Miracle-dz/UniFAR-FTUs huggingface dataset:
Stellaris: An RDF-based Information Service for AstroGrid-D

We present Stellaris, the information service of the community project AstroGrid-D. Stellaris is the core component of the AstroGrid-D middleware that enables scientists to share their resources, provides access to large datasets and integrates instruments such as robotic telescopes. Besides the many diverse types of resources, the information service also supports a wide range of use cases each using a specific schema for the metadata. In addition, Stellaris addresses the distributed and dynamic nature of collaborations in the astronomers’ community. Stellaris satisfies these requirements by adopting RDF and SPARQL for storing and querying metadata. Our paper focuses on the requirements of the community, presents the architecture of the information service in detail and discusses experiences with the prototype already in use by partners within the project.
EcoMobiFog–Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications

The emerging 5G paradigm will enable multi-radio smartphones to run high-rate stream applications. However, since current smartphones remain resource and battery-limited, the 5G era opens new challenges on how to actually support these applications. In principle, the service orchestration capability of the Fog and Cloud Computing paradigms could be an effective means of dynamically providing resource-augmentation to smartphones. Motivated by these considerations, the peculiar focus of this paper is on the joint and adaptive optimization of the resource and task allocations of mobile stream applications in 5G-supported multi-tier Mobile-Fog-Cloud virtualized ecosystems. The objective is the minimization of the computing-plus-network energy of the overall ecosystem under hard constraints on the minimum streaming rate and the maximum computing-plus-networking resources. To this end: 1) we model the target ecosystem energy by explicitly accounting for the virtualized and multi-core nature of the Fog/Cloud servers; 2) since the resulting problem is non-convex and involves both continuous and discrete variables, we develop an optimality-preserving decomposition into the cascade of a (continuous) resource allocation sub-problem and a (discrete) task-allocation sub-problem; and 3) we numerically solve the first sub-problem through a suitably designed set of gradient-based adaptive iterations, while we approach the solution of the second sub-problem by resorting to an ad-hoc-developed elitary Genetic algorithm. Finally, we design the main blocks of EcoMobiFog, a technological virtualized platform for supporting the developed solver. The extensive numerical tests confirm that the energy-delay performance of the proposed solving framework is typically within a few per-cent the benchmark one of the exhaustive search-based solution.
Spectrum Sensing in Mobile Full-Duplex Cognitive Radio

In this paper, a mobile secondary user (SU) having in-band full-duplex (FD) ability is considered. We investigate the effect of the SU mobility and residual self-interference (RSI) on sensing performance. The fading channel between primary and secondary users is considered to be time-selective frequency flat Rayleigh fading channel. The RSI channel is also assumed to be Rayleigh fading channel. We analytically derive the closed form expressions for false alarm and detection probabilities. Moreover we analyse the receiver operating characteristics (ROC) and total error rate (TER) performance using energy detector (ED). The results show that the sensing performance degrades with increase in secondary user’s speed and also due to RSI. The results are verified through Monte Carlo simulations.
Sentiment and Emotion Analysis for Context Sensitive Information Retrieval of Social Networking Sites: A Survey

Sensitive Information Retrieval (CSIR) is quite a challenging issue because of the complexities involved in the process from semantics and ontology to the huge amount of processing capacity required to make it possible in real time. Understanding the semantic gap (where context is neglected) plays a major role in elimination false positives and improving the true positives in the information retrieval process. With big data becoming ubiquitous due to the volume, velocity and variety of data being presented and analysed in almost all the domains today, context sensitive analysis and interpretation of big data becomes important. This paper presents a comprehensive survey of the existing techniques for big data analysis based on massively parallel processing techniques like GPGPUs (CUDA), Hadoop Map- Reduce and also Data Warehousing. This paper presents a discussion about the datasets that are available for research and also the applications that could be thought of by context sensitive analysis of social media data. Also this paper provides research directions for context sensitive information retrieval and sentiment analysis in big data based on massively parallel processing architecture.
Optimal control problem with a varying terminal time: Part I, stochastic maximum principle.

To establish the stochastic maximum principle for the optimal control problem under state constraints without using Ekland's variational principle, we propose a new optimal control problem, in which the terminal time follows the varying of the control via the constrained condition. Focusing on this new optimal control problem, we investigate a novel stochastic maximum principle, which differ from the traditional optimal control problem under state constraints. The optimal pair of the optimal control model can be verified via this new stochastic maximum principle. In addition, this new optimal control model can not only minimize the cost functional but also decreases the holding time.
Detection of Breast Tumour Tissue Regions in Histopathological Images using Convolutional Neural Networks

Ductal carcinoma in situ (DCIS) is considered a pre-invasive breast cancer and sometimes it can develop into an invasive ductal carcinoma. The analysis of histopathological images to detect tumour border of DCIS could provide important information for better diagnosis of patients. We present a deep learning based system to automatically identify DCIS in histopathological images. Specifically, a convolutional neural network (CNN) is first trained to predict labels of small patches cropped out of a histopathological whole slide image. Next, a sliding window method is used to produce a probability map of DCIS. Finally, given the probability map, a tumor border of DCIS is produced and delineated with the method of Marching Cubes to facilitate pathologists’ review and assessment. Evaluation of cross validation demonstrates that the CNN model of GoogleNet performs well in histology image patch classification with an overall accuracy of (98.46±0.40)% and identifies the DCIS tissue patches with a F1-score of (97.40±1.18)% (mean±variance). Moreover, around 95.6% tumour tissue within the enclosed tumour regions can be identified by our developed method. Finally, the goal of tumor border detection can be well achieved with a few post-processing steps.
Backward/Forward Method for Three-phase Power Flow Calculation in Low Voltage Distribution Networks with EV Charging Points

The paper presents comprehensive review on power flow methods, and proposed a robust and fast power flow calculation approach for low voltage three-phase balanced/unbalanced distribution networks considering the Electric Vehicles (EVs) charging points. The proposed approach adopts the Kirchhoff’s current law (KCL) and Kirchhoff’s voltage law (KVL) to determine the branch currents and node voltages. For fast calculations, a backward/forward sweep approach is adopted to update the bus voltages in each iteration. The individual mathematical modelling of distribution system components are presented. To validate the method, simulation results obtained are compared with existing methods. Further, voltage and loss sensitivities are used to determine the potential charging points and their sizes by considering percentage unbalancing of the system.
Tomography Image Reconstruction using Parallel-Beam Projection at Microwave Frequency

Tomography is a method for reconstructing an image of some object cross-section using emitted electromagnetics waves. Some techniques frequently applied for tomography covers backprojection, Algebraic Reconstruction Technique (ART), and backpropagation. In this paper, frequency selection and image reconstruction techniques for tomography are investigated using parallel-beam projection at microwave frequency. The use of some microwave frequencies of 1.5GHz, 3GHz, and 4.5GHz is intended to determine the characteristics and effect of selected frequency in producing the reconstructed image. A perforated wood is used as a target which is represented by a wood of tree trunk for object measurement. Here, the filtered backprojection and backpropagation techniques are implemented for reconstructing the image of measured object. From the result, it shows that the reconstructed image using backpropagation technique at the frequency of 3GHz has better image quality compared to the ones using filtered backprojection technique.
Predictive Optimal Switching Vector Controller based Microgrid Enabling Switching Frequency Constraint

This study proposes predictive optimal switching vector controller to generate optimal switching sequences to voltage-controlled inverters in islanded microgrid systems. The conventional pulse width modulation controllers use output voltage and current with inner and outer current loops, thereby demanding the PI (Proportional Integral) regulators and PWM modulators. They are associated with finite dynamic response transient time by PI controllers and necessitate rigorous tuning for practical implementation purposes. To complement this problem, voltage and current predictive optimal switching vector control is proposed here, which completely eliminates the PID regulators, PWM modulators and synchronous co-ordinate transformations. It uses the model of the system to predict behavior of output voltages for each sampling instant. A minimization function is then used to select the switching state for the next inverter switching instant, while limiting the over-currents caused in the inverter. A switching frequency constraint is proposed to reduce the switching losses of the inverter. The proposed control strategy is verified through simulations and is experimentally validated on a prototype of microgrid system built in the laboratory.
Motion Compatibility for Indoor Localization

Indoor localization — a device’s ability to determine its location within an extended indoor environment — is a fundamental enabling capability for mobile context-aware applications. Many proposed applications assume localization information from GPS, or from WiFi access points. However, GPS fails indoors and in urban canyons, and current WiFibased methods require an expensive, and manually intensive, mapping, calibration, and configuration process performed by skilled technicians to bring the system online for end users. We describe a method that estimates indoor location with respect to a prior map consisting of a set of 2D floorplans linked through horizontal and vertical adjacencies. Our main contribution is the notion of “path compatibility,” in which the sequential output of a classifier of inertial data producing low-level motion estimates (standing still, walking straight, going upstairs, turning left etc.) is examined for agreement with the prior map. Path compatibility is encoded in an HMMbased matching model, from which the method recovers the user’s location trajectory from the low-level motion estimates. To recognize user motions, we present a motion labeling algorithm, extracting fine-grained user motions from sensor data of handheld mobile devices. We propose “feature templates,” which allows the motion classifier to learn the optimal window size for a specific combination of a motion and a sensor feature function. We show that, using only proprioceptive data of the quality typically available on a modern smartphone, our motion labeling algorithm classifies user motions with 94.5% accuracy, and our trajectory matching algorithm can recover the user’s location to within 5 meters on average after one minute of movements from an unknown starting location. Prior information, such as a known starting floor, further decreases the time required to obtain precise location estimate.
A Novel Task-Specific Upper-Extremity Rehabilitation System with Interactive Game-Based Interface for Stroke Patients

We present a novel task-specific upper-extremity rehabilitation system that uses an instrumented cup and an interactive gaming environment to promote patient engagement during repetitive rehabilitative exercise. The designed system tracks the movement of the cup as a stroke patient uses her forearm to perform a complex goal-oriented and task-specific activity, namely, grasping, lifting, and tilting the cup to drink from it. A force sensitive resistive sensor is mounted on the cup to constantly monitor the grasp force and the cup is endowed with rich features allowing a webcam to track and estimate its location and pose with the aid of state-of-the-art machine learning algorithms. This bi-manual forearm rehabilitation system is designed to enable stroke patients to perform an activity of daily living, namely, grasping, lifting, and tilting, repetitively, at their home as an exercise, so that they can relearn the motions of arm, wrist, and hand related to drinking.
PsRPs26, a 40S Ribosomal Protein Subunit, Regulates the Growth and Pathogenicity of Puccinia striiformis f. sp. Tritici

Eukaryotic ribosomes are essential for proliferation, differentiation, and cell growth. RPs26 is a ribosomal subunit structural protein involved in the growth and development process. Little is known about the function of PsRPs26 in pathogenic fungi. In this study, we isolated the RPs26 gene, PsRPs26, from Puccinia striiformis f. sp. tritici (Pst). PsRPs26 contains a eukaryotic-specific Y62–K70 motif and is more than 90% identical with its ortholog gene in other fungi. PsRPs26 was found to be localized in both the nucleus and cytoplasm. Expression of PsRPs26 increased when wheat seedlings were inoculated with the Pst CYR31 isolate. Moreover, knockdown of PsRPs26 by a host-induced gene silencing system inhibited growth and limited urediospore production in Pst. Our discovery that PsRPs26 may contribute to the pathogenicity of Pst and open a new way in the pathogenic function of PsRPs26 in cereal rust fungi.
Cascading Application of all Optical Universal NAND Gate for Implementation of Basic Logic Gates

All optical logic gates has been a prime area of research in the past. Several notable attempts have been made and many designs have been proposed. One of the important aspects in the use of these logic gates is the feasibility of their cascaded operation which has been overlooked so far. In order to develop large scale integrated photonic circuits it is very much necessary to check whether the logic gates can operate satisfactorily when connected in cascade. This paper focuses on discussing the cascade connection applications of all optical NAND gate developed using microring resonators. Development of other optical logic gates using the universal NAND gate is proposed. Inputs applied have a fixed power of 0.25 W and the NAND gate used to develop other logic gates has output power level around 0.20 W and 0.05 W for high and low logic respectively. Logic operations such as AND, OR, XOR, XNOR are successfully demonstrated. The results obtained from cascade connection indicate the possibility of utilizing the NAND gate for construction of several combinational circuits which can be a very important step to develop large scale photonic integrated circuits.
Experimental Demonstration of EEG Signal Transmission Using VLC Deploying LabView

To diagnose the brain abnormalities and diseases such as comma, epilepsy and many more comprehensive studies of the brain is essential which is done by placing electrodes on the scalp of the brain and examining the electrical brain activity and this whole procedure is known as Electroencephalogram (EEG). Presently, the EEG in hospitals is done using wires, several electrodes which require time for preparation and in some cases involve surgery, however, the non-invasive EEG has the potential to examine the brain activity without any surgery. Additionally, the technology used in hospitals for EEG is RF-based which could interfere with electronics devices and might affect the health of the patient as due to Electromagnetic interference specifically in the areas where the use of mobile phones and RF is prohibited. Hence, keeping in view the challenges of RF based EEG in hospitals this research work focuses on the Optical transmission of EEG signals using LED/PD pair deploying Labview. Experiments were carried out in LOS condition using On-Off Keying Non-Return to Zero (OOK_NRZ) modulation scheme employing LED/PD pair. The experimental results show the improvement in SNR in comparison to available literature and show the accuracy and reliability of the proposed system.
Stackelberg Game for User Clustering and Power Allocation in Millimeter Wave-NOMA Systems

In this paper, the joint design of user clustering and power allocation is investigated in a downlink non-orthogonal multiple access-based millimeter wave (mm-wave-NOMA) system. To reduce the system overhead, hybrid precoding techniques are adopted at the base station by using the channel state information of cluster heads (CHs) only. In order to maximize the sum rate of the system, Stackelberg game-based optimization problems are formulated for two cases with different quality of some targets: Case 1 focuses on improving the data rates of CHs; and Case 2 aims to increase the rates of cluster members when the CHs’ data rates are caped. With the aid of coalitional game theory, a low complexity algorithm is proposed to dynamically allocate users into different clusters. Then, the optimal power allocation coefficients of the users in each cluster are obtained by the derived closed-form expressions. The properties of the proposed joint algorithms are analyzed in terms of Stackelberg equilibrium, the complexity, the convergence and the stability. The simulation results demonstrate that: 1) the proposed algorithms can significantly improve the sum rate and reduce the outage probability of the mm-wave-NOMA system and 2) the application of NOMA in mm-wave systems is capable of achieving promising gains over conventional orthogonal multiple access-based frameworks in both cases.
Design of Mapping Matrix for Arithmetic Bit-Interleaved Coded Modulation

Arithmetic bit-interleaved coded modulation (A-BICM) is a coded rate compatible modulation (RCM). It achieves progressive mapping to strike a trade-off between high signal-to-noise ratio (SNR) and low SNR. However, the weights for parity bits are random and not sufficient, which decreases the performance significantly. In order to address the problem, this paper proposes a novel weight mapping scheme for A-BICM. The proposed scheme aims to design the degree and the weight distribution in the mapping matrix to decrease the error probability of parity bits. Through analysis, we have found that the proposed scheme is beneficial to increase the minimum Euclidean distance of symbols. Through simulation, the proposed scheme is verified to increase the throughput of transmission and decrease the demapping complexity at the same time. Both analysis and simulation results show that the proposed scheme outperforms the original A-BICM.
Defragmentation Based Load Balancing Routing & Spectrum Assignment (DLBRSA) strategy for elastic optical networks

In this paper, we propose a defragmentation based load balancing technique for efficiently undertaking routing and spectrum assignment (RSA), and minimizing the fragmentation problem in elastic optical network (EON). In EON, after connections terminate, they leave behind spectrum gaps which con not be re-assigned. To counter this condition, in Defragmentation based Load Balancing Routing and Spectrum Assignment (DLBRSA), the existing connections are reconfigured. At the time of defragmentation, for route selection, among the available shortest routes, the route with the largest chunk of contiguous frequency slots (FSs) is chosen. DLBRSA has three advantages over the conventional Shortest Path RSA (SPRSA) technique: (1) defragmentation increases the network resource utilization, (2) load balancing on the basis of the largest available FS chunk along the route ensures uniform spectrum allocation, and (3) defragmentation ensures that the route length of the request is minimum. The performance of the DLBRSA is analyzed in reference to a benchmark, SPRSA strategy. The comparison is done on the metrics of bandwidth rejected, and the percentage of total network capacity allocated for the EON and NSFNET network topologies. These two parameters are evaluated under the two scenarios of increase in: network load (in Erlang) and number of FSs demand. Simulation results show lower bandwidth blocking probability of DLBRSA from 6.53% to 8.96% under the different load conditions. In addition, decrease of network resource utilization is observed, when we compare the proposed DLBRSA with SPRSA the existing strategy.
OCPDA: A novel approach towards detection of overloaded cloud providers in a federated environment

A federated cloud environment helps different cloud service providers (CSP) to improve their performances in terms of Quality of Service $(\mathrm {Q}\mathrm {o}\mathrm {S})$. For a federation to deliver its best, the workloads on its member CSPs should be balanced. Overloading a CSP leads to degradation in performance which is absolutely undesirable. To balance the workloads among the member CSPs of a federation, detection of the overloaded CSPs is important. The proposed model, Overloaded Cloud Provider Detection Algorithm(OCPDA), uses Multiple Linear Regression (MLR) based technique to estimate the loads of CSPs. Extensive simulation has been conducted which shows that OCPDA successfully detects overloaded CSPs in a federation.
Segmentation of ultrasound videos for cardiac cavities detection

This work aims at detecting cardiac cavities in 2D echocardiographic sequences. In this respect, we propose to extend to the case of moving images, a competitive fully automatic intraframe segmentation method. The latter handles the spatial information by characterizing the texture in each image and, involves the watershed algorithm using an optimal texture gradient image and a suitable seeds image. The novelty of our work consists in accounting for the dynamic evolution according to two strategies. The first one consists in projecting and refining the boundaries from a frame to the next one. The second strategy acts on the inputs of the watershed algorithm. Experiments are carried out on apical 2D-echocardiographic sequences and, they show the benefit that can be drawn from the proposed strategies in terms of both objective accuracy metrics and computational complexity.
Solar Charge Controller for Lithium-Ion Battery

Battery charging technology has been developed over the past several years and storage of electrical energy has become a necessity, there is vast demand for compact and portable battery charger for various applications. The lithium ion batteries are finding considerable usage in both primary and secondary applications. The objective of this work is to design a low cost, versatile, efficient and compact solar powered lithium ion battery charger. The proposed battery charger circuit has features like over voltage, over charge, short circuit, reverse polarity protection for extended battery life and uses constant current and constant voltage methods of charging. P&O MPPT algorithm is used to extract maximum power from the solar panel. And buck converter is designed and built to function as a MPPT charge controller.