Rotation invariant iris feature extraction using Gaussian Markov random fields with non-separable wavelet

Rotation invariance is important for an iris recognition system since changes of head orientation and binocular vergence may cause eye rotation. The conventional methods of iris recognition cannot achieve true rotation invariance. They only achieve approximate rotation invariance by rotating the feature vector before matching or unwrapping the iris ring at different initial angles. In these methods, the complexity of the method is increased, and when the rotation scale is beyond the certain scope, the error rates of these methods may substantially increase. In order to solve this problem, a new rotation invariant approach for iris feature extraction based on the non-separable wavelet is proposed in this paper. Firstly, a bank of non-separable orthogonal wavelet filters is used to capture characteristics of the iris. Secondly, a method of Markov random fields is used to capture rotation invariant iris feature. Finally, two-class kernel Fisher classifiers are adopted for classification. Experimental results on public iris databases show that the proposed approach has a low error rate and achieves true rotation invariance. © 2010.

Online learning in discrete hidden Markov models

We present and analyze three different online algorithms for learning in discrete Hidden Markov Models (HMMs) and compare their performance with the Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of the generalization error we draw learning curves in simplified situations and compare the results. The performance for learning drifting concepts of one of the presented algorithms is analyzed and compared with the Baldi-Chauvin algorithm in the same situations. A brief discussion about learning and symmetry breaking based on our results is also presented. © 2006 American Institute of Physics.

Non-Markov state model of peptide dynamics

A hidden Markov state model has been applied to classical molecular dynamics simulated small peptide in explicit water. The methodology allows increasing the time resolution of the model and describe the dynamics with the precision of 0.3 ps (comparing to 6 ps for the standard methodology). It also permits the investigation of the mechanisms of transitions between the conformational states of the peptide. The detailed description of one of such transitions for the studied molecule is presented. © 2012 Elsevier B.V. All rights reserved.

Consuming linked data in supply chains:enabling data visibility via linked pedigrees

The performance of a supply chain depends critically on the coordinating actions and decisions undertaken by the trading partners. The sharing of product and process information plays a central role in the coordination and is a key driver for the success of the supply chain. In this paper we propose the concept of "Linked pedigrees" - linked datasets, that enable the sharing of traceability information of products as they move along the supply chain. We present a distributed and decentralised, linked data driven architecture that consumes real time supply chain linked data to generate linked pedigrees. We then present a communication protocol to enable the exchange of linked pedigrees among trading partners. We exemplify the utility of linked pedigrees by illustrating examples from the perishable goods logistics supply chain.

A solution for information management in logistics operations of modern manufacturing chains

One dominant feature of the modern manufacturing chains is the movement of goods. Manufacturing companies would remain an unprofitable investment if the supplies/logistics of raw materials, semi-finished products or final goods are not handled in an effective way. Both levels of a modern manufacturing chain-actual production and logistics-are characterized by continuous data creation at a much faster rate than they can be meaningfully analyzed and acted upon manually. Often, instant and reliable decisions need to be taken based on huge, previously inconceivable amounts of heterogeneous, contradictory or incomplete data. The paper will highlight aspects of information flows related to business process data visibility and observability in modern manufacturing networks. An information management platform developed in the framework of the EU FP7 project ADVANCE will be presented.

Fractional Derivatives and Fractional Powers as Tools in Understanding Wentzell Boundary Value Problems for Pseudo-Differential Operators Generating Markov Processes

Mathematics Subject Classification: 26A33, 31C25, 35S99, 47D07.

Enhancing visibility in EPCIS governing agri-food supply chains via linked pedigrees

Data integration for the purposes of tracking, tracing and transparency are important challenges in the agri-food supply chain. The Electronic Product Code Information Services (EPCIS) is an event-oriented GS1 standard that aims to enable tracking and tracing of products through the sharing of event-based datasets that encapsulate the Electronic Product Code (EPC). In this paper, the authors propose a framework that utilises events and EPCs in the generation of "linked pedigrees" - linked datasets that enable the sharing of traceability information about products as they move along the supply chain. The authors exploit two ontology based information models, EEM and CBVVocab within a distributed and decentralised framework that consumes real time EPCIS events as linked data to generate the linked pedigrees. The authors exemplify the usage of linked pedigrees within the fresh fruit and vegetables supply chain in the agri-food sector.

An Application of the Subordination Chains

MSC 2010: 30C45, 30A20, 34A30

EPCIS Event-Based traceability in pharmaceutical supply chains via automated generation of linked pedigrees

In this paper we show how event processing over semantically annotated streams of events can be exploited, for implementing tracing and tracking of products in supply chains through the automated generation of linked pedigrees. In our abstraction, events are encoded as spatially and temporally oriented named graphs, while linked pedigrees as RDF datasets are their specific compositions. We propose an algorithm that operates over streams of RDF annotated EPCIS events to generate linked pedigrees. We exemplify our approach using the pharmaceuticals supply chain and show how counterfeit detection is an implicit part of our pedigree generation. Our evaluation results show that for fast moving supply chains, smaller window sizes on event streams provide significantly higher efficiency in the generation of pedigrees as well as enable early counterfeit detection.

Single-Server Queueing System with Markov-Modulated Arrivals and Service Times

Key words: Markov-modulated queues, waiting time, heavy traffic.

A New Class of Processes for Formalizing and Generalizing Individual-Based Models: The Semi-Semi-Markov Processes

2000 Mathematics Subject Classification: 60K15, 60K20, 60G20,60J75, 60J80, 60J85, 60-08, 90B15.

Lp Microlocal Supercritical Markov Branching Processes with Non-Homogeneous Poisson Immigration

2010 Mathematics Subject Classification: 60J80.

Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions

In this paper we develop set of novel Markov Chain Monte Carlo algorithms for Bayesian smoothing of partially observed non-linear diffusion processes. The sampling algorithms developed herein use a deterministic approximation to the posterior distribution over paths as the proposal distribution for a mixture of an independence and a random walk sampler. The approximating distribution is sampled by simulating an optimized time-dependent linear diffusion process derived from the recently developed variational Gaussian process approximation method. The novel diffusion bridge proposal derived from the variational approximation allows the use of a flexible blocking strategy that further improves mixing, and thus the efficiency, of the sampling algorithms. The algorithms are tested on two diffusion processes: one with double-well potential drift and another with SINE drift. The new algorithm's accuracy and efficiency is compared with state-of-the-art hybrid Monte Carlo based path sampling. It is shown that in practical, finite sample applications the algorithm is accurate except in the presence of large observation errors and low to a multi-modal structure in the posterior distribution over paths. More importantly, the variational approximation assisted sampling algorithm outperforms hybrid Monte Carlo in terms of computational efficiency, except when the diffusion process is densely observed with small errors in which case both algorithms are equally efficient. © 2011 Springer-Verlag.

OntoPedigree:modelling pedigrees for traceability in supply chains

The sharing of product and process information plays a central role in coordinating supply chains operations and is a key driver for their success. "Linked pedigrees" - linked datasets, that encapsulate event based traceability information of artifacts as they move along the supply chain, provide a scalable mechanism to record and facilitate the sharing of track and trace knowledge among supply chain partners. In this paper we present "OntoPedigree" a content ontology design pattern for the representation of linked pedigrees, that can be specialised and extended to define domain specific traceability ontologies. Events captured within the pedigrees are specified using EPCIS - a GS1 standard for the specification of traceability information within and across enterprises, while certification information is described using PROV - a vocabulary for modelling provenance of resources. We exemplify the utility of OntoPedigree in linked pedigrees generated for supply chains within the perishable goods and pharmaceuticals sectors.