A world pattern of the optimum ash levels of cleans from the washability data of typical coal seams

For every coal of specified size there is an optimal cut point, which more or less demarcates the free and fixed dirt in the coal. Technically speaking, one can expect the maximum advantage in washing a coal by separating it at this optimal cut point. A method is described for determining this point (the Optimum Degree of Washability) for any coal, from normal float-and-sink data. The Washability Number (directly calculated from the Optimum Degree of Washability) and the cleans ash at the optimum separation level are useful parameters for defining the ease or difficulty of washing. This Optimum Degree of Washability generally bears a rectilinear relation to the earlier Washability Index. While the values of Washability Number are generally higher and the optimum cleans ash level usually lower for the coal seams formed under quiescent conditions, the opposite is true for coal seams deposited under turbulent conditions. Thus, in the association of dirt (minerals) with coal (macerals), the higher the Washability Number, the greater is the heterogeneity of a coal seam inclusive of the dirt and vice versa. The Washability Number and the optimum cleans ash also undergo changes according to the size of crushing and grading of the raw coal. Characteristics on the above new basis are recorded for regional variation of some typical coal seams covering deposits of both ‘in situ’ and ‘drift’ origin, and significant observations were made regarding the trend of variation of the same seam at different sectors of the regions. In respect of deposits of drift origin, some regular sequence was also noted in the related values from the upper to the lower horizons. On the basis of typical data collected from more than 25 coal-producing countries, a worldwide projection has been made in this paper. A general pattern emerges with significant limits of variation between the coals of the Northern and Southern Hemispheres. This pattern could be better defined if further studies were conducted with coals of the same geological age and their washability characteristics were evaluated under standard conditions of test by crushing them initially to a particular top size. Further, from a knowledge of the different ranges of Washability Number, it is possible for a coal-preparation engineer to take tentative decisions on a size of crushing and a system of washing specifically suited to the beneficiation of any run-of-mine coal.     

A hybrid technique for the PAPR reduction of NOMA waveform

Non-orthogonal multiple access (NOMA) is a great contender for future cellular modulation due to its desirable properties like massive connectivity, high data rate transmission, and high spectral efficiency. However, its peak-to-average power ratio (PAPR) is significant, which becomes a significant disadvantage for the efficient operability of the NOMA waveform compared to current techniques. Several PAPR reduction algorithms like selective mapping (SLM), partial transmission sequence (PTS), and companding techniques have been proposed to lower the PAPR of multicarrier waveforms (MCWs). PTS reduces the PAPR but has high complexity. On the other hand, SLM has a less complex framework, but its PAPR performance is not as efficient as PTS. Companding methods reduce the PAPR by compressing the signals at the transmitter, which unfortunately reduces the dynamic range of the signal. In this work, we propose a hybrid algorithm (SLM + PTS) with a companding method for the first time for the NOMA waveform, which efficiently reduces the PAPR with low computational complexity. Furthermore, we compare the performances of a host of candidate algorithms like SLM, PTS, hybrid (SLM + PTS), hybrid + A law (SLM–PTS–A law), and hybrid + Mu law (SLM–PTS–Mu law). The results of the experiments show that the hybrid + Mu law did a better job than the existing PAPR reduction algorithms.     

A Novel Approximate Message Passing Detection for Massive MIMO 5G System

Massive-Multiple Inputs and Multiple Outputs (M-MIMO) is considered as one of the standard techniques in improving the performance of Fifth Generation (5G) radio. 5G signal detection with low propagation delay and high throughput with minimum computational intricacy are some of the serious concerns in the deployment of 5G. The evaluation of 5G promises a high quality of service (QoS), a high data rate, low latency, and spectral efficiency, ensuring several applications that will improve the services in every sector. The existing detection techniques cannot be utilised in 5G and beyond 5G due to the high complexity issues in their implementation. In the proposed article, the Approximation Message Passing (AMP) is implemented and compared with the existing Minimum Mean Square Error (MMSE) and Message Passing Detector (MPD) algorithms. The outcomes of the work show that the performance of Bit Error Rate (BER) is improved with minimal complexity.     

A novel voting ensemble model for spatial prediction of landslides using GIS

ABSTRACT

Inherent complex topography and drastic weather patterns together have concocted various natural disasters worldwide. In difficult terrains such as those prevalent in the North-Eastern regions of India, coupled with the factors such as population explosion and improper land use, lead them to witness some of the world’s most drastic landslides with an astonishing frequency, reckoning landslide susceptibility assessment crucial in such regions. This paper focuses on exploring a promising machine learning ensemble technique of Majority-based voting which has seldom been employed for landslide susceptibility assessment. The ensemble comprises Logistic Regression (LR), Gradient Boosted Decision Trees (GBDT) and Voting Feature Interval (VFI) to prepare landslide susceptibility zonation maps for the Brahmaputra valley region (Assam & Nagaland) and its close vicinity. In the first stage of the study, a landslide inventory for the area comprising 436 landslide locations was prepared in geographic information system (GIS), substantiated by news reports and remote sensing data. In the second stage, 16 landslide causative thematic maps including Elevation, Slope, Slope Aspect, General Curvature, Plan Curvature, Profile Curvature, Surface Roughness, Topographic Wetness Index (TWI), Stream Power Index (SPI), Slope Length, Normalized Difference Vegetation Index (NDVI), Land Use/Land Cover (LULC), Distance from Roads, Rivers, Faults and Railways were prepared. In the third stage, the landslide inventory was annexed with the causative factor maps to obtain a dataset comprising coordinates of the locations and the values of aforementioned causative factors on the corresponding coordinates. The proposed model was then trained and tested on the prepared dataset (70%:30% split). Finally, the efficiency of the new model was tested using the area under receiver operating characteristic curve (AUC of ROC). The validation results demonstrate the mettle of the proposed majority-based voting ensemble LR-GBDT-VFI (AUC: 0.98) against the conventional techniques such as Decision Trees, Support Vector Machines, Random Forest, etc. Altogether, the study offers an approach with wide scope across the field of landslide hazard assessment.     

An integrated approach to a biophysiologically based classification of floating aquatic macrophytes

Globally, invasive species are identified as one of the most serious threats to ecological stability and biodiversity. Water hyacinth (Eichhornia crassipes), an aggressive invasive aquatic species, has caused severe economic and ecological impacts in the Sacramento-San Joaquin River Delta in California. In the Delta, water hyacinth co-occurs with native pennywort (Hydrocotyle umbellata L.) and non-native water primrose (Ludwigia spp.). All of the species express a wide range of phenotypic variability, making it difficult to map them with remote sensing techniques because their spectral response is highly variable. We present an integrated approach to mapping these floating species using a sequence of hyperspectral methods, such as spectral angle mapper (SAM), linear spectral unmixing (LSU), continuum removal and several indices in a decision tree format. The ensuing tree, based on biophysiological differences between the species, was robust and consistent across three separate years and over multiple flightlines each year, spread across an area of approximately 2500 km². The most important inputs used to create the tree were reflectance in the short-wave infrared (SWIR), Red Edge Index, near-infrared (NIR) reflectance, LSU fractions and SAM rule values. The floating species were mapped with average accuracy of 88% for water hyacinth, 87% for pennywort and 71% for water primrose.     

Flexible epoxy novolac coatings: Use of cardanol‐based flexibilizers

Flexible epoxy novolac coatings were developed by reacting an epoxy novolac resin, poly[(phenylglycidyl ether)‐co‐formaldehyde] (PPGEF) with an amine curing agent, 4,4′‐diamino‐3,3′‐dimethyldicyclohexyl methane (BMCHA), cardanol based reactive diluent (Cardolite NC‐513) and two different cardanol‐based flexibilizers (Cardolite NC‐514 and Cardolite NC‐547). The flexibilizer content was varied from 5 to 10% by weight of the resin. These resins were coated onto the stainless steel panels and tested for their gloss, cross‐hatch adhesion, falling weight impact resistance, flexibility, abrasion, scratch hardness, solvent scrub resistance, and chemical resistance. The thermo‐mechanical properties of these coatings were determined by TGA, DSC, DMTA, and tensile strength measurements. The cryofractured specimens were subjected to SEM analysis. The influence of structural differences of two flexibilizers on the coating properties was investigated. These coatings exhibited excellent properties and have great potential in industrial applications. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134, 44920.     

Single-sensor hand-vein multimodal biometric recognition using multiscale deep pyramidal approach

Biometrics has emerged as a powerful technology for person authentication in various scenarios including forensic and civilian applications. Deployment of biometric solutions that use cues from multiple modalities enhances the reliability and robustness of authentication necessary to meet the increasingly stringent security requirements. However, there are two drawbacks typically associated with multimodal biometrics. Firstly, the image acquisition process in such systems is not very user-friendly, primarily due to the time and effort required to capture biometric samples belonging to multiple modalities. Secondly, the overall cost is higher as they employ multiple biometric sensors. To overcome these drawbacks, we employ a single NIR sensor-based image acquisition in the proposed approach for hand-vein recognition. From the input hand image, a palm-vein and four finger-vein subimages are extracted. These images are then enhanced by CLAHE and transformed into illumination invariant representation using center-symmetric local binary pattern (CS-LBP). Further, a hierarchical non-rigid matching technique inspired by the architecture of deep convolutional networks is employed for matching the CS-LBP features. Finally, weighted sum rule-based matching score-level fusion is performed to combine the palm-vein and the four finger-vein modalities. A set of rigorous experiments has been performed on an in-house database collected from the left and right hands of 185 subjects and the publicly available CASIA dataset. The proposed approach achieves equal error rates of 0.13% and 1.21%, and rank-1 identification rates of 100% and 100% on the in-house and CASIA datasets, respectively. Additionally, we compare the proposed approach with the state-of-the-art techniques proposed for vascular biometric recognition in the literature. The important findings are (1) the proposed approach outperforms all the existing techniques considered in this study, (2) the fusion of palm-vein and finger-vein modalities consistently leads to better performance for all the feature extraction techniques considered in this work. (3) Furthermore, our experimental results also suggest that considering the constituent palm-vein and finger-vein images instead of the entire hand-vein images achieves better performance.     

OWA Operator‐Based Hybrid Framework for Outlier Reduction in Web Mining

Web mining is the process of extracting useful information from Web resources. Handling outliers is one of the primary challenges of present Web mining techniques. The complex nature of the Web, by virtue of both data and users, makes it very difficult to mine the information and convert to knowledge base with little outlier values. In this paper, a framework for reducing outliers in regression analysis with the help of ordered weighted operators (OWA) as a multicriteria decision‐making problem is being formulated. First, a regression problem with a real‐time Web data set will be formulated followed by solving the same with the help of a variant of OWA operators. Results, thus obtained are able to prove that outliers can be reduced significantly with the help of proposed approach.     

Cuckoo and krill herd‐based k‐means++ hybrid algorithms for clustering

Clustering algorithms can be optimized using nature‐inspired techniques. Many algorithms inspired by nature, namely, firefly algorithm, ant colony optimization algorithm, and so forth, have improved clustering results. k‐means is a popular clustering technique but has limitations of local optima, which have been overcome using its various hybrids. k‐means++ is a hybrid k‐means clustering algorithm that gives the procedure to initialize centre of the clusters. In the proposed work, hybrids of nature‐inspired techniques using cuckoo and krill herd algorithm are implemented on k‐means++ algorithm to enhance cluster quality and generate optimized clusters. The designed algorithms are implemented, and the results are compared with their counterparts. Performance parameters such as accuracy, f‐measure, error rate, standard deviation, CPU time, cluster quality check, and so forth are used to measure the clustering capabilities of these algorithms. The results indicate the high performance of newly designed algorithms.     

Comparison of different sintering aids in cold sinter-assisted densification of lead zirconate titanate

Ceramics such as lead zirconate titanate (PZT) tend to dissolve incongruently, and thus pose a challenge in the cold sintering process. Moist lead nitrate has previously been shown to enable a cold sinter-assisted densification of PZT by a viscous phase sintering mechanism. In this paper, lead acetate trihydrate is demonstrated to lower the required temperature of the cold sintering step to 200°C. This densification process was described as a two-step process: cold sintering of PZT with lead acetate trihydrate and post-annealing the as-cold sintered PZT ceramics. Unlike in the case of lead nitrate, PZT densification with lead acetate trihydrate occurs by a liquid phase assisted sintering mechanism, leading to an as-cold sintered relative density of 84% at 200°C. After performing a post-anneal step at 900°C, >97% relative densities were achieved in samples that were cold sintered with lead acetate trihydrate. This step not only densified PZT but also refined the grain boundaries. In the post-annealed samples, the room-temperature relative permittivity at 100 Hz was ~1600, slightly higher than that reported in samples that used lead nitrate as a sintering aid; the loss tangent was about 3.8%. For measurements at 10 Hz, the remanent polarization in both cases was ~28 µC/cm². Both Rayleigh analysis and aging studies showed that a higher irreversible contribution to the permittivity exists in samples that used lead nitrate as a cold sintering aid.