Information-theoretic model and analysis of molecular signaling in targeted drug delivery

Targeted drug delivery (TDD) modality promises a smart localization of appropriate dose of therapeutic drugs to the targeted part of the body at reduced system toxicity. To achieve the desired goals of TDD, accurate analysis of the system is important. Recent advances in molecular communication (MC) present prospects to analyzing the TDD process using engineering concepts and tools. Specifically, the MC platform supports the abstraction of TDD process as a communication engineering problem in which the injection and transportation of drug particles in the human body and the delivery to a specific tissue or organ can be analyzed using communication engineering tools. In this paper we stand on the MC platform to present the information-theoretic model and analysis of the TDD systems. We present a modular structure of the TDD system and the probabilistic models of the MC-abstracted modules in an intuitive manner. Simulated results of information-theoretic measures such as the mutual information are employed to analyze the performance of the TDD system. Results indicate that uncertainties in drug injection/release systems, nanoparticles propagation channel and nanoreceiver systems influence the mutual information of the system, which is relative to the system's bioequivalence measure.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7728eo2020Electrical, Electronic and Computer Engineerin

Modeling a teacher in a tutorial-like system using Learning Automata

The goal of this paper is to present a novel approach to model the behavior of a Teacher in a Tutorial- like system. In this model, the Teacher is capable of presenting teaching material from a Socratic-type Domain model via multiple-choice questions. Since this knowledge is stored in the Domain model in chapters with different levels of complexity, the Teacher is able to present learning material of varying degrees of difficulty to the Students. In our model, we propose that the Teacher will be able to assist the Students to learn the more difficult material. In order to achieve this, he provides them with hints that are relative to the difficulty of the learning material presented. This enables the Students to cope with the process of handling more complex knowledge, and to be able to learn it appropriately. To our knowledge, the findings of this study are novel to the field of intelligent adaptation using Learning Automata (LA). The novelty lies in the fact that the learning system has a strategy by which it can deal with increasingly more complex/difficult Environments (or domains from which the learning as to be achieved). In our approach, the convergence of the Student models (represented by LA) is driven not only by the response of the Environment (Teacher), but also by the hints that are provided by the latter. Our proposed Teacher model has been tested against different benchmark Environments, and the results of these simulations have demonstrated the salient aspects of our model. The main conclusion is that Normal and Below-Normal learners benefited significantly from the hints provided by the Teacher, while the benefits to (brilliant) Fast learners were marginal. This seems to be in-line with our subjective understanding of the behavior of real-life Students

An Evolutionary Fuzzy Algorithm For Qos And Policy-Based Inter-Domain Routing In Heterogeneous Atm And Sdh/sonet Networks

One major issue in future B-ISDN is the problem of routing, given QoS and policy constraints. The problem has been proved to be extremely complex and heuristics have been proposed to reach near-optimal solutions . In this paper we propose the use of computational intelligence algorithms, such as fuzzy logic and genetic algorithms for route computation. We designed an inter-domain routing mechanism for global broadband connections that uses a fuzzy-logic controller to compute route suitability. Our work resulted in a system that is efficient, self-improving through use of genetic algorithms, and can be easily adapted to other environments as well. AN EVOLUTIONARY FUZZY ALGORITHM FOR QOS AND POLICY-BASED INTER-DOMAIN ROUTING IN HETEROGENEOUS ATM AND SDH/SONET NETWORKS 1 A.V.Vasilakos, K.G.Anagnostakis {vasilako,kanag}@ics.forth.gr Telecommunications & Networks Group, ICS-FORTH P.O.Box 1385, 711 10 Herakion Crete, Greece A.Pitsillides [email protected] Dept. of Computer Scie..

Peer-to-Peer media streaming: Insights and new developments

10.1109/JPROC.2011.2165330Proceedings of the IEEE99122089-2109IEEP

On distributed and coordinated resource allocation for interference mitigation in self-organizing LTE networks

We propose a distributed and coordinated radio resource allocation algorithm for orthogonal frequency division multiple access (OFDMA)-based cellular networks to self-organize efficient and stable frequency reuse patterns. In the proposed radio resource allocation algorithm, each cell independently and dynamically allocates modulation and coding scheme (MCS), resource block (RB), and transmit power to its users in a way that its total downlink (DL) transmit power is minimized, while users' throughput demands are satisfied. Moreover, each cell informs neighboring cells of the RBs that have been scheduled for its cell-edge users' DL transmissions through message passing. Accordingly, the neighboring cells abstain from assigning high transmit powers to the specified RBs. Extensive simulation results attempt to demonstrate that DL power control on a per-RB basis may play a key role in future networks, and show that the distributed minimization of DL transmit power at each cell, supported by intercell interference coordination, is able to provide a 20% improvement of network throughput, considerably reduce the number of user outages, and significantly enhance spatial reuse, as compared to cutting-edge resource allocation schemes

Autonomous composition of fuzzy granules in ambient intelligence scenarios

Pervasive and human-centric computing is beginning to be fact: with cell phones, laptops and handhelds, human beings can work pretty much anywhere. Ambient Intelligence (AmI) is a novel human-centric computer discipline based on three emergent technologies: Ubiquitous Computing, Ubiquitous Communication and Intelligent User Interfaces. The integration of aforesaid technologies opens new scenarios for improving the interaction between humans and information technology equipments realizing a human-centric computing environment. Within this aim the deliverable of tasks or services should be achieved through the usage of an invisible network of heterogeneous devices composing dynamic computational-ecosystems capable of satisfying the users requirements. Fuzzy granules, intended as a clump of objects which are drawn together by criteria like indistinguishability, similarity, proximity or functionality, can represent a powerful and, simultaneously, simple paradigm to embed intelligence into a generic AmI ecosystem in order to support people in carrying out their everyday life activities, tasks and rituals in easy and natural way. However, the strong dinamicity and the high complexity characterizing a typical AmI scenario make difficult and expensive to design ad-hoc fuzzy granules. This paper presents a framework exploiting methodologies coming from Semantic Web and Computational Intelligence areas to compose fuzzy granules in autonomous way in order to maximize the users comfort and achieve the hardware transparency and interoperability

A measurement study on potential inter-domain routing diversity

In response to Internet emergencies, Internet resiliency is investigated directly through an autonomous system (AS) level graph inferred from policy-compliant BGP paths or/and traceroute paths. Due to policy-driven inter-domain routing, the physical connectivity does not necessarily imply network reachability in the AS-level graph, i.e., many physical paths are not visible by the inter-domain routing protocol for connectivity recovery during Internet outages. We call the invisible connectivity at the routing layer, which can be quickly restored for recovering routing failures by simple configurations, as the potential routing diversities. In this paper, we evaluate two kinds of potential routing diversities, which are recognized as Internet eXchange Points (IXPs) participant reconnection and peering policy relaxation. Using the most complete dataset containing AS-level map and IXP participants that we can achieve, we successfully evaluate the ability of potential routing diversity for routing recovery during different kinds of Internet emergencies. Encouragingly, our experimental results show that 40% to 80% of the interrupted network pairs can be recovered on average beyond policy-compliant paths, with rich path diversities and a little traffic shifts. Thus, this paper implies that the potential routing diversities are promising venues to address Internet failures. © 2004-2012 IEEE