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With the application of various new mission loads in unmanned aerial vehicle (UAV), how to improve the communication quality of UAV data link has become a widespread concern. With the rapid development of UAV, multi UAVs cooperative self-organizing network can accomplish tasks more reliably, efficiently and economically than a single large UAV. This paper analyzes the characteristics of mobile self-organizing network composed of multiple UAVs, and focuses on how to improve the communication quality of UAV network. After analyzing the technical principles and system models of multiple input multiple output (MIMO) and orthogonal frequency division multiplexing (OFDM) technologies, a MIMO-OFDM communication system is constructed. The simulation results show that the application of MIMO-OFDM technology in UAV communication network can effectively improve the data transmission ability.

In this paper, a blind modulation classification method based on compressed sensing using a high-order cumulant and cyclic spectrum combined with the decision tree&ndash, support vector machine classifier is proposed to solve the problem of low identification accuracy under single-feature parameters and reduce the performance requirements of the sampling system. Through calculating the fourth-order, eighth-order cumulant and cyclic spectrum feature parameters by breaking through the traditional Nyquist sampling law in the compressed sensing framework, six different cognitive radio signals are effectively classified. Moreover, the influences of symbol length and compression ratio on the classification accuracy are simulated and the classification performance is improved, which achieves the purpose of identifying more signals when fewer feature parameters are used. The results indicate that accurate and effective modulation classification can be achieved, which provides the theoretical basis and technical accumulation for the field of optical-fiber signal detection.

With the rapid development of information technology and the increasing application of unmanned aerial vehicle (UAV) in various fields, UAV network is facing many challenges, such as complex electromagnetic environment, serious environmental interference and human interference. Therefore, higher requirements are put forward for its safety, reliability and adaptability in complex environment. In order to improve the anti-narrowband interference capability of UAV network, this paper proposes a narrowband interference suppression technology based on spread spectrum communication. The simulation results show that the method has a good effect on narrowband interference suppression, and provides an effective solution to the problem of narrowband interference suppression in UAV network.

In this paper, we propose and demonstrate a blind chromatic dispersion (CD) estimation method based on fractional Fourier transformation (FrFT). When FrFT acts on linear frequency modulated (LFM) signal with energy convergence, it is compared to fiber chromatic dispersion signal, and the best order can be obtained by scanning or search method. Through numerical simulations, reliable CD estimation is demonstrated for 28 GBaud PM-QPSK and PM-16QAM signals with a range of (-70,45) ps/nm. The average estimation error of CD estimation is 0.15% and 0.5% when the transmission distance over 100km~2000km standard single mode fiber (SSMF).

With the development of artificial intelligence technology, deep learning has been applied to automatic modulation classification (AMC) and achieved very good results. In this paper, we introduced an improved deep neural architecture for implementing radio signal identification tasks, which is an important facet of constructing the spectrum-sensing capability required by software-defined radio. The architecture of the proposed network is based on the Inception-ResNet network by changing the several kernel sizes and the repeated times of modules to adapt to modulation classification. The modules in the proposed architecture are repeated more times to increase the depth of neural network and the model’s ability to learn features. The modules in the proposed network combine the advantages of Inception network and ResNet, which have faster convergence rate and larger receptive field. The proposed network is proved to have excellent performance for modulation classification through the experiment in this paper. The experiment shows that the classification accuracy of the proposed method is highest with the varying SNR among the six methods and it peaks at 93.76% when the SNR is 14 dB, which is 6 percent higher than that of LSTM and 13 percent higher than that of MentorNet, Inception, and ResNet purely. Besides, the average accuracy from 0 to 18 dB of the proposed method is 3 percent higher than that of GAN network. It will provide a new idea for modulation classification aiming at distraction time signal.

An improved constant modulus algorithm (CMA) for dispersion compensation in 100 Gbps Dual Polarization-Quadrature Phase Shift Keying (DP-QPSK) optical signal transmission system is proposed to achieve better reliability receiving performances. In the receiver, we combine variable step size and Mean-Square Error (MSE) to improve the conventional CMA. Compared to conventional CMA, the proposed algorithm has a better performance in adapting the time varying channel and the strong noise. Meanwhile, the proposed algorithm has a faster convergence rate than the previous one. Simulation results show that the improved CMA can achieve the dispersion and polarization demultiplexing of 100 Gbps DP-QPSK optical signal transmission system efficiently.

Autonomous underwater vehicles (AUV) play an important role in human activities. They are different from of the vehicles other areas (such as air, ground). Movement, communication, target detection all involve complex hydrology and climatic condition. For special application fields it's even more complex. Due to the limitation of experimental conditions, simulation tests are adopted to assist with the increase in the sample size. The credibility of the simulation models need to be evaluated, especially for dynamic models such as control systems. This paper studies the application of several dynamic model validation methods, such as gray-Box identification and Bayesian statistics, in the AUV motion simulation model. And comparison of the methods above and some other validation methods is given, which validates the performance of the proposed methods.

With the development of wireless communication technology, cognitive radio needs to solve the spectrum sensing problem of wideband wireless signals. Due to performance limitation of electronic components, it is difficult to complete spectrum sensing of wideband wireless signals at once. Therefore, it is required that the wideband wireless signal has to be split into a set of sub-bands before the further signal processing. However, the sequence of sub-band perception has become one of the important factors, which deeply-impact wideband spectrum sensing performance. In this paper, we develop a novel approach for sub-band selection through the non-stationary multi-arm bandit (NS-MAB) model. This approach is based on a well-known order optimal policy for NS-MAB mode called discounted upper confidence bound (D-UCB) policy. In this paper, according to different application requirements, various discount functions and exploration bonuses of D-UCB are designed, which are taken as the parameters of the policy proposed in this paper. Our simulation result demonstrates that the proposed policy can provide lower cumulative regret than other existing state-of-the-art policies for sub-band selection of wideband spectrum sensing.

Time difference of arrival (TDoA) based on a group of sensor nodes with known locations has been widely used to locate targets. Two-step weighted least squares (TSWLS), constrained weighted least squares (CWLS), and Newton&ndash, Raphson (NR) iteration are commonly used passive location methods, among which the initial position is needed and the complexity is high. This paper proposes a hybrid firefly algorithm (hybrid-FA) method, combining the weighted least squares (WLS) algorithm and FA, which can reduce computation as well as achieve high accuracy. The WLS algorithm is performed first, the result of which is used to restrict the search region for the FA method. Simulations showed that the hybrid-FA method required far fewer iterations than the FA method alone to achieve the same accuracy. Additionally, two experiments were conducted to compare the results of hybrid-FA with other methods. The findings indicated that the root-mean-square error (RMSE) and mean distance error of the hybrid-FA method were lower than that of the NR, TSWLS, and genetic algorithm (GA). On the whole, the hybrid-FA outperformed the NR, TSWLS, and GA for TDoA measurement.

The cognitive wireless sensor network (CWSN) is an important development direction of wireless sensor networks (WSNs), and spectrum sensing technology is an essential prerequisite for CWSN to achieve spectrum sharing. However, the existing non-cooperative narrowband spectrum sensing technology has difficulty meeting the application requirements of CWSN at present. In this paper, we present a non-cooperative spectrum sensing algorithm for CWSN, which combines the multi-resolution technique, phase space reconstruction method, and singular spectrum entropy method to sense the spectrum of narrowband wireless signals. Simulation results validate that this algorithm can greatly improve the detection probability at a low signal-to-noise ratio (SNR) (from &minus, 19dB to &minus, 12dB), and the detector can quickly achieve the best detection performance as the SNR increases. This algorithm could promote the development of CWSN and the application of WSNs.

With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of physical defense equipment. Therefore, corresponding schemes of intrusion detection are urgently needed to defend against such attacks. Considering the serious class imbalance of the intrusion dataset, this paper proposes a method of using the synthetic minority oversampling technique (SMOTE) to balance the dataset and then uses the random forest algorithm to train the classifier for intrusion detection. The simulations are conducted on a benchmark intrusion dataset, and the accuracy of the random forest algorithm has reached 92.39%, which is higher than other comparison algorithms. After oversampling the minority samples, the accuracy of the random forest combined with the SMOTE has increased to 92.57%. This shows that the proposed algorithm provides an effective solution to solve the problem of class imbalance and improves the performance of intrusion detection.

Segmentation of skin lesions is a challenging task because of the wide range of skin lesion shapes, sizes, colors, and texture types. In the past few years, deep learning networks such as U-Net have been successfully applied to medical image segmentation and exhibited faster and more accurate performance. In this paper, we propose an extended version of U-Net for the segmentation of skin lesions using the concept of the triple attention mechanism. We first selected regions using attention coefficients computed by the attention gate and contextual information. Second, a dual attention decoding module consisting of spatial attention and channel attention was used to capture the spatial correlation between features and improve segmentation performance. The combination of the three attentional mechanisms helped the network to focus on a more relevant field of view of the target. The proposed model was evaluated using three datasets, ISIC-2016, ISIC-2017, and PH2. The experimental results demonstrated the effectiveness of our method with strong robustness to the presence of irregular borders, lesion and skin smooth transitions, noise, and artifacts.

Deep packet inspection in modern networking and security devices use ternary content addressable memory (TCAM) to match regular expressions. TCAM has its advantage in search speed but disadvantages in limited storage and high power consumption. The hierarchical encoding algorithm to save TCAM space is proposed and the codes divided into four layers. The proposal and the results are tested showing that the new algorithm has higher compression efficiency and the compression rate can reach 95–99%.

With the rapid development of information technology and the increasing application of UAV in various fields, the security problems of unmanned aerial vehicle (UAV) communication network have become increasingly prominent. It has become an important scientific challenge to design a routing protocol that can provide efficient and reliable node to node packet transmission. In this paper, an efficient Digital Signature algorithm based on the elliptic curve cryptosystem is applied to routing protocol, and an improved security method suitable for on-demand routing protocol is proposed. The UAV communication network was simulated through the NS2 simulation platform, and the execution efficiency and safety of the improved routing protocol were analyzed. In the simulation experiment, the routing protocols of ad-hoc on demand distance vector (AODV), security ad-hoc on demand distance vector (SAODV), and improved security ad-hoc on demand distance vector (ISAODV) are compared in terms of the performance indicators of packet delivery rate, throughput, and end-to-end delay under normal conditions and when attacked by malicious nodes. The simulation results show that the improved routing protocol can effectively improve the security of the UAV communication network.

With the rapid development of information technology, the problem of the network security of unmanned aerial vehicles (UAVs) has become increasingly prominent. In order to solve the intrusion detection problem of massive, high-dimensional, and nonlinear data, this paper proposes an intrusion detection method based on the deep belief network (DBN) optimized by particle swarm optimization (PSO). First, a classification model based on the DBN is constructed, and the PSO algorithm is then used to optimize the number of hidden layer nodes of the DBN, to obtain the optimal DBN structure. The simulations are conducted on a benchmark intrusion dataset, and the results show that the accuracy of the DBN-PSO algorithm reaches 92.44%, which is higher than those of the support vector machine (SVM), artificial neural network (ANN), deep neural network (DNN), and Adaboost. It can be seen from comparative experiments that the optimization effect of PSO is better than those of the genetic algorithm, simulated annealing algorithm, and Bayesian optimization algorithm. The method of PSO-DBN provides an effective solution to the problem of intrusion detection of UAV networks.

In secure communication systems, a very important problem is how to prevent wiretapping. Lots of researches on cryptography give good solutions on secure communications. But if a wire tapper can detect the existence of the transmitted information and get enough eavesdropped frames, cryptanalysis techniques can help to blindly recover the frame structures, error-control coding parameters and passwords. In this paper we propose a novel secure transmission scheme on the link level for optical fiber communication systems. Based on the blind frame synchronization technique, we propose to drop the traditionally strict frame structures in fiber communications and conceal the error-correcting-encoded blocks among random data, so that wire tappers cannot get enough coded packets to analyze and recover the transmitted information. Therefore, the proposed method is very suitable for secure communications and military communications.

With the recent increase in IP traffic owing to fiber communication, previous schemes have become inadequate for link-layer processing of IP over SONET/SDH(POS). In this study, a proposal based on [Formula: see text] processors to provide mapping or demapping of IP datagrams from or into SONET/SDH is presented, and the value of [Formula: see text] is decided based on the link layer rate of POS. Further, the mathematic model of proposed architecture are presented in detail. Then the realization procedures are implemented in a Field-Programmable Gate Array (FPGA). Both theoretical analysis and experimental test prove that the proposed scheme is efficient, portable, cost-efficient and has a lower hardware resources consumption.

There are many algorithms that can be used to fuse sensor data. The complementary filtering algorithm has low computational complexity and good real-time performance characteristics. It is very suitable for attitude estimation of small unmanned aerial vehicles (micro-UAVs) equipped with low-cost inertial measurement units (IMUs). However, its low attitude estimation accuracy severely limits its applications. Though, many methods have been proposed by researchers to improve attitude estimation accuracy of complementary filtering algorithms, there are few studies that aim to improve it from the data processing aspect. In this paper, a real-time first-order differential data processing algorithm is proposed for gyroscope data, and an adaptive adjustment strategy is designed for the parameters in the algorithm. Besides, the differential-nonlinear complementary filtering (D-NCF) algorithm is proposed by combine the first-order differential data processing algorithm with the basic nonlinear complementary filtering (NCF) algorithm. The experimental results show that the first-order differential data processing algorithm can effectively correct the gyroscope data, and the Root Mean Square Error (RMSE) of attitude estimation of the D-NCF algorithm is smaller than when the NCF algorithm is used. The RMSE of the roll angle decreases from 1.1653 to 0.5093, that of the pitch angle decreases from 2.9638 to 1.5542, and that of the yaw angle decreases from 0.9398 to 0.6827. In general, the attitude estimation accuracy of D-NCF algorithm is higher than that of the NCF algorithm.

In order to break chaotic direct sequence spreading spectrum (CD3S) signals under the multipath fading channel, a particle filter based algorithm combining blind channel equalization with chaos fitting is proposed. To implement this algorithm, the intruder substitutes a different chaotic equation into the state-space equations of the channel and the chaos fitting, and then multiple particle filters are used for blind channel equalization and chaos fitting simultaneously by implementing them in reciprocal interaction. As a result, the impact brought about by the multipath fading channel and additive noises can be overcome. Furthermore, the range-differentiating factor is used to make the inevitable chaos fitting error advantageous based on the chaos fitting method. Thus, the CD3S signals can be broken according to the range of the estimated message. Simulations show that the binary message signal can be extracted from the CD3S signals without any knowledge of the chaotic transmitter’s structure, parameters, initial value, or the channel characteristics.

Passive source localization in shallow water has always been an important and challenging problem. Implementing scientific research, surveying, and monitoring using a short, less than ten meter long, horizontal linear array has received considerable attention in the recent years. The short array can be conveniently placed on autonomous underwater vehicles and deployed for adaptive spatial sampling. However, it is usually difficult to obtain a sufficient spatial gain for localizing long-range sources due to its limited physical size. To address this problem, a localization approach is proposed which is based on matched-field processing of the likelihood of the passive source localization in shallow water, as well as inter-position processing for the improved localization performance and the enhanced stability of the estimation process. The ability of the proposed approach is examined through the two-dimensional synthetic test cases which involves ocean environmental mismatch and position errors of the short array. The presented results illustrate the localization performance for various source locations at different signal- to-noise ratios and demonstrate the build up over time of the positional parameters of the estimated source as the short array moves at a low speed along a straight line at a certain depth.

Obtaining a minimal automaton is a fundamental issue in the theory and practical implementation of deterministic finite automatons (DFAs). A minimization algorithm is presented in this paper that consists of two main phases. In the first phase, the backward depth information is built, and the state set of the DFA is partitioned into many blocks. In the second phase, the state set is refined using a hash table. The minimization algorithm has a lower time complexity O(n) than a naive comparison of transitions O(n2). Few states need to be refined by the hash table, because most states have been partitioned by the backward depth information in the coarse partition. This method achieves greater generality than previous methods because building the backward depth information is independent of the topological complexity of the DFA. The proposed algorithm can be applied not only to the minimization of acyclic automata or simple cyclic automata, but also to automata with high topological complexity. Overall, the proposal has three advantages: lower time complexity, greater generality, and scalability. A comparison to Hopcroft’s algorithm demonstrates experimentally that the algorithm runs faster than traditional algorithms.

In this paper we are interested in the hypothesis test (HT) solution in the presence of prior information and conjugated prior distribution. Truncated Bayesian sequential posterior odd test (SPOT) is introduced to predict the required test sample size in autonomous underwater vehicle (AUV) range accreditation. We focus on the connection between risks (type I and type II) and truncated threshold, required test sample size. And the computational approach based on numerical approaching is given in steps. Supported by prior information after fusion on credibility, we deeply analyzed the effect of risk base value on SPOT performance, and an array of random bootstrapping data is generated to validate the conclusion. A mature solution to small-size-sample test accreditation is proposed in this paper, which provides vital suggestions for test sample size prediction in small-size-sample parameter hypothesis test.

Based on the feature extraction of multidimensional asynchronous delay-tap plot entropy (ADTPE) and multiclass classification of support vector machine (SVM), we propose a method for recognition of multiple optical modulation formats and various data rates. We firstly present the algorithm of multidimensional ADTPE, which is extracted from asynchronous delay sampling pairs of modulated optical signal. Then, a multiclass SVM is utilized for fast and accurate classification of several widely-used optical modulation formats. In addition, a simple real-time recognition scheme is designed to reduce the computation time. Compared to the existing method based on asynchronous delay-tap plot (ADTP), the theoretical analysis and simulation results show that our recognition method can effectively enhance the tolerance of transmission impairments, obtaining relatively high accuracy. Finally, it is further demonstrated that the proposed method can be integrated in an optical transport network (OTN) with flexible expansion. Through simply adding the corresponding sub-SVM module in the digital signal processer (DSP), arbitrary new modulation formats can be recognized with high recognition accuracy in a short response time.

The paper analyzes the characteristics of shallow sea reverberation such as divergent variance and heavy tails in probability density distribution and matches it to symmetric alpha stable (SαS) distribution. After analyzing the AR model whitening method based on Gaussian distribution and Levinson–Durbin algorithm, the paper proposes and derives the generalized Levinson–Durbin (GLD) algorithm based on covariation theory of alpha stable distribution. The GLD algorithm can estimate the order and parameters of AR SαS model by iterations which are fast and effective in calculating, and then the performance tests by simulations are given. The AR model whitening method using GLD algorithm has a faster speed and a better detection performance than AR Gaussian whitening method in impulsive reverberation. At last the Monte Carlo simulating tests are used to validate the validity of the new method.

With the improvement of people’s living standard, the requirement of network is higher and higher. The rate and capacity of network communication are rising all the time, it ’s outstanding on the backbone networks. The rate of backbone network had improved from 10G to 100G in recent years. This change also put forward new requirement for optical network equipment . The key technologies of lower order cross-connect equipment is discussing in this passage. This passage mainly discusses the following aspects: time division multiplexing of STM-1 lower-order pointer interpretation and lower -order pointer generation. By using the technologies discussed in this passage, the lower -order cross-connect devices has the higher cross-connect capacity under consuming the same logic resource, and the equipment will meet the requirement of the backbone network transmission capacity.

Differential phase-shift keying (DPSK) has been widely implemented and developed in high-speed optical communication systems. The low error rate detection at high access rate is one of the considerable issues in practical engineering application. Balanced detection based on fiber Mach-Zehnder delay interferometer (MZDI) is the typical optical DPSK signal detecting method. It requires that the free spectrum range (FSR) of the MZDI equals the reciprocal of symbol period of the DPSK signal. For the reasons of ambient temperature variation and nonlinear phase noise, a dynamic frequency offset always exists between the FSR and the reciprocal of symbol period. That may introduce some optical signal-to-noise ratio (OSNR) costs and fault detections. Therefore, it is significant to inhibit the frequency offset on DPSK detection. In this paper, firstly, we discuss the effects of frequency offset on DPSK detection, and realize the conclusion that frequency offset is virtually equivalent to an additional phase difference between adjacent symbols. Secondly, through simulation, we analyze the feasibility of DPSK detection in the presence of a definite range of frequency offset, and present the quantitative computation of effective coverage, duty cycle, and optimal sampling time of symbol interference. Some issues which should be considered in practical implementation are also discussed. Finally, according to the relationship among phase difference, temperature and voltage, we propose a phase difference compensation scheme which can automatically adjust the voltage for optimal detections, and dynamically track the changing of ambient temperature and nonlinear phase noise. Furthermore, we ascertain the performance of the voltage requested for implementing the scheme. The scheme can be also developed to quadrature phase-shift keying (QPSK) and differential QPSK (DQPSK) modulation situations.

There are varies modulation formats such as ASK, OOK, BPSK, QPSK, DP-QPSK. The DP-QPSK modulation format appears as the best performing 50 Gbit/s modulation format solution with higher transmission efficiency and stronger anti-interference ability. Therefore, this topic has great significance for the study of a test system of DP-QPSK optical transmission. There is hardware and software design of the test system. Hardware design is the basis and prerequisite for the design of the test system, requiring further understanding of the composition of the test system. The software which completes the work of data processing is a crucial part of the whole test system.

The interactions of salicylic acid (SL) and two different site markers (warfarin for site I and ibuprofen for site II) with bovine serum albumin (BSA) in pH 7.4 Tris–HCl buffer have been investigated with the use of spectrofluorimetry. An equilibrium solution of BSA and SA was titrated separately with the two markers. This initial work showed that the binding of SL with BSA could be quite complex, and that there was probably a competitive interaction occurring between ibuprofen and SL. However, the spectral results were difficult to interpret clearly for the interaction of warfarin and SL in similar circumstances. To extract more information from the resolution of fluorescence excitation-emission spectra, the contour plots of the fluorescence spectra indicated that the optimal excitation wavelengths for BSA, SL, warfarin and ibuprofen were different, and were found to be at 278, 295, 306 and 218 nm, respectively. The spectral information was arranged into three-way excitation-emission fluorescence matrix (EEM) stack arrays, and was submitted for analysis by the parallel factor analysis (PARAFAC) algorithm. Firstly, it was demonstrated that the estimated excitation and emission spectral responses for SL, BSA and the site markers, warfarin and ibuprofen, agreed well with the measured spectra. Then, the interpretation of the plots of simultaneously extracted (by PARAFAC) equilibrium concentrations for the above four reactants, showed that: (i) the SL primarily appears to bind in site I but at a different location from the high-affinity binding site (HAS) for warfarin, and the interaction partially overlaps with the low-affinity binding site (LAS) for warfarin. (ii) The SL may have two LAS—one in site II where the HAS for ibuprofen is located, and the other in site I at the LAS for ibuprofen. Thus, application of the PARAFAC method for the study of competitive interaction of SL and BSA with the aid of two different site markers has extracted information unobtainable by traditional methods such as the Scatchard plot, and provided useful means of data visualization.

In order to achieve the receiving task of 100Gbps Dual Polarization-Quadrature Phase Shift Keying (DP-QPSK) optical signal acquisition instrument, improve acquisition performance of the instrument, this paper has deeply researched DP-QPSK modulation principles, demodulation techniques and the key technologies of optical signal acquisition. The theories of DP-QPSK optical signal transmission are researched. The DP-QPSK optical signal transmission model is deduced. And the clock and data recovery in high-speed data acquisition and offset correction of multi-channel data are researched. By reasonable hardware circuit design and software system construction, the utilization of high performance Advanced Telecom Computing Architecture (ATCA), this paper proposes a 100Gbps DP-QPSK optical signal acquisition instrument which is based on ATCA. The implementations of key modules are presented by comparison and argumentation. According to the modularization idea, the instrument can be divided into eight modules. Each module performs the following functions. (1) DP-QPSK coherent detection demodulation module; (2) deceleration module; (3) FPGA (Field Programmable Gate Array); (4) storage module; (5) data transmission module; (6) clock module; (7) power module; (8) JTAG debugging, configuration module; What is more, this paper has put forward two solutions to test optical signal acquisition instrument performance. The first scenario is based on a standard STM-256 optical signal format and exploits the SignalTap of QuartusII software to monitor the optical signal data. Another scenario is to use a pseudo-random signal series to generate data, acquisition module acquires a certain amount of data signals, and then the signals are transferred to a computer by the Gigabit Ethernet to analyze. Two testing results show that the bit error rate of optical signal acquisition instrument is low. And the instrument fully meets the requirements of signal receiving system. At the same time this design has an important significance in practical applications.

In the era of information, the big data, which contains huge information, brings about some problems, such as high speed transmission, storage and real-time analysis and process. As the important media for data transmission, the Internet is the significant part for big data processing research. With the large-scale usage of the Internet, the data streaming of network is increasing rapidly. The speed level in the main fiber optic communication of the present has reached 40Gbps, even 100Gbps, therefore data on the optical backbone network shows some features of massive data. Generally, data services are provided via IP packets on the optical backbone network, which is constituted with SDH (Synchronous Digital Hierarchy). Hence this method that IP packets are directly mapped into SDH payload is named POS (Packet over SDH) technology. Aiming at the problems of real time process of high speed massive data, this paper designs a process system platform based on ATCA for 40Gbps POS signal data stream recognition and packet content capture, which employs the FPGA as the CPU. This platform offers pre-processing of clustering algorithms, service traffic identification and data mining for the following big data storage and analysis with high efficiency. Also, the operational procedure is proposed in this paper. Four channels of 10Gbps POS signal decomposed by the analysis module, which chooses FPGA as the kernel, are inputted to the flow classification module and the pattern matching component based on TCAM. Based on the properties of the length of payload and net flows, buffer management is added to the platform to keep the key flow information. According to data stream analysis, DPI (deep packet inspection) and flow balance distribute, the signal is transmitted to the backend machine through the giga Ethernet ports on back board. Practice shows that the proposed platform is superior to the traditional applications based on ASIC and NP.

This paper gives a solution to the blind parameter identification of a convolutional encoder. The problem can be addressed in the context of the noncooperative communications or adaptive coding and modulations (ACM) for cognitive radio networks. We consider an intelligent communication receiver which can blindly recognize the coding parameters of the received data stream. The only knowledge is that the stream is encoded using binary convolutional codes, while the coding parameters are unknown. Some previous literatures have significant contributions for the recognition of convolutional encoder parameters in hard-decision situations. However, soft-decision systems are applied more and more as the improvement of signal processing techniques. In this paper we propose a method to utilize the soft information to improve the recognition performances in soft-decision communication systems. Besides, we propose a new recognition method based on correlation attack to meet low signal-to-noise ratio situations. Finally we give the simulation results to show the efficiency of the proposed methods.

This paper presents a grey particle filter (GPF) that incorporates the grey prediction algorithm into the particle filter (PF). The GPF self-estimates the depth of maneuvering autonomous underwater vehicle (AUV) using the data measured by the depth sensor equipped in the AUV under the condition that the prior maneuvering information is unknown and the measurement noise is time-varying. The principle of the GPF is that the particles are sampled by grey prediction algorithm and the likelihood probabilities of the grey particles are calculated by wavelet transform in real time, which only uses the historical measurement without establishing prior dynamic models. Therefore, the GPF can effectively alleviate the sample degeneracy problem which is common in the multiple model particle filter (MMFP). The performance of the MMPF and GPF are both evaluated through the experimental data. The results show that GPF has the better estimation accuracy than the MMPF.

This paper presents an adaptive particle filter (APF) for estimating the states of the nonlinear system with unknown parameters. To implement this algorithm, the process noise covariance is adjusted according to the innovation-based adaptive estimation (IAE) approach, which only takes advantage of one state model without the requirement of knowing the parameters. In addition, the Maximum-A-Posterior (MAP) method is used to estimate the parameters to improving the performance of APF. Therefore, the APF can modify the prior distribution of the particles, and then alleviate the sample degeneracy problem which is common in particle filter (PF). Numerical simulations of Logistic map are conducted to demonstrate the effectiveness of our proposed method.

Digital channelizer is used to separate the sub-band signals contained within a wideband intermediate frequency (IF) received signal. This paper presents a structure of digital channelizer which is built upon estimation of the sub-band signal number. The proposed structure is designed by employing nonuniform filter banks (NUFBs), which are implemented by merging the adjacent sub-branches of the perfect reconstruction (PR) cosine modulated filter banks (CMFBs). Using source number estimation algorithm, the estimated value of sub-band number can be obtained. By energy detection, the energy characteristic of the analysis filter banks (AFBs) can be acquired and another value of sub-band number is calculated. Comparing the estimated value and the calculated one, we can evaluate whether the design of the AFB is suitable for the received signal. The structure of digital channelizer proposed by this paper provides a potential way to channelize the unknown signal and suggests a solution for the dynamic channelizaiton. A design example is also presented to demonstrate the feasibility and simplicity of this method.

With the ever improvement of SDH backbone network at home and abroad and the exponential growth of IP services, more and more attention are paid to POS signal. POS technology not only is the most important IP transmission way of backbone network, but also will be one of the core technologies in the next generation optical transmission network. In this paper the formulas for influence of HDLC frame alignment and ESC on link transport efficiency of POS signal are educed, on the basis of deeply study of POS technology and HDLC protocol. The results of this paper can offer many references to lucubrating POS technology.

It is necessary to simulate seafloor reverberation time series for testing and evaluation of active sonar signal-processing algorithms and active sonar system-performance models. Traditionally, for an isovelocity sound-speed profile, consider simulating only the reverberation arising from direct-path propagation from the source to a scattering patch on the seafloor and back to a receiver. Actually, the effect of multipath propagation can't be ignored. Therefore, the reverberation model based on multipath propagation theory is proposed, and a method for simulating reverberation time series is presented with the seafloor is divided into range-azimuth cell. The simulation data will be useful in the evaluation of active sonar system.