Fraunhofer Institute for Integrated Circuits

Illumination variation is a big problem in object recognition, which usually requires a costly compensation prior to classification. It would be desirable to have an image-to-image transform, which uncovers only the structure of an object for an efficient matching. In this context the contribution of our work is two-fold. First, we introduce illumination invariant local structure features for object detection. For an efficient computation we propose a modified census transform which enhances the original work of Zabih and Woodfill. We show some shortcomings and how to get over them with the modified version. S6econdly, we introduce an efficient four-stage classifier for rapid detection. Each single stage classifier is a linear classifier, which consists of a set of feature lookup-tables. We show that the first stage, which evaluates only 20 features filters out more than 99% of all background positions. Thus, the classifier structure is much simpler than previous described multi-stage approaches, while having similar capabilities. The combination of illumination invariant features together with a simple classifier leads to a real-time system on standard computers (60 msec, image size: 288/spl times/384, 2GHi Pentium). Detection results are presented on two commonly used databases in this field namely the MIT+CMU set of 130 images and the BioID set of 1526 images. We are achieving detection rates of more than 90% with a very low false positive rate of 10/sup -7/%. We also provide a demo program that can be found on the Internet http://www.iis.fraunhofer.de/bv/biometrie/download/.

The increasing importance of sustainable behaviour in business has enhanced its impact on supply chain management. Firms foster sustainability in their supplier base in reaction to growing sustainability requirements in various ways, including sustainable supplier co-operation. Knowledge about the effects of sustainable supplier co-operation on firm performance is limited; therefore, this study tests antecedents and implications of sustainable supplier co-operation according to the triple bottom line. A survey of Western European firms reveals that sustainable supplier co-operation has generally positive effects on firm performance across social, green and economic dimensions. However, only green practices have positive significant effects on economic performance, not social practices (e.g., child labour rules). In contrast to practitioner perceptions, investments in sustainability, for example through sustainable supplier co-operation does indeed result in sufficient returns.

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> Multidimensional harmonic retrieval problems are encountered in a variety of signal processing applications including radar, sonar, communications, medical imaging, and the estimation of the parameters of the dominant multipath components from MIMO channel measurements. <formula formulatype="inline"> <tex>$R$</tex></formula>-dimensional subspace-based methods, such as <formula formulatype="inline"><tex>$R$</tex></formula>-D Unitary ESPRIT, <formula formulatype="inline"> <tex>$R$</tex></formula>-D RARE, or <formula formulatype="inline"><tex>$R$</tex> </formula>-D MUSIC, are frequently used for this task. Since the measurement data is multidimensional, current approaches require stacking the dimensions into one highly structured matrix. However, in the conventional subspace estimation step, e.g., via an SVD of the latter matrix, this structure is not exploited. In this paper, we define a measurement tensor and estimate the signal subspace through a higher-order SVD. This allows us to exploit the structure inherent in the measurement data already in the first step of the algorithm which leads to better estimates of the signal subspace. We show how the concepts of forward-backward averaging and the mapping of centro-Hermitian matrices to real-valued matrices of the same size can be extended to tensors. As examples, we develop the <formula formulatype="inline"><tex>$R$</tex></formula>-D standard Tensor-ESPRIT and the <formula formulatype="inline"><tex>$R$</tex></formula>-D Unitary Tensor-ESPRIT algorithms. However, these new concepts can be applied to any multidimensional subspace-based parameter estimation scheme. Significant improvements of the resulting parameter estimation accuracy are achieved if there is at least one of the <formula formulatype="inline"><tex>$R$</tex></formula> dimensions, which possesses a number of sensors that is larger than the number of sources. This can already be observed in the two-dimensional case. </para>

Obtaining reliable longitudinal information about everyday functioning from individuals with Parkinson's disease (PD) in natural environments is critical for clinical care and research. Despite advances in mobile health technologies, the implementation of digital outcome measures is hindered by a lack of consensus on the type and scope of measures, the most appropriate approach for data capture (eg, in clinic or at home), and the extraction of timely information that meets the needs of patients, clinicians, caregivers, and health care regulators. The Movement Disorder Society Task Force on Technology proposes the following objectives to facilitate the adoption of mobile health technologies: (1) identification of patient-centered and clinically relevant digital outcomes; (2) selection criteria for device combinations that offer an acceptable benefit-to-burden ratio to patients and that deliver reliable, clinically relevant insights; (3) development of an accessible, scalable, and secure platform for data integration and data analytics; and (4) agreement on a pathway for approval by regulators, adoption into e-health systems and implementation by health care organizations. We have developed a tentative roadmap that addresses these needs by providing the following deliverables: (1) results and interpretation of an online survey to define patient-relevant endpoints, (2) agreement on the selection criteria for use of device combinations, (3) an example of an open-source platform for integrating mobile health technology output, and (4) recommendations for assessing readiness for deployment of promising devices and algorithms suitable for regulatory approval. This concrete implementation guidance, harmonizing the collaborative endeavor among stakeholders, can improve assessments of individuals with PD, tailor symptomatic therapy, and enhance health care outcomes. © 2019 International Parkinson and Movement Disorder Society.

T cells, in particular CD4+ T cells, have been implicated in mediating many aspects of autoimmune inflammation. However, current evidence suggests that the role played by CD4+ T cells in the development of rheumatoid inflammation exceeds that of activated proinflammatory T-helper (Th)1 effector cells that drive the chronic autoimmune response. Subsets of CD4+ T cells with regulatory capacity, such as CD25+ regulatory T (Treg) cells and Th2 cells, have been identified, and recent observations suggest that in rheumatoid arthritis the function of these regulatory T cells is severely impaired. Thus, in rheumatoid arthritis, defective regulatory mechanisms might allow the breakdown of peripheral tolerance, after which the detrimental Th1-driven immune response evolves and proceeds to chronic inflammation. Here, we review the functional abnormalities and the contribution of different T cell subsets to rheumatoid inflammation.

Supervised learning-based methods for source localization, being data driven, can be adapted to different acoustic conditions via training and have been shown to be robust to adverse acoustic environments. In this paper, a convolutional neural network (CNN) based supervised learning method for estimating the direction of arrival (DOA) of multiple speakers is proposed. Multi-speaker DOA estimation is formulated as a multi-class multi-label classification problem, where the assignment of each DOA label to the input feature is treated as a separate binary classification problem. The phase component of the short-time Fourier transform (STFT) coefficients of the received microphone signals are directly fed into the CNN, and the features for DOA estimation are learnt during training. Utilizing the assumption of disjoint speaker activity in the STFT domain, a novel method is proposed to train the CNN with synthesized noise signals. Through experimental evaluation with both simulated and measured acoustic impulse responses, the ability of the proposed DOA estimation approach to adapt to unseen acoustic conditions and its robustness to unseen noise type is demonstrated. Through additional empirical investigation, it is also shown that with an array of M microphone our proposed framework yields the best localization performance with M-1 convolution layers. The ability of the proposed method to accurately localize speakers in a dynamic acoustic scenario with varying number of sources is also shown.

Some Facts about Psychoacoustics 2.2.1 Masking in the Frequency Domain 2.2.2 Masking in the Time Domain 2.2.3 Variability between listeners 2.3 Basic ideas of perceptual coding 2.3.1 Basic block diagram 2.3.2Additional coding tools 2.3.3Perceptual Entropy 2.4 Description of coding tools 2.4.1 Filter banks 2.4.2Perceptual models 2.4.3Quantization and coding 2.4.4Joint stereo coding 2.4.5 Prediction 2.4.6 Multi-channel: to matrix or not to matrix 2.5 Applying the basic techniques: real coding systems 2.5.1 Pointers to early systems (no detailed description) 2.5.2MPEG Audio 2.5.3MPEG-2 Advanced Audio Coding (MPEG-2 AAC) 2.5.4MPEG-4 Audio 2.

A convolution neural network (CNN) based classification method for broadband DOA estimation is proposed, where the phase component of the short-time Fourier transform coefficients of the received microphone signals are directly fed into the CNN and the features required for DOA estimation are learned during training. Since only the phase component of the input is used, the CNN can be trained with synthesized noise signals, thereby making the preparation of the training data set easier compared to using speech signals. Through experimental evaluation, the ability of the proposed noise trained CNN framework to generalize to speech sources is demonstrated. In addition, the robustness of the system to noise, small perturbations in microphone positions, as well as its ability to adapt to different acoustic conditions is investigated using experiments with simulated and real data.

Mammography is the most effective method for breast cancer screening available today. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis (CAD) systems have been proposed in the last several years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short term follow-up examination instead. We present two novel CAD approaches that both emphasize an intelligible decision process to predict breast biopsy outcomes from BI-RADS findings. An intelligible reasoning process is an important requirement for the acceptance of CAD systems by physicians. The first approach induces a global model based on decison-tree learning. The second approach is based on case-based reasoning and applies an entropic similarity measure. We have evaluated the performance of both CAD approaches on two large publicly available mammography reference databases using receiver operating characteristic (ROC) analysis, bootstrap sampling, and the ANOVA statistical significance test. Both approaches outperform the diagnosis decisions of the physicians. Hence, both systems have the potential to reduce the number of unnecessary breast biopsies in clinical practice. A comparison of the performance of the proposed decision tree and CBR approaches with a state of the art approach based on artificial neural networks (ANN) shows that the CBR approach performs slightly better than the ANN approach, which in turn results in slightly better performance than the decision-tree approach. The differences are statistically significant (p value < 0.001). On 2100 masses extracted from the DDSM database, the CRB approach for example resulted in an area under the ROC curve of A(z) = 0.89 +/- 0.01, the decision-tree approach in A(z) = 0.87 +/- 0.01, and the ANN approach in A(z) = 0.88 +/- 0.01.

As digital transformation is changing entire industries, organizations are struggling to keep up with these changes. Scholars are viewing organizational capabilities as a central mean for organizations to master digital transformation. Based on a comprehensive literature review, this study identifies a broad set of relevant organizational capabilities and introduces a conceptual framework in which organizational capabilities are clustered into seven relevant themes for managing digital transformation. These capabilities are then embedded in the logic of the dynamic capability theory, highlighting the development of organizational capabilities throughout the digital transformation process. The results reveal that a differentiated perspective on the digital transformation process is beneficial to account for changing needs of organizational capabilities during the transformation process. Just as organizations themselves change during the process, various capabilities at different time points are needed to support and enable organizations during digital transformation. The developed conceptual framework gives organizations guidance for the development of organizational capabilities throughout the digital transformation process.

Taking into account a wide variety of recent results from studies of silicon crystal growth and high temperature wafer heat treatments, a consistent picture of intrinsic point defect behavior is produced. The relevant point defect parameters: diffusivities, equilibrium concentrations and the details of the interaction of vacancies with oxygen are deduced. This set of parameters successfully explains a very wide array of experimental observations covering the temperature range 900–1410 °C. These experimental observations, which are reviewed here, include the properties of grown-in microdefects and vacancy-controlled oxygen precipitation effects in rapidly cooled wafers. The analysis of point defect behavior from observations of high temperature phenomena such as these has the great advantage of relative simplicity and transparency.

Breast cancer is the most common type of cancer among women in the western world. While mammography is regarded as the most effective tool for the detection and diagnosis of breast cancer, the interpretation of mammograms is a difficult and error-prone task. Hence, computer aids have been developed that assist the radiologist in the interpretation of mammograms. Computer-aided detection (CADe) systems address the problem that radiologists often miss signs of cancers that are retrospectively visible in mammograms. Furthermore, computer-aided diagnosis (CADx) systems have been proposed that assist the radiologist in the classification of mammographic lesions as benign or malignant. While a broad variety of approaches to both CADe and CADx systems have been published in the past two decades, an extensive survey of the state of the art is only available for CADe approaches. Therefore, a comprehensive review of the state of the art of CADx approaches is presented in this work. Besides providing a summary, the goals for this article are to identify relations, contradictions, and gaps in literature, and to suggest directions for future research. Because of the vast amount of publications on the topic, this survey is restricted to the two most important types of mammographic lesions: masses and clustered microcalcifications. Furthermore, it focuses on articles published in international journals.

Biomedical applications of deep learning algorithms rely on large expert annotated data sets. The classification of bone marrow (BM) cell cytomorphology, an important cornerstone of hematological diagnosis, is still done manually thousands of times every day because of a lack of data sets and trained models. We applied convolutional neural networks (CNNs) to a large data set of 171 374 microscopic cytological images taken from BM smears from 945 patients diagnosed with a variety of hematological diseases. The data set is the largest expert-annotated pool of BM cytology images available in the literature. It allows us to train high-quality classifiers of leukocyte cytomorphology that identify a wide range of diagnostically relevant cell species with high precision and recall. Our CNNs outcompete previous feature-based approaches and provide a proof-of-concept for the classification problem of single BM cells. This study is a step toward automated evaluation of BM cell morphology using state-of-the-art image-classification algorithms. The underlying data set represents an educational resource, as well as a reference for future artificial intelligence-based approaches to BM cytomorphology.

This paper focuses on the actual technical challenges that must be addressed in the field of inductive magnetic coupling at low-frequency (LF) and high-frequency (HF) bands for wireless power transmission (WPT), with special focus on smart objects applications. Inductive coupling is the most common operating principle for WPT, because of the high efficiency and the relatively high amount of energy that can be transferred. This paper will present some important applications that can benefit from this technology. Some of the actual challenges that must be addressed for a proper implementation of WPT in the mentioned applications as well as the actual trends will be discussed. Finally, research projects that have successfully dealt or are currently dealing with these problems will be presented.

In discussing the rationale behind the vision for JPEG Pleno and how the new standardization initiative aims to reinvent the future of imaging, the authors review plenoptic representation and its underlying practical implications and challenges in implementing real-world applications with an enhanced quality of experience.

Diffusion of platinum at low temperatures is a convenient way to characterize vacancy profiles in silicon. This article summarizes the experiments performed to find a standard procedure, discusses the pitfalls and limitations, and shows the applicability of the method. The results of experiments with float-zone and Czochralski-grown samples in the temperature range from 680 to 842 °C were found to disagree with the predictions of models published in the literature. Therefore, parameters governing the diffusion of point defects and platinum in silicon were determined for this temperature range.

The preparation and the properties of titanium dioxide (TiO2) thin films have been studied with respect to its application as a new capacitor dielectric material in low-power high-density dynamic random access memory ultralarge scale integrated circuits.The TiO2 films were deposited by a low pressure metal organic chemical vapor deposition process with tetra-iso-propyltitanate as the precursor metal organic material.The deposition was performed in a hot wall-type vertical furnace at low temperatures (300-350~Very uniform TiO2 thin films with a dielectric constant up to 70 were prepared showing the polycrystalline structure of anatase after the deposition.The electronic properties of the TiO2-silicon interface were investigated in detail using a metal-insulator-semiconductor structure.Stoichiometry, structure, as well as electrical properties of the TiO2 layers were examined before and after an annealing treatment in oxygen ambient.

The 5G mobile network brings several new features that can be applied to existing and new applications. High reliability, low latency, and high data rate are some of the features that fulfill the requirements of vehicular networks. Vehicular networks aim to provide safety for road users and several additional advantages such as enhanced traffic efficiency and in-vehicle infotainment services. This article summarizes the most important aspects of NR-V2X, which is standardized by 3GPP, focusing on sidelink communication. The main part of this work belongs to 3GPP Release 16, which is the first 3GPP release for NR-V2X, and the work/study items of the future Release 17.

ABSTRACT Papillon‐Lefèvre syndrome (PLS) is characterized by nonfunctional neutrophil serine proteases (NSPs) and fulminant periodontal inflammation of unknown cause. Here we investigated neutrophil extracellular trap (NET)‐associated aggregation and cytokine/chemokine‐release/degradation by normal and NSP‐deficient human and mouse granulocytes. Stimulated with solid or soluble NET inducers, normal neutrophils formed aggregates and both released and degraded cytokines/chemokines. With increasing cell density, proteolytic degradation outweighed release. Maximum output of cytokines/chemokines occurred mostly at densities between 2 × 10 7 and 4 × 10 7 neutrophils/cm 3 . Assessment of neutrophil density in vivo showed that these concentrations are surpassed during inflammation. Association with aggregated NETs conferred protection of neutrophil elastase against αl‐antitrypsin. In contrast, eosinophils did not influence cytokine/chemokine concentrations. The proteolytic degradation of inflammatory mediators seen in NETs was abrogated in Papillon–Lèfevre syndrome (PLS) neutrophils. In summary, neutrophil‐driven proteolysis of inflammatory mediators works as a built‐in safeguard for inflammation. The absence of this negative feedback mechanism might be responsible for the nonresolving periodontitis seen in PLS.—Hahn, J., Schauer, C., Czegley, C., Kling, L., Petru, L., Schmid, B., Weidner, D., Reinwald, C., Biermann, M. H. C., Blunder, S., Ernst, J., Lesner, A., Bäuerle, T., Palmisano, R., Christiansen, S., Herrmann, M., Bozec, A., Gruber, R., Schett, G., Hoffmann, M. H. Aggregated neutrophil extracellular traps resolve inflammation by proteolysis of cytokines and chemokines and protection from antiproteases. FASEB J. 33, 1401–1414 (2019). www.fasebj.org

In recent years, as newer technologies have evolved around the healthcare ecosystem, more and more data have been generated. Advanced analytics could power the data collected from numerous sources, both from healthcare institutions, or generated by individuals themselves via apps and devices, and lead to innovations in treatment and diagnosis of diseases; improve the care given to the patient; and empower citizens to participate in the decision-making process regarding their own health and well-being. However, the sensitive nature of the health data prohibits healthcare organizations from sharing the data. The Personal Health Train (PHT) is a novel approach, aiming to establish a distributed data analytics infrastructure enabling the (re)use of distributed healthcare data, while data owners stay in control of their own data. The main principle of the PHT is that data remain in their original location, and analytical tasks visit data sources and execute the tasks. The PHT provides a distributed, flexible approach to use data in a network of participants, incorporating the FAIR principles. It facilitates the responsible use of sensitive and/or personal data by adopting international principles and regulations. This paper presents the concepts and main components of the PHT and demonstrates how it complies with FAIR principles.