An implicit GTS allocation mechanism in IEEE 802.15.4 for time-sensitive wireless sensor networks: theory and practice

Timeliness guarantee is an important feature of the recently standardized IEEE 802.15.4 protocol, turning it quite appealing for Wireless Sensor Network (WSN) applications under timing constraints. When operating in beacon-enabled mode, this protocol allows nodes with real-time requirements to allocate Guaranteed Time Slots (GTS) in the contention-free period. The protocol natively supports explicit GTS allocation, i.e. a node allocates a number of time slots in each superframe for exclusive use. The limitation of this explicit GTS allocation is that GTS resources may quickly disappear, since a maximum of seven GTSs can be allocated in each superframe, preventing other nodes to benefit from guaranteed service. Moreover, the GTS may be underutilized, resulting in wasted bandwidth. To overcome these limitations, this paper proposes i-GAME, an implicit GTS Allocation Mechanism in beacon-enabled IEEE 802.15.4 networks. The allocation is based on implicit GTS allocation requests, taking into account the traffic specifications and the delay requirements of the flows. The i-GAME approach enables the use of one GTS by multiple nodes, still guaranteeing that all their (delay, bandwidth) requirements are satisfied. For that purpose, we propose an admission control algorithm that enables to decide whether to accept a new GTS allocation request or not, based not only on the remaining time slots, but also on the traffic specifications of the flows, their delay requirements and the available bandwidth resources. We show that our approach improves the bandwidth utilization as compared to the native explicit allocation mechanism defined in the IEEE 802.15.4 standard. We also present some practical considerations for the implementation of i-GAME, ensuring backward compatibility with the IEEE 801.5.4 standard with only minor add-ons. Finally, an experimental evaluation on a real system that validates our theoretical analysis and demonstrates the implementation of i-GAME is also presented.     

Filtered Quadrics for High‐Speed Geometry Smoothing and Clustering

Modern 3D capture pipelines produce dense surface meshes at high speed, which challenge geometric operators to process such massive data on‐the‐fly. In particular, aiming at instantaneous feature‐preserving smoothing and clustering disqualifies global variational optimizers and one usually relies on high‐performance parallel kernels based on simple measures performed on the positions and normal vectors associated with the surface vertices. Although these operators are effective on small supports, they fail at properly capturing larger scale surface structures. To cope with this problem, we propose to enrich the surface representation with filtered quadrics, a compact and discriminating range space to guide processing. Compared to normal‐based approaches, this additional vertex attribute significantly improves feature preservation for fast bilateral filtering and mode‐seeking clustering, while exhibiting a linear memory cost in the number of vertices and retaining the simplicity of convolutional filters. In particular, the overall performance of our approach stems from its natural compatibility with modern fine‐grained parallel computing architectures such as graphics processor units (GPU). As a result, filtered quadrics offer a superior ability to handle a broad spectrum of frequencies and preserve large salient structures, delivering meshes on‐the‐fly for interactive and streaming applications, as well as quickly processing large data collections, instrumental in learning‐based geometry analysis.     

The female voice and traditional discourse biases: The case of francophone African literature

This study reports on a statistical approach to Francophone African literature, addressing the issues of discourse bias and the specificity of female writing as against male. The research is based on a comparison of all the characters present in 20 novels written by male and female African authors, under the headings of importance, power and attitude. It suggests that a number of significant differences characterize the make-up of novels written by African female and male authors.Beverley Ormerod, from Jamaica, is currently associate professor of French at the University of Western Australia. Her publications concerning the literature of the French Caribbean and Francophone Africa includeAn Introduction to the French Caribbean Novel (London: Heinemann, 1985) andRomancières africaines d'expression française, with J.-M. Volet (Paris: L'Harmattan, 1994). Jean-Marie Volet, born in Switzerland and currently Honorary Research Fellow in the Department of French Studies at the University of Western Australia, is pursuing research in the fields of African literature and women's writing. His publications includeLa Parole aux Africaines (Amsterdam: Rodopi, 1993) andRomancières africaines d'expression française, with B. Ormerod (Paris: L'Harmattan, 1994). Hélène Jaccomard, a tutor in the Department of French Studies at the University of Western Australia and a translator, has research interests in autobiography and literary theory. She has published a book onLe Lecteur et la lecture dans l'autobiographie française contemporaine (Geneva: Droz, 1994) and articles, including Françoise d'Eaubonne: Accuser (la) Réception,The French Review, 68, 3 (1994).     

A branch-and-cut algorithm for quadratic assignment problems based on linearizations

The quadratic assignment problem (QAP) is one of the hardest combinatorial optimization problems known. Exact solution attempts proposed for instances of size larger than 15 have been generally unsuccessful even though successful implementations have been reported on some test problems from the QAPLIB up to size 36. In this study, we focus on the Koopmans–Beckmann formulation and exploit the structure of the flow and distance matrices based on a flow-based linearization technique that we propose. We present two new IP formulations based on the flow-based linearization technique that require fewer variables and yield stronger lower bounds than existing formulations. We strengthen the formulations with valid inequalities and report computational experience with a branch-and-cut algorithm. The proposed method performs quite well on QAPLIB instances for which certain metrics (indices) that we proposed that are related to the degree of difficulty of solving the problem are relatively high (?0.3$?0.3$). Many of the well-known instances up to size 25 from the QAPLIB (e.g. nug24, chr25a) are in this class and solved in a matter of days on a single PC using the proposed algorithm.     

A hybrid approach to medical decision support systems: combining feature selection, fuzzy weighted pre-processing and AIRS

This paper presents a hybrid approach based on feature selection, fuzzy weighted pre-processing and artificial immune recognition system (AIRS) to medical decision support systems. We have used the heart disease and hepatitis disease datasets taken from UCI machine learning database as medical dataset. Artificial immune recognition system has shown an effective performance on several problems such as machine learning benchmark problems and medical classification problems like breast cancer, diabetes, and liver disorders classification. The proposed approach consists of three stages. In the first stage, the dimensions of heart disease and hepatitis disease datasets are reduced to 9 from 13 and 19 in the feature selection (FS) sub-program by means of C4.5 decision tree algorithm (CBA program), respectively. In the second stage, heart disease and hepatitis disease datasets are normalized in the range of [0,1] and are weighted via fuzzy weighted pre-processing. In the third stage, weighted input values obtained from fuzzy weighted pre-processing are classified using AIRS classifier system. The obtained classification accuracies of our system are 92.59% and 81.82% using 50-50% training-test split for heart disease and hepatitis disease datasets, respectively. With these results, the proposed method can be used in medical decision support systems.     

Application of the Conant-Finkelstein Reaction to the Modification of PVC: Iodinated PVC

The Conant-Finkelstein reaction was applied to PVC with the aiming of replacing the chlorine atoms with iodine ones. The effect of reaction temperature with regard to the characteristics of the modified PVC was significant. Formation of a gel and degraded polymeric materials was observed when working at temperatures higher than 60 °C. The degraded polymer formed at 70 °C was insoluble and gave rise to a polyacetylene-like chain with a melting point of 60 °C. However, the reaction on PVC at lower temperatures resulted in soluble polymers which were easily amenable to spectral characterization. The molecular weights of the iodine-modified PVCs were temperature-dependent. At 40, 45, 50 and 60 °C, molecular weights lower than that of the initial PVC were measured; however, at 35, 30 and 25 °C, a gain of about 9% in molecular weight was seen. Substitution and elimination reactions occurred to different extents depending mostly on temperature. Optimal substitution was obtained at 50 °C for a reaction time of 20 h.     

Review of real-time vehicle schedule recovery methods in transportation services

This paper presents a comprehensive review on methods for real-time schedule recovery in transportation services. The survey concentrates on published research on recovery of planned schedules in the occurrence of one or several severe disruptions such as vehicle breakdowns, accidents, and delays. Only vehicle assignment and rescheduling are reviewed; crew scheduling and passenger logistics problems during disruptions are not. Real-time vehicle schedule recovery problems (RTVSRP) are classified into three classes: vehicle rescheduling, for road-based services, train-based rescheduling, and airline schedule recovery problems. For each class, a classification of the models is presented based on problem formulations and solution strategies. The paper concludes that RTVSRP is a challenging problem that requires quick and good quality solutions to very difficult and complex situations, involving several different contexts, restrictions, and objectives. The paper also identifies research gaps to be investigated in the future, stimulating research in this area.     

Indoor Human Wayfinding Performance Using Vertical and Horizontal Signage in Virtual Reality

Disorientation has many costs. It may lead to physical fatigue, stress, and frustration and can also jeopardize people's safety. Designing wayfinding aids to fit people's needs can facilitate their environmental knowledge acquisition and, therefore, improve their wayfinding performance. The scope of this article is human wayfinding in unfamiliar buildings, considering only individual pedestrian movement in an immersive virtual environment. The purpose is to investigate the use of external information at a higher level of awareness (e.g., signage) as a wayfinding aid, as well as the use of immersive virtual reality (VR) to study indoor wayfinding. Fifty‐four volunteers accomplished a wayfinding task (i.e., finding a room from the building's entrance) within a virtual building, employing two types of signage systems (i.e., vertical and horizontal conditions). A neutral condition (no signage) was also considered as a control condition to be used as a baseline. Aside from the success of the wayfinding task (getting to the destination), other performance metrics (distance traveled, time spent, number of pauses, and average speed) were analyzed and compared. Although the differences found are not statistically significant, findings suggest that participants assigned to the horizontal condition traveled smaller distances, spent less time, made fewer pauses, and moved at higher average speed than those assigned to vertical and neutral conditions. Gender‐related differences were found statistically significant only in the average speed variable (females were faster than males). © 2012 Wiley Periodicals, Inc.     

Classification-oriented structure learning in Bayesian networks for multimodal event detection in videos

We investigate the use of structure learning in Bayesian networks for a complex multimodal task of action detection in soccer videos. We illustrate that classical score-oriented structure learning algorithms, such as the K2 one whose usefulness has been demonstrated on simple tasks, fail in providing a good network structure for classification tasks where many correlated observed variables are necessary to make a decision. We then compare several structure learning objective functions, which aim at finding out the structure that yields the best classification results, extending existing solutions in the literature. Experimental results on a comprehensive data set of 7 videos show that a discriminative objective function based on conditional likelihood yields the best results, while augmented approaches offer a good compromise between learning speed and classification accuracy.     

Hidden in plain sight: low-literacy adults in a developed country overcoming social and educational challenges through mobile learning support tools

Illiteracy is often associated with people in developing countries. However, an estimated 50 % of adults in a developed country such as Canada lack the literacy skills required to cope with the challenges of today’s society; for them, tasks such as reading, understanding, basic arithmetic, and using everyday items are a challenge. Many community-based organizations offer resources and support for these adults, yet overall functional literacy rates are not improving. This is due to a wide range of factors, such as poor retention of adult learners in literacy programs, obstacles in transferring the acquired skills from the classroom to the real life, personal attitudes toward learning, and the stigma of functional illiteracy. In our research we examined the opportunities afforded by personal mobile devices in providing learning and functional support to low-literacy adults. We present the findings of an exploratory study aimed at investigating the reception and adoption of a technological solution for adult learners. ALEX^© is a mobile application designed for use both in the classroom and in daily life in order to help low-literacy adults become increasingly literate and independent. Such a solution complements literacy programs by increasing users’ motivation and interest in learning, and raising their confidence levels both in their education pursuits and in facing the challenges of their daily lives. We also reflect on the challenges we faced in designing and conducting our research with two user groups (adults enrolled in literacy classes and in an essential skills program) and contrast the educational impact and attitudes toward such technology between these. Our conclusions present the lessons learned from our evaluations and the impact of the studies’ specific challenges on the outcome and uptake of such mobile assistive technologies in providing practical support to low-literacy adults in conjunction with literacy and essential skills training.