MODELING AND PROTOCOL DESIGN FOR EMERGING WIRELESS SYSTEMS

Mobile Mesh Network based In-Transit Visibility (MMN-ITV) system facilitates global real-time tracking capability for the logistics system. In-transit containers form a multi-hop mesh network to forward the tracking information to the nearby sinks, which further deliver the information to the remote control center via satellite. The fundamental challenge to the MMN-ITV system is the energy constraint of the battery-operated containers. Coupled with the unique mobility pattern, cross-MMN behavior, and the large-spanned area, it is necessary to investigate the energy-efficient communication of the MMN-ITV system thoroughly. First of all, this dissertation models the energy-efficient routing under the unique pattern of the cross-MMN behavior. A new modeling approach, pseudo-dynamic modeling approach, is proposed to measure the energy-efficiency of the routing methods in the presence of the cross-MMN behavior. With this approach, it could be identified that the shortest-path routing and the load-balanced routing is energy-efficient in mobile networks and static networks respectively. For the MMN-ITV system with both mobile and static MMNs, an energy-efficient routing method, energy-threshold routing, is proposed to achieve the best tradeoff between them. Secondly, due to the cross-MMN behavior, neighbor discovery is executed frequently to help the new containers join the MMN, hence, consumes similar amount of energy as that of the data communication. By exploiting the unique pattern of the cross-MMN behavior, this dissertation proposes energy-efficient neighbor discovery wakeup schedules to save up to 60% of the energy for neighbor discovery. Vehicular Ad Hoc Networks (VANETs)-based inter-vehicle communications is by now growingly believed to enhance traffic safety and transportation management with low cost. The end-to-end delay is critical for the time-sensitive safety applications in VANETs, and can be a decisive performance metric for VANETs. This dissertation presents a complete analytical model to evaluate the end-to-end delay against the transmission range and the packet arrival rate. This model illustrates a significant end-to-end delay increase from non-saturated networks to saturated networks. It hence suggests that the distributed power control and admission control protocols for VANETs should aim at improving the real-time capacity (the maximum packet generation rate without causing saturation), instead of the delay itself. Based on the above model, it could be determined that adopting uniform transmission range for every vehicle may hinder the delay performance improvement, since it does not allow the coexistence of the short path length and the low interference. Clusters are proposed to configure non-uniform transmission range for the vehicles. Analysis and simulation confirm that such configuration can enhance the real-time capacity. In addition, it provides an improved trade off between the end-to-end delay and the network capacity. A distributed clustering protocol with minimum message overhead is proposed, which achieves low convergence time.

SMS length and function: A comparative study of 13- to 18-year-old girls and boys

International audience

The applicability of biased estimation in model and model order selection

Biased estimation has the advantage of reducing the mean squared error (MSE) of an estimator. The question of interest is how biased estimation affects model selection. In this paper, we introduce biased estimation to a range of model selection criteria. Specifically, we analyze the performance of the minimum description length (MDL) criterion based on biased and unbiased estimation and compare it against modern model selection criteria such as Kay's conditional model order estimator (CME), the bootstrap and the more recently proposed hook-and-loop resampling based model selection. The advantages and limitations of the considered techniques are discussed. The results indicate that, in some cases, biased estimators can slightly improve the selection of the correct model. We also give an example for which the CME with an unbiased estimator fails, but could regain its power when a biased estimator is used.

Improving the performance of model-order selection criteria by partial-model selection search

The traditional searching method for model-order selection in linear regression is a nested full-parameters-set searching procedure over the desired orders, which we call full-model order selection. On the other hand, a method for model-selection searches for the best sub-model within each order. In this paper, we propose using the model-selection searching method for model-order selection, which we call partial-model order selection. We show by simulations that the proposed searching method gives better accuracies than the traditional one, especially for low signal-to-noise ratios over a wide range of model-order selection criteria (both information theoretic based and bootstrap-based). Also, we show that for some models the performance of the bootstrap-based criterion improves significantly by using the proposed partial-model selection searching method. Index Terms— Model order estimation, model selection, information theoretic criteria, bootstrap 1. INTRODUCTION Several model-order selection criteria can be applied to find the optimal order. Some of the more commonly used information theoretic-based procedures include Akaike’s information criterion (AIC) [1], corrected Akaike (AICc) [2], minimum description length (MDL) [3], normalized maximum likelihood (NML) [4], Hannan-Quinn criterion (HQC) [5], conditional model-order estimation (CME) [6], and the efficient detection criterion (EDC) [7]. From a practical point of view, it is difficult to decide which model order selection criterion to use. Many of them perform reasonably well when the signal-to-noise ratio (SNR) is high. The discrepancies in their performance, however, become more evident when the SNR is low. In those situations, the performance of the given technique is not only determined by the model structure (say a polynomial trend versus a Fourier series) but, more importantly, by the relative values of the parameters within the model. This makes the comparison between the model-order selection algorithms difficult as within the same model with a given order one could find an example for which one of the methods performs favourably well or fails [6, 8]. Our aim is to improve the performance of the model order selection criteria in cases where the SNR is low by considering a model-selection searching procedure that takes into account not only the full-model order search but also a partial model order search within the given model order. Understandably, the improvement in the performance of the model order estimation is at the expense of additional computational complexity.

More than a backchannel : Twitter and television

Twitter is a social media service that has managed very successfully to embed itself deeply in the everyday lives of its users. Its short message length (140 characters), and one-way connections (‘following’ rather than ‘friending’) lend themselves effectively to random and regular updates on almost any form of personal or professional activity – and it has found uses from the interpersonal (e.g. boyd et al., 2010) through crisis communication (e.g. Bruns et al., 2012) to political debate (e.g. Burgess & Bruns, 2012). In such uses, Twitter does not necessarily replace existing media channels, such as the broadcast or online offerings of the mainstream media, but often complements them, providing its users with alternative opportunities to contribute more actively to the wider mediasphere. This is true especially where Twitter is used alongside television, as a simple backchannel to live programming or for more sophisticated uses. In this article, we outline four aspects – dimensions – of the way that the ‘old’ medium of television intersects and, in some cases, interacts, with the ‘new’ medium of Twitter.

Shouting from the ivory tower : a marketing approach to improve communication of academic research to entrepreneurs

Evidence-based practice in entrepreneurship requires effective communication of research findings. We focus on how research synopses can “promote” research to entrepreneurs. Drawing on marketing communications literature, we examine how message characteristics of research synopses affect their appeal. We demonstrate the utility of conjoint analysis in this context and find message length, media richness and source credibility to have positive influences. We find mixed support for a hypothesized negative influence of jargon, and for our predictions that participants’ involvement with academic research moderates these effects. Exploratory analyses reveal latent classes of entrepreneurs with differing preferences, particularly for message length and jargon.

More than a backchannel : Twitter and television

Twitter is a social media service that has managed very successfully to embed itself deeply in the daily lives of its users. Its short message length (140 characters), and one-way connections (‘following’ rather than ‘friending’), lead themselves effectively to random and regular updates on almost any form of personal or professional activity. Thus, it has found uses from the interpersonal (e.g. Boyd et al., 2010) through crisis communication (e.g. Bruns et al., 2012), to political debate (e.g. Burgess & Bruns, 2012). In such uses, Twitter does not necessarily replace existing media channels, such as broadcasting or online mainstream media, but often complements them, providing its users with alternative opportunities to contribute more actively to the wider media sphere. This is true especially where Twitter is used alongside television, as a simple backchannel to live programming or for more sophisticated uses. In this article, we outline four aspects and dimensions, of the way that the old medium of television intersects, and in some cases, interacts with the new medium of Twitter. Tweeting about the television has always been a social media form. It has also consistently provided key ‘talking points’ for western societies...

A cluster-voting approach for speaker diarization and linking of Australian broadcast news recordings

We present a clustering-only approach to the problem of speaker diarization to eliminate the need for the commonly employed and computationally expensive Viterbi segmentation and realignment stage. We use multiple linear segmentations of a recording and carry out complete-linkage clustering within each segmentation scenario to obtain a set of clustering decisions for each case. We then collect all clustering decisions, across all cases, to compute a pairwise vote between the segments and conduct complete-linkage clustering to cluster them at a resolution equal to the minimum segment length used in the linear segmentations. We use our proposed cluster-voting approach to carry out speaker diarization and linking across the SAIVT-BNEWS corpus of Australian broadcast news data. We compare our technique to an equivalent baseline system with Viterbi realignment and show that our approach can outperform the baseline technique with respect to the diarization error rate (DER) and attribution error rate (AER).

Statistical and Information-Theoretic Methods for Data-Analysis

In this Thesis, we develop theory and methods for computational data analysis. The problems in data analysis are approached from three perspectives: statistical learning theory, the Bayesian framework, and the information-theoretic minimum description length (MDL) principle. Contributions in statistical learning theory address the possibility of generalization to unseen cases, and regression analysis with partially observed data with an application to mobile device positioning. In the second part of the Thesis, we discuss so called Bayesian network classifiers, and show that they are closely related to logistic regression models. In the final part, we apply the MDL principle to tracing the history of old manuscripts, and to noise reduction in digital signals.

Computing the Stochastic Complexity of Simple Probabilistic Graphical Models

Minimum Description Length (MDL) is an information-theoretic principle that can be used for model selection and other statistical inference tasks. There are various ways to use the principle in practice. One theoretically valid way is to use the normalized maximum likelihood (NML) criterion. Due to computational difficulties, this approach has not been used very often. This thesis presents efficient floating-point algorithms that make it possible to compute the NML for multinomial, Naive Bayes and Bayesian forest models. None of the presented algorithms rely on asymptotic analysis and with the first two model classes we also discuss how to compute exact rational number solutions.

Computationally Efficient Methods for MDL-Optimal Density Estimation and Data Clustering

The Minimum Description Length (MDL) principle is a general, well-founded theoretical formalization of statistical modeling. The most important notion of MDL is the stochastic complexity, which can be interpreted as the shortest description length of a given sample of data relative to a model class. The exact definition of the stochastic complexity has gone through several evolutionary steps. The latest instantation is based on the so-called Normalized Maximum Likelihood (NML) distribution which has been shown to possess several important theoretical properties. However, the applications of this modern version of the MDL have been quite rare because of computational complexity problems, i.e., for discrete data, the definition of NML involves an exponential sum, and in the case of continuous data, a multi-dimensional integral usually infeasible to evaluate or even approximate accurately. In this doctoral dissertation, we present mathematical techniques for computing NML efficiently for some model families involving discrete data. We also show how these techniques can be used to apply MDL in two practical applications: histogram density estimation and clustering of multi-dimensional data.

Discovering compressing serial episodes from event sequences

Most pattern mining methods yield a large number of frequent patterns, and isolating a small relevant subset of patterns is a challenging problem of current interest. In this paper, we address this problem in the context of discovering frequent episodes from symbolic time-series data. Motivated by the Minimum Description Length principle, we formulate the problem of selecting relevant subset of patterns as one of searching for a subset of patterns that achieves best data compression. We present algorithms for discovering small sets of relevant non-redundant episodes that achieve good data compression. The algorithms employ a novel encoding scheme and use serial episodes with inter-event constraints as the patterns. We present extensive simulation studies with both synthetic and real data, comparing our method with the existing schemes such as GoKrimp and SQS. We also demonstrate the effectiveness of these algorithms on event sequences from a composable conveyor system; this system represents a new application area where use of frequent patterns for compressing the event sequence is likely to be important for decision support and control.

Comparative study on the catch efficiency and size selection of entangling nets in Lake Kainji, Nigeria

The relative catch performance and selectively of gillnets and trammel nets were investigated in 12 sampling stations in Lake Kainji, Nigeria. 3 types of nets with dimensions 50mx3m were constructed using 76mm and 178mm meshsizes for two gillnets, 76mm and 178mm meshsizes for the lint and ar mour nets of the trammelnets respectively. All the nets were randomly ganged together to form a fleet of nine nets each, and were set twice in each of the 12 stations which gave a total of 24 fishing operations. A total of 365 fish weighing 88.9kg and belonging to 16 different species were caught in all the nets. The trammelnet had the highest catch by number and weight constituting 60% and 69.22% of the total catch and weight respectively with a relative species Diversity Index of 0.82. This was followed by 76mm gillnet which constituted 38.63% by number, 28.09% by weight, 0.69 relative Species Diversity Index. The 178mm gillnet had the least catch of 1.37% and 2.9% by number and weight respectively with 0.25 relative Species Diversity Index. There was significant difference (P<0.05) in the number and weight of fish caught in the different nets. The minimum selection length for these species caught were the same for each net. The trammel net had a wider selection range that skewed to the right, a higher modal and median length indicating larger individual species being entangled in the net

Variability in spawning frequency and reproductive development of the narrow-barred Spanish mackerel (Scomberomorus commerson) along the west coast of Australia

The narrow-barred Spanish mackerel (Scomberomorus commerson) is widespread throughout the Indo-West Pacific region. This study describes the reproductive biology of S. commerson along the west coast of Australia, where it is targeted for food consumption and sports fishing. Development of testes occurred at a smaller body size than for ovaries, and more than 90% of males were sexually mature by the minimum legal length of 900 mm TL compared to 50% of females. Females dominated overall catches although sex ratios within daily catches vary considerably and females were rarely caught when spaw n ing. Scomberomorus commerson are seasonally abundant in coastal waters and most of the commercial catch is taken prior to the reproductive season. Spawning occurs between about August and November in the Kimberley region and between October and January in the Pilbara region. No spawning activity was recorded in the more southerly West Coast region, and only in the north Kimberley region were large numbers of fish with spawning gonads collected. Catches dropped to a minimum when spawning began in the Pilbara region, when fish became less abundant in inshore waters and inclement weather conditions limited fishing on still productive offshore reefs. Final maturation and ovulation of oocytes took place within a 24-hour period, and females spawned in the afternoon-evening every three days. A third of these spawning females released batches of eggs on consecutive days. Relationships between length, weight, and batch fecundity are presented.