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A complex IC design marginally passes CDM targets during qualification. Employing the verification tool ESRA initially reveals CDM-uncritical voltages. Detailed testing beyond the CDM target levels and an extended tool setup provide valuable findings: pin-specific CDM robustness, design improvements, and the need to treat particular on-chip conductors as inductive components during CDM.

The ahadith, prophetic traditions for the Muslims around the world, are narrations originating from the sayings and the deeds of Prophet Muhammad (pbuh). They are considered one of the fundamental sources of Islamic legislation along with the Quran. The list of persons involved in the narration of each hadith is carefully scrutinized by scholars studying the hadith, with respect to their reputation and authenticity of the hadith. This is due to the its legislative importance in Islamic principles. There were many narrators who contributed to this responsibility of preserving prophetic narrations over the centuries. But to date, no systematic and comprehensive study, based on the social network, has been adapted to understand the contribution of early hadith narrators and the propagation of hadith across generations. In this study, we represented the chain of narrators of the hadith collection from Sahih Bukhari as a social graph. Based on social network analysis (SNA) on this graph, we found that the network of narrators is a scale-free network. We identified a list of influential narrators from the companions as well as the narrators from the second and third-generation who contribute significantly in the propagation of hadith collected in Sahih Bukhari. We discovered sixteen communities from the narrators of Sahih Bukhari. In each of these communities, there are other narrators who contributed significantly to the propagation of prophetic narrations. We also found that most narrators were centered in Makkah and Madinah in the era of companions and, then, gradually the center of hadith narrators shifted towards Kufa, Baghdad and central Asia over a period of time. To the best of our knowledge, this the first comprehensive and systematic study based on SNA, representing the narrators as a social graph to analyze their contribution to the preservation and propagation of hadith., Comment: Social Network Analysis of Hadith Narrators from Sahih Bukhari

This paper maximizes the expected profits of a cyber-physical social system (CPSS) for flexible passenger transportation. In the CPSS-based transportation system users can choose from an assortment of travel offers. When shared rides are proposed, the users have a great impact on the system performance, since every passenger of the proposed shared ride is able to choose another travel option or to reject it. In the worst case, the rejections result in a not profitable transportation of the passengers accepting the shared ride. To draw attention to this problem, a mixed integer problem considering the choices of the customers is formulated and applied to synthetic data. At last, a heuristic based on the idea of forming best pairs is proposed and evaluated.

One of the key features of industry 4.0 is the automation and optimization of transportation tasks. Especially autonomous guided vehicles meet the required high flexibility of future enterprises. In this paper, a concept of an autonomous indoor post service is proposed. The whole cyber-physical system from the autonomous vehicle, which is equipped with a Lidar sensor and programmed, using a real world automotive framework, over the secure communication design, to the dynamic time-critical decision and routing optimization is explained.

Smart adaptive systems are evolving rapidly. In addition to high-performance artificial intelligence from the cloud, smart edge devices are increasingly developing. They are capable of handling constantly more complex classification or even learning tasks independently. After answering the question of how this knowledge can be learned or used at the edge, it must also be determined how this knowledge can be exchanged with a cloud. We would like to investigate this exchange of knowledge between several Edge Runtime Learning Artificial Noses and a cloud intelligence in further experiments. For this purpose, we describe an improvement of our former approaches for an artificial nose. Especially, we implemented a resource-efficient edge nose with lazy learning algorithms on a microcontroller and enabled learning at runtime. This new nose achieves classification rates of up to 92 %, almost as good as the resource-intensive previous version, and forms the basis for research into the knowledge exchange between different edge and cloud AI devices and the processes involved.

In this work, a new hybrid algorithm for disease risk classification is proposed. The proposed methodology is based on Dynamic Time Warping (DTW). This methodology can be applied to time series from various domains such as vital sign time series available in medical big data. To validate our methodology, we applied it to risk classification for sepsis, which is one of the most challenging problems within the area of medical data analysis. In the first step the algorithm uses different statistical properties of time series features. Furthermore, using differently labeled training data sets, we created a DTW Barycenter Averaging (DBA) on each feature. In the second step, validation data sets and DTW are used to validate the precision of classification and the final results are compared. The performance of our methodology is validated with real medical data and on six different criteria definitions for the sepsis diseases. Results show that our algorithm performed, in the best case, with precision and recall of 96,38% and 90,90%, respectively.

The concept of Adaptive Resolution Change (ARC) in video coding is already known from former international standards such as MPEG-4 [1]. However, in MPEG-4 linear filters are used for upsampling, which is crucial to coding video at varying resolution. With the rise of machine learning-based super-resolution methods in the last decade, powerful algorithms outperforming conventional upsampling methods were developed. This contribution introduces an ARC concept using un-coded areas within a frame of a video sequence and a Dictionary Learning (DL)-based Super-Resolution (SR) scheme. In this concept, a frame contains a picture at different resolution levels, which are spatially separated by the use of slices and tiles. Generally, a tile can be marked as coded or un-coded. Slices which do not hold any coded tiles are omitted from the bitstream. Thus, only one resolution level needs to be coded, while the other is generated at the decoder side. At the encoder a rate-distortion decision is made in order to decide, which resolution level should be coded. Simulation results show that gains with respect to the Versatile Video Coding (VVC) standard in development can be achieved at low bitrates.

Measurements of the PV modules are based on standard test conditions. However, PV modules have significant difference in performance based on the locations, environmental conditions and mounting situation. One of the environmental factors which influence the performance of the PV module is the spectrum of incident light on the PV module.In this paper, we compare the optical reflection losses, thermal losses as well as electricity production of modules under moderate (AM1.5) and desert-like spectrums (AM1.0). The results show quantitatively that thermalization has the highest loss portion with an average of 29.5% of the total incoming power density for both spectrums.The highest relative change is for absorption of light in polymer encapsulant especially between 280-400 nm with 24.3% and total resistive losses (in cell and interconnection) with 21.3% extra losses from desert to moderate conditions.Finally, we show that similar modules in desert climate produce 5% extra power gain while it suffers from 7.2% extra heat and 5% extra reflections.

Recently, neural networks were shown to improve video and image intra prediction significantly. In this paper, the properties of different architectures for neural network-based intra prediction are evaluated. This includes an analysis of the properties of convolutional neural networks used for this purpose, showing that they outperform fully connected ones especially for complex and low resolution content. Also, the usage of separate networks for luma and chroma prediction, which are able to perform a learned cross-component prediction, is proposed as this is clearly beneficial for the prediction quality. Furthermore, a new way of signaling a neural network-based intra prediction mode in HEVC is investigated. In total this improves the compression performance in terms of average BD-rate changes by −2.0% for the luma and by −1.5% for the chroma channels.

In 360° video, a complete scene is captured, as it can be seen from a single point in any direction. Since the captured 360 images are spherical, they cannot be converted to planar images without introducing geometric distortions. The nature of these distortion depends on the used projection format.This paper introduces an approach to reduce artifacts occurring when encoding 360° video which has been projected to the faces of a cube. In order to achieve this, the operation of the deblocking filter is modified such that the correct pixels with respect to the 3D geometry are used for filtering of edges.The method is evaluated on the set of sequences defined by the Joint Call for Proposals on Video Compression with Capability beyond HEVC. While the method has almost no impact on the objective coding performance, the visual quality is still clearly enhanced. Edges of the cube, previously visible as coding artifacts, are mostly removed with the proposed method.

In this paper, we show how absolute orientation measurements provided by low-cost but high-fidelity IMU sensors can be integrated into the KinectFusion pipeline. We show that integration improves both runtime, robustness and quality of the 3D reconstruction. In particular, we use this orientation data to seed and regularize the ICP registration technique. We also present a technique to filter the pairs of 3D matched points based on the distribution of their distances. This filter is implemented efficiently on the GPU. Estimating the distribution of the distances helps control the number of iterations necessary for the convergence of the ICP algorithm. Finally, we show experimental results that highlight improvements in robustness, a speed-up of almost 12%, and a gain in tracking quality of 53% for the ATE metric on the Freiburg benchmark., CVPR Workshop on Visual Odometry and Computer Vision Applications Based on Location Clues 2018

Image scale-up is a crucial task in resolution varying scalable video coding, as the coding costs for the enhancement layer depend heavily on the prediction signal generated by inter-layer prediction. In order to generate a suitable prediction signal the missing high frequencies in the base layer picture have to be reconstructed. For this purpose upscaling methods which go beyond the classical sampling theory are required. In this paper, an image scale-up method based on dictionary learning and sparse coding techniques for inter-layer prediction in scalable video coding is presented. Experimental results show that the proposed method outperforms state of the art scalable coding models in the case of 2x upscaling. In more detail, 2.35 % BD-rate savings against SHM 12.0 reference software are observed on average for an All Intra coding configuration. The maximum achieved rate savings were 6 15 % for the sequence PeopleOnStreet.

3D scene reconstruction from multi-view images has many practical applications, including games, virtual/augmented reality, and digital archives of cultural heritage. In this paper, we introduce a new application in video compression. The proposed idea is to have the decoder reconstruct a 3D scene model based on a subset of decoded frames and then reproject the 3D model to 2D for prediction or reconstruction of intermediate and/or future frames; this can also include a further motion compensation step in 2D. Structure from Motion (SfM) has been employed as a tool to estimate 3D point clouds and camera parameters. This approach has been integrated to generate additional reference pictures in an HEVC codec, and was tested so far on two 4K video sequences: A computer generated sequence with moving objects and a natural but stationary scene captured from a moving camera. Initial simulation results show around 0.8% bit-rate reduction compared to HEVC Test Model (HM16.7). It is asserted that the method offers headroom for further improvements by enhancing the reconstruction algorithms.

We present a method for the interactive exploration of tera-scale particle data sets. Such data sets arise from molecular dynamics, particle-based fluid simulation, and astrophysics. Our visualization technique provides a focus+context view of the data that runs interactively on commodity hardware. The method is based on a hybrid multi-scale rendering architecture, which renders the context as a hierarchical density volume. Fine details in the focus are visualized using direct particle rendering. In addition, clusters like dark matter halos can be visualized as semi-transparent spheres enclosing the particles. Since the detail data is too large to be stored in main memory, our approach uses an out-of-core technique that streams data on demand. Our technique is designed to take advantage of a dual-GPU configuration, in which the workload is split between the GPUs based on the type of data. Structural features in the data are visually enhanced using advanced rendering and shading techniques. To allow users to easily identify interesting locations even in overviews, both the focus and context view use color tables to show data attributes on the respective scale. We demonstrate that our technique achieves interactive performance on a one trillionpar-ticle data set from the DarkSky simulation.

In many cases, view synthesis prediction (VSP) for 3D video coding suffers from inaccurate depth maps (DMs) caused by non-reliable point correspondences in low textured areas. In addition, occlusion handling is a major issue if prediction is performed from one reference view. In this paper, we address VSP for non-coplanar 3D video sequences modified by a linear DM enhancement model. In this model, points close to edges in the DM are treated as key points, on which depth estimation is assumed to work well. Other points are assumed to have unreliable depth information and the depth is modelled by the solution of the Laplace equation. As another preprocessing stage for DMs, the influence of guided image depthmap filtering is investigated. Moreover, we present an inpainting method for occluded areas, which is based on the solution of the Laplace equation and corresponding depth information. Combining these techniques and applying it to VSP for 3D video sequences, which are captured by an arc-arranged camera array, our approach outperforms state of the art models in coding efficiency. Experimental results show bit rate savings up to 4.3 % compared to HEVC-3D for coding two views and corresponding depth in an All Intra encoder configuration.

To support users in operating a computer system with a varying set of functions, it is fundamental to understand their intentions, e.g., within an in-car infotainment system. Although the development of current in-car infotainment systems is already model-based, explicitly gathering and modeling user intentions is currently not supported. However, manually creating software that predicts user intentions is complex, error-prone and expensive. Model-based development can help in overcoming these issues. In this paper, we present an approach for modeling a user's intention based on Bayesian networks. We support developers of in-car infotainment systems by providing means to model possible intentions of users according to the current situation. We further allow modeling of user preferences and show how the modeled intentions may change during run-time as a result of the user's behavior. We demonstrate feasibility of our approach using an industrial example of an intention-aware in-car infotainment system.

In this paper we describe an approach for the realization of 3D resist patterns for implant applications, highlighting the opportunities of controlling doping depth and profiles and their influence on device parameters. We choose a grayscale litho process, where the 3D structuring of the photoresist is done by a spatially variable exposure. Pixilated grayscale mask structures are defined to achieve the desired 3D resist patterns by locally variable transmittance values. The variable transmittance values result in different resist film thicknesses after development. Up to 20 different resist film thicknesses are obtained within a single exposure shot. This enables spatial patterning of the implant depth and accordingly various novel approaches for device optimization.

In this paper the effects of soldering process parameters and used materials on wettability characteristics and the mechanical properties in photovoltaics solder joints were investigated. The soldering and wetting behaviour of solder joints and its industry standard testing methods has been well studied in electronics packaging applications [1–3]. In photovoltaics lesser extent is made to characterize solder joint formation and their influencing parameters, such as process parameters, surface finishes, fluxes, solder composition, etc. [4–6]. In several experiments we focused on a comparative characterization of different process parameters, a variation in solder alloys and the aspects of different solar cells for solar cell string assembly. The mechanical strength of the solder joint interconnection was measured by mechanical pull tests, correlating the force-way-sequence with the fracture mode alongside the pad structure. We conducted wetting balance and surface measurements on solar cell metallizations, indicating the significant difference between used cells and surface finishes on the interconnection process for the equipment suppliers.

The increasing demands to the performance of lithography tools require extensive efforts for monitoring the lithography processes. Relevant parameters for the lithography process are the critical dimension (CD), the profile of the structure and the defect density. CD and profiles are influenced by parameters like focus and dose settings of the lithography process. Another important parameter is the film thickness of the used bottom antireflective coating (BARC) and resist. Up to now the monitoring of these parameters requires the application of various measurement tools like ellipsometers for the thickness measurement, scanning electron microscope (SEM) for CD and profile analysis. The defect inspection is additionally used for focus monitoring. Here we present an investigation of the application of the scatterometry method to obtain all of these parameters on one single wafer with only one single measurement step. It turns out that the scatterometry measurement is not only much faster than the conventional methods, but even more stable and reliable. We demonstrate the potential of scatterometry to enhance the quality of process monitoring accompanied by a significant reduction of costs for the process monitoring.

We present a novel method to recycle bits of paletted data sets. We exploit that the codebook of such data can be reordered without affecting the content. Enumerating all possible permutations of N codebook entries yields an additional O(N log2 N) bits that can be used without storage overhed for the losless encoding of a limited amount of tags, meta-information, or part of the actual data.

We present 3D device modeling of RESURF or non-STI type DeNMOS device under ESD conditions. The impact of base push-out, pulse-to-pulse instability and electrical imbalance on the various phases of filamentation is discussed. A new phenomenon called “week NPN action” and the cause of early and fast failure is identified. A modification of the device is proposed which achieved an improvement of ∼5X in failure threshold (I T2 ) and ∼2X in ESD window without degrading its I/O performance.

We present differences in the ESD failure mechanisms, intrinsic behavior and various phases of filamentation of STI type DeNMOS and DePMOS devices using detailed 3D TCAD simulations, TLP and TIM experiments. The impact of localized base-push-out, power dissipation because of space charge build-up, regenerative bipolar triggering and various events during the current filamentation are compared. Measurements show that the absence of base push out in DePMOS device leads to ∼2.5X higher IT2 as compared to DeNMOS.

A novel DeMOS device with modified body and source region in grounded gate (gg) NMOS configuration for ESD protection is proposed. Detailed 3D simulations indicate a high failure threshold because of moving current filaments and self-protection from gate oxide breakdown, even for fast transients. A detailed physics of second basepushout and moving filaments is discussed.

We present experimental and simulation studies of STI type DeNMOS devices under ESD conditions. The impact of basepush-out, power dissipation because of space charge build-up and, regenerative NPN action, on the various phases of filamentation and the final thermal runaway is discussed. A modification of the device layout is proposed to achieve an improvement (∼2X) in failure threshold (I T2 ).

ESD performance of advanced FinFETs shows a delicate sensitivity to device layout and to processing. Thermal issues are experimentally correlated to gate length, fin width, electrical operation mode and are investigated by TCAD simulation. S/D implant conditions, silicide blocking, and selective epitaxial growth are studied. Reasonable ESD performance is demonstrated while margins between success and failure seem to be very narrow.

FinFET devices have been recently reported to show an unprecedented sensitivity to ESD stress. Detailed failure analyses under ESD stress for these devices reveal a uniquely new mechanism that indicates fusing of single fins. This rather unexpected phenomenon is the cause of extremely high ESD sensitivity as determined by the weakest fin. Based on device simulations, thermal properties of the fins' robustness to ESD are discussed. ESD-robust design of MuGFET circuits is envisioned, if this particular thermal behavior is taken into account for the device design.

Thin large-grained polycrystalline silicon (poly-Si) films can be formed on glass by aluminium-induced crystallisation (AIC) of amorphous silicon (a-Si) in an annealing process below the eutectic temperature of the Al/Si system. The influence of increasing the temperature during the anneal as well as the modification of the initial aluminium layer was investigated. In both cases similar or even larger silicon grains were achieved while reducing the process time.

Modern integrated circuits still exhibit unexplored ESD failure modes. In this work, the trigger voltage of the base pushout driven snapback in parasitic bipolar devices is identified as a limiting value for the ESD concept design and the cause for damage in a 0.13 /spl mu/m technology. Its strong dependence on base control by standard ESD protection elements is considered carefully. Effective countermeasures on circuit and device design level are examined by thermo-electrical device simulations and theoretical estimations.

A survey of graphics developers on the issue of texture mapping hardware for volume rendering would most likely find that the vast majority of them view limited texture memory as one of the most serious drawbacks of an otherwise fine technology. In this paper, we propose a compression scheme for static and time-varying volumetric data sets based on vector quantization that allows us to circumvent this limitation. We describe a hierarchical quantization scheme that is based on a multiresolution covariance analysis of the original field. This allows for the efficient encoding of large-scale data sets, yet providing a mechanism to exploit temporal coherence in non-stationary fields. We show, that decoding and rendering the compressed data stream can be done on the graphics chip using programmable hardware. In this way, data transfer between the CPU and the graphics processing unit (GPU) can be minimized thus enabling flexible and memory efficient real-time rendering options. We demonstrate the effectiveness of our approach by demonstrating interactive renditions of Gigabyte data sets at reasonable fidelity on commodity graphics hardware.

We prepared thin polycrystalline silicon (poly-Si) films on glass by an aluminum-induced layer exchange (ALILE) process which is based on the aluminum-induced crystallization (AlC) of amorphous silicon (a-Si). During the ALILE process a glass/Al/a-Si stack is transformed into a glass/poly-Si/Al+Si structure. We investigated both the growth of the poly-Si layer and the final Al+Si layer on top of the poly-Si layer. Furthermore, we carried out the ALILE process on large glass substrates and on metal-coated glass substrates.

We introduce a novel deep neural network to estimate a depth map from a single monocular indoor panorama. The network directly works on the equirectangular projection, exploiting the properties of indoor 360° images. Starting from the fact that gravity plays an important role in the design and construction of man-made indoor scenes, we propose a compact representation of the scene into vertical slices of the sphere, and we exploit long- and short-term relationships among slices to recover the equirectangular depth map. Our design makes it possible to maintain high-resolution information in the extracted features even with a deep network. The experimental results demonstrate that our method outperforms current state-of-the-art solutions in prediction accuracy, particularly for real-world data.