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We compute the Fourier expansion of Hecke operators on vector-valued modular forms for the Weil representation associated to a lattice L. The Hecke operators considered in this paper include operators T(p^2l) where p is a prime dividing the level of the lattice L. Additionally, an explicit formula for a general type of Gauss sum associated to a lattice L drops out as a by-product.
EMDLAB: A toolbox for analysis of single-trial EEG dynamics using empirical mode decomposition
(2015)
Background:
Empirical mode decomposition (EMD) is an empirical data decomposition technique. Recently there is growing interest in applying EMD in the biomedical field.
New method:
EMDLAB is an extensible plug-in for the EEGLAB toolbox, which is an open software environment for electrophysiological data analysis.
Results:
EMDLAB can be used to perform, easily and effectively, four common types of EMD: plain EMD, ensemble EMD (EEMD), weighted sliding EMD (wSEMD) and multivariate EMD (MEMD) on EEG data. In addition, EMDLAB is a user-friendly toolbox and closely implemented in the EEGLAB toolbox.
Comparison with existing methods:
EMDLAB gains an advantage over other open-source toolboxes by exploiting the advantageous visualization capabilities of EEGLAB for extracted intrinsic mode functions (IMFs) and Event-Related Modes (ERMs) of the signal.
Conclusions:
EMDLAB is a reliable, efficient, and automated solution for extracting and visualizing the extracted IMFs and ERMs by EMD algorithms in EEG study.
Manual tasks in industrial production are often monotonous, leading to a decrease in concentration and motivation of the worker and thus to deficiencies in the products. With quality as well as performance requirements getting more and more stringent, workers need additional support by their work environment. We developed a novel approach providing worker assistance and inline quality assurance for manual workplaces. The prototypical system Smart Workbench (SWoB) uses a multimodal sensor interface consisting of a 3D depth sensor in combination with a 2D camera to control quality aspects of the product and track the work progress. The bidirectional flow of information is handled via an image processing driven gestural interface and the displaying of advice directly on the work surface. In this paper the developed system is introduced and the current state of evaluating its industrial usage with a manual quality control and packaging task reported.
PURPOSE
Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography.
METHODS
In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization.
RESULTS
Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method's strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection.
CONCLUSIONS
The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.
A Step Towards the Automated Diagnosis of Parkinson's Disease: Analyzing Handwriting Movements
(2015)
Parkinson’s disease (PD) has affected millions of people world-wide, being its major problem the loss of movements and, consequently, the ability of working and locomotion. Although we can find several works that attempt at dealing with this problem out there, most of them make use of datasets composed by a few subjects only. In this work, we present some results toward the automated diagnosis of PD by means of computer vision-based techniques in a dataset composed by dozens of patients, which is one of the main contributions of this work. The dataset is part of a joint research project that aims at extracting both visual and signal-based information from healthy and PD patients in order to go forward the early diagnosis of PD patients. The dataset is composed by handwriting clinical exams that are analyzed by means of image processing and machine learning techniques, being the preliminary results encouraging and promising. Additionally, a new quantitative feature to measure the amount of tremor of an individual’s handwritten trace called Mean Relative Tremor is also presented.
Steigende Fallzahlen von Menschen mit Demenz, der komplexe Bedarf und eine unzureichende Versorgungsrealität erfordern innovative und nachhaltige Lösungsansätze. Der Beitrag stellt ein Versorgungsmodell für Menschen mit Demenz vor, das infolge akuter Versorgungsprobleme entwickelt wurde und das eine umfassende, patientenorientierte Versorgung über Sektorengrenzen hinweg umsetzt. Beschrieben werden Struktur und Bedingungen gelingender Kooperation und umfassender Versorgung.
Artifacts in Incomplete Data Tomography with Applications to Photoacoustic Tomography and Sonar
(2015)
We develop a paradigm using microlocal analysis that allows one to characterize the visible and added singularities in a broad range of incomplete data tomography problems. We give precise characterizations for photoacoustic and thermoacoustic tomography and sonar, and provide artifact reduction strategies. In particular, our theorems show that it is better to arrange sonar detectors so that the boundary of the set of detectors does not have corners and is smooth. To illustrate our results, we provide reconstructions from synthetic spherical mean data as well as from experimental photoacoustic data.
We propose a new algorithmic approach to the non-smooth and non-convex Potts problem (also called piecewise-constant Mumford–Shah problem) for inverse imaging problems. We derive a suitable splitting into specific subproblems that can all be solved efficiently. Our method does not require a priori knowledge on the gray levels nor on the number of segments of the reconstruction. Further, it avoids anisotropic artifacts such as geometric staircasing. We demonstrate the suitability of our method for joint image reconstruction and segmentation. We focus on Radon data, where we in particular consider limited data situations. For instance, our method is able to recover all segments of the Shepp–Logan phantom from seven angular views only. We illustrate the practical applicability on a real positron emission tomography dataset. As further applications, we consider spherical Radon data as well as blurred data.
In-plant milk-run systems are transportation systems, where materials are delivered from a central storage area to several points of use on defined routes and in short intervals. Milk-run systems generally enable frequent deliveries in smaller lot sizes with short lead times and low inventory at the points of use. In real milk-run systems, the number of deliveries per interval varies due to, e.g., variations in the production program. To enable efficient and stable milk-run system operations, these systems therefore must be able to cope with peaks in the number of deliveries. We develop different strategies for handling these delivery peaks and evaluate them with respect to delivery cost, lead time and service level using real material consumption data from two large companies from the automotive industry.
Robots should appropriately give reasons for their actions
when these actions affect a human’s action or goal space. Communicating reasons may help the human understand the robot’s intents and may initiate joint action, i. e., accepting the robot’s goals and cooperating on the robot’s actions. However, to be efficient, the communication of reasons should be limited to the necessary rather than to completeness, conforming to the Gricean Maxim of Quantity. Furthermore, what is necessary only becomes apparent as the situation evolves and hence, for seamless interaction, ongoing utterances must be adapted as they happen. We present a system that flexibly gives reasons in a reduced setting in which the robot needs to intrude a human’s personal space in order to reach its goal.
We propose to use a model of personal space to initiate communication while passing a human thereby acknowledging that humans are not just a special kind of obstacle to be avoided but potential interaction partners. As a simple form of interaction, our system communicates an apology while closely passing a human. To this end, we present a software architecture that integrates a social-spaces knowledge base and a component for incremental speech production. Incrementality ensures that the robot’s utterance can be adapted to fit the developing situation in a natural way. Observer ratings show that personal-space intrusion is perceived as both natural and polite if the robot has the capability to utter and adapt an apology in an incremental way whereas it is perceived as unfriendly if the robot intrudes personal space without saying anything. Moreover, the robot is perceived as less natural if it does not adapt.
Incremental speech synthesis aims at delivering the synthetic voice while the sentence is still being typed. One of the main challenges is the online estimation of the target prosody from a partial knowledge of the sentence's syntactic structure. In the context of HMM-based speech synthesis, this typically results in missing segmental and suprasegmental features, which describe the linguistic context of each phoneme. This study describes a voice training procedure which integrates explicitly a potential uncertainty on some contextual features. The proposed technique is compared to a baseline approach (previously published), which consists in substituting a missing contextual feature by a default value calculated on the training set. Both techniques were implemented in a HMM-based Text-To-Speech system for French, and compared using objective and perceptual measurements. Experimental results show that the proposed strategy outperforms the baseline technique for this language.
Purpose – The purpose of this paper is to demonstrate the processability of cohesive PE-HD particles in laser beam melting processes (LBM) of polymers. Furthermore, we present a characterization method for polymer particles, which can predict the quality of the powder deposition via LBM processes. Design/methodology/approach – This study focuses on the application of dry particle coating processes to increase flowability and bulk density of PE-HD particles. Both has been measured and afterwards validated via powder deposition of PE-HD particles in a LBM machine. Findings – For efficient coating in a dry particle coating process, the PE-HD particles and the attached nanoparticles need to show similar surface chemistry, i.e. both need to behave either hydrophobic or hydrophilic. It is demonstrated that dry particle coating is appropriate to enhance flowability and bulk density of PE-HD particles and hence considerably improves LBM processes and the resulting product quality. Originality/value – At present, in LBM processes mainly polyamide (PA), 12 particles are used, which are so far quite expensive in comparison to, for example, PE-HD particles. This work provides a unique and versatile method for nanoparticulate surface modification which may be applied to a wide variety of materials. After the coating, the particles are applicable for the LBM process. Our results provide a correlation between flowability and bulk density and the resulting product quality.
In diesem Beitrag wird eine Theorie zur taktorientierten Fertigungssteuerung entwickelt. Es wird der kleinste Repräsentant einer taktorientiert gesteuerten Werkstatt ermittelt - das Ein-Ressourcen- Modell. Durch die Modifikation des Trichtermodells kann ein allgemeingültiges Beschreibungsmodell des Produktionsprozesses bei taktorientierter Fertigungssteuerung erarbeitet werden - das taktorientierte Trichtermodell. Es ermöglicht die Darstellung der Arbeitsbestände im aktuellen Bearbeitungstakt und der des nachfolgenden Taktes. Weiter wird das taktbasierte Trichtermodell in ein taktbasiertes Durchlaufdiagramm überführt. Hiermit kann das Bestandsverhalten des Ein-Ressourcen-Modells über der Zeit dargestellt werden. Das optimale Bestandsverhalten pro Takt kann durch ein idealisiertes Durchlaufdiagramm beschrieben werden. Bei realer Auslastung der Ressource entstehen schwankende Arbeitsbestände pro Takt. Hierbei werden durch zu geringe Bestände pro Takt Auslastungsverluste an der Ressource erzeugt. Durch zu hohe Bestände pro Takt entstehen ohne Kapazitätsanpassungen Verspätungen.
Machine allocation planning in job-shop production is challenging due to the high variability of machining times. The tact-time based production control reduces planning effort also the flow of orders in the shop floor is more predictable. This article takes a deeper look at a single production unit controlled tact based. By enhancing the funnel model and looking at the stock behaviour of production units, a deeper understanding of tact-time based production control is gained.
The Impact of the ObamaCare Excise Tax on Innovation and Entrepreneurship – Early Empirical Findings
(2015)
This study addresses aspects of governmental influence on innovation by analyzing the impact of the ObamaCare excise tax on the medical device industry. We initially give an overview of common approaches to measuring innovativeness and entrepreneurship, empirically assess whether existing metrics are suitable for investigating the innovation performance of the U.S. medical device industry, and define a new measure (firm innovation activity) for entrepreneurship. Then we perform a quantitative analysis to explore the impact of the tax. We analyze more than 60,000 product clearances from 1996 to 2013, using the FDA database. We find a significant relationship between product counts and revenues for one segment. Contrary to the present criticism of the excise tax, we find hardly any noteworthy response in either firm innovation activity or number of products launched in the year after the tax was introduced. The 2013 reduction of new product submissions is well within the limits of typical annual fluctuations observed in previous years. This provides a first indication that the excise tax act did not have a strong impact on innovative activities through the present.
Innovation diffusion points toward how innovations spread into the market after launch. This paper investigates diffusion dynamics at market entry time and proposes a new evolution pattern at the intersection between inventions and innovations. With this in mind, we initially prove that patent filings correlate with new product introductions in the U.S. spine market. Then we test our new theory supposing that certain patent filing threshold numbers accelerate strong economic returns in terms of innovations. We find that firms hitting certain patent filing thresholds significantly increase their product launches in the mentioned market. Moreover, the results seem to indicate that economic returns of inventions may be measured substantially. Thus, this paper suggests a new research area by utilizing our proposed concept about an Innovation Outcome Trigger Value (IOTV). Furthermore, the implications may also be interesting for practitioners, since we empirically prove that inventive activities turn out to be worthwhile, indeed.
This paper presents the new open source Modelica libraryWindPowerPlants. For the economic assessment of either a wind power plant or an entire wind park, the accurate prediction of the energy output is essential. Such prediction is usually performed by means of calculations based on statistical wind data. The proposed WindPowerPlants library is capable of assessing the energy output both for statistical and real wind data based on time domain simulations.
In the presented version of the library wind turbine models are modeled with pitch control. The generator models have variable speed and an optional connector to the mains. The entire library is based on power balance conditions and losses are fully neglected. Yet, the library can be extended towards more detailed models considering different types of losses.
The structure and components of the library are presented. Simulations examples are shown and compared with reference data. The applicability of the proposed WindPowerPlants library is demonstrated and possible enhancements are discussed.
We demonstrate nonlinear coupling in a discrete optical system. This is achieved in waveguide arrays with quadratic nonlinearity, where the symmetries of the nonlinearly interacting waveguide modes are used to suppress the usually dominating nonlinear effects within individual waveguides. We derive a mathematical model to describe the nonlinear coupling in such waveguide arrays and show experimentally the profound effects of this nonlinear coupling mechanism on second-harmonic generation.
Large arrays of femtoliter-sized chambers are important tools for single molecule research as well as bioanalytical applications. We have optimized the design and fabrication of two array types consisting of 250 × 250 (62 500) femtoliter chambers either by surface etching of fused silica slides or by polydimethylsiloxane (PDMS) molding. Highly diluted solutions of β-galactosidase were enclosed in such arrays to monitor the fluorogenic reactions of hundreds of individual enzyme molecules in parallel by wide-field fluorescence microscopy. An efficient mechanical sealing procedure was developed to prevent diffusion of the fluorescent reaction product out of the chambers. Different approaches for minimizing non-specific surface adsorption were explored. The signal acquisition was optimized to grant both a large field of view and an efficient signal acquisition from each femtoliter chamber. The optimized femtoliter array has enabled a three-in-one enzyme assay system: First, the concentration of active enzyme can be determined in a digital way by counting fluorescent chambers in the array. Second, the activity of the enzyme bulk solution is given by averaging many individual substrate turnover rates without the need for knowing the exact enzyme concentration. Third-unlike conventional enzyme assays-the distribution of individual substrate turnover rates yields insight into the conformational heterogeneity in an enzyme population. The substrate turnover rates of single β-galactosidase molecules were found to be broadly distributed and independent of the type of femtoliter array. In general, both types of femtoliter arrays are highly sensitive platforms for enzyme analysis at the single molecule level and yield consistent results.