Mobile agent planning (MAP) is increasingly viewed as an important technique of information retrieval systems to provide location aware services of minimum cost in mobile computing environment. Although Hopfield-Tank neural network has been proposed for solving the traveling salesperson problem, little attention has been paid to the time constraints on resource validity for optimizing the cost of the mobile agent. Consequently, we hypothesized that Hopfield-Tank neural network can be used to solve the MAP problem. To test this hypothesis, we modify Hopfield-Tank neural network and design a new energy function to not only cope with the dynamic temporal features of the computing environment, in particular the server performance and network latency when scheduling mobile agents, but also satisfy the location-based constraints such as the starting and end node of the routing sequence must be the home site of the traveling mobile agent. In addition, the energy function is reformulated into a Lyapunov function to guarantee the convergent stable state and existence of the valid solution. The connection weights between the neurons and the activation function of state variables in the dynamic network are devised in searching for the valid solutions. Moreover, the objective function is derived to estimate the completion time of the valid solutions and predict the optimal routing path. Simulations study was conducted to evaluate the proposed model and algorithm for different time variables and various coefficient values of the energy function. The experimental results quantitatively demonstrate the computational power and speed of the proposed model by producing solutions that are very close to the minimum costs of the location-based and time-constrained distributed MAP problem rapidly. The spatio-temporal technique proposed in this work is an innovative approach in providing knowledge applicable to improving the effectiveness of solving optimization problems.

With the development of wireless communications and mobile computing technologies, the applications of moving objects have been developed in many topics, for example, traffic monitoring, mobile E-Commerce, Navigation System, and Geographic Information System. The feature of the moving objects is that objects change their locations continuously. Conventional spatial databases can not support to store the moving objects efficiently, because the databases must be updated frequently. Therefore, it is important to index moving objects for efficiently answering queries about moving objects. Among the spatial indexing methods for predicting current and future data, the approach of parametric spatial access methods has been applied largely, since it needs little memory space to preserve parametric rectangles, and it still provides good performance, so it is adopted generally. The methods of this approach include the TPR-tree, the TPR*-tree, the Bx-tree, and the Bxr-tree. Among those methods, the Bxr-tree improves CPU performance of TPR-tree by expanding query region first, and improves I/O performance of the Bxr-tree by expanding the data blocks additionally. However, the query process of the B$^x_r$-tree is too rough such that it costs too much CPU and I/O time to check the useless data. Therefore, in this thesis, we propose a new data structure and a new query processing method named Adjustable Expanded Index (AEI), to improve the disadvantages of the Bxr-tree. In our method, we let each block records the maximum and minimum speeds of each of eight directions, instead of only the maximum speed of each of four directions in the Bxr-tree method. Based on the data structure, the query region can be expanded in each of eight directions individually, instead of being expanded in each of four directions once in the Bxr-tree method. Moreover, in our AEI method, the data blocks can be expanded according to the direction toward the query region, instead of being expanded in four directions in the Bxr-tree method. In this way, the query process of AEI checks less number of data blocks because it considers the minimum speed of each of eight directions. Furthermore, the objects are divided into four groups in AEI according to their directions, while the Bxr-tree method does not. Only the objects moving to query region will be checked in the query process of AEI. Therefore, we can reduce more number of retrieved data blocks and the number of I/O operations in our method than the Bxr-tree. From our simulation, we show that the query process of the AEI method is more efficient than that of the Bxr-tree in term of the average numbers of retrieved data blocks and I/O operations.

In this thesis, a system, called Ontology-Based Spatio-Temporal Video Management System (OntoVMS) is developed in order to supply a framework which can be used for semantic data modeling and querying in video files. OntoVMS supports semantic data modeling which can be divided into concept modeling, spatio-temporal relation and trajectory data modeling. The system uses Rhizomik MPEG-7 Ontology as the core ontology. Moreover ontology expression capability is extended by automatically attaching domain ontologies. OntoVMS supports querying of all spatial relations such as directional relations (north, south ...), mixed directional relations (northeast, southwest ...), distance relations (near, far), positional relations (above, below ...) and topological relations (inside, touch ...) / temporal relations such as starts, equal, precedes / and trajectories of objects of interest. In order to enhance querying capability, compound queries are added to the framework so that the user can combine simple queries by using &quot / (&quot / , &quot / )&quot / , &quot / AND&quot / and &quot / OR&quot / operators. Finally, the use of the system is demonstrated with a semi-automatic face annotation tool.

In this thesis a natural language query interface is developed for semantic and spatio-temporal querying of MPEG-7 based domain ontologies. The underlying ontology is created by attaching domain ontologies to the core Rhizomik MPEG-7 ontology. The user can pose concept, complex concept (objects connected with an &ldquo / AND&rdquo / or &ldquo / OR&rdquo / connector), spatial (left, right . . . ), temporal (before, after, at least 10 minutes before, 5 minutes after . . . ), object trajectory and directional trajectory (east, west, southeast . . . , left, right, upwards . . . ) queries to the system. Furthermore, the system handles the negative meaning in the user input. When the user enters a natural language (NL) input, it is parsed with the link parser. According to query type, the objects, attributes, spatial relation, temporal relation, trajectory relation, time filter and time information are extracted from the parser output by using predefined rules. After the information extraction, SPARQL queries are generated, and executed against the ontology by using an RDF API. Results are retrieved and they are used to calculate spatial, temporal, and trajectory relations between objects. The results satisfying the required relations are displayed in a tabular format and user can navigate through the multimedia content.

As a result of everlasting developments in multimedia technologies, all kinds of objects tracking theory using machine vision or image process methods have been proposed. Most of the methods are based on shape of the object. For this reason, the profile of the tracked object must be known in advance. In many situations, we expect to track the object whose shape is unknown but speed or direction is explicit. For instance, speed or moving direction of the object is known. This thesis presents a spatio-temporal tracking technique, which extracts image information depending on speed of the moving object regardless of its shape. Furthermore, combination of the proposed method in spatio-temporal domain and the optical flow scheme makes the whole tracking system even more robust.

This dissertation is in the area of pervasive computing. It focuses on designing platforms for storing, querying, and computing contextual information. More specifically, we are interested in platforms for storing and querying spatio-temporal events where queries exhibit locality. Recent advances in sensor technologies have made possible gathering a variety of information on the status of users, the environment machines, etc. Combining this information with computation we are able to extract context, i.e., a filtered high-level description of the situation. In many cases, the information gathered exhibits locality both in space and time, i.e., an event is likely to be consumed in a location close to the location where the event was produced, at a time whic h is close to the time the event was produced. This dissertation builds on this observation to create better platforms for computing context. We claim three key contributions. We have studied the problem of designing and optimizing spatial organizations for exchanging context. Our thesis has original theoretical work on how to create a platform based on cells of a Voronoi diagram for optimizing the energy and bandwidth required for mobiles to exchange contextual information t hat is tied to specific locations in the platform. Additionally, we applied our results to the problem of optimizing a system for surveilling the locations of entities within a given region. We have designed a platform for storing and querying spatio-temporal events exhibiting locality. Our platform is based on a P2P infrastructure of peers organized based on the Voronoi diagram associated with their locations to store events based on their own associated locations. We have developed theoretical results based on spatial point processes for the delay experienced by a typical query in this system. Additionally, we used simulations to study heuristics to improve the performance of our platform. Finally, we came up with protocols for the replicated storage of events in order to increase the fault-tolerance of our platform. Finally, in this thesis we propose a design for a platform, based on RFID tags, to support context-aware computing for indoor spaces. Our platform exploits the structure found in most indoor spaces to encode contextual information in suitably designed RFID tags. The elements of our platform collaborate based on a set of messages we developed to offer context-aware services to the users of the platform. We validated our research with an example hardware design of the RFID tag and a software emulation of the tag's functionality. / text

Nowadays there is no need to convince anyone about the necessity of renewable energy. One of the most promising ways to obtain it is the wind power. Countries like Denmark, Germany or Spain proved that, while professionally managed, it can cover a substantial part of the overall energy demand. One of the main and specific problems related to the wind power management — development of the accurate power prediction models. Nowadays State-Of-Art systems provide predictions for a single wind turbine, wind farm or a group of them. However, the spatio-temporal propagation of the errors is not adequately considered. In this paper the potential for improving modern wind power prediction tool WPPT, based on the spatio-temporal propagation of the errors, is examined. Several statistical models (Linear, Threshold, Varying-coefficient and Conditional Parametric) capturing the cross-dependency of the errors, obtained in different parts of the country, are presented. The analysis is based on the weather forecast information and wind power prediction errors obtained for the territory of Denmark in the year 2004. / Vienas iš perspektyviausių bei labiausiai plėtojamų atsinaujinančių energijos šaltinių - vėjas. Tokios Europos Sąjungos šalys kaip Danija, Vokietija bei Ispanija savo patirtimi įrodė, jog tinkamai valdomas bei vystomas vėjo ūkis gali padengti svarią šalies energijos paklausos dalį. Pagal Europos Sąjungos direktyvą 2001/77/EC Lietuva yra įsipareigojusi iki 2010 m. pasiekti, kad elektros energijos gamyba iš atsinaujinančių energijos išteklių sudarytų 7% suvartojamos elektros energijos. Šių įsipareigojimų įvykdymui Lietuvos vyriausybės priimtu nutarimu yra nustatyta atsinaujinančių energijos išteklių naudojimo skatinimo tvarka, pagal kurią numatyta palaipsniui plėsti vėjo energijos naudojimą šalyje. Planuojama, kad iki 2010 m. bus pastatyta 200 MW bendros galios vėjo elektrinių, kurios gamins apie 2,2% visos suvartojamos elektros energijos [Marčiukaitis, 2007]. Didėjant vėjo energijos daliai energetikos sistemoje, Lietuvoje ateityje kils sistemos balansavimo problemų dėl nuolatinių vėjo jėgainių galios svyravimų. Kaip rodo kitų šalių patirtis, vėjo elektrinių galios prognozė yra efektyvi priemonė, leidžianti išspręsti šias problemas. Šiame darbe pristatyti keletas statistinių modelių bei metodų, skirtų išgaunamos vėjo energijos prognozėms gerinti. Analizė bei modeliavimas atlikti nagrinėjant Danijos WPPT (Wind Power Prediction Tool) duomenis bei meteorologines prognozes. Pagrindinis darbo tikslas - modifikuoti WPPT, atsižvelgiant į vėjo krypties bei stiprio įtaką energijos... [toliau žr. visą tekstą]

Disertaciniame darbe nagrinėjama erdvės – laiko duomenų modeliavimo problema. Dažnai erdvinių duomenų rinkiniai yra gana nedideli, o taškai, kuriuose pasklidę stebėjimai, išsidėstę netaisyklingai. Sprendžiant „erdvinį“ uždavinį, paprastai siekiama inerpoliuoti arba įvertinti erdvinį vidurkį. Laiko eilučių duomenys dažniausiai naudojami ateities reikšmėms prognozuoti. Tuo tarpu erdvės – laiko uždaviniai jungia abu uždavinių tipus. Pasiūlyta keletas originalių erdvinių laiko eilučių modeliavimo metodų. Siūlomi metodai pirmiausia analizuoja vienmates laiko eilutes, o pašalinus laikinę priklausomybė jose, laiko eilučių liekanoms vertinama erdvinė priklausomybė. Tikslas – sudaryti modelį, leidžiantį prognozuoti požymio reikšmę naujame, nestebėtame taške, nauju laiko momentu. Tokio modelio sudarymas remiasi laiko eilučių parametrų erdviniu interpoliavimu. / Space – time data modeling problem is analysed. Often spatial data sets are relatively small, and the points, where observations are taken, are located irregularly. When solving spatial task, usually we are interpolating or estimating the spatial average. Time series data usually are used to predict future values. Meanwhile, the space - time tasks combines both types of tasks. Few original modeling methods of spatial time series are proposed. The proposed methods firstly analyzes the univariate time series, and after removing temporal dependence, spatial dependence in the time series of residuals is measured. Aim of this dissertational work - to create time series model at new unobserved location by incorporating spatial interaction thru spatial interpolation of estimated time series parameters. Such a model is based on the spatial interpolation of time series parameters.

Mechanisms controlling the productivity, abundançe and taxonomic composition of phytoplankton in tropical Lake Malawi were examined by monitoring phytoplankton dynamics in 8 regions covering the length of the lake (560 km) over a 10-12 month period, and relating these dynamics to spatio-temporal changes in thermal structure, nutrient availability, and meteorological conditions. In addition, nearshore benthic photosynthetic rates were measured in 7 different months.Spatial and temporal changes in areal photosynthetic rates were due almost entirely to changes in the efficiency of light utilization by the phyroplankton community. An evaluation of potential factors which might influence tight utilization indicates that nutrient availabitity is the most important. Most of the photosynthetic N and P demand is met by internal recycling within the upper 200 m, and therefore spatio-temporal variation of phytoplankton photosynthetic rate is closely related to changes in mixing regime. It is shown that the dominant meteorological factors responsible for changes in the mixing regime were solar radiation and windspeed. A comparison with previous photosynthesis data for Lake Malawi indicates that windspeed is a dominant factor controlling interannual variability. Shallow areas of the lake were more productive than deep areas, due to more intense upwelling and more efficient internal nutrient recycling in shallow waters. Within the littoral zone, benthic photosynthetic rates were very high, accounting for 14% to 28% of total net photosynthesis within the shallow southeast arm. Phytoplankton biomass was not correlated with photosynthetic rate, indicating that biomass loss processes were important in controlling biomass variability. Changes in phytoplankton taxonomic composition were related to changes in mixing regime. Cyanobacteria and chlorophytes were dominant throughout much of the study period, but diatoms made up a significant proportion of total biomass during periods of increased turbulence and nutrient availability. An analysis of phytoplankton surface area : volume ratios revealed that organism shape and size are important determinants in species succession. Previous studies have emphasized the low variability of phytoplankton biomass and photosynthetic rates in tropical lakes, relative to temperate lakes. An inter-lake comparison reveals that this tenet does not apply to large lakes. Fluctuations in the mixing regime of large tropical lakes have an effect on phytoplankton variability similar in magnitude to the effect of fluctuating solar irradiance in large temperate lakes.

With the increasing degradation of rivers worldwide, an understanding of spatial and temporal patterns in freshwater quality is important. Water quality is highly variable in space and time, yet this is largely overlooked at the scale of stream catchments. I employed a landscape ecology approach to examine the spatial patterning of water quality in complex, impacted stream networks on the Canterbury Plains of the South Island of New Zealand, with the goal of understanding how land-use effects proliferate through stream systems. In particular, I used “snapshot” sampling events in conjunction with spatial modelling and longitudinal profiles to investigate the ways in which spatial and environmental factors influence the variability of water quality in stream networks. Spatial eigenfunction analyses showed that distance measures, which took into account variable connectivity by flow and distance along the stream between sites, explained more spatial variance in water quality than traditional distance metrics. Small upstream reaches were more spatially and temporally variable than main stems (under summer base-flow conditions). The extent of spatial variation in water quality differed between stream networks, potentially depending on linkages to groundwater and the surrounding landscape. My results indicated that the water quality of headwater streams can have a disproportionate influence over water quality throughout an entire network. I investigated spatio-temporal patterns in water quality more intensively in one stream network, the Cam River, in which I found consistent spatial pattern through time. The relative balance between nutrient inputs (pollution and groundwater) and in-stream conditions influenced the spatial pattern of water quality, as well as that of several ecosystem processes which I measured simultaneously. The spatially intensive and explicit approach has allowed identification of key factors controlling water quality and ecosystem processes throughout the Cam River. This research highlights the importance of taking a spatially explicit approach when studying stream water quality and that such an approach could be insightful and will contribute to solving current stream management problems.