Threshold dynamic time warping for spatial activity recognition

Non-invasive spatial activity recognition is a difficult task, complicated by variation in how the same activities are conducted and furthermore by noise introduced by video tracking procedures. In this paper we propose an algorithm based on dynamic time warping (DTW) as a viable method with which to quantify segmented spatial activity sequences from a video tracking system. DTW is a widely used technique for optimally aligning or warping temporal sequences through minimisation of the distance between their components. The proposed algorithm threshold DTW (TDTW) is capable of accurate spatial sequence distance quantification and is shown using a three class spatial data set to be more robust and accurate than DTW and the discrete hidden markov model (HMM). We also evaluate the application of a band dynamic programming (DP) constraint to TDTW in order to reduce extraneous warping between sequences and to reduce the computation complexity of the approach. Results show that application of a band DP constraint to TDTW improves runtime performance significantly, whilst still maintaining a high precision and recall.

Optimal H∞ insulin injection control for blood glucose regulation in diabetic patients

The theory of H/sup /spl infin// optimal control has the feature of minimizing the worst-case gain of an unknown disturbance input. When appropriately modified, the theory can be used to design a "switching" controller that can be applied to insulin injection for blood glucose (BG) regulation. The "switching" controller is defined by a collection of basic insulin rates and a rule that switches the insulin rates from one value to another. The rule employed an estimation of BG from noisy measurements, and the subsequent optimization of a performance index that involves the solution of a "jump" Riccati differential equation and a discrete-time dynamic programming equation. With an appropriate patient model, simulation studies have shown that the controller could correct BG deviation using clinically acceptable insulin delivery rates.

Asset-selling problem with an uncertain deadline, quitting offer, and search skipping option

This paper presents a discrete-time sequential stochastic asset-selling problem with an infinite planning horizon, where the process of selling the asset may reach a deadline at any point in time with a probability. It is assumed that a quitting offer is available at every point in time and search skipping is permitted. Thus, decisions must be made as to whether or not to accept the quitting offer, to accept an appearing buyer’s offer, and to conduct a search for a buyer. The main purpose of this paper is to clarify the properties of the optimal decision rules in relation to the model’s parameters.

Many routes lead to Rome : potential causes for the multi-route migration system of Red Knots, Calidris canutus islandica

Migrants, such as birds or representatives of other taxa, usually make use of several stopover sites to cover the distance between their site of origin and destination. Potentially, multiple routes exist, but often little is known about the causes and consequences of alternative migration routes. Apart from their geographical distribution, the suitability of potential sites might play an important role in the animals’ decisions for a particular itinerary. We used an optimal-migration model to test three nonmutually exclusive hypotheses leading to variations in the spring migration routes of a subspecies of Red Knot, Calidris canutus islandica, which migrates from wintering grounds in Western Europe to breeding grounds in Greenland and the Canadian Arctic: the breeding location hypothesis, the energy budget hypothesis, and the predation risk hypothesis. Varying only breeding location, the model predicted that birds breeding in the Canadian Arctic and on West Greenland stop over on Iceland, whereas birds breeding in East and Northeast Greenland migrate via northern Norway, a prediction that is supported by empirical findings. Energy budgets on stopover sites had a strong influence on the choice of route and staging times. Varying foraging-intensity and mass-dependent predation risk prompted the birds to use less risky sites, if possible. The effect of simultaneous changes in the energy budget and predation risk strongly depended on the site where these occurred. Our findings provide potential explanations for the observations that C. canutus islandica uses a diverse array of migration routes. Scrutinizing the three alternative driving forces for the choice of migratory routes awaits further, specific data collection in rapidly developing fields of research (e.g., predation risk assessment, GPS tracking). Generally, the type of modeling presented here may not only highlight alternative explanations, but also direct follow-up empirical research.

The consequences of climate-driven stop-over sites changes on migration schedules and fitness of Arctic geese

1. How climatic changes affect migratory birds remains difficult to predict because birds use multiple sites in a highly interdependent manner. A better understanding of how conditions along the flyway affect migration and ultimately fitness is of paramount interest.

2. Therefore, we developed a stochastic dynamic model to generate spatially and temporally explicit predictions of stop-over site use. For each site, we varied energy expenditure, onset of spring, intake rate and day-to-day stochasticity independently. We parameterized the model for the migration of pink-footed goose Anser brachyrhynchus from its wintering grounds in Western Europe to its breeding grounds on Arctic Svalbard.

3. Model results suggested that the birds follow a risk-averse strategy by avoiding sites with comparatively high energy expenditure or stochasticity levels in favour of sites with highly predictable food supply and low expenditure. Furthermore, the onset of spring on the stop-over sites had the most pronounced effect on staging times while intake rates had surprisingly little effect.

4. Subsequently, using empirical data, we tested whether observed changes in the onset of spring along the flyway explain the observed changes in migration schedules of pink-footed geese from 1990 to 2004. Model predictions generally agreed well with empirically observed migration patterns, with geese leaving the wintering grounds earlier while considerably extending their staging times in Norway.

Optimal management of a goose flyway : migrant management at minimum cost

1. We adopt a ‘whole flyway’ approach to modelling scenarios for protecting migratory birds, aiming at efficient and cost-effective conservation of flyway habitat.

2. We developed a model to minimize flyway management costs while safeguarding a migrating bird population. The model assumes that the intensity of the birds’ use of sites can be manipulated by varying management regimes (with concomitant costs) and that the birds make optimal use of the conditions created along their flyway.

3. We used dynamic programming to find the sequence of migratory decisions that maximizes the fitness of the migrants given a range of management scenarios, followed by a management cost estimate of all these scenarios and selection of those scenarios yielding an optimal solution from both an economic and the migrants’ perspective.

4. Using the population of pink-footed geese Anser brachyrhynchus that breed in Svalbard as an example, we calculated that the cheapest management scenario given current compensation payment rates at the various goose stopover sites yielded a 35% cost saving over current management. This cheapest scenario provides a migration itinerary that is very similar to the current itinerary used by the geese. This is fortuitous since changing environmental conditions may put the migrants at risk.

5. Synthesis and application. Given the global threats to migratory birds, developing a framework for efficient and effective conservation of flyway habitat is an urgent need. Such a framework may likewise be used to assist in controlling migrants causing conflict with agriculture, such as several goose species, in an economic and responsible fashion. Our suggested exemplified framework identified large unexplainable differences in management costs between regions. Differences in management costs between staging sites for birds make big differences to the optimal management of a flyway. Hence, to achieve efficient and effective management of migratory birds, we firstly need an objective assessment of the cost of management in different locations, followed by a modelling approach as here advocated, and followed up by a collaborative action of managers along the entire flyway.

Modelling behavioural and fitness consequences of disturbance for geese along their spring flyway

1. For migratory birds the implications of environmental change may be difficult to predict because they use multiple sites during their annual cycle. Moreover, the migrants’ use of these sites may be interdependent. Along the flyway of the Svalbard pink-footed goose Anser brachyrhynchus population, Norwegian farmers use organized scaring to minimize goose use of their grasslands in spring. We assessed the consequences of this practice for regional site use of pink-footed geese along their spring migration route.

2. We used dynamic programming to find the sequence of migratory decisions that maximizes the fitness of female geese during spring migration, assuming scaring impinges on both food-intake rates and predation risk. The parameterization of the model was based on data gathered from individually marked pink-footed geese between 1991 and 2003.

3. The effect of scaring in terms of fitness and site use was most noticeable regarding food-intake rate. Scaring resulted in a redistribution of geese along the flyway. Furthermore, the outcomes of the modelling exercises were highly dependent on whether or not the geese were omniscient or naive: at moderate scaring levels naive geese were predicted to succumb.

4. On a qualitative basis there was good correspondence between the predictions from the model and the empirical evidence gathered to date.

5. Synthesis and applications. Besides highlighting the importance of learning and changing behaviour in an adaptive fashion, our modelling exercise indicated the potential vulnerability of the geese to abrupt environmental change. In addition, the exercise emphasized the interdependence of site use along the migratory flyway. The model supports the necessity for an integrated flyway management approach. In Norway, discussion is ongoing about the future management of the spring conflict between farming interests and geese. Farmers in north and mid-Norway have announced that they will expand the scaring campaign if a long-term solution, including a compensation scheme, is not forthcoming. If scaring on such a large scale is implemented abruptly, it may have severe consequences for the population: management of both the scaring intensity and its geographical extent is urgently required.

Simulating urban growth in a developing nation's region using a cellular automata-based model

Urbanization is one of the most evident global changes. Research in the field of urban growth modelling has generated models that explore for drivers and components of the urban growth dynamics. Cellular automata (CA) modeling is one of the recent advances, and a number of CA-based models of urban growth have produced satisfactory simulations of spatial urban expansion over time. Most application and test of CA-based models of urban growth which provide likely and reliable simulations has been developed in urban regions of developed nations; urban regions in the United States, in particular. This is because most of the models were developed in universities and research centers of developed nations, and these regions have the required data, which is extensive. Most of the population growth in the world, however, occurs in the developing world. While some European countries show signs of stabilization of their population, in less developed countries, such as India, population still grows exponentially. And this growth is normally uncoordinated, which results in serious environmental and social problems in urban areas. Therefore, the use of existing dynamic–spatial models of urban growth in regions of developing nations could be a means to assist planners and decision makers of these regions to understand and simulate the process of urban growth and test the results of different development strategies. The pattern of growth of urban regions of developing nations, however, seems to be different of the pattern of developed countries. The former use to be more dense and centralized, normally expanding outwards from consolidated urban areas; while the second is normally more fragmented and sparse. The present paper aims to investigate to how extent existing CA-based urban growth models tested in developed nations can also be applied to a developing country urban area. The urban growth model was applied to Porto Alegre City, Brazil. An expected contiguous expansion from existing urban areas has been obtained as following the historical trends of growth of the region. Moreover, the model was sensitive and able to portray different pattern of growth in the study area by changing the value of its parameters.

A smith-waterman local alignment approach for spatial activity recognition

In this paper we address the spatial activity recognition problem with an algorithm based on Smith-Waterman (SW) local alignment. The proposed SW approach utilises dynamic programming with two dimensional spatial data to quantify sequence similarity. SW is well suited for spatial activity recognition as the approach is robust to noise and can accommodate gaps, resulting from tracking system errors. Unlike other approaches SW is able to locate and quantify activities embedded within extraneous spatial data. Through experimentation with a three class data set, we show that the proposed SW algorithm is capable of recognising accurately and inaccurately segmented spatial sequences. To benchmark the techniques classification performance we compare it to the discrete hidden markov model (HMM). Results show that SW exhibits higher accuracy than the HMM, and also maintains higher classification accuracy with smaller training set sizes. We also confirm the robust property of the SW approach via evaluation with sequences containing artificially introduced noise.

Finding the optimal temporal partitioning of video sequences

The existing techniques for shot partitioning either process each shot boundary independently or proceed sequentially. The sequential process assumes the last shot boundary is correctly detected and utilizes the shot length distribution to adapt the threshold for detecting the next boundary. These techniques are only locally optimal and suffer from the strong assumption about the correct detection of the last boundary. Addressing these fundamental issues, in this paper, we aim to find the global optimal shot partitioning by utilizing Bayesian principles to model the probability of a particular video partition being the shot partition. A computationally efficient algorithm based on Dynamic Programming is then formulated. The experimental results on a large movie set show that our algorithm performs consistently better than the best adaptive-thresholding technique commonly used for the task.

Prediction of pedestrians routes within a built environment in normal conditions

Modelling and prediction of pedestrian routing behaviours within known built environments has recently attracted the attention of researchers across multiple disciplines, owing to the growing demand on urban resources and requirements for efficient use of public facilities. This study presents an investigation into pedestrians' routing behaviours within an indoor environment under normal, non-panic situations. A network-based method using constrained Delaunay triangulation is adopted, and a utility-based model employing dynamic programming is developed. The main contribution of this study is the formulation of an appropriate utility function that allows an effective application of dynamic programming to predict a series of consecutive waypoints within a built environment. The aim is to generate accurate sequence waypoints for the pedestrian walking path using only structural definitions of the environment as defined in a standard CAD format. The simulation results are benchmarked against those from the A* algorithm, and the outcome positively indicates the usefulness of the proposed method in predicting pedestrians' route selection activities. © 2014 Elsevier Ltd. All rights reserved.

Skipping swans: fuelling rates and wind conditions determine differential use of migratory stopover sites of Bewick's Swans Cygnus bewickii

Some migratory birds refuel at stopover sites that they by-pass on the return trip. In theory, this skipping behaviour is only expected in time-selected migrants when the overflown site is of a lower quality than the departure site. We provide empirical evidence that quality differences in stopover sites are the cause for skipping in Bewick's Swans Cygnus bewickii tracked by satellite telemetry. Two and five complete tracks were recorded in spring and autunm, respectively, showing that the White Sea was visited for c. 2 weeks in spring, but by-passed (or visited for a few days at the most) in autumn. Skipping of the White Sea in autumn was predicted by a dynamic programming model which was based on calculated gain rates during stopover in the Pechora Delta and the White Sea. This prediction was not sensitive to plausible variations in gain rates. Relative to the Pechora Delta the White Sea is a poor site because a large tidal amplitude precludes foraging on the beds of the submerged macrophyte Fennel Pondweed Potamogeton pectinatus during high tide. The dynamic programming model predicted a fast autunm migration. However, the phenology of autunm arrival dates of Bewick's Swans on the wintering grounds revealed that only in three out of ten years a significant number of birds was able to reach the wintering grounds without refuelling. In the other years, unfavourable wind conditions along the Russian/Baltic part of the route prevented such non-stop migration.

A chain is as strong as its weakest link: assessing the consequences of habitat loss and degradation in a long-distance migratory shorebird

The conservation of migratory species represents a major challenge, as they use multiple sites, all contributing in varying degrees in sustaining high survival and reproductive success. There is particular concern for shorebirds of the East Asian-Australasian Flyway (EAAF), where declining numbers of migratory species have mostly been attributed to habitat loss along the East Asian coast. Using a stochastic dynamic programming migration model, we assessed the effect of habitat degradation scenarios along the EAAF on migration behaviour, survival and reproductive success of a long-distance migrating shorebird, the Ruddy Turnstone (Arenaria interpres). Following manipulation of habitat quality through changes in intake rate, we found that changes on the wintering (major non-breeding) ground in South Australia had the highest negative effect on reproductive success and survival. We also identified Taiwan and the Yellow Sea as sites with high importance for reproductive success. Although habitats along the East Asian coastline are currently most threatened from a range of global change processes, we highlight the importance of conserving high-quality shorebird wintering habitat in Australia. This may be of notable importance to trans-equatorial migratory shorebirds, which often make a long non-stop flight from their wintering grounds in order to skip low-latitude sites that typically provide little food.

Intensive care unit discharge policies prior to treatment completion

In this study we explore a model to optimize the Intensive Care Unit (ICU) discharging decisions prior to service completion as a result of capacity-constrained situation under uncertainty. Discharging prior to service completion, which is called demand-driven discharge or premature discharging, increases the chance that a patient to be readmitted to the ICU in the near future. Since readmission imposes an additional load on ICUs, the cost of demand-driven discharge is pertained to the surge of readmission chance and the length of stay (LOS) in the ICU after readmission. Hence, the problem is how to select a current patient in the ICU for demand-driven discharge to accommodate a new critically ill patient. In essence, the problem is formulated as a stochastic dynamic programming model. However, even in the deterministic form i.e. knowing the arrival and treatment times in advance, solving the dynamic programming model is almost unaffordable for a sizable problem. This is illustrated by formulating the problem by an integer programming model. The uncertainties and difficulties in the problem are convincing reasons to use the optimization-simulation approach. Thus, using simulations, we evaluate various scenarios by considering Weibull distribution for the LOS. While it is known that selecting a patient with the lowest readmission risk is optimum under certain conditions and supposing a memory-less distribution for LOS; we remark that when LOS is non-memory-less, considering readmission risk and remaining LOS rather than just readmission risk leads to better results.