Cooperation using a robotic ad hoc network made from Bluetooth, JXTA, OSGi and other commercial off the shelf (COTS) products

Abstract - Mobile devices in the near future will need to collaborate to fulfill their function. Collaboration will be done by communication. We use a real world example of robotic soccer to come up with the necessary structures required for robotic communication. A review of related work is done and it is found no examples come close to providing a RANET. The robotic ad hoc network (RANET) we suggest uses existing structures pulled from the areas of wireless networks, peer to peer and software life-cycle management. Gaps are found in the existing structures so we describe how to extend some structures to satisfy the design. The RANET design supports robot cooperation by exchanging messages, discovering needed skills that other robots on the network may possess and the transfer of these skills. The network is built on top of a Bluetooth wireless network and uses JXTA to communicate and transfer skills. OSGi bundles form the skills that can be transferred. To test the nal design a reference implementation is done. Deficiencies in some third party software is found, specifically JXTA and JamVM and GNU Classpath. Lastly we look at how to fix the deciencies by porting the JXTA C implementation to the target robotic platform and potentially eliminating the TCP/IP layer, using UDP instead of TCP or using an adaptive TCP/IP stack. We also propose a future areas of investigation; how to seed the configuration for the Personal area network (PAN) Bluetooth protocol extension so a Bluetooth TCP/IP link is more quickly formed and using the STP to allow multi-hop messaging and transfer of skills.

Intimate Drowning

intimate drowning 50 minute performance + installation work ice | salt | tears | This work is about death. Grief The relationships before The aftermath - of confusion, violence, isolation The never ending questions The devastating loss and paranoia "Since my wife died, I have spent the last six years treading water - trying to stop myself from drowning. Sometimes I catch myself not breathing. I have to remind myself that I can't live underwater no matter how much I want to." Grief. Loss. Tears. Fear. Sadness Water. Milk. Salt. Ice Falling. Waiting Submerged. Suffocated. Broken ties Intention. Lack of focus. Intensity of focus Fighting. Screaming. Wailing Blue. White. Black. Blackness The doors open: we walk through a gauze curtain and discover a dark space with a square of light in the middle of the room. As we walk closer to the light, we see a girl writing in charcoal on the floor around a square box filled with milk. She is writing the same thing over and over. The more she writes the more desperate she becomes: I am listening… We have to keep walking past. She isn’t writing for us. We find our seats Two people: one slowly breaking the hundreds of fragile strings that tie her to the other. The other is pleading with her to stop: Please. Please don’t. Please Avril. …Please don’t One girl facing away from us. She is slowly swimming on the spot without water. Projected next to her are images of her drowning under water. Salt falls in front of her. Behind her. A wall of salt. She is bound to the spot. Underwater and still breathing. Swimming in her own tears. She won’t escape. She wants to stay, but desires nothing Two people standing in a large square box filled with milk. They start in intimacy. The relationship begins to dissolve before us. One fights to be with/on/behind the other. The other fights her off. The milk is splashed. Why aren't they being careful? In the darkness there is scrubbing. Someone is scrubbing the floor. The other girl is on her knees trying to erase the original writing. The traces left behind that we have no control over. We only see her for a second, but hear her in the darkness. Scrubbing. It is pointless. You can't erase the past.

Understanding the physical processes of pollutant build-up and wash-off on roof surfaces

Pollutants originating with roof runoff can have a significant impact to urban stormwater quality. This signifies the importance of understanding pollutant processes on roof surfaces. Additionally, knowledge of pollutant processes on roof surfaces is important as roofs are used as the primary catchment surface for domestic rainwater harvesting. In recent years, rainwater harvesting has become one of the primary sustainable water management techniques to counteract the growing demand for potable water. Similar to all impervious services, pollutants associated with roof runoff undergo two primary processes: build-up and wash-off. The knowledge relating to these processes is limited. This paper presents outcomes of an in-depth research study into pollutant build-up and wash-off for roof surfaces. The knowledge will be important in order to develop appropriate strategies to safeguard rainwater users from possible health risks.

Bayesian versus frequentist statistical inference for investi gating a one-off cancer cluster reported to a health department

Background The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods. Methods This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution. Results Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior. Conclusion In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones), rather than objective reality. Bayesian analysis is (arguably) a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit.