Search

Two processes are necessary to solve group decision making problems: A consensus process and a selection process. The consensus reaching process is necessary to obtain a final solution with a certain level of agreement between the experts; and the selection process is necessary to obtain such a final solution. In a previous paper, we present a selection process to deal with group decision making problems with incomplete fuzzy preference relations, which uses consistency measures to estimate the incomplete fuzzy preference relations. In this paper we present a consensus model. The main novelty of this consensus model is that of being guided by both consensus and consistency measures. Also, the consensus reaching process is guided automatically, without moderator, through both consensus and consistency criteria. To do that, a feedback mechanism is developed to generate advice on how experts should change or complete their preferences in order to reach a solution with high consensus and consistency degrees. In each consensus round, experts are given information on how to change their preferences, and to estimate missing values if their corresponding preference relation is incomplete. Additionally, a consensus and consistency based induced ordered weighted averaging operator to aggregate the experts' preferences is introduced, which can be used in consensus models as well as in selection processes. The main improvement of this consensus model is that it supports the management of incomplete information and it allows to achieve consistent solutions with a great level of agreement.

When streaming packetized media data over a lossy packet network, it is desirable to use transmission strategies that minimize the expected distortion subject to a constraint on the expected transmission rate. Because the computation of such optimal strategies is usually an intractable problem, fast heuristic techniques are often used. We first show that when the graph that gives the decoding dependencies between the data packets is reducible to a tree, optimal transmission strategies can be efficiently computed with dynamic programming algorithms. The proposed algorithms are much faster than other exact algorithms developed for arbitrary dependency graphs. They are slower than previous heuristic techniques but can provide much better solutions. We also show how to apply our algorithms to find high-quality approximate solutions when the dependency graph is not tree reducible. To validate our approach, we run simulations for MPEG1 and H.264 video data. We first consider a simulated packet erasure channel. Then we implement a real video streaming system and provide experimental results for an Internet connection.

Output of the EU Framework funded collaborative research project Environmental Lifecycle Information Management and Acquisition (ELIMA) (Growth Programme GRD2-2000-30097). The resulting software framework from DMU produced two product lifecycle information management systems that facilitated pilot multi-country field trials (Indesit/Hotpoint refrigeration products - tim.cock@indesitcompany.com and Sony PlayStation consoles -bodenhoefer@sony.de ). Resulting product lifecycle information management systems were transferred and hosted at SONY and Indesit respectively. Other partners included Motorola, Cybernetix, SAT and Sheffield Hallam. Total project value was €3.9M. In this paper, a component-based software framework that can facilitate the development of lifecycle information management systems for consumer products is developed. This software framework can accommodate the management of lifecycle data at all stages, especially data that occur in distribution, usage, maintenance and end-of life stages, and use them to provide information and knowledge. The software framework is built with software components, based on a component-based concept with reference to a product lifecycle information acquisition and management model proposed within this research study. Two actual lifecycle information management systems are created using the Framework in field trials. This software framework can open new horizons for product design which are sustainable and environmentally sensitive. It also contributes to the wider exploration of middleware solutions for implementing next generation consumer products (e.g. smart home appliances) and intelligent products.

Over the past three decades, the scientific and industrial communities have injected considerable resources into perfecting orthogonal frequency division multiplex (OFDM) - the driving technology behind many applications used today (such as IEEE 802.11g, asymmetric digital subscriber line (ADSL), and digital terrestrial television). In 1997, OFDM was officially sanctioned for use in the European Telecommunications Standards Institute (ETSI) EN 300 744 standard known as digital video broadcasting-terrestrial (DVB-T). As OFDM is such a popular technology, there has been limited research over the years into alternatives. This paper proposes a novel approach to transmit parallel information through a terrestrial channel that could render it a more flexible alternative to OFDM in the future. Investigations have shown that this technique could provide a highly flexible, channel optimized modulation technique based on time-frequency multiplexing and this paper presents the preliminary phase of investigations into this new technique .

Owing to the susceptibility to the peak-to-average power ratio (PAPR) on orthogonal frequency division multiplexing (OFDM) systems, the authors introduce a novel approach based on turbo coding to improve OFDM (PAPR) performance. In the paper, it is shown that even for different types of modulation methods and coding rates, the technique is still applicable and gives better performance comparing to the conventional techniques being used (such as the clipping technique and the partial transmit sequence (PTS) technique).

Interval type-2 fuzzy sets (T2 FS) play a central role in fuzzy sets as models for words and in engineering applications of T2 FSs. These fuzzy sets are characterized by their footprints of uncertainty (FOU), which in turn are characterized by their boundaries-upper and lower membership functions (MF). In this two-part paper, we focus on symmetric interval T2 FSs for which the centroid (which is an interval type-1 FS) provides a measure of its uncertainty. Intuitively, we anticipate that geometric properties about the FOU, such as its area and the center of gravities (centroids) of its upper and lower MFs, will be associated with the amount of uncertainty in such a T2 FS. The main purpose of this paper (Part 1) is to demonstrate that our intuition is correct and to quantify the centroid of a symmetric interval T2 FS, and consequently its uncertainty, with respect to such geometric properties. It is then possible, for the first time, to formulate and solve forward problems, i.e., to go from parametric interval T2 FS models to data with associated uncertainty bounds. We provide some solutions to such problems. These solutions are used in Part 2 to solve some inverse problems, i.e., to go from uncertain data to parametric interval T2 FS models (T2 fuzzistics)

The feature selective validation (FSV) method has been proposed as a technique to allow the objective, quantified, comparison of data for inter alia validation of computational electromagnetics. In the companion paper "Feature selective validation for validation of computational electromagnetics. Part I-The FSV method," the method was outlined in some detail. This paper addresses two specific issues related to the implementation of the FSV method, namely "how well does it produce results that agree with visual assessment?" and "what benefit can it provide in a practical validation environment?" The first of these questions is addressed by comparing the FSV output to the results of an extensive survey of EMC engineers from several countries. The second is approached via a case study analysis.

The paper demonstrates our research claims that Evolutionary Computing techniques can be used for extracting information which is non-explicitly available within data, and therefore otherwise impossible to discern. It shows how our novel A.I. techniques can actually be tested and validated using real-world data. It supports the quest for research to be accepted by the business community, to show that it will eventually be useful to understand a system which potentially has obscure internal knowledge which was previously impossible to extract and therefore analyse.

A list Viterbi algorithm (LVA) finds the n most likely paths in a trellis diagram of a convolutional code. One of the most efficient LVAs is the tree-trellis algorithm of Soong and Huang. We propose a new implementation of this algorithm. Instead of storing the candidate paths in a single list sorted according to the metrics of the paths, we show that it is computationally more efficient to use several unsorted lists, where all paths of the same list have the same metric. For an arbitrary integer bit metric, both the time and space complexity of our implementation are linear in n. Experimental results for a binary symmetric channel and an additive white Gaussian noise channel show that our implementation is much faster than all previous LVAs.

We consider the problem of ratedistortion optimized streaming of packetized multimedia data over a single quality of service network using feedback and retransmissions. For a single data unit, we prove that the problem is NP-hardand provide efficient branch and bound algorithms that are much faster than the previously best solution based on dynamic programming. For a group of interdependent data units, we show how to compute optimal solutions with branch and bound algorithms. The branch and bound algorithms for a group of data units are much slower than the current state of the art, a heuristic technique known as sensitivity adaptation. However, in many realworld situations, they provide a significantly better rate-distortion performance.

The group decision-making framework with linguistic preference relations is studied. In this context, we assume that there exist several experts who may have different background and knowledge to solve a particular problem and, therefore, different linguistic term sets (multigranular linguistic information) could be used to express their opinions. The aim of this paper is to present a model of consensus support system to assist the experts in all phases of the consensus reaching process of group decision-making problems with multigranular linguistic preference relations. This consensus support system model is based on i) a multigranular linguistic methodology, ii) two consensus criteria, consensus degrees and proximity measures, and iii) a guidance advice system. The multigranular linguistic methodology permits the unification of the different linguistic domains to facilitate the calculus of consensus degrees and proximity measures on the basis of experts' opinions. The consensus degrees assess the agreement amongst all the experts' opinions, while the proximity measures are used to find out how far the individual opinions are from the group opinion. The guidance advice system integrated in the consensus support system model acts as a feedback mechanism, and it is based on a set of advice rules to help the experts change their opinions and to find out which direction that change should follow in order to obtain the highest degree of consensus possible. There are two main advantages provided by this model of consensus support system. Firstly, its ability to cope with group decision-making problems with multigranular linguistic preference relations, and, secondly, the figure of the moderator, traditionally presents in the consensus reaching process, is replaced by the guidance advice system, and in such a way, the whole group decision-making process is automated

Reliable real-time transmission of packetized embedded multimedia data over noisy channels requires the design of fast error control algorithms. For packet erasure channels, efficient forward error correction is obtained by using systematic Reed-Solomon (RS) codes across packets. For fading channels, state-of-the-art performance is given by a product channel code where each column code is an RS code and each row code is a concatenation of an outer cyclic redundancy check code and an inner rate-compatible punctured convolutional code. For each of these two systems, we propose a low-memory linear-time iterative improvement algorithm to compute an error protection solution. Experimental results for the two-dimensional and three-dimensional set partitioning in hierarchical trees coders showed that our algorithms provide close to optimal average peak signal-to-noise ratio performance, and that their running time is significantly lower than that of all previously proposed solutions.

This output is the culmination of over 10 years work on radix digit-serial computations. This research was recognized with an invitation to the Editorial Board of the IEEE Transactions on VLSI Systems. The work on digit serial computations was adopted by Professor Parhi’s Group at Minnesota University (parhi@umn.edu ) and as a result many companies are developing crypto-processors based on digital serial computations including Philips and Sun Microsystems.

Accurate and reliable decision making in oncological prognosis can help in the planning of suitable surgery and therapy, and generally, improve patient management through the different stages of the disease. In recent years, several prognostic markers have been used as indicators of disease progression in oncology. However, the rapid increase in the discovery of novel prognostic markers resulting from the development in medical technology, has dictated the need for developing reliable methods for extracting clinically significant markers where complex and nonlinear interactions between these markers naturally exist. The aim of this paper is to investigate the fuzzy k-nearest neighbor (FK-NN) classifier as a fuzzy logic method that provides a certainty degree for prognostic decision and assessment of the markers, and to compare it with: 1) logistic regression as a statistical method and 2) multilayer feedforward backpropagation neural networks an artificial neural-network tool, the latter two techniques having been widely used for oncological prognosis. In order to achieve this aim, breast and prostate cancer data sets are considered as benchmarks for this analysis. The overall results obtained indicate that the FK-NN-based method yields the highest predictive accuracy, and that it has produced a more reliable prognostic marker model than both the statistical and artificial neural-network-based methods.

The DRIFTS (Dynamically Responsive Intervention For Tremor Suppression) project aims to create wearable active orthoses that suppress upper-limb tremor while allowing natural movement. We present an overview this multidisciplinary project. This project is financially supported by the European Commission's Fifth Framework Programme and aims to create proof-of-concept prototypes of wearable active orthoses for the suppression of upper limb tremor while preserving natural movement. Another major goal of the project is the development of a prototyping and evaluation platform for the future elaboration of wearable ambulatory tremor suppression devices. The prototyping platform will be used to assess the efficacy of available sensing, actuating, and control technologies for tremor suppression.

We derive inner- and outer-bound sets for the type-reduced set of an interval type-2 fuzzy logic system (FLS), based on a new mathematical interpretation of the Karnik-Mendel iterative procedure for computing the type-reduced set. The bound sets can not only provide estimates about the uncertainty contained in the output of an interval type-2 FLS, but can also be used to design an interval type-2 FLS. We demonstrate, by means of a simulation experiment, that the resulting system can operate without type-reduction and can achieve similar performance to one that uses type-reduction. Therefore, our new design method, based on the bound sets, can relieve the computation burden of an interval type-2 FLS during its operation, which makes an interval type-2 FLS useful for real-time applications.

In electrical impedance tomography, currents are applied to the body through electrodes that are attached to the surface and the corresponding surface voltages are measured. Based on these boundary measurements, the internal admittivity distribution of the body can be reconstructed. In order to improve the image quality it is necessary and useful to apply physiologically meaningful prior information into the image reconstruction. Such prior information usually can be obtained from other sources. For example, information on the object's boundary shape and internal structure can be obtained from computed tomography and magnetic resonance imaging scan. However, this type of prior information may change from time to time and from person to person. As these changes are limited anatomically and physiologically, the prior information including the possible changes can be presented in a number of variational forms. The aim of this paper is to find which form of prior information is more compatible for a specific imaged object at the time of imaging. This paper proposes a new method for selecting the most appropriate form of prior information, through the procedure of iterative image reconstruction by using the information obtained from boundary measurements. The method is based on the principle that incompatible prior information causes errors which are able to affect the image reconstruction's convergence behavior. In this method, according to the various forms of prior information available, several image reconstruction configurations are designed. Then, through monitoring the convergence behavior in an iterative image reconstruction, the configuration with compatible prior information can be found among those different configurations. As an example, the prior information regarding the imaged object's boundary shape and internal structure was studied by computer simulation. Results were shown and discussed.

The research underpinning this paper was funded by over £150k from Brand-Rex Ltd (contact khodge@brand-rex.com), who further supported research in communication cables by over £200k. This paper led to collaboration with Dr Jorg-Hein Walling (jo.walling@sympatico.ca), head of the Canadian Delegation to the IEC) which has resulted in several conference papers to date. This paper was a major contributor to invitations to deliver tutorials on cable design at the International Wire and Cable Symposium (www.iwcs.org in 2002 – 4) and to invitations to join the TIA’s Balanced Modeling Working Group and BSI committee TC7/-/1. The authors present equations for the calculation of the attenuation constant of both unshielded and shielded cables as a function of cable dimension, material parameters and frequency of operation. An analytical approach is given for calculating the return loss of a communication channel as a function of such parameters as impedance, length and propagation constant. A transmission-line matrix (TLM) method to assess the effects on attenuation and return loss of longitudinal variations along a structured wire cable is developed. Results are presented using both analytical and modelling approaches; these are validated against measurements.