Main streams and fine rivers : thinking the aporetics of edge

This paper takes up the question (reframed by Deleuze and Guattari) of where expansion takes place: at the ends or from the centre. Despite the connotations of mediocrity that can be attributed to the term ‘mainstream’, it is possible to rethink what happens at close range as the space of radical openings. Writers can often believe that what is most abnormal or fringe will produce the highest probability of creative ‘event’. The question, however, can be posed – framed by the lineage of deconstruction – whether the key to unlocking any system of totality or closed possibility may lie in a very central (although physically peripheral) location. If, instead of the classical image, expansion may occur from re-imagined ‘middles’ rather than conventional ‘margins’, this reading of where potential can arise may offer a more resilient model than that of fragile peripheries, forever exposed to being amputated from staid centres of status and restricted participation. Drawing on the writings of Deleuze and Guattari, Derrida and Badiou, this paper seeks to unsettle any simplistic approach to the notion of edge, reinscribing it within the repetitiveness of our situations, to argue that right in the middle of the so-called mainstream, there might be the fine rivers of aporia that when encountered in thought can constitutes gates to that which is most radical in writing and other creative practices.

A finite-time stability theorem of stochastic nonlinear systems and stabilization designs

This paper focuses on the finite-time stability and stabilization designs of stochastic nonlinear systems. We first present and discuss a definition on the finite-time stability in probability of stochastic nonlinear systems, then we introduce a stochastic Lyapunov theorem on the finite-time stability, which has been established by Yin et al. We also employ this theorem to design a continuous state feedback controller that makes a class of stochastic nonlinear systems to be stable in finite time. An example and a simulation are given to illustrate the theoretical analysis.

Robust recognition and segmentation of human actions using HMMs with missing observations

This paper describes the integration of missing observation data with hidden Markov models to create a framework that is able to segment and classify individual actions from a stream of human motion using an incomplete 3D human pose estimation. Based on this framework, a model is trained to automatically segment and classify an activity sequence into its constituent subactions during inferencing. This is achieved by introducing action labels into the observation vector and setting these labels as missing data during inferencing, thus forcing the system to infer the probability of each action label. Additionally, missing data provides recognition-level support for occlusions and imperfect silhouette segmentation, permitting the use of a fast (real-time) pose estimation that delegates the burden of handling undetected limbs onto the action recognition system. Findings show that the use of missing data to segment activities is an accurate and elegant approach. Furthermore, action recognition can be accurate even when almost half of the pose feature data is missing due to occlusions, since not all of the pose data is important all of the time.

Finding the optimal temporal partitioning of video sequences

The existing techniques for shot partitioning either process each shot boundary independently or proceed sequentially. The sequential process assumes the last shot boundary is correctly detected and utilizes the shot length distribution to adapt the threshold for detecting the next boundary. These techniques are only locally optimal and suffer from the strong assumption about the correct detection of the last boundary. Addressing these fundamental issues, in this paper, we aim to find the global optimal shot partitioning by utilizing Bayesian principles to model the probability of a particular video partition being the shot partition. A computationally efficient algorithm based on Dynamic Programming is then formulated. The experimental results on a large movie set show that our algorithm performs consistently better than the best adaptive-thresholding technique commonly used for the task.

Dielectrophoresis of micro/nano particles using curved microelectrodes

Dielectrophoresis, the induced motion of polarisable particles in non-homogenous electric field, has been proven as a versatile mechanism to transport, immobilise, sort and characterise micro/nano scale particle in microfluidic platforms. The performance of dielectrophoretic (DEP) systems depend on two parameters: the configuration of microelectrodes designed to produce the DEP force and the operating strategies devised to employ this force in such processes. This work summarises the unique features of curved microelectrodes for the DEP manipulation of target particles in microfluidic systems. The curved microelectrodes demonstrate exceptional capabilities including (i) creating strong electric fields over a large portion of their structure, (ii) minimising electro-thermal vortices and undesired disturbances at their tips, (iii) covering the entire width of the microchannel influencing all passing particles, and (iv) providing a large trapping area at their entrance region, as evidenced by extensive numerical and experimental analyses. These microelectrodes have been successfully applied for a variety of engineering and biomedical applications including (i) sorting and trapping model polystyrene particles based on their dimensions, (ii) patterning carbon nanotubes to trap low-conductive particles, (iii) sorting live and dead cells based on their dielectric properties, (iv) real-time analysis of drug-induced cell death, and (v) interfacing tumour cells with environmental scanning electron microscopy to study their morphological properties. The DEP systems based on curved microelectrodes have a great potential to be integrated with the future lab-on-a-chip systems.