Advances in information and network technologies have fostered the creation and availability of a vast amount of online information, typically in the form of text documents. Information retrieval (IR) pertains to determining the relevance between a user query and documents in the target collection, then returning those documents that are likely to satisfy the user¡¦s information needs. One challenging issue in IR is word mismatch, which occurs when concepts can be described by different words in the user queries and/or documents. Query expansion is a promising approach for dealing with word mismatch in IR. In this thesis, we develop a fuzzy cluster-based query expansion technique to solve the word mismatch problem. Using existing expansion techniques (i.e., global analysis and non-fuzzy cluster-based query expansion) as performance benchmarks, our empirical results suggest that the fuzzy cluster-based query expansion technique can provide a more accurate query result than the benchmark techniques can.

Sensor nodes are deployed over sensing fields for the purpose of monitoring certain phenomena of interest. The sensor nodes perform specific measurements, process the sensed data, and send the data to a base station over a wireless channel. The base station collects data from the sensor nodes, analyses this data, and reports it to the users. Wireless sensor networks are different from traditional networks, because of the following constraints. Typically, a large number of sensor nodes need to be randomly deployed and, in most cases, they are deployed in unreachable environments; however, the sensor nodes may fail, and they are subject to power constraints. Energy is one of the most important design constraints of wireless sensor networks. Energy consumption, in a sensor node, occurs due to many factors, such as: sensing the environment, transmitting and receiving data, processing data, and communication overheads. Since the sensor nodes behave as router nodes for data propagation, of the other sensor nodes to the base station, network connectivity decreases gradually. This may result in disconnected sub networks of sensor nodes. In order to prolong the network's lifetime, energy efficient protocols should be designed for the characteristics of the wireless sensor network. Sensor nodes in different regions of the sensing field can collaborate to aggregate the data that they gathered. Data aggregation is defined as the process of aggregating the data from sensor nodes to reduce redundant transmissions. It reduces a large amount of the data traffic on the network, it requires less energy, and it avoids information overheads by not sending all of the unprocessed data throughout the sensor network. Grouping sensor nodes into clusters is useful because it reduces the energy consumption. The clustering technique can be used to perform data aggregation. The clustering procedure involves the selection of cluster heads in each of the cluster, in order to coordinate the member nodes. The cluster head is responsible for: gathering the sensed data from its cluster's nodes, aggregating the data, and then sending the aggregated data to the base station. An adaptive clustering protocol was introduced to select the heads in the wireless sensor network. The proposed clustering protocol will dynamically change the cluster heads to obtain the best possible performance, based on the remaining energy level of sensor nodes and the average energy of clusters. The OMNET simulator will be used to present the design and implementation of the adaptive clustering protocol and then to evaluate it. This research has conducted extensive simulation experiments, in order to fully study and analyse the proposed energy efficient clustering protocol. It is necessary for all of the sensor nodes to remain alive for as long as possible, since network quality decreases as soon as a set of sensor nodes die. The goal of the energy efficient clustering protocol is to increase the lifetime and stability period of the sensor network. This research also introduces a new bidirectional data gathering protocol. This protocol aims to form a bidirectional ring structure among the sensor nodes, within the cluster, in order to reduce the overall energy consumption and enhance the network's lifetime. A bidirectional data gathering protocol uses a source node to transmit data to the base station, via one or more multiple intermediate cluster heads. It sends data through energy efficient paths to ensure the total energy, needed to route the data, is kept to a minimum. Performance results reveal that the proposed protocol is better in terms of: its network lifetime, energy dissipation, and communication overheads.

Document clustering in information retrieval (IR) is considered an alternative to rank-based retrieval approaches, because of its potential to support user interactions beyond just typing in queries. Similarly, the Principle of Polyrepresentation (multi-evidence: combining multiple cognitively and/or functionally diff erent information need or information object representations for improving an IR system's performance) is an established approach in cognitive IR with plausible applicability in the domain of information seeking and retrieval. The combination of these two approaches can assimilate their respective individual strengths in order to further improve the performance of IR systems. The main goal of this study is to combine cognitive and cluster-based IR approaches for improving the eff ectiveness of (interactive) information retrieval systems. In order to achieve this goal, polyrepresentative information retrieval strategies for cluster browsing and retrieval have been designed, focusing on the evaluation aspect of such strategies. This thesis addresses the challenge of designing and evaluating an Optimum Clustering Framework (OCF) based model, implementing probabilistic document clustering for interactive IR. Thus, polyrepresentative cluster browsing strategies have been devised. With these strategies a simulated user based method has been adopted for evaluating the polyrepresentative cluster browsing and searching strategies. The proposed approaches are evaluated for information need based polyrepresentative clustering as well as document based polyrepresentation and the combination thereof. For document-based polyrepresentation, the notion of citation context is exploited, which has special applications in scientometrics and bibliometrics for science literature modelling. The information need polyrepresentation, on the other hand, utilizes the various aspects of user information need, which is crucial for enhancing the retrieval performance. Besides describing a probabilistic framework for polyrepresentative document clustering, one of the main fi ndings of this work is that the proposed combination of the Principle of Polyrepresentation with document clustering has the potential of enhancing the user interactions with an IR system, provided that the various representations of information need and information objects are utilized. The thesis also explores interactive IR approaches in the context of polyrepresentative interactive information retrieval when it is combined with document clustering methods. Experiments suggest there is a potential in the proposed cluster-based polyrepresentation approach, since statistically signifi cant improvements were found when comparing the approach to a BM25-based baseline in an ideal scenario. Further marginal improvements were observed when cluster-based re-ranking and cluster-ranking based comparisons were made. The performance of the approach depends on the underlying information object and information need representations used, which confi rms fi ndings of previous studies where the Principle of Polyrepresentation was applied in diff erent ways.

The explosion of internet traffic, advent of social media sites such as Facebook and Twitter, and increased availability of digital cameras has saturated life with images and videos. Never before has it been so important to sift quickly through large amounts of digital information. Salient Object Detection (SOD) is a computer vision topic that finds methods to locate important objects in pictures. SOD has proven to be helpful in numerous applications such as image forgery detection and traffic sign recognition. In this thesis, I outline a novel SOD technique to automatically isolate important objects from the background in images.

Recommendation is not a new phenomenon arising from the digital era, but an existing social behavior in real life. Recommendation systems facilitate such natural social recommendation behavior and alleviate information overload facing individuals. Among different recommendation techniques proposed in the literature, the collaborative filtering approach is the most successful and widely adopted recommendation technique to date. However, the traditional collaborative filtering recommendation approach ignores proximities between items. That is, all user ratings on items are deemed identically important and given an equal weight in neighborhood formation process. In this study, we proposed a cluster-based collaborative filtering recommendation approach that takes into account the content similarities of items in the collaborative filtering process. Our empirical evaluation results show that the cluster-based collaborative filtering approach improves the prediction accuracy without sacrificing the prediction coverage, using those achieved by the traditional collaborative filtering approach as performance benchmarks. Due to the sparsity problem, when a prediction is made based on few neighbors, the cluster average method could achieve a better prediction accuracy than the proposed approach. Thus, we further proposed an enhanced cluster-based collaborative filtering approach that combines our approach and the cluster average method. The empirical results suggest that the enhanced approach could result in a prediction accuracy comparable to or even better than that accomplished by the cluster average method.

Taiwan is a country with limited land resources and a dense population. Regarding national land, it should be effectively and comprehensively utilized, developed and maintained so that industries can have appropriately allocation and development under regional planning in the hope to improve national welfare. However, Taiyen Biotech Co., Ltd. (Taiwan Salt Industrial Corporation) has terminated solar salt work completely since 2002 and returned 5,243 hectares of land to National Property Administration. The re-development and use of land has remained under review consideration. The solar salt economic activities that once supported tens of thousand of people have ground to a halt due to the termination of solar salt work, leaving salt fields unattended. And salt villages have become desolate. It is a shame to see the unattended salt fields soak in sea water and turn into marshes. The author tries to study from the viewpoint of National Comprehensive Development Plan and its relation with industrial development and present three industrial cluster-based economy science park development projects. They can provide choices for the government to make effective management guidelines in National Comprehensive Development Plan. By re-invigorating regional industrial economy in the country, we may expect to build new prosperous science and technology townships. Keywords: development, National Comprehensive Development Plan, industrial cluster-based economy, project, regional economy.

This work considers the sampling design for detection problems.Firstly,we focus on studying the effect of signal shape on sampling design for Gaussian detection problem.We then investigate the sampling design for distributed detection problems and compare the performance with the single sensor context. We also propose a sampling design scheme for the cluster-based wireless sensor networks.The cluster head employs a linear combination fusion to reduce the dimension of the sampled observation.Mathematical verification and simulation result show that the performance loss caused by the dimensionality reduction is exceedingly small as compared with the benchmark scheme,which is the sampling scheme without dimensionality reduction.In particular,there is no performance loss when the identical sampling points are employed at all sensor nodes.

With the rapid proliferation of new wireless communication devices and services, the demand for the radio spectrum is increasing at a rapid rate, which leads to making the spectrum more and more crowded. The limited available spectrum and the inefficiency in the spectrum usage have led to the emergence of cognitive radio (CR) and dynamic spectrum access (DSA) technologies, which enable future wireless communication systems to exploit the empty spectrum in an opportunistic manner. To do so, future wireless devices should be aware of their surrounding radio environment in order to adapt their operating parameters according to the real-time conditions of the radio environment. From this viewpoint, spectrum sensing is becoming increasingly important to new and future wireless communication systems, which is designed to monitor the usage of the radio spectrum and reliably identify the unused bands to enable wireless devices to switch from one vacant band to another, thereby achieving flexible, reliable, and efficient spectrum utilisation. This thesis focuses on issues related to local and cooperative spectrum sensing for CR networks, which need to be resolved. These include the problems of noise uncertainty and detection in low signal to noise ratio (SNR) environments in individual spectrum sensing. In addition to issues of energy consumption, sensing delay and reporting error in cooperative spectrum sensing. In this thesis, we investigate how to improve spectrum sensing algorithms to increase their detection performance and achieving energy efficiency. To this end, first, we propose a new spectrum sensing algorithm based on energy detection that increases the reliability of individual spectrum sensing. In spite of the fact that the energy detection is still the most common detection mechanism for spectrum sensing due to its simplicity. Energy detection does not require any prior knowledge of primary signals, but has the drawbacks of threshold selection, and poor performance due to noise uncertainty especially at low SNR. Therefore, a new adaptive optimal energy detection algorithm (AOED) is presented in this thesis. In comparison with the existing energy detection schemes the detection performance achieved through AOED algorithm is higher. Secondly, as cooperative spectrum sensing (CSS) can give further improvement in the detection reliability, the AOED algorithm is extended to cooperative sensing; in which multiple cognitive users collaborate to detect the primary transmission. The new combined approach (AOED and CSS) is shown to be more reliable detection than the individual detection scheme, where the hidden terminal problem can be mitigated. Furthermore, an optimal fusion strategy for hard-fusion based cognitive radio networks is presented, which optimises sensing performance. Thirdly, the need for denser deployment of base stations to satisfy the estimated high traffic demand in future wireless networks leads to a significant increase in energy consumption. Moreover, in large-scale cognitive radio networks some of cooperative devices may be located far away from the fusion centre, which causes an increase in the error rate of reporting channel, and thus deteriorating the performance of cooperative spectrum sensing. To overcome these problems, a new multi-hop cluster based cooperative spectrum sensing (MHCCSS) scheme is proposed, where only cluster heads are allowed to send their cluster results to the fusion centre via successive cluster heads, based on higher SNR of communication channel between cluster heads. Furthermore, in decentralised CSS as in cognitive radio Ad Hoc networks (CRAHNs), where there is no fusion centre, each cognitive user performs the local spectrum sensing and shares the sensing information with its neighbours and then makes its decision on the spectrum availability based on its own sensing information and the neighbours’ information. However, cooperation between cognitive users consumes significant energy due to heavy communications. In addition to this, each CR user has asynchronous sensing and transmission schedules which add new challenges in implementing CSS in CRAHNs. In this thesis, a new multi-hop cluster based CSS scheme has been proposed for CRAHNs, which can enhance the cooperative sensing performance and reduce the energy consumption compared with other conventional decentralised cooperative spectrum sensing modes.

Master of Science / Department of Statistics / James W. Neill / Cluster based lack of fit tests for linear regression models are generally effective in detecting model inadequacy due to between- or within-cluster lack of fit. Typically, lack of fit exists as a combination of these two pure types, and can be extremely difficult to detect depending on the nature of the mixture. Su and Yang (2006) and Miller and Neill (2007) have proposed lack of fit tests which address this problem. Based on a simulation comparison of the two testing procedures, it is concluded that the Su and Yang test can be expected to be effective when the true model is locally well approximated within each specified cluster and the lack of fit is not due to an unspecified predictor variable. The Miller and Neill test accommodates a broader alternative, which can thus result in comparatively lower but effective power. However, the latter test demonstrated the ability to detect model inadequacy when the lack of fit was a function of an unspecified predictor variable and does not require a specified clustering for implementation.

Sensor nodes are deployed over sensing fields for the purpose of monitoring certain phenomena of interest. The sensor nodes perform specific measurements, process the sensed data, and send the data to a base station over a wireless channel. The base station collects data from the sensor nodes, analyses this data, and reports it to the users. Wireless sensor networks are different from traditional networks, because of the following constraints. Typically, a large number of sensor nodes need to be randomly deployed and, in most cases, they are deployed in unreachable environments; however, the sensor nodes may fail, and they are subject to power constraints. Energy is one of the most important design constraints of wireless sensor networks. Energy consumption, in a sensor node, occurs due to many factors, such as: sensing the environment, transmitting and receiving data, processing data, and communication overheads. Since the sensor nodes behave as router nodes for data propagation, of the other sensor nodes to the base station, network connectivity decreases gradually. This may result in disconnected sub networks of sensor nodes. In order to prolong the network¿s lifetime, energy efficient protocols should be designed for the characteristics of the wireless sensor network. Sensor nodes in different regions of the sensing field can collaborate to aggregate the data that they gathered. Data aggregation is defined as the process of aggregating the data from sensor nodes to reduce redundant transmissions. It reduces a large amount of the data traffic on the network, it requires less energy, and it avoids information overheads by not sending all of the unprocessed data throughout the sensor network. Grouping sensor nodes into clusters is useful because it reduces the energy consumption. The clustering technique can be used to perform data aggregation. The clustering procedure involves the selection of cluster heads in each of the cluster, in order to coordinate the member nodes. The cluster head is responsible for: gathering the sensed data from its cluster¿s nodes, aggregating the data, and then sending the aggregated data to the base station. An adaptive clustering protocol was introduced to select the heads in the wireless sensor network. The proposed clustering protocol will dynamically change the cluster heads to obtain the best possible performance, based on the remaining energy level of sensor nodes and the average energy of clusters. The OMNET simulator will be used to present the design and implementation of the adaptive clustering protocol and then to evaluate it. This research has conducted extensive simulation experiments, in order to fully study and analyse the proposed energy efficient clustering protocol. It is necessary for all of the sensor nodes to remain alive for as long as possible, since network quality decreases as soon as a set of sensor nodes die. The goal of the energy efficient clustering protocol is to increase the lifetime and stability period of the sensor network. This research also introduces a new bidirectional data gathering protocol. This protocol aims to form a bidirectional ring structure among the sensor nodes, within the cluster, in order to reduce the overall energy consumption and enhance the network¿s lifetime. A bidirectional data gathering protocol uses a source node to transmit data to the base station, via one or more multiple intermediate cluster heads. It sends data through energy efficient paths to ensure the total energy, needed to route the data, is kept to a minimum. Performance results reveal that the proposed protocol is better in terms of: its network lifetime, energy dissipation, and communication overheads.