Longitudinal Student Evaluations of a Postgraduate Unit Using Importance-Performance Analysis

The Australian tourism tertiary education sector operates in a competitive and dynamic environment, which necessitates a market orientation to be successful. Academic staff and management in the sector must regularly assess the perceptions of prospective and current students, and monitor the satisfaction levels of current students. This study is concerned with the setting and monitoring of satisfaction levels of current students, reporting the results of three longitudinal investigations of student satisfaction in a postgraduate unit. The study also addresses a limitation of a university’s generic teaching evaluation instrument. Importance-performance analysis (IPA) has been recommended as a simple but effective tool for overcoming the deficiencies of many student evaluation studies, which have generally measured only attribute importance or importance at the end of a semester. IPA was used to compare student expectations of the unit at the beginning of semester with their perceptions of performance ten weeks later. The first stage documented key benchmarks for which amendments to the unit based on student feedback could be evaluated during subsequent teaching periods.

Performance analysis of unified enterprise application security framework

Unified Enterprise application security is a new emerging approach for providing protection against application level attacks. Conventional application security approach that consists of embedding security into each critical application leads towards scattered security mechanism that is not only difficult to manage but also creates security loopholes. According to the CSIIFBI computer crime survey report, almost 80% of the security breaches come from authorized users. In this paper, we have worked on the concept of unified security model, which manages all security aspect from a single security window. The basic idea is to keep business functionality separate from security components of the application. Our main focus was on the designing of frame work for unified layer which supports single point of policy control, centralize logging mechanism, granular, context aware access control, and independent from any underlying authentication technology and authorization policy.

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.

Performance analysis of IEEE 802.11 DCF based WNCS networks

Wireless network technologies, such as IEEE 802.11 based wireless local area networks (WLANs), have been adopted in wireless networked control systems (WNCS) for real-time applications. Distributed real-time control requires satisfaction of (soft) real-time performance from the underlying networks for delivery of real-time traffic. However, IEEE 802.11 networks are not designed for WNCS applications. They neither inherently provide quality-of-service (QoS) support, nor explicitly consider the characteristics of the real-time traffic on networked control systems (NCS), i.e., periodic round-trip traffic. Therefore, the adoption of 802.11 networks in real-time WNCSs causes challenging problems for network design and performance analysis. Theoretical methodologies are yet to be developed for computing the best achievable WNCS network performance under the constraints of real-time control requirements. Focusing on IEEE 802.11 distributed coordination function (DCF) based WNCSs, this paper analyses several important NCS network performance indices, such as throughput capacity, round trip time and packet loss ratio under the periodic round trip traffic pattern, a unique feature of typical NCSs. Considering periodic round trip traffic, an analytical model based on Markov chain theory is developed for deriving these performance indices under a critical real-time traffic condition, at which the real-time performance constraints are marginally satisfied. Case studies are also carried out to validate the theoretical development.

Longitudinal evaluations of student satisfaction with a postgraduate unit using importance-performance analysis

The Australian tourism tertiary education sector operates in a competitive and dynamic environment, which necessitates a market orientation to be successful. Academic staff and management in the sector must regularly assess the perceptions of prospective and current students and monitor the satisfaction levels of current students. This study is concerned with the setting and monitoring of satisfaction levels of current students, reporting the results of three longitudinal investigations of student satisfaction in a postgraduate unit. The study also addresses a limitation of a university’s generic teaching evaluation instrument. Importance-Performance Analysis (IPA) has been recommended as a simple but effective tool for overcoming the deficiencies of many student evaluation studies, which have generally measured only attribute performance at the end of a semester. IPA was used to compare student expectations of the unit at the beginning of a semester with their perceptions of performance 10 weeks later. The first stage documented key benchmarks for which amendments to the unit based on student feedback could be evaluated during subsequent teaching periods.

Performance analysis of hybrid liquid desiccant solar cooling system

This thesis investigates the coefficient of performance (COP) of a hybrid liquid desiccant solar cooling system. This hybrid cooling system includes three sections: 1) conventional air-conditioning section; 2) liquid desiccant dehumidification section and 3) air mixture section. The air handling unit (AHU) with mixture variable air volume design is included in the hybrid cooling system to control humidity. In the combined system, the air is first dehumidified in the dehumidifier and then mixed with ambient air by AHU before entering the evaporator. Experiments using lithium chloride as the liquid desiccant have been carried out for the performance evaluation of the dehumidifier and regenerator. Based on the air mixture (AHU) design, the electrical coefficient of performance (ECOP), thermal coefficient of performance (TCOP) and whole system coefficient of performance (COPsys) models used in the hybrid liquid desiccant solar cooing system were developed to evaluate this system performance. These mathematical models can be used to describe the coefficient of performance trend under different ambient conditions, while also providing a convenient comparison with conventional air conditioning systems. These models provide good explanations about the relationship between the performance predictions of models and ambient air parameters. The simulation results have revealed the coefficient of performance in hybrid liquid desiccant solar cooling systems substantially depends on ambient air and dehumidifier parameters. Also, the liquid desiccant experiments prove that the latent component of the total cooling load requirements can be easily fulfilled by using the liquid desiccant dehumidifier. While cooling requirements can be met, the liquid desiccant system is however still subject to the hysteresis problems.

Performance analysis of business process models with advanced constructs

The importance of actively managing and analysing business processes is acknowledged more than ever in organisations nowadays. Business processes form an essential part of an organisation and their application areas are manifold. Most organisations keep records of various activities that have been carried out for auditing purposes, but they are rarely used for analysis purposes. This paper describes the design and implementation of a process analysis tool that replays, analyses and visualises a variety of performance metrics using a process definition and its corresponding execution logs. The replayer uses a YAWL process model example to demonstrate its capacity to support advanced language constructs.

Perceptions of visitor relationship marketing opportunities by destination marketers : an importance-performance analysis

Customer relationship marketing (CRM) initiatives are increasingly being adopted by businesses in the attempt to enhance brand loyalty and stimulate repeat purchases. The purpose of this study was to examine the extent to which destination marketing organisations (DMOs) around the world have developed a visitor relationship marketing (VRM) orientation. The proposition underpinning the study is that maintaining meaningful dialogue with previous visitors in some markets would represent a more efficient use of resources than above the line advertising to attract new visitors. Importance-performance analysis was utilised to measure destination marketers’ perceptions of the efficacy of CRM initiatives, and then rate their own organisation’s performance across the same range of initiatives. A key finding was that mean importance was higher than perceived performance for every item. While the small sample limits generalisability, in general there are appears to be a lack of strategic intent by DMOs to invest in VRM.

Robust performance analysis on YAWL process models with advanced constructs

The importance of actively managing and analyzing business processes is acknowledged more than ever in organizations nowadays. Business processes form an essential part of an organization and their ap-plication areas are manifold. Most organizations keep records of various activities that have been carried out for auditing purposes, but they are rarely used for analysis purposes. This paper describes the design and implementation of a process analysis tool that replays, analyzes and visualizes a variety of performance metrics using a process definition and its execution logs. Performing performance analysis on existing and planned process models offers a great way for organizations to detect bottlenecks within their processes and allow them to make more effective process improvement decisions. Our technique is applied to processes modeled in the YAWL language. Execution logs of process instances are compared against the corresponding YAWL process model and replayed in a robust manner, taking into account any noise in the logs. Finally, performance characteristics, obtained from replaying the log in the model, are projected onto the model.