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Disturbances of the cardiac conduction system causing reentry mechanisms above the atrioventricular (AV) node are induced by at least one accessory pathway with different conducting properties and refractory periods. This work aims to further develop the already existing and continuously expanding Offenburg heart rhythm model to visualise the most common supraventricular reentry tachycardias to provide a better understanding of the cause of the respective reentry mechanism.
The visualization of heart rhythm disturbance and atrial fibrillation therapy allow the optimization of new cardiac catheter ablations. With the simulation software CST (Computer Simulation Technology, Darmstadt) electromagnetic and thermal simulations can be carried out to analyze and optimize different heart rhythm disturbance and cardiac catheters for pulmonary vein isolation. Another form of visualization is provided by haptic, three-dimensional print models. These models can be produced using an additive manufacturing method, such as a 3D printer. The aim of the study was to produce a 3D print of the Offenburg heart rhythm model with a representation of an atrial fibrillation ablation procedure to improve the visualization of simulation of cardiac catheter ablation.
The basis of 3D printing was the Offenburg heart rhythm model and the associated simulation of cryoablation of the pulmonary vein. The thermal simulation shows the pulmonary vein isolation of the left inferior pulmonary vein with the cryoballoon catheter Arctic Front AdvanceTM from Medtronic. After running through the simulation, the thermal propagation during the procedure was shown in the form of different colors. The three-dimensional print models were constructed on the base of the described simulation in a CAD program. Four different 3D printers are available for this purpose in a rapid prototyping laboratory at the University of Applied Science Offenburg. Two different printing processes were used: 1. a binder jetting printer with polymer gypsum and 2. a multi-material printer with photopolymer. A final print model with additional representation of the esophagus and internal esophagus catheter was also prepared for printing.
With the help of the thermal simulation results and the subsequent evaluation, it was possible to make a conclusion about the propagation of the cold emanating from the catheter in the myocardium and the surrounding tissue. It could be measured that already 3 mm from the balloon surface into the myocardium the temperature drops to 25 °C. The simulation model was printed using two 3D printing methods. Both methods as well as the different printing materials offer different advantages and disadvantages. While the first model made of polymer gypsum can be produced quickly and cheaply, the second model made of photopolymer takes five times longer and was twice as expensive. On the other hand, the second model offers significantly better properties and was more durable overall. All relevant parts, especially the balloon catheter and the conduction, are realistically represented. Only the thermal propagation in the form of different colors is not shown on this model.
Three-dimensional heart rhythm models as well as virtual simulations allow a very good visualization of complex cardiac rhythm therapy and atrial fibrillation treatment methods. The printed models can be used for optimization and demonstration of cryoballoon catheter ablation in patients with atrial fibrillation.
The Internet of Things (IoT) application has becoming progressively in-demand, most notably for the embedded devices (ED). However, each device has its own difference in computational capabilities, memory usage, and energy resources in connecting to the Internet by using Wireless Sensor Networks (WSNs). In order for this to be achievable, the WSNs that form the bulk of the IoT implementation requires a new set of technologies and protocol that would have a defined area, in which it addresses. Thus, IPv6 Low Power Area Network (6LoWPAN) was designed by the Internet Engineering Task Force (IETF) as a standard network for ED. Nevertheless, the communication between ED and 6LoWPAN requires appropriate routing protocols for it to achieve the efficient Quality of Service (QoS). Among the protocols of 6LoWPAN network, RPL is considered to be the best protocol, however its Energy Consumption (EC) and Routing Overhead (RO) is considerably high when it is implemented in a large network. Therefore, this paper would propose the HRPL to enchance the RPL protocol in reducing the EC and RO. In this study, the researchers would present the performance of RPL and HRPL in terms of EC, Control traffic Overhead (CTO) and latency based on the simulation of the 6LoWPAN network in fixed environment using COOJA simulator. The results show HRPL protocol achieves better performance in all the tested topology in terms of EC and CTO. However, the latency of HRPL only improves in chain topology compared with RPL. We found that further research is required to study the relationship between the latency and the load of packet transmission in order to optimize the EC usage.
Seismic data has often missing traces due to technical acquisition or economical constraints. A compete dataset is crucial in several processing and inversion techniques. Deep learning algorithms, based on convolutional neural networks (CNNs), have shown alternative solutions that overcome limitation of traditional interpolation methods e.g. data regularity, linearity assumption, etc. There are two different paradigms of CNN methods for seismic interpolation. The first one, so-called deep prior interpolation (DPI), trains a CNN to map random noise to a complete seismic image using only the decimated image itself. The second one, referred as standard deep learning method, trains a CNN to map a decimated seismic image into a complete one using a dataset of complete and artificially decimated images. Within this research, we systematically compare the performance of both methods for different quantities of regular and irregular missing traces using 4 datasets. We evaluate the results of both methods using 5 well-known metrics. We found that DPI method performs better than the standard method if the percentage of missing traces is low (10%) and otherwise if the level of decimation is high (50%).
Radio frequency identification (RFID) antennas are popular for high frequency (HF) RFID, energy transfer and near field communication (NFC) applications. Particularly for wireless measurement systems the RFID/NFC technology is a good option to implement a wireless communication interface. In this context, the design of corresponding reader and transmitter antennas plays a major role for achieving suitable transmission quality. This work proves the feasibility of the rapid prototyping of a RFID/NFC antenna, which is used for the wireless communication and energy harvesting at the required frequency of 13.56 MHz. A novel and low-cost direct ink writing (DIW) technology utilizing highly viscous silver nanoparticle ink is used for this process. This paper describes the development and analysis of low-cost printed flexible RFID/NFC antennas on cost-effective substrates for a microelectronic vital parameter measurement system. Furthermore, we compare the measured technical parameters with existing copper-based counterparts on a FR4 substrate.
The monitoring of industrial environments ensures that highly automated processes run without interruption. However, even if the industrial machines themselves are monitored, the communication lines are currently not continuously monitored in todays installations. They are checked usually only during maintenance intervals or in case of error. In addition, the cables or connected machines usually have to be removed from the system for the duration of the test. To overcome these drawbacks, we have developed and implemented a cost-efficient and continuous signal monitoring of Ethernet-based industrial bus systems. Several methods have been developed to assess the quality of the cable. These methods can be classified to either passive or active. Active methods are not suitable if interruption of the communication is undesired. Passive methods, on the other hand, require oversampling, which calls for expensive hardware. In this paper, a novel passive method combined with undersampling targeting cost-efficient hardware is proposed.
The Go programming language is an increasingly popular language but some of its features lack a formal investigation. This article explains Go's resolution mechanism for overloaded methods and its support for structural subtyping by means of translation from Featherweight Go to a simple target language. The translation employs a form of dictionary passing known from type classes in Haskell and preserves the dynamic behavior of Featherweight Go programs.
In recent years, predictive maintenance tasks, especially for bearings, have become increasingly important. Solutions for these use cases concentrate on the classification of faults and the estimation of the Remaining Useful Life (RUL). As of today, these solutions suffer from a lack of training samples. In addition, these solutions often require high-frequency accelerometers, incurring significant costs. To overcome these challenges, this research proposes a combined classification and RUL estimation solution based on a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network. This solution relies on a hybrid feature extraction approach, making it especially appropriate for low-cost accelerometers with low sampling frequencies. In addition, it uses transfer learning to be suitable for applications with only a few training samples.
The desire to connect more and more devices and to make them more intelligent and more reliable, is driving the needs for the Internet of Things more than ever. Such IoT edge systems require sound security measures against cyber-attacks, since they are interconnected, spatially distributed, and operational for an extended period of time. One of the most important requirements for the security in many industrial IoT applications is the authentication of the devices. In this paper, we present a mutual authentication protocol based on Physical Unclonable Functions, where challenge-response pairs are used for both device and server authentication. Moreover, a session key can be derived by the protocol in order to secure the communication channel. We show that our protocol is secure against machine learning, replay, man-in-the-middle, cloning, and physical attacks. Moreover, it is shown that the protocol benefits from a smaller computational, communication, storage, and hardware overhead, compared to similar works.
Cryptographic protection of messages requires frequent updates of the symmetric cipher key used for encryption and decryption, respectively. Protocols of legacy IT security, like TLS, SSH, or MACsec implement rekeying under the assumption that, first, application data exchange is allowed to stall occasionally and, second, dedicated control messages to orchestrate the process can be exchanged. In real-time automation applications, the first is generally prohibitive, while the second may induce problematic traffic patterns on the network. We present a novel seamless rekeying approach, which can be embedded into cyclic application data exchanges. Although, being agnostic to the underlying real-time communication system, we developed a demonstrator emulating the widespread industrial Ethernet system PROFINET IO and successfully use this rekeying mechanism.
When designing and installing Indoor Positioning Systems, several interrelated tasks have to be solved to find an optimum placement of the Access Points. For this purpose, a mathematical model for a predefined number of access points indoors is presented. Two iterative algorithms for the minimization of localization error of a mobile object are described. Both algorithms use local search technique and signal level probabilities. Previously registered signal strengths maps were used in computer simulation.
In this work a method for the estimation of current slopes induced by inverters operating interior permanent magnet synchronous machines is presented. After the derivation of the estimation algorithm, the requirements for a suitable sensor setup in terms of accuracy, dynamic and electromagnetic interference are discussed. The boundary conditions for the estimation algorithm are presented with respect to application within high power traction systems. The estimation algorithm is implemented on a field programmable gateway array. This moving least-square algorithm offers the advantage that it is not dependent on vectors and therefore not every measured value has to be stored. The summation of all measured values leads to a significant reduction of the required storage units and thus decreases the hardware requirements. The algorithm is designed to be calculated within the dead time of the inverter. Appropriate countermeasures for disturbances and hardware restrictions are implemented. The results are discussed afterwards.
This paper presents the use of model predictive control (MPC) based approach for peak shaving application of a battery in a Photovoltaic (PV) battery system connected to a rural low voltage gird. The goals of the MPC are to shave the peaks in the PV feed-in and the grid power consumption and at the same time maximize the use of the battery. The benefit to the prosumer is from the maximum use of the self-produced electricity. The benefit to the grid is from the reduced peaks in the PV feed-in and the grid power consumption. This would allow an increase in the PV hosting and the load hosting capacity of the grid.
The paper presents the mathematical formulation of the optimal control problem
along with the cost benefit analysis. The MPC implementation scheme in the
laboratory and experiment results have also been presented. The results show
that the MPC is able to track the deviation in the weather forecast and operate
the battery by solving the optimal control problem to handle this deviation.
As cyber-attacks and functional safety requirements increase in Operational Technology (OT), implementing security measures becomes crucial. The IEC/IEEE 60802 draft standard addresses the security convergence in Time-Sensitive Networks (TSN) for industrial automation.We present the standard’s security architecture and its goals to establish end-to-end security with resource access authorization in OT systems. We compare the standard to our abstract technology-independent model for the management of cryptographic credentials during the lifecycles of OT systems. Additionally, we implemented the processes, mechanisms, and protocols needed for IEC/IEEE 60802 and extended the architecture with public key infrastructure (PKI) functionalities to support complete security management processes.
A Novel Approach of High Dynamic Current Control of Interior Permanent Magnet Synchronous Machines
(2019)
Harmonic-afflicted effects of permanent magnet synchronous machines with high power density are hardly faced by traditional current PI controllers, due to limited controller bandwidth. As a consequence, currents and lastly torque ripples appear. In this paper, a new deadbeat current controller architecture has been presented, which is capable to encounter the effects of these harmonics. This new control algorithm, here named “Hybrid-Deadbeat-Controller”, combines the stability and the low steady-state errors offered by common PI regulators with the high dynamic offered by the deadbeat control. Therefore, a novel control algorithm is proposed, capable of either compensating the current harmonics in order to get smoother currents or to control a varying reference value to achieve a smoother torque. The information needed to calculate the optimal reference currents is based on an online parameter estimation feeding an optimization algorithm to achieve an optimal torque output and will be investigated in future research. In order to ensure the stability of the controller over the whole area of operation even under the influence of effects changing the system’s parameter, this work as well focusses on the robustness of the “hybrid” dead beat controller.
This paper presents a novel low-jitter interface between a low-cost integrated IEEE802.11 chip and a FPGA. It is designed to be part of system hardware for ultra-precise synchronization between wireless stations. On physical level, it uses Wi-Fi chip coexistence signal lines and UART frame encoding. On its basis, we propose an efficient communication protocol providing precise timestamping of incoming frames and internal diagnostic mechanisms for detecting communication faults. Meanwhile it is simple enough to be implemented both in low-cost FPGA and commodity IEEE802.11 chip firmware. The results of computer simulation shows that developed FPGA implementation of the proposed protocol can precisely timestamp incoming frames as well as detect most of communication errors even in conditions of high interference. The probability of undetected errors was investigated. The results of this analysis are significant for the development of novel wireless synchronization hardware.
The following describes a new method for estimating the parameters of an interior permanent magnet synchronous machine (IPMSM). For the estimation of the parameters the current slopes caused by the switching of the inverter are used to determine the unknowns of the system equations of the electrical machine. The angle and current dependence of the machine parameters are linearized within a PWM cycle. By considering the different switching states of the inverter, several system equations can be derived and a solution can be found within one PWM cycle. The use of test signals and filter-based approaches is avoided. The derived algorithm is explained and validated with measurements on a test bench.
In recent years, the topic of embedded machine learning has become very popular in AI research. With the help of various compression techniques such as pruning, quantization and others compression techniques, it became possible to run neural networks on embedded devices. These techniques have opened up a whole new application area for machine learning. They range from smart products such as voice assistants to smart sensors that are needed in robotics. Despite the achievements in embedded machine learning, efficient algorithms for training neural networks in constrained domains are still lacking. Training on embedded devices will open up further fields of applications. Efficient training algorithms would enable federated learning on embedded devices, in which the data remains where it was collected, or retraining of neural networks in different domains. In this paper, we summarize techniques that make training on embedded devices possible. We first describe the need and requirements for such algorithms. Then we examine existing techniques that address training in resource-constrained environments as well as techniques that are also suitable for training on embedded devices, such as incremental learning. At the end, we also discuss which problems and open questions still need to be solved in these areas.
Ensuring that software applications present their users the most recent version of data is not trivial. Self-adjusting computations are a technique for automatically and efficiently recomputing output data whenever some input changes.
This article describes the software architecture of a large, commercial software system built around a framework for coarse-grained self-adjusting computations in Haskell. It discusses advantages and disadvantages based on longtime experience. The article also presents a demo of the system and explains the API of the framework.
Multiple Object Tracking (MOT) is a long-standing task in computer vision. Current approaches based on the tracking by detection paradigm either require some sort of domain knowledge or supervision to associate data correctly into tracks. In this work, we present a self-supervised multiple object tracking approach based on visual features and minimum cost lifted multicuts. Our method is based on straight-forward spatio-temporal cues that can be extracted from neighboring frames in an image sequences without supervision. Clustering based on these cues enables us to learn the required appearance invariances for the tracking task at hand and train an AutoEncoder to generate suitable latent representations. Thus, the resulting latent representations can serve as robust appearance cues for tracking even over large temporal distances where no reliable spatio-temporal features can be extracted. We show that, despite being trained without using the provided annotations, our model provides competitive results on the challenging MOT Benchmark for pedestrian tracking.
Subspace clustering aims to find all clusters in all subspaces of a high-dimensional data space. We present a massively data-parallel approach that can be run on graphics processing units. It extends a previous density-based method that scales well with the number of dimensions. Its main computational bottleneck consists of (sequentially) generating a large number of minimal cluster candidates in each dimension and using hash collisions in order to find matches of such candidates across multiple dimensions. Our approach parallelizes this process by removing previous interdependencies between consecutive steps in the sequential generation process and by applying a very efficient parallel hashing scheme optimized for GPUs. This massive parallelization gives up to 70x speedup for
the bottleneck computation when it is replaced by our approach and run on current GPU hardware. We note that depending on data size and choice of parameters, the parallelized part of the algorithm can take different percentages of the overall runtime of the clustering process, and thus, the overall clustering speedup may vary significantly between different cases. However, even
in our ”worst-case” test, a small dataset where the computation makes up only a small fraction of the overall clustering time, our parallel approach still yields a speedup of more than 3x for the complete run of the clustering process. Our method could also be combined with parallelization of other parts of the clustering algorithm, with an even higher potential gain in processing speed.
The authentication method of electronic devices, based on individual forms of correlograms of their internal electric noises, is well-known. Specific physical differences in the components – for example, caused by variations in production quality – cause specific electrical signals, i.e. electric noise, in the electronic device. It is possible to obtain this information and to identify the specific differences of the individual devices using an embedded analog-to-digital converter (ADC). These investigations confirm the possibility to identify and authenticate electronic devices using bit templates, calculated from the sequence of values of the normalized autocorrelation function of noise. Experiments have been performed using personal computers. The probability of correct identification and authentication increases with increasing noise recording duration. As a result of these experiments, an accuracy of 98.1% was achieved for a 1 second-long registration of EM for a set of investigated computers.
Active participation of industrial enterprises in electricity markets - a generic modeling approach
(2021)
Industrial enterprises represent a significant portion of electricity consumers with the potential of providing demand-side energy flexibility from their production processes and on-site energy assets. Methods are needed for the active and profitable participation of such enterprises in the electricity markets especially with variable prices, where the energy flexibility available in their manufacturing, utility and energy systems can be assessed and quantified. This paper presents a generic model library equipped with optimal control for energy flexibility purposes. The components in the model library represent the different technical units of an industrial enterprise on material, media, and energy flow levels with their process constraints. The paper also presents a case study simulation of a steel-powder manufacturing plant using the model library. Its energy flexibility was assessed when the plant procured its electrical energy at fixed and variable electricity prices. In the simulated case study, flexibility use at dynamic prices resulted in a 6% cost reduction compared to a fixed-price scenario, with battery storage and the manufacturing system making the largest contributions to flexibility.
The fast and cost-effective manufacturing of tools for thermoforming is an essential requirement to shorten the development time of products. Thus, additive processes are used increasingly in tooling for thermoforming of plastic sheets. However, a disadvantage of many additive methods is that they are highly cost-intensive, since complex systems based on laser technology and expensive metal powders are needed. Therefore, this paper examines how to work with favorable additive methods, e.g. Binder Jetting, to manufacture tools, which provide sufficient strength for thermoforming. The use of comparatively low-priced inkjet technology for the layer construction and a polymer plaster as material can be expected to result in significant cost reductions. Based on a case study using a cowling (engine bonnet) for an Unmanned Aerial Vehicle (UAV), the development of a complex tool for thermoforming is demonstrated. The object in this study is to produce a tool for a complex-shaped component in small numbers and high quality in a short time and at reasonable costs. Within the tooling process, integrated vacuum channels are implemented in additive tooling without the need for additional post-processing (for example, drilling). In addition, special technical challenges, such as the demolding of undercuts or the parting of the tool are explained. All process steps from tool design to the use of the additively manufactured tool are analyzed. Based on the manufacturing of a small series of cowlings for a UAV made of plastic sheets (ABS), it is shown, that the Binder Jetting offers sufficient mechanical and thermal strength for additive tooling. In addition, an economic evaluation of the tool manufacturing and a detailed consideration of the required manufacturing times for the different process steps are carried out. Finally, a comparison is made with conventional and alternative additive methods of tooling.
In bimodal cochlear implant (CI) / hearing aid (HA) users a constant interaural time delay in the order of several milliseconds occurs due to differences in signal processing of the devices. For MED-EL CI systems in combination with different HA types, we have quantified the respective device delay mismatch (Zirn et al. 2015). In the current study, we investigate the effect of the device delay mismatch in simulated and actual bimodal listeners on sound localization accuracy.
To deal with the device delay mismatch in actual bimodal listeners we delayed the CI stimulation according to the measured HA processing delay and two other values. With all delay values highly significant improvements of the rms error in the localization task were observed compared to the test without the delay. The results help to narrow down the optimal patient-specific delay value.
Wireless communication technologies play a major role to enable megatrends like Internet of Things (IoT) and Industry 4.0. The Narrowband Wireless WAN (NBWWAN) introduced to meet the long range and low power requirements of spatially distributed wireless communication use cases. These networks introduce additional challenges in testing because the network topology and RF characteristics become particularly complex and thus a multitude of different scenarios must be tested. This paper describes the infrastructure for automated testing of radio communication and for systematic measurements of the network performance of NBWWAN.
Deep learning models are intrinsically sensitive to distribution shifts in the input data. In particular, small, barely perceivable perturbations to the input data can force models to make wrong predictions with high confidence. An common defense mechanism is regularization through adversarial training which injects worst-case perturbations back into training to strengthen the decision boundaries, and to reduce overfitting. In this context, we perform an investigation of 3 × 3 convolution filters that form in adversarially- trained models. Filters are extracted from 71 public models of the ℓ ∞ -RobustBench CIFAR-10/100 and ImageNet1k leaderboard and compared to filters extracted from models built on the same architectures but trained without robust regularization. We observe that adversarially-robust models appear to form more diverse, less sparse, and more orthogonal convolution filters than their normal counterparts. The largest differences between robust and normal models are found in the deepest layers, and the very first convolution layer, which consistently and predominantly forms filters that can partially eliminate perturbations, irrespective of the architecture.
IoT networks are increasingly used as entry points for cyberattacks, as often they offer low-security levels, as they may allow the control of physical systems and as they potentially also open the access to other IT networks and infrastructures. Existing intrusion detection systems (IDS) and intrusion prevention systems (IPS) mostly concentrate on legacy IT networks. Nowadays, they come with a high degree of complexity and adaptivity, including the use of artificial intelligence. It is only recently that these techniques are also applied to IoT networks. In this paper, we present a survey of machine learning and deep learning methods for intrusion detection, and we investigate how previous works used federated learning for IoT cybersecurity. For this, we present an overview of IoT protocols and potential security risks. We also report the techniques and the datasets used in the studied works, discuss the challenges of using ML, DL and FL for IoT cybersecurity and provide future insights.
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.
An Empirical Investigation of Model-to-Model Distribution Shifts in Trained Convolutional Filters
(2021)
We present first empirical results from our ongoing investigation of distribution shifts in image data used for various computer vision tasks. Instead of analyzing the original training and test data, we propose to study shifts in the learned weights of trained models. In this work, we focus on the properties of the distributions of dominantly used 3x3 convolution filter kernels. We collected and publicly provide a data set with over half a billion filters from hundreds of trained CNNs, using a wide range of data sets, architectures, and vision tasks. Our analysis shows interesting distribution shifts (or the lack thereof) between trained filters along different axes of meta-parameters, like data type, task, architecture, or layer depth. We argue, that the observed properties are a valuable source for further investigation into a better understanding of the impact of shifts in the input data to the generalization abilities of CNN models and novel methods for more robust transfer-learning in this domain.
An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks
(2021)
Decision explanations of machine learning black-box models are often generated by applying Explainable AI (XAI) techniques. However, many proposed XAI methods produce unverified outputs. Evaluation and verification are usually achieved with a visual interpretation by humans on individual images or text. In this preregistration, we propose an empirical study and benchmark framework to apply attribution methods for neural networks developed for images and text data on time series. We present a methodology to automatically evaluate and rank attribution techniques on time series using perturbation methods to identify reliable approaches.
Neural networks have a number of shortcomings. Amongst the severest ones is the sensitivity to distribution shifts which allows models to be easily fooled into wrong predictions by small perturbations to inputs that are often imperceivable to humans and do not have to carry semantic meaning. Adversarial training poses a partial solution to address this issue by training models on worst-case perturbations. Yet, recent work has also pointed out that the reasoning in neural networks is different from humans. Humans identify objects by shape, while neural nets mainly employ texture cues. Exemplarily, a model trained on photographs will likely fail to generalize to datasets containing sketches. Interestingly, it was also shown that adversarial training seems to favorably increase the shift toward shape bias. In this work, we revisit this observation and provide an extensive analysis of this effect on various architectures, the common L_2-and L_-training, and Transformer-based models. Further, we provide a possible explanation for this phenomenon from a frequency perspective.
Analysis of Amplitude and Phase Errors in Digital-Beamforming Radars for Automotive Applications
(2020)
Fundamentally, automotive radar sensors with Digital-Beamforming (DBF) use several transmitter and receiver antennas to measure the direction of the target. However, hardware imperfections, tolerances in the feeding lines of the antennas, coupling effects as well as temperature changes and ageing will cause amplitude and phase errors. These errors can lead to misinterpretation of the data and result in hazardous actions of the autonomous system. First, the impact of amplitude and phase errors on angular estimation is discussed and analyzed by simulations. The results are compared with the measured errors of a real radar sensor. Further, a calibration method is implemented and evaluated by measurements.
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.
The applicability of characteristics of local magnetic fields for more precise determination of localization of subjects and/or objects in indoor environments, such as railway stations, airports, exhibition halls, showrooms, or shopping centers, is considered. An investigation has been carried out to find out whether and how low-cost magnetic field sensors and mobile robot platforms can be used to create maps that improve the accuracy and robustness of later navigation with smartphones or other devices.
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).
Apache Hadoop is a well-known open-source framework for storing and processing huge amounts of data. This paper shows the usage of the framework within a project of the university in cooperation with a semiconductor company. The goal of this project was to supplement the existing data landscape by the facilities of storing and analyzing the data on a new Apache Hadoop based platform.
Wireless communication networks are crucial for enabling megatrends like the Internet of Things (IoT) and Industry 4.0. However, testing these networks can be challenging due to the complex network topology and RF characteristics, requiring a multitude of scenarios to be tested. To address this challenge, the authors developed and extended an automated testbed called Automated Physical TestBed (APTB). This testbed provides the means to conduct controlled tests, analyze coexistence, emulate multiple propagation paths, and model dependable channel conditions. Additionally, the platform supports test automation to facilitate efficient and systematic experimentation. This paper describes the extended architecture, implementation, and performance evaluation of the APTB testbed. The APTB testbed provides a reliable and efficient solution for testing wireless communication networks under various scenarios. The implementation and performance verification of the testbed demonstrate its effectiveness and usefulness for researchers and industry practitioners.
Bei dem vorgestellten Ansatz soll der Auftreffpunkt des Pfeils durch die Kreuzkorrelation von Audio-Signalen bestimmt werden. Das Auftreffen des Pfeils erzeugt ein charakteristisches Geräusch, welches von mehreren Mikrofonen in bestimmter Anordnung um die Dartscheibe herum in elektrische Signale umgewandelt wird. Mithilfe der Schallgeschwindigkeit und den Zeitdifferenzen, welche die Schallwelle zu den einzelnen Mikrofonen benötigt soll dann der Auftreffpunkt berechnet werden.
In this paper, we study the runtime performance of symmetric cryptographic algorithms on an embedded ARM Cortex-M4 platform. Symmetric cryptographic algorithms can serve to protect the integrity and optionally, if supported by the algorithm, the confidentiality of data. A broad range of well-established algorithms exists, where the different algorithms typically have different properties and come with different computational complexity. On deeply embedded systems, the overhead imposed by cryptographic operations may be significant. We execute the algorithms AES-GCM, ChaCha20-Poly1305, HMAC-SHA256, KMAC, and SipHash on an STM32 embedded microcontroller and benchmark the execution times of the algorithms as a function of the input lengths.
Biodegradable metals have entered the implant market in recent years, but still do not show fully satisfactory degradation behaviour and mechanical properties. In contrast, it has been shown that pure molybdenum has an excellent combination of the required properties in this respect. We report on PM based screen printing of thin-walled molybdenum tubes as a processing step for medical stent manufacture. We also present data on the in vivo degradation and biocompatibility of molybdenum. The degradation of molybdenum wires implanted in the aorta of rats was evaluated by SEM and EDX. Biocompatibility was assessed by histological investigation of organs and analysis of molybdenum levels in tissue extracts and body fluids. Degradation rates of up to 13.5 μm/y were observed after 12 months. No histological changes or elevated molybdenum levels in organ tissues were observed. In summary, the results further underline that molybdenum is a highly promising biodegradable metallic material.
Complex tourism products with intangible service components are difficult to explain to potential customers. This research elaborates the use of virtual reality (VR) in the field of shore excursions. A theoretical research model based on the technology acceptance model was developed, and hypotheses were proposed. Cruise passengers were invited to test 360° excursion images on a landing page. Data was collected using an online questionnaire. Finally, data was analyzed using the PLS-SEM method. The results provide theoretical implications on technology acceptance model (TAM) research in the field of cruise tourism. Furthermore, the results and implications indicate the potential of virtual 360° shore excursion presentations for the cruise industry.
In recent times, 5G has found applications in several public as well as private networks. There is a growing need to make it compatible with diverse services without compromising security. Current security options for authenticating devices into a home network are 5G Authentication and Key Agreement (5G-AKA) and Extensible Authentication Protocol (EAP)-AKA'. However, for specific use cases such as private networks, more customizable and convenient authentication mechanisms are required. The current mobile networks use authentication based only on SIM cards, but as 5G is being applied in fields like IIoT and automation, even in Non-Public-Networks (NPNs), there is a need for a simpler method of authentication. Certificate-based authentication is one such mechanism that is passwordless and works solely on the information present in the digital certificate that the user holds. The paper suggests an authentication mechanism that performs certificate-based mutual authentication between the UE and the Home network. The proposed concept identifies both the user and network with digital certificates and intends to carry out primary authentication with the help of it. In this work we conduct a study on presently available authentication protocols for 5G networks, both theoretically and experimentally in hardware as well as virtual environments. On the basis of the analysis a series of proposed steps for certificate primary authentication are presented.
The present work ties in with the problem of bicycle road assessment that is currently done using expensive special measuring vehicles. Our alternative approach for road condition assessment is to mount a sensor device on a bicycle which sends accelerometer and gyroscope data via WiFi to a classification server. There, a prediction model determines road type and condition based on the sensor data. For the classification task, we compare different machine learning methods with each other, whereby validation accuracies of 99% can be achieved with deep residual networks such as InceptionTime. The main contribution of this work with respect to comparable work is that we achieve excellent accuracies on a realistic dataset classifying road conditions into nine distinct classes that are highly relevant for practice.
Currently, many theoretical as well as practically relevant questions towards the transferability and robustness of Convolutional Neural Networks (CNNs) remain unsolved. While ongoing research efforts are engaging these problems from various angles, in most computer vision related cases these approaches can be generalized to investigations of the effects of distribution shifts in image data. In this context, we propose to study the shifts in the learned weights of trained CNN models. Here we focus on the properties of the distributions of dominantly used 3×3 convolution filter kernels. We collected and publicly provide a dataset with over 1.4 billion filters from hundreds of trained CNNs, using a wide range of datasets, architectures, and vision tasks. In a first use case of the proposed dataset, we can show highly relevant properties of many publicly available pre-trained models for practical applications: I) We analyze distribution shifts (or the lack thereof) between trained filters along different axes of meta-parameters, like visual category of the dataset, task, architecture, or layer depth. Based on these results, we conclude that model pre-training can succeed on arbitrary datasets if they meet size and variance conditions. II) We show that many pre-trained models contain degenerated filters which make them less robust and less suitable for fine-tuning on target applications. Data & Project website: https://github.com/paulgavrikov/cnn-filter-db.
Generative adversarial networks (GANs) provide state-of-the-art results in image generation. However, despite being so powerful, they still remain very challenging to train. This is in particular caused by their highly non-convex optimization space leading to a number of instabilities. Among them, mode collapse stands out as one of the most daunting ones. This undesirable event occurs when the model can only fit a few modes of the data distribution, while ignoring the majority of them. In this work, we combat mode collapse using second-order gradient information. To do so, we analyse the loss surface through its Hessian eigenvalues, and show that mode collapse is related to the convergence towards sharp minima. In particular, we observe how the eigenvalues of the are directly correlated with the occurrence of mode collapse. Finally, motivated by these findings, we design a new optimization algorithm called nudged-Adam (NuGAN) that uses spectral information to overcome mode collapse, leading to empirically more stable convergence properties.
Transformer models have recently attracted much interest from computer vision researchers and have since been successfully employed for several problems traditionally addressed with convolutional neural networks. At the same time, image synthesis using generative adversarial networks (GANs) has drastically improved over the last few years. The recently proposed TransGAN is the first GAN using only transformer-based architectures and achieves competitive results when compared to convolutional GANs. However, since transformers are data-hungry architectures, TransGAN requires data augmentation, an auxiliary super-resolution task during training, and a masking prior to guide the self-attention mechanism. In this paper, we study the combination of a transformer-based generator and convolutional discriminator and successfully remove the need of the aforementioned required design choices. We evaluate our approach by conducting a benchmark of well-known CNN discriminators, ablate the size of the transformer-based generator, and show that combining both architectural elements into a hybrid model leads to better results. Furthermore, we investigate the frequency spectrum properties of generated images and observe that our model retains the benefits of an attention based generator.
The paper compares different anti-windup strategies for the current control of inverter-fed permanent magnet synchronous machines (PMSM) controlled by pulse-width modulation. In this respect, the focus is on the drive behavior with a relatively large product of stator frequency and sampling time. A requirement for dynamically high-quality anti-windup measures is, among other things, a sufficiently accurate decoupling of the stator current direct axis and quadrature axis components even at high stator frequencies. Discrete-time models of the electrical subsystem of the PMSM are well suited for this purpose, of which the method found to be the most accurate in a preliminary investigation is used as the basis for all anti-windup methods examined. Simulation studies and measurement results document the performance of the compared methods.
Time Sensitive Networking (TSN) provides mechanisms to enable deterministic and real-time networking in industrial networks. Configuration of these mechanisms is key to fully deploy and integrate TSN in the networks. The IEEE 802.1 Qcc standard has proposed different configuration models to implement a TSN configuration. Up until now, TSN and its configuration have been explored mostly for Ethernet-based industrial networks. However, they are still considered “work-in-progress” for wireless networks. This work focuses on the fully centralized model and describes a generic concept to enable the configuration of TSN mechanisms in wireless industrial networks. To this end, a configuration entity is implemented to conFigure the wireless end stations to satisfy their requirements. The proposed solution is then validated with the Digital Enhanced Cordless Telecommunication ultra-low energy (DECT ULE) wireless communication protocol.
This paper presents an extended version of a previously published Bayesian algorithm for the automatic correction of the positions of the equipment on the map with simultaneous mobile object trajectory localization (SLAM) in underground mine environment represented by undirected graph. The proposed extended SLAM algorithm requires much less preliminary data on possible equipment positions and uses an additional resample move algorithm to significantly improve the overall performance.
It seems to be a widespread impression that the use of strong cryptography inevitably imposes a prohibitive burden on industrial communication systems, at least inasmuch as real-time requirements in cyclic fieldbus communications are concerned. AES-GCM is a leading cryptographic algorithm for authenticated encryption, which protects data against disclosure and manipulations. We study the use of both hardware and software-based implementations of AES-GCM. By simulations as well as measurements on an FPGA-based prototype setup we gain and substantiate an important insight: for devices with a 100 Mbps full-duplex link, a single low-footprint AES-GCM hardware engine can deterministically cope with the worst-case computational load, i.e., even if the device maintains a maximum number of cyclic communication relations with individual cryptographic keys. Our results show that hardware support for AES-GCM in industrial fieldbus components may actually be very lightweight.
Current Harmonics Control Algorithm for inverter-fed Nonlinear Synchronous Electrical Machines
(2023)
Current harmonics are a well known challenge of electrical machines. They can be undesirable as they can cause instabilities in the control, generate additional losses and lead to torque ripples with noise. However, they can also be specifically generated in new methods in order to improve the machine behavior. In this paper, an algorithm for controlling current harmonics is proposed. It can be described as a combination of different PI controllers for defined angles of the machine with repetitive control characteristics for whole revolutions. The controller design is explained and important points where linearization is necessary are shown. Furthermore, the limits are analyzed and, for validation, measurement results with a permanently excited synchronous machine on the test bench are considered.
Fused Filament Fabrication (FFF) is a widespread additive manufacturing technology, mostly in the field of printable polymers. The use of filaments filled with metal particles for the manufacture of metallic parts by FFF presents specific challenges regarding debinding and sintering. For aluminium and its alloys, the sintering temperature range overlaps with the temperature range of thermal decomposition of many commonly used “backbone” polymers, which provide stability to the green parts. Moreover, the high oxygen affinity of aluminium necessitates the use of special sintering regimes and alloying strategies. Therefore, it is challenging to achieve both low porosity and low levels of oxygen and carbon impurities at the same time. Feedstocks compatible with the special requirements of aluminium alloys were developed. We present results on the investigation of debinding/sintering regimes by Fourier Transform Infrared spectroscopy (FTIR) based In-Situ Process Gas Analysis and discuss optimized thermal treatment strategies for Al-based FFF.
Seismic data processing involves techniques to deal with undesired effects that occur during acquisition and pre-processing. These effects mainly comprise coherent artefacts such as multiples, non-coherent signals such as electrical noise, and loss of signal information at the receivers that leads to incomplete traces. In this work, we employ a generative solution, since it can explicitly model complex data distributions and hence, yield to a better decision-making process. In particular, we introduce diffusion models for multiple removal. To that end, we run experiments on synthetic and on real data, and we compare the deep diffusion performance with standard algorithms. We believe that our pioneer study not only demonstrates the capability of diffusion models, but also opens the door to future research to integrate generative models in seismic workflows.
To demonstrate how deep learning can be applied to industrial applications with limited training data, deep learning methodologies are used in three different applications. In this paper, we perform unsupervised deep learning utilizing variational autoencoders and demonstrate that federated learning is a communication efficient concept for machine learning that protects data privacy. As an example, variational autoencoders are utilized to cluster and visualize data from a microelectromechanical systems foundry. Federated learning is used in a predictive maintenance scenario using the C-MAPSS dataset.
An important step in seismic data processing to improve inversion and interpretation is multiples attenuation. Radon-based algorithms are often used for discriminating primaries and multiples. Recently, deep learning (DL), based on convolutional neural networks (CNNs) has shown promising results in demultiple that could mitigate the challenges of Radon-based methods. In this work, we investigate new different strategies to train a CNN for multiples removal based on different loss functions. We propose combined primaries and multiples labels in the loss for training a CNN to predict primaries, multiples, or both simultaneously. We evaluate the performance of the CNNs trained with the different strategies on 400 clean and noisy synthetic data, considering 3 metrics. We found that training a CNN to predict the multiples and then subtracting them from the input image is the most effective strategy for demultiple. Furthermore, including the primaries labels as a constraint during the training of multiples prediction improves the results. Finally, we test the strategies on a field dataset. The CNNs trained with different strategies report competitive results on real data compared with Radon demultiple. As a result, effectively trained CNN models can potentially replace Radon-based demultiple in existing workflows.
This paper describes a thorough analysis of using PPO to learn kick behaviors with simulated NAO robots in the simspark environment. The analysis includes an investigation of the influence of PPO hyperparameters, network size, training setups and performance in real games. We believe to improve the state of the art mainly in four points: first, the kicks are learned with a toed version of the NAO robot, second, we improve the reliability with respect to kickable area and avoidance of falls, third, the kick can be parameterized with desired distance and direction as input to the deep network and fourth, the approach allows to integrate the learned behavior seamlessly into soccer games. The result is a significant improvement of the general level of play.
In recent years, Physical Unclonable Functions (PUFs) have gained significant attraction in the Internet of Things (IoT) for security applications such as cryptographic key generation and entity authentication. PUFs extract the uncontrollable production characteristics of physical devices to generate unique fingerprints for security applications. One common approach for designing PUFs is exploiting the intrinsic features of sensors and actuators such as MEMS elements, which typically exist in IoT devices. This work presents the Cantilever-PUF, a PUF based on a specific MEMS device – Aluminum Nitride (AlN) piezoelectric cantilever. We show the variations of electrical parameters of AlN cantilevers such as resonance frequency, electrical conductivity, and quality factor, as a result of uncontrollable manufacturing process variations. These variations, along with high thermal and chemical stability, and compatibility with silicon technology, makes AlN cantilever a decent candidate for PUF design. We present a cantilever design, which magnifies the effect of manufacturing process variations on electrical parameters. In order to verify our findings, the simulation results of the Monte Carlo method are provided. The results verify the eligibility of AlN cantilever to be used as a basic PUF device for security applications. We present an architecture, in which the designed Cantilever-PUF is used as a security anchor for PUF-enabled device authentication as well as communication encryption.
The often-occurring short-term orders of manufactured products require a high machine availability. This requirement increases the importance of predictive maintenance solutions for bearings used in machines. There are, among others, hybrid solutions that rely on a physical model. For their usage, knowing the different degradation stages of bearings is essential. This research analyzes the underlying failure mechanisms of these stages theoretically and in a practical example of the well-known FEMTO dataset used for the IEEE PHM 2012 Data Challenge to provide this knowledge. In addition, it shows for which use cases the usage of low-frequency accelerometers is sufficient. The analysis provides that the degradation stages toward the end of the bearing life can also be detected with low-frequency accelerometers. Further, the importance of high-frequency accelerometers to detect bearing faults in early degradation stages is pointed out. These aspects have not been paid attention to by industry and research until now, despite providing a considerable cost-saving potential.
Recently, adversarial attacks on image classification networks by the AutoAttack (Croce and Hein, 2020b) framework have drawn a lot of attention. While AutoAttack has shown a very high attack success rate, most defense approaches are focusing on network hardening and robustness enhancements, like adversarial training. This way, the currently best-reported method can withstand about 66% of adversarial examples on CIFAR10. In this paper, we investigate the spatial and frequency domain properties of AutoAttack and propose an alternative defense. Instead of hardening a network, we detect adversarial attacks during inference, rejecting manipulated inputs. Based on a rather simple and fast analysis in the frequency domain, we introduce two different detection algorithms. First, a black box detector that only operates on the input images and achieves a detection accuracy of 100% on the AutoAttack CIFAR10 benchmark and 99.3% on ImageNet, for epsilon = 8/255 in both cases. Second, a whitebox detector using an analysis of CNN feature maps, leading to a detection rate of also 100% and 98.7% on the same benchmarks.
Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality
(2023)
Diffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images. This raises strong concerns about their potential for malicious purposes. In this paper, we propose using the lightweight multi Local Intrinsic Dimensionality (multiLID), which has been originally developed in context of the detection of adversarial examples, for the automatic detection of synthetic images and the identification of the according generator networks. In contrast to many existing detection approaches, which often only work for GAN-generated images, the proposed method provides close to perfect detection results in many realistic use cases. Extensive experiments on known and newly created datasets demonstrate that the proposed multiLID approach exhibits superiority in diffusion detection and model identification.Since the empirical evaluations of recent publications on the detection of generated images are often mainly focused on the "LSUN-Bedroom" dataset, we further establish a comprehensive benchmark for the detection of diffusion-generated images, including samples from several diffusion models with different image sizes.The code for our experiments is provided at https://github.com/deepfake-study/deepfake-multiLID.
Recent work has investigated the distributions of learned convolution filters through a large-scale study containing hundreds of heterogeneous image models. Surprisingly, on average, the distributions only show minor drifts in comparisons of various studied dimensions including the learned task, image domain, or dataset. However, among the studied image domains, medical imaging models appeared to show significant outliers through "spikey" distributions, and, therefore, learn clusters of highly specific filters different from other domains. Following this observation, we study the collected medical imaging models in more detail. We show that instead of fundamental differences, the outliers are due to specific processing in some architectures. Quite the contrary, for standardized architectures, we find that models trained on medical data do not significantly differ in their filter distributions from similar architectures trained on data from other domains. Our conclusions reinforce previous hypotheses stating that pre-training of imaging models can be done with any kind of diverse image data.
In many application areas, Deep Reinforcement Learning (DRL) has led to breakthroughs. In Curriculum Learning, the Machine Learning algorithm is not randomly presented with examples, but in a meaningful order of increasing difficulty. This has been used in many application areas to further improve the results of learning systems or to reduce their learning time. Such approaches range from learning plans created manually by domain experts to those created automatically. The automated creation of learning plans is one of the biggest challenges.In this work, we investigate an approach in which a trainer learns in parallel and analogously to the student to automatically create a learning plan for the student during this Double Deep Reinforcement Learning (DDRL). Three Reward functions, Friendly, Adversarial, and Dynamic based on the learner’s reward are compared. The domain for evaluation is kicking with variable distance, direction and relative ball position in the SimSpark simulated soccer environment.As a result, Statistic Curriculum Learning (SCL) performs better than a random curriculum with respect to training time and result quality. DDRL reaches a comparable quality as the baseline and outperforms it significantly in shorter trainings in the distance-direction subdomain reducing the number of required training cycles by almost 50%.
The EREMI project is a 2-year project funded under the ERASMUS+ framework programme and its team has developed and will validate an advanced higher education program, including life-long learning, on the interdisciplinary topic of resource efficiency in manufacturing industries and the overall system optimization of low or not digitized physical infrastructure. All of these will be achieved by applying IoT technologies towards efficient industrial systems, and by utilizing a high-level educated human capital on these economically, politically, and technically crucial and highly relevant topics for the rapidly developing industries and economies of intensively economically and industrially transforming countries - Bulgaria, North Macedonia, and Romania. Efficiency will be attained by utilizing the experience and expertise of the involved German partner organisation.
To deal with frequent power outages in developing countries, people turn to solutions like uninterruptible power supply (UPS), which stores electric energy during normal operating hours and use it to meet energy needs during rolling blackout intervals. Locally produced UPSs of poorer power quality are widely accessible in the marketplaces, and they have a negative impact on power quality. The charging and discharging of the batteries in these UPSs generate significant amount of power losses in weak grid environments. The Smart-UPS is our proposed smart energy metering (SEM) solution for low voltage consumers that is provided by the distribution company. It does not require batteries, therefore there is no power loss or harmonic distortion due to corresponding charging and discharging. Through load flow and harmonic analysis of both traditional UPS and Smart-UPS systems on ETAP, this paper examines their impact on the harmonics and stability of the distribution grid. The simulation results demonstrate that Smart-UPS can assist fixing power quality issues in a developing country like Pakistan by providing cleaner energy than the battery-operated traditional UPSs.
Background: A disturbed synchronization of the ventricular contraction can cause a highly developed systolic heart failure in affected patients, which can often be explained by a diseased left bundle branch block (LBBB). If medication remains unresponsive, the concerned patients will be treated with a cardiac resynchronization therapy (CRT) system. The aim of this study was to integrate His bundle pacing into the Offenburg heart rhythm model in order to visualize the electrical pacing field generated by His bundle pacing.
Methods: Modelling and electrical field simulation activities were performed with the software CST (Computer Simulation Technology) from Dessault Systèms. CRT with biventricular pacing is to be achieved by an apical right ventricular electrode and an additional left ventricular electrode, which is floated into the coronary vein sinus. This conventional type of biventricular pacing leads to a reduction of the left ventricular ejection fraction. Furthermore, the non-responder rate of the CRT therapy is about one third of the CRT patients.
Results: His bundle pacing represents a physiological alternative to conventional cardiac pacing and cardiac resynchronization. An electrode implanted in the His bundle emits a stronger electrical pacing field than the electrical pacing field of conventional cardiac pacemakers. The pacing of the His bundle was performed by the Medtronic Select Secure 3830 electrode with pacing voltage amplitudes of 3 V, 2 V and 1.5 V in combination with a pacing pulse duration of 1 ms.
Conclusions: Compared to conventional cardiac pacemaker pacing, His bundle pacing is capable of bridging LBBB conduction disorders in the left ventricle. The His bundle pacing electrical field is able to spread via the physiological pathway in the right and left ventricles for CRT with a narrow QRS-complex in the surface ECG.
Electrode modelling and simulation of diagnostic and pulmonary vein isolation in atrial fibrillation
(2022)
Background: Pulmonary vein isolation (PVI) using cryoballoon catheters are a recognized method for the treatment of atrial fibrillation (AF). This method offers shorter treatment duration in contrast to the classical therapy with high-frequency (HF) ablation.
Purpose: The aim of this study was to integrate different cryoballoon catheters and a HF catheter into a heart rhythm model and to compare them by means of static and dynamic electromagnetic and thermal simulation in use under AF.
Methods: The cryoballoon catheters from Medtronic and the HF ablation catheter from Osypka were modelled virtually with the aid of manufacturer specifications and the CST (Computer Simulation Technology, Darmstadt) simulation program. The cryoballoon catheter was located in the lower left pulmonary vein of the virtual heart rhythm model for the realization of pulmonary vein isolation (PVI) by cryoenergy. The simulated temperature at the balloon surface was -50°C during the simulation.
Results: During a simulated 20 second application of a cryoballoon catheter at -50°C, a temperature of -24°C was measured at a depth of 0.5 mm in the myocardium. At a depth of 1 mm the temperature was -3°C, at 2 mm depth 18°C and at 3 mm depth 29°C. Under the 15 second application of a RF catheter with a 8 mm electrode and a power of 5 W at 420 kHz, the temperature at the tip of the electrode was 110°C. At a depth of 0.5 mm in the myocardium, the temperature was 75°C, at a depth of 1 mm 58°C, at 2 mm depth 45°C and at 3 mm depth 38°C.
Conclusions: The simulation of temperature profiles during the virtual application of several catheter models in the heart rhythm model allows the static and dynamic simulation of PVI by cryoballoon ablation and RF ablation. The three-dimensional simulation can be used to improve ablation applications by creating a model in personalized cardiac rhythm therapy from MRI or CT data of a heart and finding a favourable position for ablation of AF.
Background: The application of high-frequency ablation is used for the treatment of tachycardia arrhythmias and is a respected method. Ablation with high frequency current leads to the targeted heat destruction of myocardial tissue at specific sites and thus prevents the pathological propagation of excitation through these structures.
Purpose: The aim of this study was to simulate heat propagation during RF ablation with modeled electrodes in different sizes and materials. The simulation was performed on atrioventricular node re-entry tachycardia (AVNRT), atrioventricular re-entry tachycardia (AVRT) and atrial flutter (AFL).
Methods: Using the modeling and simulation software CST, ablation catheters with 4 mm and 8 mm tip electrodes were modeled from gold and platinum for each. The designed catheters correspond to the manufacturer"s specifications of Medtronic, Biotronik and Osypka. The catheters were integrated into the Offenburg heart rhythm model to simulate and compare the heat propagation during an ablation application, which also takes into account the blood flow in the four heart chambers. A power of 5 W - 40 W was simulated for the 4 mm electrodes and a power of 50 W - 80 W for the 8 mm electrodes.
Results: During the simulated HF ablation application, the temperature at the ablation electrode was measured at different powers. This is 40.67°C at 5 W, 44.34°C at 10 W, 51.76°C at 20 W, 59.0°C at 30 W, and 66.33°C at 40 W. The measured temperature during 40 W application is 39.5°C at 0,5 mm depth in the myocardium and 37.5°C at 2 mm depth.
In the simulation, the 8 mm platinum electrode reached an ablation temperature of 72.85°C at its tip during an applied power of 60 W. In contrast, the 8 mm platinum electrode reached a depth of 5 mm at 39.5 C° and at a depth of 2 mm at 37.5 °C. In contrast, the 8 mm gold electrode reached a temperature of 64.66°C with the same performance. This is due to the thermal properties of gold, which has a better thermal conductivity than platinum.
Conclusions: CST offers the possibility to carry out a static and dynamic simulation of a heart model and the ablation electrodes integrated in it during an HF ablation. In variation with different electrode sizes and materials, therapy methods for the treatment of AVNRT, AVRT and AFL can be optimized
Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of uncertainty for climate modeling. Many climate models do not include aerosols in sufficient detail. In order to achieve higher accuracy, aerosol microphysical properties and processes have to be accounted for. This is done in the ECHAM-HAM global climate aerosol model using the M7 microphysics model, but increased computational costs make it very expensive to run at higher resolutions or for a longer time. We aim to use machine learning to approximate the microphysics model at sufficient accuracy and reduce the computational cost by being fast at inference time. The original M7 model is used to generate data of input-output pairs to train a neural network on it. By using a special logarithmic transform we are able to learn the variables tendencies achieving an average score of . On a GPU we achieve a speed-up of 120 compared to the original model.
Encapsulant-free N.I.C.E. modules have strong ecological advantages compared to conventional laminated modules but suffer generally from lower electrical performance. Via long-term outdoor monitoring of fullsize industrial modules of both types with identical solar cells, we investigated if the performance difference remains constant over time and which parameters influence its value. After assessing about a full year’s data, two obvious levers for N.I.C.E. optimization are identified: The usage of textured glass and transparent adhesives on the module rear side. Also, the performance loss could be alleviated using tracking systems due to lower AOI values. Our measurements show additionally that N.I.C.E. module surfaces are in average about 2.5°C cooler compared to laminated modules. With these findings, we lay out a roadmap to reduce today’s LIV gap of about 5%rel by different optimizations.
Smart Home-/Smart-Building-Anwendungen sind ein stetig wachsender Markt. Smart Gardening ist ein Beispiel dafür, Nutzern mehr Komfort und eine bessere Lebensqualität zu Hause oder in Bürogebäuden zu ermöglichen. Im Rahmen dieses Beitrags wird die Entwicklung eines Indoor-Smart-Gardening-Systems mit dem Fokus auf energieautarkes Arbeiten vorgestellt. Herzstück des Systems ist ein 3D-gedruckter Blumentopf für einzelne Pflanzen mit integrierter Elektronik zum Monitoring der wichtigsten Pflanzenparameter und einem integrierten Wasserreservoir mit Tauchpumpe für das automatisierte Bewässern der Pflanze. Energy Harvesting per Solarzellen ermöglicht ein energieautarkes Arbeiten des Blumentopfes. Eine selbstentwickelte Low-Power-Funkschnittstelle im Blumentopf und ein externes Gateway ermöglichen die drahtlose Vernetzung mehrerer Pflanzen. Das Gateway dient zur Auswertung der Pflanzenparameter, der Ansteuerung der im Netzwerk vorhandenen Blumentöpfe und als Benutzerinterface.
Smart Home or Smart Building applications are a growing market. An increasing challenge is to design energy efficient Smart Home applications to achieve sustainable and green homes. Using the example of the development of an Indoor Smart Gardening system with wireless monitoring and automated watering this paper is discussing in particular the design issue of energy autonomous working sensors and actuators for home automation. Most important part of the presented Smart Gardening system is a 3D printed smart flower pot for single plants. The smart flower pot has integrated a water reservoir for automated plant irrigation and an electronic for monitoring important plant parameters and the water level of the water reservoir. Energy harvesting with solar cells enables energy autonomous working of the flower pot. A low-power wireless interface also integrated in the flowerpot and an external gateway based on a Raspberry Pi 3 enables wireless networking of multiple of those flower pots. The gateway is used for evaluating the plant parameters and as a user interface. Particularly the architecture of the energy autonomous wireless flower pot will be considered, because fully energy autonomous sensors and actuators for home automation could not be implemented without special concepts for the energy supply and the overall electronic.
In this paper we report on further success of our work to develop a multi-method energy optimization which works with a digital twin concept. The twin concept serves to replicate production processes of different kinds of production companies, including complex energy systems and test market interactions to then use them for model predictive optimizing. The presented work finally reports about the performed flexibility assessment leading to a flexibility audit with a list of measures and the impact of energy optimizations made related to interactions with the local power grid i.e., the exchange node of the low voltage distribution grid. The analysis and continuous exploration of flexibilities as well as the exchange with energy markets require a “guide” leading to continuous optimization with a further tool like the Flexibility Survey and Control Panel helping decision-making processes on the day-ahead horizon for real production plants or the investment planning to improve machinery, staff schedules and production
infrastructure.
Elektronische Türschilder zur Darstellung von Informationen sind insbesondere in öffentlichen Gebäuden zwischenzeitlich weit verbreitet. Die Varianz dieser elektronischen Türschilder reicht vom Tablet-basierten Türschild bis hin zum PC-basierten Türschild mit externem Bildschirm. Zumeist werden die Systeme mit 230 V betrieben. Bei einer großen Summe von Türschildern in öffentlichen Gebäuden kann dies zu einem signifikanten Umsatz an Energie führen. Im Rahmen dieses Papers wird die Entwicklung eines energieautarken arbeiten Türschildes vorgestellt, bei dem ein E-Paper-Display zum Einsatz kommt. Das Türschild lässt sich per Smartphone-App und NFC-Schnittstelle konfigurieren. Es wird insbesondere auf das Low-Power-Hardware-Design der Elektronik und energetische Aspekte eingegangen.
Spinal cord stimulation (SCS) is the most commonly used technique of neurostimulation. It involves the stimulation of the spinal cord and is therefore used to treat chronic pain. The existing esophageal catheters are used for temperature monitoring during an electrophysiology study with ablation and transesophageal echocardiography. The aim of the study was to model the spine and new esophageal electrodes for the transesophageal electrical pacing of the spinal cord, and to integrate them in the Offenburg heart rhythm model for the static and dynamic simulation of transesophageal neurostimulation. The modeling and simulation were both performed with the electromagnetic and thermal simulation software CST (Computer Simulation Technology, Darmstadt). Two new esophageal catheters were modelled as well as a thoracic spine based on the dimensions of a human skeleton. The simulation of directed transesophageal neurostimulation is performed using the esophageal balloon catheter with an electric pacing potential of 5 V and a trapezoidal signal. A potential of 4.33 V can be measured directly at the electrode, 3.71 V in the myocardium at a depth of 2 mm, 2.68 V in the thoracic vertebra at a depth of 10 mm, 2.1 V in the thoracic vertebra at a depth of 50 mm and 2.09 V in the spinal cord at a depth of 70 mm. The relation between the voltage delivered to the electrodes and the voltage applied to the spinal cord is linear. Virtual heart rhythm and catheter models as well as the simulation of electrical pacing fields and electrical sensing fields allow the static and dynamic simulation of directed transesophageal electrical pacing of the spinal cord. The 3D simulation of the electrical sensing and pacing fields may be used to optimize transesophageal neurostimulation.
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.
Estimation of Scattering and Transfer Parameters in Stratified Dispersive Tissues of the Human Torso
(2021)
The aim of this study is to understand the effect of the various layers of biological tissues on electromagnetic radiation in a certain frequency range. Understanding these effects could prove crucial in the development of dynamic imaging systems under operating environments during catheter ablation in the heart. As the catheter passes through some arterial paths in the region of interest inside the heart through the aorta, a three-dimensional localization of the catheter is required. In this paper, a study is given on the detection of the catheter by using electromagnetic waves. Therefor, an appropriate model for the layers of the human torso is defined and simulated without and with an inserted electrode.
Soiling is an important issue in the renewable energy sector since it can result in significant yield losses, especially in regions with higher pollution or dust levels. To mitigate the impact of soiling on photovoltaic (PV) plants, it is essential to regularly monitor and clean the panels, as well as develop accurate soiling predictions that can affect cleaning strategies and enhance the overall performance of PV power plants. This research focuses on the problem of soiling loss in photovoltaic power plants and the potential to improve the accuracy of soiling predictions. The study examines how soiling can affect the efficiency and productivity of the modules and how to measure and predict soiling using machine learning (ML) algorithms. The research includes analyzing real data from large-scale ground-mounted PV sites and comparing different soiling measurement methods. It was observed that there were some deviations in the real soiling loss values compared to the expected values for some projects in southern Spain, thus, the main goal of this work is to develop machine learning models that could predict the soiling more accurately. The developed models have a low mean square error (MSE), indicating the accuracy and suitability of the models to predict the soiling rates. The study also investigates the impact of different cleaning strategies on the performance of PV power plants and provides a powerful application to predict both the soiling and the number of cleaning cycles.
Wireless sensor networks have found their way into a wide range of applications, among which environmental monitoring systems have attracted increasing interests of researchers. Main challenges for these applications are scalability of the network size and energy efficiency of the spatially distributed nodes. Nodes are mostly battery-powered and spend most of their energy budget on the radio transceiver module. In normal operation modes most energy is spent waiting for incoming frames. A so-called Wake-On-Radio (WOR) technology helps to optimize trade-offs between energy consumption, communication range, complexity of the implementation and response time. We already proposed a new protocol called SmartMAC that makes use of such WOR technology. Furthermore, it gives the possibility to balance the energy consumption between sender and receiver nodes depending on the use case. Based on several calculations and simulations, it was predicted that the SmartMAC protocol was significantly more efficient than other schemes being proposed in recent publications, while preserving a certain backward compatibility with standard IEEE802.15.4 transceivers. To verify this prediction, we implemented the SmartMAC protocol for a given hardware platform. This paper compares the realtime performance of the SmartMAC protocol against simulation results, and proves the measured values are very close to the estimated values. Thus we believe that the proposed MAC algorithms outperforms all other Wake-on-Radio MACs.
The visual-inertial mapping and localization system maplab is analyzed by its implementation and subsequent evaluation. The mapping or localization is based on environmental feature detection. In addition to creating maps, there is also the option of fusion of several maps and thus mapping extensive areas and using them for further analysis of data. In this way, various software tools can be used to optimize the existing data sets.
Two sensor components are needed: an inertial measuring unit (IMU) and a monochrome camera, which are combined by a hardware rig and put into operation for the analysis of the visual-inertial system. System calibration is crucial for precision and system functioning and is based on nonlinear dynamic state estimation. This ensures the best possible estimate of the position of the environmental feature and the map. Maplab is particularly suitable for mapping rooms or small building complexes as the implementation and evaluation of the results in different application scenarios show. Special emphasis is laid on the evaluation of larger scenarios, in which is shown, that the system is struggling to keep up geometric consistencies and thus provide an accurate map.
In this study, various imaging algorithms for the localization of objects have been investigated. Therefore, an Ultra-Wideband (UWB) radar based experimental setup with a circular antenna array is designed as part of this work. This concept could be particularly useful in microwave medical imaging applications. In order to validate its applicability in microwave imaging, different imaging algorithms have been evaluated and compared by means of our experimental setup. Accurate imaging results have been achieved with our system under multiple test-scenarios.
Evaluierung von Kalman Filter Konfigurationen zur Roboterlokaliserung mittels Sensordatenfusion
(2023)
In dieser Arbeit werden drei verschiedene Konfigurationen der von Tom Moore, für das Robot Operating System, entwickelte Kalman-Filter vorgestellt. Diese bilden die Grundlage für eine Lokalisierung mittels Sensorfusion in dem verwendeten ROS-Framework. Ziel dieser Arbeit ist der Aufbau und die Verifikation einer Lokalisierung für ein mobiles Robotersystem Husky A200 der Firma Clearpath Robotics. Hierzu wurden die Möglichkeiten des bestehenden Systems untersucht und mehrere Versionen von Lokalisierungsfiltern konfiguriert. Am an Ende, wird eine Verifikation der Ergebnisse in verschiedenen Szenarien gegeneinandergestellt. Hierzu werden die Ergebnisse einer Variante des Extended Kalman-Filters in 2D (EKF2D), eine Variante des Unscented Kalman-Filter in 2D (UKF2D) und eine Variante des Extended Kalman-Filters in 3D (EKF3D) verifiziert und verglichen. Die Untersuchungen ergaben das der EKF2D die besten und robustesten Ergebnisse für eine Lokalisierung erbringt, trotz, im Vergleich zu der UKF2D Variante, 17,3 % höhere Endpositionsabweichung aufweist. Die in diesem Projekt gewählte EKF3D Konfigurationsvariante eignet sich, wegen seinen starken Ungenauigkeiten in der Höhenbestimmung nicht für eine aussagekräftige Positionsbestimmung.
Narrowband Internet-of-Things (NB-IoT) is a 3rd generation partnership project (3GPP) standardized cellular technology, adopted for 5G and optimized for massive Machine Type Communication (mMTC). Applications are anticipated around infrastructure monitoring, asset management, smart city and smart energy applications. In this paper, we evaluate the suitability of NB-IoT for private (campus) networks in industrial environments, including complex cloud-based applications around process automation. An end-to-end system has been developed, comprising of a sensor unit connected to a NB-IoT modem, a base station (gNodeB) equipped with a beamforming array and a local (private) network architecture comprising a sensor management system in the edge cloud. The experimental study includes field tests in realistic industrial environments with latency, reliability and coverage measurements. The results show a good suitability of NB-IoT for process automation with high scalability, low-power requirements and moderate latency requirements.
The precise positioning of mobile systems is a prerequisite for any autonomous behavior, in an industrial environment as well as for field robotics. The paper describes the set up for an experimental platform and its use for the evaluation of simultaneous localization and mapping (SLAM) algorithms. Two approaches are compared. First, a local method based on point cloud matching and integration of inertial measurement units is evaluated. Subsequent matching makes it possible to create a three-dimensional point cloud that can be used as a map in subsequent runs. The second approach is a full SLAM algorithm, based on graph relaxation models, incorporating the full sensor suite of odometry, inertial sensors, and 3D laser scan data.
The aim of this work is the application and evaluation of a method to visually detect markers at a distance of up to five meters and determine their real-world position. Combinations of cameras and lenses with different parameters were studied to determine the optimal configuration. Based on this configuration, camera images were taken after proper calibration. These images are then transformed into a bird's eye view using a homography matrix. The homography matrix is calculated with four-point pairs as well as with coordinate transformations. The obtained images show the ground plane un distorted, making it possible to convert a pixel position into a real-world position with a conversion factor. The proposed approach helps to effectively create data sets for training neural networks for navigation purposes.
The present work describes an extension of current slope estimation for parameter estimation of permanent magnet synchronous machines operated at inverters. The area of operation for current slope estimation in the individual switching states of the inverter is limited due to measurement noise, bandwidth limitation of the current sensors and the commutation processes of the inverter's switching operations. Therefore, a minimum duration of each switching state is necessary, limiting the final area of operation of a robust current slope estimation. This paper presents an extension of existing current slope estimation algorithms resulting in a greater area of operation and a more robust estimation result.
Das hier vorgestellte System verbindet das neue Konzept der Peer-to-Peer-Navigation mit dem Einsatz von Augmented Reality zur Unterstützung von bettseitig durchgeführten externen Ventrikeldrainagen. Das sehr kompakte und genaue Gesamtsystem beinhaltet einen Patiententracker mit integrierter Kamera, eine Augmented-Reality-Brille mit Kamera und eine Punktionsnadel bzw. einen Pointer mit zwei Trackern, mit dessen Hilfe die Anatomie des Patienten aufgenommen wird. Die exakte Position und Richtung der Punktionsnadel wird unter Zuhilfenahme der aufgenommenen Landmarken berechnet und über die Augmented-Reality-Brille für den Chirurgen sichtbar auf dem Patienten dargestellt. Die Methode zur Kalibrierung der statischen Transformationen zwischen Patiententracker und daran befestigter Kamera beziehungsweise zwischen den Trackern der Punktionsnadel sind für die Genauigkeit sehr wichtig und werden hier vorgestellt. Das Gesamtsystem konnte in vitro erfolgreich getestet werden und bestätigt den Nutzen eines Peer-to-Peer-Navigationssystems.
Facial image manipulation is a generation task where the output face is shifted towards an intended target direction in terms of facial attribute and styles. Recent works have achieved great success in various editing techniques such as style transfer and attribute translation. However, current approaches are either focusing on pure style transfer, or on the translation of predefined sets of attributes with restricted interactivity. To address this issue, we propose FacialGAN, a novel framework enabling simultaneous rich style transfers and interactive facial attributes manipulation. While preserving the identity of a source image, we transfer the diverse styles of a target image to the source image. We then incorporate the geometry information of a segmentation mask to provide a fine-grained manipulation of facial attributes. Finally, a multi-objective learning strategy is introduced to optimize the loss of each specific tasks. Experiments on the CelebA-HQ dataset, with CelebAMask-HQ as semantic mask labels, show our model’s capacity in producing visually compelling results in style transfer, attribute manipulation, diversity and face verification. For reproducibility, we provide an interactive open-source tool to perform facial manipulations, and the Pytorch implementation of the model.
In this paper, we describe a first publicly available fine-grained product recognition dataset based on leaflet images. Using advertisement leaflets, collected over several years from different European retailers, we provide a total of 41.6k manually annotated product images in 832 classes. Further, we investigate three different approaches for this fine-grained product classification task, Classification by Image, by Text, as well as by Image and Text. The approach "Classification by Text" uses the text extracted directly from the leaflet product images. We show, that the combination of image and text as input improves the classification of visual difficult to distinguish products. The final model leads to an accuracy of 96.4% with a Top-3 score of 99.2%. We release our code at https://github.com/ladwigd/Leaflet-Product-Classification.
Formal Description of Use Cases for Industry 4.0 Maintenance Processes Using Blockchain Technology
(2019)
Maintenance processes in Industry 4.0 applications try to achieve a high degree of quality to reduce the downtime of machinery. The monitoring of executed maintenance activities is challenging as in complex production setups, multiple stakeholders are involved. So, full transparency of the different activities and of the state of the machine can only be supported, if these stakeholders trust each other. Therefore, distributed ledger technologies, like Blockchain, can be promising candidates for supporting such applications. The goal of this paper is a formal description of business and technical interactions between non-trustful stakeholders in the context of Industry 4.0 maintenance processes using distributed ledger technologies. It also covers the integration of smart contracts for automated triggering of activities.
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
Current training methods for deep neural networks boil down to very high dimensional and non-convex optimization problems which are usually solved by a wide range of stochastic gradient descent methods. While these approaches tend to work in practice, there are still many gaps in the theoretical understanding of key aspects like convergence and generalization guarantees, which are induced by the properties of the optimization surface (loss landscape). In order to gain deeper insights, a number of recent publications proposed methods to visualize and analyze the otimization surfaces. However, the computational cost of these methods are very high, making it hardly possible to use them on larger networks. In this paper, we present the GradVis Toolbox, an open source library for efficient and scalable visualization and analysis of deep neural network loss landscapes in Tesorflow and PyTorch. Introducing more efficient mathematical formulations and a novel parallelization scheme, GradVis allows to plot 2d and 3d projections of optimization surfaces and trajectories, as well as high resolution second order gradient information for large networks.
The paper describes a systematic approach for a precise short-time cloud coverage prediction based on an optical system. We present a distinct pre-processing stage that uses a model based clear sky simulation to enhance the cloud segmentation in the images. The images are based on a sky imager system with fish-eye lens optic to cover a maximum area. After a calibration step, the image is rectified to enable linear prediction of cloud movement. In a subsequent step, the clear sky model is estimated on actual high dynamic range images and combined with a threshold based approach to segment clouds from sky. In the final stage, a multi hypothesis linear tracking framework estimates cloud movement, velocity and possible coverage of a given photovoltaic power station. We employ a Kalman filter framework that efficiently operates on the rectified images. The evaluation on real world data suggests high coverage prediction accuracy above 75%.
We demonstrate how to exploit group sparsity in order to bridge the areas of network pruning and neural architecture search (NAS). This results in a new one-shot NAS optimizer that casts the problem as a single-level optimization problem and does not suffer any performance degradation from discretizating the architecture.
In this paper, we propose a unified approach for network pruning and one-shot neural architecture search (NAS) via group sparsity. We first show that group sparsity via the recent Proximal Stochastic Gradient Descent (ProxSGD) algorithm achieves new state-of-the-art results for filter pruning. Then, we extend this approach to operation pruning, directly yielding a gradient-based NAS method based on group sparsity. Compared to existing gradient-based algorithms such as DARTS, the advantages of this new group sparsity approach are threefold. Firstly, instead of a costly bilevel optimization problem, we formulate the NAS problem as a single-level optimization problem, which can be optimally and efficiently solved using ProxSGD with convergence guarantees. Secondly, due to the operation-level sparsity, discretizing the network architecture by pruning less important operations can be safely done without any performance degradation. Thirdly, the proposed approach finds architectures that are both stable and well-performing on a variety of search spaces and datasets.
Novel manufacturing technologies, such as printed electronics, may enable future applications for the Internet of Everything like large-area sensor devices, disposable security, and identification tags. Printed physically unclonable functions (PUFs) are promising candidates to be embedded as hardware security keys into lightweight identification devices. We investigate hybrid PUFs based on a printed PUF core. The statistics on the intra- and inter-hamming distance distributions indicate a performance suitable for identification purposes. Our evaluations are based on statistical simulations of the PUF core circuit and the thereof generated challenge-response pairs. The analysis shows that hardware-intrinsic security features can be realized with printed lightweight devices.