A Scheduling Technique Wireless Personal for Bandwidth Communication Allocation in Networks

Future generation wireless personalcommunication networks (PCN) are expected to providemultimedia capable wireless extensions of fixedATM/B-ISDN. This paper presents a scheduling techniquefor PCN based on TDMA and the leaky bucket regulator, thewell known bandwidth enforcement mechanism for fixedATM. The main objective of the proposed technique is toensure fair and efficient treatment of various types oftraffic on the air interface, includingconstant-bit-rate (CBR) voice and variable-bit-rate(VBR) video. Two alternative priority mechanisms areintroduced and their performance is evaluated. Theperformance comparison of the alternatives reveals aninteresting tradeoff between fairness and quality ofservice (QoS).     

A Multiagent Platform for Ubiquitous Service Provision

The Virtual Home Environment is very important in contemporary mobile telecommunications infrastructure as it caters for the ubiquitous provision of services irrespective of network, location and user device. The universality of systems like Universal Mobile Telecommunications System and wi-fi increases the need for the rapid introduction of efficient VHE schemes. In this paper, we study the adoption of Mobile Agents for handling the VHE functionality. Mobile agents are nicely harmonized with the broader idea of VHE as they allow the autonomous execution of tasks by components that roam from node to node and network to network. We present the detailed modeling of a VHE provisioning architecture and investigate its suitability for different use cases and technical options (e.g., end user devices). The adoption of mobile agents for the ubiquitous provision of telecommunication services is quite promising in terms of efficiency. Through a series of experiments we quantify the performance benefits stemming from the adoption of mobile agents in contrast to conventional service provisioning schemes.     

A formally specified ontology management API as a registry for ubiquitous computing systems

Recently, several standards have emerged for ontology markup languages that can be used to formalize all kinds of knowledge. However, there are no widely accepted standards yet that define APIs to manage ontological data. Processing ontological information still suffers from the heterogeneity imposed by the plethora of available ontology management systems. Moreover, ubiquitous computing environments usually comprise software components written in a variety of different programming languages, which makes it particularly difficult to establish a common ontology management API with programming language agnostic semantics. We implemented an ontological Knowledge Base Server, which can expose the functionality of arbitrary off-the-shelf ontology management systems via a formally specified and well defined API. A case study was carried out in order to demonstrate the feasibility of our approach to use a formally specified ontology management API to implement a registry for ubiquitous computing systems.     

HISYCOL a hybrid computational intelligence system for combined machine learning: the case of air pollution modeling in Athens

The analysis of air quality and the continuous monitoring of air pollution levels are important subjects of the environmental science and research. This problem actually has real impact in the human health and quality of life. The determination of the conditions which favor high concentration of pollutants and most of all the timely forecast of such cases is really crucial, as it facilitates the imposition of specific protection and prevention actions by civil protection. This research paper discusses an innovative threefold intelligent hybrid system of combined machine learning algorithms HISYCOL (henceforth). First, it deals with the correlation of the conditions under which high pollutants concentrations emerge. On the other hand, it proposes and presents an ensemble system using combination of machine learning algorithms capable of forecasting the values of air pollutants. What is really important and gives this modeling effort a hybrid nature is the fact that it uses clustered datasets. Moreover, this approach improves the accuracy of existing forecasting models by using unsupervised machine learning to cluster the data vectors and trace hidden knowledge. Finally, it employs a Mamdani fuzzy inference system for each air pollutant in order to forecast even more effectively its concentrations.     

Optimizing drug regimens in cancer chemotherapy by an efficacy-toxicity mathematical model.

In cancer chemotherapy, it is important to design treatment strategies that ensure a desired rate of tumor cell kill without unacceptable toxicity. To optimize treatment, we used a mathematical model describing the pharmacokinetics of anticancer drugs, antitumor efficacy, and drug toxicity. This model was associated with constraints on the allowed plasma concentrations, drug exposure, and leukopenia. Given a schedule of drug administrations, the mathematical model optimized the drug doses that can minimize the tumor burden while limiting toxicity at the level of the white blood cells. The main result is that the optimal drug administration is an initial high-dose chemotherapy up to saturation of constraints associated with normal cell toxicity and a maintenance continuous infusion at a moderate rate. Data related to etoposide investigations were used in a feasibility study. Simulations with the optimized protocol showed better performances than usual clinical protocols. Model-based optimal drug doses provide for greater cytoreduction, while limiting the risk of unacceptable toxicity.     

Detecting invasive species with a bio-inspired semi-supervised neurocomputing approach: the case of Lagocephalus sceleratus

The need to protect the environment and biodiversity and to safeguard public health require the development of timely and reliable methods for the identification of particularly dangerous invasive species, before they become regulators of ecosystems. These species appear to be morphologically similar, despite their strong biological differences, something that complicates their identification process. Additionally, the localization of the broader space of dispersion and the development of invasive species are considered to be of critical importance in the effort to take proper management measures. The aim of this research is to create an advanced computational intelligence system for the automatic recognition, of invasive or another unknown species. The identification is performed based on the analysis of environmental DNA by employing machine learning methods. More specifically, this research effort proposes a hybrid bio-inspired computational intelligence detection approach. It employs extreme learning machines combined with an evolving Izhikevich spiking neuron model for the automated identification of the invasive fish species “Lagocephalus sceleratus” extremely dangerous for human health.

     

Influence of a cyclic butylene terephthalate oligomer on the processability and thermoelectric properties of polycarbonate/MWCNT nanocomposites

The thermoelectric properties of melt-processed nanocomposites consisting of a polycarbonate (PC) thermoplastic matrix filled with commercially available carboxyl (–COOH) functionalized multi-walled carbon nanotubes (MWCNTs) were evaluated. MWCNTs carrying carboxylic acid moieties (MWCNT-COOH) were used due the p-doping that the carboxyl groups facilitate, via electron withdrawing from the electron-rich π-conjugated system. Preliminary thermogravimetric analysis (TGA) of MWCNT-COOH revealed that the melt-mixing was limited at low temperatures due to thermal decomposition of the MWCNT functional groups. Therefore, PC was mixed with 2.5 wt% MWCNT-COOH (PC/MWCNT-COOH) at 240 °C and 270 °C. In order to reduce the polymer melt viscosity, a cyclic butylene terephthalate (CBT) oligomer was utilized as an additive, improving additionally the electrical conductivity of the nanocomposites. The melt rheological characterization of neat PC and PC/CBT blends demonstrated a significant decrease of the complex viscosity by the addition of CBT (10 wt%). Optical and transmission electron microscopy (OM, TEM) depicted an improved MWCNT dispersion in the PC/CBT polymer blend. The electrical conductivity was remarkably higher for the PC/MWCNT-COOH/CBT composites compared to the PC/MWCNT-COOH ones. Namely, the PC/MWCNT-COOH/CBT processed at 270 °C exhibited the best values with electrical conductivity; σ = 0.05 S/m, Seebeck coefficient; S = 13.55 μV/K, power factor; PF = 7.60 × 10⁻⁶μW/m K⁻², and thermoelectric figure of merit; ZT = 7.94 × 10⁻⁹. The PC/MWCNT-COOH/CBT nanocomposites could be ideal candidates for large-scale thermal energy harvesting, even though the presently obtained ZT values are still too low for commercial applications.     

A mixed integer optimisation model for data classification

In this work, a mixed integer linear programming (MILP) model is proposed for the multi-class data classification problem using a hyper-box representation. The latter representation is particularly suitable for capturing disjoint data regions. The objective function used is the minimisation of the total number of misclassified data samples. In order to improve the training and testing accuracy of our approach, an iterative solution procedure is developed to assign potential multiple boxes to each single class. Finally, the applicability of the proposed approach is demonstrated through a number of illustrative examples. According to the computational results obtained, the proposed optimisation-based approach is competitive in terms of prediction accuracy when compared with various standard classifiers.