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...     

An Adaptive Transmission Mode Selection Scheme for Cellular Underlaid D2D Communication

In this paper, we propose to use Artificial Bee Colony (ABC) optimization to solve the joint mode selection, channel assignment, and power allocation (JMSCPA) problem to maximize system throughput and spectral efficiency. JMSCPA is a problem where the allocation of channel and power depends on the mode selection. Such problems require two step solution and are called bi-level optimization problems. As bi-level optimization increases the complexity and computational time, we propose a modified version of single-level ABC algorithm aided with the adaptive transmission mode selection algorithm to allocate the cellular, reuse, and dedicated modes to the DUs along with channel and power allocation based on the network traffic load scenarios. A single variable, represented by the users (CUs and DUs) is used to allocate mode selection, and channel allocation to solve the JMSCPA problem, leading to a simpler solution with faster convergence, and significant reduction in the computational complexity which scales linearly with the number of users. Further, the proposed solution avoids premature stagnation of conventional ABC into local minima by incorporating a modification in its update procedure. The efficacy of the ABC-aided approach, as compared to the results reported in the literature, is validated by extensive numerical investigations under different simulation scenarios.

     

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.

     

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)...