Sequential estimation of multipath MIMO-OFDM channels

Wireless “MIMO” systems, employing multiple transmit and receive antennas, promise a significant increase of channel capacity, while orthogonal frequency-division multiplexing (OFDM) is attracting a good deal of attention due to its robustness to multipath fading. Thus, the combination of both techniques is an attractive proposition for radio transmission. The goal of this paper is the description and analysis of a new and novel pilot-aided estimator of multipath block-fading channels. Typical models leading to estimation algorithms assume the number of multipath components and delays to be constant (and often known), while their amplitudes are allowed to vary with time. Our estimator is focused instead on the more realistic assumption that the number of channel taps is also unknown and varies with time following a known probabilistic model. The estimation problem arising from these assumptions is solved using Random-Set Theory (RST), whereby one regards the multipath-channel response as a single set-valued random entity.Within this framework, Bayesian recursive equations determine the evolution with time of the channel estimator. Due to the lack of a closed form for the solution of Bayesian equations, a (Rao–Blackwellized) particle filter (RBPF) implementation ofthe channel estimator is advocated. Since the resulting estimator exhibits a complexity which grows exponentially with the number of multipath components, a simplified version is also introduced. Simulation results describing the performance of our channel estimator demonstrate its effectiveness.

Sequential estimation of time-varying multipath channel for MIMO-OFDM systems

In this paper, we introduce a pilot-aided multipath channel estimator for Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems. Typical estimation algorithms assume the number of multipath components and delays to be known and constant, while theiramplitudes may vary in time. In this work, we focus on the more realistic assumption that also the number of channel taps is unknown and time-varying. The estimation problem arising from this assumption is solved using Random Set Theory (RST), which is a probability theory of finite sets. Due to the lack of a closed form of the optimal filter, a Rao-Blackwellized Particle Filter (RBPF) implementation of the channel estimator is derived. Simulation results demonstrate the estimator effectiveness.

Evolving from inferences to decisions in the interpretation of scientific evidence

Le travail d'un(e) expert(e) en science forensique exige que ce dernier (cette dernière) prenne une série de décisions. Ces décisions sont difficiles parce qu'elles doivent être prises dans l'inévitable présence d'incertitude, dans le contexte unique des circonstances qui entourent la décision, et, parfois, parce qu'elles sont complexes suite à de nombreuse variables aléatoires et dépendantes les unes des autres. Etant donné que ces décisions peuvent aboutir à des conséquences sérieuses dans l'administration de la justice, la prise de décisions en science forensique devrait être soutenue par un cadre robuste qui fait des inférences en présence d'incertitudes et des décisions sur la base de ces inférences. L'objectif de cette thèse est de répondre à ce besoin en présentant un cadre théorique pour faire des choix rationnels dans des problèmes de décisions rencontrés par les experts dans un laboratoire de science forensique. L'inférence et la théorie de la décision bayésienne satisfont les conditions nécessaires pour un tel cadre théorique. Pour atteindre son objectif, cette thèse consiste de trois propositions, recommandant l'utilisation (1) de la théorie de la décision, (2) des réseaux bayésiens, et (3) des réseaux bayésiens de décision pour gérer des problèmes d'inférence et de décision forensiques. Les résultats présentent un cadre uniforme et cohérent pour faire des inférences et des décisions en science forensique qui utilise les concepts théoriques ci-dessus. Ils décrivent comment organiser chaque type de problème en le décomposant dans ses différents éléments, et comment trouver le meilleur plan d'action en faisant la distinction entre des problèmes de décision en une étape et des problèmes de décision en deux étapes et en y appliquant le principe de la maximisation de l'utilité espérée. Pour illustrer l'application de ce cadre à des problèmes rencontrés par les experts dans un laboratoire de science forensique, des études de cas théoriques appliquent la théorie de la décision, les réseaux bayésiens et les réseaux bayésiens de décision à une sélection de différents types de problèmes d'inférence et de décision impliquant différentes catégories de traces. Deux études du problème des deux traces illustrent comment la construction de réseaux bayésiens permet de gérer des problèmes d'inférence complexes, et ainsi surmonter l'obstacle de la complexité qui peut être présent dans des problèmes de décision. Trois études-une sur ce qu'il faut conclure d'une recherche dans une banque de données qui fournit exactement une correspondance, une sur quel génotype il faut rechercher dans une banque de données sur la base des observations faites sur des résultats de profilage d'ADN, et une sur s'il faut soumettre une trace digitale à un processus qui compare la trace avec des empreintes de sources potentielles-expliquent l'application de la théorie de la décision et des réseaux bayésiens de décision à chacune de ces décisions. Les résultats des études des cas théoriques soutiennent les trois propositions avancées dans cette thèse. Ainsi, cette thèse présente un cadre uniforme pour organiser et trouver le plan d'action le plus rationnel dans des problèmes de décisions rencontrés par les experts dans un laboratoire de science forensique. Le cadre proposé est un outil interactif et exploratoire qui permet de mieux comprendre un problème de décision afin que cette compréhension puisse aboutir à des choix qui sont mieux informés. - Forensic science casework involves making a sériés of choices. The difficulty in making these choices lies in the inévitable presence of uncertainty, the unique context of circumstances surrounding each décision and, in some cases, the complexity due to numerous, interrelated random variables. Given that these décisions can lead to serious conséquences in the admin-istration of justice, forensic décision making should be supported by a robust framework that makes inferences under uncertainty and décisions based on these inferences. The objective of this thesis is to respond to this need by presenting a framework for making rational choices in décision problems encountered by scientists in forensic science laboratories. Bayesian inference and décision theory meets the requirements for such a framework. To attain its objective, this thesis consists of three propositions, advocating the use of (1) décision theory, (2) Bayesian networks, and (3) influence diagrams for handling forensic inference and décision problems. The results present a uniform and coherent framework for making inferences and décisions in forensic science using the above theoretical concepts. They describe how to organize each type of problem by breaking it down into its différent elements, and how to find the most rational course of action by distinguishing between one-stage and two-stage décision problems and applying the principle of expected utility maximization. To illustrate the framework's application to the problems encountered by scientists in forensic science laboratories, theoretical case studies apply décision theory, Bayesian net-works and influence diagrams to a selection of différent types of inference and décision problems dealing with différent catégories of trace evidence. Two studies of the two-trace problem illustrate how the construction of Bayesian networks can handle complex inference problems, and thus overcome the hurdle of complexity that can be present in décision prob-lems. Three studies-one on what to conclude when a database search provides exactly one hit, one on what genotype to search for in a database based on the observations made on DNA typing results, and one on whether to submit a fingermark to the process of comparing it with prints of its potential sources-explain the application of décision theory and influ¬ence diagrams to each of these décisions. The results of the theoretical case studies support the thesis's three propositions. Hence, this thesis présents a uniform framework for organizing and finding the most rational course of action in décision problems encountered by scientists in forensic science laboratories. The proposed framework is an interactive and exploratory tool for better understanding a décision problem so that this understanding may lead to better informed choices.

Non-random fertilization in mice correlates with the MHC and something else

One evolutionary explanation for the success of sexual reproduction assumes that sex is an advantage in the coevolutionary arms race between pathogens and hosts. Accordingly, an important criterion in mate choice and maternal selection thereafter could be the allelic specificity at polymorphic loci involved in parasite-host interactions, e.g. the MHC (major histocompatibility complex). The MHC has been found to influence mate choice and selective abortions in mice and humans. However, it could also influence the fertilization process itself, i.e. (i) the oocyte's choice for the fertilizing sperm, and (ii) the outcome of the second meiotic division after the sperm has entered the egg. We tested both hypotheses in an in vitro fertilization experiment with two inbred mouse strains congenic for their MHC. The genotypes of the resulting blastocysts were determined by polymerase chain reaction. We found nonrandom MHC combinations in the blastocysts which may result from both possible choice mechanisms. The outcome changed significantly over time, indicating that a choice for MHC combinations during fertilization may be influenced by one or several external factors.

Simple estimation of incident HIV infection rates in notification cohorts based on window periods of algorithms for evaluation of line-immunoassay result patterns.

BACKGROUND: Tests for recent infections (TRIs) are important for HIV surveillance. We have shown that a patient's antibody pattern in a confirmatory line immunoassay (Inno-Lia) also yields information on time since infection. We have published algorithms which, with a certain sensitivity and specificity, distinguish between incident (< = 12 months) and older infection. In order to use these algorithms like other TRIs, i.e., based on their windows, we now determined their window periods. METHODS: We classified Inno-Lia results of 527 treatment-naïve patients with HIV-1 infection < = 12 months according to incidence by 25 algorithms. The time after which all infections were ruled older, i.e. the algorithm's window, was determined by linear regression of the proportion ruled incident in dependence of time since infection. Window-based incident infection rates (IIR) were determined utilizing the relationship 'Prevalence = Incidence x Duration' in four annual cohorts of HIV-1 notifications. Results were compared to performance-based IIR also derived from Inno-Lia results, but utilizing the relationship 'incident = true incident + false incident' and also to the IIR derived from the BED incidence assay. RESULTS: Window periods varied between 45.8 and 130.1 days and correlated well with the algorithms' diagnostic sensitivity (R(2) = 0.962; P<0.0001). Among the 25 algorithms, the mean window-based IIR among the 748 notifications of 2005/06 was 0.457 compared to 0.453 obtained for performance-based IIR with a model not correcting for selection bias. Evaluation of BED results using a window of 153 days yielded an IIR of 0.669. Window-based IIR and performance-based IIR increased by 22.4% and respectively 30.6% in 2008, while 2009 and 2010 showed a return to baseline for both methods. CONCLUSIONS: IIR estimations by window- and performance-based evaluations of Inno-Lia algorithm results were similar and can be used together to assess IIR changes between annual HIV notification cohorts.

Towards robust network based complex systems : from evolutionary cellular automata to biological models

Abstract Sitting between your past and your future doesn't mean you are in the present. Dakota Skye Complex systems science is an interdisciplinary field grouping under the same umbrella dynamical phenomena from social, natural or mathematical sciences. The emergence of a higher order organization or behavior, transcending that expected of the linear addition of the parts, is a key factor shared by all these systems. Most complex systems can be modeled as networks that represent the interactions amongst the system's components. In addition to the actual nature of the part's interactions, the intrinsic topological structure of underlying network is believed to play a crucial role in the remarkable emergent behaviors exhibited by the systems. Moreover, the topology is also a key a factor to explain the extraordinary flexibility and resilience to perturbations when applied to transmission and diffusion phenomena. In this work, we study the effect of different network structures on the performance and on the fault tolerance of systems in two different contexts. In the first part, we study cellular automata, which are a simple paradigm for distributed computation. Cellular automata are made of basic Boolean computational units, the cells; relying on simple rules and information from- the surrounding cells to perform a global task. The limited visibility of the cells can be modeled as a network, where interactions amongst cells are governed by an underlying structure, usually a regular one. In order to increase the performance of cellular automata, we chose to change its topology. We applied computational principles inspired by Darwinian evolution, called evolutionary algorithms, to alter the system's topological structure starting from either a regular or a random one. The outcome is remarkable, as the resulting topologies find themselves sharing properties of both regular and random network, and display similitudes Watts-Strogtz's small-world network found in social systems. Moreover, the performance and tolerance to probabilistic faults of our small-world like cellular automata surpasses that of regular ones. In the second part, we use the context of biological genetic regulatory networks and, in particular, Kauffman's random Boolean networks model. In some ways, this model is close to cellular automata, although is not expected to perform any task. Instead, it simulates the time-evolution of genetic regulation within living organisms under strict conditions. The original model, though very attractive by it's simplicity, suffered from important shortcomings unveiled by the recent advances in genetics and biology. We propose to use these new discoveries to improve the original model. Firstly, we have used artificial topologies believed to be closer to that of gene regulatory networks. We have also studied actual biological organisms, and used parts of their genetic regulatory networks in our models. Secondly, we have addressed the improbable full synchronicity of the event taking place on. Boolean networks and proposed a more biologically plausible cascading scheme. Finally, we tackled the actual Boolean functions of the model, i.e. the specifics of how genes activate according to the activity of upstream genes, and presented a new update function that takes into account the actual promoting and repressing effects of one gene on another. Our improved models demonstrate the expected, biologically sound, behavior of previous GRN model, yet with superior resistance to perturbations. We believe they are one step closer to the biological reality.

Syndrome de l'intestin irritable: un diagnostic d'exclusion. [Irritable bowel syndrome: an exclusion diagnosis?].

The irritable bowel syndrome has been considered a diagnosis of exclusion and multiple diagnostic procedures were often performed in order to exclude an organic disorder. Nowadays, studies show that in young patients, who match the clinical criteria of irritable bowel syndrome and show no alarm features, the prevalence of underlying organic disorders is low, or at least not higher than in the general population. Based on these findings, current recommendations suggest that no extra diagnostic tests have to be performed in those patients, apart from the serological tests in search of celiac disease, which are recommended for patients presenting an irritable bowel syndrome with diarrhea or a mixed-type irritable bowel syndrome.

A New Model for Designing a Product Line Using TURF Analysis

This paper introduces the approach of using TURF analysis to design a product line through a binary linear programming model. This improves the efficiency of the search for the solution to the problem compared to the algorithms that have been used to date. Furthermore, the proposed technique enables the model to be improved in order to overcome the main drawbacks presented by TURF analysis in practice.

Symptomatic in-hospital deep vein thrombosis and pulmonary embolism following hip and knee arthroplasty among patients receiving recommended prophylaxis: a systematic review.

CONTEXT: Symptomatic venous thromboembolism (VTE) after total or partial knee arthroplasty (TPKA) and after total or partial hip arthroplasty (TPHA) are proposed patient safety indicators, but its incidence prior to discharge is not defined. OBJECTIVE: To establish a literature-based estimate of symptomatic VTE event rates prior to hospital discharge in patients undergoing TPHA or TPKA. DATA SOURCES: Search of MEDLINE, EMBASE, and the Cochrane Library (1996 to 2011), supplemented by relevant articles. STUDY SELECTION: Reports of incidence of symptomatic postoperative pulmonary embolism or deep vein thrombosis (DVT) before hospital discharge in patients who received VTE prophylaxis with either a low-molecular-weight heparin or a subcutaneous factor Xa inhibitor or oral direct inhibitor of factors Xa or IIa. DATA EXTRACTION AND SYNTHESIS: Meta-analysis of randomized clinical trials and observational studies that reported rates of postoperative symptomatic VTE in patients who received recommended VTE prophylaxis after undergoing TPHA or TPKA. Data were independently extracted by 2 analysts, and pooled incidence rates of VTE, DVT, and pulmonary embolism were estimated using random-effects models. RESULTS: The analysis included 44,844 cases provided by 47 studies. The pooled rates of symptomatic postoperative VTE before hospital discharge were 1.09% (95% CI, 0.85%-1.33%) for patients undergoing TPKA and 0.53% (95% CI, 0.35%-0.70%) for those undergoing TPHA. The pooled rates of symptomatic DVT were 0.63% (95% CI, 0.47%-0.78%) for knee arthroplasty and 0.26% (95% CI, 0.14%-0.37%) for hip arthroplasty. The pooled rates for pulmonary embolism were 0.27% (95% CI, 0.16%-0.38%) for knee arthroplasty and 0.14% (95% CI, 0.07%-0.21%) for hip arthroplasty. There was significant heterogeneity for the pooled incidence rates of symptomatic postoperative VTE in TPKA studies but less heterogeneity for DVT and pulmonary embolism in TPKA studies and for VTE, DVT, and pulmonary embolism in TPHA studies. CONCLUSION: Using current VTE prophylaxis, approximately 1 in 100 patients undergoing TPKA and approximately 1 in 200 patients undergoing TPHA develops symptomatic VTE prior to hospital discharge.

Effectiveness of chronic obstructive pulmonary disease-management programs: systematic review and meta-analysis

BACKGROUND: Disease-management programs may enhance the quality of care provided to patients with chronic diseases, such as chronic obstructive pulmonary disease (COPD). The aim of this systematic review was to assess the effectiveness of COPD disease-management programs. METHODS: We conducted a computerized search of MEDLINE, EMBASE, CINAHL, PsychINFO, and the Cochrane Library (CENTRAL) for studies evaluating interventions meeting our operational definition of disease management: patient education, 2 or more different intervention components, 2 or more health care professionals actively involved in patients' care, and intervention lasting 12 months or more. Programs conducted in hospital only and those targeting patients receiving palliative care were excluded. Two reviewers evaluated 12,749 titles and fully reviewed 139 articles; among these, data from 13 studies were included and extracted. Clinical outcomes considered were all-cause mortality, lung function, exercise capacity (walking distance), health-related quality of life, symptoms, COPD exacerbations, and health care use. A meta-analysis of exercise capacity and all-cause mortality was performed using random-effects models. RESULTS: The studies included were 9 randomized controlled trials, 1 controlled trial, and 3 uncontrolled before-after trials. Results indicate that the disease-management programs studied significantly improved exercise capacity (32.2 m, 95% confidence interval [CI], 4.1-60.3), decreased risk of hospitalization, and moderately improved health-related quality of life. All-cause mortality did not differ between groups (pooled odds ratio 0.84, 95% CI, 0.54-1.40). CONCLUSION: COPD disease-management programs modestly improved exercise capacity, health-related quality of life, and hospital admissions, but not all-cause mortality. Future studies should explore the specific elements or characteristics of these programs that bring the greatest benefit.

Validation of the potentially avoidable hospital readmission rate as a routine indicator of the quality of hospital care

BACKGROUND: The hospital readmission rate has been proposed as an important outcome indicator computable from routine statistics. However, most commonly used measures raise conceptual issues. OBJECTIVES: We sought to evaluate the usefulness of the computerized algorithm for identifying avoidable readmissions on the basis of minimum bias, criterion validity, and measurement precision. RESEARCH DESIGN AND SUBJECTS: A total of 131,809 hospitalizations of patients discharged alive from 49 hospitals were used to compare the predictive performance of risk adjustment methods. A subset of a random sample of 570 medical records of discharge/readmission pairs in 12 hospitals were reviewed to estimate the predictive value of the screening of potentially avoidable readmissions. MEASURES: Potentially avoidable readmissions, defined as readmissions related to a condition of the previous hospitalization and not expected as part of a program of care and occurring within 30 days after the previous discharge, were identified by a computerized algorithm. Unavoidable readmissions were considered as censored events. RESULTS: A total of 5.2% of hospitalizations were followed by a potentially avoidable readmission, 17% of them in a different hospital. The predictive value of the screen was 78%; 27% of screened readmissions were judged clearly avoidable. The correlation between the hospital rate of clearly avoidable readmission and all readmissions rate, potentially avoidable readmissions rate or the ratio of observed to expected readmissions were respectively 0.42, 0.56 and 0.66. Adjustment models using clinical information performed better. CONCLUSION: Adjusted rates of potentially avoidable readmissions are scientifically sound enough to warrant their inclusion in hospital quality surveillance.