Miracle-dz/UniFAR-FTUs huggingface dataset:
Solar Power Prediction Models: Classification Based on Time Horizon, Input, Output and Application

Large number of solar power prediction models such as Artificial Neural Networks (ANN), Numerical Weather Prediction (NWP), Support Vector Regression (SVR), Bayesian approaches and hybrids are exist in literature. Different models are used for different type of applications such as system operation, market operation based on their time horizon. Also, different models use different types of inputs. There are less literature available for the classification of these models based on application, time horizon, input types and predicted outputs. Therefore, this paper classify the solar power prediction models based on time horizon, input types, application and predicted output type and also gives most suitable model for each class. It concludes that position of sun and earth is same corresponding to previous year similar day, only difference is cloud cover and cloud movement, wind speed and temperature. Compared to wind speed and temperature cloud movement and cloud speed have high co relation with solar power. Image processing based on sky imagery data for real time cloud cover and cloud movement along with previous years similar day sky data will give accurate predictions.
A Polarization-Independent Tunable Microwave Absorber with Wide Tuning Range

In this paper, a tunable microwave absorber has been presented for L-band applications. The proposed design consists of square loops which are connected through varactor diodes across the diagonals. By controlling the reverse voltage of the varactors, the absorption frequency can be tuned for a wide frequency range. The novelty of the proposed design lies in its four-fold symmetry, which makes the structure polarization-insensitive unlike the earlier reported tunable absorbers. Furthermore, a prototype of the proposed structure has been fabricated where the active components are regulated using a novel biasing technique. The measured results also show good agreement with the simulated responses under normal incidence as well as for different polarization angles.
Comparing the Performance Potentials of Interval and General Type-2 Rule-Based Fuzzy Systems in Terms of Sculpting the State Space

This paper provides application-independent perspectives on why improved performance usually occurs as one goes from an interval type-2 (IT2) fuzzy system to a general type-2 (GT2) fuzzy system. This is achieved by using the horizontal-slice representation of a GT2 fuzzy set and GT2 fuzzy system and by examining first- and second-order rule partitions as well as novelty partitions for the horizontal slices. It demonstrates that, for triangle and trapezoid secondary membership functions, the numbers of first- and second-order rule partitions are exactly the same for IT2 and GT2 fuzzy systems, but that a maximum amount of change always occurs in every second-order rule partition of a GT2 fuzzy system. This does not always occur in such partitions of an IT2 fuzzy system. Furthermore, when type reduction (TR) is used in a GT2 fuzzy system, the total number of novelty partitions is directly proportional to the number of horizontal slices; consequently, there are many more such partitions in a GT2 fuzzy system that uses TR than occur in an IT2 fuzzy system that also uses TR. It is the author's conjecture that it is the maximum changes that occur in every second-order rule partition, as well as the greater number of novelty partitions when TR is used, that provide a GT2 fuzzy system with the potential to outperform an IT2 fuzzy system.
A content analysis based Recommender System using Categorization of Online Text Resources on Five-Dimensional Human Cognitive System

A Recommendation System (RS) facilitates customers to purchase items of their choice. A user prefers to get relevant suggestions of his choice. Since, the acceptance of recommendation makes satisfaction to user and it increases the profits of website owner. Therefore, RS has become an important component of e-commerce websites now days. Normally, users give reviews of items as feedback on e-commerce websites. These reviews provide meaningful information about items for other users. The other users use these reviews to decide what he may like from the available options of items. Therefore. , Recommender systems (RSs) of e-commerce website largely use the information available in online reviews given by users to generate the recommendations. However. , in the influence of social events and latest trends of items. , the acceptance of recommended items does not achieve an expected level of satisfaction in user, in spite of inclusion of customer review in computation of recommendation. This paper introduces an innovative approach in RS that uses the information of social events in the computation of recommendations. The approach uses content analysis on online news of social events. The supervised learning method is used in content analysis. The content analysis has two phases namely. , news filtration and categorization computation. In news filtration. , news is filtered based on their content which may influence the choice of user. The filtered news is then categorized using five-dimensional Human Cognitive System (HCS). The paper has discussed the computation of recommendation using the content analysis of online news using HCS and natural language processing (NLP) on review of products. The final computation of recommendation has enhanced the acceptability by user. It is shown with the help of confusion matrix.
Analysis, Design, and Implementation of Accurate ZVS Angle Control for EV Battery Charging in Wireless High-Power Transfer

To acquire high efficiency and reduce electromagnetic interference in wireless high-power charging for electric vehicles (EVs), it is of great importance to realize accurate zero voltage switching angle (ZVSA) control of the inverter. However, the traditional zero-crossing detection cannot be applied in high power because of considerable noise and poor accuracy in wide power range applications. In this paper, to suppress the noise in high power, a current hysteresis comparator is adopted, and an enhanced phase detection methodology is proposed to measure the phase of resonant current by using a reference signal produced by a processor. Meanwhile, to improve the accuracy of ZVSA, a uniform time delay compensation method (UTDCM) by considering the whole time delays synthetically is proposed, and a uniform time delay equation can be obtained to guarantee the high accuracy of phase detection, especially in the wide power range. Finally, a novel control strategy with the ZVSA loop based on UTDCM is proposed for battery charging in wireless high-power transfer system. A 500-W wireless power transfer prototype is built to verify the accuracy of ZVSA based on UTDCM and the system efficiency can achieve 94.17% with $k$ = 0.22.
Design of Frequency Modulated Fuzzy Logic Controller for Switched Capacitor Converter

Switched capacitor converter control schemes that are reported in literature fall in the category of ON-OFF control strategies. Other strategies include frequency modulated compensators, such as PI or PID control schemes. This paper proposes the use of fuzzy logic control based control schemes for such systems. The system under test is a unity switched capacitor converter, with specified load requirements, the controller is designed and tested under different operating conditions.
Resolving Multiple Sequence Alignment Problem Using an Intelligent Cooperative Algorithm

In this paper, we aim to present a novel intelligent method based on stochastic techniques in order to tackle the multiple sequence alignment (MSA) problem. In this manner, the metropolis criterion is used to generate the initial population of particle swam optimization (PSO) algorithm. Numerical results on BaliBASE benchmark have shown the potent of our developed approach SAPSO to find good solutions comparing to those given by other existing methods.
An Online Adaptive Modulation Scheme for Energy Harvesting Nodes Using Bayesian Decision Theory

Energy harvesting (EH) has emerged as a promising technique in next general wireless communication. Plenty of studies have already focused on the energy management to solve the offline problem in wireless transmission network with two hop nodes. In this paper, we propose an online transmission scheme for the source node to choose QAM level under causal system information based on Bayesian Decision Theory. Taking the result of the offline scheme as training data, we make a statistic of the conditional probability for the channel state and energy state. Then we use Naive Bayes Classifiers to calculate the posterior probability for every QAM level. With the probability matrix, the source node can determine the suitable QAM level under casual system information. The simulation result shows that the online scheme improves the total system throughput compared to the fixed 64QAM scheme but still litter worse than the offline scheme.
Recognition of Bangla Handwritten Characters using Feature Combinations

The success of any character recognition system largely depends on how well the distinguishing features of different characters describe the classes of characters. This paper studies the effect of the combinations of three different feature sets (i.e. chain code histogram (CH), longest run (LR) and Gabor wavelet based (GW) feature) on Bangla handwritten character recognition in order to maximize the number of discriminative features among different character classes. Different combinations of the three feature sets, namely LR & GW, CH & GW, LR & CH, and LR, CH & GW tested on a standard database of Bangla characters revealed that the combination of LR and CH features yielded better recognition accuracy compared to the other cases. It was also observed that size of the feature vectors in the combination played a key role in the recognition process.
A Sub-FBLMS Algorithm for Clutter Cancellation in Bistatic Noise Radar

In order to improve the cancellation performance in case of channel mismatch, in this paper, the subband frequency domain block least mean squares (sub-FBLMS) algorithm is proposed. The sub-FBLMS algorithm has some advantages, like fast implementation of FBLMS algorithm, fast convergence behavior of subband filtering, less filter order due to decimation operation with respect to FBLMS algorithm. Finally, simulation results demonstrate the effectiveness of the sub-FBLMS algorithm.
Tur e Iraj, Caín y Abel: Semejanzas iconográficas en la interminable lucha del mal y el bien.

ABSTRACT: The duality between good and evil is a common topic in all civilizations regardless of chronology or geographical location. It is a conflict reflected in art history, and on the present occasion, through the iconographic types of Cain killing Abel as well as Tur murdering Iraj. The roots of these myths are deeply embedded in two highly relevant written sources, the Bible and the Shahnameh or The Book of Kings . The latter was written by the great Iranian poet Ferdowsi. The legends, despite having different origins, nevertheless have many similarities and they share a particular iconographic type. I analyze each of the myths along with their pictorial representations, which connect the thought of different cultures. KEYWORDS: Tur, Iraj, Cain, Abel, Shahnameh , Bible, envy, iconographic type. RESUMEN: La dualidad entre el bien y el mal es un tema comun en todas las civilizaciones independientemente de la cronologia o la localizacion geografica. Un conflicto reflejado en la historia del arte y en esta ocasion, figurado a traves de los tipos iconograficos de Cain matando a Abel asi como Tur asesinando a Iraj. Mitos que hunden su raiz en dos fuentes escritas de gran relevancia, la Biblia y el Shahnameh o Libro de los Reyes , esta ultima redactada por el gran poeta irani Ferdousi. Las leyendas, pese a tener un origen distinto, poseen numerosas similitudes y comparten un tipo iconografico concreto. Analizamos cada uno de los mitos junto con sus representaciones pictoricas, conectando el pensamiento de las distintas culturas. PALABRAS CLAVES: Tur, Iraj, Cain, Abel, Shahnameh, Biblia, envidia, tipo iconografico.
Performance Analysis of Energy Detection Based Cognitive Radio over GBK Fading Channel

A Generalised Bessel K fading distribution (GBK) has flexible parameters and own the capability to capture the multipath as well as shadowing effect. Moreover, it reduces to several well known multipath fading cases such as exponential distribution, generalised K distribution, generalised Gamma distribution, Half Gaussian, K distribution, Gamma distribution. In this work, our goal is to obtain analytical expression of average probability of detection (APD) in case of GBK fading channel and then to carry out the system performance analysis by applying square law selection (SLS) diversity scheme. Effect of different shaping parameters on system performance is also examined.
A Novel Dual-Stator Radial-Flux Machine With Diametrically Magnetized Cylindrical Permanent Magnets

This paper presents a dual-stator permanent magnet synchronous machine (DS-PMSM) with diametrically magnetized cylindrical PMs (DMCPMs). The advantages of the proposed DS-PMSM are its small cogging torque and torque ripple, sinusoidal (low harmonic) air-gap flux density waveforms, and a high pole-pair number structure, which can be used to increase the torque density. To confirm the advantages of the proposed DS-PMSMs, first, the cogging torques produced with conventional arc PMs and cylindrical PMs are compared. Next, a DS-PMSM with DMCPMs is designed, and its cogging torque is calculated. Then, all the relevant machine characteristics, including the cogging torque, air-gap flux density, back electromotive force, and torque–angle characteristics, are predicted by the two- and three-dimensional finite-element method. Finally, the machine is prototyped, and the validity of its advantages is investigated with the experimental tests.
Precession Period Extraction of Axisymmetric Space Target from RCS Sequence via Convolutional Neural Network

The precession period was used to identify space targets in radar target recognition, especially the axisymmetric ballistic missile warheads and the similar shaped decoys. There are many precession period extraction methods based on RCS sequence. However, these methods have many restrictions and often yield poor results under noise condition. Aiming at extracting the precession period from RCS sequence, this paper designed a one-dimensional convolutional neural network. The precession period extraction is converted to a signal parameter estimation problem where the RCS sequence is the input signal and the period is the expected parameter. The proposed method was trained and validated on simulated RCS sequences and compared with spectral method, including CAUTOC, CAMDF, CAUTOC/CAMDF and trigonometric fitting method. The results showed that the proposed method yielded more accurate estimation results. Moreover, it can tell that there is no valid period by yielding a value that is not in the range [2, N /2]where $N$ is the length of the RCS sequence.
A Survey on Gene Selection for Microarray Cancer Classification Based on Soft Computing Techniques

Recent developments in the field of soft computing have affected a large number of research works of data mining in a positive way. Processing and analysis of large data become an easy task when using soft computing techniques and especially for NP-Complete problems. This paper focuses on the cancer classification field which is an important research field in Bioinformatics. The efficiency in cancer classification depends on the competence in selecting the most promising genes using a suitable gene selection algorithm. By using soft computing techniques, the performances of gene selection algorithms are enhanced and efficient results are obtained. In this paper, a detailed literature has been done on gene selection and cancer classification and a detailed review have been made on the various gene selection algorithms based on soft computing techniques. A comparison is made based on different aspects of the existing gene selection algorithms. Apart from this, the future of gene selection and classification along with the various research gaps has been discussed.
A system for the automatic detection of glaucoma using retinal images

Glaucoma is an optic neuropathy and it principal cause of blindness in the world. In this paper, a system able to treat and analyze the Visual Field (VF) images and Optical Coherence Tomography of the Ganglion Cell Layer (OCT-GCL) images is proposed, in order to help early detection of glaucoma in its early stages. The proposed approach is based on calculating the percentage of healthy, sick and dead regions of VF and OCT-GCL images. In order to carry out this calculation, we combined the thresholding methods with morphological operators and median filter to extract all regions. These algorithms developed were tested on a set of images of a local database composed of 58 OCT-GCL images and 21 VF images. The results obtained are satisfactory and confirmed by experts in ophthalmology.
Performance Analysis of Non-Converged and Converged Medium Access Control Protocols for Radio-over-Fiber Networks

For supporting the upcoming applications like interactive gaming, 3D-TV, etc., a high data rate needs to be provided to the wireless mobile users. To facilitate these requirements, and to keep the mobility of the users intact, a wireless network that has an optical backbone with a huge bandwidth capacity is required. Radio-over-Fiber (RoF) networks prove to be a promising solution that can provide a high data rate along with a very low delay. In this paper, we propose a converged medium access control (MAC) protocol for RoF networks. In addition, the proposed converged MAC protocol is compared with a state-of-art non-converged MAC protocol. The simulation results show that the converged MAC protocol for RoF network offers much lower delay and high channel utilization as compared to the non-converged and hence converged RoF networks prove to be a better choice for the evolution of next generation networks.
State-Space Digital Filters with Minimum Weighted Round-off Noise and Pole Sensitivity Subject to $\pmb{l}_{2}$ -Scaling Constraints

In this paper, we investigate the problem of minimizing roundoff noise and pole sensitivity simultaneously subject to l2-scaling constraints for state-space digital filters. A measure for evaluating roundoff noise and pole sensitivity is introduced. An efficient iterative technique for minimizing this measure is then explored by relaxing the constraints into a single constraint on matrix trace and solving the relaxed problem with an effective matrix iteration scheme. Finally, the validity and effectiveness of the proposed technique is demonstrated by a numerical example.
STAP Based Detector for Multi-Dynamic Radar in Homogeneous Clutter Scenario

Multi-Static-Radar (MSR) systems are emerging technology due to exploitation of spatial diversity nature, offering improved Performance for various configuration of deployment of vis a vis mono-static radar. The deployment challenges becomes more difficult when all radar are considered as airborne radar and creates scenario of Multi-Dynamic radar(MDR). The radar signal characteristics for MDR scenario dynamically changing based on geometrical configuration. The Transmit and Receive pairs in MDR case is considered on moving airborne-platform such that Airborne radar with multiple receiver(Rx) mounted on different Unmanned Aerial Vehicles (UAVs), such configuration considered as Multi-dynamic radar for airborne surveillance systems. Space Time Adaptive Processing(STAP) is considered as multi-dimensional adaptive-filtering algorithms that simultaneously combine and process the signals received from the array elements (spatial domain) and multiple pulses (Temporal domain) of coherent radar waveform to suppress the interference. STAP for multi-dynamic radar scenario improve detection of stealth or low rcs targets obscured by mainlobe clutter and jamming environments as detection performance depends on Receivers coordinated network. A typical interference scenario involves the detection of a low rcs targets(SNR) in the presence of a large interfering signal, it is desirable to attenuate the interfering signal while preserving the integrity of low rcs target and an appropriate dynamic range. This paper discusses the reduced rank algorithm to reduce the rank of sample covariance matrix in sample starved homogeneous environment. The two solutions namely, multi-stage Weiner filter and Principal Component (PC) have been shown. Keywords: Bi-static Radar, Multi-dynamic Radar, Multi-static Radar, STAP, clutter, jammer, MWF, PC.
Optimization Framework for Flavour and Nutrition Balanced Recipe: A Data Driven Approach

Food has been playing a major part of human civilization as not only being a physiological need, but being a major factor for defining the culture and society. The choice of the food is mainly depended on both flavour and nutrient but the biasness towards to the flavour factor has lead the human to effect badly on their healthier lifestyle. Recipe recommendation literature typically considers either flavour or nutrient factor. Various flavour traits are also preventing the promotion of healthy foods while maintaining the palatability. Our data driven flavour and nutrient optimization framework consists of classification model which achieved 79.546 % prediction accuracy when detecting the cuisine, generalized flavour recommendation approach and personalized nutrient recommendation approach when deciding the optimization task.
Weighted Hard-Reliability Decoding Method for Non-binary LDPC Codes

In this paper, we propose a weighted hard-reliability based one step majority-logic decoding algorithm for NON-Binary Low-Density Parity-Check (NB-LDPC) codes. To improve the information reliable of check nodes and the use efficiency of receive message, a weight reliability message method is proposed where only the weight values generated in the decoding initialization are reserved for the iterate decoding process. We also propose a new message reliability updating rule for each iterate decoding, in which only the unreliable variable nodes are updated. Simulation results show that our proposed weighted iterative hard-reliability (WIHRB) algorithm significantly improves the error-floor performance compared to the conventional truncate iterative hard-reliability (TIHRB) algorithms.
Recommendation on Keyword Combination of News Headlines

The use of social networking sites have been growing dramatically. This phenomenon promotes most news agencies to use social media platforms rather than traditional media platforms for news releases. The main differences between social and traditional media platforms are “Real-time news release” and “cross-platform extensions Contents posted on social media platforms require speed and richness. Therefore, web news editors find it relatively difficult to write an article with good title in a short time that attracts audience to click in and read through. In this study, we analyzed selected “web news headlines with high page views” to identify catchy headlines and similarities of keywords. We used dimension reduction method as our primary technique to identify the best keyword combinations that help news editors to write attractive news for higher page views and more actual readers on the social media platforms.