Applying ISOMAP to the learning of hyperspectral image

In this paper, we present the application of a non-linear dimensionality reduction technique for the learning and probabilistic classification of hyperspectral image. Hyperspectral image spectroscopy is an emerging technique for geological investigations from airborne or orbital sensors. It gives much greater information content per pixel on the image than a normal colour image. This should greatly help with the autonomous identification of natural and manmade objects in unfamiliar terrains for robotic vehicles. However, the large information content of such data makes interpretation of hyperspectral images time-consuming and userintensive. We propose the use of Isomap, a non-linear manifold learning technique combined with Expectation Maximisation in graphical probabilistic models for learning and classification. Isomap is used to find the underlying manifold of the training data. This low dimensional representation of the hyperspectral data facilitates the learning of a Gaussian Mixture Model representation, whose joint probability distributions can be calculated offline. The learnt model is then applied to the hyperspectral image at runtime and data classification can be performed.

Secure data aggregation in wireless sensor networks

A Wireless Sensor Network (WSN) is a set of sensors that are integrated with a physical environment. These sensors are small in size, and capable of sensing physical phenomena and processing them. They communicate in a multihop manner, due to a short radio range, to form an Ad Hoc network capable of reporting network activities to a data collection sink. Recent advances in WSNs have led to several new promising applications, including habitat monitoring, military target tracking, natural disaster relief, and health monitoring. The current version of sensor node, such as MICA2, uses a 16 bit, 8 MHz Texas Instruments MSP430 micro-controller with only 10 KB RAM, 128 KB program space, 512 KB external ash memory to store measurement data, and is powered by two AA batteries. Due to these unique specifications and a lack of tamper-resistant hardware, devising security protocols for WSNs is complex. Previous studies show that data transmission consumes much more energy than computation. Data aggregation can greatly help to reduce this consumption by eliminating redundant data. However, aggregators are under the threat of various types of attacks. Among them, node compromise is usually considered as one of the most challenging for the security of WSNs. In a node compromise attack, an adversary physically tampers with a node in order to extract the cryptographic secrets. This attack can be very harmful depending on the security architecture of the network. For example, when an aggregator node is compromised, it is easy for the adversary to change the aggregation result and inject false data into the WSN. The contributions of this thesis to the area of secure data aggregation are manifold. We firstly define the security for data aggregation in WSNs. In contrast with existing secure data aggregation definitions, the proposed definition covers the unique characteristics that WSNs have. Secondly, we analyze the relationship between security services and adversarial models considered in existing secure data aggregation in order to provide a general framework of required security services. Thirdly, we analyze existing cryptographic-based and reputationbased secure data aggregation schemes. This analysis covers security services provided by these schemes and their robustness against attacks. Fourthly, we propose a robust reputationbased secure data aggregation scheme for WSNs. This scheme minimizes the use of heavy cryptographic mechanisms. The security advantages provided by this scheme are realized by integrating aggregation functionalities with: (i) a reputation system, (ii) an estimation theory, and (iii) a change detection mechanism. We have shown that this addition helps defend against most of the security attacks discussed in this thesis, including the On-Off attack. Finally, we propose a secure key management scheme in order to distribute essential pairwise and group keys among the sensor nodes. The design idea of the proposed scheme is the combination between Lamport's reverse hash chain as well as the usual hash chain to provide both past and future key secrecy. The proposal avoids the delivery of the whole value of a new group key for group key update; instead only the half of the value is transmitted from the network manager to the sensor nodes. This way, the compromise of a pairwise key alone does not lead to the compromise of the group key. The new pairwise key in our scheme is determined by Diffie-Hellman based key agreement.

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.

Using Fiber Bragg Grating (FBG) sensors to measure vertical displacements of bridges : a preliminary study

In many bridges, vertical displacements are the most relevant parameter for monitoring in the both short and long term. However, it is difficult to measure vertical displacements of bridges and yet they are among the most important indicators of structural behaviour. Therefore, it prompts a need to develop a simple, inexpensive and yet more practical method to measure vertical displacements of bridges. With the development of fiber-optics technologies, fiber Bragg grating (FBG) sensors have been widely used in structural health monitoring. The advantages of these sensors over the conventional sensors include multiplexing capabilities, high sample rate, small size and electro magnetic interference (EMI) immunity. In this paper, methods of vertical displacement measurements of bridges are first reviewed. Then, FBG technology is briefly introduced including principle, sensing system, characteristics and different types of FBG sensors. Finally, the methodology of vertical displacement measurements using FBG sensors is presented and a trial test is described. It is concluded that using FBG sensors is feasible to measure vertical displacements of bridges. This method can be used to understand global behaviour of bridge‘s span and can further develop for structural health monitoring techniques such as damage detection.

Multiview point cloud kernels for semisupervised learning [Lecture Notes]

In semisupervised learning (SSL), a predictive model is learn from a collection of labeled data and a typically much larger collection of unlabeled data. These paper presented a framework called multi-view point cloud regularization (MVPCR), which unifies and generalizes several semisupervised kernel methods that are based on data-dependent regularization in reproducing kernel Hilbert spaces (RKHSs). Special cases of MVPCR include coregularized least squares (CoRLS), manifold regularization (MR), and graph-based SSL. An accompanying theorem shows how to reduce any MVPCR problem to standard supervised learning with a new multi-view kernel.

Intrinsically photosensitive melanopsin retinal ganglion cell contributions to the post-illumination pupil response and circadian rhythm

Intrinsically photosensitive retinal ganglion cells (ipRGCs) in the eye transmit the environmental light level, projecting to the suprachiasmatic nucleus (SCN) (Berson, Dunn & Takao, 2002; Hattar, Liao, Takao, Berson & Yau, 2002), the location of the circadian biological clock, and the olivary pretectal nucleus (OPN) of the pretectum, the start of the pupil reflex pathway (Hattar, Liao, Takao, Berson & Yau, 2002; Dacey, Liao, Peterson, Robinson, Smith, Pokorny, Yau & Gamlin, 2005). The SCN synchronizes the circadian rhythm, a cycle of biological processes coordinated to the solar day, and drives the sleep/wake cycle by controlling the release of melatonin from the pineal gland (Claustrat, Brun & Chazot, 2005). Encoded photic input from ipRGCs to the OPN also contributes to the pupil light reflex (PLR), the constriction and recovery of the pupil in response to light. IpRGCs control the post-illumination component of the PLR, the partial pupil constriction maintained for > 30 sec after a stimulus offset (Gamlin, McDougal, Pokorny, Smith, Yau & Dacey, 2007; Kankipati, Girkin & Gamlin, 2010; Markwell, Feigl & Zele, 2010). It is unknown if intrinsic ipRGC and cone-mediated inputs to ipRGCs show circadian variation in their photon-counting activity under constant illumination. If ipRGCs demonstrate circadian variation of the pupil response under constant illumination in vivo, when in vitro ipRGC activity does not (Weng, Wong & Berson, 2009), this would support central control of the ipRGC circadian activity. A preliminary experiment was conducted to determine the spectral sensitivity of the ipRGC post-illumination pupil response under the experimental conditions, confirming the successful isolation of the ipRGC response (Gamlin, et al., 2007) for the circadian experiment. In this main experiment, we demonstrate that ipRGC photon-counting activity has a circadian rhythm under constant experimental conditions, while direct rod and cone contributions to the PLR do not. Intrinsic ipRGC contributions to the post-illumination pupil response decreased 2:46 h prior to melatonin onset for our group model, with the peak ipRGC attenuation occurring 1:25 h after melatonin onset. Our results suggest a centrally controlled evening decrease in ipRGC activity, independent of environmental light, which is temporally synchronized (demonstrates a temporal phase-advanced relationship) to the SCN mediated release of melatonin. In the future the ipRGC post-illumination pupil response could be developed as a fast, non-invasive measure of circadian rhythm. This study establishes a basis for future investigation of cortical feedback mechanisms that modulate ipRGC activity.

Using Fiber Bragg Grating (FBG) sensors for vertical displacement measurement of bridges

In many bridges, vertical displacements are one of the most relevant parameters for structural health monitoring in both the short and long terms. Bridge managers around the globe are always looking for a simple way to measure vertical displacements of bridges. However, it is difficult to carry out such measurements. On the other hand, in recent years, with the advancement of fiber-optic technologies, fiber Bragg grating (FBG) sensors are more commonly used in structural health monitoring due to their outstanding advantages including multiplexing capability, immunity of electromagnetic interference as well as high resolution and accuracy. For these reasons, using FBG sensors is proposed to develop a simple, inexpensive and practical method to measure vertical displacements of bridges. A curvature approach for vertical displacement measurement using curvature measurements is proposed. In addition, with the successful development of a FBG tilt sensors, an inclination approach is also proposed using inclination measurements. A series of simulation tests of a full-scale bridge was conducted. It shows that both the approaches can be implemented to determine vertical displacements for bridges with various support conditions, varying stiffness (EI) along the spans and without any prior known loading. These approaches can thus measure vertical displacements for most of slab-on-girder and box-girder bridges. Moreover, with the advantages of FBG sensors, they can be implemented to monitor bridge behavior remotely and in real time. Further recommendations of these approaches for developments will also be discussed at the end of the paper.

Gaseous and particle emissions from an ethanol fumigated compression ignition engine

A 4-cylinder Ford 2701C test engine was used in this study to explore the impact of ethanol fumigation on gaseous and particle emission concentrations. The fumigation technique delivered vaporised ethanol into the intake manifold of the engine, using an injector, a pump and pressure regulator, a heat exchanger for vaporising ethanol and a separate fuel tank and lines. Gaseous (Nitric oxide (NO), Carbon monoxide (CO) and hydrocarbons (HC)) and particulate emissions (particle mass (PM2.5) and particle number) testing was conducted at intermediate speed (1700 rpm) using 4 load settings with ethanol substitution percentages ranging from 10-40 % (by energy). With ethanol fumigation, NO and PM2.5 emissions were reduced, whereas CO and HC emissions increased considerably and particle number emissions increased at most test settings. It was found that ethanol fumigation reduced the excess air factor for the engine and this led to increased emissions of CO and HC, but decreased emissions of NO. PM2.5 emissions were reduced with ethanol fumigation, as ethanol has a very low “sooting” tendency. This is due to the higher hydrogen-to-carbon ratio of this fuel, and also because ethanol does not contain aromatics, both of which are known soot precursors. The use of a diesel oxidation catalyst (as an after-treatment device) is recommended to achieve a reduction in the four pollutants that are currently regulated for compression ignition engines. The increase in particle number emissions with ethanol fumigation was due to the formation of volatile (organic) particles; consequently, using a diesel oxidation catalyst will also assist in reducing particle number emissions.

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.

From the Editor

The five articles appearing in this issue of the journal reflect the breadth of project management research and the cross-fertilization of ideas from other management disciplines with project management. These articles focus on the evolution of project management theory (“A Bibliometric View on the Use of Contingency Theory in Project Management Research” by Hanisch and Wald), interfirm knowledge management and learning in project networks (“Exploring Negotiation Through Boundary Objects in Global Design Project Networks” by Di Marco, Alin, and Taylor), learning within one project organization (“Organizational Learning in Project-Based Companies: A Process Thinking Approach” by Koskinen), identifying the factors affecting project performance (“Factors Causing Design Schedule Delays in Turnkey Projects in Taiwan: An Empirical Study of Power Distribution Substation Projects” by Yau and Yang), and considering the antecedents of negative employee behavior and its consequences for project team performance (“Counteracting Free-Riding With Team Morale—An Experimental Study” by He).

Fiber Bragg grating strain modulation based on nonlinear string transverse force amplifier

The only effective method of Fiber Bragg Grating (FBG) strain modulation has been by changing the distance between its two fixed ends. We demonstrate an alternative being more sensitive to force based on the nonlinear amplification relationship between a transverse force applied to a stretched string and its induced axial force. It may improve the sensitivity and size of an FBG force sensor, reduce the number of FBGs needed for multi-axial force monitoring, and control the resonant frequency of an FBG accelerometer.

Static vertical displacement measurement of bridges using Fiber Bragg Grating (FBG) sensors

Vertical displacements are one of the most relevant parameters for structural health monitoring of bridges in both the short and long terms. Bridge managers around the globe are always looking for a simple way to measure vertical displacements of bridges. However, it is difficult to carry out such measurements. On the other hand, in recent years, with the advancement of fiber-optic technologies, fiber Bragg grating (FBG) sensors are more commonly used in structural health monitoring due to their outstanding advantages including multiplexing capability, immunity of electromagnetic interference as well as high resolution and accuracy. For these reasons, using FBG sensors is proposed to develop a simple, inexpensive and practical method to measure vertical displacements of bridges. A curvature approach for vertical displacement measurements using curvature measurements is proposed. In addition, with the successful development of FBG tilt sensors, an inclination approach is also proposed using inclination measurements. A series of simulation tests of a full- scale bridge was conducted. It shows that both of the approaches can be implemented to determine vertical displacements for bridges with various support conditions, varying stiffness (EI) along the spans and without any prior known loading. These approaches can thus measure vertical displacements for most of slab-on-girder and box-girder bridges. Besides, the approaches are feasible to implement for bridges under various loading. Moreover, with the advantages of FBG sensors, they can be implemented to monitor bridge behavior remotely and in real time. A beam loading test was conducted to determine vertical displacements using FBG strain sensors and tilt sensors. The discrepancies as compared with dial gauges reading using the curvature and inclination approaches are 0.14mm (1.1%) and 0.41mm (3.2%), respectively. Further recommendations of these approaches for developments will also be discussed at the end of the paper.

Kernel analysis on Grassmann manifolds for action recognition

Modelling video sequences by subspaces has recently shown promise for recognising human actions. Subspaces are able to accommodate the effects of various image variations and can capture the dynamic properties of actions. Subspaces form a non-Euclidean and curved Riemannian manifold known as a Grassmann manifold. Inference on manifold spaces usually is achieved by embedding the manifolds in higher dimensional Euclidean spaces. In this paper, we instead propose to embed the Grassmann manifolds into reproducing kernel Hilbert spaces and then tackle the problem of discriminant analysis on such manifolds. To achieve efficient machinery, we propose graph-based local discriminant analysis that utilises within-class and between-class similarity graphs to characterise intra-class compactness and inter-class separability, respectively. Experiments on KTH, UCF Sports, and Ballet datasets show that the proposed approach obtains marked improvements in discrimination accuracy in comparison to several state-of-the-art methods, such as the kernel version of affine hull image-set distance, tensor canonical correlation analysis, spatial-temporal words and hierarchy of discriminative space-time neighbourhood features.