Energy-efficient medium access control in wireless sensor networks

Medium access control for wireless sensor networks has been an active
research area in the past decade. This chapter discusses a set of important medium access control (MAC) attributes and possible design trade-offs in protocol design, with an emphasis on energy efficiency. Then we categorize existing MAC protocols into five groups, outline the representative protocols, and compare their advantages and disadvantages in the context of wireless sensor network. Finally, thoughts for practitioners are presented and open research issues are also discussed.

The fishery, biology and management of three inland water-bodies, Vietnam

This study was carried out from 1997 to 2004 and its findings have significantly enhanced our understanding on fishery, biology, and management of three inland water bodies in Vietnam. The study also provides insights into development of management guidelines and recommendations for future fisheries development in the research area.

Parallelization of dissipative particle dynamics simulation

The dissipative particle dynamics simulation is usually used to study polymer in mesoscopic space. The traditional methods are resource intensive, especially when the scale of research is large. Therefore, improving computing efficiency is a key point in this research area. Two major issues are addressed in this paper. First, the DPD methods are analysed and the most time-consuming parts are identified: conservative force, dissipative force and random force. Second, we describe how to parallelize the existing serial application in the Windows Compute Cluster Server (WCCS) platform. The results show that the parallel algorithm not only effectively shortens the computing time, but also improves the resource utilization rate.

Interventions to prevent obesity in 0-5 year olds : an updated systematic review of the literature

The small number and recency of the early childhood obesity-prevention literature identified in a previous review of interventions to prevent obesity, promote healthy eating, physical activity, and/or reduce sedentary behaviors in 0-5 year olds suggests this is a new and developing research area. The current review was conducted to provide an update of the rapidly emerging evidence in this area and to assess the quality of studies reported. Ten electronic databases were searched to identify literature published from January 1995 to August 2008. Inclusion criteria: interventions reporting child anthropometric, diet, physical activity, or sedentary behavior outcomes and focusing on children aged 0-5 years of age. Exclusion criteria: focusing on breastfeeding, eating disorders, obesity treatment, malnutrition, or school-based interventions. Two reviewers independently extracted data and assessed study quality. Twenty-three studies met all criteria. Most were conducted in preschool/childcare (n = 9) or home settings (n = 8). Approximately half targeted socioeconomically disadvantaged children (n = 12) and three quarters were published from 2003 onward (n = 17). The interventions varied widely although most were multifaceted in their approach. While study design and quality varied most studies reported their interventions were feasible and acceptable, although impact on behaviors that contribute to obesity were not achieved by all. Early childhood obesity-prevention interventions represent a rapidly growing research area. Current evidence suggests that behaviors that contribute to obesity can be positively impacted in a range of settings and provides important insights into the most effective strategies for promoting healthy weight from early childhood.

Estimating survival and movements using both live and dead recoveries : a case study of oystercatchers confronted with habitat change

1. Animals facing partial habitat loss can try to survive in the remaining habitat or emigrate. Effects on survival and movements should be studied simultaneously since survival rates may be underestimated if emigrants are not considered, and since emigrants may experience reduced survival.

2. We analysed movements and survival of adult wintering oystercatcher Haematopus ostralegus in response to the 1986–1987 partial closure of the Oosterschelde in the Dutch Delta. This reduced by one-third the tidal area of this major European wintering area for waders.

3. We developed a novel variant of a multistate capture–recapture model allowing simultaneous estimation of survival and movement between sites using a mixture of data (live recaptures and dead recoveries). We used a two-step process, first estimating movements between sites followed by site-specific survival rates.

4. Most birds were faithful to their ringing site. Winter survival was negatively affected by winter severity and was lowest among birds changing wintering site (i.e. moving outside of the Oosterschelde).

5. During mild winters, survival rates were very high, and similar to before the closure in both changed and unchanged sectors of the Oosterschelde. However, the combined effect of habitat loss with severe winters decreased the survival of birds from changed sectors and induced emigration.

6. The coastal engineering project coincided with three severe winters and high food stock, making assessment of its effects difficult. However, the habitat loss seems to have had less impact on adult survival and movements than did winter severity.

7. Synthesis and applications. Human-induced habitat change may result in population decline through costly emigration or reduced survival or reproduction of individuals that stay. Long-term monitoring of marked individuals helps to understand how populations respond to environmental change, but site-specific survival and movement rates should be integrated in the same model in order to maximize the information yield. Our modelling approach facilitates this because it allows the inclusion of recoveries from outside the study area.

A formal model of mixed-initiative interaction in design exploration

Computer-based environments for supporting design are complex software artifacts. These tools need to use sound computational formalisms as well as address issues of human usability. The development of interactive and usable generative systems is a significant research area in design computation. Though classical search techniques play a central role in the generative kernels of these "closed-world" systems, the open-ended exploration of design spaces is the desirable goal. In this paper, we present a formal model of exploration that combines search with user driven exploration. We describe the role of interaction and agency in an experimental mixed-initiative design support system.

Video abstraction : a systematic review and classification

The demand for various multimedia applications is rapidly increasing due to the recent advance in the computing and network infrastructure, together with the widespread use of digital video technology. Among the key elements for the success of these applications is how to effectively and efficiently manage and store a huge amount of audio visual information, while at the same time providing user-friendly access to the stored data. This has fueled a quickly evolving research area known as video abstraction. As the name implies, video abstraction is a mechanism for generating a short summary of a video, which can either be a sequence of stationary images (keyframes) or moving images (video skims). In terms of browsing and navigation, a good video abstract will enable the user to gain maximum information about the target video sequence in a specified time constraint or sufficient information in the minimum time. Over past years, various ideas and techniques have been proposed towards the effective abstraction of video contents. The purpose of this article is to provide a systematic classification of these works. We identify and detail, for each approach, the underlying components and how they are addressed in specific works. © 2007 ACM.

Temporal PDMs for gait classification

Gait classification is a developing research area, particularly with regards to biometrics. It aims to use the distinctive spatial and temporal characteristics of human motion to classify differing activities. As a biometric, this extends to recognising different people by the heterogeneous aspects of their gait. This research aims to use a modified deformable model, the temporal PDM, to distinguish the movements of a walking and miming person. The movement of 2D points on the moving form is used to provide input into the model and classify the type of gait present.

Do consistent individual differences in metabolic rate promote consistent individual differences in behavior?

Consistent individual differences (CIDs) in behavior are a widespread phenomenon in animals, but the proximate reasons for them are unresolved. We discuss evidence for the hypothesis that CIDs in energy metabolism, as reflected by resting metabolic rate (RMR), promote CIDs in behavior patterns that either provide net energy (e.g. foraging activity), and/or consume energy (e.g. courtship activity). In doing so, we provide a framework for linking together RMR, behavior, and life-history productivity. Empirical studies suggest that RMR is (a) related to the capacity to generate energy, (b) repeatable, and (c) correlated with behavioral output (e.g. aggressiveness) and productivity (e.g. growth). We conclude by discussing future research directions to clarify linkages between behavior and energy metabolism in this emerging research area.

A comparative analysis of decision trees vis-à-vis other computational data mining techniques in automotive insurance fraud detection

The development and application of computational data mining techniques in financial fraud detection and business failure prediction has become a popular cross-disciplinary research area in recent times involving financial economists, forensic accountants and computational modellers. Some of the computational techniques popularly used in the context of - financial fraud detection and business failure prediction can also be effectively applied in the detection of fraudulent insurance claims and therefore, can be of immense practical value to the insurance industry. We provide a comparative analysis of prediction performance of a battery of data mining techniques using real-life automotive insurance fraud data. While the data we have used in our paper is US-based, the computational techniques we have tested can be adapted and generally applied to detect similar insurance frauds in other countries as well where an organized automotive insurance industry exists.

Community perception of mobile payment in e-Government services

Mobile payment and e-Government are emerging topics in the research area of Information Systems. This research addresses the preferred e-Government services for mobile payment, the suitable payment methods of mobile payment, and adoption factors of e-Government services. The research derives the notion of technology adoption and related constructs, however, as no particular Information Systems adoption theory was adequate to study emerging systems of electronic payment, the research did not utilise any specific theory. Rather, the research uses the characteristics of mobile payment as revealed in the contemporary research to develop an adoption model based on user perceptions regarding mobile payments in the context of e-Government services. A survey study on the use of mobile devices to pay for e-Government services was conducted in Western Australia. The findings of this research contribute conceptually and practically by recommending suitable services and mobile payment methods. This paper also addresses the positive and negative factors impacting the adoption of mobile payment for e-Government services.

An effective deferentially private data releasing algorithm for decision tree

Differential privacy is a strong definition for protecting individual privacy in data releasing and mining. However, it is a rigid definition introducing a large amount of noise to the original dataset, which significantly decreases the quality of data mining results. Recently, how to design a suitable data releasing algorithm for data mining purpose is a hot research area. In this paper, we propose a differential private data releasing algorithm for decision tree construction. The proposed algorithm provides a non-interactive data releasing method through which miner can obtain the complete dataset for data mining purpose. With a given privacy budget, the proposed algorithm generalizes the original dataset, and then specializes it in a differential privacy constrain to construct decision trees. As the designed novel scheme selection operation can fully utilize the allocated privacy budget, the data set released by the proposed algorithm can yield better decision tree models than other method. Experimental results demonstrate that the proposed algorithm outperforms existing methods for private decision tree construction.

Extensions to knowledge acquisition and effect of multimodal representation in unsupervised learning

The phenomenal behaviour and composition of human cognition is yet to be defined comprehensibly. Developing the same, artificially, is a foremost research area in artificial intelligence and related fields. In this chapter we look at advances made in the unsupervised learning paradigm (self organising methods) and its potential in realising artificial cognitive machines. The first section delineates intricacies of the process of learning in humans with an articulate discussion of the function of thought and the function of memory. The self organising method and the biological rationalisations that led to its development are explored in the second section. The next focus is the effect of structure restrictions on unsupervised learning and the enhancements resulting from a structure adapting learning algorithm. Generation of a hierarchy of knowledge using this algorithm will also be discussed. Section four looks at new means of knowledge acquisition through this adaptive unsupervised learning algorithm while the fifth examines the contribution of multimodal representation of inputs to unsupervised learning. The chapter concludes with a summary of the extensions outlined.

Palinurid lobster aquaculture: nutritional progress and considerations for successful larval rearing

The closed-cycle rearing of palinurid lobsters in commercially relevant quantities currently represents one of the most difficult challenges facing modern-day aquaculture. The length and complexity of the larval life cycle exacerbate the problem, comprising the major bottleneck to their successful aquaculture. The general consensus is that developments in the key research area of nutrition will provide the necessary breakthroughs to make the closed-cycle rearing of lobster a reality. Due to the cryptic nature of feeding preferences and complex larval morphology, a commercially formulated feed for their culture does currently not exist. Nevertheless, there has been a wealth of research conducted to elucidate many of the unknowns concerning larval nutritional requirements. This review presents a synthesis of this information, ranging from investigations of larval morphology and feeding behaviour, hatchery nutrition practices, the elucidation of wild prey items and the nutritional content of wild-caught larval species. Based on the information available, this review culminates with a ‘best guess’ formulation for a larval spiny lobster diet, taking aspects concerning both the physical and chemical attributes of formulation into consideration. It is concluded that larval spiny lobsters are likely comparatively low in relation to other larval species with respect to their quantitative dietary requirements. Ultimately, the overall success of the larval cycle appears to be dictated by the stockpiling of lipids to fuel an energy-demanding metamorphosis and a subsequent nonfeeding puerulus phase. These insights provide valuable direction for the formulation of nutritionally complete feeds to permit the closed-loop culture of spiny lobster species.

Big data related technologies, challenges and future prospects

Big data is the large or complex data that exceed the processing capacity of conventional data processing systems. This book provides a big picture in this broad research area, covering all the phases of its value chains. The authors have attempted to survey most of the relevant technologies in each phrase of big data. The book is recommended for readers interested in advanced research in big data, also for industry practitioners who are interested in building big data applications. If the reader is not with necessary technical background, complementary readings may be needed.