Miracle-dz/UniFAR-FTUs huggingface dataset:
Polarization-Reconfigurable Cylindrical Dielectric Resonator Antenna Excited by Dual Probe With Tunable Feed Network

A polarization-reconfigurable cylindrical dielectric resonator antenna (DRA) based on a tunable feed network is presented in this paper. $\textrm {HEM}_{11\delta } $ modes excited by two-port side-fed probes are adapted to design the proposed DRA. Polarization reconfigurable characteristic can be obtained by a tunable feed network that consists of Wilkinson equal power divider, parasitic elements used for phase shifters, and p-i-n diodes between them. By controlling the four pairs of p-i-n diode switches to active corresponding elements as effective phase shifters, two sources with equal amplitude and 0° or ±90° phase differences are obtained to generate linear-polarized (LP) or circular-polarized (CP) states, respectively. A fully functional prototype of the proposed antenna with the dimension of $0.55\lambda \times 0.55\lambda \times 0.14\lambda $ has been designed, fabricated, and tested. The results indicate that the proposed DRA can achieve the bandwidths of more than 30% for both impedance bandwidth and 3-dB axial ratio bandwidth at CP states, and two LP states with the impedances of 6.3% and 9.5%. Stable directional patterns and realized gains are obtained for all the states. These performances make the proposed DRA suitable for polarization diversity scenarios in communication systems.
Optimal Capacitor Placement in Distribution Network for Loss Reduction and Voltage Profile Improvement

This paper presents a method for optimal multi objective capacitor placement in the medium voltage distribution network. The purpose of the proposed algorithm is to determine the optimal sites and sizes for capacitor installation in distribution network for power loss reduction and voltage profile improvements. The search for the set of candidate solutions is performed using an evolution inspired, multi objective genetic algorithm, while the conflict among candidate solutions from Pareto front is solved using the fuzzy multi criteria decision making based on Bellman-Zadeh method. In order to demonstrate its relevance, the problem is tested on the model of a real distribution system, currently operated in Bosnia and Herzegovina. The results indicate that Distribution System Operator (DSO) can benefit from optimal capacitor placement in distribution network. The proposed algorithm was proved to have a practical relevance as it can be applied to realistic distribution system. The presented results are relevant to the DSO for planning purposes, for customers because it has potential to lower the power distribution cost and finally for regulators who are overseeing the network planning and long term management process.
Lessons Learned from Event Detection from Arabic Tweets: The Case of Jordan Flash Floods near Dead Sea

Event detection is essential for decision makers to understand the events surrounding their real world. Social media microblogging platforms play a significant role in our life. One of these platforms is Twitter, which has an extreme high exchange rate and accordingly has become a valuable and relevant source for many political and social events. Event detection from social media attracted the attention of researchers in different natural languages. Extracting and detecting events from Arabic tweets is still under investigation. In this paper, we have used the Python Natural Language Toolkit (NLTK) library to develop two classifiers for filtering and detecting extracted events from Arabic tweets. The first classifier filters the collected tweets using two passes. The first pass identifies the hashtags while the second pass does a shallow analysis on the tweets content. The second classifier analyzes the text extracted from the tweets. As a case study, we present the tragic events of the Jordan flash floods near the Dead Sea. The model successfully filtered all the collected tweets and picked the ones describing the incidents within that region. Analyzed data revealed important information to learn from this lesson for the future. The solution can be generalized and adapted to other problems.
Dendry: a procedural model for dendritic patterns

We introduce Dendry, a procedural function that generates dendritic patterns and is locally computable. The function is controlled by parameters such as the level of branching, the degree of local smoothing, random seeding and local disturbance parameters, and the range of the branching angles. It is also controlled by a global control function that defines the overall shape and can be used, for example, to initialize local minima. The algorithm returns the distance to a tree structure which is implicitly constructed on the fly, while requiring a small memory footprint. The evaluation can be performed in parallel for multiple points and scales linearly with the number of cores. We demonstrate an application of our model to the generation of terrain heighfields with consistent river networks. A quad core implementation of our algorithm takes about ten seconds for a 512 × 512 resolution grid on the CPU.
Fuzzy Tracking Control of a Standalone PV Pumping System based on an Induction Motor

This paper traits the modeling of a PV pumping system composed of an induction motor (IM) connected to a centrifugal pump and subject to variable solar irradiations. A Maximum Power Point Tracker based on Perturb and Observe (P&O) algorithm is applied for the objective of ameliorating the performance of the pumping system. A Takagi-Sugeno (TS) fuzzy controller, based on a reference tracking TS model, is implemented to the IM. First, A mathematical model in the synchronous d-q frame rotating is developed, that is later converted to a TS model with field-oriented control strategy. After that, basing on the $\mathrm{H}^{\infty}$ performance, a fuzzy state feedback controller is established to insure the reference trajectory tracking, provided by the TS reference model. The fuzzy observer is applied to estimate the rotor flux which is inaccessible. The control law developed is based on the minimization of the disturbances effect on the tracking error according to the $\mathrm{H}^{\infty}$ criterion. The control strategy is based on the Lyapunov theorem to guarantee the trajectory tracking performance. Thus, stability conditions, the controller and observer gains are formulated as linear matrix inequalities (LMI). At last, simulation is done to illustrate the tracking performance of the developed T-S tracking controller.
Neural Binary Representation Learning for Large-Scale Collaborative Filtering

Integrating hashing into collaborative filtering (CF) has been a promising solution to address the efficiency problem of large-scale recommender systems, i.e., hashing users and items into binary codes and then making recommendations in Hamming space. However, most of the existing hashing methods for CF solely focus on modeling the user-item similarity (a.k.a. preference) but omit the user–user and item–item similarities, which cannot well preserve the original geometry in the vector space. Another tough issue is how to effectively tackle the encoding loss from the continuous vector space to the discrete Hamming space. In this paper, we propose a neural binary representation (NBR) learning approach by combining hashing with a neural network for the large-scale CF tasks. Our NBR problem is formulated as a Hamming similarity loss plus two anchor smoothing terms, which jointly preserve the intrinsic user–item, user–user, and item–item similarities. To compensate the encoding loss introduced by hashing, the anchors for users and items are pre-learned using both user–item interactions and the side-information of users and items through an AutoEncoder model. In particular, to solve the NBR problem, we develop a computationally efficient algorithm, which learns the binary codes in a fast bit-by-bit way and achieves less quantization deviation than the conventional CF hashing schemes. The extensive experiments on three benchmarks validate the superiority of our NBR approach in comparison to the state-of-the-art CF hashing methods.
Towards Adaptive Multimedia System for Assisting Children with Arabic Learning Difficulties

Children with learning difficulties (LD) are increasing dramatically in the Arab world. Such children require quick intervention especially during the early childhood years. This paper presents a dynamic multimedia system for helping children with LD to overcome their learning problems. We introduce an approach to automatically convert modern standard Arabic children’s stories to the finest representative images that can efficiently illustrate the meaning of words. Specifically, first, we apply natural language processing techniques to analyze the text in stories and we extract keywords of all characters and events in each sentence. Second, we apply an image captioning process through a pre-trained deep learning model for all retrieved images from our multimedia database as well as the Google search engine. Third, using sentence similarities, most significant images are retrieved back by selecting top highest similarity values. The proposed system aims to better enhance understanding, communications, and thinking skills for children with LD in elementary schools and special education centers.
Enabling a Blockchain-Based IoT Edge

Within many industry verticals, the Internet of Things provides the means to collect and analyze crucial data for optimizing system performance. The technical evolution of IoT is shifting its computational capabilities away from centralized cloud platforms to a decentralized IoT edge. The IoT edge faces challenges in providing fully decentralized security mechanisms, as well as built-in guarantees for user-data privacy. In recent years, blockchain has been viewed as a potential fabric for a secure, decentralized IoT edge due to its inherent features of fault tolerance, transparency, and enforcement of service level agreements through smart contracts. Despite their virtues, blockchains have their fair share of challenges in being integrated into IoT. In this article, we discuss these challenges, as well as candidate solutions for integrating blockchains into the IoT edge.
Performance evaluation of dereferencing methods for estimating information flow in laminar connectivity models*

Multi-layered brain structures contain canonical microcircuits that specialize in region-specific function. Information flow across the layers is typically inferred using multivariate techniques that operate on local field potentials (LFPs). These methods (e.g., Granger Causality (GC)) are sensitive to the presence of a common reference that corrupts LFPs recorded with a multi-contact electrode and introduces spurious covariations. Using models of reference-noise corrupted signals with laminar interactions, we evaluated the efficacy of three dereferencing methods - bipolar subtraction, current source density (CSD), and common average referencing. We examined which method best recovered the underlying functional interactions between layers. Each dereferencing method introduced different types of error, often to alarming levels of false prediction. While CSD and bipolar subtraction methods performed best, they often predicted spurious connections, exhibited GC power in incorrect frequency bands, and/or missed salient relationships between layers. Though the confounds in this model may not be present in evaluations of functional connectivity between brain areas, these findings call for a reassessment of dereferencing methods used in the context of evaluating information flow within laminar neural tissue.
Recognizing Motor Imagery Between Hand and Forearm in the Same Limb in a Hybrid Brain Computer Interface Paradigm: An Online Study

Brain computer interfaces (BCIs) based on motor imagery (MI) play an important role in helping to improve and restore the loss of physical function. However, traditional MI-BCIs are limited to the motion intention of gross limb, which places many restrictions on their applications. This study proposes a hybrid paradigm based on MI and the steady-state somatosensory evoked potential, with the aim of improving the spatial resolution of MI recognition. Twelve subjects participated in this study. They performed MI tasks under MI and hybrid conditions. In the MI condition, subjects only performed MI tasks, whereas, in the hybrid condition, they received an electrical stimulus while performing the same tasks. Under the hybrid condition, subjects were required not to pay extra attention to the electrical stimulation. The MI task included imagining clenching the right hand and lifting the right forearm. The classifier was built using the filter bank common spatial pattern algorithm and a support vector machine, and online experiments were used to verify the recognition of two MI tasks. During the online experiments, all subjects were able to output different commands at a recognition accuracy far higher than the random level. The average classification accuracy of the hybrid condition reached 76.39%, with a maximum value of 88.34%, which was about 11% higher than that of the MI condition. Moreover, based on offline data, the classification performance using the event-related desynchronization (ERD) feature under the hybrid condition did not differ significantly from that under the MI condition, indicating that the introduction of electrical stimulation did not interfere in the separability of ERD. The proposed paradigm improved the efficiency of decoding multiple MI locations within a single limb. Despite the introduction of external stimuli, users could still drive the new system in the same way as MI in traditional MI-BCI.
Global Optimal Resource Allocation for Efficient FD-D2D Enabled Cellular Network

This paper tackles the resource allocation (RA) problem of a full-duplex (FD) device-to-device (D2D) communications enabled cellular network. In the considered scenario, multiple FD-D2D pairs share the uplink channels of the regular cellular users (CUs) which leads to mutual interference between the two communication types. Within this interference environment, this paper aims to properly allocate the network’s resources, such as the transmit power and the channels, to maximize a network-centric metric like the weighted-sum rate (WSR) and the global energy efficiency (GEE). The complex coupling between the mutual interference of the different links, as well as the flexibility of assigning the channels to the users, results in a non-convex RA optimization problem, for which the global optimal solution is hard to obtain. This paper is a first and innovative approach that globally solves the RA problem of an FD-D2D-based cellular network. In particular, we show that the global optimal solution can be achieved by decoupling the original problem into two sub-problems as power allocation (PA) and channel assignment (CA). The PA sub-problem is solved by means of monotonic optimization theory. Precisely, we propose a new polyblock-based algorithm, MARIO, which efficiently converges to the global solution of the PA problem. Then, based on the optimal PA solution, the CA problem reduces to an assignment problem, which can be solved by Khun–Munkers algorithm. Further, we propose a sub-optimal solution by solving the original RA problem in the reverse order, i.e., first assigning the channel and then allocating the power. The simulation results show the effectiveness of the proposed algorithms and provide important insights on the solution design parameters such as the proximity distance and the self-interference cancellation capability.
Measuring the turnover rates of Arabidopsis proteins using deuterium oxide: an auxin signaling case study.

Rapid environmental responses in plants rely on endogenous signaling mechanisms, which in many cases are mediated by changes in protein turnover rates. It is therefore necessary to develop methods for measuring protein dynamics that monitor large sets of plant proteins to begin to apply a systems biology approach to the study of plant behavior. The use of stable isotope labeling strategies that are adaptable to proteomic methods is particularly attractive for this purpose. Here, we explore one example of such methods that is particularly suitable for plants at the seedling stage, where measurement of amino acid and protein turnover rates is accomplished using a heavy water labeling strategy. The method is backed by microarray evaluation to define its feasibility for specific experimental approaches, and the CULLIN-ASSOCIATED AND NEDDYLATION DISSOCIATED 1 (CAND1) and TRANSPORT INHIBITOR RESPONSE 1 (TIR1) proteins are used to illustrate the potential utility in understanding hormonal signaling regulation. These studies provide insight not only into the potential utility of the method, but also address possible areas of concern regarding the use of heavy water labeling during plant growth. These considerations suggest a prescription for specific experimental designs that minimize interference resulting from the induction of treatment-specific gene expression in the results obtained.
Interactive System for Language and Communication Stimulation Directed to Young Children

We present an Interactive System for Language and Communication Stimulation (ISLACS) directed to young children. The system is a technological educational application intended to stimulate learning abilities, developed as a native mobile application for devices with Android operating system. It is specially designed for children between 2 and 4 years of age, to stimulate language, contingency and attention, through cognitive tasks in the form of interactive mini-games. The activities teach the association between images with the corresponding infrequently used spoken words, and images of letters with the corresponding sounds (i.e. phonemes). The mini-games are based on multimedia resources, mainly videos of an educator, real objects and animals. The software evaluates progress by measuring interaction parameters such as correct answers and response time and has an algorithm to randomize the trials across children. The application is tested in 314 2-4 year-old children. Each child played a minimum of 8 sessions every other day, each one presenting 6 words and 4 letters, and testing for 3 words and 2 letters. Results showed that ISLACS is an attractive game for young children, which significantly succeeded to teach children the sounds of the letters and a repertory of nearly 48 infrequent words in Spanish, in a brief intervention.
Performance of Self-Corrected Min-Sum Decoding Algorithm for Decoders with Quantized Input

In this paper we explore the Min-Sum decoding algorithm for LDPC codes with self-correction (SC-MS) modification and how it affects the error correction performance for quantized input data. Three levels of quantization are observed: 3-, 2- and 1-bit.It was shown that the self-corrected modification significantly improves the performance of Min-Sum decoding algorithm with quantized input in comparison with basic Min-Sum decoding algorithm. Convergence of the algorithm is also better than for basic Min-Sum algorithm.Results of error correction performance and convergence are presented for two codes: CCSDS (8176, 7156) and IEEE 802.3an (2048, 1723) LDPC. The results are also presented for the SC APP-based and basic Min-Sum decoding algorithms.
Channel Selection Improves MEG-based Brain-Computer Interface

This study investigates the effect of channel selection on the performance of a Magnetoencephalography (MEG)-based brain-computer interface (BCI) system in terms of classification accuracy (CA). Although many efforts are currently being undertaken to develop BCI using MEG, the major concern still is low accuracy. MEG systems involve data recording from a large number of channels which may provide a better spatio-temporal resolution for assessing brain patterns, however, a large numbers of channels result in a large number of features, which further make feature learning a challenging task. In this study, we evaluated the performance of two state-of-the-art channel selection methods, i.e. class-correlation (CC) and ReliefF (RF) across six binary classification tasks with a MEG dataset of 15 healthy participants. Both CC and RF methods provided a statistically significant increase in the CA (range: 20.91 - 24.22%) compared to baseline (i.e. using 204 channels) with bandpower features from the alpha (8-12 Hz) and beta frequency bands (13-30 Hz). Moreover, both methods reduce the optimum number of channels significantly (from 204 to the range of 1-22). Reducing the number of features can significantly reduce the computational cost and increase the chances of numerical stability which are key considerations in neurofeedback (online) applications.
Optimizing Spatial Contrast of a New Checkerboard Stimulus for Eliciting Robust SSVEPs

Most existing steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) adopted low- or medium-frequency stimuli towards high information transfer rates (ITRs). However, the intense flashes will deteriorate the user experience. With the increase of stimulation frequency, the discomfort caused by flickering stimuli can be significantly reduced, but the amplitude of SSVEP is also decreased, which greatly increases the difficulty of SSVEP detection. To cope with the tradeoff between SSVEP intensity and user experience, this study proposed a new checkerboard stimulus scheme with spatially alternated flickering and background squares. This study compared black-white, black, and white-background checkerboard stimuli, together with a traditional single flickering stimulus in eliciting SSVEPs at different frequencies. The data analysis results showed that the signal-to-noise ratio (SNR) of SSVEP for the black-background checkerboard stimulus was comparable to the single flickering stimulus, both of which were significantly higher than the other two checkerboard stimuli in the high-frequency range (40 Hz and 60 Hz). Additionally, with a consensus of reduction in subjective visual irritation, the black-background checkerboard stimulus can lead to improved user experience of the SSVEP-based BCIs.
Sliding Mode Observer based sensorless control of five phase PMSM in electric vehicle

The impact of the third order current harmonic cannot be ignored in a five-phase permanent magnet synchronous motor (PMSM), on the contrary of the three-phase PMSM. So, to realize the speed sensorless control for PMSM, a Sliding Mode Observer (SMO) is suggested with consideration the impact of third order harmonic. At the outset, a Sliding Mode Control (SMC) with injection of the third order current harmonic is designed for speed sensorless control of PMSM model for Electric Vehicle (EV). Then, Sliding Mode Observer is built to estimate the motor speed, torque and current in (d,q) frame, in order to improve the performance of sensorless control strategy. Besides, the stability of the SMC and SMO are studied using Lyapunov stability criteria. The feasibility of this technique will be evaluated using MATLAB/Simulink platform