In this paper, we present our comprehensive approach to the implementation of extracurricular STEM courses, drawing from 17 years of experience in the field. Our methodology encompasses various aspects, including administrative procedures, strategies for promotion, and didactic methods. Additionally, we provide insights into participant numbers and a practical illustration of our didactic approach through specific course examples, offering a nuanced understanding of our proposed framework.

Velocities measured by radar sensors suffer from ambiguities caused by aliasing effects associated with signal processing. These ambiguities are highly undesired as they affect otherwise very accurate speed measurements. For automotive applications utilizing radar sensors, this means that measured velocities are potentially unreliable which can lead to unsafe conditions for automated driving functionalities. This work presents the first approach to disambiguate radial velocities measured by radar sensors based on Deep Learning methods. By utilizing the presented method, radial velocity estimates can be obtained which are both highly accurate while being reliable as ambiguities are resolved.

Selecting an optimal neural network architecture tailored to a specific dataset is a time-consuming task due to numerous design possibilities. Neural Architecture Search (NAS) provides strategies to identify well performing networks in a limited timeframe. Zero-cost proxies offer a training-free approach to find potential architectures within a predefined search space. However, relying solely on these proxies often leads to unreliable results across diverse search spaces and datasets. In this paper, we present an empirical study of extended zero-cost proxies, termed few-cost proxies, obtained by training for a restricted number of epochs. Our analysis demonstrates that these few-cost proxies significantly enhance the ranking performance. Furthermore, novel few-cost proxies introduced in this study outperform previous methods significantly, achieving a Spearman correlation of 0.89 compared to the second-highest score of 0.847 on TSS-Cifar10, showcasing their effectiveness in the context of NAS.

In this paper, we propose a novel method for vectorizing Wave Digital structures using Automatic Differentiating Wave Digital Filters (ADWDFs), maintaining modularity through a backend/frontend construction approach, and enabling parallelization as well as hardware acceleration by design. As demonstrated by a proof-of-concept example, our approach achieves up to a 10 times speedup compared to a non-vectorized reference implementation.

Due to its key role in many real world applications such as autonomous driving, semantic segmentation often has to be applied on edge devices with limited computational resources. At runtime, the requirements for the segmentation system can change significantly depending on the given circumstances. For example, in some cases the focus is on a fast segmentation of certain particularly important classes, in other cases it is more relevant to distinguish a large number of classes. Besides many works aiming at a resource efficient but still accurate semantic segmentation, the possibility to adapt the segmentation model to specific circumstances is missing. In this work, we present a novel approach for flexible semantic segmentation that builds on recent developments in segmentation foundation models and prompt tuning. The approach offers the possibility to trade off inference time and accuracy, to flexibly select the classes to be segmented, and to add new target classes to an existing system just by transferring new prompts. Evaluation on an edge device shows that the inference time can be significantly reduced with fewer classes and smaller prompts, and that accuracy increases with larger prompts at the expense of a longer inference time.

The prediction of road users’ future motion is a critical task in supporting advanced driver-assistance systems (ADAS). It plays an even more crucial role for autonomous driving (AD) in enabling the planning and execution of safe driving maneuvers. Based on our previous work, Context-Aware Scene Prediction Network (CASPNet), an improved system, CASPNet++, is proposed. In this work, we focus on further enhancing the interaction modeling and scene understanding to support the joint prediction of all road users in a scene using spatiotemporal grids to model future occupancy. Moreover, an instance-based output head is introduced to provide multimodal trajectories for agents of interest. In extensive quantitative and qualitative analysis, we demonstrate the scalability of CASPNet++ in utilizing and fusing diverse environmental input sources such as HD maps, Radar detection, and Lidar segmentation. Tested on the urban-focused prediction dataset nuScenes, CASPNet++ reaches state-of-the-art performance. The model has been deployed in a testing vehicle, running in real-time at 20 Hz with moderate computational resources alongside a machine learning-based perception system.

This paper presents a novel approach for refining the semantic granularity of remote sensing image segmentation datasets using openly available cadastral data, thus minimizing the annotation effort. A two-step method is proposed: (1) a supervised learning step for training a semantic segmentation model on fine-grained cadastre labels to extract class prototypes, and (2) an unsupervised learning step utilizing hierarchical clustering on the extracted prototypes to generate a class hierarchy. The method is demonstrated on building segmentation in aerial imagery data, resulting in models capable of predicting new aggregated semantic classes without extra effort in dataset annotation. Evaluation of the proposed method shows its ability to generate meaningful hierarchical relationships among labels and achieve high segmentation performance.

We present WUP-CD, a high-resolution aerial dataset for building change detection consisting of both orthorectified aerial imagery and corresponding elevation information ac-quired at two points in time. Detailed analysis of the dataset using several state-of-the-art deep learning methods allows us to show that the best results are obtained using elevation data only, highlighting its importance for future data acquisition and model development. The dataset is available for down-load11https://github.com/tritolol/WUP-CD.

In existing semi-supervised learning (SSL) approaches, the contributions of labeled and unlabeled data points to the training objective are expressed as two separated terms for the respective sets. A single weight specifies how much the unlabeled samples contribute to the overall loss and is carefully scaled in the course of the training. In this work, we propose a SSL framework in which the contribution of each unlabeled sample is individually scaled based on the confidence generated by a teacher model. We demonstrate that our scaling mechanism enables the student model to efficiently learn from heavily interpolated and augmented samples and achieve state-of-the-art performance.

To train a well performing neural network for semantic segmentation, it is crucial to have a large dataset with available ground truth for the network to generalize on unseen data. In this paper we present novel point cloud augmentation methods to artificially diversify a dataset. Our sensor-centric methods keep the data structure consistent with the lidar sensor capabilities. Due to these new methods, we are able to enrich low-value data with high-value instances, as well as create entirely new scenes. We validate our methods on multiple neural networks with the public SemanticKITTI [3] dataset and demonstrate that all networks improve compared to their respective baseline. In addition, we show that our methods enable the use of very small datasets, saving annotation time, training time and the associated costs.

The rising demand for Active Safety systems in automotive applications stresses the need for a reliable short-term to midterm trajectory prediction. Anticipating the unfolding path of road users, one can act to increase the overall safety. In this work, we propose to train neural networks for movement understanding by predicting trajectories in their natural form, as a function of time. Predicting polynomial coefficients allows us to increase accuracy and improve generalisation.

Interest in accurate semantic geospatial data has increased with application areas such as the creation of realistic virtual worlds or autonomous driving. Curb detection is an important component that is especially needed for modeling road sections. By leveraging static point cloud data, we show the potential of modern 3D deep learning methods on this problem and present a post-processing step that reliably detects complex curb pathways and improves the overall result. Since datasets used in related work are mostly unavailable or not complex enough, we provide a dataset to fill this gap and serve as a basis for future work. We show that our approach generalizes well to unknown data, underlining its usefulness in real-world applications.

Lidar sensors are widely used in various applications, ranging from scientific fields over industrial use to integration in consumer products. With an ever growing number of different driver assistance systems, they have been introduced to automotive series production in recent years and are considered an important building block for the practical realisation of autonomous driving. However, due to the potentially large amount of Lidar points per scan, tailored algorithms are required to identify objects (e.g. pedestrians or vehicles) with high precision in a very short time. In this work, we propose an algorithmic approach for real-time instance segmentation of Lidar sensor data. We show how our method leverages the properties of the Euclidean distance to retain three-dimensional measurement information, while being narrowed down to a two-dimensional representation for fast computation. We further introduce what we call "skip connections", to make our approach robust against over-segmentation and improve assignment in cases of partial occlusion. Through detailed evaluation on public data and comparison with established methods, we show how these aspects enable state-of-the-art performance and runtime on a single CPU core.

Due to their ability to model the manifold of natural images, generative adversarial networks (GANs) have been adapted to semi-supervised learning and shown promising results. Using this property of GANs, manifold regularization has been incorporated into feature-matching GANs (FM-GAN) [14] and shown improvement over FM-GAN. In this work, we present a novel approach for performing manifold regularization in semi- supervised classification using the FM-GAN framework. Our approach, mixup manifold regularization (MUMR), regularizes the networks to favor linear behavior so that linear interpolations of noise vectors lead to linear interpolations of the associated predictions. We show that our approach is able to achieve state-of-the-art results when compared to other GANs- and perturbation-based approaches.

Abstract The last decade has witnessed important advancements in the field of computer vision and scene understanding, enabling applications such us autonomous vehicles. Radar is a commonly adopted sensor in automotive industry, but its suitability to machine learning techniques still remains an open question. In this work, we propose a neural network (NN) based solution to efficiently process radar data. We introduce RadarPCNN, an architecture specifically designed for performing semantic segmentation on radar point clouds. It uses PointNet $$++$$ + + as a building-block—enhancing the sampling stage with mean-shift—and an attention mechanism to fuse information. Additionally, we propose a machine learning radar pre-processing module that confers the network the ability to learn from radar features. We show that our solutions are effective, yielding superior performance than the state-of-the-art.

Due to the effectiveness of anomaly/outlier detection, one-class algorithms have been extensively studied in the past. The representatives include the shallow-structure methods and deep networks, such as the one-class support vector machine (OC-SVM), one-class extreme learning machine (OC-ELM), deep support vector data description (Deep SVDD), and multilayer OC-ELM (ML-OCELM/MK-OCELM). However, existing algorithms are generally built on the minimum mean-square-error (mse) criterion, which is robust to the Gaussian noises but less effective in dealing with large outliers. To alleviate this deficiency, a robust maximum correntropy criterion (MCC)-based OC-ELM (MC-OCELM) is first proposed and then further extended to a hierarchical network to enhance its capability in characterizing complex and large data (named HC-OCELM). The gradient derivation combining with a fixed-point iterative updation scheme is adopted for the output weight optimization. Experiments on many benchmark data sets are conducted for effectiveness validation. Comparisons to many state-of-the-art approaches are provided for the superiority demonstration.

This paper develops iterative learning control laws for a subclass of uncertain spatially interconnected systems. Model uncertainties appear both in the state (represented by the norm bounded description) and the output equation (represented by a polytopic description) of the dynamics modeled in the 2D systems setting. A particular example of an RLC ladder circuit provides an example to motivate the analysis. The first stage is to write the dynamics in 2D model form and then to apply the stability theory for a distinct class of 2D systems known as repetitive processes is used to develop the ILC law in the case of differential dynamics. This results in a design algorithm that considers the mixed uncertainties in the model and can be applied using linear matrix inequalities.

With an increasing significance of climate change and the progressive development of new intelligent devices in the sense of an Internet of Things (IoT), attempts are being made to recover unused ambient energy and convert it into electricity in order to supply said devices, known as energy harvesting. To raise awareness to the topic among students and next-generation engineers, we propose an energy harvesting based teaching platform for physical computing that allows students to experience the concept and design of energy harvesting systems playfully and creatively. Therefore, a self-powered energy harvesting hardware (gaming) platform (EHHP) has been developed involving a variety of components and peripherals which enables a wide range of software and hardware development teaching possibilities in physical computing. Piezoelectric elements and amorphous silicon solar cells in combination with supercapacitors were selected as energy sources which sufficiently enable several hours of operation under gameplay conditions. Besides, a software framework is presented that can be adapted to the level of experience or age of the students and can thus be kept completely modular. Further, since the concept involves mechanical design, CAD construction in combination with rapid prototyping by means of 3D printing techniques can be conducted by students.

Based on PointPillars[1] and SECOND[2] we propose a novel LiDAR object detection system focused on speed, while not neglecting detection performance with the help of included occupancy grid features. We achieve this by replacing the voxelgrid defined in cartesian coordinates as introduced by Zhou et al. in VoxelNet[3] by a grid in a polar coordinate system. Doing this we can significantly reduce the number of required grid cells, while still keeping a good grid resolution in areas of highest point density close to the sensor. Because of this strong reduction on resolution we have a loss in performance which we try to regain with extending the feature network by adding occupancy grid and height map features. Furthermore we integrate the ground truth augmentation introduced in SECOND[2], injecting additional ground truth objects into the limited number of point clouds to increase variance. We achieve performance close to the state of the art, while reaching inference speeds around 12ms.

The applications of Machine Learning and Neural Networks (NN) are nearly unlimited and the application of artificial intelligence has gained much interest in recent years, because of their great performance in various tasks. Deep Convolutional Neural Networks (CNNs) are a state-of-the-art technique for visual recognition in image- and video data. However, the application range of a specific CNN is very limited, because the CNN is adapted for a specific task with an exclusive dataset for training. It needs to be rebuilt from scratch when the input- or output parameters are just slightly changing, including the collection of a new dataset for training. To reduce those cost and time expensive issues, transfer learning can be beneficial, where the outcome of an already pre-trained Neural Network, the source data, is fitted to the target dataset of a new task. In the case of object recognition, there are several use cases where pre-trained Deep CNNs are applied. But those Deep CNNs can not only be used for visual recognition. In this work the approach is made, to use transfer learning on DCNNs for spoken letter recognition, although the target data is very dissimilar from the source data, to show the range of application for transfer learning. Moreover, this application is trained with a very small dataset.

Implementation and evaluation of image/video processing systems on FPGA hardware require a video path for input/output of data. We propose a portable low-cost video pipelining method by means of a USB 2.0 interface in conjunction with a Raspberry Pi single board computer. An application of our approach is demonstrated by a 2-D spatio-temporal Wave Digital Filter for motion feature extraction.

Implementation and evaluation of image/video processing systems on FPGA hardware require a video path for input/output of data. We propose a portable low-cost video pipelining method by means of a USB 2.0 interface in conjunction with a Raspberry Pi single board computer, whereby virtually any video sources/sinks can be used as input/output with realtime capabilities. An application of our approach is presented by the implementation and evaluation of a 2-D spatio-temporal Wave Digital Filter for motion feature extraction.

In recent years the research community and industry have paid high attention to the field of machine learning, especially deep learning. Nowadays many real-world classification or rather prediction applications are implemented by neural network models. We propose a multi-view fusion neural network with application in the manufacturing industry. Image information of multiple cameras is fused and used by the proposed model to predict the state of a manufacturing machine. Experiments show that the overall classification performance is increased from a baseline of 92.7% to 99.5% by the fusion model.

This paper presents the combination of the adversarial nets framework with a kernel-based statistical dependency measurement for learning interpretable representations without any labeled data and with an autoencoder. Thus, the loss function consists of three terms and, based on the loss function, a new architecture emerges. Experiments show the functionality and potential of the architecture and the proposed kernel-based statistical dependency criterion quantitatively and qualitatively. Furthermore, the improvements are presented on a dataset consisting of industrial images.

We propose a combination of optical tracker, Probability Hypothesis Density (PHD) Filter and group tracking for tracking vehicle head lights and tail lights from an in vehicle, forward facing camera. We propose these systems are advantageous, because they can bridge several frames without outside detections and lead to more stable tracks than just using a traditional tracker, like a Kalman filter, on it's own. Additionally PHD does not need track-data association, but moves association uncertainty into the tracker, where it can be incorporated in covariance and noise calculations. Evaluation is performed with a closed source detector and a private data set. This evaluation proves the stability of the tracks and the trackers ability to bridge large amounts of time without external detections. This makes it a suitable choice for high difficulty situations that lead to the external detector missing light sources.

With the ever increasing application of Convolutional Neural Networks to customer products the need emerges for models to efficiently run on embedded, mobile hardware. Slimmer models have therefore become a hot research topic with various approaches which vary from binary networks to revised convolution layers. We offer our contribution to the latter and propose a novel convolution block which significantly reduces the computational burden while surpassing the current state-of-the-art. Our model, dubbed EffNet, is optimised for models which are slim to begin with and is created to tackle issues in existing models such as MobileNet and ShuffleNet.

Implementations of wave digital filters (WDF) require quantization of finite word lengths. We present a method for finite word length optimization of a 2-D-WDF, using magnitude truncation for quantization and weighted quantization error variances, constrained by a limited shared memory bus width. Moreover, we define a rule to obtain word lengths for various image sizes and arbitrary bus widths, whereby long simulation times of the filter can be avoided during design time. After all, an application of our approach offers a constant improvement by 23 dB, compared to an intuitive unbalanced approach.

Convolutional neural networks (CNNs) have demonstrated their superiority in numerous computer vision tasks, yet their computational cost results prohibitive for many real-time applications such as pedestrian detection which is usually performed on low-consumption hardware. In order to alleviate this drawback, most strategies focus on using a two-stage cascade approach. Essentially, in the first stage a fast method generates a significant but reduced amount of high quality proposals that later, in the second stage, are evaluated by the CNN. In this work, we propose a novel detection pipeline that further benefits from the two-stage cascade strategy. More concretely, the enriched and subsequently compressed features used in the first stage are reused as the CNN input. As a consequence, a simpler network architecture, adapted for such small input sizes, allows to achieve real-time performance and obtain results close to the state-of-the-art while running significantly faster without the use of GPU. In particular, considering that the proposed pipeline runs in frame rate, the achieved performance is highly competitive. We furthermore demonstrate that the proposed pipeline on itself can serve as an effective proposal generator.

Many n-D signal processing applications in industrial environments require real-time processing of data. We propose a technique for realization of motion feature extraction in video images on low performance computing hardware. The realization is based on a 2-D spatio-temporal wave digital filter (WDF) using SDRAM-based shift operators. This enables the usage of different effective shared bus widths for the connection between a DDR2-SDRAM and the WDF arithmetic subsystems, which is demonstrated by two different configurations. The FPGA board is processing real-time video signals from an image sensor and the filtered output can be observed on a display.

Increasing miniaturization of electronic systems and nano-technology enables more and more the realization of real time 3-D spatio-temporal signal processing and control systems. However, while stability is well understood for 1-D systems, open problems remain in the higher dimensional case, e.g. for 3D system realizations, especially with respect to computational complexity and thus hardware effort. We propose a basic and thus fast stability test for systems that can be given in a standard 3-D Roesser-like state space model. Most practical systems can be represented in that manner or at least be transformed into an equivalent system so that the test is applicable to most real world problems. The test itself is inspired by passivity assumptions which for example guarantee stability of electrical reactance networks. It represents a sufficient condition for stability and comprises eigenvalue computations of a sum of Gramian matrices of the same size as the system matrix, which leads in this step to the same complexity as a 1-D stability test. The latter can thus also be applied to adaptive systems, where the system matrix is periodically changing.

This paper focuses on dynamic orientation estimation of a vehicle-mounted mono camera. In particular, the pitch and roll angles of the camera relative to the road surface. Information about the orientation angles is included in the homography (projective transformation) between two images of a planar road surface. The extraction of angles from a homography matrix is possible but not recommended due to parameter ambiguities. For this reason we do not estimate a full homography matrix but reduce the parameter space to two parameters. In this area of parameter estimation there are mainly two different approaches: The optical flow based approach and the image registration based approach. In order to decide which of these approaches is more favorable for the angle estimation problem, we develop one optical flow based and one image registration based angle estimation algorithm. We are the first directly evaluating and comparing both approaches with each other with the help of an artificial image sequence as well as real-world driving scenarios. In addition, this paper lifts the common limitation of roll angle of zero degree for dynamic camera orientation estimation. Our research finds that there is only a small difference between the parameter estimation results of the optical flow and image registration based approach.

Visual tomography is a relatively new method for 3D scene reconstruction. It is adopted from medical tomography and based on multiple images from different viewpoints of a scene. In this context, multidimensional spectra and filtering techniques are the key technology for the reconstruction process. Visual tomography differs from classical tomography in several aspects which leads to new challenges with respect to mathematical description. The present paper examines the influence of image background on reconstruction quality. This background problem does not appear in classical medical tomography applications. In particular, the influence of multidimensional sampling and restrictions with respect to the number of view angles can be analyzed by using multidimensional signal theoretical concepts. The differences between ideal (no background) and real acquisition conditions are examined. Visual tomography has the potential for innovative new fields of applications, where Enabling Technologies for Societal Challenges are the focus of our considerations. Demographic change leads to a high interest for enabling mobility for elderly people with physical disabilities. Walking frames equipped with such technologies will be able to assist such people in real day environments.

This paper proposes a method to analyse human-made environments regarding the existence of descending stairs and steps to assists visually impaired and furthermore disabled people, that are not able to use traditional supports like blind canes. Those people are heavily limited in their daily lives, since wrong decisions caused by the lack of information can easily lead to accidents. We use depth data acquired with a low-resolution Time-of-Flight (ToF) camera to perceive the scene in front a mobile vehicle (rollator) to provide the user with detailed information about potentially hazardous situations. Experiments with affected persons have shown the ability of the system to help them understand the environment and, in particular, avoid falls from descending stairs.

To avoid the occurrence of noncomputable, delay-free loops, classic Wave Digital Filters (WDFs) usually exhibit a tree-like topology. For the realization of prototype circuits that contain ring-like subnetworks, prior approaches require the decomposition of the structure and thus neglect the notion of modularity of the original Wave Digital concept. In this paper, a new modular approach based on Multidimensional Wave Digital Filters (MDWDFs) is presented. For this, the contractivity property of WDFs is shown. On that basis, the new approach is studied with respect to possible side-effects and an appropriate modification is proposed that counteracts these effects and significantly improves the convergence behaviour.

In this paper a 2D systems setting is used to develop new results on modeling and the stability analysis of electrical multi element ladder circuits from the repetitive process standpoint.

A novel approach for a decision fusion and reasoning system for vision-based traffic sign recognition is presented. This module consists of several steps. In the first stage, a track-based Bayesian fusion scheme is used to fuse the classification results from each frame to obtain a fusion result for each track to decide whether a sign is present, as well as to determine the sign type. In order to determine the sign type, the temporal fusion scheme has been combined with a decision tree. In the second stage, the system combines and fuses probable identical objects which help to further reduce failures in the recognition process. The decision is based on the fusion results, as well as a position cue. Finally, a reasoning module is used to decide which of the passed signs should be shown to the driver. In addition to these modules, a general evaluation method for multi-class tracking systems is shown. While some failures are observed from the evaluation on object level, the additional post processing steps improve the system in such a way that the finally presented signs are almost always correct on the test set.

This paper presents a new hardware-efficient method of eliminating aliasing artifacts in time direction occurring in filter results of velocity or directional filters applied to image sequences recorded by a camera. Aim is to deliver reliable input data for object detection in a driver assistance system, for example. Applications designed for mass products like these demand low production costs and therefore cheap sensors and hardware. Thus, resolution as well as frame rate of the captured image sequences are often limited and therefore Shannon's sampling theorem is not always satisfied. For the case that aliasing only occurs in the pass band, a method to remove unwanted aliasing components from the filter result without any additional filter hardware is proposed and demonstrated by means of examples.

Stability of multidimensional (k-D) systems is still a challenging field of work. Well known and established stability measures may lead to complex mathematical problems, while simple tests are restricted to special cases of n-D systems. A new stability test for certain discrete 2-D system realizations given in a Roesser model description is proposed. This test is suitable for signals bounded with respect to both coordinate directions, like images. The 2-D system is observed in real operation, i.e. considering a sequence of processing, which leads to a 1-D state space description. The resulting 1-D system matrix is some kind of a block Toeplitz matrix that allows definition of the test.

In this paper a new crosswalk detection strategy is presented. The application area is narrowed down to driver assistance systems to guarantee a reliable detection result and to benefit from the properties of a vehicle mounted camera. The main contribution of this paper is an efficient method to detect line segments that are typical for road markings like lanes and crosswalks. Therefore an adaptive Haar-like filter is applied by means of an integral image, where the pass-band of the filter is adjusted according to the distance from the vehicle. The detected segments are then combined to regions of interest by means of prior knowledge of crosswalk dimensions. These regions are then passed to a simple classifying module that utilizes a set of moment invariants for classification. The final detection result is then tracked by a standard Kalman filter.

In this paper a new anti-aliasing approach for motion-based object detection using linear shift invariant (LSI) filters is presented. Mainly originating from the field of signal processing, LSI-filter-based motion detection has been topic of research for a long time, though due to low computational power of contemporary computers the developed systems have been unfeasible for application in mass products like driver assistance systems. However, recent progress in hardware in conjunction with decreasing costs makes using linear filters a very interesting alternative of increasing performance of common approaches. One of the most important factors falsifying object detection results obtained using linear shift invariant (LSI) is aliasing. This is mostly caused by the low temporal resolution of the camera signal which leads to the fact that the sampling theorem does not hold for objects moving at high speed in the image plane. In general, aliasing errors cannot be removed after sampling. Under certain conditions, however, their impact on the filter result can be successfully decreased using the approach presented in this paper. The applicability of the new approach is demonstrated using scenes recorded by a camera installed in a blind spot warning system.

Although recent statistics demonstrate a decrease in pedestrian fatalities, the absolute number of accident related deaths is sufficiently high to justify research in the area of vulnerable road user protection. Research has shown that situation awareness, which requires a significant amount of context information, is critical to timely intervention. Altered pedestrian behavior, due to traffic regulation, requires context information. For example, crosswalk presence and location knowledge can be of importance in pedestrian crossing scenarios. Therefore this paper discusses the implementation of a crosswalk detection algorithm, using Fourier transformation, augmented bipolarity, inverse perspective mapping template matching and edge orientation ratios for classification.

In practice, n-D signal processing problems often show the property that underlying signals are unbounded with respect to only one direction, like the spatio-temporal signal of a line-scan camera, for example. Thus, the use of systems which meet the conditions of practical BIBO stability, mainly known from the field of control theory, seems to be feasible. In this paper the practical BIBO stability concept introduced by Agathoklis and Bruton [1] is analyzed with respect to its applicability in the field of signal processing applications. It is shown that practical stability in its original form is not sufficient in signal processing if not further conditions are supposed. This is done by comparing the 2D Fourier transform of the actually measured impulse response of exemplary systems to the frequency response expected on basis of the transfer function.

Vision-based object detection is a core part of many automotive collision warning systems. Especially due to the extensive information an image can hold and the decreasing costs of computational power, it has attracted more and more interest in the last decade. In this paper we propose a motion-based approach to simplify the detection of moving objects in order to improve available methods and make them more efficient. We interpret the image sequence containing the moving object (e.g. a vehicle or a crossing pedestrian) as a three-dimensional signal, not just as a sequence of image matrices. That way we can benefit from the advanced theoretical knowledge on system description and handling from the field of signal processing. In detail, three-dimensional velocity filters implemented using wave digital filters are used to oppress every object in the image that is not moving in a certain direction at a certain velocity.

This paper considers the general filtering problem for a distinct class of two-dimensional (2-D) discrete linear systems, i.e. information propagation in two independent directions, known as discrete linear repetitive processes which are of both system-theoretic and applications interest. In particular, new results on the design of filters with guaranteed levels of performance are developed. These take the form of algorithms for the design of an Hinfinand l2-linfindynamic output feedback filter which guarantees that the resulting filtering error process is stable and has prescribed disturbance attenuation performance as measured by Hinfinand l2-linfinnorms.

This paper addresses the problem of stability for two-dimensional systems with delays in the state. To solve this problem, the Lyapunov second method is used. The resulting condition is written in terms of linear matrix inequalities and it is dependent on the size of delays. This fact allows us to reduce the conservatism in the stability analysis of two-dimensional systems with state delays. A simulation example is given to illustrate the theoretical developments.

Network synchronization of flat multihop ad hoc networks is considerably different from conventional systems that are usually hierarchically organized. Lately, connected dominating sets (CDSs) are used in ad hoc networks to structure even larger scaled topographies into smaller portions. While these virtual infrastructures are initially utilized for efficient flooding or routing purposes, this paper presents a global network synchronization scheme that also uses a CDS in order to synchronize network members in an efficient manner. An extension to the predictive timer synchronization function (PTSF) is presented that has been tailored to this specific application and allows to synchronize even widely distributed systems by supporting multihop operation

In the field of multidimensional stability theory so called scattering Hurwitz polynomials (in the continuous case) and scattering Schur polynomials (in the discrete) case play a crucial role. In the present paper a parametric representation for real two-variable scattering Schur polynomials is given. The following features of this model makes it best suited for the computer based design of 2-D systems, namely no dependencies between the real valued parameters, coverage of the whole class of 2-D scattering Schur polynomials, and the coefficients of the polynomials are rational functions of the parameters. The synthesis of two-dimensional (2-D) lossless networks and Householder matrices form the basis of our considerations.

Due to the increasing calculation power of dedicated hardware, real-time image processing becomes practicable at reasonable expense. Nevertheless the algorithms concerned have to be adapted to real-time requirements. The subject treated in this paper is tie detection for automated railway inspection. An edge detector is proposed in which the sensitivity is adaptable to the local brightness of the processed image. The required effort is the performance of a convolution operation with subsequent decision concerning the sign of the values obtained.