1,249 research outputs found
Identifying stochastic oscillations in single-cell live imaging time series using Gaussian processes
Multiple biological processes are driven by oscillatory gene expression at
different time scales. Pulsatile dynamics are thought to be widespread, and
single-cell live imaging of gene expression has lead to a surge of dynamic,
possibly oscillatory, data for different gene networks. However, the regulation
of gene expression at the level of an individual cell involves reactions
between finite numbers of molecules, and this can result in inherent randomness
in expression dynamics, which blurs the boundaries between aperiodic
fluctuations and noisy oscillators. Thus, there is an acute need for an
objective statistical method for classifying whether an experimentally derived
noisy time series is periodic. Here we present a new data analysis method that
combines mechanistic stochastic modelling with the powerful methods of
non-parametric regression with Gaussian processes. Our method can distinguish
oscillatory gene expression from random fluctuations of non-oscillatory
expression in single-cell time series, despite peak-to-peak variability in
period and amplitude of single-cell oscillations. We show that our method
outperforms the Lomb-Scargle periodogram in successfully classifying cells as
oscillatory or non-oscillatory in data simulated from a simple genetic
oscillator model and in experimental data. Analysis of bioluminescent live cell
imaging shows a significantly greater number of oscillatory cells when
luciferase is driven by a {\it Hes1} promoter (10/19), which has previously
been reported to oscillate, than the constitutive MoMuLV 5' LTR (MMLV) promoter
(0/25). The method can be applied to data from any gene network to both
quantify the proportion of oscillating cells within a population and to measure
the period and quality of oscillations. It is publicly available as a MATLAB
package.Comment: 36 pages, 17 figure
Energy-efficient thermal-aware multiprocessor scheduling for real-time tasks using TCPNs
We present an energy-effcient thermal-aware real-time global scheduler for a set of hard real-time (HRT) tasks running on a multiprocessor system. This global scheduler fulfills the thermal and temporal constraints by handling two independent variables, the task allocation time and the selection of clock frequency. To achieve its goal, the proposed scheduler is split into two stages. An off-line stage, based on a deadline partitioning scheme, computes the cycles that the HRT tasks must run per deadline interval at the minimum clock frequency to save energy while honoring the temporal and thermal constraints, and computes the maximum frequency at which the system can run below the maximum temperature. Then, an on-line, event-driven stage performs global task allocation applying a Fixed-Priority Zero-Laxity policy, reducing the overhead of quantum-based or interval-based global schedulers. The on-line stage embodies an adaptive scheduler that accepts or rejects soft RT aperiodic tasks throttling CPU frequency to the upper lowest available one to minimize power consumption while meeting time and thermal constraints. This approach leverages the best of two worlds: the off-line stage computes an ideal discrete HRT multiprocessor schedule, while the on-line stage manage soft real-time aperiodic tasks with minimum power consumption and maximum CPU utilization
Control techniques for thermal-aware energy-efficient real time multiprocessor scheduling
La utilización de microprocesadores multinúcleo no sólo es atractiva para la industria sino que en muchos ámbitos es la única opción. La planificación tiempo real sobre estas plataformas es mucho más compleja que sobre monoprocesadores y en general empeoran el problema de sobre-diseño, llevando a la utilización de muchos más procesadores /núcleos de los necesarios. Se han propuesto algoritmos basados en planificación fluida que optimizan la utilización de los procesadores, pero hasta el momento presentan en general inconvenientes que los alejan de su aplicación práctica, no siendo el menor el elevado número de cambios de contexto y migraciones.Esta tesis parte de la hipótesis de que es posible diseñar algoritmos basados en planificación fluida, que optimizan la utilización de los procesadores, cumpliendo restricciones temporales, térmicas y energéticas, con un bajo número de cambios de contexto y migraciones, y compatibles tanto con la generación fuera de lÃnea de ejecutivos cÃclicos atractivos para la industria, como de planificadores que integran técnicas de control en tiempo de ejecución que permiten la gestión eficiente tanto de tareas aperiódicas como de desviaciones paramétricas o pequeñas perturbaciones.A este respecto, esta tesis contribuye con varias soluciones. En primer lugar, mejora una metodologÃa de modelo que representa todas las dimensiones del problema bajo un único formalismo (Redes de Petri Continuas Temporizadas). En segundo lugar, propone un método de generación de un ejecutivo cÃclico, calculado en ciclos de procesador, para un conjunto de tareas tiempo real duro sobre multiprocesadores que optimiza la utilización de los núcleos de procesamiento respetando también restricciones térmicas y de energÃa, sobre la base de una planificación fluida. Considerar la sobrecarga derivada del número de cambios de contexto y migraciones en un ejecutivo cÃclico plantea un dilema de causalidad: el número de cambios de contexto (y en consecuencia su sobrecarga) no se conoce hasta generar el ejecutivo cÃclico, pero dicho número no se puede minimizar hasta que se ha calculado. La tesis propone una solución a este dilema mediante un método iterativo de convergencia demostrada que logra minimizar la sobrecarga mencionada.En definitiva, la tesis consigue explotar la idea de planificación fluida para maximizar la utilización (donde maximizar la utilización es un gran problema en la industria) generando un sencillo ejecutivo cÃclico de mÃnima sobrecarga (ya que la sobrecarga implica un gran problema de los planificadores basados en planificación fluida).Finalmente, se propone un método para utilizar las referencias de la planificación fuera de lÃnea establecida en el ejecutivo cÃclico para su seguimiento por parte de un controlador de frecuencia en lÃnea, de modo que se pueden afrontar pequeñas perturbaciones y variaciones paramétricas, integrando la gestión de tareas aperiódicas (tiempo real blando) mientras se asegura la integridad de la ejecución del conjunto de tiempo real duro.Estas aportaciones constituyen una novedad en el campo, refrendada por las publicaciones derivadas de este trabajo de tesis.<br /
A hardware scheduler based on task queues for FPGA-based embedded real-time systems
A hardware scheduler is developed to improve real-time performance of soft-core processor based computing systems. A hardware scheduler typically accelerates system performance at the cost of increased hardware resources, inflexibility and integration difficulty. However, the reprogrammability of FPGA-based systems removes the problems of inflexibility and integration difficulty. This paper introduces a new task-queue architecture to better support practical task controls and maintain good resource scaling. The scheduler can be configured to support various algorithms such as time sliced priority scheduling, Earliest Deadline First and Least Slack Time. The hardware scheduler reduces scheduling overhead by more than 1,000 clock cycles and raises the system utilization bound by a maximum 19.2 percent. Scheduling jitter is reduced from hundreds of clock cycles in software to just two or three cycles for most operations. The additional resource cost is no more than 17 percent of a typical softcore system for a small scale embedded application
A STUDY ON DYNAMIC SYSTEMS RESPONSE OF THE PERFORMANCE CHARACTERISTICS OF SOME MAJOR BIOPHYSICAL SYSTEMS
Dynamic responses of biophysical systems - performance characteristic
Maximising microprocessor reliability through game theory and heuristics
PhD ThesisEmbedded Systems are becoming ever more pervasive in our society, with most
routine daily tasks now involving their use in some form and the market predicted
to be worth USD 220 billion, a rise of 300%, by 2018. Consumers expect
more functionality with each design iteration, but for no detriment in perceived
performance. These devices can range from simple low-cost chips to expensive
and complex systems and are a major cost driver in the equipment design
phase. For more than 35 years, designers have kept pace with Moore's Law, but
as device size approaches the atomic limit, layouts are becoming so complicated
that current scheduling techniques are also reaching their limit, meaning that
more resource must be reserved to manage and deliver reliable operation. With
the advent of many-core systems and further sources of unpredictability such as
changeable power supplies and energy harvesting, this reservation of capability
may become so large that systems will not be operating at their peak efficiency.
These complex systems can be controlled through many techniques, with
jobs scheduled either online prior to execution beginning or online at each time
or event change. Increased processing power and job types means that current
online scheduling methods that employ exhaustive search techniques will not
be suitable to define schedules for such enigmatic task lists and that new techniques
using statistic-based methods must be investigated to preserve Quality
of Service.
A new paradigm of scheduling through complex heuristics is one way to
administer these next levels of processor effectively and allow the use of more
simple devices in complex systems; thus reducing unit cost while retaining reliability a key goal identified by the International Technology Roadmap for Semi-conductors for Embedded Systems in Critical Environments. These changes
would be beneficial in terms of cost reduction and system
exibility within the
next generation of device. This thesis investigates the use of heuristics and
statistical methods in the operation of real-time systems, with the feasibility of
Game Theory and Statistical Process Control for the successful supervision of
high-load and critical jobs investigated. Heuristics are identified as an effective
method of controlling complex real-time issues, with two-person non-cooperative
games delivering Nash-optimal solutions where these exist. The simplified algorithms for creating and solving Game Theory events allow for its use within
small embedded RISC devices and an increase in reliability for systems operating
at the apex of their limits. Within this Thesis, Heuristic and Game Theoretic
algorithms for a variety of real-time scenarios are postulated, investigated, refined and tested against existing schedule types; initially through MATLAB
simulation before testing on an ARM Cortex M3 architecture functioning as a
simplified automotive Electronic Control Unit.Doctoral Teaching Account from the EPSRC
Scheduling Techniques for Operating Systems for Medical and IoT Devices: A Review
Software and Hardware synthesis are the major subtasks in the implementation of hardware/software systems. Increasing trend is to build SoCs/NoC/Embedded System for Implantable Medical Devices (IMD) and Internet of Things (IoT) devices, which includes multiple Microprocessors and Signal Processors, allowing designing complex hardware and software systems, yet flexible with respect to the delivered performance and executed application. An important technique, which affect the macroscopic system implementation characteristics is the scheduling of hardware operations, program instructions and software processes. This paper presents a survey of the various scheduling strategies in process scheduling. Process Scheduling has to take into account the real-time constraints. Processes are characterized by their timing constraints, periodicity, precedence and data dependency, pre-emptivity, priority etc. The affect of these characteristics on scheduling decisions has been described in this paper
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