Investigation of transition between spark ignition and controlled auto-ignition combustion in a V6 direct-injection engine with cam profile switching

Controlled auto-ignition (CAI) combustion, also known as Homogeneous Charge Compression Ignition (HCCI) can be achieved by trapping residuals with early exhaust valve closure in a direct fuel injection in-cylinder four-stroke gasoline engines (through the employment of low-lift cam profiles). Due to the operating region being limited to low and mid-load operation for CAI combustion with a low-lift cam profile, it is important to be able to operate SI combustion at high-load with a normal cam profile. A 3.0L prototype engine was modified to achieve CAI combustion, using a Cam Profile Switching mechanism which has the capability to switch between high and low-lift cam-profiles. A strategy was used where a high-profile could be used for SI combustion and a low-lift profile was used for CAI combustion. Initial analysis showed that for transitioning from SI to CAI combustion, misfire occurred on the first CAI transitional cycle. Subsequent experiments showed that the throttle opening position and switching time could be controlled avoiding misfire. Further work investigated transitioning at different loads and from CAI to SI combustion

Further investigation of a contactless patient-electrode interface of an Electrical Impedance Mammography system

The Sussex Mk4 Electrical Impedance Mammography (EIM) system is a novel instrument, designed for the detection of early breast cancer, based upon Electrical Impedance Tomography (EIT). Many innovations in the field have been incorporated in the design improving both signal distribution and response. This paper investigates the behaviour of the contactless patient-electrode interface. The interface was studied in detail using phantom and healthy volunteer, in-vivo, data. Our findings show the necessity for the careful design of electrode enclosure so that the response of the system is not affected by the unpredictable positioning of the breast; it closely mimics those conditions seen when using the phantom. The paper includes a number of possible designs and their individual characteristics. In addition an explanation on the unanticipated effects and solutions for such are described. © 2010 IOP Publishing Ltd

DDSL: Efficient Subgraph Listing on Distributed and Dynamic Graphs

Subgraph listing is a fundamental problem in graph theory and has wide applications in areas like sociology, chemistry, and social networks. Modern graphs can usually be large-scale as well as highly dynamic, which challenges the efficiency of existing subgraph listing algorithms. Recent works have shown the benefits of partitioning and processing big graphs in a distributed system, however, there is only few work targets subgraph listing on dynamic graphs in a distributed environment. In this paper, we propose an efficient approach, called Distributed and Dynamic Subgraph Listing (DDSL), which can incrementally update the results instead of running from scratch. DDSL follows a general distributed join framework. In this framework, we use a Neighbor-Preserved storage for data graphs, which takes bounded extra space and supports dynamic updating. After that, we propose a comprehensive cost model to estimate the I/O cost of listing subgraphs. Then based on this cost model, we develop an algorithm to find the optimal join tree for a given pattern. To handle dynamic graphs, we propose an efficient left-deep join algorithm to incrementally update the join results. Extensive experiments are conducted on real-world datasets. The results show that DDSL outperforms existing methods in dealing with both static dynamic graphs in terms of the responding time

Generation of high-energy monoenergetic heavy ion beams by radiation pressure acceleration of ultra-intense laser pulses

A novel radiation pressure acceleration (RPA) regime of heavy ion beams from laser-irradiated ultrathin foils is proposed by self-consistently taking into account the ionization dynamics. In this regime, the laser intensity is required to match with the large ionization energy gap when the successive ionization of high-Z atoms passing the noble gas configurations [such as removing an electron from the helium-like charge state $(\text{Z}-2)^+$ to $(\text{Z}-1)^+$]. While the target ions in the laser wing region are ionized to low charge states and undergo rapid dispersions due to instabilities, a self-organized, stable RPA of highly-charged heavy ion beam near the laser axis is achieved. It is also found that a large supplement of electrons produced from ionization helps preserving stable acceleration. Two-dimensional particle-in-cell simulations show that a monoenergetic $\text{Al}^{13+}$ beam with peak energy $1\ \text{GeV}$ and energy spread of $5\%$ is obtained by lasers at intensity $7\times10^{20}\ \text{W}/\text{cm}^2$.Comment: 5 pages, 4 figure

Searching for sub-millisecond pulsars from highly polarized radio sources

Pulsars are among the most highly polarized sources in the universe. The NVSS has catalogued 2 million radio sources with linear polarization measurements, from which we have selected 253 sources, with polarization percentage greater than 25%, as targets for pulsar searches. We believe that such a sample is not biased by selection effects against ultra-short spin or orbit periods. Using the Parkes 64m telescope, we conducted searches with sample intervals of 0.05 ms and 0.08 ms, sensitive to submillisecond pulsars. Unfortunately we did not find any new pulsars.Comment: 2 pages 1 figure. To appear in "Young Neutron Stars and Their Environments" (IAU Symposium 218, ASP Conference Proceedings), eds F. Camilo and B. M. Gaensle

Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor

Neuromorphic computing is a new paradigm for design of both the computing hardware and algorithms inspired by biological neural networks. The event-based nature and the inherent parallelism make neuromorphic computing a promising paradigm for building efficient neural network based architectures for control of fast and agile robots. In this paper, we present a spiking neural network architecture that uses sensory feedback to control rotational velocity of a robotic vehicle. When the velocity reaches the target value, the mapping from the target velocity of the vehicle to the correct motor command, both represented in the spiking neural network on the neuromorphic device, is autonomously stored on the device using on-chip plastic synaptic weights. We validate the controller using a wheel motor of a miniature mobile vehicle and inertia measurement unit as the sensory feedback and demonstrate online learning of a simple 'inverse model' in a two-layer spiking neural network on the neuromorphic chip. The prototype neuromorphic device that features 256 spiking neurons allows us to realise a simple proof of concept architecture for the purely neuromorphic motor control and learning. The architecture can be easily scaled-up if a larger neuromorphic device is available.Comment: 6+1 pages, 4 figures, will appear in one of the Robotics conference