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Many programmable matter systems have been proposed and realized recently, each often tailored to a specific task or physical setting. In our work on self-organizing particle systems, we abstract away from specific settings and instead describe programmable matter as a collection of simple computational elements (called particles) with limited computational power that each perform fully distributed, local, asynchronous algorithms to solve system-wide problems of movement, configuration, and coordination. Here, we focus on the compression problem, where we seek fully distributed and asynchronous algorithms that lead the system to gather together as tightly as possible, that is, to converge to a configuration with small perimeter, where we measure the perimeter of a configuration by the length of the walk along the configuration boundary. We present a Markov chain based algorithm that solves the compression problem under the geometric amoebot model, using the triangular lattice as the underlying graph, for particle systems that begin in a connected configuration with no holes. The Markov chain M takes as input a bias parameter $\lambda$, where $\lambda>1$ corresponds to particles favoring inducing more lattice triangles within the particle system. We prove that during the execution of M, the particles stay connected and no holes form. We furthermore prove M is a reversible and ergodic Markov chain, which leads to our main result: for all $\lambda>2+\sqrt{2}$, there is a constant $\alpha>1$ such that at stationarity the particles are $\alpha$-compressed, meaning the perimeter of the particle configuration is at most $\alpha$ times the minimum perimeter for those particles. We additionally show $\lambda>1$ is not enough to guarantee compression: for all $0

Many programmable matter systems have been proposed and realized recently, each often tailored to a specific task or physical setting. In our work on self-organizing particle systems, we abstract away from specific settings and instead describe programmable matter as a collection of simple computational elements (called particles) with limited computational power that each perform fully distributed, local, asynchronous algorithms to solve system-wide problems of movement, configuration, and coordination. Here, we focus on the compression problem, where we seek fully distributed and asynchronous algorithms that lead the system to gather together as tightly as possible, that is, to converge to a configuration with small perimeter, where we measure the perimeter of a configuration by the length of the walk along the configuration boundary. We present a Markov chain based algorithm that solves the compression problem under the geometric amoebot model, using the triangular lattice as the underlying graph, for particle systems that begin in a connected configuration with no holes. The Markov chain M takes as input a bias parameter $\lambda$, where $\lambda>1$ corresponds to particles favoring inducing more lattice triangles within the particle system. We prove that during the execution of M, the particles stay connected and no holes form. We furthermore prove M is a reversible and ergodic Markov chain, which leads to our main result: for all $\lambda>2+\sqrt{2}$, there is a constant $\alpha>1$ such that at stationarity the particles are $\alpha$-compressed, meaning the perimeter of the particle configuration is at most $\alpha$ times the minimum perimeter for those particles. We additionally show $\lambda>1$ is not enough to guarantee compression: for all $0

Many programmable matter systems have been proposed and realized recently, each often tailored to a specific task or physical setting. In our work on self-organizing particle systems, we abstract away from specific settings and instead describe programmable matter as a collection of simple computational elements (called particles) with limited computational power that each perform fully distributed, local, asynchronous algorithms to solve system-wide problems of movement, configuration, and coordination. Here, we focus on the compression problem, where we seek fully distributed and asynchronous algorithms that lead the system to gather together as tightly as possible, that is, to converge to a configuration with small perimeter, where we measure the perimeter of a configuration by the length of the walk along the configuration boundary. We present a Markov chain based algorithm that solves the compression problem under the geometric amoebot model, using the triangular lattice as the underlying graph, for particle systems that begin in a connected configuration with no holes. The Markov chain M takes as input a bias parameter $\lambda$, where $\lambda>1$ corresponds to particles favoring inducing more lattice triangles within the particle system. We prove that during the execution of M, the particles stay connected and no holes form. We furthermore prove M is a reversible and ergodic Markov chain, which leads to our main result: for all $\lambda>2+\sqrt{2}$, there is a constant $\alpha>1$ such that at stationarity the particles are $\alpha$-compressed, meaning the perimeter of the particle configuration is at most $\alpha$ times the minimum perimeter for those particles. We additionally show $\lambda>1$ is not enough to guarantee compression: for all $0

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Learning systems are defined on three levels of complexity, the trained system, the adaptive system, and the self-organizing system. The functional purpose of each is discussed in terms of the removal of noise and a theorem stating a necessary condition for adaption is stated and proven. The theorem is then applied to a simple form of adaptive system, and it is shown that for a linear threshold device employed within its 'natural capacity', two bits per weight are necessary. The information theoretic model of the self- organizing system is translated into a goal directed control system model. This self-organizing control system model is analyzed and shown to have a simple performance surface that can be searched by relaxation methods. Experiments are discussed.

Learning systems are defined on three levels of complexity, the trained system, the adaptive system, and the self-organizing system. The functional purpose of each is discussed in terms of the removal of noise and a theorem stating a necessary condition for adaption is stated and proven. The theorem is then applied to a simple form of adaptive system, and it is shown that for a linear threshold device employed within its 'natural capacity', two bits per weight are necessary. The information theoretic model of the self- organizing system is translated into a goal directed control system model. This self-organizing control system model is analyzed and shown to have a simple performance surface that can be searched by relaxation methods. Experiments are discussed.

Learning systems are defined on three levels of complexity, the trained system, the adaptive system, and the self-organizing system. The functional purpose of each is discussed in terms of the removal of noise and a theorem stating a necessary condition for adaption is stated and proven. The theorem is then applied to a simple form of adaptive system, and it is shown that for a linear threshold device employed within its 'natural capacity', two bits per weight are necessary. The information theoretic model of the self- organizing system is translated into a goal directed control system model. This self-organizing control system model is analyzed and shown to have a simple performance surface that can be searched by relaxation methods. Experiments are discussed.

Learning systems are defined on three levels of complexity, the trained system, the adaptive system, and the self-organizing system. The functional purpose of each is discussed in terms of the removal of noise and a theorem stating a necessary condition for adaption is stated and proven. The theorem is then applied to a simple form of adaptive system, and it is shown that for a linear threshold device employed within its 'natural capacity', two bits per weight are necessary. The information theoretic model of the self- organizing system is translated into a goal directed control system model. This self-organizing control system model is analyzed and shown to have a simple performance surface that can be searched by relaxation methods. Experiments are discussed.

Learning systems are defined on three levels of complexity, the trained system, the adaptive system, and the self-organizing system. The functional purpose of each is discussed in terms of the removal of noise and a theorem stating a necessary condition for adaption is stated and proven. The theorem is then applied to a simple form of adaptive system, and it is shown that for a linear threshold device employed within its 'natural capacity', two bits per weight are necessary. The information theoretic model of the self- organizing system is translated into a goal directed control system model. This self-organizing control system model is analyzed and shown to have a simple performance surface that can be searched by relaxation methods. Experiments are discussed.

Learning systems are defined on three levels of complexity, the trained system, the adaptive system, and the self-organizing system. The functional purpose of each is discussed in terms of the removal of noise and a theorem stating a necessary condition for adaption is stated and proven. The theorem is then applied to a simple form of adaptive system, and it is shown that for a linear threshold device employed within its 'natural capacity', two bits per weight are necessary. The information theoretic model of the self- organizing system is translated into a goal directed control system model. This self-organizing control system model is analyzed and shown to have a simple performance surface that can be searched by relaxation methods. Experiments are discussed.

This article is from Sensors (Basel, Switzerland) , volume 9 . Abstract The reliable operation of modern infrastructures depends on computerized systems and Supervisory Control and Data Acquisition (SCADA) systems, which are also based on the data obtained from sensor networks. The inherent limitations of the sensor devices make them extremely vulnerable to cyberwarfare/cyberterrorism attacks. In this paper, we propose a reputation system enhanced with distributed agents, based on unsupervised learning algorithms (self-organizing maps), in order to achieve fault tolerance and enhanced resistance to previously unknown attacks. This approach has been extensively simulated and compared with previous proposals.

Many of the current bio-inspired delivery networks set their focus on search, e.g., by using artificial ants. If the network size and, therefore, the search space gets too large, the users experience high delays until the requested content can be consumed. In previous work, we proposed different replication strategies to reduce the search space. In this report we further evaluate measures for storage load balancing, because peers are most likely limited in space. We periodically apply clean-ups if a certain storage level is reached. For our evaluations we combine the already introduced replication measures with least recently used (LRU), least frequently used (LFU) and a hormone-based clean-up. The goal is to elaborate a combination that leads to low delays while the replica utilization is high.

Urban transportation is a complex phenomenon. Since many agents are constantly interacting in parallel, it is difficult to predict the future state of a transportation system. Because of this, optimization techniques tend to give obsolete solutions, as the problem changes before it can be optimized. An alternative lies in seeking adaptive solutions. This adaptation can be achieved with self-organization. In a self-organizing transportation system, the elements of the system follow local rules to achieve a global solution. Like this, when the problem changes the system can adapt by itself to the new configuration. In this chapter, I will review recent, current, and future work on self-organizing transportation systems. Self-organizing traffic lights have proven to improve traffic flow considerably over traditional methods. In public transportation systems, simple rules are being explored to prevent the "equal headway instability" phenomenon. The methods we have used can be also applied to other urban transportation systems and their generality is discussed.

Contemporary threats exhibit unique characteristics that challenge classical security systems. In response to these threats, the DoD has an increased focus on applying proven SE techniques to SoS, which provide more functionality and performance than the sum of the constituent systems. Tools and techniques are necessary to facilitate evolution of today's systems. One major need is the ability to develop agile SoS architectures that respond to contemporary threats. This paper focuses on one characteristic of contemporary threats, self-organization. Different SoS are reviewed for the driving forces that support their self-organizing architecture. Necessary characteristics are identified and general constructs are put forward as candidate architectural patterns. These candidate patterns are now available for evaluation and employment consideration. They can be applied when problems common to the pattern manifest in new environments. They also add to the body of knowledge for SoS SE.

Source: Digital Library of India Scanning Centre: IISc, Bangalore Source Library: Carnegie Mellon University Date Accessioned: 7/9/2015 20:41 The Digital Library of India was a project under the auspices of the Government of India.

Source: Digital Library of India Scanning Centre: IISc, Bangalore Source Library: Carnegie Mellon University Date Accessioned: 7/9/2015 20:41 The Digital Library of India was a project under the auspices of the Government of India.

The new approach is based on the use of the low power radiation constant source as the sounding element having the interval of the radiation interruption (dark pulse) for a while commensurable with time of radiation and usual lidar systems. The following is used: the special time organization of an entering coming signal digitization, when registration time in each readout is commensurable in due course passages by a signal of all zone of measurements is beside; restoration of the environment characteristics with the necessary permission is reached by special processing of the received readout of the signal taken in pairs and differing on time of digitization on the interval corresponding of such permission; identification of the structure of the inhomogeneities is decided by a special method of structural and statistical analysis; identification of anomalies in the structure is decided by a special method of nonlinear filtering.

The new approach is based on the use of the low power radiation constant source as the sounding element having the interval of the radiation interruption (dark pulse) for a while commensurable with time of radiation and usual lidar systems. The following is used: the special time organization of an entering coming signal digitization, when registration time in each readout is commensurable in due course passages by a signal of all zone of measurements is beside; restoration of the environment characteristics with the necessary permission is reached by special processing of the received readout of the signal taken in pairs and differing on time of digitization on the interval corresponding of such permission; identification of the structure of the inhomogeneities is decided by a special method of structural and statistical analysis; identification of anomalies in the structure is decided by a special method of nonlinear filtering.

This paper studies the problem of self-organizing heterogeneous LTE systems. We propose a model that jointly considers several important characteristics of heterogeneous LTE system, including the usage of orthogonal frequency division multiple access (OFDMA), the frequency-selective fading for each link, the interference among different links, and the different transmission capabilities of different types of base stations. We also consider the cost of energy by taking into account the power consumption, including that for wireless transmission and that for operation, of base stations and the price of energy. Based on this model, we aim to propose a distributed protocol that improves the spectrum efficiency of the system, which is measured in terms of the weighted proportional fairness among the throughputs of clients, and reduces the cost of energy. We identify that there are several important components involved in this problem. We propose distributed strategies for each of these components. Each of the proposed strategies requires small computational and communicational overheads. Moreover, the interactions between components are also considered in the proposed strategies. Hence, these strategies result in a solution that jointly considers all factors of heterogeneous LTE systems. Simulation results also show that our proposed strategies achieve much better performance than existing ones.

This study has been devoted to extension and further development of search algorithms of utility for self-organizing control systems. The four major results of this study are as follows: Self-organizing search methods developed in the previous study (AMRL-TR-73-76) have been extended to higher- dimensional multi-modal problems and have been shown to be very effective. A composite search algorithm incorporating both the pdf-guided search and the guided accelerated random search was found to be more effective than either search algorithm alone. Clustering analysis has been shown to be a valuable tool for assessing the complexity of a search surface. The number of modes (peaks), their locations relative to each other, their shape and volume, and the estimated maximum performance value within each are all adaptively determined via clustering. A new method for image encoding has been formulated that provides image reconstruction of similar quality to methods currently in use. This procedure also can find regions of possible interest within the image because of its ability to treat the image as a whole rather than line-by-line. This characteristic considerably enhances its value as a tool in image pattern recognition and classification.

For practical industrial applications, the development of trainable robots is an important and immediate objective. Therefore, the developing of flexible intelligence directly applicable to training is emphasized. It is generally agreed upon by the AI community that the fusion of expert systems, neural networks, and conventionally programmed modules (e.g., a trajectory generator) is promising in the quest for autonomous robotic intelligence. Autonomous robot development is hindered by integration and architectural problems. Some obstacles towards the construction of more general robot control systems are as follows: (1) Growth problem; (2) Software generation; (3) Interaction with environment; (4) Reliability; and (5) Resource limitation. Neural networks can be successfully applied to some of these problems. However, current implementations of neural networks are hampered by the resource limitation problem and must be trained extensively to produce computationally accurate output. A generalization of conventional neural nets is proposed, and an architecture is offered in an attempt to address the above problems.

To say Earth systems are complex, is not the same as saying they are a complex system. A complex system, in the technical sense, is a group of -agents (individual interacting units, like birds in a flock, sand grains in a ripple, or individual units of friction along a fault zone), existing far from equilibrium, interacting through positive and negative feedbacks, forming interdependent, dynamic, evolutionary networks, that possess universality properties common to all complex systems (bifurcations, sensitive dependence, fractal organization, and avalanche behaviour that follows power-law distributions.) Chaos/complex systems theory behaviors are explicit, with their own assumptions, approaches, cognitive tools, and models that must be taught as deliberately and systematically as the equilibrium principles normally taught to students. We present a learning progression of concept building from chaos theory, through a variety of complex systems, and ending with how such systems result in increases in complexity, diversity, order, and/or interconnectedness with time--that is, evolve. Quantitative and qualitative course-end assessment data indicate that students who have gone through the rubrics are receptive to the ideas, and willing to continue to learn about, apply, and be influenced by them. The reliability/validity is strongly supported by open, written student comments.

The voices of millions of unified citizens will speak far louder than the divisive individual voices from wall street! Check this out: Inequality: mother of all evils? image.guardian.co.uk More money won't fix broken Britain, but sharing the money could. That is the conclusion of The Spirit Level, a groundbreaking study which concludes inequality is at root of all society's problems, from violent crime to teenage pregnancy. On these pages Tom Clark presents the evidence so you can make up your own mind This is what democracy looks like: www.youtube.com Also see: Edwards v. South Carolina, 372 US 229, 83 S. Ct. 680, 9 L. Ed. 2d 697 (1963), the Court held that the conviction of 187 African American students for demonstrating on the grounds of the state capitol in Columbia, South Carolina, had infringed on their "constitutionally protected rights of free speech, free assembly, and freedom to petition for redress of their grievances." The shocking truth about the crackdown on Occupy www.guardian.co.uk In other words, for the DHS to be on a call with mayors, the logic of its chain of command and accountability implies that congressional overseers, with the blessing of the White House, told the DHS to authorise mayors to order their police forces -- pumped up with millions of dollars of hardware and training from the DHS -- to make war on peaceful citizens. PLEASE SHARE THIS! Check this story out about police pensions: Check this story out about police pensions: www.bloomberg.comcc

During the period of 9/1/2003 through 8/31/2004 we have developed solutions for two critical problems faced by self-reconfigurable systems: (1) Autonomous discovery and response to unexpected topology changes; (2) A new distributed functional language called DH2 for programming of self-reconfigurable systems using hormone-inspired computational methods. These results are published by Dr Wei-Min Shen and his students Behnam Salemi, Maks Krivokon, and Michael Rubenstein.

Earth systems increase in complexity, diversity, and interconnectedness with time, driven by tectonic/solar energy that keeps the systems far from equilibrium. The evolution of Earth systems is facilitated by three evolutionary mechanisms: "elaboration," "fractionation," and "self-organization," that share universality features not found in more familiar equilibrium systems. These features include: (1) evolution to sensitive dependent critical states, (2) avalanches of changes following power law distributions with fractal organization, and (3) dynamic behaviour as strange attractors that often exhibit bistable behaviour. We propose a new approach to teaching Earth systems theory, where theoretical underpinnings of evolutionary mechanisms are introduced, followed by explorations of how the mechanisms interact to integrate the lithosphere, atmosphere, hydrosphere, and biosphere into a unitary evolutionary system. We incorporate conceptual and computer-based interactive models (included here as educational resources) within our lesson plans that illustrate a hierarchy of principles and experimental outcomes for evolutionary mechanisms. Application of this educational framework requires explicating complex systems mechanisms and their interactions, exploring their applicability to Earth systems, and imbedding them in high school as well as college introductory and upper level Earth Science classrooms to put all Earth systems on a comprehensive, integrated, universal evolutionary theoretical foundation.

Our technologies complexify our environments. Thus, new technologies need to deal with more and more complexity. Several efforts have been made to deal with this complexity using the concept of self-organization. However, in order to promote its use and understanding, we must first have a pragmatic understanding of complexity and self-organization. This paper presents a conceptual framework for speaking about self-organizing systems. The aim is to provide a methodology useful for designing and controlling systems developed to solve complex problems. First, practical notions of complexity and self-organization are given. Then, starting from the agent metaphor, a conceptual framework is presented. This provides formal ways of speaking about "satisfaction" of elements and systems. The main premise of the methodology claims that reducing the "friction" or "interference" of interactions between elements of a system will result in a higher "satisfaction" of the system, i.e. better performance. The methodology discusses different ways in which this can be achieved. A case study on self-organizing traffic lights illustrates the ideas presented in the paper.

Many proposals have already been made for realizing programmable matter, ranging from shape-changing molecules, DNA tiles, and synthetic cells to reconfigurable modular robotics. Envisioning systems of nano-sensors devices, we are particularly interested in programmable matter consisting of systems of simple computational elements, called particles, that can establish and release bonds and can actively move in a self-organized way, and in shape formation problems relevant for programmable matter in those self-organizing particle systems (SOPS). In this paper, we present a general algorithmic framework for shape formation problems in SOPS, and show direct applications of this framework to the problems of having the particle system self-organize to form a hexagonal or triangular shape. Our algorithms utilize only local control, require only constant-size memory particles, and are asymptotically optimal both in terms of the total number of movements needed to reach the desired shape configuration.

Aspects of problem formulation and problem solution are discussed. In particular, the relevance of these matters to the field of intelligent machines and some connections with the theory and application of dynamic programming are treated. The practical application of scientific philosophy as a technique to guide research and to avoid undue waste of time, energy, and talent is developed.

This dissertation researches the mathematical translation of resource provisioning policy into mathematical terms and parameters to solve the on-line service placement problem. A norm called the Provisioning Norm is introduced. Theorems presented in the work show the Provisioning Norm utility function and greedy, random, local search effectively and efficiently solve the on-line problem. Caching of placements is shown to reduce the cost of change but does not improve response time performance. The use of feedback control theory is shown to be effective at significantly improving performance but increases the cost of change. The theoretical results are verified using a decentralized, self-organizing testbed of web servers. The testbed places services on servers on-line using feedback control by profiling the service and node resources. Web servers share service profiles and find new service placement solutions using parallel searches based on the Provisioning Norm.

The general aim of this work is to develop and demonstrate a prototype structural health monitoring system for thermal protection systems that incorporates piezoelectric acoustic emission (AE) sensors to detect the occurrence and location of damaging impacts, and an optical fiber Bragg grating (FBG) sensor network to evaluate the effect of detected damage on the thermal conductivity of the TPS material. Following detection of an impact, the TPS would be exposed to a heat source, possibly the sun, and the temperature distribution on the inner surface in the vicinity of the impact measured by the FBG network. A similar procedure could also be carried out as a screening test immediately prior to re-entry. The implications of any detected anomalies in the measured temperature distribution will be evaluated for their significance in relation to the performance of the TPS during re-entry. Such a robust TPS health monitoring system would ensure overall crew safety throughout the mission, especially during reentry

We study the predictability of large events in self-organizing systems. We focus on a set of models which have been studied as analogs of earthquake faults and fault systems, and apply methods based on techniques which are of current interest in seismology. In all cases we find detectable correlations between precursory smaller events and the large events we aim to forecast. We compare predictions based on different patterns of precursory events and find that for all of the models a new precursor based on the spatial distribution of activity outperforms more traditional measures based on temporal variations in the local activity.

The general aim of this work is to develop and demonstrate a prototype structural health monitoring system for thermal protection systems that incorporates piezoelectric acoustic emission (AE) sensors to detect the occurrence and location of damaging impacts, such as those from Micrometeoroid Orbital Debris (MMOD). The approach uses an optical fiber Bragg grating (FBG) sensor network to evaluate the effect of detected damage on the thermal conductivity of the TPS material. Following detection of an impact, the TPS would be exposed to a heat source, possibly the sun, and the temperature distribution on the inner surface in the vicinity of the impact measured by the FBG network. A similar procedure could also be carried out as a screening test immediately prior to re-entry. The implications of any detected anomalies in the measured temperature distribution will be evaluated for their significance in relation to the performance of the TPS during reentry. Such a robust TPS health monitoring system would ensure overall crew safety throughout the mission, especially during reentry.

There is tremendous interest in the design of intelligent machines capable of autonomous learning and skillful performance under complex environments. A major task in designing such systems is to make the system plastic and adaptive when presented with new and useful information and stable in response to irrelevant events. A great body of knowledge, based on neuro-physiological concepts, has evolved as a possible solution to this problem. Adaptive resonance theory (ART) is a classical example under this category. The system dynamics of an ART network is described by a set of differential equations with nonlinear functions. An approach for designing self-organizing networks characterized by nonlinear differential equations is proposed.

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