Solvent‐Induced Morphology in Polymer‐Based Systems for Organic Photovoltaics

Studies on the influence of four different solvents on the morphology and photovoltaic performance of bulk‐heterojunction films made of poly(3‐hexylthiophene) (P3HT) and [6,6]‐phenyl‐C₆₁ butyric acid methyl ester (PCBM) via spin‐coating for photovoltaic applications are reported. Solvent‐dependent PCBM cluster formation and P3HT crystallization during thermal annealing are investigated with optical microscopy and grazing‐incidence wide‐angle X‐ray scattering (GIWAXS) and are found to be insufficient to explain the differences in device performance. A combination of atomic force microscopy (AFM), X‐ray reflectivity (XRR), and grazing‐incidence small‐angle X‐ray scattering (GISAXS) investigations results in detailed knowledge of the inner film morphology of P3HT:PCBM films. Vertical and lateral phase separation occurs during spin‐coating and annealing, depending on the solvent used. The findings are summarized in schematics and compared with the IV characteristics. The main influence on the photovoltaic performance arises from the vertical material composition and the existence of lateral phase separation fitting to the exciton diffusion length. Absorption and photoluminescence measurements complement the structural analysis.     

A Best Practice Guide to Resource Forecasting for Computing Systems

Recently, measurement-based studies of software systems have proliferated, reflecting an increasingly empirical focus on system availability, reliability, aging, and fault tolerance. However, it is a nontrivial, error-prone, arduous, and time-consuming task even for experienced system administrators, and statistical analysts to know what a reasonable set of steps should include to model, and successfully predict performance variables, or system failures of a complex software system. Reported results are fragmented, and focus on applying statistical regression techniques to monitored numerical system data. In this paper, we propose a best practice guide for building empirical models based on our experience with forecasting Apache web server performance variables, and forecasting call availability of a real-world telecommunication system. To substantiate the presented guide, and to demonstrate our approach in a step by step manner, we model, and predict the response time, and the amount of free physical memory of an Apache web server system, as well as the call availability of an industrial telecommunication system. Additionally, we present concrete results for a) variable selection where we cross benchmark three procedures, b) empirical model building where we cross benchmark four techniques, and c) sensitivity analysis. This best practice guide intends to assist in configuring modeling approaches systematically for best estimation, and prediction results.     

Energy-efficient Rate Adaptation MAC Protocol for Ad Hoc Wireless Networks

In this paper, two novel energy-efficient rate adaptation schemes are presented. The proposed protocols use the Distributed Power Control (DPC) algorithm to predict the channel state and determine the necessary transmission power which optimizes the energy consumption. The first proposed rate adaptation scheme heuristically alters the modulation rate to balance the energy-efficiency and the required throughput which is estimated with queue fill ratio. Moreover, the back-off scheme is incorporated to mitigate congestion and reduce packet losses due to buffer overflows thus minimizing energy consumption. Consequently, the nodes will conserve energy when the traffic is low, offer higher throughput when needed and save energy during congestion by limiting transmission rates. The second rate adaptation scheme employs dynamic programming (DP) principle to analytically select modulation rate and a burst size to be used during transmission. The proposed quadratic cost-function minimizes the energy consumption while alleviating network congestions and buffer overflows. The proposed DP solution renders a Riccati equation ultimately providing an optimal rate selection. The simulation results indicate that an increase in throughput by 96% and energy-efficiency by 131% is observed when compared to other available protocols, for example Receiver Based AutoRate (RBAR).

Spatiotemporal EEG/MEG source analysis based on a parametric noise covariance model

A method is described to incorporate the spatiotemporal noise covariance matrix into a spatiotemporal source analysis. The essential feature is that the estimation problem is split into two parts. First, a model is fitted to the observed noise covariance matrix. This model is a Kronecker product of a spatial and a temporal matrix. The spatial matrix models the spatial covariances by a function dependent on sensor distance. The temporal matrix models the temporal covariances as lag dependent. In the second part, sources are estimated given this noise model, which can be done very efficiently due to the Kronecker formulation. An application to real electroencephalogram (EEG) data shows that the noise model fits the data very well. Simulation results show that the resulting source estimates are more precise than those obtained from a standard analysis neglecting the noise covariance. In addition, the estimated standard errors of the source parameter estimates are far more precise than those obtained from a standard analysis. Finally, the source parameter standard errors are used to investigate the effects of temporal sampling. It is shown that increasing the sampling by a factor x, decreases the standard errors of all source parameters with the square root of x.     

Digital canvas removal in paintings

The periodic structure of the underlying support of paintings on canvas can become quite prominent and disturbing in high resolution digital recordings. In this paper, we construct a new model and method for the digital removal of canvas which is considered as a noise component superimposed on the painting artwork. The high resolution of the images prohibits the efficient application of existing adaptive denoising filters. Hence, a two-step approach is proposed. First a (smoothing) Wiener filter is applied to the complete image. The second step consists of a spatially adaptive extension with low-complexity to obtain a generic digital canvas removal filter.     

TiN Nanoparticles for Enhanced THz Generation in TDS Systems

By virtue of the surface plasmon resonance effect, plasmonic nanoparticles (NPs) can localize the light field and significantly enhance the performance of some optoelectronic devices. In this work, NPs are employed for an enhanced generation of terahertz radiation from LT-GaAs-based antennas. Therefore, we have prepared plasmonic TiN NPs by direct ultrasonication (ULS) and pulsed laser ablation (PLA) techniques. The zeta potential, particle size, and absorbance were used to characterize the NPs in their colloidal forms in a comparison to commercial Au NPs. A layer of polydispersed titanium nitride (TiN) NPs prepared by PLA and deposited on the surface of an LT-GaAs device shows a significant improvement of terahertz signal generation from these devices with an enhancement of the peak to peak amplitude of 100%.     

Modeling and Identification of Elastic Robot Joints With Hysteresis and Backlash

This paper presents a novel approach to the modeling and identification of elastic robot joints with hysteresis and backlash. The model captures the dynamic behavior of a rigid robotic manipulator with elastic joints. The model includes electromechanical submodels of the motor and gear from which the relationship between the applied torque and the joint torsion is identified. The friction behavior in both presliding and sliding regimes is captured by generalized Maxwell-slip model. The hysteresis is described by a Preisach operator. The distributed model parameters are identified from experimental data obtained from internal system signals and external angular encoder mounted to the second joint of a 6-DOF industrial robot. The validity of the identified model is confirmed by the agreement of its prediction with independent experimental data not previously used for model identification. The obtained models open an avenue for future advanced high-precision control of robotic manipulator dynamics.     

Self-Assembled Perovskite Nanoislands on CH3NH3PbI3 Cuboid Single Crystals by Energetic Surface Engineering

Organometal perovskite single crystals have been recognized as a promising platform for high-performance optoelectronic devices, featuring high crystallinity and stability. However, a high trap density and structural nonuniformity at the surface have been major barriers to the progress of single crystal-based optoelectronic devices. Here, the formation of a unique nanoisland structure is reported at the surface of the facet-controlled cuboid MAPbI₃ (MA = CH₃NH₃⁺) single crystals through a cation interdiffusion process enabled by energetically vaporized CsI. The interdiffusion of mobile ions between the bulk and the surface is triggered by thermally activated CsI vapor, which reconstructs the surface that is rich in MA and CsI with reduced dangling bonds. Simultaneously, an array of Cs-Pb-rich nanoislands is constructed on the surface of the MAPbI₃ single crystals. This newly reconstructed nanoisland surface enhances the light absorbance over 50% and increases the charge carrier mobility from 56 to 93 cm² V⁻¹ s⁻¹. As confirmed by Kelvin probe force microscopy, the nanoislands form a gradient band bending that prevents recombination of excess carriers, and thus, enhances lateral carrier transport properties. This unique engineering of the single crystal surface provides a pathway towards developing high-quality perovskite single-crystal surface for optoelectronic applications.     

Data-parallel intra decoding for block-based image and video coding on massively parallel architectures

With the increasing number of processor cores available in modern computing architectures, task or data parallelism is required to maximally exploit the available hardware and achieve optimal processing speed. Current state-of-the-art data-parallel processing methods for decoding image and video bitstreams are limited in parallelism by dependencies introduced by the coding tools and the number of synchronization points introduced by these dependencies, only allowing task or coarse-grain data parallelism. In particular, entropy decoding and data prediction are bottleneck coding tools for parallel image and video decoding. We propose a new data-parallel processing scheme for block-based intra sample and coefficient prediction that allows fine-grain parallelism and is suitable for integration in current and future state-of-the-art image and video codecs. Our prediction scheme enables maximum concurrency, independent of slice or tile configuration, while minimizing synchronization points. This paper describes our data-parallel processing scheme for one- and two-dimensional prediction and investigates its application to block-based image and video codecs using JPEG XR and H.264/AVC Intra as a starting point. We show how our scheme enables faster decoding than the state-of-the-art wavefront method with speedup factors of up to 21.5 and 7.9 for JPEG XR and H.264/AVC Intra coding tools respectively. Using the H.264/AVC Intra coding tool, we discuss the requirements of the algorithm and the impact on decoded image quality when these requirements are not met. Finally, we discuss the impact on coding rate in order to allow for optimal parallel intra decoding.     

Using an eye tracker for accurate eye movement artifact correction

We present a new method to correct eye movement artifacts in electroencephalogram (EEG) data. By using an eye tracker, whose data cannot be corrupted by any electrophysiological signals, an accurate method for correction is developed. The eye-tracker data is used in a Kalman filter to estimate which part of the EEG is of ocular origin. The main assumptions for optimal correction are summed and their validity is proven. The eye-tracker-based correction method is objectively evaluated on simulated data of four different types of eye movements and visually evaluated on experimental data. Results are compared to three established correction methods: Regression, Principal Component Analysis, and Second-Order Blind Identification. A comparison of signal to noise ratio after correction by these methods is given in Table II and shows that our method is consistently superior to the other three methods, often by a large margin. The use of a reference signal without electrophysiological influences, as provided by an eye tracker, is essential to achieve optimal eye movement artifact removal.