This paper proposes a novel technique for 3D mesh segmentation using multiple 2D pose footprints. Such a problem has been targeted many times in the literature, but still requires further development especially in the area of automation. The proposed algorithm applies cognition theory and provides a generic technique to form a 3D bounding contour from a seed vertex on the 3D mesh. Forming the cutlines is done in both 2D and 3D spaces to enrich the available information for the search processes. The main advantage of this technique is the possibility to operate without any object-dependent parameters. The parameters that can be used will only be related to the used cognition theory and the seeds suggestion, which is another advantage as the algorithm can be generic to more than one theory of segmentation or to different criterion. The results are competitive against other algorithms, which use object-dependent or tuning parameters. This plus the autonomy and generality features, provides an efficient and usable approach for segmenting 3D meshes and at the same time to reduce the computation load.
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-achip
This paper reports on robotic and haptic technologies and capabilities developed for the law
enforcement and defence community within Australia by the Centre for Intelligent Systems
Research (CISR). The OzBot series of small and medium surveillance robots have been
designed in Australia and evaluated by law enforcement and defence personnel to determine
suitability and ruggedness in a variety of environments. Using custom developed digital
electronics and featuring expandable data busses including RS485, I<sup>2</sup>C, RS232, video and
Ethernet, the robots can be directly connected to many off the shelf payloads such as gas
sensors, x-ray sources and camera systems including thermal and night vision.
Differentiating the OzBot platform from its peers is its ability to be integrated directly with haptic
technology or the 'haptic bubble' developed by CISR. Haptic interfaces allow an operator to
physically 'feel' remote environments through position-force control and experience realistic
force feedback. By adding the capability to remotely grasp an object, feel its weight, texture and
other physical properties in real-time from the remote ground control unit, an operator's
situational awareness is greatly improved through Haptic augmentation in an environment
where remote-system feedback is often limited.
This paper describes the design, simulation, fabrication and experimental analysis of a passive micromixer for the mixing
of biological solvents. The mixer consists of a T-junction, followed by a serpentine microchannel. The serpentine has
three arcs, each equipped with circular barriers that are patterned as two opposing triangles. The barriers are engineered
to induce periodic perturbations in the flow field and enhance the mixing. CFD (Computational Fluid Dynamics) method
is applied to optimise the geometric variables of the mixer before fabrication. The mixer is made from PDMS
(Polydimethylsiloxane) using photo- and soft-lithography techniques. Experimental measurements are performed using
yellow and blue food dyes as the mixing fluids. The mixing is measured by analysing the composition of the flow's
colour across the outlet channel. The performance of the mixer is examined in a wide range of flow rates from 0.5 to 10
μl/min. Mixing efficiencies of higher than 99.4% are obtained in the experiments confirming the results of numerical
simulations. The proposed mixer can be employed as a part of lab-on-a-chip for biomedical applications.
Joule heating is a significant problem for microfluidic chips with electrokinetically driven flows. In this paper, we will present the modeling results of the Joule heating of a Polymethylmethacrylate (PMMA) polymer separation chip using both experimental and computational methods. The temperature distributions on the surface of the chip were measured by an advanced thermograph system. The numerical study was carried out using the multiphysics computational fluid dynamics (CFD) package CFD-Ace+. Different solutions and operating conditions were studied. Both the measurements and CFD data revealed that the heat generation was approximately uniform and the subsequent temperature increase was also uniform along the channel except for regions near the liquid ports. The highest temperature increase was observed along the centerline of the channel and the temperature reduced significantly away from the channel. At an electrical field of 45kV/m, the Joule heating effect was negligible for the solution used, even though at such a high electric field significant heating effect has been observed for micro capillary flows in literature. At a higher electrical field (68-120kV/m), the Joule heating could cause an increase of temperature of up to 40°C.
Integration of microheaters in microfluidic systems has a wide range of applications such as sensing, actuating and biomedical devices. This study focused on the investigation of the thermal performance of a nickel microheater fabricated on a printed circuit board using a LIGA process. Both experimental and computational methods were used in conjunction with preliminary theoretical analysis. The microheater was tested both in air and water. The temperature distributions of the microheater were measured by an advanced thermography system. The numerical study was carried out using the multiphysics CFD package CFD-Ace+. The temperature of the microheater increased approximately linearly with the input power for the experimental conditions. The microheater heated up exponentially at the start of power supply. At a supply of 120mA electrical current, the microheater could heat up at a rate of around 90°C/s. During the cooling stage, the rate was much higher and could reach 800°C/s. When placed in the microchannel with air flow, the heater could heat the flow effectively without causing significant increase in the chip temperature. The CFD results were validated by comparing temperature distributions on the chip surface and on the heater surface. The validated CFD results allowed more detailed investigations of thermal and fluid flow within the microchannel and the whole chip.
The paper presents a application method of detecting moving ground target based on micro accelerometer. Because vehicles moving over ground generate a succession of impacts, the soil disturbances propagate away from the source as seismic waves. Thus, we can detect moving ground vehicles by means of detecting seismic signals using a seismic tranasducer, and automatically classify and recognize them by data fusion method. The detection system on the basis of MEMS technology is small volume, light weight, low poer, low cost and can work under poor circumstances. In order to recognize vehicle targets, seismic properties of typical vehicle targets are researched in the paper. A data fusion technique of artifical neural networks (NAA) is applied to recognition of seismic signals for vehicle targets. An improved back propagation (BP) algorithm and ANN architecture have been presented to improve learning speed. The improved BP algorithm had been used recognition of vehicle targets in the outdoor environment. Through experiments, it can be proven that target seismic properties acquired are correct. ANN data fusion is effective to solve the recognition problem of moving vehicle target, and the micro accelerometer can be used in target recognition.
This paper introduces a novel methodology for texture object detection using genetic algorithms. The method employs a kind of high performance detection filter defined as 2D masks, which are derived using genetic algorithm operating. The population of filters iteratively evaluated according to a statistical performance index corresponding to object detection ability, and evolves into an optimal filter using the evolution principles of genetic search. Experimental results of texture object detection in high resolution satellite images are presented to illustrate the merit and feasibility of the proposed method.
This paper introduces a novel methodology for object detection using genetic algorithms and morphological processing. The method employs a kind of object oriented structuring element, which are derived using genetic algorithm operating. The population of morphological filters iteratively evaluated according to a statistical performance index corresponding to object extraction ability, and evolves into an optimal structuring elements using the evolution principles of genetic search. Experimental results of object extraction in high resolution satellite images are presented to illustrate the merit and feasibility of the proposed method.
Fiber identification has been a very important task in many industries such as wool growing, textile processing, archaeology, histochernical engineering, and zoology. Over the years, animal fibers have been identified using physical and chemical approaches. Recently, objective identification of animal fibers has been developed based on the cuticular information of fibers. Effective and accurate extraction of representative features is essential to animal fiber identification and classification. In the current work, two different strategies are developed for this purpose. In the first method, explicit features are extracted using image processing. However, only implicit features are used in the second method with an unsupervised artificial neural network. It is found that the use of explicit features increases the accuracy of fiber identification but requires more effort on processing images and solid knowledge of what features are representative ones.