B-GWO based multi-UAV deployment and power allocation in NOMA assisted wireless networks

Wireless Networks - Unmanned Aerial Vehicles (UAVs) deployed as flying base stations is a promising technology for enhancing the quality of service (QoS) and quick recovery from unexpected damages...     

Learning to hash: a comprehensive survey of deep learning-based hashing methods

Explosive growth of big data demands efficient and fast algorithms for nearest neighbor search. Deep learning-based hashing methods have proved their efficacy to learn advanced hash functions that suit the desired goal of nearest neighbor search in large image-based data-sets. In this work, we present a comprehensive review of different deep learning-based supervised hashing methods particularly for image data-sets suggested by various researchers till date to generate advanced hash functions. We categorize prior works into a five-tier taxonomy based on: (i) the design of network architecture, (ii) training strategy based on nature of data-set, (iii) the type of loss function, (iv) the similarity measure and, (v) the nature of quantization. Further, different data-sets used in prior works are reported and compared based on various challenges in the characteristics of images that are part of the data-sets. Lastly, different future directions such as incremental hashing, cross-modality hashing and guidelines to improve design of hash functions are discussed. Based on our comparative review, it has been observed that generative adversarial networks-based hashing models outperform other methods. This is due to the fact that they leverage more data in the form of both real world and synthetically generated data. Furthermore, it has been perceived that triplet-loss-based loss functions learn better discriminative representations by pushing similar patterns together and dis-similar patterns away from each other. This study and its observations shall be useful for the researchers and practitioners working in this emerging research field.

     

Studies on geotechnical properties of Prosopis juliflora ash treated black cotton soil

Bulletin of Engineering Geology and the Environment - The pavements of the Beijing Capital International Airport (BCIA), China, where a buried fault zone lies beneath, have undergone continuous...     

Performance Investigations and PAPR Reduction Analysis Using Very Efficient and Optimized Amended SLM Algorithm for Wireless Communication OFDM System

Wireless Personal Communications - The orthogonal frequency division multiplexing (OFDM) is the most encouraging multi-carrier modulation system chosen for the high data rates but the objective is...     

A QoS Aware Resource Placement Approach Inspired on the Behavior of the Social Spider Mating Strategy in the Cloud Environment

Wireless Personal Communications - The efficient management of resource sharing plays a crucial role in the cloud execution environment. The constraints such as heterogeneity and dynamic nature of...     

A Survey of Various Schedulers Used for Fair Bandwidth Allocation in WiMAX: IEEE 802.16

ABSTRACT

Wireless Interoperability for Multiple Access (WiMAX) is one of the emerging fields of high-speed wireless communication that has enormous capabilities due to its range and the connection speed. Like wireless local area networks (LANs), WiMAX networks implement multiple quality of service (QoS) frameworks at the Media Access Control (MAC) level for assured data, voice, and video services. The question of ensuring QoS is basically how to distribute available resources to users in order to satisfy QoS parameters such as latency, jitter and throughput requirements. IEEE 802.16 standard does not have any particular guidelines on scheduling of incoming and outgoing data. This has caught the attention of researchers working on WiMAX. This article discusses the various issues in WiMAX along with a classification of various scheduling approaches based upon the type of scheduler for the sake of better understanding the scheduling problem and analyzing various available theories.     

An MRI brain disease classification system using PDFB-CT and GLCM with kernel-SVM for medical decision support

Multimedia Tools and Applications - The automatic binary classification of normal and abnormal subjects using magnetic resonance (MR) brain images has made remarkable progress in recent years. This...     

Study of Corrosion Behavior and In Vitro Bioactivity of Single NbSi2 and Duplex NbSi2/Nb5Si3 Coatings on Nb Substrates for Biomedical Applications

Protection of Metals and Physical Chemistry of Surfaces - Single NbSi2 and duplex NbSi2/Nb5Si3 coatings were successfully applied on Nb substrates using halide activated pack cementation (HAPC)...     

Effect of cutting process on the stress corrosion susceptibility of AISI 304L stainless steel

During manufacturing of a component, cutting, turning, grinding, and milling operations are inevitable and these operations induce surface residual stresses. In this study, it is shown that, depending on the process employed for cutting, residual stresses generated at the cut surfaces can vary widely and they can, in turn, make the cut surfaces of austenitic stainless steel (SS) prone to stress corrosion cracking (SCC). An austenitic SS 304L plate was cut using three different procesess: bandsaw cutting, cutting using the cut-off wheel, and shearing. Surface residual stress measurement using the X-ray diffraction (XRD) technique is carried out close to the cutting edges and on the cross-section. SCC susceptibility studies were carried out as per ASTM G36 in 45% boiling magnesium chloride solution. Optical microscopic examination showed the presence of cracks, and confocal microscopy was used to measure the depth of cracks. The study confirmed that high tensile residual stresses present in the cut surfaces produced by cut-off wheel and shear cutting make the surfaces susceptible to SCC while the surfaces produced by bandsaw cutting are resistant to SCC. Hence, it is shown that there is a definite risk of SCC for product forms of austenitic SS with cut surfaces produced using cutting processes that generate high tensile residual stresses stored for a long period of time in a susceptible environment.