Factory intersystem communication devices of networks, systems, complexes and electronic computing m
Information Technology is the concept involving the development, maintenance, and use of computer systems, software, and networks for the processing and distribution of data. Often in the context of a business or other enterprise. We shall call it information technology IT. Executives cite robust customer demand and the uptake of emerging product and service categories as key contributors to the positive sentiment.VIDEO ON THE TOPIC: Hub, Switch, & Router Explained - What's the difference?
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- History of the Internet
- Wireless Network Design for Emerging IIoT Applications: Reference Framework and Use Cases
- Recent Trends and Advances in Wireless and IoT-enabled Networks
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Recently, studies of active systems e. While from a theoretical point of view, collective dynamics represents an interesting case of study, it can also serve as a solid infrastructure for developing new technologies [5,6,7,8]. Therefore, developing computational methods capable of efficiently reproducing the dynamics of confined systems is fundamental. In this context, we elaborate a discrete model to perform a parametric study of a confined active system, spanning a wide range of possible values of the characteristic quantities of the system, such as geometrical configurations or particle peculiarities.
In this way, it is possible to associate these properties with the collective emergent dynamics. We follow the dynamics of self-propelled active particles confined in a channel with single-file condition.
The channel is represented by a 1D lattice, and active particles move within it with a constant velocity. In particular, we consider a system consisting of two microchambers, containing a number of particles N, connected through a microchannel with a length L.
This geometrical configuration has been already studied in molecular dynamics simulations MDSs , showing an interesting self-sustained density oscillation of particles. We take into account short-range interactions, namely the excluded volume effect and the relative pushes between adjacent particles, responsible for the formation of aggregated states.
In fact, self-propelled particles generally interact through non-reflecting collisions, with a subsequent formation of clusters . We also consider long-range interactions amongst particles in the same cluster, which leads to the rise of collective dynamics.
We find the density oscillations relying on the formation of long active clusters. These clusters must be long enough to allow the formation of long-lasting flows of particles in the channel.
Results show that in channels with L It is also very interesting to note that the oscillatory behavior does not depend on the number of swimmers N. These results can be the starting point for designing microstructured-devices based on the collective dynamics of living organisms. These devices would be capable of, for instance, controlling bacteria diffusion or transport passive matter. References  Paoluzzi M.
Matter 24 , Natl Acad. USA , E 91, , Machine learning algorithms are typically trained and tested based on classification or regression error. While the Kullback-Liebler or other information theoretic metrics may be utilized, these metrics often measure relative performance without a clear sense of what constitutes an absolute standard of success. The interpretation of information theoretic metrics is clarified by translating them into a probability which can be compared with the classification metrics.
Furthermore via the weighted generalized mean of predicted probabilities, which is a translation of the Tsallis and Renyi generalizations of entropy, the contrast between decisive and robust algorithms can be measured.
These probabilistic metrics of learning performance can be split into components related to the discrimination power of the underlying features and the accuracy of the learned models. The components are related to the entropy and divergence components of the cross-entropy between the model and source distributions.
The probabilistic metrics are embedded in a plot of the calibration curves contrasting predicted and measured distributions providing a clear visualization of the accuracy and robustness of machine learning algorithms.
Over the past few years, vulnerability of pharmaceutical supply chains to disruption has affected heath care through the United States. Designing a system which is robust for these disruptions is complex and requires adaptive decision process that dynamically changes over time. Despite from inevitable occurrence of exogenous disruptions that can happen in any system, endogenous disruptions as irrational decisions can reinforce the vulnerability of supply chain and cause system to collapse.
Using system dynamics simulation helps us to capture the complex interaction among components of a pharmaceutical supply chain and try to design safety stock policies for varying exogenous stochastic shocks to the system.
In addition to that we characterize how disperse events spatially and temporarily in area and time can propagate disruptions in a system. We present a method of endowing agents in an agent-based model with sophisticated cognitive capabilities and a naturally tunable level of intelligence.
Often, agent-based models use random behavior or greedy algorithms for maximizing objectives such as a predator always chasing after the closest prey.
However, random behavior is too simplistic in many circumstances and greedy algorithms, as well as classic AI planning techniques, can be brittle in the context of the unpredictable and emergent situations in which agents may find themselves.
Our method centers around representing agent cognition as an independently defined, but connected, agent-based model. To that end, we have implemented our method in the NetLogo agent-based modeling platform, using the recently released LevelSpace extension, which we developed to allow NetLogo models to interact with other NetLogo models.
Our method works as follows: The modeler defines what actions an agent can take e. Similar to Monte Carlo tree search methods used in game AI, during each tick of the simulation each agent runs a settable number of micro-simulations using its cognitive model, with initial conditions based on their surroundings, tracking what actions they take, and how well they meet their objectives as a consequence of those actions. The agent then selects an action based on the results of these micro-simulations.
As an illustrative example, and to begin to understand how this type of cognition interacts with complex systems, we present a modification of a classic predator-prey model, in which the animals have been equipped with the cognitive faculties described above.
Based on the Wolf-Sheep Predation model included with NetLogo, the model contains wolves, sheep, and grass. In the classic model, wolves and sheep move randomly and reproduce when they have sufficient energy. Sheep eat grass and wolves eat sheep. Grass grows back at a set rate. In our extension, we define a simplified version of the model that represents how the wolves and sheep think the world works.
We then use NetLogo LevelSpace to equip each of the wolves and sheep with this cognitive model, which they then use to perform short-term simulations of their surroundings and select actions that lead to the best outcomes.
This cognitive model naturally allows sheep, for instance, to realize that if they move towards a particular patch of grass, a wolf might eat them or another sheep may arrive there first. However, the cognitive model also automatically adapts to special circumstances that the sheep may find itself in.
For instance, if the sheep is about to starve to death, they will be more willing to risk being eaten by a wolf if it means getting food. The naturally adaptive capabilities and emergent decision making sets this agent cognition method apart from traditional agent AI. To understand the impact of this cognitive model on the dynamics of the predator-prey model, we performed experiments in which the number and length of the micro-simulations that the sheep use is varied, while the wolves are left with their original random behavior.
Simulation length, however, achieves peak performance at a relatively small number of ticks; when the simulations are too long, sheep performance drops. Thus, we find that giving the agents even limited cognitive capabilities results in dramatic changes to the systems long-term behavior. Motivated by the increasing attention given to automated information campaigns and their potential to influence information ecosystems online, we argue that agent-based models of opinion dynamics provide a useful environment for understanding and assessing social influence strategies.
This approach allows us to build theory about the efficacy of various influence strategies, forces us to be precise and rigorous about our assumptions surrounding such strategies, and highlights potential gaps in existing models. We present a case study illustrating these points in which we adapt a strategy, viz.
We treat it as a simple agent strategy situated within three models of opinion dynamics using three different mechanisms of social influence. We present early findings from this work suggesting that a simple amplification strategy is only successful in cases where it is assumed that any given agent is capable of being influenced by almost any other agent, and is likewise unsuccessful in cases that assume agents have more restrictive criteria for who may influence them.
The outcomes of this case study suggest ways in which the amplification strategy can be made more robust, and thus more relevant for extrapolating to real-world strategies. We discuss how this methodology might be applied to more sophisticated strategies and the broader benefits of this approach as a complement to empirical methods.
We are interested in practical applications of AI and Data Science across different industries and aspects of our daily lives, and in particular how are AI and the Big Data disrupting different industries as well as various aspects of economic, social and other human endeavor.
In that context, we specifically identify health care, the energy sector and education to be among those domains in which the AI- and Big Data-triggered disruption is already in progress, with much more to come in the future. We first briefly discuss how is the landscape from technology use to business models to impact on people working on those industries of each of these three domains already being considerably by the emergence of scaleable, practical applied AI and "big data" analytics; some of the discussion is based on our own research addressing some of the major challenges those industries face.
We then outline our prediction on further changes that we think are very likely to befall these industries. While most technology-driven and especially, AI and Big Data driven changes that health care, the energy sector and education esp.
In particular, those changes will require forward-looking, technology-aware industry leaders and policy makers capable of and willing to embrace change and re-invent their organizations and industries, in order to not merely survive but actually strive while riding on the wave of the ongoing, not-slowing-down-anytime-soon AI- and Big Data-driven technology revolution.
Complex Networks theory is considered as a formal tool for describing and analyzing the interaction backbone of a wide range of real complex systems. The concept of line graph offers a good representation of the network properties when it is appropriate to give more importance to the edges of a network than to its nodes.
It is possible to consider two different approaches on a directed and weighted network G in order to define the PageRank of each edge of G:. We can show that both approaches are equivalent, even though it is clear that one approach has clear computational advantages over the other. As an application, we analyze human mobility in the Madrid Metro System in order to locate the segments with the highest passenger flow on a standard working day, distinguishing between the morning and the afternoon time periods.
The symbolic dynamics and recurrence plots are basic methods of nonlinear dynamics for analyzing complex systems. Although the conventional methods have made great strides in understanding genetic patterns, they are required to analyze the so-called junk DNA with complex funtions.
In this presentation, firstly, the metric representation of a genome borrowed from the symbolic dynamics is proposed to form a fractal pattern in a plane. Due to the metric repsentation method, the recurrence plot technique of the genome is established to analyze the recurrence structures of nucleotide strings.
Then, by using the metric repsentation and recurrece plot methos, the recurrence distance distributions in bacterial and aechaeal complete genomes are identified. The mechanism of the recurrence structures are analyzed.
Further, the Synechocystis sp. PCC genome as one of oldest unicellular organism is taken as an example to make detailed analysis of the periodic and non-periodic recurrence structures.
The periodic recurrence structures are generated by periodic transfer of several substrings in long periodic or non-periodic nucleotide strings embedded in the coding regions of genes. The non-periodic recurrence structures are generated by non-periodic transfer of several substrings covering or overlapping with the coding regions of genes. In the periodic and non-periodic transfer, some gaps divide the long nucleotide strings into the substrings and prevent their global transfer.
Due to the comparison of the relative positions and lengths, the substrings concerned with the non-periodic recurrence structures are almost identical to the mobile elements annotated in the genome.
The mobile elements are thus endowed with the basic results on the recurrence structures. Networks are one of the most frequently used modelling paradigms for dynamical systems. Investigations towards synchronization phenomena in networks of coupled oscillators have attracted considerable attention, and so has the analysis of chaotic behaviour and corresponding phenomena in networks of dynamical systems to name just a few.
Here, we discuss another related challenge that originates from the fact that network inference in the Inverse Problem typically relies on statistical methods and selection criteria. When a network is reconstructed, two types of errors can occur: false positive and false negative errors about the presence or absence of links.
We analyse analytically the impact of these two errors on the vertex degree distribution. Moreover, an analytic formula of the density of the biased vertex degree distribution is presented. In the Inverse Problem, the aim is to reconstruct the original network.
We formulate an equation that enables us to calculate analytically the vertex degree distribution of the original network if the biased one and the probabilities of false positive and false negative errors are given. When the dimension of the network is relatively large, numerical issues arise and consequently the truncated singular value decomposition is used to calculate the original network vertex degree distribution. The outcomes of this work are general results that enable to reconstruct analytically the vertex degree distribution of any network.
This method is a powerful tool since the vertex degree distribution is a key characteristic of networks. The impact sector is the sector that uses business to achieve environmental and social positive impact in a sustainable manner.
This article was published in: Embedded Systems Programming , 7 11 , November , pp. Bhargav P. Upender barg utrc.
Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks. Sign in Help View Cart. Article Tools. Add to my favorites.
To develop a solid understanding of communications technology, one must be firmly grounded in a wide range of basic concepts in both the voice and data domains. Telecommunications is defined as the transfer of information over a distance. The information can be audio for example, voice , image, video, computer data or any combination. The voice or video information can be transferred in its native analog format over an analog network, or it can be transformed into a digital format for transfer over a digital network. Similarly, computer data can be transferred in its native digital format over a digital network, or it can be transformed into an analog format for transfer over an analog network. In the voice and video domains, the telephones accept acoustic analog audio inputs, and the cameras accept reflected analog light inputs on the transmit side of the data transfer, and telephones and TV monitors recreate them on the receive side. In the domain of data communications, the data may take the forms of text, graphics and images in a wide variety of formats including documents, spreadsheets, e-mail, customer records, performance records, traffic tallies, still images and video.
This chapter looks at the inter-system communication components that occur in the embedded motion systems this book is focusing on. A field bus is a part of a system which provides the communication between several components in that system for example an actuator or a sensor. A bus is a cable with an interface on the two ends. A bus system is a collective noun for all buses, this means that there is a distinguish between:.
The following cross-references are used in this glossary: See refers you from a nonpreferred term to the preferred term or from an abbreviation to the spelled-out form. See also refers you to a related or contrasting term. An object cannot be constructed from an abstract class; that is, it cannot be instantiated. See also parent class.
Embedded Control Systems Design/Field busses
The history of the Internet has its origin in the efforts of wide area networking that originated in several computer science laboratories in the United States , United Kingdom , and France. In the early s the NSF funded the establishment for national supercomputing centers at several universities, and provided interconnectivity in with the NSFNET project, which also created network access to the supercomputer sites in the United States from research and education organizations. Commercial Internet service providers ISPs began to emerge in the very late s.
Not a MyNAP member yet? Register for a free account to start saving and receiving special member only perks. Humans have long dreamed of possessing the capability to communicate with each other anytime, anywhere. Kings, nation-states, military forces, and business cartels have sought more and better ways to acquire timely information of strategic or economic value from across the globe. Travelers have often been willing to pay premiums to communicate with family and friends back home.
History of the Internet
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Albers O. Camp J. Percher B. Jouga L. Holtmanns M.
Wireless Network Design for Emerging IIoT Applications: Reference Framework and Use Cases
Recently, studies of active systems e. While from a theoretical point of view, collective dynamics represents an interesting case of study, it can also serve as a solid infrastructure for developing new technologies [5,6,7,8]. Therefore, developing computational methods capable of efficiently reproducing the dynamics of confined systems is fundamental.
Recent Trends and Advances in Wireless and IoT-enabled Networks
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Industrial Internet of Things IIoT applications, featured with data-centric innovations, are leveraging the observability, control, and analytics, as well as the safety of industrial operations. The wireless system design for IIoT applications is inherently a joint effort between operational technology OT engineers, information technology IT system architects, and wireless network planners. In this paper, we propose a new reference framework for the wireless system design in IIoT use cases. The framework presents a generic design process and identifies the key questions and tools of individual procedures. Specifically, we extract impact factors from distinct domains including industrial operations and environments, data service dynamics, and the IT infrastructure.
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