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.

Delay-Induced Synchronization of Identical Linear Multiagent Systems.

This paper studies a class of fast consensus algorithms for a group of identical multiagent systems each described by the linear state-space model. By using both the current and delayed state information, the proposed delay-induced consensus algorithm is shown to achieve synchronization with a faster convergence speed than the standard one when the eigenvalues of the open-loop system, control parameters, the Laplacian matrix of the network, and the delay satisfy certain conditions. In addition, some sufficient or necessary and sufficient conditions are established to guarantee the closed-loop stability of the delay-induced consensus algorithm, where an extra control parameter on the coupling strength is introduced to adjust the convergence speed of the closed-loop system flexibly. We then show that the delay-induced algorithm is robust to the small intrinsic communication or input delays, i.e., the proposed delay-induced consensus algorithm may also produce a faster convergence speed than the standard one even if there exist small intrinsic communication or input delays. Furthermore, we extend the results from the case of an undirected communication topology to those of a directed communication topology and a switching communication topology. Several simulation examples are presented to illustrate the theoretical results.

Linguistic models and linguistic modeling.

The study is concerned with a linguistic approach to the design of a new category of fuzzy (granular) models. In contrast to numerically driven identification techniques, we concentrate on budding meaningful linguistic labels (granules) in the space of experimental data and forming the ensuing model as a web of associations between such granules. As such models are designed at the level of information granules and generate results in the same granular rather than pure numeric format, we refer to them as linguistic models. Furthermore, as there are no detailed numeric estimation procedures involved in the construction of the linguistic models carried out in this way, their design mode can be viewed as that of a rapid prototyping. The underlying algorithm used in the development of the models utilizes an augmented version of the clustering technique (context-based clustering) that is centered around a notion of linguistic contexts-a collection of fuzzy sets or fuzzy relations defined in the data space (more precisely a space of input variables). The detailed design algorithm is provided and contrasted with the standard modeling approaches commonly encountered in the literature. The usefulness of the linguistic mode of system modeling is discussed and illustrated with the aid of numeric studies including both synthetic data as well as some time series dealing with modeling traffic intensity over a broadband telecommunication network.

ANASA-a stochastic reinforcement algorithm for real-valued neural computation.

This paper introduces ANASA (adaptive neural algorithm of stochastic activation), a new, efficient, reinforcement learning algorithm for training neural units and networks with continuous output. The proposed method employs concepts, found in self-organizing neural networks theory and in reinforcement estimator learning algorithms, to extract and exploit information relative to previous input pattern presentations. In addition, it uses an adaptive learning rate function and a self-adjusting stochastic activation to accelerate the learning process. A form of optimal performance of the ANASA algorithm is proved (under a set of assumptions) via strong convergence theorems and concepts. Experimentally, the new algorithm yields results, which are superior compared to existing associative reinforcement learning methods in terms of accuracy and convergence rates. The rapid convergence rate of ANASA is demonstrated in a simple learning task, when it is used as a single neural unit, and in mathematical function modeling problems, when it is used to train various multilayered neural networks.