694,365 research outputs found
Multi-task Implementation for Image Reconstruction of an AER Communication
Address-Event-Representation (AER) is a communication protocol
for transferring spikes between bio-inspired chips. Such systems may consist of
a hierarchical structure with several chips that transmit spikes among them in
real time, while performing some processing. There exist several AER tools to
help in developing and testing AER based systems. These tools require the use
of a computer to allow the processing of the event information, reaching very
high bandwidth at the AER communication level. We propose to use an
embedded platform based on multi-task operating system to allow both, the
AER communication and the AER processing without a laptop or a computer.
We have connected and programmed a Gumstix computer to process Address-
Event information and measured the performance referred to the previous AER
tools solutions. In this paper, we present and study the performance of a new
philosophy of a frame-grabber AER tool based on a multi-task environment,
composed by the Intel XScale processor governed by an embedded GNU/Linux
system.Ministerio de Ciencia e Innovación TEC2006-11730-C03-0
Embedding Multi-Task Address-Event- Representation Computation
Address-Event-Representation, AER, is a communication protocol that is
intended to transfer neuronal spikes between bioinspired chips. There are
several AER tools to help to develop and test AER based systems, which may
consist of a hierarchical structure with several chips that transmit spikes
among them in real-time, while performing some processing. Although these
tools reach very high bandwidth at the AER communication level, they require
the use of a personal computer to allow the higher level processing of the
event information. We propose the use of an embedded platform based on a
multi-task operating system to allow both, the AER communication and
processing without the requirement of either a laptop or a computer. In this
paper, we present and study the performance of an embedded multi-task AER
tool, connecting and programming it for processing Address-Event
information from a spiking generator.Ministerio de Ciencia e Innovación TEC2006-11730-C03-0
Efficient data processing and quantum phenomena: Single-particle systems
We study the relation between the acquisition and analysis of data and
quantum theory using a probabilistic and deterministic model for photon
polarizers. We introduce criteria for efficient processing of data and then use
these criteria to demonstrate that efficient processing of the data contained
in single events is equivalent to the observation that Malus' law holds. A
strictly deterministic process that also yields Malus' law is analyzed in
detail. We present a performance analysis of the probabilistic and
deterministic model of the photon polarizer. The latter is an adaptive
dynamical system that has primitive learning capabilities. This additional
feature has recently been shown to be sufficient to perform event-by-event
simulations of interference phenomena, without using concepts of wave
mechanics. We illustrate this by presenting results for a system of two chained
Mach-Zehnder interferometers, suggesting that systems that perform efficient
data processing and have learning capability are able to exhibit behavior that
is usually attributed to quantum systems only.Comment: http://www.compphys.net/dl
Performance management of event processing systems
This thesis is a study of performance management of Complex Event Processing (CEP) systems. Since CEP systems have distinct characteristics from other well-studied computer systems such as batch and online transaction processing systems and database-centric applications, these characteristics introduce new challenges and opportunities to the performance management for CEP systems. Methodologies used in benchmarking CEP systems in many performance studies focus on scaling the load injection, but not considering the impact of the functional capabilities of CEP systems. This thesis proposes the approach of evaluating the performance of CEP engines’ functional behaviours on events and develops a benchmark platform for CEP systems: CEPBen. The CEPBen benchmark platform is developed to explore the fundamental functional performance of event processing systems: filtering, transformation and event pattern detection. It is also designed to provide a flexible environment for exploring new metrics and influential factors for CEP systems and evaluating the performance of CEP systems. Studies on factors and new metrics are carried out using the CEPBen benchmark platform on Esper. Different measurement points of response time in performance management of CEP systems are discussed and response time of targeted event is proposed to be used as a metric for quality of service evaluation combining with the traditional response time in CEP systems. Maximum query load as a capacity indicator regarding to the complexity of queries and number of live objects in memory as a performance indicator regarding to the memory management are proposed in performance management of CEP systems. Query depth is studied as a performance factor that influences CEP system performance
Spike Processing on an Embedded Multi-task Computer: Image Reconstruction
There is an emerging philosophy, called Neuro-informatics, contained
in the Artificial Intelligence field, that aims to emulate how living beings do tasks
such as taking a decision based on the interpretation of an image by emulating spiking
neurons into VLSI designs and, therefore, trying to re-create the human brain at
its highest level. Address-Event-Representation (AER) is a communication protocol
that has embedded part of the processing. It is intended to transfer spikes between
bioinspired chips. An AER based system may consist of a hierarchical structure with
several chips that transmit spikes among them in real-time, while performing some
processing. There are several AER tools to help to develop and test AER based systems.
These tools require the use of a computer to allow the higher level processing of
the event information, reaching very high bandwidth at the AER communication level.
We propose the use of an embedded platform based on a multi-task operating system
to allow both, the AER communication and processing without the requirement of either
a laptop or a computer. In this paper, we present and study the performance of a
new philosophy of a frame-grabber AER tool based on a multi-task environment. This
embedded platform is based on the Intel XScale processor which is governed by an
embedded GNU/Linux system. We have connected and programmed it for processing
Address-Event information from a spiking generator.Ministerio de Educación y Ciencia TEC2006-11730-C03-0
Real-Time Context-Aware Microservice Architecture for Predictive Analytics and Smart Decision-Making
The impressive evolution of the Internet of Things and the great amount of data flowing through the systems provide us with an inspiring scenario for Big Data analytics and advantageous real-time context-aware predictions and smart decision-making. However, this requires a scalable system for constant streaming processing, also provided with the ability of decision-making and action taking based on the performed predictions. This paper aims at proposing a scalable architecture to provide real-time context-aware actions based on predictive streaming processing of data as an evolution of a previously provided event-driven service-oriented architecture which already permitted the context-aware detection and notification of relevant data. For this purpose, we have defined and implemented a microservice-based architecture which provides real-time context-aware actions based on predictive streaming processing of data. As a result, our architecture has been enhanced twofold: on the one hand, the architecture has been supplied with reliable predictions through the use of predictive analytics and complex event processing techniques, which permit the notification of relevant context-aware information ahead of time. On the other, it has been refactored towards a microservice architecture pattern, highly improving its maintenance and evolution. The architecture performance has been evaluated with an air quality case study
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A modular hybrid simulation framework for complex manufacturing system design
For complex manufacturing systems, the current hybrid Agent-Based Modelling and Discrete Event Simulation (ABM–DES) frameworks are limited to component and system levels of representation and present a degree of static complexity to study optimal resource planning. To address these limitations, a modular hybrid simulation framework for complex manufacturing system design is presented. A manufacturing system with highly regulated and manual handling processes, composed of multiple repeating modules, is considered. In this framework, the concept of modular hybrid ABM–DES technique is introduced to demonstrate a novel simulation method using a dynamic system of parallel multi-agent discrete events. In this context, to create a modular model, the stochastic finite dynamical system is extended to allow the description of discrete event states inside the agent for manufacturing repeating modules (meso level). Moreover, dynamic complexity regarding uncertain processing time and resources is considered. This framework guides the user step-by-step through the system design and modular hybrid model. A real case study in the cell and gene therapy industry is conducted to test the validity of the framework. The simulation results are compared against the data from the studied case; excellent agreement with 1.038% error margin is found in terms of the company performance. The optimal resource planning and the uncertainty of the processing time for manufacturing phases (exo level), in the presence of dynamic complexity is calculated
Stereo Matching in Address-Event-Representation (AER) Bio-Inspired Binocular Systems in a Field-Programmable Gate Array (FPGA)
In stereo-vision processing, the image-matching step is essential for results, although it
involves a very high computational cost. Moreover, the more information is processed, the more time
is spent by the matching algorithm, and the more ine cient it is. Spike-based processing is a relatively
new approach that implements processing methods by manipulating spikes one by one at the time
they are transmitted, like a human brain. The mammal nervous system can solve much more complex
problems, such as visual recognition by manipulating neuron spikes. The spike-based philosophy
for visual information processing based on the neuro-inspired address-event-representation (AER)
is currently achieving very high performance. The aim of this work was to study the viability of a
matching mechanism in stereo-vision systems, using AER codification and its implementation in
a field-programmable gate array (FPGA). Some studies have been done before in an AER system
with monitored data using a computer; however, this kind of mechanism has not been implemented
directly on hardware. To this end, an epipolar geometry basis applied to AER systems was studied
and implemented, with other restrictions, in order to achieve good results in a real-time scenario.
The results and conclusions are shown, and the viability of its implementation is proven.Ministerio de Economía y Competitividad TEC2016-77785-
Event-driven Principles and Complex Event Processing for Self-adaptive Network Analysis and Surveillance Systems
Event-driven approaches and Complex Event Processing (CEP) have the potential to aid in tackling the complex requirements and challenges of monitoring contemporary computer networks. The applicability of such methods, however, depends on, e.g., architectural considerations, data processing performance, or usability. In this thesis, we study the applicability of event-driven principles and CEP for analyzing and surveying computer networks and present ways for improving the applicability of these paradigms. The main contributions that are presented and discussed in this thesis are: an analysis of important properties of network analysis and surveillance, the introduction of a corresponding Event-driven Architecture (EDA) for addressing these requirements, the empirical evaluation of the proposed EDA using a prototype implementation, the development of cooperative and self-adaptive methods for addressing performance and usability issues, and the development of techniques for improving the integration of components implemented in different languages in event-driven systems.
Assuring and maintaining the proper operation of computer networks is as crucial as assuring the proper operation of the Information Technology (IT) systems they connect. However, collecting and analyzing information about computer networks, which is required for assuring their proper operation, is increasingly challenging because of, e.g., the growing logical and spatial extent of computer networks, accelerated changes in computer network structures and network traffic, or near real-time requirements. Furthermore, a wide variety of methods for network analysis and surveillance exists and for acquiring comprehensive information at optimal resource requirements these various methods have to be combined with a converging approach.
Based on the results of an analysis of important properties and requirements for network analysis and surveillance, we propose an approach which leverages event-driven paradigms such as EDA and CEP for addressing the complex mix of requirements in this field and for enabling convergence of the various existing methods. We evaluate our proposed approach with a case study and performance benchmarks using a prototype. Our results show that our approach is a good fit for addressing the complex mix of requirements and that it is feasible from a performance perspective. In contrast to other related recent research, which is limited to specific use cases, we propose a generic and versatile event-driven approach for universal network analysis and surveillance.
Moreover, we present techniques for further improving network analysis and surveillance. While our general approach already constitutes an important improvement, we also propose and investigate further innovations. Based on the evaluation of our approach, we consider distributed operation, usability, performance in distributed deployments and of sensors, integration of data sources, and the interoperation of implementations in different programming languages in event-driven systems as most important aspects for further improvement.
For improving the operation, usability, and performance in distributed contexts, we develop an approach for cooperative and self-adaptive data acquisition using the example of packet capturing. In order to research ways for advancing the operation of sensors and integration of data sources, we use the example of packet capturing with the Java Virtual Machine (JVM), for which we develop and analyze various improvements at various abstraction levels such as data extraction via a Domain Specific Language (DSL) or self-adaptive adjustments based on performance constraints. Even though packet capturing with the JVM was already employed in other research, these studies only consider the overall systems such that neither the specific implications of JVM-based packet capturing nor methods for improving the performance in this scenario were discussed in detail yet. Furthermore, we analyze the impact of programming language barriers in event-driven systems and present a batch-based approach for increasing the data exchange throughput.
In conclusion, we improve the state-of-the-art of network analysis and surveillance. Our work aims on taking the next step towards holistic network analysis and surveillance by addressing distribution, convergence, usability, and performance aspects. We demonstrate the benefits and evaluate the applicability of event-driven data processing paradigms and show how self-adaptivity and cooperation can further improve the capabilities
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