Abstract—A new abstract model of computation, the computational manifold, provides a framework for approaching the problems of consciousness, awareness, cognition and symbolic processing in the central nervous system. Physical properties involving space, time and frequency, such as the surface

Abstract—Natural gas storage facilities form critical components in the energy networks of most industrialized nations. Storage facilities are typically very large underground salt caverns or depleted gas reservoirs that have undergone an expensive conversion process. Storage is extremely important because of the strong seasonality in natural gas prices due to demand patterns. We study the effects of competition when firms share space and injection/withdrawal resources and behave in a rational, self-interested manner to optimize utilization (typically using software systems to assist in decision support). Strategic interaction occurs between firms because injectability and deliverability depend on the total inventory stored. We analyze the interaction that occurs in a pre-commitment game where firms commit to inject until a certain time and subsequently withdraw the commodity until it is exhausted. We compute equilibrium and welfare maximizing outcomes and calculate losses in efficiency. We also study the effects of incorporating a “use-it-or-lose-it ” policy on overall welfare. Our results indicate that significant inefficiencies can occur in both settings and, therefore, have significant implications for the regulatory systems for storage facilities, price stability in the gas markets and the operational policies for storage companies. Index Terms—Energy system modelling, decision optimization, software trading agents, game theory, equilibrium analysis.

Abstract- In the paper a model of the traffic in the LAN is presented. In the model the most important components influ-encing the network traffic are taken into account. Namely, the transmission protocols and information buffering, operational systems and queuing algorithms as well as users ' behavior working with the network applications are considered. The model is based on an "on-off " function. The network traffic observed at the physical layer is a superposition of many se-quential and self-similar "on-off " processes. It has been shown that self-similarity of the traffic measured by Hurst parameter changing from almost 1.0 for very low frequencies to 0.5 for high frequencies. Implications, which are derived from the model, are described in the conclusion. Index Ternm-network protocols, traffic model, components traffic I.

An increasing number of applications execute over a set of computing and communication resources and have end-to-end quality of service (QoS) requirements. Given a system and an application with specific flow structure and QoS requirements, this paper describes an integrated QoS-based allocation and routing algorithm that determines which computing and communication resources to utilize for this application. More specifically, the algorithm finds the best flow path for an application, subject to application flow and QoS constraints, so as to optimize system objectives (e.g., minimize total system utilization or balance the load). The paper also introduces a-optimal algorithm that, relative to the optimal algorithm, significantly reduces the run-time of the algorithm with a minor degradation in the optimality of the solution. The-optimal algorithm is guaranteed to produce a result that is at most-units sub-optimal. The paper also analyzes the optimality and run time of the solution as a function of for two multimedia scenarios. Rules of thumb for setting are also given.

Abstract — This work presents an algorithm for generating the GA-based (Genetic Algorithm) classifier for microarray data classification. The microarray dataset comprises of a small number of samples with very high features. In order to construct the GA-based classifier, a number of informative features (genes) are selected. These features are divided into 2 groups (10 features or less in each group). The summation of gene expression values selected by GA in each group is then calculated and compared between groups. If the summation of the first group is greater than the other, it is classified as class 1; otherwise, it is classified as class 2. In the experiment, 3 microarray benchmark datasets for the 2-class problem are used. There are Lymphoma, Leukemia and Colon datasets. 10-Folds cross validation is used to test the performance of the proposed method. The experimental results show that the proposed GA-based classifier yields a good effectiveness in the 2-class microarray data classification comparing with the other methods.

Abstract—We propose novel information content estimators for diffusion tensor images using binless approaches based on nearest-neighbour distances. Combining these estimators with existing tensor distance metrics allows us to generate entropy estimates that are consistent and accurate for diffusion tensor data. Further, we are able to obtain such estimators without having to reduce the dimensionality of the tensor data to the point where a binning estimator can be reliably used. We test our estimators in the context of noise estimation, image segmentation, and image registration. Results on 12 datasets from LBAM and 50 datasets from LONI show our estimators more accurately reflect the underlying DTI data and provide faster convergence rates for image segmentation and registration algorithms. Keywords-diffusion tensor imaging; entropy; mutual information; noise estimation; image segmentation; image registration I.

Abstract- This paper presents a new economic cost-effective solution known as web and telephony based method for tracking and tracing of goods and small and medium sized third party logistic providers. Considering that these companies usually operate on very flat margins, a comparison is made of the available track and trace technologies like GPS, Mobile Phone Approximately GPS and Java based cell tracking in terms of costs, operating risks, and other evaluation criteria. Keywords- track and trace, small and medium sized third party logistic providers, interactive voice response system I.

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Abstract—One of the ideas of agile software development is to respond to changes rather than following a plan. Constantly changing businesses result in changing requirements, to be handled in the development process. Therefore, it is essential that the underlying software architecture is capable of managing agile business processes. However, criticism on agile software development states that it fails to pay attention to architectural and design issues and therefore is bound to engender suboptimal design-decisions. In this paper we propose an architectural framework for agile software development, that by explicitly separating computational, coordinational, and communicational models offers a high degree of flexibility regarding architectural and design changes introduced by agile business processes. The framework strength is facilitated by combining the characteristics and properties of state-of-the-art middleware architectural styles captured in a simple API. The benefit of our approach is a clear architectural design with minimized effects of changes the models have on each other, accompanied by an efficient realization of new business requirements.

Abstract—The complexity and performance requirements of embedded software are continuously increasing, making Multiprocessor System-on-Chip (MPSoC) architectures more and more important in the domain of embedded and cyber-physical systems. Using multiple cores in a single system reduces problems concerning energy consumption, heat dissipation, and increases performance. Nevertheless, these benefits do not come for free. Porting existing, mostly sequential, applications to MPSoCs requires extracting efficient parallelism to utilize all available cores. Many embedded applications, like network services and multimedia tasks for voice-, image- and video processing, are operating on data streams and thus have a streaming-based structure. Despite the abundance of parallelism in streaming applications, it is a non-trivial task to split and efficiently map sequential applications to MPSoCs. Therefore, we present an algorithm which automatically extracts pipeline parallelism from sequential ANSI-C applications. The presented tool employs an integer linear programming (ILP) based approach enriched with an adequate cost model to automatically control the granularity of the parallelization. By applying our tool to real-life applications, it can be shown that our approach is able to speed up applications by a factor of up to 3.9x on a four-core MPSoC architecture, compared to a sequential execution.