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Many commonly well-performing convolutional neural network models have shown to be susceptible to input data perturbations, indicating a low model robustness. Adversarial attacks are thereby specifically optimized to reveal model weaknesses, by generating small, barely perceivable image perturbations that flip the model prediction. Robustness against attacks can be gained for example by using adversarial examples during training, which effectively reduces the measurable model attackability. In contrast, research on analyzing the source of a model’s vulnerability is scarce. In this paper, we analyze adversarially trained, robust models in the context of a specifically suspicious network operation, the downsampling layer, and provide evidence that robust models have learned to downsample more accurately and suffer significantly less from aliasing than baseline models.
Estimating the Robustness of Classification Models by the Structure of the Learned Feature-Space
(2022)
Over the last decade, the development of deep image classification networks has mostly been driven by the search for the best performance in terms of classification accuracy on standardized benchmarks like ImageNet. More recently, this focus has been expanded by the notion of model robustness, \ie the generalization abilities of models towards previously unseen changes in the data distribution. While new benchmarks, like ImageNet-C, have been introduced to measure robustness properties, we argue that fixed testsets are only able to capture a small portion of possible data variations and are thus limited and prone to generate new overfitted solutions. To overcome these drawbacks, we suggest to estimate the robustness of a model directly from the structure of its learned feature-space. We introduce robustness indicators which are obtained via unsupervised clustering of latent representations from a trained classifier and show very high correlations to the model performance on corrupted test data.
Despite the success of convolutional neural networks (CNNs) in many academic benchmarks for computer vision tasks, their application in the real-world is still facing fundamental challenges. One of these open problems is the inherent lack of robustness, unveiled by the striking effectiveness of adversarial attacks. Current attack methods are able to manipulate the network's prediction by adding specific but small amounts of noise to the input. In turn, adversarial training (AT) aims to achieve robustness against such attacks and ideally a better model generalization ability by including adversarial samples in the trainingset. However, an in-depth analysis of the resulting robust models beyond adversarial robustness is still pending. In this paper, we empirically analyze a variety of adversarially trained models that achieve high robust accuracies when facing state-of-the-art attacks and we show that AT has an interesting side-effect: it leads to models that are significantly less overconfident with their decisions, even on clean data than non-robust models. Further, our analysis of robust models shows that not only AT but also the model's building blocks (like activation functions and pooling) have a strong influence on the models' prediction confidences. Data & Project website: https://github.com/GeJulia/robustness_confidences_evaluation
Over the last years, Convolutional Neural Networks (CNNs) have been the dominating neural architecture in a wide range of computer vision tasks. From an image and signal processing point of view, this success might be a bit surprising as the inherent spatial pyramid design of most CNNs is apparently violating basic signal processing laws, i.e. Sampling Theorem in their down-sampling operations. However, since poor sampling appeared not to affect model accuracy, this issue has been broadly neglected until model robustness started to receive more attention. Recent work in the context of adversarial attacks and distribution shifts, showed after all, that there is a strong correlation between the vulnerability of CNNs and aliasing artifacts induced by poor down-sampling operations. This paper builds on these findings and introduces an aliasing free down-sampling operation which can easily be plugged into any CNN architecture: FrequencyLowCut pooling. Our experiments show, that in combination with simple and Fast Gradient Sign Method (FGSM) adversarial training, our hyper-parameter free operator substantially improves model robustness and avoids catastrophic overfitting. Our code is available at https://github.com/GeJulia/flc_pooling
To achieve Germany's climate targets, the industrial sector, among others, must be transformed. The decarbonization of industry through the electrification of heating processes is a promising option. In order to investigate this transformation in energy system models, high-resolution temporal demand profiles of the heat and electricity applications for different industries are required. This paper presents a method for generating synthetic electricity and heat load profiles for 14 industry types. Using this methodology, annual profiles with a 15-minute resolution can be generated for both energy demands. First, daily profiles for the electricity demand were generated for 4 different production days. These daily profiles are additionally subdivided into eight end-use application categories. Finally, white noise is applied to the profile of the mechanical drives. The heat profile is similar to the electrical but is subdivided into four temperature ranges and the two applications hot water and space heating. The space heating application is additionally adjusted to the average monthly outdoor temperature. Both time series were generated for the analysis of an electrification of industrial heat application in energy system modelling.
The contribution of the RoofKIT student team to the SDE 21/22 competition is the extension of an existing café in Wuppertal, Germany, to create new functions and living space for the building with simultaneous energetic upgrading. A demonstration unit is built representing a small cut-out of this extension. The developed energy concept was thoroughly simulated by the student team in seminars using Modelica. The system uses mainly solar energy via PVT collectors as the heat source for a brine-water heat pump (space heating and hot water). Energy storage (thermal and electrical) is installed to decouple generation and consumption. Simulation results confirm that carbon neutrality is achieved for the building operation, consuming and generating around 60 kWh/m2a.
Featherweight Go (FG) is a minimal core calculus that includes essential Go features such as overloaded methods and interface types. The most straightforward semantic description of the dynamic behavior of FG programs is to resolve method calls based on run-time type information. A more efficient approach is to apply a type-directed translation scheme where interface-values are replaced by dictionaries that contain concrete method definitions. Thus, method calls can be resolved by a simple lookup of the method definition in the dictionary. Establishing that the target program obtained via the type-directed translation scheme preserves the semantics of the original FG program is an important task.
To establish this property we employ logical relations that are indexed by types to relate source and target programs. We provide rigorous proofs and give a detailed discussion of the many subtle corners that we have encountered including the need for a step index due to recursive inter- faces and method definitions.
One of the major challenges impeding the energy transition is the intermittency of solar and wind electricity generation due to their dependency on weather changes. The demand-side energy flexibility contributes considerably to mitigate the energy supply/demand imbalances resulting from external influences such as the weather. As one of the largest electricity consumers, the industrial enterprises present a high demand-side flexibility potential from their production processes and on-site energy assets. In this direction, methods are needed with a focus on enabling the energy flexibility and ensure an active participation of such enterprises in the electricity markets especially with variable prices of electricity. This paper presents a generic model library for an industrial enterprise implemented with optimal control for energy flexibility purposes. The components in the model library represent the typical technical units of an industrial enterprise on material, media, and energy flow levels with their operative constraints. A case study of a plastic manufacturing plant using the generic model library is also presented, in which the results of two simulation with different electricity prices are compared and the behavior of the model can be assessed. The results show that the model provides an optimal scheduling of the manufacturing system according to the variations in the electricity prices, and ensures an optimal control for utilities and energy systems needed for the production.
Solar energy plays a central role in the energy transition. Clouds generate locally large fluctuations in the generation output of photovoltaic systems, which is a major problem for energy systems such as microgrids, among others. For an optimal design of a power system, this work analyzed the variability using a spatially distributed sensor network at Stuttgart Airport. It has been shown that the spatial distribution partially reduces the variability of solar radiation. A tool was also developed to estimate the output power of photovoltaic systems using irradiation time series and assumptions about the photovoltaic sites. For days with high fluctuations of the estimated photovoltaic power, different energy system scenarios were investigated. It was found the approach can be used to have a more realistic representation of aggregated PV power taking spatial smoothing into account and that the resulting PV power generation profiles provide a good basis for energy system design considerations like battery sizing.
Peer-to-peer energy trading and local electricity markets have been widely discussed as new options for the transformation of the energy system from the traditional centralized scheme to the novel decentralized one. Moreover, it has also been proposed as a more favourable alternative for already expiring feed in tariff policies that promote investment in renewable energy sources. Peer-to-peer energy trading is usually defined as the integration of several innovative technologies, that enable both prosumers and consumers to trade electricity, without intermediaries, at a consented price. Furthermore, the techno-economic aspects go hand in hand with the socio-economic aspects, which represent at the end significant barriers that need to be tackled to reach a higher impact on current power systems. Applying a qualitative analysis, two scalable peer-to-peer concepts are presented in this study and the possible participant´s entry probability into such concepts. Results show that consumers with a preference for environmental aspects have in general a higher willingness to participate in peer-to-peer energy trading. Moreover, battery storage systems are a key technology that could elevate the entry probability of prosumers into a peer-to-peer market.
Due to the increasing aging of the population, the number of elderly people requiring care is growing in most European countries. However, the number of caregivers working in nursing homes and on daily care services is declining in countries like Germany or Italy. This limits the time for interpersonal communication. Furthermore, as a result of the Covid-19 pandemic, social distancing during contact restrictions became more important, causing an additional reduction of personal interaction. This social isolation can strongly increase emotional stress. Robotic assistance could contribute to addressing this challenge on three levels: (1) supporting caregivers to respond individually to the needs of patients and residents in nursing homes; (2) observing patients’ health and emotional state; (3) complying with high hygiene standards and minimizing human contact if required. To further the research on emotional aspects and the acceptance of robotic assistance in care, we conducted two studies where elderly participants interacted with the social robot Misa. Facial expression and voice analysis were used to identify and measure the emotional state of the participants during the interaction. While interpersonal contact plays a major role in elderly care, the findings reveal that robotic assistance generates added value for both caregivers and patients and that they show emotions while interacting with them.
Robust scheduling problem is a major decision problem that is addressed in the literature, especially for remanufacturing systems; this problem is complex because of the high uncertainty and complex constraints involved. Generally, the existing approaches are dedicated to specific processes and do not enable the quick and efficient generation and evaluation of schedules. With the emergence of the Industry 4.0 paradigm, data availability is now considered an opportunity to facilitate the decision-making process. In this study, a data-driven decisionmaking process is proposed to treat the robust scheduling problem of remanufacturing systems in uncertain environments. In particular, this process generates simulation models based on a data-driven modeling approach. A robustness evaluation approach is proposed to answer several decision questions. An application of the decision process in an industrial case of a remanufacturing system is presented herein, illustrating the impact of robustness evaluation results on real-life decisions.
Most recently, the federal government in Germany published new climate goals in order reach climate neutrality by 2045. This paper demonstrates a path to a cost optimal energy supply system for the German power grid until the year 2050. With special regard to regionality, the system is based on yearly myopic optimization with the required energy system transformation measures and the associated system costs. The results point out, that energy storage systems (ESS) are fundamental for renewables integration in order to have a feasible energy transition. Moreover, the investment in storage technologies increased the usage of the solar and wind technologies. Solar energy investments were highly accompanied with the installation of short-term battery storage. Longer-term storage technologies, such as H2, were accompanied with high installations of wind technologies. The results pointed out that hydrogen investments are expected to overrule short-term batteries if their cost continues to decrease sharply. Moreover, with a strong presence of ESS in the energy system, biomass energy is expected to be completely ruled out from the energy mix. With the current emission reduction strategy and without a strong presence of large scale ESS into the system, it is unlikely that the Paris agreement 2° C target by 2050 will be achieved, let alone the 1.5° C.
In the railway technical centers, scheduling the maintenance activities is a very complex task, it consists in ordering, in the time, all the maintenance operations on the workstations, while respecting the number of resources, precedence constraints, and the workstations' availabilities. Currently, this process is not completely automatic. For improving this situation, this paper presents a mathematical model for the maintenance activities scheduling in the case of railway remanufacturing systems. The studied problem is modeled as a flexible job-shop, with the possibility for a job to be executed several times on a stage. MILP formulation is implemented with the Makespan as an objective, representing the time for remanufacturing the train. The aim is to create a generic model for optimizing the planning of the maintenance activities and improving the performance of the railway technical centers. At last, numerical results are presented, discussing the impact of the instances size on the computing time to solve the described problem.
Lithium-ion batteries show strongly nonlinear behaviour regarding the battery current and state of charge. Therefore, the modelling of lithium-ion batteries is complex. Combining physical and data-driven models in a grey-box model can simplify the modelling. Our focus is on using neural networks, especially neural ordinary differential equations, for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis for the grey-box model. Unknown parameters and dependencies are then replaced by learnable parameters and neural networks. We use experimental full-cycle data and data from pulse tests of a lithium iron phosphate cell to train the model. Finally, we test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model.
We consider the local group of agents for exchanging the time-series data value and computing the approximation of the mean value of all agents. An agent represented by a node knows all local neighbor nodes in the same group. The node has the contact information of other nodes in other groups. The nodes interact with each other in synchronous rounds to exchange the updated time-series data value using the random call communication model. The amount of data exchanged between agent-based sensors in the local group network affects the accuracy of the aggregation function results. At each time step, the agent-based sensor can update the input data value and send the updated data value to the group head node. The group head node sends the updated data value to all group members in the same group. Grouping nodes in peer-to-peer networks show an improvement in Mean Squared Error (MSE).
Data is ever increasing in the computing world. Due to advancement of cloud technology the dynamics of volumes of data and its capacity has increased within a short period of time and will keep increasing further. Providing transparency, privacy, and security to the cloud users is becoming more and more challenging along with the volume of data and use of cloud services. We propose a new approach to address the above mentioned challenge by recording the user events in the cloud ecosystem into log files and applying MAR principle namely 1) Monitoring 2) Analyzing and 3) Reporting.
E-Tutoren-Ausbildung: Lernerfahrungen reflektieren – Lehrhandlungskompetenzen dialogisch aufbauen
(2014)
To provide proper solutions to the problem of device dependant content delivery, a fine categorization of the application target devices is needed. Earlier attempts provided two different presentations for desktop and mobile platforms. The mobile platform presentation was divided into three categories, based on a general classification (PDA, Smartphone or mobile phone). In order to improve the on mobile device presentation a finer categorization is introduced. In this paper, our focus is to clarify the concept of this more flexible presentation module, in which the delivered content depends on the efficiency of the device based on a selected set of capabilities.
Physik durch Informatik
(2022)
Selbsttests in Lernmanagementsystemen (LMS) ermöglichen es Studierenden, den eigenen Lernfortschritt einzuschätzen. Das didaktische Konzept Physik durch Informatik (PDI) ist charakterisiert durch die Nutzung einer Programmiersprache zur Lösungseingabe bei Mathematik und Physik-Aufgaben. Im Gegensatz zur Lösungseingabe durch Zahlenwerte oder im Antwort-Auswahl-Verfahren erfordert die Implementierung einer Lösung in einer Programmiersprache eine höhere Kompetenzstufe.
The improvements in the hardware and software of communication devices have allowed running Virtual Reality (VR) and Augmented Reality (AR) applications on those. Nowadays, it is possible to overlay synthetic information on real images, or even to play 3D on-line games on smart phones or some other mobile devices. Hence the use of 3D data for business and specially for education purposes is ubiquitous. Due to always available at hand and always ready to use properties of mobile phones, those are considered as most potential communication devices. The total numbers of mobile phone users are increasing all over the world every day and that makes mobile phones the most suitable device to reach a huge number of end clients either for education or for business purposes. There are different standards, protocols and specifications to establish the communication among different communication devices but there is no initiative taken so far to make it sure that the send data through this communication process will be understood and used by the destination device. Since all the devices are not able to deal with all kind of 3D data formats and it is also not realistic to have different version of the same data to make it compatible with the destination device, it is necessary to have a prevalent solution. The proposed architecture in this paper describes a device and purpose independent 3D data visibility any time anywhere to the right person in suitable format. There is no solution without limitation. The architecture is implemented in a prototype to make an experimental validation of the architecture which also shows the difference between theory and practice.
This paper shows the results of the evaluation of two sets of mobile web design guidelines concerning mobile learning. The first set of guidelines is concerned with the usage of text on mobile device screens. The second set is concerned with the usage of images on mobile devices. The evaluation is performed by eye tracking (objective) as well as questionnaires and interviews (subjective) respectively.
The idea of this game is to use a flashcard system to create a short story in a foreign language. The story is developed by a group of people by exchanging sentences via a flashcard system. This way, people can learn from each other without fear of making mistakes because the group members are anonymous.
Flashcards are a well known and proven method to learn and memorise. Such a way of learning is perfectly suited for “learning on the way,” but carrying all the flashcards could be awkward. In this scenario, a mobile device (mobile phone) is an adequate solution. The new mobile device operating system Android from Google allows for writing multimedia-enriched applications.
Electrode modelling and simulation of diagnostic and pulmonary vein isolation in atrial fibrillation
(2022)
Logging information is more precious as it contains the execution of a system; it is produced by millions of events from simple application logins to random system errors. Most of the security related problems in the cloud ecosystem like intruder attacks, data loss, and denial of service, etc. could be avoided if Cloud Service Provider (CSP) or Cloud User (CU) analyses the logging information. In this paper we introduced few challenges, which are place of monitoring, security, and ownership of the logging information between CSP and CU.
Also we proposed a logging architecture to analyze the behaviour of the cloud ecosystem, to avoid data breaches and other security related issues at the CSP space. So that we believe our proposed architecture can provide maximum trust between CU and CSP.
The advantages of the coupling-of-modes (COM) formalism and the transmission-matrix approach are combined to create exact and computationally efficient analysis and synthesis CAD tools for the design of SAW-resonator filters. The models for the filter components, especially gratings, interdigital transducers (IDTs). and multistrip couplers (MSCs), are based on the COM approach, which delivers closed-form expressions. In order to determine the relevant COM parameters, the integrated COM differential equations are compared with analytically derived expressions from the transmission-matrix approach. The most important second-order effects such as energy storage, propagation loss and mechanical and electrical loading are fully taken into account. As an example, the authors investigate a two-pole, acoustically coupled resonator filter at 914.5 MHz on AT quartz. Excellent agreement between theory and measurement is found.
Structures for interconnecting active microwave semiconductor-devices, e.g. FET's and MIC's, with the electrical surrounding or with each other have to be designed more and more carefully when increasing the desired upper frequency limit. Therefore, several connecting structures for device embedding have been examined. Mainly, their applicability for the frequency range from 10 GHz to 100 GHz was considered. Additionally, different equivalent circuits were developed to approximately describe their behaviour for CAD-applications.
In short-reach connections, large-diameter multimode fibres allow for robust and easy connections. Unfortunately, their propagation properties depend on the excitation conditions. We propose a launching technique using a fibre stub that can tolerate fabrication tolerances in terms of tilts and off-sets to a large extent. A study of the influence of displaced connectors along the transmission link shows that the power distributions approach a steady-state power distribution very similar to the initial distribution established by the proposed launching scheme.
iSign - internet based simulation of guided wave propagation - ist eine Lernumgebung für Online-Laborversuche. Die Client-Serverarchitektur nutzt server-seitig das Tool F3D, das elektromagnetische Felder in 3D-Strukturen berechnet. Ein Apache-Webserver (unter Linux) bedient den Theorie-/Aufgaben-Teil und die Lernsystemadministration. Ein HPUX Simulationsserver steuert und kontrolliert den mehrstufigen Simulationsvorgang. Eine MySQL-Datenbank erlaubt dynmaische Webseiten-Generierung und Simulations-, Projekt- und Userdatenhaltung. Java-Applets, JavaServer Pages und JavaBeans erzeugen die interaktive Client-Oberfläche zur Eingabe, Ergebnisdarstellung und für Online-Virtual Reality. Die einheitlich gestaltete Benutzeroberfläche verbirgt die Systemkomplexität.
Die hochfrequente, feldnumerische Analyse mit der Finite-Differenzen Methode erfordert die Diskretisierung der zu untersuchenden Struktur in einem nichtäquidistanten Gitter. Vorschriften zur Diskretisierung kreiszylindrischer Strukturen wie sie z.B. bei Durchkontaktierungen auftreten, werden untersucht und eine optimierte Lösung vorgestellt.
Virtual-Reality-Darstellung elektromagnetischer Felder in dreidimensionalen Mikrowellenstrukturen
(2000)
Untersuchungen haben gezeigt, daß der Mensch ein Vielfaches an Informationen in Form von visuellen Eindrücken, im Gegensatz zur textuellen Darstellung, verarbeiten kann. Mit Hilfe des numerischen Feld-Simulationsprogramms F3D können Mikrowellenstrukturen auf die Wechselwirkung mit elektromagnetischen Feldern untersucht werden. Das Programm F3D2VRML stellt die Ergebnisse in einer dreidimensionalen Virtual-Reality-Darstellung (VR) dar.
Damit ist es dem Betrachter möglich, mehr Informationen aufzunehmen, da die Informationen mit Formen und Farben im dreidimensionalen Raum visualisiert werden.
MPC-Workshop Februar 2020
(2021)
MPC-Workshop Juli 2018
(2018)
MPC-Workshop Februar 2016
(2016)
MPC-Workshop Juli 2015
(2015)
MPC-Workshop Februar 2015
(2015)
MPC-Workshop Juli 2014
(2014)
MPC-Workshop Februar 2014
(2014)
MPC-Workshop Juli 2013
(2013)
MPC-Workshop Februar 2013
(2013)
MPC-Workshop Juli 2012
(2012)
MPC-Workshop Februar 2012
(2012)
MPC-Workshop Juli 2011
(2011)
MPC-Workshop Februar 2011
(2011)
Tagungsband zum Workshop der Multiprojekt-Chip-Gruppe Baden-Württemberg, Reutlingen, 9. Juli 2010
(2010)
Tagungsband zum Workshop der Multiprojekt-Chip-Gruppe Baden-Württemberg, Göppingen, 5. Februar 2010
(2010)
Tagungsband zum Workshop der Multiprojekt-Chip-Gruppe Baden-Württemberg, Karlsruhe, 10. Juli 2009
(2009)
Tagungsband zum Workshop der Multiprojekt-Chip-Gruppe Baden-Württemberg, Künzelsau, 6. Februar 2009
(2009)
Tagungsband zum Workshop der Multiprojekt-Chip-Gruppe Baden-Württemberg, Konstanz, 4. Juli 2008
(2008)
MPC-Workshop Juli 2007
(2007)
MPC-Workshop Februar 2007
(2007)
MPC-Workshop Juli 2006
(2006)
MPC-Workshop Februar 2006
(2006)
MPC-Workshop Juli 2005
(2005)
MPC-Workshop Februar 2005
(2005)
MPC-Workshop Juli 2004
(2004)
MPC-Workshop Februar 2004
(2004)
MPC-Workshop Juli 2003
(2003)
MPC-Workshop Januar 2003
(2003)
MPC-Workshop Juni 2002
(2002)
MPC-Workshop Januar 2002
(2002)
MPC-Workshop Juli 2001
(2001)
MPC-Workshop Februar 2001
(2001)
In this work a set of nonlinear coupled COM equations at interacting frequencies is derived on the basis of nonlinear electro-elasticity. The formalism is presented with the aim of describing intermodulation distortion of third-order (IMD3) and triple beat. The resulting COM equations are translated to the P-matrix formalism, where care is taken to obtain the correct frequency dependence. The scheme depends on two frequency-independent constants for an effective third-order nonlinearity. One of these two constants is negligibly small in the systems considered here. The P-matrix approach is applied to single filters and duplexers on LiTaO 3 (YXl)/42° operating in different frequency ranges. Both IMD3 and triple beat show good agreement with measurement.
A Nonlinear FEM Model to Calculate Third-Order Harmonic and Intermodulation in TC-SAW Devices
(2018)
Nonlinearities in Temperature Compensated SAW (TC-SAW) devices in the 2 GHz range are investigated using a nonlinear finite element model by simultaneously considering both third-order intermodulation distortion (IMD3)and third harmonic (H3). In the employed perturbation approach, different contributions to the total H3, the direct and indirect contribution, are discussed. H3 and IMD3 measurements were fitted simultaneously using scaling factors for SiO 2 film and Cu electrode nonlinear material tensors in TC-SAW devices. We employ a P-Matrix simulation as intermediate step: Firstly, measurement and nonlinear P-Matrix calculations for finite devices are compared and coefficients of the P-Matrix simulation are determined. The nonlinear tensor data of the different materials involved in periodic nonlinear finite element method (FEM) computations are optimized to fit periodic P-Matrix calculations by introducing scaling factors. Thus, the contribution of different materials to the nonlinear behavior of TC-SAW devices is obtained and the role of materials is discussed.
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.
Network landscape of recent time contains many different network technologies, a wide range of end-devices with a large scale of capabilities and power, and an 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, will increase their diversity and variety. In this paper software architecture has been proposed to establish device and content format independent communication including 3D imaging and virtual reality data as content. As experimental validation the concept is implemented in collaborative Language Learning Game (LLG), which is a learning tool for language acquisition.