Identification and specification of relationships as the foundation for service bundling

Service bundling can be regarded as an option for service providers to strengthen their competitive advantages, cope with dynamic market conditions and heterogeneous consumer demand. Despite these positive effects, actual guidance for the identification of service bundles and the act of bundling itself can be regarded as a gap. Previous research has resulted in a conceptualization of a service bundling method relying on a structured service description in order to fill this gap. This method addresses the reasoning about the suitability of services to be part of a bundle based on analyzing existing relationships between services captured by a description language. This paper extends the aforementioned research by presenting an initial set of empirically derived relationships between services in existing bundles that can subsequently be utilized to identify potential new bundles. Additionally, a gap analysis points out to what extent prominent ontologies and service description languages accommodate for the identified relationships.

Service identification through value chain analysis and prioritization

In a resource constrained business world, strategic choices must be made on process improvement and service delivery. There are calls for more agile forms of enterprises and much effort is being directed at moving organizations from a complex landscape of disparate application systems to that of an integrated and flexible enterprise accessing complex systems landscapes through service oriented architecture (SOA). This paper describes the deconstruction of an enterprise into business services using value chain analysis as each element in the value chain can be rendered as a business service in the SOA. These business services are explicitly linked to the attainment of specific organizational strategies and their contribution to the attainment of strategy is assessed and recorded. This contribution is then used to provide a rank order of business service to strategy. This information facilitates executive decision making on which business service to develop into the SOA. The paper describes an application of this Critical Service Identification Methodology (CSIM) to a case study.

An identification of factors influencing police workplace motivation

Police work tasks are diverse and require the ability to take command, demonstrate leadership, make serious decisions and be self directed (Beck, 1999; Brunetto & Farr-Wharton, 2002; Howard, Donofrio & Boles, 2002). This work is usually performed in pairs or sometimes by an officer working alone. Operational police work is seldom performed under the watchful eyes of a supervisor and a great amount of reliance is placed on the high levels of motivation and professionalism of individual officers. Research has shown that highly motivated workers produce better outcomes (Whisenand & Rush, 1998; Herzberg, 2003). It is therefore important that Queensland police officers are highly motivated to provide a quality service to the Queensland community. This research aims to identify factors which motivate Queensland police to perform quality work. Researchers acknowledge that there is a lack of research and knowledge in regard to the factors which motivate police (Beck, 1999; Bragg, 1998; Howard, Donofrio & Boles, 2002; McHugh & Verner, 1998). The motivational factors were identified in regard to the demographic variables of; age, sex, rank, tenure and education. The model for this research is Herzberg’s two-factor theory of workplace motivation (1959). Herzberg found that there are two broad types of workplace motivational factors; those driven by a need to prevent loss or harm and those driven by a need to gain personal satisfaction or achievement. His study identified 16 basic sub-factors that operate in the workplace. The research utilised a questionnaire instrument based on the sub-factors identified by Herzberg (1959). The questionnaire format consists of an initial section which sought demographic information about the participant and is followed by 51 Likert scale questions. The instrument is an expanded version of an instrument previously used in doctoral studies to identify sources of police motivation (Holden, 1980; Chiou, 2004). The questionnaire was forwarded to approximately 960 police in the Brisbane, Metropolitan North Region. The data were analysed using Factor Analysis, MANOVAs, ANOVAs and multiple regression analysis to identify the key sources of police motivation and to determine the relationships between demographic variables such as: age, rank, educational level, tenure, generation cohort and motivational factors. A total of 484 officers responded to the questionnaire from the sample population of 960. Factor analysis revealed five broad Prime Motivational Factors that motivate police in their work. The Prime Motivational Factors are: Feeling Valued, Achievement, Workplace Relationships, the Work Itself and Pay and Conditions. The factor Feeling Valued highlighted the importance of positive supportive leaders in motivating officers. Many officers commented that supervisors who only provided negative feedback diminished their sense of feeling valued and were a key source of de-motivation. Officers also frequently commented that they were motivated by operational police work itself whilst demonstrating a strong sense of identity with their team and colleagues. The study showed a general need for acceptance by peers and an idealistic motivation to assist members of the community in need and protect victims of crime. Generational cohorts were not found to exert a significant influence on police motivation. The demographic variable with the single greatest influence on police motivation was tenure. Motivation levels were found to drop dramatically during the first two years of an officer’s service and generally not improve significantly until near retirement age. The findings of this research provide the foundation of a number of recommendations in regard to police retirement, training and work allocation that are aimed to improve police motivation levels. The five Prime Motivational Factor model developed in this study is recommended for use as a planning tool by police leaders to improve motivational and job-satisfaction components of police Service policies. The findings of this study also provide a better understanding of the current sources of police motivation. They are expected to have valuable application for Queensland police human resource management when considering policies and procedures in the areas of motivation, stress reduction and attracting suitable staff to specific areas of responsibility.

Fingerprint analysis using wavelet transform with application to compression and feature extraction

The main goal of this research is to design an efficient compression al~ gorithm for fingerprint images. The wavelet transform technique is the principal tool used to reduce interpixel redundancies and to obtain a parsimonious representation for these images. A specific fixed decomposition structure is designed to be used by the wavelet packet in order to save on the computation, transmission, and storage costs. This decomposition structure is based on analysis of information packing performance of several decompositions, two-dimensional power spectral density, effect of each frequency band on the reconstructed image, and the human visual sensitivities. This fixed structure is found to provide the "most" suitable representation for fingerprints, according to the chosen criteria. Different compression techniques are used for different subbands, based on their observed statistics. The decision is based on the effect of each subband on the reconstructed image according to the mean square criteria as well as the sensitivities in human vision. To design an efficient quantization algorithm, a precise model for distribution of the wavelet coefficients is developed. The model is based on the generalized Gaussian distribution. A least squares algorithm on a nonlinear function of the distribution model shape parameter is formulated to estimate the model parameters. A noise shaping bit allocation procedure is then used to assign the bit rate among subbands. To obtain high compression ratios, vector quantization is used. In this work, the lattice vector quantization (LVQ) is chosen because of its superior performance over other types of vector quantizers. The structure of a lattice quantizer is determined by its parameters known as truncation level and scaling factor. In lattice-based compression algorithms reported in the literature the lattice structure is commonly predetermined leading to a nonoptimized quantization approach. In this research, a new technique for determining the lattice parameters is proposed. In the lattice structure design, no assumption about the lattice parameters is made and no training and multi-quantizing is required. The design is based on minimizing the quantization distortion by adapting to the statistical characteristics of the source in each subimage. 11 Abstract Abstract Since LVQ is a multidimensional generalization of uniform quantizers, it produces minimum distortion for inputs with uniform distributions. In order to take advantage of the properties of LVQ and its fast implementation, while considering the i.i.d. nonuniform distribution of wavelet coefficients, the piecewise-uniform pyramid LVQ algorithm is proposed. The proposed algorithm quantizes almost all of source vectors without the need to project these on the lattice outermost shell, while it properly maintains a small codebook size. It also resolves the wedge region problem commonly encountered with sharply distributed random sources. These represent some of the drawbacks of the algorithm proposed by Barlaud [26). The proposed algorithm handles all types of lattices, not only the cubic lattices, as opposed to the algorithms developed by Fischer [29) and Jeong [42). Furthermore, no training and multiquantizing (to determine lattice parameters) is required, as opposed to Powell's algorithm [78). For coefficients with high-frequency content, the positive-negative mean algorithm is proposed to improve the resolution of reconstructed images. For coefficients with low-frequency content, a lossless predictive compression scheme is used to preserve the quality of reconstructed images. A method to reduce bit requirements of necessary side information is also introduced. Lossless entropy coding techniques are subsequently used to remove coding redundancy. The algorithms result in high quality reconstructed images with better compression ratios than other available algorithms. To evaluate the proposed algorithms their objective and subjective performance comparisons with other available techniques are presented. The quality of the reconstructed images is important for a reliable identification. Enhancement and feature extraction on the reconstructed images are also investigated in this research. A structural-based feature extraction algorithm is proposed in which the unique properties of fingerprint textures are used to enhance the images and improve the fidelity of their characteristic features. The ridges are extracted from enhanced grey-level foreground areas based on the local ridge dominant directions. The proposed ridge extraction algorithm, properly preserves the natural shape of grey-level ridges as well as precise locations of the features, as opposed to the ridge extraction algorithm in [81). Furthermore, it is fast and operates only on foreground regions, as opposed to the adaptive floating average thresholding process in [68). Spurious features are subsequently eliminated using the proposed post-processing scheme.

Spectral coding methods for speech compression and speaker identification

This thesis investigates aspects of encoding the speech spectrum at low bit rates, with extensions to the effect of such coding on automatic speaker identification. Vector quantization (VQ) is a technique for jointly quantizing a block of samples at once, in order to reduce the bit rate of a coding system. The major drawback in using VQ is the complexity of the encoder. Recent research has indicated the potential applicability of the VQ method to speech when product code vector quantization (PCVQ) techniques are utilized. The focus of this research is the efficient representation, calculation and utilization of the speech model as stored in the PCVQ codebook. In this thesis, several VQ approaches are evaluated, and the efficacy of two training algorithms is compared experimentally. It is then shown that these productcode vector quantization algorithms may be augmented with lossless compression algorithms, thus yielding an improved overall compression rate. An approach using a statistical model for the vector codebook indices for subsequent lossless compression is introduced. This coupling of lossy compression and lossless compression enables further compression gain. It is demonstrated that this approach is able to reduce the bit rate requirement from the current 24 bits per 20 millisecond frame to below 20, using a standard spectral distortion metric for comparison. Several fast-search VQ methods for use in speech spectrum coding have been evaluated. The usefulness of fast-search algorithms is highly dependent upon the source characteristics and, although previous research has been undertaken for coding of images using VQ codebooks trained with the source samples directly, the product-code structured codebooks for speech spectrum quantization place new constraints on the search methodology. The second major focus of the research is an investigation of the effect of lowrate spectral compression methods on the task of automatic speaker identification. The motivation for this aspect of the research arose from a need to simultaneously preserve the speech quality and intelligibility and to provide for machine-based automatic speaker recognition using the compressed speech. This is important because there are several emerging applications of speaker identification where compressed speech is involved. Examples include mobile communications where the speech has been highly compressed, or where a database of speech material has been assembled and stored in compressed form. Although these two application areas have the same objective - that of maximizing the identification rate - the starting points are quite different. On the one hand, the speech material used for training the identification algorithm may or may not be available in compressed form. On the other hand, the new test material on which identification is to be based may only be available in compressed form. Using the spectral parameters which have been stored in compressed form, two main classes of speaker identification algorithm are examined. Some studies have been conducted in the past on bandwidth-limited speaker identification, but the use of short-term spectral compression deserves separate investigation. Combining the major aspects of the research, some important design guidelines for the construction of an identification model when based on the use of compressed speech are put forward.