Performance of pulsed plasma thruster at low discharge energy

As the size of satellites scales down, low-power and compact propulsion systems such as the pulsed plasma thruster(PPT) are needed for stabilizing these miniature satellites in orbit. Most PPT systems are operated at 2 J or more of discharge energy. In this work, the performance of a PPT with a side-fed, tongue-flared electrode configuration operated within a lower discharge energy range of 0.5-2.5 J has been investigated. Ablation and charring of the polytetrafluoroethylene propellant surface were analyzed through field-effect scanning electron microscopy imaging and energy-dispersive X-ray spectroscopy. When the discharge energy fell below 2 J, inconsistencies occurred in the specific impulse and the thrust efficiency due to the measurement of the low mass bit. At energy ≥2 J, the performance parameters are compared with other PPT systems of similar configuration and discussed in depth.

Survey of Indoor Localization Based on Deep Learning

This study comprehensively examines the current state of deep learning (DL) usage in indoor positioning.It emphasizes the significance and efficiency of convolutional neural networks (CNNs) and recurrent neuralnetworks (RNNs). Unlike prior studies focused on single sensor modalities like Wi-Fi or Bluetooth, this researchexplores the integration of multiple sensor modalities (e.g.,Wi-Fi, Bluetooth, Ultra-Wideband, ZigBee) to expandindoor localization methods, particularly in obstructed environments. It addresses the challenge of precise objectlocalization, introducing a novel hybrid DL approach using received signal information (RSI), Received SignalStrength (RSS), and Channel State Information (CSI) data to enhance accuracy and stability. Moreover, thestudy introduces a device-free indoor localization algorithm, offering a significant advancement with potentialobject or individual tracking applications. It recognizes the increasing importance of indoor positioning forlocation-based services. It anticipates future developments while acknowledging challenges such as multipathinterference, noise, data standardization, and scarcity of labeled data. This research contributes significantly toindoor localization technology, offering adaptability, device independence, and multifaceted DL-based solutionsfor real-world challenges and future advancements. Thus, the proposed work addresses challenges in objectlocalization precision and introduces a novel hybrid deep learning approach, contributing to advancing locationcentricservices.While deep learning-based indoor localization techniques have improved accuracy, challenges likedata noise, standardization, and availability of training data persist. However, ongoing developments are expectedto enhance indoor positioning systems to meet real-world demands.

An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis

Handwriting is a unique and significant human feature that distinguishes them from one another.There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification.However,such systems are susceptible to forgery,posing security risks.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.Our innovative method is intricately designed,encompassing five distinct phases:data collection,preprocessing,feature extraction,significant feature selection,and classification.One key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting(BHW),setting the foundation for our comprehensive approach.Post-preprocessing,we embarked on an exhaustive feature extraction process,encompassing integration with kinematic,statistical,spatial,and composite features.This meticulous amalgamation resulted in a robust set of 91 features.To enhance the efficiency of our system,we employed an analysis of variance(ANOVA)F test and mutual information scores approach,meticulously selecting the most pertinent features.In the identification phase,we harnessed the power of cutting-edge deep learning models,notably the Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM).These models underwent rigorous training and testing to accurately discern individuals based on their handwriting characteristics.Moreover,our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM,capitalizing on fine motor features for enhanced individual classifications.Crucially,our experimental results underscore the superiority of our approach.The CNN,BiLSTM,and hybrid models exhibited superior performance in individual classification when compared to prevailing state-of-the-art techniques.This validates our method’s efficacy and underscores its potential to outperform existing technologies,marking a significant stride forward in the realm of individual identification through handwriting analysis.

Effect of Helix Pitch Angle on the Modal Dispersion Characteristic of Step-Index Optical Fiber

The general characteristic equation is derived for the helically cladded step-index optical fiber. The dispersion curves are drawn for the different pitch angles Ψ and mode order ν = 1. The effect of helix pitch angle on the dispersion characteristics and also on the modal cut-off condition is examined. Except for the lowest order mode, all the modes appear in pairs. The lowest order mode displays the negative dispersion for the some value of normalized frequency V and depends on the helix pitch angle Ψ.

Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

In software-defined networks(SDNs),controller placement is a critical factor in the design and planning for the future Internet of Things(IoT),telecommunication,and satellite communication systems.Existing research has concentrated largely on factors such as reliability,latency,controller capacity,propagation delay,and energy consumption.However,SDNs are vulnerable to distributed denial of service(DDoS)attacks that interfere with legitimate use of the network.The ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design,especially in critical applications such as military,health care,and financial services networks requiring high availability.We propose a mathematical model for planning the deployment of SDN smart backup controllers(SBCs)to preserve service in the presence of DDoS attacks.Given a number of input parameters,our model has two distinct capabilities.First,it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating conditions.Second,it recommends an optimal number of smart backup controllers for use with different levels of DDoS attacks.The goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the parameters.Our simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.

A low power 50Hz argon plasma for surface modification of polytetrafluoroethylene

The characteristics of a low power 50 Hz argon plasma for surface treatment of polytetrafluoroethylene(PTFE)film is presented in this article.The current–voltage behavior of the discharge and time-varying intensity of the discharge showed that a DC glow discharge was generated in reversed polarity at every half-cycle.At discharge power between 0.5 and 1 W,the measured electron temperature and density were 2–3 eV and∼10^(8) cm^(−3),respectively.The optical emission spectrum of the argon plasma showed presence of some‘impurity species’such as OH,N_(2) and H,which presumably originated from the residual air in the discharge chamber.On exposure of PTFE films to the argon glow plasma at pressure 120 Pa and discharge power 0.5 to 1 W,the water contact angle reduced by 4%to 20%from the original 114°at pristine condition,which confirms improvement of its surface wettability.The increase in wettability was attributed to incorporation of oxygen-containing functional groups on the treated surface and concomitant reduction in fluorine as revealed by the XPS analysis and increase in surface roughness analyzed from the atomic force micrographs.Ageing upon storage in ambient air showed retention of the induced increase in surface wettability.

Design of a Five-Band Dual-Port Rectenna for RF Energy Harvesting

This paper proposed the design of a dual-port rectifier with multifrequency operations.The RF rectifier is achieved using a combination of L-section inductive impedance matching network(IMN)at Port-1 with a multiple stubs impedance transformer at Port-2.The fabricated prototype can harvest RF signal from GSM/900,GSM/1800,UMTS/2100,Wi-Fi/2.45 and LTE/2600 frequency bands at(0.94,1.80,2.10,2.46,and 2.63 GHz),respectively.The rectifier occupies a small portion of a PCB board at 0.20λg×0.15λg.The proposed circuit realized a measured peak RF-to-dc(radio frequency direct current)power conversion efficiency(PCE)of(21%,22.76%,25.33%,21.57%,and 22.14%)for an input power of−20 dBm.The RF harvester attains a measured peak RF-to-dc PCE of 72.70%and an output dc voltage of 154 mV for an input power of 3 dBm at 2.46 GHz.Measurement of the proposed rectifier in the ambiance gives a peak dc output voltage of 376.1 mV from the five signal tones.Similarly,a low-powered bq25504-674 evaluation module(EVM)is integrated with the rectifier.The module boost and drive the rectifier output dc voltage to 945 mV.The performance of the proposed rectifier in the ambiance environment makes it a suitable module for low-powered RF applications.

Performance analysis of a hybrid optical CDMA/DWDM system against inter-symbol interference and four wave mixing

This paper reports the implementation and theoretical model for analyzing an optical CDMA/DWDM hybrid system to reduce two major problems,the Inter-Symbol Interference(ISI)and the Four-Wave Mixing(FWM)effects and improve the performance of optical subscriber access networks by using Zero Cross-Correlation(ZCC)optical codes,which helps to reduce the effect of the Multiple Access Interference(MAI).A squeezing method is used in the proposed hybrid system to completely suppress the ISI.In this method,the sequence interval of the signature code is squeezed into a duration of less than one-bit.The hybrid system is capable of accommodating 120 optical CDMA users carried by only 10 DWDM wavelengths spaced by 0.2 nm with 60 Gb/s/wavelength transmitted over 105.075 km of optical fiber.The result shows that the optimum interval of the code sequence is a quarter(i.e.,25%)of the bit duration.Moreover,the results reveal that the CDMA technology based on the spread spectrum is capable of increasing the nonlinear tolerance of the proposed hybrid system as the energy of bits is distributed over the chip sequence code.Also,the number of ones/weight and the positions between them have a significant effect on the performance of the proposed hybrid system.

Notch-δ-doped InP Gunn diodes for low-THz band applications

The viability of the indium phosphide(InP)Gunn diode as a source for low-THz band applications is analyzed based on a notch-δ-doped structure using the Monte Carlo modeling.The presence of theδ-doped layer could enhance the current harmonic amplitude(A0)and the fundamental operating frequency(f0)of the InP Gunn diode beyond 300 GHz as compared with the conventional notch-doped structure for a 600-nm length device.With its superior electron transport properties,the notch-δ-doped InP Gunn diodes outperform the corresponding gallium arsenide(GaAs)diodes with up to 1.35 times higher in f0 and 2.4 times larger in A0 under DC biases.An optimized InP notch-δ-doped structure is estimated to be capable of generating 0.32-W radio-frequency(RF)power at 361 GHz.The Monte Carlo simulations predict a reduction of 44%in RF power,when the device temperature is increased from 300 K to 500 K;however,its operating frequency lies at 280 GHz which is within the low-THz band.This shows that the notch-δ-doped InP Gunn diode is a highly promising signal source for low-THz sensors,which are in a high demand in the autonomous vehicle industry.

DWDM混合光学系统中帧间和信道内四波混频效应的抑制（英文）

本文提出了光码多分址(CDMA)和光密集波分复用(DWDM)的混合系统,全面研究了四波混频(FWM)的影响。在这个系统中,主要存在两个四波混频问题:包括多址干扰(MAI)和码间干扰(ISI)的帧间四波混频和信道内四波混频。结果表明,综合考虑信道间和信道内四波混频的影响,最佳发射功率可选为18 d Bm。当发射功率大于18 d Bm时,混合系统的误码率(BER)将增加。基于此,本文提出了一种电光相位调制器(EOPM)模块,将其放置在波分复用器之后,通过抑制信道内四波混频的影响,同时调制所有波长信号的相位,从而增加混合系统的非线性容限,这极大地改善了基于OOK传输的光学CDMA-DWDM混合系统的性能。此外,由于多对角线(MD)结构具有零互相关特性,通过使用多对角线识别序列码可以减少多址干扰的影响。结果还表明,CDMA技术与色散相结合有助于降低信道间四波混频的影响。此外,识别序列码间隔在减轻码间干扰中起着至关重要的作用,如结果所示,当识别序列码间隔压缩至比特持续时间的25%时,可以避免码间干扰,此时所提出的混合系统的性能最佳。

Metamaterial-Based Compact Antenna with Defected Ground Structure for 5G and Beyond

In this paper,a unit cell of a single-negative metamaterial structure loaded with a meander line and defected ground structure(DGS)is investigated as the principle radiating element of an antenna.The unit cell antenna causes even or odd mode resonances similar to the unit cell structure depending on the orientation of the microstrip feed used to excite the unit cell.However,the orientation which gives low-frequency resonance is considered here.The unit cell antenna is then loaded with a meander line which is parallel to the split bearing side and connects the other two sides orthogonal to the split bearing side.This modified structure excites another mode of resonance at high frequency when a meander line defect is loaded on the metallic ground plane.Specific parameters of the meander line structure,the DGS shape,and the unit cell are optimized to place these two resonances at different frequencies with proper frequency intervals to enhance the bandwidth.Finally,the feed is placed in an offset position for better impedance matching without affecting the bandwidth The compact dimension of the antenna is 0.25λL×0.23λL×0.02λL,whereλL is the free space wavelength with respect to the center frequency of the impedance bandwidth.The proposed antenna is fabricated and measured.Experimental results reveal that the modified design gives monopole like radiation patterns which achieves a fractional operating bandwidth of 26.6%,from 3.26 to 4.26 GHz for|S11|<−10 dB and a pick gain of 1.26 dBi is realized.In addition,the simulated and measured crosspolarization levels are both less than−15 dB in the horizontal plane.

An Efficient Stacked-LSTM Based User Clustering for 5G NOMA Systems

Non-orthogonal multiple access(NOMA)has been a key enabling technology for the fifth generation(5G)cellular networks.Based on the NOMA principle,a traditional neural network has been implemented for user clustering(UC)to maximize the NOMA system’s throughput performance by considering that each sample is independent of the prior and the subsequent ones.Consequently,the prediction of UC for the future ones is based on the current clustering information,which is never used again due to the lack of memory of the network.Therefore,to relate the input features of NOMA users and capture the dependency in the clustering information,time-series methods can assist us in gaining a helpful insight into the future.Despite its mathematical complexity,the essence of time series comes down to examining past behavior and extending that information into the future.Hence,in this paper,we propose a novel and effective stacked long short term memory(S-LSTM)to predict the UC formation of NOMA users to enhance the throughput performance of the 5G-based NOMA systems.In the proposed strategy,the S-LSTM is modelled to handle the time-series input data to improve the predicting accuracy of UC of the NOMA users by implementing multiple LSTM layers with hidden cells.The implemented LSTM layers have feedback connections that help to capture the dependency in the clustering information as it propagates between the layers.Specifically,we develop,train,validate and test the proposed model to predict the UC formation for the futures ones by capturing the dependency in the clustering information based on the time-series data.Simulation results demonstrate that the proposed scheme effectively predicts UC and thereby attaining near-optimal throughput performance of 98.94%compared to the exhaustive search method.

CT Brain Image:Abnormalities Recognition and Segmentation

In order to develop an automated segmentation system for Computed Tomography(CT)brain images,a new approach which consists of several unsupervised segmentation techniques was introduced. The system segments the CT brain images into three partitions,i.e.,abnor malities,cerebrospinal fluid(CSF) ,and brain matter. Our approach consists of two phase-segmentation methods.In the first phase segmentation,k-means and fuzzy c-means(FCM)methods were implemented to segment and transform the images into the binary images. Based on the connected component in binary images,a decision tree was employed for the annotation of normal or abnormal regions. In the second phase segmentation,the modified FCM with population-diameter independent(PDI)segmentation was applied to segment the images into CSF and brain matter. The experimental results have shown that our proposed systemis feasible and yield satisfactory results.

Heat Transfer with Viscous Dissipation in Couette-Poiseuille Flow under Asymmetric Wall Heat Fluxes

Analytical solutions of temperature distributions and the Nusselt numbers in forced convection are reported for flow through infinitely long parallel plates, where the upper plate moves in the flow direction with constant velocity and the lower plate is kept stationary. The flow is assumed to be laminar, both hydro-dynamically and thermally fully developed, taking into account the effect of viscous dissipation of the flowing fluid. Both the plates being kept at specified and at different constant heat fluxes are considered as thermal boundary conditions. The solutions obtained from energy equation are in terms of Brinkman number, dimensionless velocity and heat flux ratio. These parameters greatly influence and give complete understanding on heat transfer rates that has potentials for designing and analyzing energy equipment and processes.

Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire network.Nevertheless,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack straightforwardly.Furthermore,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been detected.However,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure problem.Moreover,recently the high frequencies of DDoS attacks have increased dramatically.In this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has proposed.Our proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS occurrence.The obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

Magnetic resonance cholangiopancreatography image enhancement for automatic disease detection

AIM:To sufficiently improve magnetic resonance cholangiopancreatography(MRCP) quality to enable reliable computer-aided diagnosis(CAD).METHODS:A set of image enhancement strategies that included filters(i.e.Gaussian,median,Wiener and Perona-Malik),wavelets(i.e.contourlet,ridgelet and a non-orthogonal noise compensation implementation),graph-cut approaches using lazy-snapping and Phase Unwrapping MAxflow,and binary thresholding using a fixed threshold and dynamic thresholding via histogram analysis were implemented to overcome the adverse characteristics of MRCP images such as acquisition noise,artifacts,partial volume effect and large inter-and intra-patient image intensity variations,all of which pose problems in application development.Subjective evaluation of several popular pre-processing techniques was undertaken to improve the quality of the 2D MRCP images and enhance the detection of the significant biliary structures within them,with the purpose of biliary disease detection.RESULTS:The results varied as expected since each algorithm capitalized on different characteristics of the images.For denoising,the Perona-Malik and contourlet approaches were found to be the most suitable.In terms of extraction of the significant biliary structures and removal of background,the thresholding approaches performed well.The interactive scheme performed the best,especially by using the strengths of the graphcut algorithm enhanced by user-friendly lazy-snapping for foreground and background marker selection.CONCLUSION:Tests show promising results for some techniques,but not others,as viable image enhancement modules for automatic CAD systems for biliary and liver diseases.

A Spectrum Sharing Model that Counters Eavesdropping:An Energy Efficiency View

Wireless transmission is subject to eavesdropping.When wireless transmission ceases,the assigned frequency channel is unused,wasting the spectral opportunity given.In this study,a spectrum sharing model that reduces spectral wastage and protects against eavesdropping is proposed.First,cognitive radio(CR)shares the channel access with primary user(PU).When the CR senses that the channel is idle,CR can seize the unoccupied channel for its own use.If the channel is detected to be occupied by PU,CR transmits artificial noise to jam any potential eavesdropper.To what extent is this operation beneficial to the CR?The main concern of this study is the energy efficiency(μ)of CR,i.e.,the ratio of channel throughput to its energy consumption.The relationship betweenμand the percentage of frame duration allocated for sensing(τ)was investigated.This study contributes a novel theoretical expression that allows us to find the optimalμandτvalues,denoted byμ∗andτ∗.With the availability of this expression,the relationships between(μ∗,τ∗)and other important system parameters can be understood thoroughly.Our investigation reveals that strong CR signal will result in highμ∗without the need of increasingτ∗.On the other hand,a strong primary signal allows a shortτ∗and it improvesμ∗.High sampling rate for sensing may be unnecessary,as it does not improveμ∗significantly.A more demanding target probability of detection requires a higher sensing duration,but it has insignificant impact onμ∗.

A Partial Double-Pass S-Band Erbium-Doped Fibre Amplifier

An efficient and low noise short wavelength band erbium-doped fibre amplifier （S-band EDFA） is proposed and demonstrated using double-pass configuration. This amplifier provides a gain of 1500 nm signal as high as 26.9 dB, which is 9.6 dB higher than the two-stage single-pass amplifier. The corresponding noise figure obtained is 7.5 dB, which is of the same level as in the single-pass amplifier and more than 2 dB lower than the previously reported double-pass amplifier [IEIOE Electron. Express 2 （2005） 182]. The gain enhancement is due to the double pass-propagation of the test signal in the second stage, which increases the effective erbium-doped fibre （EDF） length. The low noise is attributed to the optical circulator between EDFs, which prevents the backward amplified spontaneous emission from propagating into the input part of the amplifier. The proposed amplifier is expected to play an important role in the development of a practical S-band EDFA.

Improved Radio Network Dimensioning for Real-Time Polling Service on IEEE 802.16 Wireless Networks with QoS Consideration

Recently, applications of real-time polling service (rtPS) in IEEE 802.16 wireless networks have gained considerable popularity. These applications generate large amounts of real time traffic in the network and thus maintaining the quality of service (QoS) such as packet delay requirement in rtPS dominant networks is critical. Existing dimensioning methodology does not consider QoS parameters of rtPS in network dimensioning. Moreover, exhaustive and time-consuming simulations are required to evaluate the performance and QoS of rtPS. To overcome this problem, we propose an improved radio network dimensioning framework which considers QoS parameters of rtPS in network dimensioning. In this framework, an analytical model is developed to evaluate the capacity and performance of rtPS in IEEE 802.16 wireless networks. The proposed framework provides a fast and accurate means of finding the trade-off between system load and packet delay, thus providing network operators with an analytical tool that jointly considers coverage, capacity and QoS requirements for obtaining the minimum number of sites required. The accuracy of the proposed model is validated through simulations.

Clustering Indoor Location Data for Social Distancing and Human Mobility to Combat COVID-19

The world is experiencing the unprecedented time of a pandemic caused by the coronavirus disease(i.e.,COVID-19).As a countermeasure,contact tracing and social distancing are essential to prevent the transmission of the virus,which can be achieved using indoor location analytics.Based on the indoor location analytics,the human mobility on a site can be monitored and planned to minimize human’s contact and enforce social distancing to contain the transmission of COVID-19.Given the indoor location data,the clustering can be applied to cluster spatial data,spatio-temporal data and movement behavior features for proximity detection or contact tracing applications.More specifically,we propose the Coherent Moving Cluster(CMC)algorithm for contact tracing,the density-based clustering(DBScan)algorithm for identification of hotspots and the trajectory clustering(TRACLUS)algorithm for clustering indoor trajectories.The feature extraction mechanism is then developed to extract useful and valuable features that can assist the proposed system to construct the network of users based on the similarity of the movement behaviors of the users.The network of users is used to model an optimization problem to manage the human mobility on a site.The objective function is formulated to minimize the probability of contact between the users and the optimization problem is solved using the proposed effective scheduling solution based on OR-Tools.The simulation results show that the proposed indoor location analytics system outperforms the existing clustering methods by about 30%in terms of accuracy of clustering trajectories.By adopting this system for human mobility management,the count of close contacts among the users within a confined area can be reduced by 80%in the scenario where all users are allowed to access the site.