A prototypical extension to head-mounted displays is also presented. Location-based services or smart home applications all depend on an accurate indoor positioning system.
Basically one divides these systems into token-based and token-free localization systems. In this work, we focus on the token-free system based on smart floor technology. Smart floors can typically be built using pressure sensors or capacitive sensors. However, these set-ups are often hard to deploy as mechanical or electrical features are required below the surface and even harder to replace when detected a sensor malfunctioning.
Therefore we present a novel indoor positioning system using an uncommon form of passive electric field sensing EPS , which detects the electric potential variation caused by body movement. The EPS-based smart floor set-up is easy to install by deploying a grid of passive electrode wires underneath any non-conductive surfaces. Easy maintenance is also ensured by the fact that the sensors are not placed underneath the surface, but on the side. Due to the passive measuring nature, low power consumption is achieved as opposed to active capacitive measurement.
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The proposed architecture achieves a high position accuracy and an excellent spatial resolution. Based on our evaluation conducted in our living lab, we measure a mean positioning error of only Jabhe, Ali Khesrau; Kuijper, Arjan [1. More and more tasks whether classification, detection, segmentation, etc. However, Deep Neural Network DNN -based classification models have been foundto be vulnerable to adversarial attacks - maliciously and digitally manipulated image data -resulting in misclassification of the model.
While only observed in Medical Imaging, adversarial examples define an established securitythreat to computer vision tasks. Furthermore, recent research has demonstrated theexistence of adversarial examples in the physical world - perturbations by physical artifactscausing misclassification instead of digitally perturbed data fed into the model. Since the existenceof adversarial examples in the physical world is not yet explored in Medical Imagingand trends are towards DL methods starting to get approved for clinical practice, this problemneeds more attention.
Using the example of skin lesion classification, as it is likely to be the next aspirant for afully automated medical product, this work throws light on the potential threat adversarialexamples in the physical world pose to patients, physicians, and the healthcare system by generatinga new dataset "Physical Attacks in Dermoscopy" PADv1 and evaluate susceptibilityand robustness of five state-of-the-art DNN-based skin lesion classifiers under physical attack.
All architectures were trained on the HAM dataset, a comprehensive challenge-verifiedbenchmark dataset for Machine Learning. Results show that on average the accuracy of thesearchitectures drops by Furthermore, a visualization of Gradient-weighted class activationmaps is provided to help understand or even interpret a models decision-making on clean andattacked image data.
In this thesis the Linoc prototyping toolkit is presented. The toolkits focus lies on its usability in order to be adapted in future research and novel use cases. Its strength is the possibility to change its configuration at run time. A common obstacle in the beginning of a project is the time required to familiarize with present tools and systems, before the actual project can be attended to.
This can be made worse by dependencies on previous work, often not fully documented and without training given from the original designer. Good toolkits can help to overcome this problem by providing a layer of abstraction and allowing to work on a higher level.
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If the toolkit however requires too much time to familiarize or behaves too restrictive, its goal has been missed and no benefits are generated. A usage study found good reception, a steep learning curve and an interest to use the toolkit in the future. Technical benchmarks show a detectable range equal to its predecessors and in a demonstration it was shown that the toolkit can actually be used in projects.
Boutros, Fadi; Kuijper, Arjan [1. Gutachten]; Damer, Naser [2. Automated face recognition has gained wider deployment ground after the recent accuracy gains achieved by deep learning techniques. Despite the rapid performance gains, face recognition still suffers from very critical issues. One of the recently uncovered, and very sensitive, challenges is the ethnicity bias in face recognition decision. This is the case, unfortunately, even in the latest commercial and academic face recognition system. In , the National Institute of Standards and Technology NIST published the latest report regarding the evaluation result of commercial face recognition solutions from several major biometric vendors.
The report specifically evaluated and demonstrated the variance of the error rates of the evaluated solutions based on demographic variations. This thesis is one of the first research efforts in addressing the decision bias challenge in biometrics.
It builds its hypothesis on the strong assumption that ethnicity bias is caused by the relative under-representation of certain ethnicities in training databases. This work introduces a novel ethnicity-driven data augmentation approach to reduce biometric bias. The proposed approach successfully utilize a generative image model to generate new face images that preserve the identity of their source images while partially transforming their ethnicity to the targeted ethnicity group.
A large-scale ethnicity-labeled face images database is created in order to develop and evaluate the proposed approach. To achieve that, part of this thesis focused on creating an ethnicity classifier to annotate face images, achieving accuracy in the state-of-the-art range. The proposed ethnicity-driven face generative model is developed based on the ethnicity labeled images to generate realistically and high-resolution face images, depending on a limit amount of training data.
More importantly, the thesis proves that the proposed augmentation approach strongly preserves the identity of the input images and partially transforms the ethnicity. The augmented images are used as part of the training data of a face recognition model. The achieved verification results prove that the proposed ethnicity augmentation methods significantly and consistently reduced the ethnicity bias of the face recognition model. For examples, the ERR was reduced from 0. Moreover, the overall performance of the face recognition model was improved. However, this improvement was more significant, as intended, in the targeted ethnicity groups.
Das Setzen eines perkutanen Zugangs durch einen Roboter ist erstrebenswert, um die Genauigkeit von Eingriffen, wie Biopsien, thermale und non-thermale Ablation, zu verbessern. Als Optimierungsverfahren werden der Amoeba Algorithmus und der Gradientenabstieg, in Verbindung mit verschieden Metriken und Modellen getestet. Eine fehlerhafte Initialisierung konnte von den Optimierern nicht ausgeglichen werden.
Bei der Merkmal-Extraaktionstechnik werden die Mittelpunkte der vier Kugeln bestimmt und zu einem Modell registriert. Zur Bestimmung der Kugelmittelpunkte wurde die Hough-Transformation verwendet. Die Translationsabweichung ist dabei auf Sub-Voxel-Spacing genau. Frank, Sebastian; Kuijper, Arjan. Nowadays, there is an increasing trend towards automated driving. This is supported by both driver assistance systems getting more and more available and powerful, and research for car manufacturing industries. As a consequence, driver hands and feet are less involved in vehicle control.
Increasing automation will even let them become idle. Recent gesture recognition mainly focuses on hand interaction. This work focuses on possibilities for feet gesture interaction. Many gesture recognition systems rely on computing intensive, privacy concerns causing video systems.
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Furthermore, these systems require a line of sight and therefore visible interior design integration. The proposed system shows that invisibly integrated capacitive proximity sensors can do the job, too.capsdutiti.gq
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They do not cause privacy issues and they can be integrated under non-conductive materials. Therefore, there is no visible interior design impact. The proposed solution distinguishes between four feet gestures. There is no limitation to feet movement. Further, an evaluation including six participants and a vehicle legroom mockup shows the system function.
This work contributes to the basis of driver foot gesture recognition pointing to further applications and more comprehensive investigations. We show how to overcome the single weakness of an existing fully automatic system for acquisition of spatially varying optical material behavior of real object surfaces. While the expression of spatially varying material behavior with spherical dependence on incoming light as a 4D texture an ABTF material model allows flexible mapping onto arbitrary 3D geometry, with photo-realistic rendering and interaction in real time, this very method of texture-like representation exposes it to common problems of texturing, striking in two disadvantages.
Firstly, non-seamless textures create visible artifacts at boundaries. Secondly, even a perfectly seamless texture causes repetition artifacts due to their organised placement in large numbers over a 3D surface. We have solved both problems through our novel texture synthesis method that generates a set of seamless texture variations randomly distributed over the surface at shading time. When compared to regular 2D textures, the inter-dimensional coherence of the 4D ABTF material model poses entirely new challenges to texture synthesis, which includes maintaining the consistency of material behavior throughout the 4D space spanned by the spatial image domain and the angular illumination hemisphere.
In addition, we tackle the increased memory consumption caused by the numerous variations through a fitting scheme specifically designed to reconstruct the most prominent effects captured in the material model. View article PDF. Ramesh, Supradha; Ben Hmida, Helmi [1. Anomaly detection is the task of finding instances in a dataset that are different from the normal data.
Today, anomaly detection is a core part of many IoT applications and finding abnormal instances is crucial in many applications. For example in network intrusion detection, identification of failures in mechanical systems and in smart sensors and abnormal usage of resources and diagnosis in the medical domain. In all of these applications, the amount of stored data has increased dramatically in the last decade, resulting in a strong demand for algorithms suitable for these crucial challenges.
Existing research on anomaly detection has been fragmented across different application domains.
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Without a good understanding of how different techniques are related to each other and what the strengths and weaknesses of the techniques are, a large number of algorithms are created for the problem of anomaly detection in the field of IoT for specific use cases. This causes scalability issues in reusing the algorithm.
In many solutions, the foundation of data are not taken into account, which leads to poor performance of the anomaly detection. The main goal of this thesis is to bridge this gap and to provide an efficient recommendation on anomaly detection algorithm, based on the good understanding of algorithms and characteristics of data.
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