Miracle-dz/UniFAR-FTUs huggingface dataset:
Miniaturization and Gain Enhancement of Microstrip Patch Antenna Using Defected Ground with EBG

—In this research work, we have proposed an inset feed rectangular microstrip patch antenna operating at 2.45 GHz in the Industrial, Scientific and Medical (ISM) band for wireless communication. This conventional design of antenna has been modified with defected ground (DG) plane. When this is optimized back to 2.45 GHz, a miniaturization of 37.9 % of patch size has been achieved along with the enhancement in radiation performance. This design is then modified and loaded with mushroom-type Electronic Band-Gap (EBG) structure on the top of dielectric FR-4 substrates. This creates a band-gap region that suppresses the surface waves considerably, thus improves the overall performance and functionality of the proposed antenna. Radiation characteristics such as S-parameters, directivity, gain, efficiency, bandwidth, VSWR and Z11 of microstrip antenna have been performed and compared. The simulated results show that the antenna gain has been increased throughout the entire bandgap of EBG operating in 2.25 to 4.25 GHz. Moreover, rest of radiation performances remain highly preserved.
Social Network Site Skills for Communication Professionals: Conceptualization, Operationalization, and an Empirical Investigation

Background: The primary goal of the current study was to conceptualize and operationalize social networking sites (SNS) skills for communication professionals. Literature review: The development of the definition and measurement instrument began with a critical look at internet skills literature. We propose a three-fold instrument for measuring SNS skills: communication, content creation, and strategic SNS skills. Research questions: 1. Which skills do communication professionals need to effectively use SNS? 2. Which SNS use-related factors contribute to the level of SNS skills? Method: We ensured that the proposed measures reflected typical SNS uses for communication professionals. All items in the instrument used a scale that contained statements about SNS skills, with answer formats that ranged from “Not at all true of me” to “Very true of me. ” After the development of a first survey instrument, we conducted cognitive interviews that resulted in some amendments to the proposed skill items to improve clarity. The SNS skill scales and their characteristics were then tested in a pilot study. Results: The resulting instrument was refined in the final study among 203 German communication professionals. Conclusion: The findings suggest a recommendation for the use of a 15-item survey measure for communication, content creation, and strategic SNS skills in professional contexts. Our findings furthermore suggest that communication and content creation SNS skills are better developed than strategic SNS skills. Finally, several SNS use variables were identified as predictors of SNS skills.
Online social networking services and spam detection approaches in opinion mining - a review

Social network platforms have gained enormous growth in present days. Users are more attracted towards social network sites such as Twitter, Facebook, micro blogs and YouTube to express their opinion and views and share the knowledge. With the tremendous increase in technological growth and with the diversity of products, online e-commerce servicing sites are becoming competitor's for each other to increase their proliferation in business offering to post positive product spam reviews. Users generally express their opinion based on different sentiment orientations, ratings and the features of the product. This tends to create ambiguity at the customers end in arriving at a decision based on the criticism, building fake opinion on the products. Opinion spam detection provides scope in building opinion from the reviews of various networking sites. A novel framework using meta-heuristic and k-means clustering approach is proposed for identifying opinion spam detection using flower pollination, grey wolf and moth flame. Amazon automotive product dataset is chosen for analysis and it is observed that grey wolf algorithm performs better than flower pollination and moth flame optimisation algorithms in terms of improved convergence speed, mean, standard deviation, variance and elapsed run time.
Smooth point-to-point trajectory planning for robot manipulators by using radial basis functions

SUMMARY The paper introduces the use of radial basis functions (RBFs) to generate smooth point-to-point joint trajectories for robot manipulators. First, Gaussian RBF interpolation is introduced taking into account boundary conditions. Then, the proposed approach is compared with classical planning techniques based on polynomial and trigonometric models. Also, the trajectory planning problem involving via-points is solved using the proposed RBF interpolation technique. The obtained trajectories are then compared with those synthesized using algebraic and trigonometric splines. Finally, the proposed method is tested for the six-joint PUMA 560 robot in two cases (minimum time and minimum time-jerk) and results are compared with those of other planning techniques. Numerical results demonstrate the advantage of the proposed technique, offering an effective solution to generate trajectories with short execution time and smooth profile.
Artificial neural network based identification of Bharatanatyam Mudra images using eigen values

This paper presents a three stage methodology for identification of Mudra images of Bharatanatyam dance. In the first stage, acquired images of Bharatanatyam Mudras are preprocessed to obtain contours of Mudras using canny edge detector. In the second stage, eigen values are extracted as features. In the third stage, an artificial neural network is used for recognition and classification of unknown Mudras. The work finds application in e-learning of Bharatanatyam dance in particular and dances in general and automation of commentary during concerts.
Efficient resource management in the cloud: From simulation to experimental validation using a low‐cost Raspberry Pi testbed

Within the context of cloud computing, efficient resource management is of great importance as it can result in higher scalability and significant energy and cost reductions over time. Because of the high complexity and costs of cloud environments, however, newly developed resource allocation strategies are often only validated by means of simulations, for example, by using CloudSim or custom‐developed simulation tools.
Integrating Cooperative Learning into the Combined Blended Learning Design Model: Implications for Students' Intrinsic Motivation

Extensive research has been done on the implementation of cooperative learning (CL) in a face-to-face classroom. However, only a few studies could be found on the implementation of CL in a blended learning environment. The implementation of CL in such an environment is a challenging goal for facilitators. It requires a commitment to change and the willingness to take risks, it takes time and requires planning. This article reports on research done to develop a holistic blended learning (BL) design model. The development of the model was based on a synthesis of a number of pedagogical models, which focus specifically on the integration of technology. The model was then used as a tool to design a module with the integration of CL in a BL environment. It was evident from the findings of the qualitative data that the students' intrinsic motivation (IM) improved after implementing the CL–BL module design.
Migrating mission-critical applications in federated cloud: a case study

Although virtualisation, elasticity and resource sharing enable new levels of flexibility, convenience and economy benefits, they also add new challenges and more areas for potential failures and security vulnerabilities, which represent the major concerns for companies and public organisations that want to shift their business and mission critical applications and sensitive data to the cloud. This paper discusses some technical issues, which must be addressed to migrate mission-critical applications to public clouds. Moreover, by using a case study, an approach to broker cloud infrastructure needs to satisfy more restrictive critical requirements is presented.
Design and Comparative Study of Advanced Adaptive Control Schemes for Position Control of Electronic Throttle Valve

This paper investigates the performance of two different adaptive control schemes for controlling the angular position of an electronic throttle (ET) plate. The adaptive backstepping controller and adaptive sliding mode backstepping controller are the controllers under consideration. The control design based on these adaptive controllers is firstly addressed and the stability analysis of each controller has been presented and the convergence of both position and estimation errors for both controllers have been proved. A comparison study of the performance of both controllers has been conducted in terms of system transient characteristics and the behavior of their associated adaptive gain. The simulation has been implemented within the environment of the MATLAB package.
Gravitational microlensing of active galactic nuclei dusty tori

We investigated the gravitational microlensing of active galactic nuclei dusty tori in the case of lensed quasars in the infrared domain. The dusty torus is modelled as a clumpy two-phase medium. To obtain spectral energy distributions and images of tori at different wavelengths, we used the 3D Monte Carlo radiative transfer code skirt. A ray-shooting technique has been used to calculate microlensing magnification maps. We simulated microlensing by the stars in the lens galaxy for different configurations of the lensed system and different values of the torus parameters, in order to estimate (a) amplitudes and time-scales of high magnification events and (b) the influence of geometrical and physical properties of dusty tori on light curves in the infrared domain. We found that, despite their large size, dusty tori could be significantly affected by microlensing in some cases, especially in the near-infrared domain (rest frame). The very long time-scales of such events, in the range from several decades to hundreds of years, are limiting the practical use of this method to study the properties of dusty tori. However, our results indicate that, when studying flux ratios between the images in different wavebands of lensed quasars, one should not disregard the possibility that the near- and mid-infrared flux ratios could be under the influence of microlensing.
Dictionary-induced least squares framework for multi-view dimensionality reduction with multi-manifold embeddings

This study proposes a novel dimensionality reduction (DR) method for multi-view datasets. The principal component analysis (PCA) idea of minimising least squares reconstruction errors is extended to consider both data distribution and penalty weights called dictionary to recover outliers free global structures from missing and noisy data points. In this way, PCA is viewed as a special instance of the authors' proposed dictionary induced least squares framework (DLS). Furthermore, to appropriately handle multi-view DR, we combine the DLS with multiple manifold embeddings (DLSME). Therefore it can obtain lower projections while maintaining a balance between preserving global structures with DLS and local structures with multi-manifold embeddings. Extensive experiments on object and face recognition datasets verify that the DLS achieves better classification results with lower dimensional projections than PCA. Also, on many multi-view datasets of visual recognition and web image annotation, the DLSME method demonstrates more effectiveness than Graph-Laplacian PCA (gLPCA), robust PCA-optimal mean, canonical correlation analysis (CCA), bilinear models (BLM), neighbourhood preserving embedding, locality preserving projections, and locality sensitive discriminant analysis.
Reappearance of Command-Following Is Associated With the Recovery of Language and Internal-Awareness Networks: A Longitudinal Multiple-Case Report

The recovery of patients with disorders of consciousness is a real challenge, especially at the chronic stage. After a severe brain injury, patients can regain some slight signs of consciousness, while not being able to functionally communicate. This entity is called the minimally conscious state (MCS), which has been divided into MCS- and MCS+, respectively based on the absence or presence of language-related signs of consciousness. In this series of cases we aimed to describe retrospectively the longitudinal recovery of specific language-related behaviors using neuroimaging measurement in severely brain-injured patients. Among 209 chronic MCS patients admitted to our center from 2008 to 2018, 19 were assessed at two time points by means of behavioral and neuroimaging assessments. Three of them met our inclusion criteria and were diagnosed as MCS- during their first stay and had recovered command-following when they were reassessed (i.e., MCS+). As compared to their first assessments, when the three patients were in a MCS+, they showed less hypometabolism and/or higher gray matter volume in brain regions such as the precuneus and thalamus, as well as the left caudate and temporal/angular cortices known to be involved in various aspects of semantics. According to these preliminary results, the reappearance of language-related behaviors was concomitant with the recovery of metabolism and gray matter in neural regions that have been associated with self-consciousness and language processing. Prospective studies should be conducted to deepen our understanding of the neural correlates of the recovery of language-related behaviors in chronic MCS.
Key Distribution Protocol for Industrial Internet of Things Without Implicit Certificates

The deployment of the Internet of Things (IoT) in industry, called the Industrial IoT (IIoT), is supporting the introduction of very desirable improvements, such as increasing production flexibility, self-organization, and real-time and quick response to events. However, security and privacy challenges are still to be well addressed. The IIoT requires different properties to achieve secure and reliable systems and these requirements create extra challenges considering the limited processing and communication power available to IIoT field devices. In this research article, we present a key distribution protocol for IIoT that is computationally and communicationally lightweight (requires a single message exchange) and handles node addition and revocation, as well as fast rekeying. The scheme can also resist the consequences of node capture attacks (we assume that captured nodes can be detected by the gateway and previous works have shown this assumption to be acceptable in practice), server impersonation attacks and provides forward/backward secrecy. We show formally the correctness of our protocol and evaluate its energy consumption under realistic scenarios using a real embedded platform compared to previous state-of-the-art key-exchange protocols, to show our protocol reliability for IIoT.
A general framework for task-constrained motion planning with moving obstacles

SUMMARY Consider the practically relevant situation in which a robotic system is assigned a task to be executed in an environment that contains moving obstacles. Generating collision-free motions that allow the robot to execute the task while complying with its control input limitations is a challenging problem, whose solution must be sought in the robot state space extended with time. We describe a general planning framework which can be tailored to robots described by either kinematic or dynamic models. The main component is a control-based scheme for producing configuration space subtrajectories along which the task constraint is continuously satisfied. The geometric motion and time history along each subtrajectory are generated separately in order to guarantee feasibility of the latter and at the same time make the scheme intrinsically more flexible. A randomized algorithm then explores the search space by repeatedly invoking the motion generation scheme and checking the produced subtrajectories for collisions. The proposed framework is shown to provide a probabilistically complete planner both in the kinematic and the dynamic case. Modified versions of the planners based on the exploration–exploitation approach are also devised to improve search efficiency or optimize a performance criterion along the solution. We present results in various scenarios involving non-holonomic mobile robots and fixed-based manipulators to show the performance of the planner.
Low-light image enhancement with strong light weakening and bright halo suppressing

Low-light enhancement methods suffer from the over-enhancement problem which could induce the loss of the important texture and make images look unnatural. Moreover, some low-light images contain strong light areas that must be weakened to improve the visual effect. In this study, an enhancement method with strong light weakening and bright halo suppressing is presented. Firstly, the bright channel prior is applied to the inverted image to weaken the strong light both in and around the strong light areas. Then, a dehazing-type of algorithm with the dark channel prior is employed via superpixel segmentation to enhance the low-light image. Finally, a revised non-local denoising method is proposed to further refine the enhanced image. Experimental results showed that the proposed method achieved better visual effects compared with other state-of-the-art methods. Besides, the quantitative evaluation showed that the authors' method outperforms the other methods both in the aspect of enhancement and denoising.
Runtime Power Management of Battery Electric Vehicles for Extended Range With Consideration of Driving Time

Installation of a large-capacity battery pack is a straightforward method to extend the range of battery electric vehicles (BEV or all-electric vehicles). However, at the same time, a large-capacity battery pack not only occupies a big space but also significantly increases the vehicle weight, which directly impacts the fuel economy and vehicle performance. This implies that increasing the battery capacity has an obvious limitation in extending the EV range. In this paper, we introduce a system-level framework to extend the range of BEV with the consideration of the vehicle dynamics, electric powertrain characteristics, road slopes, payload, and regenerative braking. This paper particularly takes into account driving time so that the resultant BEV power management does not impractically slow down the vehicle velocity. The BEV power management framework derives energy-aware velocity planning, i.e., a desirable instantaneous velocity at each distance step (or at each time instant). The major technical contributions of this paper compared with the previous work include: 1) practically applicable velocity planning for production BEVs from superb BEV power model fidelity; 2) new performance metrics to consider both driving energy and driving time: energy-delay product (EDP), energy-square-delay product, and energy-cubic-delay product; 3) heuristics to derive EDP-aware velocity planning; 4) comparative analysis of the velocity planning between BEV and internal combustion engine vehicles; and 5) analysis of the model fidelity impact on the energy-aware velocity planning. The proposed method results in up to a 46.2% improvement of the EDP compared with the least-energy constant velocity driving.
Why Have a Pet Amphibian? Insights From YouTube

The desire to own a pet amphibian is growing, and with it a growth in amphibian trade and in negative impacts on native populations, including disease transmission and invasive amphibian populations. We know very little about how or why people choose amphibians as pets, but amphibian owners share large numbers of videos on freely accessible platforms, such as YouTube. We aimed to use videos of captive amphibians to determine which species are kept, their life-history stage and the types of videos uploaded. We watched and categorized 1162 videos by video type, type of amphibian behavior and amphibian taxonomy (superfamily, family and species). We used data on the amphibian trade from the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), on conservation status from the International Union for Conservation of Nature (IUCN) red list, and on potential environmental impact from published Environmental Impact Classification of Alien Taxa (EICAT) records, to determine potential conflicts of owning pet amphibians. We recorded 173 captive species in 847 videos with a taxonomic overrepresentation of salamandroids and pipoids, and an underrepresentation of ranoids and plethodontoids. When compared to videos of wild amphibian species, videos of captive animals featured disproportionate amounts of adults feeding, being handled and moving. The videos watched had a smaller proportion of threatened amphibian species, but a higher proportion of invasive species, than would be expected by chance, with the proportion present in CITES appendices (18%) being non-significant. We suggest that such data can be used to profile potential pets for trade and attempt to avoid conflicts with threatened and highly impacting alien species.
Experimental Analysis on Weight  ${K}$ -Nearest Neighbor Indoor Fingerprint Positioning

Wi-Fi deployed inside a building can be used for positioning indoor users. A commonly used technology is weighted <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula>-nearest neighbor (WKNN) fingerprint which positions a user based on <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> nearest reference points measured beforehand. The challenge lies in how to configure the value of <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> to obtain the best positioning accuracy. In this paper, we propose a self-adaptive WKNN (SAWKNN) algorithm with a dynamic <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula>. By adjusting the value of <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> based on the signal strength, SAWKNN can obtain a better positioning accuracy than traditional WKNN. In particular, a significant percentage of the SAWKNN positioning makes use of a value <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math></inline-formula> = 1. The performance of the proposed algorithm has been evaluated in real-world experiments.
An RIP Condition for Exact Support Recovery With Covariance-Assisted Matching Pursuit

The covariance-assisted matching pursuit (CAMP) algorithm has recently been proposed for recovering sparse signals <inline-formula><tex-math notation="LaTeX">$\boldsymbol {f}$</tex-math></inline-formula> from noisy linear measurements based on <italic>a priori</italic> knowledge of the covariance and mean of the nonzero coefficients of <inline-formula><tex-math notation="LaTeX">$\boldsymbol {f}$</tex-math></inline-formula>. It utilizes the <italic>a priori</italic> knowledge by incorporating the Gauss–Markov theorem into the orthogonal matching pursuit (OMP) algorithm and has a significantly better reconstruction performance than OMP. This letter develops sufficient conditions of exact support recovery of any <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>-sparse signals <inline-formula><tex-math notation="LaTeX">$\boldsymbol {f}$</tex-math></inline-formula> via CAMP in <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> iterations, under the <inline-formula><tex-math notation="LaTeX">$\ell _2$</tex-math></inline-formula>-bounded and Gaussian noises. These sufficient conditions are based on the restricted isometry constant of the sensing matrix and minimum magnitude of the nonzero elements of <inline-formula><tex-math notation="LaTeX">$\boldsymbol {f}$</tex-math></inline-formula>, and are much better than the existing ones.