Study on the method of freeway incident detection using wireless positioning terminal

Improving the performance of a incident detection system was essential to minimize the effect of incidents. A new method of incident detection was brought forward in this paper based on an in-car terminal which consisted of GPS module, GSM module and control module as well as some optional parts such as airbag sensors, mobile phone positioning system (MPPS) module, etc. When a driver or vehicle discovered the freeway incident and initiated an alarm report the incident location information located by GPS, MPPS or both would be automatically send to a transport management center (TMC), then the TMC would confirm the accident with a closed-circuit television (CCTV) or other approaches. In this method, detection rate (DR), time to detect (TTD) and false alarm rate (FAR) were more important performance targets. Finally, some feasible means such as management mode, education mode and suitable accident confirming approaches had been put forward to improve these targets.

Security metrics for object-oriented class designs

Measuring quality attributes of object-oriented designs (e.g. maintainability and performance) has been covered by a number of studies. However, these studies have not considered security as much as other quality attributes. Also, most security studies focus at the level of individual program statements. This approach makes it hard and expensive to discover and fix vulnerabilities caused by design errors. In this work, we focus on the security design of an object oriented application and define a number of security metrics. These metrics allow designers to discover and fix security vulnerabilities at an early stage, and help compare the security of various alternative designs. In particular, we propose seven security metrics to measure Data Encapsulation (accessibility) and Cohesion (interactions) of a given object-oriented class from the point of view of potential information flow.

When the object speaks, a postcolonial encounter : anthropological, representations and Aboriginal's women's self-presentations

As an Aboriginal woman currently reviewing feminist literature in Australia, I have found that representations of Aboriginal women's gender have been generated predominantly by women anthropologists. Australian feminists utilise this literature in their writing and teaching and accept its truths without question; the most often quoted ethnographic text is Diane Bell's Daughters of the Dreaming (1983a).1 Feminists' lack of critical engagement with this literature implies that they are content to accept women anthropologists' representations because Aboriginal women are not central to their constructions of feminism.2 Instead the Aboriginal woman is positioned on the margins, a symbol of difference; a reminder that it is feminists who are the bearers of true womanhood.

Early detection : a strategy to reduce risk and severity?

Despite changes in surgical techniques, radiotherapy targeting and the apparent earlier detection of cancers, secondary lymphoedema is still a significant problem for about 20–30% of those who receive treatment for cancer, although the incidence and prevalence does seem to be falling. The figures above generally relate to detection of an enlarged limb or other area, but it seems that about 60% of all patients also suffer other problems with how the limb feels, what can or cannot be done with it and a range of social or psychological issues. Often these ‘subjective’ changes occur before the objective ones, such as a change in arm volume or circumference. For most of those treated for cancer lymphoedema does not develop immediately, and, while about 60–70% develop it in the first few years, some do not develop lymphoedema for up to 15 or 20 years. Those who will develop clinically manifest lymphoedema in the future are, for some time, in a latent or hidden phase of lymphoedema. There also seems to be some risk factors which are indicators for a higher likelihood of lymphoedema post treatment, including oedema at the surgical site, arm dominance, age, skin conditions, and body mass index (BMI).

Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filter is developed to remove background noise and generate an edge map prior to the Hough transform being employed to detect straight lines. An improved Hough transform is used by performing knowledge-based line clustering in Hough space to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.

Classification of Airborne LIDAR Intensity Data Using Statistical Analysis and Hough Transform with Application to Power Line Corridors

Light Detection and Ranging (LIDAR) has great potential to assist vegetation management in power line corridors by providing more accurate geometric information of the power line assets and vegetation along the corridors. However, the development of algorithms for the automatic processing of LIDAR point cloud data, in particular for feature extraction and classification of raw point cloud data, is in still in its infancy. In this paper, we take advantage of LIDAR intensity and try to classify ground and non-ground points by statistically analyzing the skewness and kurtosis of the intensity data. Moreover, the Hough transform is employed to detected power lines from the filtered object points. The experimental results show the effectiveness of our methods and indicate that better results were obtained by using LIDAR intensity data than elevation data.

Human brain regions required for the dividing and switching of attention between two features of a single object

This combined PET and ERP study was designed to identify the brain regions activated in switching and divided attention between different features of a single object using matched sensory stimuli and motor response. The ERP data have previously been reported in this journal [64]. We now present the corresponding PET data. We identified partially overlapping neural networks with paradigms requiring the switching or dividing of attention between the elements of complex visual stimuli. Regions of activation were found in the prefrontal and temporal cortices and cerebellum. Each task resulted in different prefrontal cortical regions of activation lending support to the functional subspecialisation of the prefrontal and temporal cortices being based on the cognitive operations required rather than the stimuli themselves.

A Vision Based Target Detection System for Docking of an Autonomous Underwater Vehicle

This paper describes the development and preliminary experimental evaluation of a visionbased docking system to allow an Autonomous Underwater Vehicle (AUV) to identify and attach itself to a set of uniquely identifiable targets. These targets, docking poles, are detected using Haar rectangular features and rotation of integral images. A non-holonomic controller allows the Starbug AUV to orient itself with respect to the target whilst maintaining visual contact during the manoeuvre. Experimental results show the proposed vision system is capable of robustly identifying a pair of docking poles simultaneously in a variety of orientations and lighting conditions. Experiments in an outdoor pool show that this vision system enables the AUV to dock autonomously from a distance of up to 4m with relatively low visibility.

A nested real-time PCR assay has an increased sensitivity suitable for detection of viruses in aerosol studies

Aims: Influenza is commonly spread by infectious aerosols; however, detection of viruses in aerosols is not sensitive enough to confirm the characteristics of virus aerosols. The aim of this study was to develop an assay for respiratory viruses sufficiently sensitive to be used in epidemiological studies. Method: A two-step, nested real-time PCR assay was developed for MS2 bacteriophage, and for influenza A and B, parainfluenza 1 and human respiratory syncytial virus. Outer primer pairs were designed to nest each existing real-time PCR assay. The sensitivities of the nested real-time PCR assays were compared to those of existing real-time PCR assays. Both assays were applied in an aerosol study to compare their detection limits in air samples. Conclusions: The nested real-time PCR assays were found to be several logs more sensitive than the real-time PCR assays, with lower levels of virus detected at lower Ct values. The nested real-time PCR assay successfully detected MS2 in air samples, whereas the real-time assay did not. Significance and Impact of the Study: The sensitive assays for respiratory viruses will permit further research using air samples from naturally generated virus aerosols. This will inform current knowledge regarding the risks associated with the spread of viruses through aerosol transmission.

Early objective detection of lymphoedema

The aetiology of secondary lymphoedema seems to be multifactorial, with acquired abnormalities as well as pre-existing conditions being contributory factors. Many characteristics bear inconsistent relationships to lymphoedema risk, and the few that are consistently associated with an increased risk of developing the condition, do not alone distinguish the at-risk population. Further, our current prevention and management recommendations are not backed by strong evidence. Consequently, there remains much to be learned about who gets it, how can it be prevented and how can we best treat it. Nonetheless, it is clear that lymphoedema is associated with adverse side effects, which have a profound impact on daily life, and that preliminary evidence suggests that early detection may lead to more effective treatment and lack of treatment may lead to progression. These represent important reasons as to why lymphoedema deserves clinical attention. However, several pragmatic issues must be considered when discussing whether a routine objective measure of lymphoedema could be integrated among the standard clinical care of those undertaking treatment for cancers known to be associated with the development of lymphoedema.

Intrusion detection techniques in wireless local area networks

This research investigates wireless intrusion detection techniques for detecting attacks on IEEE 802.11i Robust Secure Networks (RSNs). Despite using a variety of comprehensive preventative security measures, the RSNs remain vulnerable to a number of attacks. Failure of preventative measures to address all RSN vulnerabilities dictates the need for a comprehensive monitoring capability to detect all attacks on RSNs and also to proactively address potential security vulnerabilities by detecting security policy violations in the WLAN. This research proposes novel wireless intrusion detection techniques to address these monitoring requirements and also studies correlation of the generated alarms across wireless intrusion detection system (WIDS) sensors and the detection techniques themselves for greater reliability and robustness. The specific outcomes of this research are: A comprehensive review of the outstanding vulnerabilities and attacks in IEEE 802.11i RSNs. A comprehensive review of the wireless intrusion detection techniques currently available for detecting attacks on RSNs. Identification of the drawbacks and limitations of the currently available wireless intrusion detection techniques in detecting attacks on RSNs. Development of three novel wireless intrusion detection techniques for detecting RSN attacks and security policy violations in RSNs. Development of algorithms for each novel intrusion detection technique to correlate alarms across distributed sensors of a WIDS. Development of an algorithm for automatic attack scenario detection using cross detection technique correlation. Development of an algorithm to automatically assign priority to the detected attack scenario using cross detection technique correlation.

The effect of language models on phonetic decoding for spoken term detection

Spoken term detection (STD) popularly involves performing word or sub-word level speech recognition and indexing the result. This work challenges the assumption that improved speech recognition accuracy implies better indexing for STD. Using an index derived from phone lattices, this paper examines the effect of language model selection on the relationship between phone recognition accuracy and STD accuracy. Results suggest that language models usually improve phone recognition accuracy but their inclusion does not always translate to improved STD accuracy. The findings suggest that using phone recognition accuracy to measure the quality of an STD index can be problematic, and highlight the need for an alternative that is more closely aligned with the goals of the specific detection task.

Spoken term detection using fast phonetic decoding

While spoken term detection (STD) systems based on word indices provide good accuracy, there are several practical applications where it is infeasible or too costly to employ an LVCSR engine. An STD system is presented, which is designed to incorporate a fast phonetic decoding front-end and be robust to decoding errors whilst still allowing for rapid search speeds. This goal is achieved through mono-phone open-loop decoding coupled with fast hierarchical phone lattice search. Results demonstrate that an STD system that is designed with the constraint of a fast and simple phonetic decoding front-end requires a compromise to be made between search speed and search accuracy.

Experimenting with an intrusion detection system for encrypted networks

Network-based Intrusion Detection Systems (NIDSs) analyse network traffic to detect instances of malicious activity. Typically, this is only possible when the network traffic is accessible for analysis. With the growing use of Virtual Private Networks (VPNs) that encrypt network traffic, the NIDS can no longer access this crucial audit data. In this paper, we present an implementation and evaluation of our approach proposed in Goh et al. (2009). It is based on Shamir's secret-sharing scheme and allows a NIDS to function normally in a VPN without any modifications and without compromising the confidentiality afforded by the VPN.

Enhancing digital road map with lane details extracted from large-scale stereo aerial imagery using object-oriented image analysis

Precise, up-to-date and increasingly detailed road maps are crucial for various advanced road applications, such as lane-level vehicle navigation, and advanced driver assistant systems. With the very high resolution (VHR) imagery from digital airborne sources, it will greatly facilitate the data acquisition, data collection and updates if the road details can be automatically extracted from the aerial images. In this paper, we proposed an effective approach to detect road lane information from aerial images with employment of the object-oriented image analysis method. Our proposed algorithm starts with constructing the DSM and true orthophotos from the stereo images. The road lane details are detected using an object-oriented rule based image classification approach. Due to the affection of other objects with similar spectral and geometrical attributes, the extracted road lanes are filtered with the road surface obtained by a progressive two-class decision classifier. The generated road network is evaluated using the datasets provided by Queensland department of Main Roads. The evaluation shows completeness values that range between 76% and 98% and correctness values that range between 82% and 97%.