Plasma potential measurements using an emissive probe made of oxide cathode

A novel emissive probe consisting of an oxide cathode coating is developed to achieve a low operating temperature and long service life.The properties of the novel emissive probe are investigated in detail,in comparison with a traditional tungsten emissive probe,including the operating temperature,the electron emission capability and the plasma potential measurement.Studies of the operating temperature and electron emission capability show that the tungsten emissive probe usually works at a temperature of 1800 K-2200 K while the oxide cathode emissive probe can function at about 1200 K-1400 K.In addition,plasma potential measurements using the oxide cathode emissive probe with different techniques have been accomplished in microwave electron cyclotron resonance plasmas with different discharge powers.It is found that a reliable plasma potential can be obtained using the improved inflection point method and the hot probe with zero emission limit method,while the floating point method is invalid for the oxide cathode emissive probe.

Detecting and Preventing of Attacks in Cloud Computing Using Hybrid Algorithm

Cloud computing is the technology that is currently used to provide users with infrastructure,platform,and software services effectively.Under this system,Platform as a Service(PaaS)offers a medium headed for a web development platform that uniformly distributes the requests and resources.Hackers using Denial of service(DoS)and Distributed Denial of Service(DDoS)attacks abruptly interrupt these requests.Even though several existing methods like signature-based,statistical anomaly-based,and stateful protocol analysis are available,they are not sufficient enough to get rid of Denial of service(DoS)and Distributed Denial of Service(DDoS)attacks and hence there is a great need for a definite algorithm.Concerning this issue,we propose an improved hybrid algorithm which is a combination of Multivariate correlation analysis,Spearman coefficient,and mitigation technique.It can easily differentiate common traffic and attack traffic.Not only that,it greatly helps the network to distribute the resources only for authenticated requests.The effects of comparing with the normalized information have shown an extra encouraging detection accuracy of 99%for the numerous DoS attack as well as DDoS attacks.

Research and Implementation of Credit Investigation Sharing Platform Based on Double Blockchain

As the development of the modern economy is increasingly insep-arable from credit support,the traditional credit investigation mode has yet to meet this demand.Because of the difficulties in conventional credit data sharing among credit investigation agencies,poor data portability,and centralized supervision,this paper proposes a data-sharing scheme for credit investigation agencies based on a double blockchain.Given the problems such as difficult data sharing,difficult recovery of damaged data,and accessible data leakage between institutions and users with non-traditional credit inves-tigation data other than credit,this paper proposes a data-sharing scheme for credit investigation subjects based on the digital envelope.Based on the above two solutions,this paper designs a double blockchain credit data-sharing plat-form based on the“public chain+alliance chain”from credit investigation agencies’and visiting subjects’perspectives.The sharing platform uses the alliance chain as the management chain to solve the problem of complex data sharing between credit bureaus and centralized supervision,uses the public chain as the use chain to solve the problem of complex data sharing between the access subject and the credit bureaus,uses the interplanetary file system and digital envelope and other technologies to solve the problem of difficult recovery of damaged data,data leakage,and other issues.After the upload test,the average upload speed reaches 80.6 M/s.The average download speed of the system is 88.7 M/s after the download test.The multi-thread stress test tests the linkage port on the system package,and the average response time for the hypertext transfer protocol(HTTP)is 0.6 ms.The system performance and security analysis show that the sharing platform can provide safe and reliable credit-sharing services for organizations and users and high working efficiency.

Construction of Regular Rate-Compatible LDPC Convolutional Codes

In this paper, we propose a new method to derive a family of regular rate-compatible low-density parity-check（RC-LDPC） convolutional codes from RC-LDPC block codes. In the RC-LDPC convolutional family, each extended sub-matrix of each extended code is obtained by choosing specified elements from two fixed matrices HE1K and HE1K, which are derived by modifying the extended matrices HE1 and HE2 of a systematic RC-LDPC block code. The proposed method which is based on graph extension simplifies the design, and prevent the defects caused by the puncturing method. It can be used to generate both regular and irregular RC-LDPC convolutional codes. All resulted codes in the family are systematic which simplify the encoder structure and have maximum encoding memories which ensure the property. Simulation results show the family collectively offer a steady improvement in performance with code compatibility over binary-input additive white Gaussian noise channel（BI-AWGNC）.

Entanglement dynamics of a double two-photon Jaynes-Cummings model with Kerr-like medium

In this paper, the entanglement dynamics of a double two-photon Jaynes-Cummings model with Kerr-like medium is investigated. It is shown that initial entanglement has an interesting subsequent time evolution, including the so-called entanglement sudden death effect. It is also shown analytically that the Kerr-like medium can repress entanglement sudden death and enhance the degree of atom-atom entanglement. A more interesting fact is that the Kerr effect is more obvious when each of the two cavities with have the Kerr-like medium than only one of them with the Kerr-like medium.

Enhancing parameter precision of optimal quantum estimation by quantum screening

We propose a scheme of quantum screening to enhance the parameter-estimation precision in open quantum systems by means of the dynamics of quantum Fisher information. The principle of quantum screening is based on an auxiliary system to inhibit the decoherence processes and erase the excited state to the ground state. By comparing the case without quantum screening, the results show that the dynamics of quantum Fisher information with quantum screening has a larger value during the evolution processes.

Dynamics of quantum Fisher information in a two-level system coupled to multiple bosonic reservoirs

We consider the optimal parameter estimation for a two-level system coupled to multiple bosonic reservoirs. By using quantum Fisher information （QFI）, we investigate the effect of the Markovian reservoirs＇ number N on QFI in both weak and strong coupling regimes for a two-level system surrounded by N zero-temperature reservoirs of field modes initially in the vacua. The results show that the dynamics of QFI non-monotonically decays to zero with revival oscillations at some time in the weak coupling regime depending on the reservoirs＇ parameters. Furthermore, we also present the relations between the QFI flow, the flows of energy and information, and the sign of the decay rate to gain insight into the physical processes characterizing the dynamics.

Analysis of atmospheric effects on the continuous variable quantum key distribution

Atmospheric effects have significant influence on the performance of a free-space optical continuous variable quantum key distribution(CVQKD)system.In this paper,we investigate how the transmittance,excess noise and interruption probability caused by atmospheric effects affect the secret-key rate(SKR)of the CVQKD.Three signal wavelengths,two weather conditions,two detection schemes,and two types of attacks are considered in our investigation.An expression aims at calculating the interruption probability is proposed based on the Kolmogorov spectrum model.The results show that a signal using long working wavelength can propagate much further than that of using short wavelength.Moreover,as the wavelength increases,the influence of interruption probability on the SKR becomes more significant,especially within a certain transmission distance.Therefore,interruption probability must be considered for CVQKD by using long-signal wavelengths.Furthermore,different detection schemes used by the receiver will result in different transmission distances when subjected to individual attacks and collective attacks,respectively.

Performance of OFDM System with Constant Amplitude Modulation

The Orthogonal Frequency Division Multiplexing (OFDM) technique has recently received considerable attention for wireless networks. Despite its advantages, it has a major drawback of its high Peak-to-Average Power Ratio (PAPR) value which affects the system efficiency and the cost. In this paper, a proposed system is discussed to achieve 0 dB PAPR value. It depends on a proposed block, called Constant Amplitude (CA) modulation. The whole characteristic mathematical analysis is presented for the proposed system. Additionally, the complexity evolution is explained. Afterwards, many MATLAB simulation programs are executed. Time and frequency domain behaviors are presented. Furthermore, in-band distortion introduced by the proposed CA modulation is calculated in terms of Error Vector Magnitude (EVM). Moreover, the proposed system outperforms the conventional one when compared in terms of PAPR, equalization, and BER under Additive White Gaussian Noise (AWGN) channel and multipath fading channels. In addition, the impact of the proposed scheme design parameter is studied.

Intrusion Detection Using Federated Learning for Computing

The integration of clusters,grids,clouds,edges and other computing platforms result in contemporary technology of jungle computing.This novel technique has the aptitude to tackle high performance computation systems and it manages the usage of all computing platforms at a time.Federated learning is a collaborative machine learning approach without centralized training data.The proposed system effectively detects the intrusion attack without human intervention and subsequently detects anomalous deviations in device communication behavior,potentially caused by malicious adversaries and it can emerge with new and unknown attacks.The main objective is to learn overall behavior of an intruder while performing attacks to the assumed target service.Moreover,the updated system model is send to the centralized server in jungle computing,to detect their pattern.Federated learning greatly helps the machine to study the type of attack from each device and this technique paves a way to complete dominion over all malicious behaviors.In our proposed work,we have implemented an intrusion detection system that has high accuracy,low False Positive Rate(FPR)scalable,and versatile for the jungle computing environment.The execution time taken to complete a round is less than two seconds,with an accuracy rate of 96%.

Research and analysis of Brillouin distributed sensing system based on quasi-single-mode few-mode fiber

A distributed fiber sensor was fabricated by splicing two single-mode fibers(SMFs)using the few-mode fiber(FMF)technique.A Brillouin optical time domain analysis(BOTDA)system was developed to measure the sensor’s temperature and bending performance.Two-mode and four-mode step FMFs were combined to splice the few-mode segment.The results indicate that the temperature response coefficients of the few-mode segment are only slightly higher than those of the connected single-mode segment,measuring at 1.13 MHz/℃and 1.12 MHz/℃,respectively.The minimum bending radius for the sensor is 0.9 cm,and the four-mode bending response curve is superior to that of the two-mode one,proving that 4-SI-FMF offers better bending sensitivity.

Secrecy Capacity Maximization for a UAV-Assisted MEC System

Unmanned aerial vehicle(UAV)communication has attracted wide attentions in the mobile edge computing(MEC)system owing to its high-flexibility and simple operation auxiliary communication mode.Users can offload computing tasks to UAVs,which serves as edge nodes.Meanwhile,UAVs forward the tasks onto a cloud center or base station for processing,thereby shortening the implementation time of tasks.Nevertheless,the offloading links of an UAV-assisted MEC system adopt a radio broadcasting mode.Several eavesdroppers might be present in the environment to eavesdrop the data sent by users and UAVs,thereby causing significant effects on the secrecy performance.An optimized iterative algorithm is proposed in this paper to realize the maximum secrecy capacity of the MEC system and further improve the secrecy performance of an UAV-assisted MEC system and assure secrecy transmit.By doing so,the secrecy transmit problems of the two-staged offloading model of the UAV-assisted MEC system are solved.The maximum secrecy capacity of the system is obtained through joint optimization of the UAV positions,transmit power of the UAV,task offloading ratio,and allocation of offloading users considering the limited time and energy of an UAV.Simulation results demonstrate that the proposed iterative algorithm can effectively improve the secrecy capacity of the system.

MU-GAN:Facial Attribute Editing Based on Multi-Attention Mechanism

Facial attribute editing has mainly two objectives:1)translating image from a source domain to a target one,and 2)only changing the facial regions related to a target attribute and preserving the attribute-excluding details.In this work,we propose a multi-attention U-Net-based generative adversarial network(MU-GAN).First,we replace a classic convolutional encoder-decoder with a symmetric U-Net-like structure in a generator,and then apply an additive attention mechanism to build attention-based U-Net connections for adaptively transferring encoder representations to complement a decoder with attribute-excluding detail and enhance attribute editing ability.Second,a self-attention(SA)mechanism is incorporated into convolutional layers for modeling long-range and multi-level dependencies across image regions.Experimental results indicate that our method is capable of balancing attribute editing ability and details preservation ability,and can decouple the correlation among attributes.It outperforms the state-of-the-art methods in terms of attribute manipulation accuracy and image quality.Our code is available at https://github.com/SuSir1996/MU-GAN.

Protecting entanglement by detuning:in Markovian environments vs in non-Markovian environments

The models of two qubits separately trapped in two independent Markovian or non-Markovian environments have been investigated. The distinction of the two-qubit entanglement dynamics in different environments has also been discussed in detail. The results show that, in non-Markovian environments, the possible usage time of entanglement can be extended due to its memory effect. On the other hand, we note that, compared to Markovian environments, the two-qubit entanglement could be protected better in non-Markovian environments by modulating the detuning between qubits and cavities. Finally, an intuitive physicM interpretation for these results is given.

IRS-Aided SWIPT Systems with Power Splitting and Artificial Noise

Intelligent reflecting surface(IRS) is regarded as a promising technology because it can achieve higher passive beamforming gain. In particular, the IRS assisted simultaneous wireless information and power transfer(SWIPT) system can make the information decoding receivers(IDRs) have a higher signal-to-noise ratio(SNR), and the energy harvesting receivers(EHRs) have the guarantee of minimum harvested energy threshold. Motivated by the above,in this paper, we use the power splitting(PS) at the user and introduce artificial noise(AN) into the access point(AP), so that the user in system can harvest energy and decode information simultaneously,further improve the security of user. We jointly optimize the beamforming matrix at AP, the reflection phase shift at IRS and the PS ratio, in order to maximize the user’s achievable secrecy rate, subject to the user’s minimum harvested energy threshold and AP’s transmission power. Due to the introduction of PS ratio, the coupling between variables is increased,and the complexity of the problem is significantly increased. Furthermore, the problem is non-convex, so we propose an efficient algorithm based on Taylor Formula, semi-definite relaxation(SDR) and alternating optimization(AO) to get the solution. Numerical results show that the proposed IRS-SWIPT system with PS and AN achieves significant performance improvement compared with other benchmark scheme.

Improved Face Recognition Method Using Genetic Principal Component Analysis

An improved face recognition method is proposed based on principal component analysis （PCA） compounded with genetic algorithm （GA）, named as genetic based principal component analysis （GPCA）. Initially the eigenspace is created with eigenvalues and eigenvectors. From this space, the eigenfaces are constructed, and the most relevant eigenfaees have been selected using GPCA. With these eigenfaees, the input images are classified based on Euclidian distance. The proposed method was tested on ORL （Olivetti Research Labs） face database. Experimental results on this database demonstrate that the effectiveness of the proposed method for face recognition has less misclassification in comparison with previous methods.

基于小波域HMM模型的自适应图像水印算法

An adaptive image watermarking algorithm based on HMM in wavelet domain is proposed. The algorithm is abstracted as follows: 1) the vector HMM model is employed to describe the statistical characteristic of image wavelet coefficients and the resulting HMM based detector achieves significant improvements in performance compared to the conventional correlation detector;2) adaptive watermark embedding based on HVS analysis; 3) a novel embedding strategy which is optimized for the HMM tree structure is adopted; 4) the strategy of dynamical threshold is applied in watermark detection. High robust results are achieved against Stirmark attacks, such as JPEG compression, adding noise, median cut and filter.

Directional Source Localization Based on RSS-AOA Combined Measurements

Source localization plays an indispensable role in many applications.This paper addresses the directional source localization problem in a three-dimensional(3D)wireless sensor network using hybrid received-signal-strength(RSS)and angle-of-arrival(AOA)measurements.Both the position and transmission orientation of the source are to be estimated.In the considered positioning scenario,the angle and range measurements are respectively corresponding to the AOA model and RSS model that integrates the Gaussian-shaped radiation pattern.Given that the localization problem is non-convex and the unknown parameters therein are coupled together,this paper adopts the second-order cone relaxation and alternating optimization techniques in the proposed estimation algorithm.Moreover,to provide a performance benchmark for any localization method,the corresponding Cramer-Rao lower bounds(CRLB)of estimating the unknown position and transmission orientation of the source are derived.Numerical and simulation results demonstrate that the presented algorithm effectively resolves the problem,and its estimation performance is close to the CRLB for the localization with the hybrid measurements.

Low Area PRESENT Cryptography in FPGA Using TRNG-PRNG Key Generation

Lightweight Cryptography(LWC)is widely used to provide integrity,secrecy and authentication for the sensitive applications.However,the LWC is vulnerable to various constraints such as high-power consumption,time consumption,and hardware utilization and susceptible to the malicious attackers.In order to overcome this,a lightweight block cipher namely PRESENT architecture is proposed to provide the security against malicious attacks.The True Random Number Generator-Pseudo Random Number Generator(TRNG-PRNG)based key generation is proposed to generate the unpredictable keys,being highly difficult to predict by the hackers.Moreover,the hardware utilization of PRESENT architecture is optimized using the Dual port Read Only Memory(DROM).The proposed PRESENT-TRNGPRNG architecture supports the 64-bit input with 80-bit of key value.The performance of the PRESENT-TRNG-PRNG architecture is evaluated by means of number of slice registers,flip flops,number of slices Look Up Table(LUT),number of logical elements,slices,bonded input/output block(IOB),frequency,power and delay.The input retrieval performances analyzed in this PRESENT-TRNG-PRNG architecture are Peak Signal to Noise Ratio(PSNR),Structural Similarity Index(SSIM)and Mean-Square Error(MSE).The PRESENT-TRNG-PRNG architecture is compared with three different existing PRESENT architectures such as PRESENT On-TheFly(PERSENT-OTF),PRESENT Self-Test Structure(PRESENT-STS)and PRESENT-Round Keys(PRESENT-RK).The operating frequency of the PRESENT-TRNG-PRNG is 612.208 MHz for Virtex 5,which is high as compared to the PRESENT-RK.