55 research outputs found

    Personalized Health Monitoring Using Evolvable Block-based Neural Networks

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    This dissertation presents personalized health monitoring using evolvable block-based neural networks. Personalized health monitoring plays an increasingly important role in modern society as the population enjoys longer life. Personalization in health monitoring considers physiological variations brought by temporal, personal or environmental differences, and demands solutions capable to reconfigure and adapt to specific requirements. Block-based neural networks (BbNNs) consist of 2-D arrays of modular basic blocks that can be easily implemented using reconfigurable digital hardware such as field programmable gate arrays (FPGAs) that allow on-line partial reorganization. The modular structure of BbNNs enables easy expansion in size by adding more blocks. A computationally efficient evolutionary algorithm is developed that simultaneously optimizes structure and weights of BbNNs. This evolutionary algorithm increases optimization speed by integrating a local search operator. An adaptive rate update scheme removing manual tuning of operator rates enhances the fitness trend compared to pre-determined fixed rates. A fitness scaling with generalized disruptive pressure reduces the possibility of premature convergence. The BbNN platform promises an evolvable solution that changes structures and parameters for personalized health monitoring. A BbNN evolved with the proposed evolutionary algorithm using the Hermite transform coefficients and a time interval between two neighboring R peaks of ECG signal, provides a patient-specific ECG heartbeat classification system. Experimental results using the MIT-BIH Arrhythmia database demonstrate a potential for significant performance enhancements over other major techniques

    Swarm autonomic agents with self-destruct capability

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    Systems, methods and apparatus are provided through which in some embodiments an autonomic entity manages a system by generating one or more stay alive signals based on the functioning status and operating state of the system. In some embodiments, an evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy. The evolvable neural interface receives and generates heartbeat monitor signals and pulse monitor signals that are used to generate a stay alive signal that is used to manage the operations of the synthetic neural system. In another embodiment an asynchronous Alice signal (Autonomic license) requiring valid credentials of an anonymous autonomous agent is initiated. An unsatisfactory Alice exchange may lead to self-destruction of the anonymous autonomous agent for self-protection

    Swarm autonomic agents with self-destruct capability

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    Systems, methods and apparatus are provided through which in some embodiments an autonomic entity manages a system by generating one or more stay alive signals based on the functioning status and operating state of the system. In some embodiments, an evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy. The evolvable neural interface receives and generates heartbeat monitor signals and pulse monitor signals that are used to generate a stay alive signal that is used to manage the operations of the synthetic neural system. In another embodiment an asynchronous Alice signal (Autonomic license) requiring valid credentials of an anonymous autonomous agent is initiated. An unsatisfactory Alice exchange may lead to self-destruction of the anonymous autonomous agent for self-protection

    Autonomic and Apoptotic, Aeronautical and Aerospace Systems, and Controlling Scientific Data Generated Therefrom

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    A self-managing system that uses autonomy and autonomicity is provided with the self-* property of autopoiesis (self-creation). In the event of an agent in the system self-destructing, autopoiesis auto-generates a replacement. A self-esteem reward scheme is also provided and can be used for autonomic agents, based on their performance and trust. Art agent with greater self-esteem may clone at a greater rate compared to the rate of an agent with lower self-esteem. A self-managing system is provided for a high volume of distributed autonomic/self-managing mobile agents, and autonomic adhesion is used to attract similar agents together or to repel dissimilar agents from an event horizon. An apoptotic system is also provided that accords an "expiry date" to data and digital objects, for example, that are available on the internet, which finds usefulness not only in general but also for controlling the loaning and use of space scientific data

    System and method of self-properties for an autonomous and automatic computer environment

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    Systems, methods and apparatus are provided through which in some embodiments self health/urgency data and environment health/urgency data may be transmitted externally from an autonomic element. Other embodiments may include transmitting the self health/urgency data and environment health/urgency data together on a regular basis similar to the lub-dub of a heartbeat. Yet other embodiments may include a method for managing a system based on the functioning state and operating status of the system, wherein the method may include processing received signals from the system indicative of the functioning state and the operating status to obtain an analysis of the condition of the system, generating one or more stay alive signals based on the functioning status and the operating state of the system, transmitting the stay-alive signal, transmitting self health/urgency data, and transmitting environment health/urgency data. Still other embodiments may include an autonomic element that includes a self monitor, a self adjuster, an environment monitor, and an autonomic manager

    Systems, methods and apparatus for quiesence of autonomic systems with self action

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    Systems, methods and apparatus are provided in which an autonomic unit or element is quiesced. A quiesce component of an autonomic unit can cause the autonomic unit to self-destruct if a stay-alive reprieve signal is not received after a predetermined time

    Intrinsically Evolvable Artificial Neural Networks

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    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

    Systems, methods, and apparatus for otoacoustic protection of autonomic systems

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    Systems, methods and apparatus are provided through which in some embodiments an autonomic unit transmits an otoacoustic signal to counteract a potentially harmful incoming signal

    Otoacoustic protection in biologically-inspired systems

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    Systems, methods and apparatus are provided through which in some embodiments an autonomic unit transmits an otoacoustic signal to counteract a potentially harmful incoming signal

    Técnicas de Adquisición y Procesamiento de Señales Electrocardiográficas en la Detección de Arritmias Cardíacas

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    The development of ambulatory monitoring systems and its electrocardiographic (ECG) signal processing techniques has become an important field of investigation, due to its relevance in the early detection of cardiovascular diseases such as the arrhythmias. The current trend of this technology is oriented to the use of portable equipment and mobile devices such as Smartphones, which have been widely accepted due to the technical characteristics and common integration in daily life. A fundamental characteristic of these systems is their ability to reduce the most common types of noise by means of digital signal processing techniques.  Among the most used techniques are the adaptive filters and the Discrete Wavelet Transform (DWT) which have been successfully implemented in several studies. There are systems that integrate classification stages based on artificial intelligence, which increases the performance in the process of arrhythmias detection. These techniques are not only evaluated for their functionality but for their computational cost, since they will be used in real-time applications, and implemented in embedded systems. This paper shows a review of each of the stages in the construction of a standard ambulatory monitoring system, for the contextualization of the reader in this type of technology.El desarrollo de sistemas de  monitoreo  ambulatorio  y  sus  técnicas  de  procesamiento  de  la  señal  electrocardiográfica (ECG) se han convertido en un importante campo de investigación, debido a su relevancia en la detección temprana de enfermedades cardiovasculares, tales como arritmias. La tendencia actual de esta tecnología está orientada al uso de equipos portátiles y dispositivos móviles como los Smartphones, que han sido ampliamente aceptados debido a sus características técnicas y a su integración, cada vez más común, en la vida diaria. Una característica fundamental de estos sistemas es su capacidad de reducir los tipos más comunes de ruido mediante técnicas de procesamiento de señales digitales. Entre las técnicas más utilizadas se encuentran los filtros adaptativos y la Transformada Discreta Wavelet (DWT, por sus siglas en inglés), los cuales han sido implementados exitosamente en diversos estudios. Así mismo, se reportan sistemas que integran etapas de clasificación basadas en inteligencia artificial, con lo cual se aumenta el rendimiento en el proceso de detección de arritmias. En este sentido, estas técnicas no solo son evaluadas por su funcionalidad, sino por su costo computacional, debido a que deben ser utilizadas en aplicaciones en tiempo real, e implementadas en sistemas embebidos. Este documento presenta una revisión del estado del arte de cada una de las etapas en la construcción de un sistema de monitoreo ambulatorio estándar, para la contextualización del lector en este tipo de tecnologías
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