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In the presented work we compare machine learning techniques in the context of lane change behavior performed by humans in a semi-naturalistic simulated environment. We evaluate different learning approaches using differing feature combinations in order to identify appropriate feature, best feature combination, and the most appropriate machine learning technique for the described task. Based on the data acquired from human drivers in the traffic simulator NISYS TRS 1 , we trained a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able to predict lane changes up to 1.5 sec in beforehand.
Practical application of object detection systems, in research or industry, favors highly optimized black box solutions. We show how such a highly optimized system can be further augmented in terms of its reliability with only a minimal increase of computation times, i.e. preserving realtime boundaries. Our solution leaves the initial (HOG-based) detector unchanged and introduces novel concepts of non-linear metrics and fusion of ROIs. In this context we also introduce a novel way of combining feature vectors for mean-shift grouping. We evaluate our approach on a standarized image database with a HOG detector, which is representative for practical applications. Our results show that the amount of false-positive detections can be reduced by a factor of 4 with a negligable complexity increase. Although introduced and applied to a HOG-based system, our approach can easily be adapted for different detectors.
The Bitcoin whitepaper states that security of the system is guaranteed as long as honest miners control more than half of the current total computational power. The whitepaper assumes a static difficulty, thus it is equally hard to solve a cryptographic proof-of-work puzzle for any given moment of the system history. However, the real Bitcoin network is using an adaptive difficulty adjustment mechanism. In this paper we introduce and analyze a new kind of attack on a mining difficulty retargeting function used in Bitcoin. A malicious miner is increasing his mining profits from the attack, named coin-hopping attack, and, as a side effect, an average delay between blocks is increasing. We propose an alternative difficulty adjustment algorithm in order to reduce an incentive to perform coin-hopping, and also to improve stability of inter-block delays. Finally, we evaluate the presented approach and show that the novel algorithm performs better than the original algorithm of Bitcoin.
Web based security applications have become increasingly important in the past years. Especially in times of blockchain based crypto currencies, user authentication is a critical aspect for the overall security, integrity and acceptance of such systems. While blockchain technologies provide a decentralized approach, the client side still largely relies on centralized security approaches. Those centralized approaches are easier to implement, but at the same time bear the risk of usual security flaws. Therefore, this paper presents a decentralized approach for increasing the security by adding a decentralized two-factor authentication mechanism to the execution of
operations.
Detection of influential actors in social media plays an important role for increasing the quality and efficiency of work and services in many fields such as education, marketing, etc. This work aims to introduce a new approach for the characterization of influential actors in online social media, such as Twitter. We present on a model of influence of an actor that is based on the attractiveness of the actor in terms of the number of other new actors with which he or she has established relations over time. We have used this concept and measure of influence to determine optimal seeds in a simulation of influence maximization using two empirically collected social networks for the underlying graphs.
Industry 4.0 is known as the fourth industrial revolution which refers to the integration of technologies that make the factories interoperable by seamlessly connecting machines, employees and sensors for communication. In Industry 4.0, one of the key features is the use of new technologies to recognize the current context. Thus, the employees are supported with contextual information for speeding up decision-making during various processes related to planning, production, maintenance, etc. As a contribution to this area, the work described here aims to introduce a cyber-physical system (CPS) approach to provide context-based and intelligent support to employees in heavy industries using new technologies, especially in the field of mobile devices. In this work, mobile device sensors and image processing techniques are used to recognize the context which requires specific support. In addition, new scenarios and associated processes are developed to support the employees on the basis of new, flexible, adaptive and mobile technologies.
To enable a robotic assistant to autonomously reach for and transport objects while avoiding obstacles we have generalized the attractor dynamics approach established for vehicles to trajectory formation in robot arms. This approach is able to deal with the time-varying environments that occur when a human operator moves in a shared workspace. Stable fixed points (attractors) for the heading direction of the end-effector shift during movement and are being tracked by the system. This enables the attractor dynamics approach to avoid the spurious states that hamper potential field methods. Separating planning and control computationally, the approach is also simpler to implement. The stability properties of the movement plan make it possible to deal with fluctuating and imprecise sensory information. We implement this approach on a seven degree of freedom anthropomorphic arm reaching for objects on a working surface. We use an exact solution of the inverse kinematics, which enables us to steer the spatial position of the elbow clear of obstacles. The straight-line trajectories of the end-effector that emerge as long as the arm is far from obstacles make the movement goals of the robotic assistant predictable for the human operator, improving man-machine interaction
For any kind of assistant systems, the ability to interact with the human operator and taking into account his or her assumptions and expectations, is the basis for a reasonable behavior. As a consequence the human behavior have to be studied in order to generate driver models that are learned from human driving data. In this work we focus on the improvement of the immersion in driving simulation environment by developing and implementing a cheap and efficient method for head tracking. We also explain why head tracking feedback is crucial for the quality of collected behavioural data, especially for simulators with close screen distances.
In this paper we present an approach for contextual big data analytics in social networks, particularly in Twitter. The combination of a Rich Context Model (RCM) with machine learning is used in order to improve the quality of the data mining techniques. We propose the algorithm and architecture of our approach for real-time contextual analysis of tweets. The proposed approach can be used to enrich and empower the predictive analytics or to provide relevant context-aware recommendations.
The term “Cloud Computing” does not primarily specify new types of core technologies but rather addresses features to do with integration, interoperability and accessibility. Although not new, virtualization and automation are core features that characterize Cloud Computing. In this paper, we intend to explore the possibility of integrating cloud services with educational scenarios without re-defining neither the technology nor the usage scenarios from scratch. Our suggestion is based on certain solutions that have already been implemented and tested for specific cases.
It is common to have a large noise and/or a strong interference around the frequency band of a Power Line Communication (PLC) system due to the fact that the PLC channel is not designed for communication. If there are no efficient operations at the receiver to suppress this out-of-band noise and interference to some extent, the Signal-to-Noise Ratio (SNR) will decrease and system will suffer performance loss consequently. Normally, the effort of Analog Front End (AFE) on the suppression of out-of-band interference is finite and it is uneconomic to change the AFE structure to make a performance improvement. Therefore, an appropriate structure of Digital Front End (DFE) at the receiver is necessary to reduce the impact of out-of-band noise and interference furthermore. In this paper, three different kinds of DFE structure at the receiver are introduced: classic DFE, time domain Nyquist windowing and Equivalent Complex Baseband (ECB) approach. The performance of these DFE structures is compared, not only from the aspect of out-of-band suppression, but also from the system overhead they need.
In recent years, hardware for the production and consumption of virtual reality content has reached level of prices that make it affordable to everyone. Accordingly schools and universities are showing increased interest in implementations of virtual reality technologies for supporting their innovative educational activities. Hence, this paper presents a flexible architecture for supporting the development of virtual reality learning scenarios conveniently deployed for educational purposes. We also suggest an example of such
educational scenario for medical purposes deployable with the suggested architecture. In addition, we developed and used a questionnaire answered by 17 medical students in order to derive additional requirements for refining such scenarios. Then, we present these efforts while aiming at deployments usable also for additional domains. Finally, we summarize and mention aspects we will address
in our coming efforts while deploying such activities.
In this paper we describe an architecture for behavioral organization based on dynamical systems. This architecture
enables the generation of complex behavioral sequences, which is demonstrated using the example of approaching and
passing a door. The behavioral sequence is generated by activating and deactivating the elementary behaviors dependent
on sensory information and internal logical conditions. The architecture is demonstrated on a mobile KOALA robot and
in simulation as well.
In the field of magnetic inductance tomography,
signal processing is a real challenge. This is due to the divergent
nature of magnetic fields. The sensitivity, i.e. the change in the
receiving signal by means of an electrically conductive sample
in a measuring volume depends strongly on the positioning
of the sample. Objects that are located near the transmitting
or receiving coils are very well locatable, where objects in
larger distance are hard to detect. In this paper an approach
is presented that improves the topology of the magnetic fields
in the ”magnetic induction tomography” (MIT) by changing
geometric constructions and current patterns of coils so far,
as to allow a sharper localization of objects within the space.
The aim is to level the distribution of the sensitivity in the
measuring volume, so that electrically conductive objects with
a larger distance between transmitting and receiving unit can
be detected with almost the same signal intensity as objects
close to the transmitting and receiving unit. The simulation tool
Comsolic is used for the geometric modeling making a finite
element analysis (FEA). The subsequent signal processing and
analysis of the simulation results are implemented in Matlabic .
Within this FEA the coil geometries and current patterns are
changed numerically, so that the minimum object size, that is
still detectable, is, compared to the known MIT, reduced and the
sensitivity of the system is improved. To validate the simulation in
Comsolic , first simulation results are compared with analytical
models and analyses.
The astronomy domain provides rich opportunities for learning about natural phenomena. It can involve and motivate a variety of mathematical and physical knowledge and skills. However it is difficult to connect astronomic observations to modelling and calculation tools and to embed them into educational scenarios. It is particularly this challenge which is focused in this paper. Concretely, we build on an existing collaborative modelling framework (Cool Modes) and extend it with specific representations to support learning activities in astronomy. A first field test has been conducted with these extensions.
This contribution demonstrates the efficient embedding of a single depth-camera into the automotive environment making mid-air gesture interaction for mobile applications viable in such a scenario. In this setting a new human-machine interface is implemented to give an idea of future improvements in automation processes in industrial applications. Our system is based on a data-driven approach by learning hand poses as well as gestures from a large database in order to apply them on mobile devices. We register any movement in a nearby driver area and crop data efficiently with the means of PCA transforming it into so-called feature vectors which present the input for our multi-layer perceptrons (MLPs). After MLP classification, the interpretation of user input is sent via WiFi to a tablet PC mounted into the car interior visualizing an infotainment system which the user is able to interact with. We demonstrate that by this setup hand gestures as well as hand poses are easily and efficiently interpretable insofar as that they become an intuitive and supplementary means of interaction for automotive HMI in mobile scenarios realizable in real-time.
In recent years, the number of reasonable powerful mobile devices increased. In 2011, the number of smartphones(e.g.)increased to more than 300 million units. A lot of research has already been conducted with respect of mobile devices acting as Cloud Service consumers, but
still not much effort is put on mobile devices in the role of Cloud Service providers. Therefore, this paper presents an approach that allows to utilize mobile devices like smart phones or tablets as Cloud Service providers. In order to make this a reasonable approach, some of the occurring problems are discussed and it is shown how the presented architecture is able to overcome these problems. Last
but not least, this paper
describes some performance
tests of the chosen implementa
tion for mobile Web Services.
CORA is a robotic assistant whose task is to collaborate with a human operator on simple manipulation or handling tasks. Its sensory channels comprising vision, audition, haptics, and force sensing are used to extract perceptual information about speech, gestures and gaze of the operator, and object recognition. The anthropomorphic robot arm makes goal-directed movements to pick up and hand over objects. The human operator may mechanically interact with the arm by pushing it away (haptics) or by taking an object out of the robot's gripper (force sensing). The design objective has been to exploit the human operator's intuition by modeling the mechanical structure, the senses, and the behaviors of the assistant on human anatomy, human perception, and human motor behavior.
We extend the attractor dynamics approach to generate goal-directed movement of a redundant, anthropomorphic arm while avoiding dynamic obstacles and respecting joint limits. To make the robot's movements human-like, we generate approximately straight-line trajectories by using two heading direction angles of the tool-point quite analogously to how movement is represented in the primate central nervous system. Two additional angles control the tool's spatial orientation so that it follows the tool-point's collision-free path. A fifth equation governs the redundancy angle, which controls the elevation of the elbow so as to avoid obstacles and respect joint limits. These variables make it possible to generate movement while sitting in an attractor (or, in the language of the potential field approach, in a minimum). We demonstrate the approach on an assistant robot, which interacts with human users in a shared workspace
The presented work formulates an framework in which early prediction of drivers lane change behavior is realized. We aim to build a representation of drivers lane change behavior in order to recognize and to predict driver's intentions as a first step towards a realistic driver model. In the test bed of the Institute of Neuroinformatik, based on the traffic simulator NISYS TRS 1, 10 individuals have driven in the experiments and they performed more then 150 lane change maneuvers. Lane-offset, distance to the front car and time to contact, were recorded. The acquired data was used to train - in parallel- a recurrent neural network, a feed forward neural network and a set of support vector machines. In the followed test drives the system was able of performing a lane change prediction time of 1.5 sec beforehand. The proposed approach describes a framework for lane-change detection and prediction, which will serve as a prerequisite for a successful driver model.
Generating collision free reaching movements for redundant manipulators using dynamical systems
(2010)
For autonomous robots to manipulate objects in unknown environments, they must be able to move their arms without colliding with nearby objects, other agents or humans. The simultaneous avoidance of multiple obstacles in real time by all link segments of a manipulator is still a hard task both in practice and in theory. We present a systematic scheme for the generation of collision free movements for redundant manipulators in scenes with arbitrarily many obstacles. Based on the dynamical systems approach to robotics, constraints are formulated as contributions to a dynamical system that erect attractors for targets and repellors for obstacles. These contributions are formulated in terms of variables relevant to each constraint and then transformed into vector fields over the manipulator joint velocity vector as an embedding space in which all constraints are simultaneously observed. We demonstrate the feasibility of the approach by implementing it on a real anthropomorphic 8-degrees-of-freedom redundant manipulator. In addition, performance is characterized by detecting failures in a systematic simulation experiment in randomized scenes with varying numbers of obstacles.
Generating flexible collision-free reaching move-
ments is a standard task for autonomous articulated robots that
is critical especially when such systems interact with humans in
a service robotics setting. Current solutions are still challenging
to put into practice. Here we generalize an approach
first
used to plan end-effector movement that is based on attractor
dynamical systems. We show, how different contributions to
the motion planning dynamics can be formulated in constraint-
specific reference frames and then transformed into the frame
of the joint velocity vector. We implement this system on an
8 DoF redundant manipulator and show its feasibility in a
simulation. A systematic experiment with randomly generated
obstacle scenes characterizes the performance of the system.
Especially challenging confi
gurations of obstacles are discussed
to illustrate how the method solves these cases
This paper deals with the question how to integrate smart devices in Java appli-
cations. It will outline how different smart devices can be used to enrich learning
environments, we will point to some of the problems one has to face while dealing
with smart devices, a differentiation of smart devices will be done and we will give an
overview about existing Java Virtual Machines available for different smart devices.
Furthermore we will tackle the question of the communication between different smart
devices and also between different kinds of smart devices. An outlook to the future
work will also be given at the end of this work
Integrating Orientation Constraints into the Attractor Dynamics Approach for Autonomous Manipulation
(2010)
For the rod shape measurement of hot rolled round steel bars (rods) the high frequency eddy current method is especially well suited as it requires no contact point and is not limited to below the Curie temperature. Defects of the rod's shape can be detected by measuring the impedance spectrum of the RLC-oscillator. In the first laboratory setup an Agilent impedance analyser was used for initial tests. Nevertheless, this setup cannot be applied in a steel plant due to the difficult environmental conditions. Hence, a vector network analyser for passive impedance measurement that is applicable in these surroundings was developed.
In this paper we describe our efforts to foster educational interoperability in scenarios using mobile and wireless technologies to support hands-on scientific experimentation and learning. A special focus is given to the idea that innovative uses of mobile and wireless technologies enhance the learners' scientific experience. Specific contributions include the creation of new applications to support interoperability between different mobile devices, thus to provide "glue" between different learning situations. We describe a number of educational scenarios as well as the technologies and the architectural principles behind them.
Methods of red-hot rod shape testing require a robust non-contact measurement principle as a touch point could lead to damages to the rod and the detection unit. Therefore a new basic approach based on high frequency eddy current (HFEC) has been investigated. Due to the robustness and the ability to determine the rod shape even above the Curie temperature this principle is especially well suited and can be implemented in the production process directly. The first automatic measurement setup was successfully developed with promising results. Hereby a defect of ovality was detected with a parallel RLC-oscillator. The capacity of this RLC-oscillator is constant whereas the inductance is the measurement part that varies due to eddy current interactions with the rod.
Fat content of liver is an essential parameter to decide whether a liver is suitable for transplantation or not. The determination of fat content is often challenging and usually there is not enough time to bring a specimen to a pathologic laboratory. That is why transplantation clinics need a technique to measure the fat content of a graft. In this paper the theoretical basics and an existing laboratory setup are presented.
The harmonic and interharmonic analysis recommendations are contained in the latest International Electrotechnical Commission (IEC) standards on power quality. Measurement and analysis experiences have shown that great difficulties arise in the interharmonics detection and measurement with acceptable levels of accuracy. In this paper, the spectral leakage problems of the discrete Fourier transform due to synchronization errors of interharmonics are analyzed. The time-domain averaging is investigated for the processing of harmonics in the framework of the IEC standards. A difference filter is proposed to detect interharmonics and can be compatible with the IEC standards. Simulations and the field results show the usefulness of the proposed methods.
The transurethral resection (TUR) is a standard technique in urological treatment procedures. Both, monopolar and bipolar electrosurgical systems, are used for TUR. Whereas electrical and physical processes in surgery surroundings are well understood for monopolar systems, there is no sufficient data base for the assessment of the processes with the use of bipolar systems. In this context a multi-electrode measuring system was developed to visualize the spatial potential distribution around bipolar electrosurgical devices as a first step to risk analysis. To simulate the anatomic surroundings of a transurethral surgery a cylinder filled with isotonic saline solution was used as a complexity reduced experimental environment.
The bipolar transurethral resection is a further development of monopolar transurethral resection, being the gold standard in surgical treatment of prostate and bladder diseases. To create the metrological basis for understanding of electrical and physical processes during bipolar transurethral resection an experimental set-up to visualize spatial potential distribution around bipolar devices was developed. A hardware based signal conditioning and specific undersampling are presented as data acquisition methods for a sampling rate up to 1 MS/s. These methods are compared with the possibilities of a high speed data acquisition card. For more than four measuring channels and depending on the output bandwidth of the electrosurgical generator either hardware based signal conditioning or specific undersampling is suggested.
We present an architecture based on the Dynamic Field Theory for the problem of scene representation. At the core of this architecture are three-dimensional neural fields linking feature to spatial information. These three-dimensional fields are coupled to lower-dimensional fields that provide both a close link to the sensory surface and a close link to motor behavior. We highlight the updating mechanism of this architecture, both when a single object is selected and followed by the robot's head in smooth pursuit and in multi-item tracking when several items move simultaneously
The WWW is the killerapp of the internet. In recent years an enormously increasing number of Web Applications, as a means of human-to-computer interaction, showed up, that allows a visitor of a certain website to interact with the website. Additionally the approach of Web Services was introduced in order to allow computer-to-computer Interaction on the basis of standardized protocols. This paper shows how the gap between Web Applications and Web Services can be closed by making Web Applications available to computer-to-computer interaction by a systematic approach.
In recent years the diversity and the ownership of mobile devices steadily increased while the prices for this kind of devices decreased to a level that allows many students to own reasonably powerful devices. As mobile devices are also being used in learning scenarios, the challenge of today is the integration of multiple heterogeneous devices into existing and upcoming learning scenarios. This paper describes an architecture that allows easy integration of various kinds of mobile and non-mobile devices. The presented architecture will be exemplified by a group discussion scenario in a heterogeneous learning environment. The paper concludes with the description of a pilot study using the described system.
This paper presents an approach towards a mobile learning environment, which is flexible in terms of supported scenarios, supported devices and input channels. The approach makes use of existing and commonly used channels like SMS, Twitter or Face book to increase acceptance and ease-of-use of mobile devices in learning scenarios. Envisaged application scenarios are described along with technical details for their realization.
The first robots are currently appearing on the consumer market. Initially they are targeted at rather simple applications such as entertainment and home convenience. For more complex areas, these robots will need to collaborate and interactively communicate with their human users, which requires appropriate man-machine interaction technologies and considerable cognitive abilities on the robot's side. Consumer acceptance will strongly depend on the integrated system. Thus, system integration and evaluation of the integrated system is becoming increasingly important. This paper describes our approach to construct a robotic assistance system. We present experience with an integrated technology demonstration and exposure of the integrated system to the public.
Bipolar electrosurgical systems are used for the treatment of benign prostatic hyperplasia (BPH) in urology. In order to analyse electrothermal processes during surgery the power loss density distribution around a bipolar resectoscope is calculated out of the measured potential distribution in isotonic saline solution ex situ. During further analysis power loss density values act as input for the Penne's bioheat equation. To achieve results, which are as realistic as possible, a method to obtain power loss density values, depending on the observed tissue or medium in the operating field, is presented. Applying this method, the power loss density distribution in isotonic saline solution at 25 °C is compared to the distribution calculated for the average conductivity of biological tissue in the region of interest.
In this contribution we present a novel approach to transform data from time-of-flight (ToF) sensors to be interpretable by Convolutional Neural Networks (CNNs). As ToF data tends to be overly noisy depending on various factors such as illumination, reflection coefficient and distance, the need for a robust algorithmic approach becomes evident. By spanning a three-dimensional grid of fixed size around each point cloud we are able to transform three-dimensional input to become processable by CNNs. This simple and effective neighborhood-preserving methodology demonstrates that CNNs are indeed able to extract the relevant information and learn a set of filters, enabling them to differentiate a complex set of ten different gestures obtained from 20 different individuals and containing 600.000 samples overall. Our 20-fold cross-validation shows the generalization performance of the network, achieving an accuracy of up to 98.5% on validation sets comprising 20.000 data samples. The real-time applicability of our system is demonstrated via an interactive validation on an infotainment system running with up to 40fps on an iPad in the vehicle interior.
Detection of air trapping in chronic obstructive pulmonary disease by low frequency ultrasound
(2012)
Background: Spirometry is regarded as the gold standard for the diagnosis of COPD, yet the condition is widely underdiagnosed. Therefore, additional screening methods that are easy to perform and to interpret are needed. Recently, we demonstrated that low frequency ultrasound (LFU) may be helpful for monitoring lung diseases. The objective of this study was to evaluate whether LFU can be used to detect air trapping in COPD. In addition, we evaluated the ability of LFU to detect the effects of short-acting bronchodilator medication.Methods: Seventeen patients with COPD and 9 healthy subjects were examined by body plethysmography and LFU. Ultrasound frequencies ranging from 1 to 40 kHz were transmitted to the sternum and received at the back during inspiration and expiration. The high pass frequency was determined from the inspiratory and the expiratory signals and their difference termed F. Measurements were repeated after inhalation of salbutamol.Results: We found signi ficant differences in F between COPD subjects and healthy subjects. These differences were already significant at GOLD stage 1 and increased with the severity of COPD. Sensitivity for detection of GOLD stage 1 was 83% and for GOLD stages worse than 1 it was 91%. Bronchodilator effects could not be detected reliably.Conclusions: We conclude that low frequency ultrasound is cost-effective, easy to perform and suitable for detecting air trapping. It might be useful in screening for COPD
Background:
Detection of influential actors in social media such as Twitter or Facebook plays an important role for improving the quality and efficiency of work and services in many fields such as education and marketing.
Methods:
The work described here aims to introduce a new approach that characterizes the influence of actors by the strength of attracting new active members into a networked community. We present a model of influence of an actor that is based on the attractiveness of the actor in terms of the number of other new actors with which he or she has established relations over time.
Results:
We have used this concept and measure of influence to determine optimal seeds in a simulation of influence maximization using two empirically collected social networks for the underlying graphs.
Conclusions:
Our empirical results on the datasets demonstrate that our measure stands out as a useful measure to define the attractors comparing to the other influence measures.
Background:
Influential actors detection in social media such as twitter or Facebook can play a major role in gathering opinions on particular topics, improving the market
-
ing efficiency, predicting the trends, etc.
Proposed methods:
This work aims to extend our formally defined
T
measure to
present a new measure aiming to recognize the actor’s influence by the strength of
attracting new important actors into a networked community. Therefore, we propose a
model of the actor’s influence based on the attractiveness of the actor in relation to the
number of other attractors with whom he/she has established connections over time.
Results and conclusions:
Using an empirically collected social network for the
underlying graph, we have applied the above-mentioned measure of influence in
order to determine optimal seeds in a simulation of influence maximization. We study
our extended measure in the context of information diffusion because this measure is
based on a model of actors who attract others to be active members in a community.
This corresponds to the idea of the IC simulation model which is used to identify the
most important spreaders in a set of actors.
Keywords: Actor influence, Social media networks, Twitter, IC model, Information
diffusion, Independent cascade model, T measure
Photoluminescence (PL) in GaN or InGaN layers monitored during epitaxial growth at high temperatures permits a quasi-continuous in situ characterization of opto-electronic properties. Therefore, epitaxial parameters can now be optimized at the earliest possible stage. A pulsed and high-power UV laser was required for PL excitation at high temperatures. Herein, the underlying nonlinear mechanism was studied via time-resolved PL experiments and rate equation-based modeling. A temperature-activated and saturable path for quenching over defects was identified. Beyond the saturation threshold, reasonably-intensive PL sets in. At high temperatures not only is the near band gap-PL present, but also—as a new observation—a defect-assisted PL emerges. Apart from these specific electronic transitions in high-temperature PL of GaN, a simple, but reasonably predictive model of the luminescent thin film has been set up to track down interference fringes in the PL spectra. It is worth mentioning that the spectral PL modulation (aiming at the Purcell effect) is often mixed up with ordinary Fabry–Pérot interference. A distinction has become key to properly analyze the spectral signatures of high-temperature PL in order to provide a reliable in situ characterization of GaN layers during epitaxial growth
This experimental study demonstrates for the first time a solid-state circuitry and design for a simple compact copper coil (without an additional bulky permanent magnet or bulky electromagnet) as a contactless electromagnetic acoustic transducer (EMAT) for pulse echo operation at MHz frequencies. A pulsed ultrasound emission into a metallic test object is electromagnetically excited by
an intense MHz burst at up to 500 A through the 0.15 mm filaments of the transducer. Immediately thereafter, a smoother and quasi “DC-like” current of 100 A is applied for about 1 ms and allows an
echo detection. The ultrasonic pulse echo operation for a simple, compact, non-contacting copper coil is new. Application scenarios for compact transducer techniques include very narrow and
hostile environments, in which, e.g., quickly moving metal parts must be tested with only one, non-contacting ultrasound shot. The small transducer coil can be operated remotely with a cable
connection, separate from the much bulkier supply circuitry. Several options for more technical and fundamental progress are discussed.
Optimization of Encircling Eddy Current Sensors for Online Monitoring of Hot Rolled Round Steel Bars
(2014)
Modern manufacturing industries are continually working on quality enhancements for the hot rolling process of round products. One method for improving the finalisation of the rods is the implementation of an automatic size control system. As a result of these trends over the last few years, there has been an increasing demand for more accurate online measurements. Thus the reason for the research performed for this thesis. A particular challenge throughout this research was dealing with the temperature changes (up to 1200°C) from the in- and output of the fervent rolling stocks, and the effect this temperature changes had on the sensors. Furthermore, there is also high demand for developing fast and practical electronic measuring equipment, capable of measuring during high transport velocities (up to 120 m/s). The eddy current principle is just one of the very few methods available which can with-stand such harsh industrial environments. In fact, eddy current sensors are already being integrated into online monitoring tasks for hot rolling processes. The measurement uncertainty, however, is still considerably large for process control purposes. One reason for this lies within the ability for eddy current detectors to receive signals influenced by outward forces, i.e. forces dependent on its location, its geometry, the outside temperature and the material properties of a particular target. Thus the current accuracy for a cross-sectional area measurement, for example, is no higher than 1%. As a result, this thesis investigates the magnitude of all individual influential factors on the eddy current detectors, using model-based analysis techniques. The analytical model provides a solution for all rotationally symmetrical targets and the FEA model covers all of the other influencing parameters in a more time consuming manner. This thesis then provides different methods which are developed to separate the cross-sectional area measurement of a rod from all of the other influencing parameters. In addition, a material tracking approach for round products is developed. Two different kinds of prototypes, capable of measuring approximately 466 Tons of red-hot steel rods during the production process, are finally introduced in this thesis. The usefulness of the eddy current principle is validated by the provided field test results. The count accuracy for the identification of 2876 bars was found to be 99.93%, and the average measurement accuracy for the cross-sectional area experiments was reduced to ± 0.29 % when including all of the findings.
A Large and Quick Induction Field Scanner for Examining the Interior of Extended Objects or Humans
(2017)
This study describes the techniques and signal properties of a large, powerful, and linear-scanning 1.5 MHz induction field scanner. The mechanical system is capable of quickly reading the volume of relative large objects, e.g., a test person. The general approach mirrors Magnetic Induction Tomography (MIT), but the details differ considerably from currently-described MIT systems: the setup is asymmetrical, and it operates in gradiometric modalities, either with coaxial excitation with destructive interference or with a single excitation loop and tilted receivers. Following this approach, the primary signals were almost completely nulled, and test objects' real or imaginary imprint was obtained directly. The coaxial gradiometer appeared advantageous: exposure to strong fields was reduced due to destructive interference. Meanwhile, the signals included enhanced components at higher spatial frequencies, thereby obtaining a gradually improved capability for localization. For robust signals, the excitation field can be powered towards the rated limits of human exposure to time-varying magnetic fields. Repeated measurements assessed the important signal integrity, which is affected by the scanner´s imperfections, particularly any motions or respiratory changes in living beings during or between repeated scans. The currently achieved and overall figure of merit for artifacts was 58 dB for inanimate test objects and 44 dB for a test person. Both numbers should be understood as worst case levels: a repeated scan with intermediate breathing and drift/dislocations requires 50 seconds, whereas a single measurement (with respiratory arrest) takes only about 5 seconds.
Recognition of emotions from multimodal cues is of basic interest for the design of many adaptive interfaces in human-machine interaction (HMI) in general and human-robot interaction (HRI) in particular. It provides a means to incorporate non-verbal feedback in the course of interaction. Humans express their emotional and affective state rather unconsciously exploiting their different natural communication modalities such as body language, facial expression and prosodic intonation. In order to achieve applicability in realistic HRI settings, we develop person-independent affective models. In this paper, we present a study on multimodal recognition of emotions from such auditive and visual cues for interaction interfaces. We recognize six classes of basic emotions plus the neutral one of talking persons. The focus hereby lies on the simultaneous online visual and accoustic analysis of speaking faces. A probabilistic decision level fusion scheme based on Bayesian networks is applied to draw benefit of the complementary information from both – the acoustic and the visual – cues. We compare the performance of our state of the art recognition systems for separate modalities to the improved results after applying our fusion scheme on both DaFEx database and a real-life data that captured directly from robot. We furthermore discuss the results with regard to the theoretical background and future applications.
To reduce the number of traffic accidents and to increase the drivers comfort, the thought of designing driver assistance systems arose in the past years. Fully or partly autonomously guided vehicles, particularly for road traffic, pose high demands on the development of reliable algorithms. Principal problems are caused by having a moving observer in predominantly natural environments. At the Institut fur Neuroinformatik methods for analyzing driving relevant scenes by computer vision are developed in cooperation with several partners from the automobile industry. We present a solution for a driver assistance system. We concentrate on the aspects of video-based scene analysis and organization of behavior.
Based on the concepts of dynamic field theory (DFT), we present an architecture that autonomously generates scene representations by controlling gaze and attention, creating visual objects in the foreground, tracking objects, reading them into working memory, and taking into account their visibility. At the core of this architecture are three-dimensional dynamic neural fields (DNFs) that link feature to spatial information. These three-dimensional fields couple into lower dimensional fields, which provide the links to the sensory surface and to the motor systems. We discuss how DNFs can be used as building blocks for cognitive architectures, characterize the critical bifurcations in DNFs, as well as the possible coupling structures among DNFs. In a series of robotic experiments, we demonstrate how the DNF architecture provides the core functionalities of a scene representation.
Relevance & Research Question: Smartphones have become an integrated part in everyday life facilitating communication, information access, entertainment and organization anytime and anywhere. However, the omnipresence of such devices can evoke psychological dependencies and the need of being always connected resulting in discomfort when the smartphone is not accessible. While few studies have found heightened anxiety during smartphone absence (e.g. Cheever, Rosen, Carrier, & Chavez, 2014), such research is scarce. Therefore, we aimed at expanding existing research asking whether the mere imagination of smartphone absence suffices to trigger anxiety and affect user’s context evaluations.
Global software development changes the requirements in terms of soft competency and increases the complexity of social interaction by including intercultural aspects. While soft competency is often seen as crucial for the success of global software development projects, the concrete competence requirements remain unknown. Internationalization competency represents one of the first attempts to structure and describe the soft competence requirements for global software developers. Based on the diversity of tasks, competence requirements will differ among the various phases of software development. By conducting a survey on the importance of internationalization competences for the different phases of global software development, we identified differences in terms of competence importance and requirements in the phases. “Adaptability” (of one's working style) and “Cultural Awareness” were the main differences. “Cultural Awareness” distinguishes requirements engineering and software design from testing and implementation while “Adaptability” distinguishes implementation and software design from requirements engineering and testing.
The goal of this paper is to define relevant barriers to the exchange of Open Educational Resources in local public administrations. Building upon a cultural model, eleven experts were interviewed and asked to evaluate several factors, such as openness in discourse, learning at the workplace, and superior support, among others. The result is a set of socio-cultural factors that shape the use of Open Educational Resources in public administrations. Significant factors are, in this respect, the independent choice of learning resources, the spirit of the platform, the range of available formats and access to technologies. Practitioners use these factors to elaborate on the readiness of public administrations towards the use of open e-Learning systems. To academic debates on culture in e-Learning, the results provide an alternative model that is contextualized to meet the demands of public sector contexts. Overall, the paper contributes to the lack of research about open e-Learning systems in the public sector, as well as regarding culture in the management of learning and knowledge exchange.
The open education movement has witnessed ups and downs from initial interest in transparency and openness, followed by a lack of reuse of open educational resources (OER) and the massive boost of interest in massive open online courses (MOOCs). This article addresses educators' online behaviors and perceptions regarding participation in collaborative development of OER in online settings. Using a data-driven approach to study educators' perceptions, this article presents multiple considerations for collaborative OER development and validates a new model explaining educators' intention to participate in collaborative action. The findings reveal the contradictory nature of emotional ownership of knowledge: a critical enabling factor for commitment and a barrier to knowledge exchange in an open and transparent manner. The findings also show how outcome expectations regarding increase in reputation and status in the network do not influence the intention to share knowledge. Further interviews with idea-sharing platform users enable us to explain the favorable settings to resolve the dilemma of emotional ownership. The study contributes not only to further development of the open education movement but also to theory development of educators’ collaborative behaviors online.
This article presents a omparative study of the barriers to open e-learning in public administrations in Luxembourg, Germany, Montenegro and Ireland. It discusses the current state of open e-learning of public administration employees at the local government level and derives the barriers to such learning. This paper's main contribution is its presentation of an empirical set of barriers in the four European countries. The results allow informed assumptions about which barriers will arise in the forthcoming use of open-source e-learning technology, particularly open educational resources as means of learning. Furthermore, this study offers a contextualised barrier framework that allows the systematic capture and comparison of challenges for future studies in the field. Other practical contributions include providing advice about open e-learning programmes, systematising lessons learned and addressing managerial implications.
In this paper we discuss how group processes can be influenced by designing specific tools in computer supported collaborative leaning. We present the design of a shared workspace application for co-constructive tasks that is enriched by certain functions that are able to track, analyze and feed back parameters of collaboration to group members. Thereby our interdisciplinary approach is mainly based on an integrative methodology for analyzing collaboration behavior and patterns in an implicit manner combined with explicit surveyed data of group members’ attitudes and its immediate feedback to the groups. In an exploratory study we examined the influence of this feedback function. Although we could only analyze ad-hoc groups in this study, we detected some benefits of our methodology which might enrich real life Learning Communities’ collaboration processes. The data analysis in our study showed advantages of this feedback on processes of a group’s well-being as well as parameters of participation. These results provide a basis for further empirical work on problem solving groups that are supported by means of parallel interaction analysis as well as its re-use as information resource.
This paper describes an educational application that combines handhelds (PDAs) and programmable Lego bricks in a classroom scenario that deals with the problem of letting a robot escape from a maze. It is specific to our setting that the problem can be solved both in the physical world by steering a Lego robot and in a simulated software environment on a PDA or on a PC. This approach enables the students to generate successful sets of rules in the simulation and to test these sets of rules later in physical mazes, or to create new types of mazes as challenges for known rule sets. In this paper we describe the technical setting for this scenario, different pedagogical scenarios and we will report an evaluation with a group of students in a school environment.
In this work we report the first quasi-continuous in-situ photoluminescence study of growing InGaN LED structures inside an industrial-grade metal-organic vapor phase epitaxy (MOVPE) reactor at growth temperature. The photoluminescence spectra contain information about temperature, thickness and composition of the epitaxial layers. Furthermore, the in-situ spectra – even at an early stage of the growth of the active region – can be used to predict the photoluminescence emission wavelength of the structure at room temperature. In this study an accuracy of this predicted wavelength in the range of ± 1.3 nm (2σ) is demonstrated. This technique thus appears suitable for closed-loop control of the emission wavelength of InGaN LEDs already during growth.
A simple copper coil without a voluminous stationary magnet can be utilized as a non-contacting transmitter and as a detector for ultrasonic vibrations in metals. Advantages of such compact EMATs without (electro-)magnet might be: applications in critical environments (hot, narrow, presence of iron filings…), potentially superior fields (then improved ultrasound transmission and more sensitive ultrasound detection).
The induction field of an EMAT strongly influences ultrasound transduction in the nearby metal. Herein, a simplified analytical method for field description at high liftoff is presented. Within certain limitations this method reasonably describes magnetic fields (and resulting eddy currents, inductances, Lorentz forces, acoustic pressures) of even complex coil arrangements. The methods can be adapted to conventional EMATS with a separate stationary magnet.
Increased distances (liftoff) are challenging and technically relevant, and this practical question is addressed: with limited electrical power and given free space between transducer and target metal, what would be the most efficient geometry of a circular coil? Furthermore, more complex coil geometries (“butterfly coil”) with a concentrated field and relatively higher reach are briefly investigated.
The production and deformation of perforated sheets introduces high levels of mechanical stress into the material. In a significant fraction, such stress levels lead to crack formation in the processed sheets. Additionally, the material might be thinned and weakened in the exposed areas; these areas tend to crack at any later dates. Currently no measuring device for the detection of such material cracks or narrowing in perforated sheet metals is in practical use. Such device should be able to test the deformed circumference of the processed sheets within the very limited time of the production cycles. This paper describes the physical principles and a metrological implementation of a potential method for fast crack detection in perforated sheet metals. Even a critical material thinning - prior to the formation of a crack - can be observed. The measuring task appears to be solvable on the basis of high frequency electromagnetic fields.
Nowadays, teachers and students utilize different ICT devices for conducting innovative and educational activities from anywhere at any time. The enactment of these activities relies on robust communication and computational infrastructures used for supporting technological devices enabling better accessibility to educational resources and pedagogical scaffolds, wherever and whenever necessary. In this paper, we present EDU.Tube: an interactive environment that relies on web and mobile solutions offered to teachers and students for authoring and incorporating educational interactions at specific moments along the time line of occasional YouTube video-clips. The teachers and students could later experience these authored artefacts while interacting from their stationary or mobile devices. We describe our efforts related to the design, deployment and evaluation of an educational activity supported by the EDU.Tube environment. Furthermore, we illustrate the specific teachers’ and students’ efforts practiced along the different phases of this educational activity. The evaluation of this activity and results are presented, followed by a discussion of these findings, as well as some recommendations for future research efforts further elaborating on EDU.Tube’s aspects in relation to learning analytics.
The paper provides a contextualization process to adapt Open Knowledge Resources for the need of public administrations. By help of a matching strategy, culture and context profiles of learners and learning resources are compared. The comparison allows to draw inferences how to contextualize an open knowledge resource for own learning needs. An example is illustrated and future research fields are proposed.
We describe the general concept, system architecture, hardware, and the behavioral abilities of Cora (Cooperative Robot Assistant, see Fig. 1), an autonomous non mobile robot assistant. Outgoing from our basic assumption that the behavior to perform determines the internal and external structure of the behaving system, we have designed Cora anthropomorphic to allow for humanlike behavioral strategies in solving complex tasks. Although Cora was built as a prototype of a service robot system to assist a human partner in industrial assembly tasks, we will show that Cora’s behavioral abilities are also conferrable in a household environment. After the description of the hardware platform and the basic concepts of our approach, we present some experimental results by means of an assembly task.
The transurethral resection (TUR) is a standard technique in urological treatment procedures. Both, monopolar and bipolar electrosurgical systems, are used for TUR. Whereas electrical and physical processes in surgery surroundings are well understood for monopolar systems, there is no sufficient data base for the assessment of the processes with the use of bipolar systems. In this context a multi-electrode measuring system was developed to visualize the spatial potential distribution around bipolar electrosurgical devices as a first step to risk analysis. To simulate the anatomic surroundings of a transurethral surgery a cylinder filled with isotonic saline solution is used as a complexity reduced experimental environment. Investigations about time stability and a qualitative assessment of the experimental set-up show deviations of measured values less than 5%. This is sufficient for further analysis of calculated power loss density distributions based on measured potential distributions. The spatial potential distribution around a bipolar devices is presented by horizontal and vertical sections through the experimental environment.
To analyze the electric field around bipolar resectoscopes, used in urology, in terms of reasons for late complications after a surgical treatment a flexible multielectrode system was developed to measure the 3-D potential distribution. A high spatial resolution is achieved with the least possible individual measurements under the conditions of a quasi-static electric field. A flexible arrangement and positioning of the measuring points in the vertical direction of the experimental environment enable an adjustable spatial resolution and the selection of the region of interest. The existing influence of the multielectrode system on the measuring results is described and a correction method is presented to achieve significant results. Thus, the multielectrode system is usable for a comparative study of bipolar resectoscopes varying in the arrangement of resection and return electrode.
Gallium Nitride (GaN) and Indium Gallium Nitride (InGaN) have become important semiconductor materials for the LED lighting industry. Recently, a photoluminescence (PL) technique for direct in-situ characterization of GaN and InGaN layers during epitaxial growth in a planetary metalorganic vapor phase epitaxy (MOVPE) reactor was reported. The PL signals reveal – at the earliest possible stage – information about current layer thickness, temperature, composition, surface roughness, and self-absorption. Thus, the PL data is valuable for both controlling and optimizing the growth parameters, thereby promising both better devices and a better yield for the LED industry. This technical report describes an extension of this PL technique to close coupled showerhead (CCS) reactors with narrow optical viewports. In contrast to the wide aperture optics in previous investigations, a compact and all-fiber optical probe without voluminous lens optics, filter elements or beam splitters was used.