Energy-Efficient Implementation of BCD to Excess-3 Code Converter for Nano-Communication Using QCA Technology

Code converters are essential in digital nano communication;therefore,a low-complexity optimal QCA layout for a BCD to Excess-3 code converter has been proposed in this paper.A QCA clockphase-based design technique was adopted to investigate integration with other complicated circuits.Using a unique XOR gate,the recommended circuit’s cell complexity has been decreased.The findings produced using the QCADesigner-2.0.3,a reliable simulation tool,prove the effectiveness of the current structure over earlier designs by considering the number of cells deployed,the area occupied,and the latency as design metrics.In addition,the popular tool QCAPro was used to estimate the energy dissipation of the proposed design.The proposed technique reduces the occupied space by∼40%,improves cell complexity by∼20%,and reduces energy dissipation by∼1.8 times(atγ=1.5EK)compared to the current scalable designs.This paper also studied the suggested structure’s energy dissipation and compared it to existing works for a better performance evaluation.

Physical Layer Security of 6G Vehicular Networks with UAV Systems:First Order Secrecy Metrics,Optimization,and Bounds

The mobility and connective capabilities of unmanned aerial vehicles(UAVs)are becoming more and more important in defense,commercial,and research domains.However,their open communication makes UAVs susceptible toundesirablepassive attacks suchas eavesdroppingor jamming.Recently,the inefficiencyof traditional cryptography-based techniques has led to the addition of Physical Layer Security(PLS).This study focuses on the advanced PLS method for passive eavesdropping in UAV-aided vehicular environments,proposing a solution to complement the conventional cryptography approach.Initially,we present a performance analysis of first-order secrecy metrics in 6G-enabled UAV systems,namely hybrid outage probability(HOP)and secrecy outage probability(SOP)over 2×2 Nakagami-m channels.Later,we propose a novel technique for mitigating passive eavesdropping,which considers first-order secrecy metrics as an optimization problem and determines their lower and upper bounds.Finally,we conduct an analysis of bounded HOP and SOP using the interactive Nakagami-m channel,considering the multiple-input-multiple-output configuration of the UAV system.The findings indicate that 2×2 Nakagami-mis a suitable fadingmodel under constant velocity for trustworthy receivers and eavesdroppers.The results indicate that UAV mobility has some influence on an eavesdropper’s intrusion during line-of-sight-enabled communication and can play an important role in improving security against passive eavesdroppers.

Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

Manual investigation of chest radiography(CXR)images by physicians is crucial for effective decision-making in COVID-19 diagnosis.However,the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques.This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies,including normal cases.Texture information is extracted using gray co-occurrence matrix(GLCM)-based features,while vessel-like features are obtained using Frangi,Sato,and Meijering filters.Machine learning models employing Decision Tree(DT)and RandomForest(RF)approaches are designed to categorize CXR images into common lung infections,lung opacity(LO),COVID-19,and viral pneumonia(VP).The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding diagnosis.The ML model validation using performance measures,including an accuracy of approximately 91.8%with an RF-based classifier,supports the usefulness of the feature set and classifier model in categorizing the four different pathologies.Furthermore,the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased analysis.This analysis reveals varying natural pixel distributions in CXR images belonging to the normal,COVID-19,LO,and VP groups,motivating the incorporation of additional features such as mean,standard deviation,skewness,and percentile based on the filtered images.Notably,the study achieves a considerable improvement in categorizing COVID-19 from LO,with a true positive rate of 97%,further substantiating the effectiveness of the methodology implemented.

Performance optimization of tri-gate junctionless FinFET using channel stack engineering for digital and analog/RF design

This manuscript explores the behavior of a junctionless tri-gate FinFET at the nano-scale region using SiGe material for the channel.For the analysis,three different channel structures are used:(a)tri-layer stack channel(TLSC)(Si-SiGe-Si),(b)double layer stack channel(DLSC)(SiGe-Si),(c)single layer channel(SLC)(S_(i)).The I−V characteristics,subthreshold swing(SS),drain-induced barrier lowering(DIBL),threshold voltage(V_(t)),drain current(ION),OFF current(IOFF),and ON-OFF current ratio(ION/IOFF)are observed for the structures at a 20 nm gate length.It is seen that TLSC provides 21.3%and 14.3%more ON current than DLSC and SLC,respectively.The paper also explores the analog and RF factors such as input transconductance(g_(m)),output transconductance(gds),gain(gm/gds),transconductance generation factor(TGF),cut-off frequency(f_(T)),maximum oscillation frequency(f_(max)),gain frequency product(GFP)and linearity performance parameters such as second and third-order harmonics(g_(m2),g_(m3)),voltage intercept points(VIP_(2),VIP_(3))and 1-dB compression points for the three structures.The results show that the TLSC has a high analog performance due to more gm and provides 16.3%,48.4%more gain than SLC and DLSC,respectively and it also provides better linearity.All the results are obtained using the VisualTCAD tool.

Implementation of High-Q Embedded Band Pass Filter in Wireless Communication

At 12.8 MHz center frequency,the advanced miniaturized polymer-based planar high quality factor(Q)passive elements embedded bandpassfilter works in the L-band.Because most of the demands operate inside the spectrum,the wideband or high-speed operation necessary to enhance must be acquired in microwave frequency ranges.The channel has a quiet,high-performance micro-filter with wideband rejection.Capacitors and inductors are used in the high quality factor(Q)passive components,and related networks are incorporated in thefilter.Embedded layers are concatenated using Three-Dimensional Integrated Circuit(3D-IC)integration,parasitics are removed,and interconnection losses are negotiated using de-embedding methods.A wireless application-based Liquid Crystalline Polymer(LCP)viewpoint is employed as a substrate material in this work.The polymer processes,their properties,and the incorporated high-Q Band Pass Filter Framework.The suggestedfilter model is computed and manufactured utilizing the L-band frequency spectrum,decreasing total physical length by 31%while increasing bandwidth by 45%.

Modeling Target Detection and Performance Analysis of Electronic Countermeasures for Phased Radar

Interference is a key factor in radar return misdetection.Strong interference might make it difficult to detect the signal or targets.When interference occurs in the sidelobes of the antenna pattern,Sidelobe Cancellation(SLC)and Sidelobe Blanking are two unique solutions to solve this problem(SLB).Aside from this approach,the probability of false alert and likelihood of detection are the most essential parameters in radar.The chance of a false alarm for any radar system should be minimal,and as a result,the probability of detection should be high.There are several interference cancellation strategies in the literature that are used to sustain consistent false alarms regardless of the clutter environment.With the necessity for interference cancellation methods and the constant false alarm rate(CFAR),the Maisel SLC algorithm has been modified to create a new algorithm for recognizing targets in the presence of severe interference.The received radar returns and interference are simulated as non-stationary in this approach,and side-lobe interference is cancelled using an adaptive algorithm.By comparing the performance of adaptive algorithms,simulation results are shown.In a severe clutter situation,the simulation results demonstrate a considerable increase in target recognition and signal to noise ratio when compared to the previous technique.

A Review and Analysis of Localization Techniques in Underwater Wireless Sensor Networks

In recent years,there has been a rapid growth in Underwater Wireless Sensor Networks(UWSNs).The focus of research in this area is now on solving the problems associated with large-scale UWSN.One of the major issues in such a network is the localization of underwater nodes.Localization is required for tracking objects and detecting the target.It is also considered tagging of data where sensed contents are not found of any use without localization.This is useless for application until the position of sensed content is confirmed.This article’s major goal is to review and analyze underwater node localization to solve the localization issues in UWSN.The present paper describes various existing localization schemes and broadly categorizes these schemes as Centralized and Distributed localization schemes underwater.Also,a detailed subdivision of these localization schemes is given.Further,these localization schemes are compared from different perspectives.The detailed analysis of these schemes in terms of certain performance metrics has been discussed in this paper.At the end,the paper addresses several future directions for potential research in improving localization problems of UWSN.

Falcon Optimization Algorithm-Based Energy Efficient Communication Protocol for Cluster-Based Vehicular Networks

Rapid development in Information Technology(IT)has allowed several novel application regions like large outdoor vehicular networks for Vehicle-to-Vehicle(V2V)transmission.Vehicular networks give a safe and more effective driving experience by presenting time-sensitive and location-aware data.The communication occurs directly between V2V and Base Station(BS)units such as the Road Side Unit(RSU),named as a Vehicle to Infrastructure(V2I).However,the frequent topology alterations in VANETs generate several problems with data transmission as the vehicle velocity differs with time.Therefore,the scheme of an effectual routing protocol for reliable and stable communications is significant.Current research demonstrates that clustering is an intelligent method for effectual routing in a mobile environment.Therefore,this article presents a Falcon Optimization Algorithm-based Energy Efficient Communication Protocol for Cluster-based Routing(FOA-EECPCR)technique in VANETS.The FOA-EECPCR technique intends to group the vehicles and determine the shortest route in the VANET.To accomplish this,the FOA-EECPCR technique initially clusters the vehicles using FOA with fitness functions comprising energy,distance,and trust level.For the routing process,the Sparrow Search Algorithm(SSA)is derived with a fitness function that encompasses two variables,namely,energy and distance.A series of experiments have been conducted to exhibit the enhanced performance of the FOA-EECPCR method.The experimental outcomes demonstrate the enhanced performance of the FOA-EECPCR approach over other current methods.

Visual Enhancement of Underwater Images Using Transmission Estimation and Multi-Scale Fusion

The demand for the exploration of ocean resources is increasing exponentially.Underwater image data plays a significant role in many research areas.Despite this,the visual quality of underwater images is degraded because of two main factors namely,backscattering and attenuation.Therefore,visual enhancement has become an essential process to recover the required data from the images.Many algorithms had been proposed in a decade for improving the quality of images.This paper aims to propose a single image enhancement technique without the use of any external datasets.For that,the degraded images are subjected to two main processes namely,color correction and image fusion.Initially,veiling light and transmission light is estimated tofind the color required for correction.Veiling light refers to unwanted light,whereas transmission light refers to the required light for color correction.These estimated outputs are applied in the scene recovery equation.The image obtained from color correction is subjected to a fusion process where the image is categorized into two versions and applied to white balance and contrast enhancement techniques.The resultants are divided into three weight maps namely,luminance,saliency,chromaticity and fused using the Laplacian pyramid.The results obtained are graphically compared with their input data using RGB Histogram plot.Finally,image quality is measured and tabulated using underwater image quality measures.

Modelling of Wideband Concentric Ring Frequency Selective Surface for 5G Devices

Frequency selective surfaces(FSSs)play an important role in wireless systems as these can be used as filters,in isolating the unwanted radiation,in microstrip patch antennas for improving the performance of these antennas and in other 5G applications.The analysis and design of the double concentric ring frequency selective surface(DCRFSS)is presented in this research.In the sub-6 GHz 5G FR1 spectrum,a computational synthesis technique for creating DCRFSS based spatial filters is proposed.The analytical tools presented in this study can be used to gain a better understanding of filtering processes and for constructing the spatial filters.Variation of the loop sizes,angles of incidence,and polarization of the concentric rings are the factors which influence the transmission coefficient as per the thorough investigation performed in this paper.A novel synthesis approach based on mathematical equations that may be used to determine the physical parameters ofDCRFSSbased spatial filters is presented.The proposed synthesis technique is validated by comparing results from high frequency structure simulator(HFSS),Ansys electronic desktop circuit editor,and an experimental setup.Furthermore,the findings acquired from a unit cell are expanded to a 2×2 array,which shows identical performance and therefore proves its stability.

Design and Analysis of Graphene Based Tunnel Field Effect Transistor with Various Ambipolar Reducing Techniques

The fundamental advantages of carbon-based graphene material,such as its high tunnelling probability,symmetric band structure(linear dependence of the energy band on the wave direction),low effective mass,and characteristics of its 2D atomic layers,are the main focus of this research work.The impact of channel thickness,gate under-lap,asymmetric source/drain doping method,workfunction of gate contact,and High-K material on Graphene-based Tunnel Field Effect Transistor(TFET)is analyzed with 20 nm technology.Physical modelling and electrical characteristic performance have been simulated using the Atlas device simulator of SILVACO TCAD with user-defined material syntax for the newly included graphene material in comparison to silicon carbide(SiC).The simulation results in significant suppression of ambipolar current to voltage characteristics of TFET and modelled device exhibits a significant improvement in subthreshold swing(0.0159 V/decade),the ratio of Ion/Ioff(1000),and threshold voltage(-0.2 V with highly doped p-type source and 0.2 V with highly doped n-type drain)with power supply of 0.5 V,which make it useful for low power digital applications.

Deep Neural Network Based Detection and Segmentation of Ships for Maritime Surveillance

In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture.Auto-matic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies.Waterways being an important medium of transport require continuous monitoring for protection of national security.The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea.This paper proposes a deep learning based model capable enough to classify between ships and no-ships as well as to localize ships in the original images using bounding box tech-nique.Furthermore,classified ships are again segmented with deep learning based auto-encoder model.The proposed model,in terms of classification,provides suc-cessful results generating 99.5%and 99.2%validation and training accuracy respectively.The auto-encoder model also produces 85.1%and 84.2%validation and training accuracies.Moreover the IoU metric of the segmented images is found to be of 0.77 value.The experimental results reveal that the model is accu-rate and can be implemented for automatic ship detection in water bodies consid-ering remote sensing satellite images as input to the computer vision system.

Reconfigurable Logic Design of CORDIC Based FFT Architecture for 5G Communications

There are numerous goals in next-generation cellular networks(5G),which is expected to be available soon.They want to increase data rates,reduce end-to-end latencies,and improve end-user service quality.Modern networks need to change because there has been a significant rise in the number of base stations required to meet these needs and put the operators’low-cost constraints to the test.Because it can withstand interference from other wireless networks,and Adaptive Complex Multicarrier Modulation(ACMM)system is being looked at as a possible choice for the 5th Generation(5G)of wireless networks.Many arithmetic units need to be used on the hardware side of multicarrier systems to do the pulse-shaping filters and inverse FFT.The main goal of this study is to adapt complex multicarrier modulation(ACMM)for baseband transmission with low complexity and the ability to change it.We found that this is the first recon-figurable architecture that lets you choose how many subcarriers a subband has while still having the same amount of hardware resources as before.Also,under the new design with a single selection line,it selects from a set of filters.The baseband modulating signal is evaluated and tested using a Field-Programmable Gate Array(FPGA)device.This device is available from a commercial source.New technology outperforms current technology in terms of computational com-plexity,simple design,and ease of implementation.Additionally,it has a higher power spectrum density,spectral efficiency,a lower bit error rate,and a higher peak to average power ratio than existing technology.

Performance Assessment and Configuration Analysis in the Study of SCADA System (Supervisory Control and Data Acquisition)

Queuing models are used to assess the functionality and aesthetics of SCADA systems for supervisory control and data collection.Here,the main emphasis is on how the queuing theory can be used in the system’s design and analysis.The analysis’s findings indicate that by using queuing models,cost-performance ratios close to the ideal might be attained.This article discusses a novel methodology for evaluating the service-oriented survivability of SCADA systems.In order to evaluate the state of service performance and the system’s overall resilience,the framework applies queuing theory to an analytical model.As a result,the SCADA process is translated using the M^(X)/G/1 queuing model,and the queueing theory is used to evaluate this design’s strategy.The supplemental variable technique solves the queuing problem that comes with the subsequent results.The queue size,server idle time,utilization,and probabilistic generating factors of the distinct operating strategies are estimated.Notable examples were examined via numerical analysis using mathematical software.Because it is used frequently and uses a statistical demarcation method,this tactic is completely acceptable.The graphical representation of this perspective offers a thorough analysis of the alleged limits.

Computer Modelling of Compact 28/38 GHz Dual-Band Antenna for Millimeter-Wave 5G Applications

A four-element compact dual-band patch antenna having a common ground plane operating at 28/38 GHz is proposed formillimeter-wave communication systems in this paper.Themultiple-input-multiple-output(MIMO)antenna geometry consists of a slotted ellipse enclosed within a hollow circle which is orthogonally rotated with a connected partial ground at the back.The overall size of the four elements MIMO antenna is 2.24λ×2.24λ(at 27.12GHz).The prototype of four-element MIMOresonator is designed and printed using Rogers RTDuroid 5880 withε_(r)=2.2 and loss tangent=0.0009 and having a thickness of 0.8 mm.It covers dual-band having a fractional bandwidth of 15.7%(27.12-31.34 GHz)and 4.2%(37.21-38.81 GHz)for millimeter-wave applications with a gain of more than 4 dBi at both bands.The proposed antenna analysis in terms ofMIMOdiversity parameters(Envelope Correlation Coefficient(ECC)and Diversity Gain(DG))is also carried out.The experimental result in terms of reflection coefficient,radiation pattern,gain and MIMOdiversity parameter correlates very well with the simulated ones that show the potential of the proposed design for MIMO applications at millimeter-wave frequencies.

Design and Energy Estimation of QCA Based Simple Data Path Selector Cum Router Unit for Nano Communication

Quantum dot cellular automata(QCA)is promising nanotechnology due to the three main advantages:faster speed,nanoscale size,and ultrasmall power consumption.This paper proposed a simple data path selector cum router as the‘multiplexerchannel-demultiplexer’unit using QCA,an unavoidable building block of nano communication.A Simple 2×2 block and the extended 4×4 block of data path selectors have been proposed in this article.The layouts of the proposed designs have been verified in QCADesigner,and the energy dissipation has been evaluated using two tools,QCAPro and QCQDesigner-E(QDE).The suggested designs reached a significant improvement in cell complexity(cell count)and covered area over the existing designs.In precise,the proposed 2×2(4×4)block shows 86%(63%)lower cell complexity and 87%(37%)smaller area than the prior reported similar designs.In addition,the currently reported 2×2(4×4)unit has 86%(60%)lower QDE based energy dissipation compared with prior reported designs.

PID and Fuzzy Logic Control of Ball and Beam System Using Particle Swarm Optimization

In this research, we carried out the modeling of the ball and beam system (BBS) within the MATLAB/Simulink framework by applying both proportional-integral-derivative (PID) and fuzzy logic control strategies to govern the dynamics of this constructed model. The underlying non-linear dynamic equations adjusting the behavior of the BBS system are based on Newton’s second law of motion. The physical installation of the BBS, designed for potential real-time application, comprises a lengthy beam subject to movement through the action of a DC servomotor, with a ball traversing the beam in a reciprocating manner. A distance sensor is strategically placed in front of the beam to determine the exact position of the ball. In this system, an electrical control signal applied to the DC servomotor causes the beam to pivot about its horizontal axis, thereby enabling the ball to move freely along the beam's length. To avoid the risk of losing the ball equilibrium on the beam and to achieve precise system control, a mathematical model was devised and implemented within the MATLAB/Simulink environment. The use of the particle swarm optimization (PSO) algorithm was aimed at tackling the task of refining and optimizing the PID controller specifically designed for the linearized ball and beam control system. The presented system is controlled using both PID and fuzzy logic, and the use of the PSO algorithm enhances the system’s responsiveness efficiency.

Modeling of OFDM Based Transceiver for Broadband Powerline Communication

In the recent past power line communication has emerged as an attractive choice for high speed data transfer and is looked upon as inexpensive and reliable media suitable for broadband internet access, home and office automation, in-vehicle data communication etc. In this paper we present an architecture for the physical layer of a PLC transceiver based on Orthogonal Frequency Division Multiplexing (OFDM) and the impact on multipath distortion for PLC transmission in terms of bit error rate. Since there is no standard PLC channel model available, a widely accepted multipath channel model is used for simulation purpose. Simulation results as well as FPGA synthesis verify the effectiveness of the proposed architecture for PLC modem design at 110 Mbps data rate.

Bio-Inspired Intelligent Routing in WSN: Integrating Mayfly Optimization and Enhanced Ant Colony Optimization for Energy-Efficient Cluster Formation and Maintenance

Wireless Sensor Networks(WSNs)are a collection of sensor nodes distributed in space and connected through wireless communication.The sensor nodes gather and store data about the real world around them.However,the nodes that are dependent on batteries will ultimately suffer an energy loss with time,which affects the lifetime of the network.This research proposes to achieve its primary goal by reducing energy consumption and increasing the network’s lifetime and stability.The present technique employs the hybrid Mayfly Optimization Algorithm-Enhanced Ant Colony Optimization(MFOA-EACO),where the Mayfly Optimization Algorithm(MFOA)is used to select the best cluster head(CH)from a set of nodes,and the Enhanced Ant Colony Optimization(EACO)technique is used to determine an optimal route between the cluster head and base station.The performance evaluation of our suggested hybrid approach is based on many parameters,including the number of active and dead nodes,node degree,distance,and energy usage.Our objective is to integrate MFOA-EACO to enhance energy efficiency and extend the network life of the WSN in the future.The proposed method outcomes proved to be better than traditional approaches such as Hybrid Squirrel-Flying Fox Optimization Algorithm(HSFLBOA),Hybrid Social Reindeer Optimization and Differential Evolution-Firefly Algorithm(HSRODE-FFA),Social Spider Distance Sensitive-Iterative Antlion Butterfly Cockroach Algorithm(SADSS-IABCA),and Energy Efficient Clustering Hierarchy Strategy-Improved Social Spider Algorithm Differential Evolution(EECHS-ISSADE).

Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant attention.Previously developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic features.The images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human actions.Poor lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging task.The unimodal-based HAR approaches are not suitable in a real-time environment.Therefore,an updated HAR model is developed using multiple types of data and an advanced deep-learning approach.Firstly,the required signals and sensor data are accumulated from the standard databases.From these signals,the wave features are retrieved.Then the extracted wave features and sensor data are given as the input to recognize the human activity.An Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition process.Additionally,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition process.An experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR model.The EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,respectively.The result proved that the developed model is effective in recognizing human action by taking less time.Additionally,it reduces the computation complexity and overfitting issue through using an optimization approach.