Miracle-dz/UniFAR-FTUs huggingface dataset:
RASS-SDSS galaxy cluster survey. VI. The dependence of the cluster SFR on the cluster global properties

We aim at quantifying the relationships between star formation in cluster galaxies and global cluster properties. Using a subsample of 79 nearby clusters from the RASS-SDSS galaxy cluster catalogue of Popesso et al. (2005, A&A, 433, 431), we perform a regression analysis between the cluster integrated star formation rate (ΣSFR), the cluster total stellar mass (M * ), the fractions of star forming (f SF ) and blue (f b ) galaxies and other cluster global properties, namely its richness (N gal , i.e. the total number of cluster members within the cluster virial radius, corrected for incompleteness), velocity dispersion (σ o ), virial mass (M 200 ), and X-ray luminosity (L X ). All cluster global quantities are corrected for projection effects before the analysis. Galaxy SFRs and stellar masses are taken from the catalog of Brinchmann et al. (2004), which is based on SDSS spectra. We only consider galaxies with M, ≤ -20.25 in our analysis, and exclude AGNs. We find that both ΣSFR and M * are correlated with all the cluster global quantities. A partial correlation analysis shows that all the correlations are induced by the fundamental one between ΣSFR and N gal , hence there is no evidence that the cluster properties affect the mean SFR or M * per galaxy. The relations between ΣSFR and M * , on the one side, and both N gal and M 200 , on the other side, are linear, i.e. we see no evidence that different clusters have different SFR or different M * per galaxy and per unit mass. The fraction f SF does not depend on any cluster property considered, while f b does depend on L X . We note that a significant fraction of star-forming cluster galaxies are red (∼25% of the whole cluster galaxy population). We conclude that the global cluster properties are unable to affect the SF properties of cluster galaxies, but the presence of the X-ray luminous intra-cluster medium can affect their colors, perhaps through the ram-pressure stripping mechanism.
Large-Scale Power Base’s Impact on Low Frequency Oscillation Characteristic in UHVAC Power Transmission System

The dynamic stability of ultra-high voltage alternating current (UHVAC) transmission system with large-scale power bases has not been clearly discussed before. This paper studies the low frequency oscillation (LFO) characteristic of UHVAC system through complex torque coefficient method (CTCM) and finds out the reasons why UHVAC system is more vulnerable to LFO problem. It is pointed out that the bulk capacity and long distance make UHVAC system’s damping torque coefficient much smaller than conventional system’s, which lets UHVAC system’s operation point closer to the safety edge and decreases system’s dynamic stability. The comparisons between UHVAC system and conventional system in practical multi-machine systems further validate the theoretical analysis. Finally, the suggestions for UHVAC systems are given based on the analysis in the view of dynamic stability.
Texture Based Approach for Image Watermarking

Internet is the most popular thing for human now days. There space unit varied problems and challenges in information security in net system just like the genuineness of content or matter is crucial issue for resolution the matter of repeating, modifying, associate in Nursing distributing the intellectual properties in an nonlocal method. With the ascension of net the varied digital ways has been projected to guard the multimedia system data from the non-authorized accesses use and alter. Among all the projected ways the watermarking technique is that the commonest technique for safeguarding the multimedia system information for unauthorized access. a completely unique methodology is projected. Secure information extraction from transform based image watermarking techniques is extracted in this proposed work. Signal process attacks and Geometric attacks are used to examine the information which is extracted from primary image. In this proposed work the watermark images are secured by testing them against unauthorized detection techniques. To make this work secure a correlation based detector is applied. Results show that the watermarked image is secure and robust against several attacks which includes all kind of unauthorized Detection attacks, also Signal Processing attacks and Geometric attacks as well.
Machine Learning-based Fall Detection in Geriatric Healthcare Systems

Intelligent IoT-based ambient assisted living systems (AALS) have been a major research focus area in recent times. According to the studies conducted by the Govt. of India, elderly population in India has reached 8.3% of the total population [40]. Per the National Program for Health Care of the Elderly (NPHCE), the elderly population in India has tripled over the last 50 years, and is projected to increase to 33.32 million by 2021 and 300.96 million by 2051 [41]. Application of machine learning in AALS, such as fall detection, therefore, has the potential to have huge public impact. In this paper, we propose a fall detection system that takes into account not only various wearable sensor node parameter readings for a subject, but also his biological and physiological profile. The profile is used to determine a fall risk category for the subject. We performed machine learning experiments using public datasets for fall detection which included wearable sensor node readings. The algorithms were then retrained by feeding in the risk categorization of the subject, and results from this analyses are presented. The objective of the experiments was to find out the impact of a subject's risk categorization on the accuracy of fall detection. The algorithms presented here form part of a comprehensive geriatric healthcare system under development, which comprises wearable sensor nodes, coordinator nodes, an indoor localization framework and cloud-hosted application servers. A brief overview of the system capabilities is also presented.
SAR Target Recognition with Deep Learning

The automated detection and classification of objects in imagery is an important topic for many applications in remote sensing. These can include the counting of cars and ships and the tracking of military vehicles for the defense and intelligence industry. Synthetic aperture radar (SAR) provides day/night and all-weather imaging capabilities. SAR is a powerful data source for Deep Learning (DL) algorithms to provide automatic target recognition (ATR) capabilities. DL classification was shown to be extremely effective on multi-spectral satellite imagery during the IARPA Functional Map of the World (fMoW). In our work we look to extend these techniques to SAR. We start by applying ResNet-18 to the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The MSTAR program, sponsored by DARPA and AFRL, consists of SAR collections of military style targets using an aerial X-band radar with one-foot resolution. We achieved an overall classification accuracy of 99% on 10 different classes of targets, confirming previously published results. We then extend this classifier to investigate an emerging target and the effects of limited training data on system performance.
Recursive Digital Filter Based Control for Power Quality Improvement of Grid Tied Solar PV System

The proposed work deals with the implementation of a control approach for an active power transfer between solar photovoltaic (PV) array and grid/ load alongwith power quality improvement by eliminating harmonics and compensating reactive power required by the load in the distribution system. The PV array is directly connected across the DC link of a voltage source converter (VSC). The power is transferred to the grid/ load through VSC, which performs the function of distribution static compensator (DSTATCOM) by injecting compensating currents in the proposed system. The PV array is operated at maximum power by employing the usage of perturb and observe (P&O) maximum power point tracking (MPPT) approach in order to extract the available maximum power from the solar PV array. A feed-forward term for the solar PV array power, is incorporated for the enhancement of dynamic response. The prominence of the control algorithm lies in the efficient switching of VSC, by generating reference grid currents, which are obtained through indirect current control technique. The recursive digital filter is utilized for processing the load currents and extracting the active power component of the load currents. Therefore, the active power component of load currents obtained, are used for generating the reference grid currents in the proposed system. A prototype of the system is developed in the laboratory and its performance is studied for varying loads, changing solar insolation, and voltage swell and voltage sag conditions.
Comparison of Various SLAM Systems for Mobile Robot in an Indoor Environment

This article presents a comparative analysis of a mobile robot trajectories computed by various ROS-based SLAM systems. For this reason we developed a prototype of a mobile robot with common sensors: 2D lidar, a monocular and ZED stereo cameras. Then we conducted experiments in a typical office environment and collected data from all sensors, running all tested SLAM systems based on the acquired dataset. We studied the following SLAM systems: (a) 2D lidar-based: GMapping, Hector SLAM, Cartographer; (b) monocular camera-based: Large Scale Direct monocular SLAM (LSD SLAM), ORB SLAM, Direct Sparse Odometry (DSO); and (c) stereo camera-based: ZEDfu, Real-Time Appearance-Based Mapping (RTAB map), ORB SLAM, Stereo Parallel Tracking and Mapping (S-PTAM). Since all SLAM methods were tested on the same dataset we compared results for different SLAM systems with appropriate metrics, demonstrating encouraging results for lidar-based Cartographer SLAM, Monocular ORB SLAM and Stereo RTAB Map methods.
Genomic Analysis of a Nutrient Response in Arabidopsis Reveals Diverse Expression Patterns and Novel Metabolic and Potential Regulatory Genes Induced by Nitrate

Microarray and RNA gel blot analyses were performed to identify Arabidopsis genes that responded to nitrate at both low (250 μM) and high (5 to 10 mM) nitrate concentrations. Genes involved directly or indirectly with nitrite reduction were the most highly induced by nitrate. Most of the known nitrate-regulated genes (including those encoding nitrate reductase, the nitrate transporter NRT1, and glutamate synthase) appeared in the 40 most strongly nitrate-induced genes/clones on at least one of the microarrays of the 5524 genes/clones investigated. Novel nitrate-induced genes were also found, including those encoding (1) possible regulatory proteins, including an MYB transcription factor, a calcium antiporter, and putative protein kinases; (2) metabolic enzymes, including transaldolase and transketolase of the nonoxidative pentose pathway, malate dehydrogenase, asparagine synthetase, and histidine decarboxylase; and (3) proteins with unknown functions, including nonsymbiotic hemoglobin, a senescence-associated protein, and two methyltransferases. The primary pattern of induction observed for many of these genes was a transient increase in mRNA at low nitrate concentrations and a sustained increase when treated with high nitrate concentrations. Other patterns of induction observed included transient inductions after both low and high nitrate treatments and sustained or increasing amounts of mRNA after either treatment. Two genes, AMT1;1 encoding an ammonium transporter and ANR1 encoding a MADS-box factor, were repressed by nitrate. These findings indicate that nitrate induces not just one but many diverse responses at the mRNA level in Arabidopsis.
A T-S fuzzy ranging model used in wireless sensor network

Wireless Sensor node location is very important for communication in Wireless Sensor Network (WSN). The accuracy of ranging significantly affects the accuracy of localization of the node. However, WSN nodes are often randomly fixed in an unknown environment. Traditional ranging model depend on the environment parameter too much. Considering the effect of these factors, this paper presents an adaptive Fuzzy TS[1] ranging model (FUZZY-TS). The simulation experiment indicates that this method is feasible and effective for improving ranging accuracy and will help to improve the accuracy of localization of node.
NPClu : An Approach for Clustering Non-point Objects

The vast majority of clustering algorithms and approaches deal with sets of objects, where objects are points in the multidimensional Euclidean space, each occupying zero hyper volume. There is a wealth of application domains that produce data sets in which the objects occupy hyperspace. Such application domains include spatiotemporal databases, medical applications etc. It is clear that clustering such data sets cannot be achieved by existing algorithms. In this paper we propose NPClu an approach for clustering sets of objects that are spatially extended. We experimentally evaluated the performance of our approach to show
A Comparison of Switching Techniques of DC-DC Converter for Improved Electric Vehicle Efficiency

The choice of an efficient dc-dc converter stage plays an important role in its designing, with proper selection of pulse width modulation (PWM) technique. In this paper ac-dc and dc-dc stages are implemented with PWM and asymmetrical pulse width modulation (APWM) technique respectively. The ac-dc stage vanishes the use of front end rectifier w hereas, in second stage there is no use of snubber circuit which helps dc-dc converter stage to decide zero voltage switching (ZVS) during turn-on process and zero current switching (ZCS) during turn-off process of electric vehicle (EV) battery charging. These switching is viable by using APWM and efficiency o f t he c onverter is also increase for 1.2 KW system in comparison with previously proposed PWM technique.
Variation in the Venom of Parasitic Wasps, Drift, or Selection? Insights From a Multivariate QST Analysis

Differentiation of traits among populations can evolve by drift when gene flow is low relative to drift or selection when there are different local optima in each population (heterogeneous selection), whereas homogeneous selection tends to prevent evolution of such a differentiation. Analyses of geographical variations in venom composition have been done in several taxa such as wasps, spiders, scorpions, cone snails and snakes, but surprisingly never in parasitoid wasps, although their venom should constrain their ability to succeed on locally available hosts. Such a study is now facilitated by the development of an accurate method (quantitative digital analysis) that allows analyzing the quantitative variation of large sets of proteins from several individuals. This method was used here to analyse the venom-based differentiation of four samples of Leptopilina boulardi and five samples of L. heterotoma from populations along a 300 km long south-north gradient in the Rhone-Saone valley (South-East of France). A major result is that the composition of the venom allows to differentiate the populations studied even when separated by few kilometers. We further analyzed these differentiations on the populations (reared under similar conditions to exclude environmental variance) with a QST analysis which compared the variance of a quantitative trait (Q) among the subpopulations (S) to the total variance (T). We also used random forest clustering analyses to detect the venom components the most likely to be adapted locally. The signature of the natural selection was strong for L. heterotoma and L. boulardi. For the latter, the comparison with the differentiation observed at some neutral markers revealed that differentiation was partly due to some local adaptation. The combination of methods used here appears to be a powerful framework for population proteomics and for the study of eco-evolutionary feedbacks between proteomic level and population and ecosystem levels. This is of interest not only for studying field evolution at an intermediate level between the genome and phenotypes, or for understanding the role of evolution in chemical ecology, but also for more applied issues in biological control.
Surgical gesture recognition with time delay neural network based on kinematic data

Automatic gesture recognition during surgical procedures is an enabling technology for improving advanced assistance features in surgical robotic systems (SRSs). Examples of such advanced features are user-specific feedback during execution of complex actions, prompt detection of safety-critical situations and autonomous execution of procedure sub-steps. Video data are available for all minimally invasive surgical procedures, but SRS could also provide accurate movements measurements based on kinematic data. Kinematic data provide low dimensional features for gesture recognition that would enable on-line processing during data acquisition. Therefore, we propose a Time Delay Neural Network (TDNN) applied to kinematic data for introducing temporal modelling in gesture recognition. Weevaluate accuracy and precision of the proposed method on public benchmark dataset for surgical gesture recognition (JIGSAWS). To evaluate the generalization capability of the proposed method, we acquired a new dataset introduing a different training exercise executed in virtual environment.The dataset is publicly available to enable other methods to be tested on it. The obtained results are comparable with other methods available in literature keeping also computational performance compatible with on-line processing during surgical procedure. The proposed method and the novel dataset are key-components in the development of future autonomous SRSs with advanced situation awareness capabilities.
2D face liveness detection: An overview

Face recognition based on 2D images is a widely used biometric approach. This is mainly due to the simplicity and high usability of this approach. Nonetheless, those solutions are vulnerable to spoof attacks made by non-real faces. In order to identify malicious attacks on such biometric systems, 2D face liveness detection approaches are developed. In this work, face liveness detection approaches are categorized based on the type of liveness indicator used. This categorization helps understanding different spoof attacks scenarios and their relation to the developed solutions. A review of the latest works dealing with face liveness detection works is presented. A discussion is made to link the state of the art solutions with the presented categorization along with the available and possible future datasets. All that aim to provide a clear path for the future development of innovative face liveness detection solutions.
Detection of Hurriedly Created Abnormal Profiles in Recommender Systems

Recommender systems try to predict the preferences of users for specific items. These systems suffer from profile injection attacks, where the attackers have some prior knowledge of the system ratings and their goal is to promote or demote a particular item introducing abnormal (anomalous) ratings. The detection of both cases is a challenging problem. In this paper, we propose a framework to spot anomalous rating profiles (outliers), where the outliers hurriedly create a profile that injects into the system either random ratings or specific ratings, without any prior knowledge of the existing ratings. The proposed detection method is based on the unpredictable behavior of the outliers in a validation set, on the user-item rating matrix and on the similarity between users. The proposed system is totally unsupervised, and in the last step it uses the k-means clustering method automatically spotting the spurious profiles. For the cases where labeling sample data is available, a random forest classifier is trained to show how supervised methods outperforms unsupervised ones. Experimental results on the MovieLens 100k and the MovieLens 1M datasets demonstrate the high performance of the proposed schemata.
Feature Extraction and Classification Based on Spatial-Spectral ConvLSTM Neural Network for Hyperspectral Images

In recent years, deep learning has presented a great advance in hyperspectral image (HSI) classification. Particularly, Long Short-Term Memory (LSTM), as a special deep learning structure, has shown great ability in modeling long-term dependencies in the time dimension of video or the spectral dimension of HSIs. However, the loss of spatial information makes it quite difficult to obtain the better performance. In order to address this problem, two novel deep models are proposed to extract more discriminative spatial-spectral features by exploiting the Convolutional LSTM (ConvLSTM) for the first time. By taking the data patch in a local sliding window as the input of each memory cell band by band, the 2-D extended architecture of LSTM is considered for building the spatial-spectral ConvLSTM 2-D Neural Network (SSCL2DNN) to model long-range dependencies in the spectral domain. To take advantage of spatial and spectral information more effectively for extracting a more discriminative spatial-spectral feature representation, the spatial-spectral ConvLSTM 3-D Neural Network (SSCL3DNN) is further proposed by extending LSTM to 3-D version. The experiments, conducted on three commonly used HSI data sets, demonstrate that the proposed deep models have certain competitive advantages and can provide better classification performance than other state-of-the-art approaches.
An Empir ical Investigation of a Systematic ObjectOr iented Inspection Technique

Software inspection is a well-recognised defect detection technique, but recent research has highlighted that its level of performance on object-oriented code may be suffering due to the highly delocalised nature of the software. This paper presents the results of an empirical investigation, which compared the traditional ad-hoc inspection approach with a systematic, abstraction-driven inspection technique, designed to help with the problem of delocalisation. The results showed no significant difference in relation to defect detection performance between the use of the ad-hoc and systematic inspection techniques. However, the systematic technique did seem to encourage a deeper understanding of the code being inspected and may also help discover different defects from the ad-hoc approach. Inspectors also seemed to appreciate the rigour that the systematic technique imposed. This paper suggests that a systematic, abstraction-driven reading strategy offers some potential but that issues regarding the difference between the static and dynamic views of object-oriented code, as well supporting the efficient construction of abstractions needs to be addressed.
A Methodology for the Analysis of 802.11a Links in Industrial Environments

Wireless solutions are becoming more and more attractive for the communication systems of manufacturing plants. If the advantages of wireless are clear and have been extensively discussed, also the drawbacks need to be considered and, among them, the impact of an industrial environment on error rate and throughput of the wireless links. These performance aspects get even more critical when developing wireless solutions for safety-critical industrial communications and using off-the-shelf components. The first step in evaluating these solutions is to characterize the channel behavior of the selected wireless platform for a set of real-life scenarios representative of different operating conditions. We present a methodology and software tool aimed at analyzing WLAN channel behaviors and we apply it to evaluate the use of 802.11a consumer chipsets for a typical safety critical industrial plant application.