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Teaching Turkish as a foreign language (TTFL) has recently gained much importance in modern life. For some reason, a large number of people and students with dissimilar background come from other countries so as to start off a new life primarily in İstanbul and/or many other cities in Turkey. Many of whom need to ensure their arrival and long term accommodations by visiting a language center. Some language institutes and foreign language centers, therefore have been trying to meet these needs and give a certificate in accordance with European language portfolio (ELP) levels. Through this paper it was attempted to clarify some certain questions showing up in this teaching performance of this outstanding language “Turkish”: “What type of challenges foreign students face in Turkish courses and how language teachers/instructors help them to get through these problems, what activities teachers carry out in the classroom and what materials and games they use in courses.” This paper reports the findings of a study aiming to reveal what mistakes the students make while learning their first, second or even more foreign languages and this study evaluates them through the more frequently used language teaching methods. Abbreviations: TTFL: Teaching Turkish as a Foreign Language L: Language FL: Foreign Language

This paper takes up the increasingly popular topic of drones — including Unmanned Aerial Vehicles (UAVs), small unmanned aerial Systems (sUAS), Remotely Piloted Aircraft (RPA) and a vast panoply of commercial drones and copters — to argue that our analysis should lie not so much on drones as objects, but as assemblages of the vertical. Drones, I argue, constitute a socio-technical assemblage of the sky and vertical space, which means that our focus should be not (only) on their technological development and capacities (although this is certainly important) but on their wider effects and affects. The latter of these include increasing algorithmic data collection and circulation. In the second part of the paper, I examine what I call "algorithmic governance" inspired by the recent governing algorithms conference at NYU but also drawing from Foucault's work on governmentality. Algorithmic governance constitutes "conduct of conduct" to modulate how "data shadows" or Deleuzian "dividuals" can be created, sold, and passed forward. My particular concern is how this takes place in a geographic register.

International audience; Satellite Image Time Series (SITS) are large datasets containing spatiotemporal information about the surface of the Earth. In order to exploit the potential of such series, SITS analysis techniques have been designed for various applications such as earthquake monitoring, urban expansion assessment or glacier dynamic analysis. In this paper, we present an unsupervised technique for browsing SITS in preliminary explorations, before deciding whether to start deeper and more time consuming analyses. Such methods are lacking in today’s analyst toolbox, especially when it comes to stimulating the reuse of the ever growing list of available SITS. The method presented in this paper builds a summary of a SITS in the form of a set of maps depicting spatiotemporal phenomena. These maps are selected using an entropy-based ranking and a swap randomization technique. The approach is general and can handle either optical or radar SITS. As illustrated on both kinds of SITS, meaningful summaries capturing crustal deformation and environmental phenomena are produced. They can be computed on demand or precomputed once and stored together with the SITS for further usage.

The present study focuses on serially occurring narrations of ‘everyday’ life, more specifically on birthing as narrated by mothers on online forums; the underlying idea being that these narrations happen against the background of cultural narratives.The present paper uses word embedding models to detect typical topics and actors in these narrations. The calculation of word embeddings automatically constructs semantic spaces, where semantic relations (synonymy in particular) can be modeled. This method offers a way to think of synonymy as ‘functional equivalence in discourse’.The present study relies on previous work with n-grams (Bubenhofer, 2018). N-grams are sequences of words that often appear together; their sequential order in different narrations gives insight in narrative patterns. A further step in the analysis is the construction of ‘narrative topoi’, which is achieved through clustering neighboring vectors. The emerging clusters can in turn be grouped into five narrative elements of ‘telling a birth story’: 1) disruption of daily life, 2) personnel, 3) body, 4) fear, 5) joy. While it seems obvious that certain themes ‘belong’ into the narration of a delivery, it is less obvious with what vocabulary these themes are expressed.The presented method of clustering word-embedding-profiles adds tremendously to the modelling of a narrative. Its advantages lie in its potential to show lexical variation, as it also includes rare, non-conformative orthographical variants. Furthermore, it allows for a discourse-specific (and usage-based) view on semantic relations. The same applies to relations between semantic clusters. Seen from a discourse linguistics or cultural analysis perspective, word embeddings renew our understanding of semantics. This shows particularly fruitful if used to analyze (discourse dependent) derivations between semantic spaces.

In this paper, we study ∗-g-frames in tensor products of Hilbert C∗-modules. We show that a tensor product of two ∗-g-frames is a ∗-g-frame, and we get some result.

Crowdfunding is now established as a valid alternative to conventional methods of financing for startups. Unfortunately, to date, research has not investigated how backers can be encouraged to support entrepreneurs beyond funding. The aim of this study is to design and evaluate certain design elements for reward-based crowdfunding platforms that can engage backers in co-creational activities for product development. The study uses a design science research (DSR) approach and the theoretical concept of psychological ownership to inform a new design and then experimentally test that design. The results suggest that the derived artifacts positively influence co-creational activities in crowdfunding and that feelings of psychological ownership play an important mediating role. The contribution of this research is threefold. First, this paper extends crowdfunding’s application potential from merely a method of financing to a method of value creation with customers for product development. Second, the study advances DSR by applying a new DSR approach that shows whether a design performs as hypothesized by theory. Third, this research allows the exploration of backers’ individual behavior as opposed to their collective behavior.

The fusion of complementary information from co-registered multi-modal image data enables a more detailed and more robust understanding of an image scene or specific objects, and is important for several applications in the field of remote sensing. In this paper, the benefits of combining RGB, near infrared (NIR) and thermal infrared (TIR) aerial images for the task of semantic vehicle segmentation through deep neural networks are investigated. Therefore, RGB, NIR and TIR image triplets acquired by the Modular Aerial Camera System (MACS) are precisely co-registered through the application of a virtual camera system and subsequently used for the training of different neural network architectures. Various experiments were conducted to investigate the influence of the different sensor characteristics and an early or late fusion within the network on the quality of the segmentation results.

Building semantic segmentation is a crucial task for building information modeling (BIM). Current research generally exploits terrestrial image data, which provides only limited view of a building. By contrast, oblique imagery acquired by unmanned aerial vehicle (UAV) can provide richer information of both the building and its surroundings at a larger scale. In this paper, we present a novel pipeline for building semantic segmentation from oblique UAV images using a fully convolutional neural network (FCN). To cope with the lack of UAV image annotations at facade level, we leverage existing ground-view facades databases to simulate various aerial-view images based on estimated homography, yielding abundant synthetic aerial image annotations as training data. The FCN is trained end-to-end and tested on full-tile UAV images. Experiments demonstrate that the incorporation of simulated views can significantly boost the prediction accuracy of the network on UAV images and achieve reasonable segmentation performance.

Summary This paper presents a technique that aimed to accomplish an efficient balance between video compression using H.265 protocol and retention of 8K resolution. The study implements multi-level of optimization in the encoding process using H.265 where JPEG2000 standards play a crucial role. The study also applies a novel concept of orthogonal projection that manages pixels metadata required in every frame transition followed by motion compensation. By using multiple file formats of 30 video datasets, the outcome of the study is found to be accomplishing approximately 49% of enhancement in data quality and around 59% of improvement in video compression in comparison to the existing techniques of HEVC-based video compression.

Summary Inverted pendulum on cart belongs to a class of highly non-linear, multivariable and complex system which is currently a keen area of interest for researchers in the field of control engineering and robotics. This paper considers stabilisation and control of inverted pendulum on cart moving on an inclined surface. The task of controlling inverted pendulum on inclined surface is much difficult as compared to that moving on horizontal surface. An offline control of the proposed system has been successfully achieved using proportional-integral-derivative (PID) and fuzzy controllers. A Matlab-Simulink model of the proposed system has been built using mathematical equations derived from Newton's second law. The study compares the proposed techniques in terms of settling time, overshoot, undershoot and steady state error. The simulation results are illustrated with the help of graphs and tables which confirms the validity of the proposed techniques.

Summary Breast cancer is significant health problem diagnosed mostly in women worldwide. Therefore, early detection of breast cancer is performed with the help of digital mammography, which can reduce mortality rate. This paper presents wrapper based feature selection approach for wavelet co-occurrence feature (WCF) using Genetic Fuzzy System (GFS) in mammogram classification problem. The performance of GFS algorithm is explained using mini-MIAS database. WCF features are obtained from detail wavelet coefficients at each level of decomposition of mammogram image. At first level of decomposition, 18 features are applied to GFS algorithm, which selects 5 features with an average classification success rate of 39.64%. Subsequently, at second level it selects 9 features from 36 features and the classification success rate is improved to 56.75%. For third level, 16 features are selected from 54 features and average success rate is improved to 64.98%. Lastly, at fourth level 72 features are applied to GFS, which selects 16 features and thereby increasing average success rate to 89.47%. Hence, GFS algorithm is the effective way of obtaining optimal set of feature in breast cancer diagnosis.

Summary In presence of environmental noise, speakers tend to emphasize their vocal effort to improve the audibility of voice. This involuntary adjustment is known as Lombard effect (LE). Due to LE the signal to noise ratio of speech increases, but at the same time the loudness, pitch and duration of phonemes changes. Hence, accuracy of automatic speech recognition systems degrades. In this paper, the effect of unsupervised equalization of Lombard effect is investigated for Hindi vowel classification task using Hindi database designed at TIFR Mumbai, India. Proposed Quantile-based Dynamic Cepstral Normalization MFCC (QCN-MFCC) along with baseline MFCC features have been used for vowel classification. Hidden Markov Model (HMM) is used as classifier. It is observed that QCN-MFCC features have given a maximum improvement of 5.97% and 5% over MFCC features for context-dependent and context-independent cases respectively. It is also observed that QCN-MFCC features have given improvement of 13% and 11.5% over MFCC features for context-dependent and context-independent classification of mid vowels.

Summary Cryptographic techniques are employed for minimizing security hazards to sensitive information. To make the systems more robust, cyphers or crypts being used need to be analysed for which cryptanalysts require ways to automate the process, so that cryptographic systems can be tested more efficiently. Evolutionary algorithms provide one such resort as these are capable of searching global optimal solution very quickly. Cuckoo Search (CS) Algorithm has been used effectively in cryptanalysis of conventional systems like Vigenere and Transposition cyphers. Linear Feedback Shift Register (LFSR) is a crypto primitive used extensively in design of cryptosystems. In this paper, we analyse LFSR based cryptosystem using Cuckoo Search to find correct initial states of used LFSR. Primitive polynomials of degree 11, 13, 17 and 19 are considered to analyse text crypts of length 200, 300 and 400 characters. Optimal solutions were obtained for the following CS parameters: Levy distribution parameter ( β ) = 1.5 and Alien eggs discovering probability ( p a ) = 0.25.

Summary In recent years, the coal mine safety production situation in our country improved year by year, but severe accidents still occurred; the accidents caused great economic loss to the national economy. According to statistical analysis, almost all of the coal mine accidents will expose the hidden danger in before, most of the accidents caused due to safety management not reaching the designated position and the hidden danger management does not take any decision in time. Based on the coal mine safety management holes in our country, the coal mine macro, meso and micro safety management system was established in this paper, which includes meaning and conception of the theories of the macro, meso and micro safety management, and also includes the matching hardware equipment, in order to achieve the hidden danger's closed-loop control and dynamic early warning in the process of coal mine production.

Summary In this paper, we present a software tool capable of predicting the final quality of triple play services by using the most common assessment metrics. The quality of speech and video in network environment is a growing concern of all the internet service providers to carry the multimedia traffic without the excessive delays and losses, which degrade the quality of multimedia as it is perceived by the end users. Prediction mathematical model is based on results obtained from many performed testing scenarios simulating real behavior in the network. Based on the proposed model, speech or video quality is calculated with regard to policies applied for packet processing by routers and to the level of total network utilization. The application cannot only predict QoS parameters but also generate the source code of particular QoS policy setting according to the user interaction and apply the policy to the routers in the network. Contribution of the work consists of a new software tool enables network administrators and designers to improve and optimize network traffic efficiently.

Summary A cellular neural network (CNN) is a massively parallel analog array processor capable of solving various complex processing problems by using specific templates that characterize the synaptic connections. The hardware implementation and applications of CNN have attracted a great deal of attention. Recently, memristors with nanometer-scale and variable gradual conductance have been exploited to make compact and programmable electric synapses. This paper proposes and studies a novel memristive CNN (Mt-CNN) with time-variant templates realized by memristor crossbar synaptic circuits. The template parameters are estimated analytically. The Mt-CNN provides a promising solution to hardware realization of real-time template updating processes, which can be used to effectively deal with various complicated problems of cascaded processing. Its effectiveness and advantages are demonstrated by practical examples of edge detection on noisy images.

Summary Variable weight synthesis method is a safety assessment method which is using the varied weight principle, based on analytic hierarchy process and combining the theory of grey relevancy, it reduce influence that secondary cause is not obvious in assessment process, and make the result more correct and dependable. In the paper, variable weight principle and the assessment method were expounded in detail.

Most travel demand models simplify the representation of trips from/to external zones. Despite the higher frequency of internal daily travel, long-distance travel cannot be neglected due to its high contribution to the travelled distance. In this paper we integrate two different models that represent those two travel segments. MITO is an agent-based model for travel demand implemented in the metropolitan area of Munich. DEMO is a macroscopic model that generates travel demand in Germany. DEMO is used to represent the external, long-distance travel from, to or through the MITO study area. DEMO demand is disaggregated and is jointly assigned with MITO trips using the transport model MATSim. The integration significantly improves the validation of the model against traffic counts and facilitates the simulation capabilities when the scenarios affect major roads.

Innovative products are interacting with the users based on smart sensors and algorithms. Logistics as one example is changing when automated guided vehicles are integrated in the process, supporting or even replacing workers. The way these products are interacting with humans and how they react to certain situations, will determine usability and user experience and therefore the success of use. Developing such products is based on innovative concepts that need to be evaluated and refined at early project stages. Using virtual-reality based user scenarios is one adequate option to do so. This paper describes technical as well as study-based approaches on how potential concepts are realized as virtual prototypes and evaluated by real users. It concludes with the evaluation results of a pilot study but also with general limitations and benefits as best practice advice for this kind of virtual prototyping techniques., aw&I Conference, Bd. 3 (2018): innteract2018

In this work, we propose a novel approach to prioritize the depth map computation of multi-view stereo (MVS) to obtain compact 3D point clouds of high quality and completeness at low computational cost. Our prioritization approach operates before the MVS algorithm is executed and consists of two steps. In the first step, we aim to find a good set of matching partners for each view. In the second step, we rank the resulting view clusters (i.e. key views with matching partners) according to their impact on the fulfillment of desired quality parameters such as completeness, ground resolution and accuracy. Additional to geometric analysis, we use a novel machine learning technique for training a confidence predictor. The purpose of this confidence predictor is to estimate the chances of a successful depth reconstruction for each pixel in each image for one specific MVS algorithm based on the RGB images and the image constellation. The underlying machine learning technique does not require any ground truth or manually labeled data for training, but instead adapts ideas from depth map fusion for providing a supervision signal. The trained confidence predictor allows us to evaluate the quality of image constellations and their potential impact to the resulting 3D reconstruction and thus builds a solid foundation for our prioritization approach. In our experiments, we are thus able to reach more than 70% of the maximal reachable quality fulfillment using only 5% of the available images as key views. For evaluating our approach within and across different domains, we use two completely different scenarios, i.e. cultural heritage preservation and reconstruction of single family houses., Comment: This paper was accepted to ISPRS Journal of Photogrammetry and Remote Sensing (https://www.journals.elsevier.com/isprs-journal-of-photogrammetry-and-remote-sensing) on March 21, 2018. The official version will be made available on ScienceDirect (https://www.sciencedirect.com)

Exploratory visual analysis is often required to assist human operator to understand and interpret Earth Observation (EO) images. Optimal image representation offers cognitive support in discovering relevant facts about the scene with respect to a particular application. This is of crucial importance for training data sets selection in all Machine Learning tasks, particularly in the design of active learning tools for multispectral (MS) EO data. This paper proposes a deep neural network (DNN) based method to compress, learn and reveal the most significant information included in the spectral bands of EO data in support of relevant visualization for image content analysis. The advanced method uses a DNN to discover the most suggestive pseudo-color representation able to highlight the entire MS image content better than the particular 3 bands selection (R, G, B). We propose the use of information theory and the concept of mutual information to rank the spectral bands based on the amount of information contained, by applying the minimum-redundancy-maximum-relevance (mRMR) criterion on a the image so that we obtain the ranked bands. A DNN stacked autoencoder based paradigm is developed in order to extract and compress in three bands the overall information from the MS EO data. The developed method is demonstrated and validated for Sentinel 2 dataset.

Modern spaceborne synthetic aperture radar (SAR) sensors, such as TerraSAR-X/TanDEM-X and COSMO-SkyMed, can deliver very high resolution (VHR) data beyond the inherent spatial scales (on the order of 1m) of buildings, constituting invaluable data source for large-scale urban mapping. Processing this VHR data with advanced interferometric techniques, such as SAR tomography (TomoSAR), enables the generation of 3-D (or even 4-D) TomoSAR point clouds from space. In this paper, we present a novel and generic workflow that exploits these TomoSAR point clouds in a way that is capable to automatically produce benchmark annotated (buildings/non-buildings) SAR datasets. These annotated datasets (building masks) have been utilized to construct and train the state-of-the-art deep Fully Convolution Neural Networks with an additional Conditional Random Field represented as a Recurrent Neural Network to detect building regions in a single VHR SAR image. The results of building detection are illustrated and validated over TerraSAR-X VHR spotlight SAR image covering approximately 39 km2 - almost the whole city of Berlin - with mean pixel accuracies of around 93.84%.

Significantly outperforming traditional machine learning methods, deep convolutional neural networks have gained increasing popularity in the application of image classification and segmentation. Nevertheless, deep learning-based methods usually require a large amount of training data, which is quite labor-intensive and time-demanding. To deal with the problem in generating training data, we propose in this paper a novel approach to generate image annotations by transferring labels from aerial images to UAV images and refine the annotations using a densely connected CRF model with an embedded naive Bayes classifier. The generated annotations not only present correct semantic labels, but also preserve accurate class boundaries. To validate the utility of these automatic annotations, we deploy them as training data for pixel-wise image segmentation and compare the results with the segmentation using manual annotations. Experiment results demonstrate that the automatic annotations can achieve comparable segmentation accuracy as the manual annotations.

Summary In the recent years, population based meta-heuristic are developed to solve non-linear optimization problems. These problems are difficult to solve using traditional methods. Simple optimization (SOPT) algorithm is one of the simple and efficient meta-heuristic techniques to solve the non-linear optimization problems. In this paper, SOPT is compared with some of the well-known meta-heuristic techniques viz. Artificial Bee Colony algorithm (ABC), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolutions (DE). For comparison, SOPT algorithm is coded in MATLAB and 25 standard test functions for unconstrained optimization having different characteristics are run for 30 times each. The results of experiments are compared with previously reported results of other algorithms. Promising and comparable results are obtained for most of the test problems. To improve the performance of SOPT, an improvement in the algorithm is proposed which helps it to come out of local optima when algorithm gets trapped in it. In almost all the test problems, improved SOPT is able to get the actual solution at least once in 30 runs.

Summary The laying and commissioning of new transmission line is very difficult due to socio-economic problems, like environmental clearances, right of way, etc. Therefore, there is an emphasis on better utilization of available transmission infrastructure. FACTS devices can provide reactive power compensation, transmission capability enhancement, and voltage and stability improvement. FACTS devices operate under the command of system operator who analyses its demand by the data acquired through traditional SCADA system, state estimation algorithms and PMUs. SCADA together with PMU give accurate information about the operational state of power system. This paper proposes a scheme to automate the FACTS devices in collaboration with PMUs in a more efficient way. Highly precised data from PMUs can be fed to intelligent controllers for effective analyzing and automating the FACTS device through control command. Thus, this combination can provide real time control of reactive power, together with enhancement of power handling capability and stability improvement.

Summary The customer's review plays an important role in deciding the purchasing behaviour for online shopping as a customer prefers to get the opinion of other customers by observing their opinion through online products’ reviews, blogs and social networking sites, etc. The customer's reviews reflect the customer's sentiments and have a substantial significance for the products being sold online including electronic gadgets, movies, house hold appliances and books. Hence, extracting the exact features of the products by analyzing the text of reviews requires a lot of efforts and human intelligence. In this paper we intend to analyze the online reviews available for books and extract book-features from the reviews using human intelligence. We have proposed a technique to categorize the features of books from the reviews of the customers. The extracted features may help in deciding the books to be recommended for readers. The ultimate goal of the work is to fulfil the requirement of the user and provide them their desired books. Thus, we have evaluated our categorization method by users themselves, and surveyed qualified persons for the concerned books. The survey results show high precision of the features categorized which clearly indicates that proposed method is very useful and appealing. The proposed technique may help in recommending the best books for concerned people and may also be generalized to recommend any product to the users.

Summary The world today is experiencing an exponential growth in cyberspace. Nevertheless, India too has witnessed a significant ascend in Internet activities and it is quite assertive to say that such phenomenal growth in access to information on one hand leads to empowered individuals and organization and on the other hand also poses new challenges to government and citizens. To make the cyber world safe is the need of the hour. Putting up deterrent measures against cybercrime is essential to national cyber security in protecting critical infrastructure of the nation as well as for individuals. In this regard, the prime objective of the government is to prevent cyber attacks and to protect the country's critical infrastructure. It also focuses on reducing vulnerability to cyber attacks so as to reduce and minimize damage and recovery time. To prevent the cyber crimes, individuals and governments need to clearly understand the crime schemes in the cyberspace and the contemporary and continuing Internet trends and behaviours of these criminals. This paper gives a brief outline of categories of cybercrimes. These crimes are categorized as crimes against individuals, property, organizations and governments. Various Internet crime scheme are evaluated and behaviour of criminals to perform the cybercrimes has been analyzed. A critical evaluation of report of cybercrime complaints under IT Act 2000 has been presented.

Summary Selection of a suitable robotic manipulator, out of the available conventional configurations, is one way to employ a robot. However, with the increasing applications of robotic assistance, the concept of task-oriented customised design for prescribed working locations in a given environment, has got attention of several researchers. One of the challenges in this direction is the realisation of specific designs, involving unconventional values of robotic parameters. This paper discusses the D–H parameters based adaptive modules, which can be adjusted according to the given values of robotic parameters. The modules are available in three types with similar architecture. The details of this basic length unit, adaptive twist unit and extended length unit are discussed. Focus of the work is to validate the adaptability of the proposed architecture through the modelling, simulation and development of standard configuration.

Summary Selecting the right vendor is an important business decision made by any organization. The decision involves multiple criteria and if the objectives vary in preference and scope, then nature of decision becomes multiobjective. In this paper, a vendor selection problem has been formulated as an intutionistic fuzzy multiobjective optimization where appropriate number of vendors is to be selected and order allocated to them. The multiobjective problem includes three objectives: minimizing the net price, maximizing the quality, and maximizing the on time deliveries subject to supplier's constraints. The objection function and the demand are treated as intutionistic fuzzy sets. An intutionistic fuzzy set has its ability to handle uncertainty with additional degrees of freedom. The Intutionistic fuzzy optimization (IFO) problem is converted into a crisp linear form and solved using optimization software Tora. The advantage of IFO is that they give better results than fuzzy/crisp optimization. The proposed approach is explained by a numerical example.

Earth observation (EO) has become a valuable source of comprehensive, reliable, and persistent information for a wide number of applications. However, dealing with the complexity of land cover is sometimes difficult, as the variety of EO sensors reflects in the multitude of details recorded in several types of image data. Their properties dictate the category and nature of the perceptible land structures. The data heterogeneity hampers proper understanding, preventing the definition of universal procedures for content exploitation. The main shortcomings are due to the different human and sensor perception on objects, as well as to the lack of coincidence between visual elements and similarities obtained by computation. In order to bridge these sensory and semantic gaps, the paper presents a compound framework for EO image information extraction. The proposed approach acts like a common ground between the user's understanding, who is visually shortsighted to the visible domain, and the machines numerical interpretation of a much wider information. A hierarchical data representation is considered. At first, basic elements are automatically computed. Then, users can enforce their judgement on the data processing results until semantic structures are revealed. This procedure completes a user-machine knowledge transfer. The interaction is formalized as a dialogue, where communication is determined by a set of parameters guiding the computational process at each level of representation. The purpose is to maintain the data-driven observable connected to the level of semantics and to human awareness. The proposed concept offers flexibility and interoperability to users, allowing them to generate those results that best fit their application scenario. The experiments performed on different satellite images demonstrate the ability to increase the performances in case of semantic annotation by adjusting a set of parameters to the particularities of the analyzed data.

With the advent of Smart Grid systems, the digitalization of electrical grid infrastructures aims to improve energy saving and efficiency. The International Electrotechnical Commission (IEC) is one of the organizations that create and manage norms and standards in areas related to electricity and electronics, such as IEC 61850. In recent years, the research community have proposed mappings of different communication protocols to IEC 61850. However, most of the proposals focused on heavyweight interaction paradigms and protocols such as Common Object Request Broker Architecture (CORBA), Data Distribution Service (DDS), HTTP-REST or Web Services. With the proliferation of the Internet of Things (IoT), new lightweight protocols are appearing opening new perspectives to the standard. Hence, this paper firstly presents a validation of a mapping of the IEC 61850 standard to the Constrained Application Protocol (CoAP) and then compares its performance implementing the IEC's mapping against HTTP-REST and Web Services/SOAP. For comparison purposes, the communication latency, the number of total bytes sent, and the size of the overhead are presented for each of the three approaches. To conclude, future perspectives on the suitability of lightweight protocols to the IEC 61850 are provided.

To improve the classification of hyperspectral images, this paper proposes an approach for multi-sensor data fusion of LiDAR and hyperspectral data using extinction profiles and Orthogonal Total Variation Component Analysis (OTVCA). Results on the benchmark Houston data indicate the superior performance of the proposed approach compared to other approaches used in the experiments based on classification accuracies.

A traffic data collection system using wireless radio frequency (RF) technologies, e.g. Bluetooth or Wi-Fi, allows for area wide measurement of traffic. This includes travel times on the whole road network. This paper aims to present the technical overview of a newly developed ITS system called DYNAMIC. This system supports indirect data acquisition by moving detectors, processing and provision of floating observer information on the basis of mobile wireless RF data. DYNAMIC consists of sensor hardware and software components like data collection, data import, transport mode detection, data processing, data fusion and visualization.

A major research area in remote sensing is the problem of multi-sensor data fusion. Especially the combination of images acquired by different sensor types, e.g. active and passive, is a difficult task. Over the last years deep learning methods have proven their high potential for remote sensing applications. In this paper we will show how a deep learning method can be valuable for the problem of optical and SAR image matching. We investigate the possible of conditional generative adversarial networks (cGANs) for the generation of artificial templates. Contrary to common template generation approaches for image matching, the generation of templates using cGANs does not require the extraction of features. Our results show the possibility of realistic SAR-like template generation from optical images through cGANs and the potential of these templates for enhancing the matching of optical and SAR images by means of reliability and accuracy.

The purpose of this paper is helping managers and leaders in healthcare develop viable organizations that deliver high-level services. Health organizations such as hospitals are exceedingly complex. Therefore, we use Integrative Systems Methodology, a framework designed especially as an enabler for coping with complexity. Within that framework, we combine quantitative and qualitative methods to describe and explain organizational phenomena evolving over time. Rather than a large survey, we use a real-life case study. A single-case setting has been chosen, to enable long-term and in-depth exploration. The case spans 30 years, covering the evolution of the oncological care system of Carinthia, which is a federal state of Austria. The contribution of the chapter is in providing deep insights. It lays open the structures underlying the viability of health organizations. The chapter also provides well-grounded advice for how to build a robust health organization in a context of complexity and change.

In this paper, we present a method for regularizing noisy 3D reconstructions, which is especially well suited for scenes containing planar structures like buildings. At horizontal structures, the input model is divided into slices and for each slice, an inside/outside labeling is computed. With the outlines of each slice labeling, we create an irregularly shaped volumetric cell decomposition of the whole scene. Then, an optimized inside/outside labeling of these cells is computed by solving an energy minimization problem. For the cell labeling optimization we introduce a novel smoothness term, where lines in the images are used to improve the regularization result. We show that our approach can take arbitrary dense meshed point clouds as input and delivers well regularized building models, which can be textured afterwards.

GNSS/IMU navigation systems offer low-cost and robust solution to navigate UAVs. Since redundant measurements greatly improve the reliability of navigation systems, extensive researches have been made to enhance the efficiency and robustness of GNSS/IMU by additional sensors. This paper presents a method for integrating reference data, images taken from UAVs, barometric height data and GNSS/IMU data to estimate accurate and reliable pose parameters of UAVs. We provide improved pose estimations by integrating multi-sensor observations in an EKF algorithm with IMU motion model. The implemented methodology has demonstrated to be very efficient and reliable for automatic pose estimation. The calculated position and attitude of the UAV especially when we removed the GNSS from the working cycle clearly indicate the ability of the purposed methodology.

Fault localization in RTL design using dynamic dependency graphs.Reverse debugging for RTL designs.Scalable and fast approach applicable to large designs.Manual debugging effort can be reduced by more than 50ź%. Display Omitted Debugging is a time consuming task in hardware design. In this paper a new debugging approach based on the analysis of dynamic dependency graphs is presented. Powerful techniques for software debugging, including reverse debugging, dynamic forward and backward slicing, and spectrum-based fault localization are combined and adapted for hardware designs. A case study on designs with multiple faults approved the power of the proposed debugging methodology reducing the debugging time to 50% in comparison to conventional techniques.

In this paper, we present the application of an automatic service composition system in the context of collaborative industrial manufacturing. We discuss how our system could support a production-as-a-service paradigm and present the architecture we created to realize this potential. The basis of our approach are embedded semantic descriptions of a production cell's available resources (machines and human workers). Using these descriptions and given a specification of the product to be manufactured, our system is able to derive a collaborative plan that composes its available resources to achieve that manufacturing goal. This ability to flexibly reconfigure industrial equipment in a goal-driven way is becoming increasingly important in the industrial domain, where mass customization leads to ever smaller lot sizes and potentially even to a lot-size-one world. It can revolutionize the way we think about manufacturing: instead of highly optimized integrated high-volume production lines, this would allow individual manufacturing cells to offer their capabilities as a service and compete with other cells in a paradigm that we refer to as UberManufacturing.

Massive graphs are becoming increasingly common in a variety of domains such as social networks and web analytics. One approach to overcoming the challenges of size is to sample the graph, and perform analytics on the smaller graph. However, to be useful, the sample must maintain the properties of interest in the original graph. In this paper, we analyze the quality of five representative sampling algorithms in how well they preserve graph structure, the bisimulation structure of graphs in particular. As part of this study, we also develop a new scalable algorithm for computing bisimulation partitions of massive graphs. We empirically demonstrate the superior performance of our new algorithm in both sequential and distributed settings.

In this paper, we propose a new scheme for script and nature identification. The objective is to discriminate between machine-printed/handwritten and Latin/Arabic scripts at word level. It is relatively a complex task due to possible use of multi-fonts and sizes, complexity and variation in handwriting. In the proposed script identification system, we extract features from word images using Co-occurrence Matrix of Oriented Gradients (Co-MOG). The classification is done using different classifiers. Extensive experimentation has been carried on 24000 words, extracted from standard databases. An average identification accuracy of 99.85% is achieved by k Nearest Neighbors (k-NN) classifier which clearly outperforms results of some existing systems.