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This paper describes the magmaOffenburg 3D simulation team trying to qualify for RoboCup 2011. While last year’s TDP focused on the tool set created for 3D simulation in this year we describe the further improvement in this tools as well as some new features we implemented focusing on heterogeneous robot models which seem to be used in RoboCup 2012.
An additional tool was written to simply generate situation-dependent strategies. Furthermore some tools, described last year, are now integrated in one single GUI to easy things up.
Existing approaches solving multi-vehicle pickup and delivery problems with soft time windows typically use common benchmark sets to verify their performance. However, there is a gap from these benchmark sets to real world problems with respect to instance size and problem complexity. In this paper we show that a combination of existing approaches together with improved heuristics is able to deal with the instance sizes and complexity of real world problems. The cost savings potential of the heuristics is compared to human dispatching plans generated from the data of a European carrier.
Today's network landscape consists of quite different network technologies, wide range of end-devices with large scale of capabilities and power, and immense quantity of information and data represented in different formats. Research on 3D imaging, virtual reality and holographic techniques will result in new user interfaces (UI) for mobile devices and will increase their diversity and variety. A lot of efforts are being done in order to establish open, scalable and seamless integration of various technologies and content presentation for different devices including mobile considering individual situation of the end user. This is very difficult because various kinds of devices used by different users or in different times/parallel by the same user which are not predictable and have to be recognized by the system in order to identify device capabilities. Not only the devices but also Content and User Interfaces are big issues because they could include different kinds of data format like text, image, audio, video, 3D Virtual Reality data and other upcoming formats. A very suitable and useful example of the use of such a system is mobile learning because of the large amount of varying devices with significantly different features and functionalities. This is true not only to support different learners, e.g. all learners within one learning community, but also to support the same learner using different equipment parallel and/or at different times. Those applications may be significantly enhanced by including virtual reality content presentation. Whatever the purposes are, it is impossible to develop and adapt content for all kind of devices including mobiles individually due to different capabilities of the devices, cost issues and author‘s requirement. A solution should be found to enable the automation of the content adaptation process.
The concept of m-learning which differs from other forms of e-learning covers a wide range of possibilities opened up by the convergence of new mobile technologies, wireless communication structure and distance learning development. This process of converging has launched some new goals to support m-learning where heterogeneity of devices, their operating systems (Linux, Windows, Symbian, Android etc) and supported markup languages (WML, XHTML etc), adaptive content, preferences or characteristics of user have become some of the major problems to be solved. To facilitate the learning process even more and to establish literally anytime anywhere learning, learning material/content should be available to the user always even if the user is in offline. Multiple devices used by the same user should also be synchronized among themselves and with server to provide updated learning content and to give a freedom to the user to choose any device as per his/her convenience. In this paper software architecture has been proposed to solve these problems and has been implemented by using a multidimensional flashcard learning system which synchronizes among all the devices that are being used by the user.
In large aircrafts the cabling is very complex and often causes reliability problems. This is specially true for modern In-flight Entertainment (IFE) systems, where every passenger can select a preferred movie, play computer games or be able to communicate with other travellers. Due to EMC problems, wireless communication systems (WiFi etc.) didn't succeed in solving these problems. In this paper an innovative communication system is proposed which perfectly supplements an aircraft IFE system. The key innovation of this system is to use structures that are essential parts of the airframe for data transfer, such as seat rails. Those rails consist of rectangular shapes and could easily be modified to fulfill the function of waveguides for microwaves. A waveguide as part of the seat rail would provide enormous benefits for aircrafts, such as a large bandwidth and consequently high data rates, no problems with EMC, unlimited flexibility of seat configuration, mechanical robustness with associated increase of reliability and a few additional advantages related to aircrafts such as reduction of weight and costs.
In previous work we [1] and other authors (e.g. [2]) have shown that agent-based systems are successful in optimizing delivery plans of single logistics companies and are meanwhile successfully productive in industry. In this paper we show that agent-based systems are particularly useful to also optimize transport across logistics companies. In intercompany optimization, privacy is of major importance between the otherwise competing companies. Some data has to be treated strictly private like the cost model or the constraint model. Other data like order information has to be shared. However, typically the amount of orders released to other companies has also to be limited. We show that our agent-based approach can be easily fine tuned to trade off privacy against the benefit of cooperation.
Introduction: Patient selection for cardiac resynchronization therapy (CRT) requires quantification of left ventricular conduction delay (LVCD). After implantation of biventricular pacing systems, individual AV delay (AVD) programming is essential to ensure hemodynamic response. To exclude adverse effects, AVD should exceed individual implant-related interatrial conduction times (IACT). As result of a pilot study, we proposed the development of a programmer-based transoesophageal left heart electrogram (LHE) recording to simplify both, LVCD and IACT measurement. This feature was implemented into the Biotronik ICS3000 programmer simultaneously with 3-channel surface ECG.
Methods: A 5F oesophageal electrode was perorally applied in 44 heart failure CRT-D patients (34m, 10f, 65±8 yrs., QRS=162±21ms). In position of maximum left ventricular deflection, oesophageal LVCD was measured between onsets of QRS in surface ECG and oesophageal left ventricular deflection. Then, in position of maximum left atrial deflection (LA), IACT in VDD operation (As-LA) was calculated by difference between programmed AV delay and the measured interval from onset of left atrial deflection to ventricular stimulus in the oesophageal electrogram. IACT in DDD operation (Ap-LA) was measured between atrial stimulus and LA..
Results: LVCD of the CRT patients was characterized by a minimum of 47ms with mean of 69±23ms. As-LA and Ap-LA were found to be 41±23ms and 125±25ms, resp., at mean. In 7 patients (15,9%), IACT measurement in DDD operation uncovered adverse AVD if left in factory settings. In this cases, Ap-LA exceeded the factory AVD. In 6 patients (13,6%), IACT in VDD operation was less than or equal 10ms indicating the need for short AVD.
Conclusion: Response to CRT requires distinct LVCD and AVD optimization. The ICS3000 oesophageal LHE feature can be utilized to measure LVCD in order to justify selection for CRT. IACT measurement simplifies AV delay optimization in patients with CRT systems irrespective of their make and model.
In-vivo and in-vitro comparison of implant-based CRT optimization - What provide new algorithms?
(2011)
Introduction: In cardiac resynchronization therapy (CRT), individual AV delay (AVD) optimization can effectively increase hemodynamics and reduce non-responder rate. Accurate, automatic and easily comprehensible algorithms for the follow-up are desirable. QuickOpt is the first attempt of a semi-automatic intracardiac electrogram (IEGM) based AVD algorithm. We aimed to compare its accuracy and usefulness by in-vitro and in-vivo studies.
Methods: Using the programmable ARSI-4 four-chamber heart rhythm and IEGM simulator (HKP, Germany), the QuickOpt feature of an Epic HF system (St. Jude, USA) was tested in-vitro by simulated atrial IEGM amplitudes between 0.3 and 3.5mV during both, manual and automatic atrial sensing between 0.2 and 1.0mV. Subsequently, in 21 heart failure patients with implanted biventricular defibrillators, QuickOpt was performed in-vivo. Results of the algorithm for VDD and DDD stimulation were compared with echo AV delay optimization.
Results: In-vitro simulations demonstrated a QuickOpt measuring accuracy of ± 8ms. Depending on atrial IEGM amplitude, the algorithm proposed optimal AVD between 90 and 150ms for VDD and between 140 and 200ms for DDD operation, respectively. In-vivo, QuickOpt difference between individual AVD in DDD and VDD mode was either 50ms (20pts) or 40ms (1pt). QuickOpt and echo AVD differed by 41 ± 25ms (7 – 90ms) in VDD and by 18 ± 24ms (17-50ms) in DDD operation. Individual echo AVD difference between both modes was 73 ± 20ms (30-100ms).
Conclusion: The study demonstrates the value of in-vitro studies. It predicted QuickOpt deficiencies regarding IEGM amplitude dependent AVD proposals constrained to fixed individual differences between DDD and VDD mode. Consequently, in-vivo, the algorithm provided AVD of predominantly longer duration than echo in both modes. Accepting echo individualization as gold standard, QuickOpt should not be used alone to optimize AVD in CRT patients.
Introduction: To simplify AV delay (AVD) optimization in cardiac resynchronization therapy (CRT), we reported that the hemodynamically optimal AVD for VDD and DDD mode CRT pacing can be approximated by individually measuring implant-related interatrial conduction intervals (IACT) in oesophageal electrogram (LAE) and adding about 50ms. The programmer-based St Jude QuickOpt algorithm is utilizing this finding. By automatically measuring IACT in VDD operation, it predicts the sensed AVD by adding either 30ms or 60ms. Paced AVD is strictly 50ms longer than sensed AVD. As consequence of those variations, several studies identified distinct inaccuracies of QuickOpt. Therefore, we aimed to seek for better approaches to automate AVD optimization.
Methods: In a study of 35 heart failure patients (27m, 8f, age: 67±8y) with Insync III Marquis CRT-D systems we recorded telemetric electrograms between left ventricular electrode and superior vena cava shock coil (LVtip/SVC = LVCE) simultaneously with LAE. By LVCE we measured intervals As-Pe in VDD and Ap-Pe in DDD operation between right atrial sense-event (As) or atrial stimulus (Ap), resp., and end of the atrial activity (Pe). As-Pe and Ap-Pe were compared with As-LA an Ap-LA in LAE, respectively.
Results: End of the left atrial activity in LVCE could clearly be recognized in 35/35 patients in VDD and 29/35 patients in DDD operation. We found mean intervals As-LA of 40.2±24.5ms and Ap-LA of 124.3±20.6ms. As-Pe was 94.8±24.1ms and Ap-Pe was 181.1±17.8ms. Analyzing the sums of As-LA + 50ms with duration of As-Pe and Ap-LA + 50ms with duration of Ap-Pe, the differences were 4.7±9.2ms and 4.2±8.6ms, resp., only. Thus, hemodynamically optimal timing of the ventricular stimulus can be triggered by automatically detecting Pe in LVCE.
Conclusion: Based on minimal deviations between LAE and LVCE approach, we proposed companies to utilize the LVCE in order to automate individual AVD optimization in CRT pacing.