Deep Learning Network for Classifying Target of Same Shape using RCS Time Series

The main intension of this work is to find the warhead and decoy classification and identification. Classification of radar target is one of the utmost imperatives and hardest practical problems in finding out the missile. Detection of target in the pool of decoys and debris is one of the major radas technologies widely used in practice. In this study we mainly focus on the radar target recognition in different shapes like cone, cylinder and sphere based on radar cross section (RCS). RCS is a critical element of the radar signature that is used in this work to identify the target. The concept is to focus on new technique of ML for analyzing the input data and to attain a better accuracy. Machine learning has had a significant impact on the entire industry as a result of its high computational competency for target prediction with precise data analysis. We investigated various machine learning classifiers methods to categorize available radar target data. This chapter summarizes conventional and deep learning technique used for classification of radar target

An overview on wireless sensor networks and finding optimal location of nodes

In this review, our aim is to make a brief description about technology of Wireless Sensor Network (WSN) and its capability to pave the way in order to make connection between physical and virtual world based on Internet worldwide network. Hence, in this technology, sensor nodes play an important role to transmit data from a node to other defined nodes in its broaden range. Due to gain most optimal state from WSNs, subject of localization for radio frequency networks has a great importance in many technical applications such as military devices to detect specified local points to attack or defend, civil engineering and in general sensor networks. The main technology to obtain direct locations is GPS (Global Positioning System). After expressing a brief history on introduction part, we will go through in order to interrogate on main structure of WSNs regarding mathematical formulations and algorithms to find best and optimal access points based on Localization action. Then, we summarize algorithms and approaches to develop in order to introduce the best strategy in order to access nodes in the best possible state in WSNs. As a result, we conclude about the mentioned issues in order of comparison and reaching a final result. Therefore, final aim of this review is to explain efficiency and reliability of localization based on different opinions. Results show this overwhelming technology can be completely modified in order to find new solutions to find nodes in most optimal nodes based on spontaneous structure of WSNs

A fuzzy-based optimization technique for the energy and spectrum efficiencies trade-off in cognitive radio-enabled 5G network

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Optimized Swarm Enabled Deep Learning Technique for Bone Tumor Detection using Histopathological Image

Cancer subjugates a community that lacks proper care. It remains apparent that research studies enhance novel benchmarks in developing a computer-assisted tool for prognosis in radiology yet an indication of illness detection should be recognized by the pathologist. In bone cancer (BC), Identification of malignancy out of the BC’s histopathological image (HI) remains difficult because of the intricate structure of the bone tissue (BTe) specimen. This study proffers a new approach to diagnosing BC by feature extraction alongside classification employing deep learning frameworks. In this, the input is processed and segmented by Tsallis Entropy for noise elimination, image rescaling, and smoothening. The features are excerpted employing Efficient Net-based Convolutional Neural Network (CNN) Feature Extraction. ROI extraction will be employed to enhance the precise detection of atypical portions surrounding the affected area. Next, for classifying the accurate spotting and for grading the BTe as typical and a typical employing augmented XGBoost alongside Whale optimization (WOA). HIs gathering out of prevailing scales patients is acquired alongside texture characteristics of such images remaining employed for training and testing the Neural Network (NN). These classification outcomes exhibit that NN possesses a hit ratio of 99.48 percent while this occurs in BT classification

Design and Synthesis of Multi-Mode Bandpass Filter for Wireless Applications

In this paper, a compact bandpass filter with improved band stop and band pass characteristics for wireless applications is built with four internal conductive poles in a single resonating cavity, which adds novel quad-resonating modes to the realization of band pass filter. This paper covers the design and testing of the S-band combline coaxial cavity filter which is beneficial in efficient filtering functions in wireless communication system design. The metallic cavity high Q coaxial resonators have the advantages of narrowband, low loss, better selectivity and high potential for power handling, as compared to microstrip filter in the application to determine the quality factor of motor oils. Furthermore, the tuning of coupling screws in the combline filter allows in frequency and bandwidth adjustments. An impedance bandwidth of 500 MHz (fractional bandwidth of 12.8%) has been achieved with an insertion loss of less than 2.5 dB and return loss of 18 dB at the resonant frequency. Four-pole resonating cavity filters have been developed with the center frequency of 4.5 GHz. Insert loss at 0 dB and estimated bandwidth at 850 MHz and a quality factor of 4.3 for the band pass frequencies between 4 and 8 GHz is seen in the simulated result

Skin Lesion Extraction Using Multiscale Morphological Local Variance Reconstruction Based Watershed Transform and Fast Fuzzy C-Means Clustering

Early identification of melanocytic skin lesions increases the survival rate for skin cancer patients. Automated melanocytic skin lesion extraction from dermoscopic images using the computer vision approach is a challenging task as the lesions present in the image can be of different colors, there may be a variation of contrast near the lesion boundaries, lesions may have different sizes and shapes, etc. Therefore, lesion extraction from dermoscopic images is a fundamental step for automated melanoma identification. In this article, a watershed transform based on the fast fuzzy c-means (FCM) clustering algorithm is proposed for the extraction of melanocytic skin lesion from dermoscopic images. Initially, the proposed method removes the artifacts from the dermoscopic images and enhances the texture regions. Further, it is filtered using a Gaussian filter and a local variance filter to enhance the lesion boundary regions. Later, the watershed transform based on MMLVR (multiscale morphological local variance reconstruction) is introduced to acquire the superpixels of the image with accurate boundary regions. Finally, the fast FCM clustering technique is implemented in the superpixels of the image to attain the final lesion extraction result. The proposed method is tested in the three publicly available skin lesion image datasets, i.e., ISIC 2016, ISIC 2017 and ISIC 2018. Experimental evaluation shows that the proposed method achieves a good result

An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks

In recent years, the underwater wireless sensor network (UWSN) has received a significant interest among research communities for several applications, such as disaster management, water quality prediction, environmental observance, underwater navigation, etc. The UWSN comprises a massive number of sensors placed in rivers and oceans for observing the underwater environment. However, the underwater sensors are restricted to energy and it is tedious to recharge/replace batteries, resulting in energy efficiency being a major challenge. Clustering and multi-hop routing protocols are considered energy-efficient solutions for UWSN. However, the cluster-based routing protocols for traditional wireless networks could not be feasible for UWSN owing to the underwater current, low bandwidth, high water pressure, propagation delay, and error probability. To resolve these issues and achieve energy efficiency in UWSN, this study focuses on designing the metaheuristics-based clustering with a routing protocol for UWSN, named MCR-UWSN. The goal of the MCR-UWSN technique is to elect an efficient set of cluster heads (CHs) and route to destination. The MCR-UWSN technique involves the designing of cultural emperor penguin optimizer-based clustering (CEPOC) techniques to construct clusters. Besides, the multi-hop routing technique, alongside the grasshopper optimization (MHR-GOA) technique, is derived using multiple input parameters. The performance of the MCR-UWSN technique was validated, and the results are inspected in terms of different measures. The experimental results highlighted an enhanced performance of the MCR-UWSN technique over the recent state-of-art techniques

Authentication and Resource Allocation Strategies during Handoff for 5G IoVs Using Deep Learning

One of the most sought-after applications of cellular technology is transforming a vehicle into a device that can connect with the outside world, similar to smartphones. This connectivity is changing the automotive world. With the speedy growth and densification of vehicles in Internet of Vehicles (IoV) technology, the need for consistency in communication amongst vehicles becomes more significant. This technology needs to be scalable, secure, and flexible when connecting products and services. 5G technology, with its incredible speed, is expected to power the future of vehicular networks. Owing to high mobility and constant change in the topology, cooperative intelligent transport systems ensure real time connectivity between vehicles. For ensuring a seamless connectivity amongst the entities in vehicular networks, a significant alternative to design is support of handoff. This paper proposes a scheme for the best Road Side Unit (RSU) selection during handoff. Authentication and security of the vehicles are ensured using the Deep Sparse Stacked Autoencoder Network (DS2AN) algorithm, developed using a deep learning model. Once authenticated, resource allocation by RSU to the vehicle is accomplished through Deep-Q learning (DQL) techniques. Compared with the existing handoff schemes, Reinforcement Learning based on the MDP (RL-MDP) has been found to have a 13% lesser decision delay for selecting the best RSU. A higher level of security and minimum time requirement for authentication is achieved using DS2AN. The proposed system simulation results demonstrate that it ensures reliable packet delivery, significantly improving system throughput, upholding tolerable delay levels during a change of RSUs

Investigation of AlGaN Channel HEMTs on β-Ga2O3 Substrate for High-Power Electronics

The wider bandgap AlGaN (Eg > 3.4 eV) channel-based high electron mobility transistors (HEMTs) are more effective for high voltage operation. High critical electric field and high saturation velocity are the major advantages of AlGaN channel HEMTs, which push the power electronics to a greater operating regime. In this article, we present the DC characteristics of 0.8 µm gate length (LG) and 1 µm gate-drain distance (LGD) AlGaN channel-based high electron mobility transistors (HEMTs) on ultra-wide bandgap β-Ga2O3 Substrate. The β-Ga2O3 substrate is cost-effective, available in large wafer size and has low lattice mismatch (0 to 2.4%) with AlGaN alloys compared to conventional SiC and Si substrates. A physics-based numerical simulation was performed to investigate the DC characteristics of the HEMTs. The proposed HEMT exhibits sheet charge density (ns) of 1.05 × 1013 cm−2, a peak on-state drain current (IDS) of 1.35 A/mm, DC transconductance (gm) of 277 mS/mm. The ultra-wide bandgap AlGaN channel HEMT on β-Ga2O3 substrate with conventional rectangular gate structure showed 244 V off-state breakdown voltage (VBR) and field plate gate device showed 350 V. The AlGaN channel HEMTs on β-Ga2O3 substrate showed an excellent performance in ION/IOFF and VBR. The high performance of the proposed HEMTs on β-Ga2O3 substrate is suitable for future portable power converters, automotive, and avionics applications