models of different fidelity with machine learning techniques to obtain efficient representations (surrogate models) suitable for nearly real-time applications. Additionally, we propose an importance sampling strategy and a novel approach to model damage propagation for dynamical systems. The methodology is assessed for the FDI and RUL estimation of an aircraft electromechanical actuator (EMA) for secondary flight controls. The results show that the proposed method allows for a high precision in the evaluation of the system RUL, while outperforming common model-based techniques in terms of computational time.Comment: 57 page

    SpiNNaker - A Spiking Neural Network Architecture

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    20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and capable of modelling spiking neural networks of the scale of a mouse brain in biological real time. This machine, hosted at the University of Manchester in the UK, is freely available under the auspices of the EU Flagship Human Brain Project. This book tells the story of the origins of the machine, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over. It also presents exemplar applications from ‘Talk’, a SpiNNaker-controlled robotic exhibit at the Manchester Art Gallery as part of ‘The Imitation Game’, a set of works commissioned in 2016 in honour of Alan Turing, through to a way to solve hard computing problems using stochastic neural networks. The book concludes with a look to the future, and the SpiNNaker-2 machine which is yet to come

    SpiNNaker - A Spiking Neural Network Architecture

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    20 years in conception and 15 in construction, the SpiNNaker project has delivered the world’s largest neuromorphic computing platform incorporating over a million ARM mobile phone processors and capable of modelling spiking neural networks of the scale of a mouse brain in biological real time. This machine, hosted at the University of Manchester in the UK, is freely available under the auspices of the EU Flagship Human Brain Project. This book tells the story of the origins of the machine, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over. It also presents exemplar applications from ‘Talk’, a SpiNNaker-controlled robotic exhibit at the Manchester Art Gallery as part of ‘The Imitation Game’, a set of works commissioned in 2016 in honour of Alan Turing, through to a way to solve hard computing problems using stochastic neural networks. The book concludes with a look to the future, and the SpiNNaker-2 machine which is yet to come

    Computer modeling and signal analysis of cardiovascular physiology

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    This dissertation aims to study cardiovascular physiology from the cellular level to the whole heart level to the body level using numerical approaches. A mathematical model was developed to describe electromechanical interaction in the heart. The model integrates cardio-electrophysiology and cardiac mechanics through excitation-induced contraction and deformation-induced currents. A finite element based parallel simulation scheme was developed to investigate coupled electrical and mechanical functions. The developed model and numerical scheme were utilized to study cardiovascular dynamics at cellular, tissue and organ levels. The influence of ion channel blockade on cardiac alternans was investigated. It was found that the channel blocker may significantly change the critical pacing period corresponding to the onset of alternans as well as the alternans’ amplitude. The influence of electro-mechanical coupling on cardiac alternans was also investigated. The study supported the earlier assumptions that discordant alternans is induced by the interaction of conduction velocity and action potential duration restitution at high pacing rates. However, mechanical contraction may influence the spatial pattern and onset of discordant alternans. Computer algorithms were developed for analysis of human physiology. The 12-lead electrocardiography (ECG) is the gold standard for diagnosis of various cardiac abnormalities. However, disturbances and mistakes may modify physiological waves in ECG and lead to wrong diagnoses. This dissertation developed advanced signal analysis techniques and computer software to detect and suppress artifacts and errors in ECG. These algorithms can help to improve the quality of health care when integrated into medical devices or services. Moreover, computer algorithms were developed to predict patient mortality in intensive care units using various physiological measures. Models and analysis techniques developed here may help to improve the quality of health care

    Establishing and optimising unmanned airborne relay networks in urban environments

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    This thesis assesses the use of a group of small, low-altitude, low-power (in terms of communication equipment), xed-wing unmanned aerial vehicles (UAVs) as a mobile communication relay nodes to facilitate reliable communication between ground nodes in urban environments. This work focuses on enhancing existing models for optimal trajectory planning and enabling UAV relay implementation in realistic urban scenarios. The performance of the proposed UAV relay algorithms was demonstrated and proved through an indoor simulated urban environment, the rst experiment of its kind.The objective of enabling UAV relay deployment in realistic urban environments is addressed through relaxing the constraints on the assumptions of communication prediction models assumptions, reducing knowledge requirements and improving prediction efficiency. This thesis explores assumptions for urban environment knowledge at three different levels: (i) full knowledge about the urban environment, (ii) partially known urban environments, and (iii) no knowledge about the urban environment. The work starts with exploring models that assume the city size, layout and its effects on wireless communication strength are known, representing full knowledge about the urban environment. [Continues.]</div

    Vibration attenuation by mass redistribution

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    A nontraditional approach for active structural vibration attenuation was proposed using mass redistribution. The focus was on pendulum structures where the objective was to examine the effectiveness of mass reconfiguration along or within a structure to attenuate its vibrational energy. The mechanics associated with a translating mass along a rotating structure give rise to a Coriolis inertia force which either opposes or increases angular oscillations, thereby producing positive or negative damping, respectively. A strategy of cycling the mass to maximize attenuation and minimize amplification required the mass be moved at twice the frequency of the structural vibrations and be properly coordinated with the angular oscillations. The desired coordination involved moving the mass away from the pivot as the pendulum nears its vertical position and moving the mass towards the pivot when the pendulum nears its maximum angular excursion. System mass reconfiguration was analyzed by studying various mass displacement profiles including sinusoidal, piece-wise constant velocity and modified proportional and derivative action patterns. These strategies were optimized for various time intervals to maximize the rate of energy attenuation or minimize the final energy state. For small amplitude oscillations with sinusoidal mass motion, the dynamic behavior was modeled by Mathieu-Hill equations to explain the beating phenomenon that occurred when the frequency of the mass motion remained constant. Several control systems were designed to generate aforementioned mass reconfiguration profiles. The methodologies included human operator, modified proportional and derivative action, knowledge or rule based and artificial neural network controllers. The human operator system improved with experience and was the most effective. Other systems depended on the chosen parameterization or the implementation of self-adjusting parameters. Several unique tools were developed during the course of this research, as referenced herein
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