Description:
Although remotely sensed data have been widely explored for forest applications, passive remote sensing techniques are limited in their ability to capture forest structural complexity, particularly in uneven-aged, mixed species forests with multiple canopy layers. Generally, these techniques are only able to provide information on horizontal (two-dimensional) forest extent. The vertical forest structure (or the interior of the canopy and understorey vegetation) cannot be mapped using these passive remote sensing techniques. Fortunately, it has been shown that active remote sensing techniques via airborne LiDAR (light detection and ranging) with capability of canopy penetration yields such high density sampling that detailed description of the forest structure in three-dimensions can be obtained. Accordingly, much interest is attached to exploring the application of this approach for identifying the distribution of designated vegetation communities. However, the suitability of LiDAR data for the classification of forests with complex structures, particularly for cool temperate rainforest and neighbouring uneven-aged mixed forests in a severely disturbed landscape has hitherto remained untested. This study applied airborne LiDAR data for the classification of cool temperate rainforest dominated by Myrtle Beech (Nothofagus cunninghamii) and adjacent forests including naturally regenerated Mountain Ash (Eucalyptus regnans), mixed forest consisting of overstorey Mountain Ash and understorey Myrtle Beech, Silver Wattle (Acacia dealbata), and hardwood plantation dominated by Shining Gum (Eucalyptus nitens) in the Strzelecki Ranges, Victoria, Australia. LiDAR data were extracted within each of the forest plots. Nonground laser returns were used to generate forest height profiles for the analysis of the spatial distribution of vertical forest structure for the plots dominated by different forest types. The k-means clustering algorithm was performed on each of the plots to stratify the vertical forest structure into three layers, representing the overstorey, mid-storey and lower storey of the plot-level forests. Variables were then calculated from the LiDAR data based on the three-layered structure for each plot. The statistical analyses, which included oneway ANOVA (analysis of variance) and the post hoc tests, identified effective variables for forest type classifications. Linear discriminant analysis with cross-validation was carried out to classify the forest types and assess the classification accuracy using error matrixes. This study demonstrated the applicability of airborne LiDAR for the classification of the Australian cool temperate rainforest and adjacent forests in the study area.
Description:
MODSIM 2011 - 19th International Congress on Modelling and Simulation - Sustaining Our Future: Understanding and Living with Uncertainty
Description:
Terrorism has progressed to a global phenomenon as a terrorist attack has an immediate effect on society not only in the targeted area, but also across the rest of the world. Acts of terrorism are extremely difficult to predict or provide early warning in most cases. In consideration of Australia, which is to a certain extent insulated from the rest of the world by virtue of the sea barrier, there is a history of terrorist incidents reported back to the 1970s. Since the attack on New York in September 2001, the level of terrorism alert to Australia has increased significantly with a current 'Medium' national level of threat. Critical Infrastructure (CI), which is considered essential for contemporary social human existence, has been impacted by multiple and variable external threats in modern times. The destruction at Chernobyl in 1986 and more recent events such as the terrorist incidents at Madrid in 2004, London in 2005, Moscow in 2011 and the tsunami in Japan in 2011 indicate the vulnerability of this infrastructure. Such events translate to threats from both natural disasters referred to as all hazard origin and human interventions such as terrorism. Subsequently, some private and government organisations of CI now regularly rehearse and simulate models of both terrorist incidents and all hazard events as a proactive protection strategy and business continuity process. These models are implemented in a form of scripted Crisis Simulation Exercises (CSE) which simulate a crisis within an organisation in order to strengthen an organisation's ability to manage crisis situations. CI organisations which adopt these strategies are able to mitigate impact of these crises and therefore, are considered to reflect a more resilient organisation to the effects of external impact. CSEs test plans, procedures, equipment and personnel to industry standards required. Within the spectrum of counter-terrorism in particular, the CSEs are becoming more sophisticated and reflective of reality with incorporation of live actions to ensure credibility and reality. The simulated scenario may include a variety of attack methodologies such as biological, chemical, cyber and conventional bombs/blasts and bullets to maintain exercise standards with continuously developing technology of terrorist attacks. This paper defines the topic of terrorism with the profile of terrorists, and examines the terrorism concept and environment both in Australia and internationally including future considerations. It also provides an overview of the simulated framework for mitigation of crisis associated with CI protection with an Australian perspective, suitable for CI protection worldwide. Additionally, this paper examines the concept of terrorism simulation, illustrating a strong case for future simulation progression with some innovative ideas and futuristic predictions as to where terrorist simulations may advance to across the future.
Description:
As with any contract of employment, the mutual expectations of the employer and the apprentice/trainee are very important. Apprenticeships and traineeships have greater expectations than other employment contracts of employment because of the training component of the contract. This paper reports on some of the findings of a major NCVER-funded national project examining mutual expectations in apprenticeships and traineeships through the concept of the psychological contract. The paper focuses on the differences between employers and apprentices/trainees, in the expectations each party has of the other and in the extent to which the expectations are perceived to have been met.
Description:
Existing contention based nonpersistent medium access control protocols in Wireless Sensor Network (WSN) do not perform well in high contention. Their performances are affected by occurrence of collision due to uniform probability distribution in choosing Time Slot (TS) during backoff period. To address this issue nonuniform probability distribution was proposed. However success rate still drops for higher number of contenders. In this paper we propose CSMA/s (Collision Sense Multiple Access /per Slot based), a new approach in nonuniform contender-aware probability distribution for choosing TS in the backoff period. Rather than taking a premeditated fixed value for contender population size, our proposed scheme embeds neighborhood population size into its bedrock to automatically converge to the actual number of contenders which enables the contender to adaptively choose TS in the backoff period for reducing collision. This method produces better success rate and lower latency for even very high number of contenders.
Description:
Color image fusion of visible and infra-red imagery can play an important role in multi-sensor night vision systems that are an integral part of modern warfare. Image fusion minimizes the amount of required bandwidth by transmitting the fused image rather than multiple sensor images. Color image fusion can be achieved by combining inputs from original colored sensors or by employing pseudo colorization and color transfer to grayscale images. Various quality measures have been proposed for multi-sensor grayscale image fusion techniques; but no appropriate quality measure has been devised for the quality evaluation of multi-sensor color image fusion. In this paper, we propose a novel color image fusion quality measure, Color Fusion Objective Index (CFOI) based on colorfulness, gradient similarity and mutual information techniques. Experimental results show the effectiveness of CFOI to evaluate the color and salient feature extraction introduced by color fusion techniques into the final fused imagery as well as its consistency with subjective evaluation.
Description:
The detection of Distributed Denial of Service (DDos) attacks is very important for maintaining the security of networks and the Internet. This paper introduces a novel iterative consensus process based on Hybrid Bipartite Graph Formulation (HGBF) consensus function for DDos attacks detection. First, the features are extracted during feature extraction process based on the analysis of network traffic. Second, several clustering algorithms are applied in combination with the silhouette index to obtain a collection of independent initial clusterings. Third, the HGBF consensus function and silhouette index are used to find an appropriate consensus clustering of the initial clusterings. Fourth, this new consensus clustering is added to the pool of initial clusterings replacing another clustering with the worst Silhouette index. Fifth, the process continues iteratively until the Silhouette index of the resulting consensus clusterings stabilizes. This iterative consensus clustering process can improve the quality of the clusters. The experimental results demonstrate that our iterative consensus process is effective and can be used in practice for detecting the separate phased of DDos attacks.
Description:
The detection of Distributed Denial of Service (DDos) attacks is very important for maintaining the security of networks and the Internet. This paper introduces a novel iterative consensus process based on Hybrid Bipartite Graph Formulation (HGBF) consensus function for DDos attacks detection. First, the features are extracted during feature extraction process based on the analysis of network traffic. Second, several clustering algorithms are applied in combination with the silhouette index to obtain a collection of independent initial clusterings. Third, the HGBF consensus function and silhouette index are used to find an appropriate consensus clustering of the initial clusterings. Fourth, this new consensus clustering is added to the pool of initial clusterings replacing another clustering with the worst Silhouette index. Fifth, the process continues iteratively until the Silhouette index of the resulting consensus clusterings stabilizes. This iterative consensus clustering process can improve the quality of the clusters. The experimental results demonstrate that our iterative consensus process is effective and can be used in practice for detecting the separate phased of DDos attacks.
Description:
Control dependencies are one of the major limitations to increase the performance of pipelined processors. This paper deals with eliminating penalties in pipelined processor. We present our discussion in the light of MIPS pipelined processor architecture. Here we present an improved pipelined processor architecture eliminating branch and jump penalty. In the proposed architecture CPI for branch and jump instruction is less than that of MIPS architecture. We also have shown the design of the required cache memory cell for the improved architecture.
Description:
Second International Conference on Computer Engineering and Applications (ICCEA), 2010
Description:
This paper examines the value of real-time traffic information gathered through Geographic Information Systems for achieving an optimal vehicle routing within a dynamically stochastic transportation network. We present a systematic approach in determining the dynamically varying parameters and implementation attributes that were used for the development of a Web-based transportation routing application integrated with real-time GIS services. We propose and implement an optimal routing algorithm by modifying Dijkstra’s algorithm in order to incorporate stochastically changing traffic flows. We describe the significant features of our Web application in making use of the real-time dynamic traffic flow information from GIS services towards achieving total costs savings and vehicle usage reduction. These features help users and vehicle drivers in improving their service levels and productivity as the Web application enables them to interactively find the optimal path and in identifying destinations effectively.
Description:
In this paper, we present an automated color transfer based video fusion method to attain real-time color night vision capability for night-time video surveillance. We utilize simple RGB Color transfer technique to fused pseudo colored video frames without conversion to any uncorrelated color space. We investigated that final color fusion results greatly depend on the selection of target color Image. Therefore, rather than using any arbitrary target color image based on mere general visual anticipation, we have automated target color image selection using structural similarity and color saturation. We further apply color enhancement to improve final appearance of color fused images. Subjective and objective quality evaluations greatly indicate the effectiveness of our color video fusion method for nighttime video surveillance applications.
Description:
The diagnosis of many sleep disorders is a labour intensive task that involves the specialised interpretation of numerous signals including brain wave, breath and heart rate captured in overnight polysomnogram sessions. The automation of diagnoses is challenging for data mining algorithms because the data sets are extremely large and noisy, the signals are complex and specialist's analyses vary. This work reports on the adaptation of approaches from four fields; neural networks, mathematical optimisation, financial forecasting and frequency domain analysis to the problem of automatically determing a patient's stage of sleep. Results, though preliminary, are promising and indicate that combined approaches may prove more fruitful than the reliance on a approach.
Description:
The diagnosis of many sleep disorders is a labour intensive task that involves the specialised interpretation of numerous signals including brain wave, breath and heart rate captured in overnight polysomnogram sessions. The automation of diagnoses is challenging for data mining algorithms because the data sets are extremely large and noisy, the signals are complex and specialist's analyses vary. This work reports on the adaptation of approaches from four fields; neural networks, mathematical optimisation, financial forecasting and frequency domain analysis to the problem of automatically determing a patient's stage of sleep. Results, though preliminary, are promising and indicate that combined approaches may prove more fruitful than the reliance on a approach.
Description:
Nowadays, body area wireless sensor networks (BAWSNs) applications are increasingly being used in in-house health monitoring systems. These applications have stringent timing requirements and often run continuously without interruptions. Hence, it becomes imperative to determine the operational continuity of the BAWSN applications by measuring their availability. The BAWSN applications rely on the collection of data within a critical time from all of the source sensor nodes rather than the data from an individual source. Subsequently, the measure of availability for a BAWSN application should be based on the time and the data delivery from all the sensor nodes. Taking into account these specific characteristics and the constraints of the BAWSN, we develop a model to measure the availability of a BAWSN application based on the unavailable time. The proposed model is evaluated through a series of experiments conducted in our existing Assistive Care Loop Framework (ACLF). Furthermore, we also develop an analogous theoretical model to evaluate the availability of a BAWSN application