Magnetostrictive Position Sensors (MPS), widely used for accurate displacement and velocity measurements, operate based on the Time-of-Flight (ToF) of structure-borne ultrasonic waves induced by magnetic interactions. However, their measurement accuracy can be significantly affected by External Magnetic Interference (EMI), which distorts the characteristic waveforms essential for precise ToF estimation. This study investigates the abilities of a Self-x concept to improve measurement accuracy by the integration of hybrid artificial intelligence techniques (Hybrid-AI) for the classification and mitigation of EMI-induced errors in MPS. The proposed approach is particularly suited for industrial environments characterized by diverse EMI sources. An additional external sensor is not required. Experimental evaluations confirm a significant improvement by self-monitoring and self-adjustments.

Efficiency and flexibility are continual demands on future production systems, and modularization has emerged as a strategy to address these challenges. In the process industry, the Module Type Package (MTP) standard was introduced to simplify the integration and configuration of modular production systems. Transferring the MTP technology to other indus-trial domains is an active research field. This paper describes how the MTP can be used to integrate robotic systems into mod-ular manufacturing plants without compromising the MTP standard. This fills a gap in existing literature and provides promising scientific and commercial opportunities. The study presents a layered concept encompassing all necessary compo-nents for the integration of robotic systems, from the robot's control to the orchestration of the modular plant. A prototypical implementation demonstrates the feasibility of the proposed ap-proach, successfully performing a typical robotic task, a Pick-and-Place operation, using MTP-based services. The imple-mented services, though representing basic robot functionali-ties, lay the groundwork for more complex services or nested service structures. In conclusion, this research contributes to the understanding of the potential integration of robotic systems in MTP-based modular production environments, paving the way for more sophisticated applications, and addressing open questions for future exploration. The prototypical implementation is publicly available on GitHub.

Magnetostrictive Position Sensors (MPS) are used for precise distance and velocity measurements. They utilize magnetostriction to generate structure-borne sound waves and work on the basis of Time-of-Flight (ToF) calculations. However, external electromagnetic interference (EMI) can impact the accuracy of these sensors by interacting with the magnetic fields of magnetostriction. To address this issue, a novel hybrid approach utilizing both neural and symbolic AI has been developed to classify the intensity of EMI. This system is based on the combination of Log-Likelihood Ratios (LLRs). This study’s findings are particularly significant for industrial environments with numerous sources of external electromagnetic interference, where precise measurement is critical.

Ideally, modern modular production plants are controlled via services with uniform interfaces and state machines. But in detail, services are of diverse nature. Due to this diversity, orchestration systems face a challenge of correctly "understanding" the available services and recognizing how and when these services can be used. This paper identifies fundamental service properties, enabling the categorization of services. Furthermore, the authors propose to assign an information model containing the service properties to each service. Consequently, an orchestration system can independently recognize, interpret, and accordingly coordinate the information about service properties and variants; they become explorable. Orchestration systems are thus put in a position to deal explicitly with different services of diverse control methods on their own.

Due to the distributed architecture of cyber-physical production systems (CPPS), the design of automation software is difficult and error-prone. It requires process domain-specific knowledge and a service-oriented approach to be able to design a requirement-specific solution. Previous software design methods for CPPS have several drawbacks. Either these insufficiently take into account the properties of distributed systems or, in the case of Domain-Specific Languages (DSL), their focus lies on interaction logic rather than on production-specific specialties. Similarly, the possibilities for using information from the Asset Administration Shell (AAS) have so far not been integrated. Therefore, we propose a new service-oriented hierarchical DSL consisting of layers with different levels of abstraction, allowing successive and distributed software design with different degrees of detail. Hence, both process engineers and software engineers can work together during the software developing process. By incorporating information from the AAS, a static and also dynamic parameterization of the individual DSL services becomes possible. Finally, a case-study in the field of process engineering demonstrates the advantages and the applicability of the proposed approach.

Over the past years, widespread use of applications based on 3D image processing has increased rapidly. It is being employed in various fields, such as research, medicine and automation. Plenoptic camera system is used to capture light-field that can be exploited to estimate the 3D depth of the scene. The respective algorithms consist of a large number of computation-intensive instructions. It eventually leads to the problem of large execution time of the algorithm. Moreover, they require substantial amount of memory cells for the storage of intermediate and final results. Desktop GPU based solutions are power intensive and therefore cannot be used in the mobile applications with low energy requirements. The idea presented in this paper is to use the FPGA based hardware design to improve the performance of a 3D depth estimation algorithm by utilizing the advantage of concurrent execution. The algorithm is implemented, evaluated and the results show that FPGA design reduces the respective execution time significantly.

Magnetostrictive position sensors (MPS) are used for absolute long-range as well as for high-precision linear position measurement. The working principle of these sensors is based on a time-of-flight (TOF) measurement of a structure-borne sound wave within a magnetostrictive waveguide. State-of-the-art techniques only determine the position based on the measured TOF. Physically related parameters like velocity or acceleration are calculated by derivation of the determined position. In this paper, a novel method for real-time simultaneous estimation of position and velocity of a position marker is presented. Related to the position markers velocity eddy currents are induced within the MPS housing. The influence of the magnetic fields generated by this eddy currents leads to a velocity dependent impact on the structure-borne sound wave that is detected and evaluated by means of an artificial neural network. The training dataset for the velocity estimation is created from recorded datasets itself, without an external velocity reference system (self-training). Therefore, the electrical representation of the detected structure-borne sound wave is digitalized at high speed. Using the presented method, the velocity data could be used either as an additional independent parameter for consistency checking (self-diagnostics), or for the improvement of the position measurement by signal fusion (self-optimization).

Future cyber-physical production systems (CPPS) constitute a complex and dynamic network of services and plant components such as actuators and sensors. Consequently, the manual technical specification and design of these systems are a complex and time-consuming task involving extensive expert knowledge. In scope of CPPS, current approaches to reduce the engineering effort focus on manufacturing technology. There are initial approaches in the domain of process engineering available. However, these neither consider the knowledge-supported definition of recipe-based operations nor the assignment of dynamic service networks to process modules. The objective of this contribution is the design of a concept and a systematic approach to automate the engineering of batch process plants respecting dynamic service networks and process modules using a knowledge-based assistance system. For this purpose, a declarative recipe description is combined with an ontological model. This enables an automatic inference of technical requirements. Based on this information, a multistage orchestration algorithm selects and combines process modules and networked services to find appropriate engineering solutions. Finally, a comprehensive case-study demonstrates that the proposed approach is able to automate the target-oriented selection and combination of process modules and service networks.

A major aspect of Cyber-physical production systems is to increase adaptability regarding the physical structure of manufacturing components as well as of the applied software systems. This is motivated by the increasing engineering efforts of manufacturing systems due to changing requirements, varying product variants and also malfunctions. In context of the method plug and produce physical adaptability, the customization of the plant layout of a production system, is an integral part. Physical adaptions become necessary when e.g. the production process is changed, the process performance requires to be optimized or manufacturing components need to be replaced. Consequently, a technique for determining appropriate manufacturing modules for a given production process is needed. In contrast to current methods this paper presents a novel approach for physical adaptability especially respecting the discrete system behavior of manufacturing modules. For this purpose, subgraph isomorphism is combined with a semantic matching filter together with additional matching criteria. Both, matching filter and matching criteria are tailored to the use case of plug and produce. Based on a case-study the proposed method is evaluated. The matching quality is assessed using macro-averaged precision and the normalized discounted cumulative gain.

This paper presents a model-based approach to generate configurable software architectures of so called Cloud-based Cyber-Physical Systems (CCPS). Cloud computing makes dynamically scalable computing power and data storage available. These advantages are included in the design of Cloud-based Cyber-Physical Systems. The designed platform architecture is evaluated by two scenarios of automation engineering. The different scenarios of camera-based process monitoring are analyzed and subject of a timing analysis, an energy consumption estimation as well as a cost analysis. Significant improvements concerning processing time are shown and the energy optimal solution is identified. Moreover the most cost-effective solution is highlighted.

Despite its age the inspection of specular surfaces is still a topic of ongoing research. While sensory approaches to inspect such surfaces based on deflectometry are increasingly used in practice, the evaluation techniques using the acquired signals (images and reconstruction results) are often not sufficient. This work addresses the challenge of detecting defects with different characteristics on specular surfaces by using robust multiscale detection and classification. In order to process the signals obtained by deflectometry efficiently in all relevant scales, a method for generating an optimized biorthogonal wavelet filter bank with strong correlation to any number of anomaly classes is proposed. The filter bank is optimized for each defect class to obtain a sparse scale space representation. In addition a Bayesian classification approach is presented to classify defects like dents and pimples directly in the scale space.

Due to the separability of its filters, the wavelet transform is suboptimal for transform coding of natural images. The contourlet transform is an approach to overcome this problem by employing directional filters. In contrast to the great number of filters available for the wavelet transform, few filters exist for use with contourlets. We describe a method for the design of directional filters, which can be tailored to specific image contents. The method numerically optimizes the coding gain of directional filters within the lifting framework. Thereto, a link is derived between the coefficients of directional filters and coding gain. An example of an optimized filter is presented and its superiority over a standard filter for contourlet based image coding is shown.

In this paper, a geometric method for estimating the face pose (roll and yaw angles) from a single uncalibrated view is presented. The symmetric structure of the human face is exploited by taking the mirror image (horizontal flip) of a test face image as a virtual second view. Facial feature point correspondences are established between the given test and its mirror image using an active appearance model. Thus, the face pose estimation problem is cast as a two-view rotation estimation problem. By using the bilateral symmetry, roll and yaw angles are estimated without the need for camera calibration. The proposed pose estimation method is evaluated on synthetic and natural face datasets, and the results are compared with an eigenspace-based method. It is shown that the proposed symmetry-based method shows performance that is comparable to the eigenspace-based method for both synthetic and real face image datasets.

Recently, the conventional lifting scheme that is widely used for the construction of wavelets and 2-channel filter banks has been extended to M-channel filter banks (M > 2). This extension brings up the beneficial properties of the lifting scheme to a broader range of applications, like discrete cosine transforms. There exist many hand-crafted lifting-based VLSI architectures, which mostly concentrate on a single and specific target application having a fixed data throughput and resource consumption. However, the reusability of such architectures is limited due to the lack of scalability and flexibility. To overcome this issue, we present a novel design methodology for automatic synthesis of VLSI architectures that are suitable for arbitrary lifting-based M-channel filter banks and transforms. The methodology offers wide-ranging design space exploration with varying resource consumption and data throughput tradeoffs, as it is desired in modern system-on-chip design.

We present an efficient VLSI architecture focusing on M-channel multirate filter banks using the lifting scheme. Since the M-channel lifting factorization results in a signal flow graph with a variable structure, the architecture must have a high degree of flexibility to allow the implementation of basically different lifting factorizations. The proposed architecture is convenient to realize arbitrary lifting factorizations on a variable amount of arithmetic resources, leading to an adaptability for the real-time requirements of various DSP applications.

The application of SAR speckle reduction filters for the purpose of ultrasonic imaging is introduced. A new adaptive filter is proposed which yields a significant improvement of sharpness in edge areas and better smoothing in homogeneous regions. The new filter incorporates high statistical moments which are weighted by their normalized local entropy determined by a moment window around the central pixel. Furthermore, a recursive version, which is algorithmically more efficient, is suggested. The results are illustrated by applying the filter to noise-corrupted artificial edge areas and ultrasonic agar phantoms. As a practical application the diagnosis of immune rejections in transplanted kidneys is demonstrated.>