Information Theoretic Weighted Fuzzy Clustering Ensemble

In order to improve performance and robustness of clustering, it is proposed to generate and aggregate a number of primary clusters via clustering ensemble technique. Fuzzy clustering ensemble approaches attempt to improve the performance of fuzzy clustering tasks. However, in these approaches, cluster (or clustering) reliability has not paid much attention to. Ignoring cluster (or clustering) reliability makes these approaches weak in dealing with low-quality base clustering methods. In this paper, we have utilized cluster unreliability estimation and local weighting strategy to propose a new fuzzy clustering ensemble method which has introduced Reliability Based weighted co-association matrix Fuzzy C-Means (RBFCM), Reliability Based Graph Partitioning (RBGP) and Reliability Based Hyper Clustering (RBHC) as three new fuzzy clustering consensus functions. Our fuzzy clustering ensemble approach works based on fuzzy cluster unreliability estimation. Cluster unreliability is estimated according to an entropic criterion using the cluster labels in the entire ensemble. To do so, the new metric is defined to estimate the fuzzy cluster unreliability; then, the reliability value of any cluster is determined using a Reliability Driven Cluster Indicator (RDCI). The time complexities of RBHC and RBGP are linearly proportional with the number of data objects. Performance and robustness of the proposed method are experimentally evaluated for some benchmark datasets. The experimental results demonstrate efficiency and suitability of the proposed method.     

Miniaturization of monopole antenna using high refractive index metamaterial loading

In this article, miniaturization of the wire monopole antenna with the help of high refractive index (HRI) metamaterial is presented. For the first time, HRI medium is realized by using the array of single ring split ring resonator. By surrounding the wire monopole with the array of SR‐SRRs, the effective wavelength is squeezed in the vicinity of the near field dominance. By the loading of the monopole with such an HRI medium, the size of the antenna is reduced from 29.5 to 16 mm, without any deformation in the current distribution and radiation pattern corresponding to the fundamental resonance. The simulated and measured results are agreed to the same.     

A compact dual‐band zeroth‐order resonator antenna loaded with meander line inductor

A novel zeroth‐order resonator (ZOR) meta‐material (MTM) antenna with dual‐band is suggested using compound right/left handed transmission line as MTM. In this article, suggested antenna consists of patch through series gap, two meander line inductors, and two circular stubs. The MTM antenna is compact in size which shows dual‐band properties with first band centered at 2.47 GHz (2.05‐2.89 GHz) and second band is centered at 5.9 GHz (3.70‐8.10 GHz) with impedance bandwidth of (S₁₁ < ? 10 dB) 34.69% and 72.45%, respectively. At ZOR mode (2.35 GHz), the suggested antenna has overall dimension of 0.197λ_o × 0.07λ_o × 0.011λ_o with gain of 1.65 dB for ZOR band and 3.35 dB for first positive order resonator band which covers the applications like Bluetooth (2.4 GHZ), TV/Radio/Data (3.700‐6.425 GHz), WLAN (5‐5.16 GHz), C band frequencies (5.15‐5.35, 5.47‐5.725, or 5.725‐5.875 GHz) and satellite communication (7.25‐7.9 GHz). The radiation patterns of suggested structure are steady during the operating band for which sample antenna has been fabricated and confirmed experimentally. It exhibits novel omnidirectional radiation characteristics in phi = 0° plane with lower cross‐polarization values.     

Coaxial‐fed high‐gain triple‐band zeroth‐order circularly polarized antenna using pseudo‐open termination and asymmetric unit cells

A coaxial‐fed tri‐band zeroth‐order resonance (ZOR) circularly polarized antenna with higher gains for all the excited ZORs is designed and analyzed in this paper. Epsilon negative transmission line (ENG TL) and pseudo‐open termination (P‐OT) unit cells with different series capacitances (C_S and C_S1 ) resonate shunt ZOR (f_sh ) and two series ZORs (f_se and f_se1 ), respectively. Asymmetric unit cell concept is applied to ENG‐TL and P‐OT unit cells to create vertical and horizontal components, and the 90° phase shift is provided by the ZOR, resulting in circular polarization (CP). Left‐hand CP (LHCP) is achieved by creating two 90° right bends to the extended stubs in ENG TL and P‐OT unit cells. Higher gains for all the excited ZORs are achieved by shifting the shorting pins of ENG TL and P‐OT unit cells far away from the center position. After fabrication, the measured resonances occur at 4.64 GHz (f_sh ), 4.04 GHz (f_se ), and 3.86 GHz (f_se1 ) with fractional bandwidths of 1.62%, 1.73%, and 1.6%, respectively. The measured LHCP peak gains are 4.05 dBic (f_sh ), 3.85 dBic (f_se ), and 3.94 dBic (f_se1 ). The average axial ratio obtained is less than 3‐dB in the 10‐dB fractional bandwidth of the proposed antenna.     

Passenger flow prediction in bus transportation system using deep learning

The forecasting of bus passenger flow is important to the bus transit system’s operation. Because of the complicated structure of the bus operation system, it’s difficult to explain how passengers travel along different routes. Due to the huge number of passengers at the bus stop, bus delays, and irregularity, people are experiencing difficulties of using buses nowadays. It is important to determine the passenger flow in each station, and the transportation department may utilize this information to schedule buses for each region. In Our proposed system we are using an approach called the deep learning method with long short-term memory, recurrent neural network, and greedy layer-wise algorithm are used to predict the Karnataka State Road Transport Corporation (KSRTC) passenger flow. In the dataset, some of the parameters are considered for prediction are bus id, bus type, source, destination, passenger count, slot number, and revenue These parameters are processed in a greedy layer-wise algorithm to make it has cluster data into regions after cluster data move to the long short-term memory model to remove redundant data in the obtained data and recurrent neural network it gives the prediction result based on the iteration factors of the data. These algorithms are more accurate in predicting bus passengers. This technique handles the problem of passenger flow forecasting in Karnataka State Road Transport Corporation Bus Rapid Transit (KSRTCBRT) transportation, and the framework provides resource planning and revenue estimation predictions for the KSRTCBRT.

     

Efficient Beamformer for Low Mobility Indoor Communication Using Sparse Adaptive Algorithm

Wireless Personal Communications - The proposed beamforming model exploits the underlying sparseness of the adaptive filter as impulse response of wireless channel shows some extent of sparse...     

Generalized Pythagorean Fuzzy Geometric Aggregation Operators Using Einstein t‐Norm and t‐Conorm for Multicriteria Decision‐Making Process

The objective of this paper is to present some series of geometric‐aggregated operators under Pythagorean fuzzy environment by relaxing the condition that the sum of the degree of membership functions is less than one with the square sum of the degree of membership functions is less than one. Under these environments, aggregator operators, namely, Pythagorean fuzzy Einstein weighted geometric, Pythagorean fuzzy Einstein ordered weighted geometric, generalized Pythagorean fuzzy Einstein weighted geometric, and generalized Pythagorean fuzzy Einstein ordered weighted geometric operators, are proposed in this paper. Some of its properties have also been investigated in details. Finally, an illustrative example for multicriteria decision‐making problems of alternatives is taken to demonstrate the effectiveness of the approach.     

A study of the thermal,dynamic mechanical,and tribological properties of polyphenylene sulfide composites reinforced with carbon nanofibers

The thermal, dynamic mechanical, and tribological properties of polyphenylene sulfide (PPS) composites reinforced with carbon nanofiber (CNF) were studied. Dynamic mechanical thermal analysis (DMTA) and differential scanning calorimetry (DSC) were used to study the viscoelastic properties and thermal transitions. In order to study the tribological properties, friction and wear tests in a pin-on-disk configuration were performed. The changes in melting point, crystallization temperature, and glass transition temperature were found to be small as a result of reinforcement. Steady state wear rates of the reinforced composites sliding against the counterface of roughness 0.13–0.15 μm Ra were significantly lower than that of the unreinforced PPS. When the composites were tested against the smoother counterface of 0.06–0.11 μm Ra, the wear rates were higher. The coefficient of friction in all the cases was not practically affected by the presence of CNF. The transfer films formed on the counterface during sliding were examined by optical microscopy and atomic force microscopy (AFM). The variation of wear is discussed in terms of the texture and topography of transfer film.