Reconfigurable Low-Density Parity-Check (LDPC) codes decoders adapted to multiple standards attracte more and more attentions in thesatellite application. However, due to the memory resource is limited on the satellite and sensitive to the space radiation effect, the conventional reconfigurable decoders are difficult to be applied to on-board processing for their high memory requirements. This paper presents a novel reconfigurable decoder with layered pipelined architecture to achieve high throughput and realizes low complexity by combining the structural characteristics of different LDPC codes. The memory size is reduced by simplifying the storage of the channel intrinsic Logarithm Likelihood Ratio (LLR) messages and the passed messages in the process of iterative decoding. The decoder is fabricated in the TSMC 0.13 μm standard CMOS technology and the result shows that each branch can achieve a throughput up to 1.5 Gbps with 7.8 mm² core area occupancy and can save 40% storage resource at most.

A new wireless resources allocation scheme is proposed to enhance network performance for both cellular and D2D (Device-to-Device) terminal users in a hybrid network. The frequency resource unit and power control can be formed as a sum-rate optimization problem with the constraints of power limitation and the ratio of the rate requirement. The ratio can be utilized to distinguish the rate requirements of both cellular and D2D users with different QoS (Quality of Service). By using the Lagrange Multiplier Process, this study derives the optimal formula of power and frequency resource unit allocation for both cellular and D2D terminal users. The optimal formula shows that the relationship between the transmission power and frequency resource unit allocation is a problem of mutual constraint. And then the wireless resource allocation scheme algorithm is given. Especially the power allocation can be resolved by the water-filling procedure. The simulation results show that the proposed new resource allocation scheme improves the sum-throughput significantly as well as fairness.

Addressing the problem of maximizing the mutual information of  MIMO channels with finite alphabet inputs, a low-complexity algorithm of linear precoding is designed. According to the mercury/water-filling theory, the algorithm integrates the precoding method based on the uniformly rotated Space-Time Linear Constellation Precoding (ST-LCP) matrix and the method of maximizing the minimum distance between output vector signals, where the one with higher mutual information is chosen for precoding. Then, in the precoding technique based on the uniformly rotated ST-LCP matrix, the singular value matrix of MIMO channels is selected to be the power allocation matrix, and two modifications are designed including the local search and the power addition on the singular value matrix. Finally, the computational cost of mutual information is further decreased by exploiting the symmetry of the finite alphabet set. The proposed algorithm obtains mutual information close to the theoretical maximum under various channel and SNR conditions, and reduces or even avoids search, resulting in much lower computational complexity. The simulation results verify the effectiveness of the proposed algorithm.

In Cognitive Radio (CR) systems, Secondary Users (SUs) access to the channels of Primary Users (PUs) by using opportunistic scheduling and multiple access scheme. To analyze the delay performance, the stochastic network calculus is deployed. A stochastic service model is constructed for the two main access schemes, i.e. Contention-Free (CF) access and Contention-Based (CB) access, as well as opportunistic scheduling. And then, a stochastic service curve is derived for the service model, after which, the delay bound is obtained for the SUs. A comparison is made for the computational results of different network configurations, which demonstrates the correctness of the delay bound. And this frame work gives light to the improvement of multiple access scheme for the SUs.

To mitigate the strong interference from the Base Station (BS) to Device-to-Device (D2D) receivers when the downlink resource is shared by the D2D and cellular users, a novel null-space based precoding scheme is proposed. The proposed scheme takes into account the interference mitigation for D2D users when designing the downlink precoders of the BS. It allows the transmitted signal of BS to aim at the scheduled cellular user while lying in the interference-free subspace of the BS-D2D channel, so as to avoid the interference to the D2D receivers. To make the design more practical, the performance of the proposed scheme under imperfect channel estimations is further analyzed. Simulation results show that substantial gains in D2D throughput can be obtained by using the proposed precoding scheme, but such gain is largely dependent on the accurate channel state information of the BS to D2D receiver.

In order to tackle the defects of the Location Management (LM) scheme used in second generation (2G) and third generation (3G) mobile networks, forth generation (4G) network Long Term Evolution (LTE) uses a new LM scheme, which is called a Tracking Area List (TAL)-based LM scheme and the signaling cost of the TAL-based LM scheme is determined by the allocation of TALs. This paper develops a mathematical model of embedded Markov chain to analyze the signaling cost of the TAL-based scheme for a local User Equipment (UE) whose activity region is relatively fixed. Mathematical formulas for the LU cost and the paging cost are derived. With these formulas, an optimal TAL allocation strategy that can minimize the signaling cost can be found.

A method is proposed to construct self-orthogonal codes over the finite field  by F_q using cyclic self- orthogonal codes over the finite ring  F_q+uF_q. A Gray map from  F_q+uF_q  to F^p_q is introduced, and a sufficient and necessary condition for the existence of cyclic self-orthogonal codes over  is given. It is proved that the Gray image of a cyclic self-orthogonal code with length n over  F_q+uF_q  is a self-orthogonal code with length pn over  F_q. Further, some self-orthogonal codes over  F_q with good parameters are constructed.

Designated verifier signature scheme has important applications in the security of network information, which make the verifier can not transmit a signature to any third party. The scheme can be used to strengthen the privacy of the signer, in which anyone can not verify the validity of the signature except the designated verifier. In this paper, a strong designated verifier signature scheme from Multivariate Public Key Cryptosystems (MPKCs) is proposed, the proposed scheme satisfies the security properties of correctness, non-transferability and unforgeability if the underling MPKC is secure against the known attacks such as algebraic attacks, linearization equations attack, rank attack, and differential attack etc.. Furthermore, a specific strong designated verifier signature scheme from multivariate pFLASH signature system is given as an example, which has an obvious advantage of efficiency. The main advantage of the proposed scheme is that it is secure under the quantum computing attack.

The conventional coefficients scanning pattern of the non-square quadtree transform can not fit the energy distribution better enough, which worsens the coding efficiency. In order to improve the coding efficiency, a non-square quadtree transform coefficients energy distribution model is proposed and the coefficients scanning pattern is improved in this paper. The position distribution in spatial frequency domain of the non-square quadtree transform block is deduced. Based on this distribution, the energy distribution model is proposed theoretically. The model is verified by experiment. It is found from the experiment result that the energy decrease gradually from the top-left corner to lower-right corner, and the energy in lower left corner is close to that in upper right corner. According to the energy distribution model, the quantized coefficients scanning pattern in non-square quadtree transform is rebuilt to fit the entropy coding, where the scanning pattern prefers to an ascending order in coefficients. Experiment results show that the proposed scanning pattern achieves average 1.32%~3.74% bitrate saving and 9.05%~13.19% bitrates saving for the non-square transformed blocks with negligible loss in coding computational complexity and objective video quality, compared with the scanning pattern in NSQT.

For the long-term application requirements of data gathering queries in event-driven Wireless Sensor Networks (WSNs), based on the compressed sensing theory, a data gathering method with long network lifetime is designed by combining the constructing process of data gathering tree with the hybrid compressed sensing data gathering techniques. When a data gathering query arrives, a data gathering tree for the query is constructed. Using the ideas of hybrid compressed sensing, energy consumption of forwarding and aggregating nodes is analyzed, and data gathering trees are constructed aiming at maximizing the minimum residual energy among the nodes after the realization of current data gathering query. The simulation results show that the proposed method can make full use of the energy resources of nodes, significantly improve the network energy efficiency and achieve the goal of extending network lifetime.

The previous fair packet sampling algorithm of Sketch Guided Sampling (SGS) has large estimation error for mice flows. A Fair packet Sampling algorithm based on Different Counting Methods between elephant and mice flows (DCMFS) is proposed, and the quantificational influence of hash collisions on estimation error of SGS is thoroughly analyzed. The key innovation is to use an elephant and mice flows differentiating counter to count the mice flows one by one and count the elephant ones by counting sketch.[w1] The theoretical analysis and evaluation on real traffic traces show that each mice flow can be estimated accurately by DCMFS and the elephant ones’ estimation error can reach the theoretical value of SGS. Due to the unequal length counter structure, DCMFS does not introduce much more counting space than SGS and Adaptive Non-Linear Sampling (ANLS). Though the replacement of counters in DCMFS introduces more time complexity than SGS, it can still support the 10 Gbps line-speed processing.

As the key technology in the next-generation Internet, locator/identifier separation mechanism can efficiently solve the scalability problem of current networks. For identifier and locator separation networks, the mapping system design is an important issue. In this paper, the combination of D1HT and Chord two-layer mapping system is proposed based on Locator/Identifier Separation Protocol-Distributed Hash Table (LISP-DHT). In addition, the Chord architecture is improved. The proposed mapping system solves the problem that the logic topology does not match the physical topology, and it reduces the redundancy of information. According to simulation results, this mapping system shortens the average query delay and the average path length, and it finally improves the performance of the system.

For the characteristic of energy-constrained in wireless sensor networks, considering routing strategy into the designing of the projection matrix, a Compressed Sensing algorithm based on Data Fusion Tree (CS-DFT) is proposed. It minimizes communication consumption by means of sparse random projection, and relevance between projection matrix and sparse basis is decreased in order to guarantee the data reconstruction quality while data fusion tree is generating. Simulation results show that, the proposed algorithm not only achieves a balance between reconstruction quality and energy consumption, but also has high adaptability to operate on a variety of data originated from different sparse basis.

In order to overcome the energy hole problem due to the uneven distribution of energy consumption in the static terrestrial Wireless Sensor Networks (WSNs) and underwater WSNs, and overcome the long data gathering delay problem in WSNs with single mobile sink node, the Grid-based Lifetime Optimization Algorithm (GLOA) is proposed for the mobile WSNs. In the GLOA algorithm, the movement of multiple sink nodes is considered. The monitoring region is divided into many grids of the same size. The anchor points are identified according to the grid potential value. Anchor points are assigned to different sink nodes. The path selection optimization model is proposed and shortest mobile path is obtained. The mobile method or static gathering method is used to cyclically gather data. The simulation results show that compared with Ratio_w algorithm or TPGF algorithm, the GLOA algorithm is able to prolong the network lifetime, reduce and balance the node energy consumption. Compared with the LOA_SMSN algorithm, the GLOA algorithm is able to decrease the data gathering delay. Under specified conditions, the proposed algorithm outperforms Ratio_w, TPGF or LOA_SMSN algorithms.

The existing alarm information correlation analysis methods take no consideration of the alarm significance, so they are unable to reflect individual differences between the alarms. In order to solve the problem, a new alarm information relevance mining mechanism based on wavelet neural network is proposed in this paper. The three key attributes of alarm information, alarm level, alarm type and alarm equipment type are considered as the inputs of the wavelet neural network respectively. Further, the weight corresponds to the importance of individual attribute, which can be determined reasonably by training with history sample. Finally, the association rules can be mined accurately. Results show that the proposed algorithm can consider multiple influence factors and history sample comprehensively, and the obtained weight can scientifically reflect the importance of the alarms; moreover, the association rules can reflect the correlation between the alarms more accurately.

In view of the localization technology for mobile nodes in Wireless Sensor Networks (WSN) being more error and worse real-time, a new algorithm called Local Sampling and Filtering-Color Dynamic Localization (LSF- CDL) which filters the samples by the RGB difference sequences is proposed in this paper. Using the collected signals, LSF-CDL adopts the overlapping signal region of beacon nodes which is able to communicate with the mobile node directly as the new local sampling area. Also the proportion factor of distance is used to weight the average hop distance which optimizes the calculation of hop distance in CDL. By comparing the RGB difference sequences, samples can be filtered out. And the absolute values of the samples’ difference sequences are used as the weighted standards to calculate the coordinates of the mobile node. The simulation results show that the proposed algorithm has good localization effect, which can obviously reduce the location error by more than 33% compared to the other classic algorithms such as Efficient Color-theory based Dynamic Localization (E-CDL) and Monte Carlo Localization (MCL).

Handling appearance variations is a very challenging issue for visual tracking. In this paper, an effective superpixel based L1 tracking method (SuperPixel-L1 tracker, SPL1) is proposed to deal with the above problem in a particle filter framework. First, the mid-level visual cue with structural information is exploited to construct the dictionary and model the object appearance. Then each candidate state defined by a particle is solved via L1 minimization. The candidate with the smallest reconstruction error is selected as the tracking result. Finally, the online dictionary updating strategy is further improved. The dictionary needs to be updated regardless of whether the object is occluded or not. The initial frame information is retained during the updating process to reduce the possibility of the object drift. Simulation results show that SPL1 tracker can still stably track the object under the circumstance of long-term occlusion, large scale and illumination changes.

This research focuses on the issue of chaotic time series prediction. A virtual feature extraction method for forecasting performance improvement is proposed. Firstly, details and smooths of the chaotic time series are extracted by shift invariant wavelet algorithms. Then the relationship between the linear and nonlinear components is extrapolated from additive to functional one. Finally, a novel virtual feature expression is given based on the above wavelet details and smooths for forecasting. Experiment results of forecasting on Mackey-Glass and real Mississippi River flow series show that the proposed method is superior over some existing methods, which also demonstrate the effectiveness of the proposed virtual feature extraction method. Moreover, the results may provide a decision-making reference for a variety of chaotic areas, such as control, hydrology, and meteorology.

It always takes a very high computational cost to build a Boosting cascade classifier. To speed up the training procedure of the Boosting cascade, this paper proposes an extended training method which utilizes the sharing information among different stage classifiers. The proposed method takes advantage of the sharing information among different stage classifiers at two levels. First, at the classifier level, the last stage classifier is taken as the first feature, and is re-used to learn a new weak classifier to adapt the newly collected training samples of the current stage. Secondly, at the feature level, all the selected features from all the previous stage classifiers are re-used to learn new weak classifiers which adapt to the newly collected training samples of the current stage. Finally, the newly learned weak classifiers and newly selected features are added in the current stage classifier. The experimental results on frontal face detection show that the proposed method improves the training speed of the Boosting cascade classifier largely. The training speed of the proposed method is about 10 times faster than that of the traditional method. To be exact, the training time of a frontal face detector is reduced from about 3 days to 8 hours.

In order to solve the deficiency of existing visibility detection methods, this paper presents a novel algorithm by working out the least square calculated from apparent luminance of roads. First, on the basis of camera self-calibration, the region of interest with constant height and even illumination is extracted. Then apparent luminance values of roads are figured out as real reference values. After that, an initial value of extinction coefficient is selected properly, with which a series of apparent luminance values are calculated according to Koschmieder theory. Finally, the objective function of extinction coefficient optimization is established based on the least square between calculated values and reference values of apparent luminance. Experimental results show that the proposed visibility detection algorithm has high accuracy, fast speed, fine robustness and broad application prospects.

The frequency variance of single-frequency signal with rectangular envelope in time domain equals to the effective bandwidth in frequency domain, and the frequency variance is a function of Signal to Noise Ratio (SNR). When the SNR drops, the calculated frequency variance error of azimuth of target line spectrum enhances. As a result, the Variance of frequency detector which weights bearing spectrum using the variance of peak frequency of each azimuth losts its target. The gain of traditional Alpha-beta filter is constant, thus it fails to correct frequency estimates effectively. The paper puts forward a post-processing method which sums the power spectrum of each azimuth several times and puts it as a gain corresponding to each frequency. When the gain corresponding to the frequency observation is large, the new information in the recursive process counts more, whereas the gain corresponding to the frequency observation is small, then the predicted value counts more. At last the bearing spectrums are rectified by using the modified variance of estimated frequency of each azimuth as weight. Simulation and experiment results show that under circumstance of low SNR, the modified post-processing method can extract weak line spectrum target and suppress interference background.

In order to improve the robustness of Adaptive Matched Field Processing (AMFP), a Conditional Probability Constraint Matched Field Processing (MFP-CPC) is proposed. The algorithm derives the posterior probability density of the source locations from Bayesian Criterion, then the main lobe of AMFP is protected and the side lobe is restricted by the posterior probability density, so MFP-CPC not only has the merit of high resolution as AMFP, but also improves the robustness. To evaluate the proposed algorithm, the canonical test case of the Naval Research Laboratory (NRL) is used. The results show that MFP-CPC is better than Bartlett and Minimum Variance Distoritionless Response (MVDR). Its robustness is like Bartlett, and its main lobe is the same as that of MVDR, meanwhile its side lobe is lower about 6 to 8 dB than the latter.

This paper devotes efforts to develope sparse signal recovery algorithms for Compressed Sensing (CS). The proposed algorithmic framework is called as Iterative Detection Estimation with Thresholding (IDET). IDET takes the One-Stage Thresholding (OST) as the reference for support detection, and devises support detection methods depended on the character of sparse signals. This study presents an implementation of IDET, which detects a support set by thresholding the result of the Iterative Hard Thresholding (IHT) iteration and estimates the reconstructed signal by solving a truncated least-squares problem on the support set, and it iterates these two steps until stop condition is met. The key to IDET lies in support detection, so this study explores three support detection strategies for fast decaying signals. The experimental results show IDET exhibits superior reconstruction performance than other accelerated algorithm for IHT.

A method of single channel source separation and parameters estimation of multi-component Sinusoid Frequency Modulation (SFM) signal is proposed, the generalized periodic is derived and it can be used to estimate the modulation frequency by singular value decomposition. The estimated modulation frequency can be used as a known quantity, then by searching the discrete points and optimization calculating with the trust region algorithm, the Frequency Modulation (FM) initial phase, the FM index and the carrier frequency are determined. Finally, the amplitude and the initial phase can be determined by calculating the inner product. In addition, the proposed method can estimate the Signal Noise Ratio (SNR) by using eigenvalue decomposition, and the SNR can be used to determine the threshold adaptively. In the simulation, the method is demonstrated by separating a specific signal, and it is supposed that the proposed method is effective in different SNRs.

In high dynamic environment, the conventional interference suppression algorithms based on covariance matrix are ineffective because of the dramatic change of interference Direction Of Arrival (DOA) in consecutive snapshots. In this paper, a high dynamic interference suppression algorithm with few snapshots for satellite navigation system is proposed to solve the problem of snapshot deficiency. By utilizing the sparse property of spatial spectrum, the DOAs of interferences are estimated by using few snapshots, even only one snapshot. Then the interference subspace is formed based on the estimated DOAs, and orthogonal subspace projection algorithm is used to suppress interference. Simulations demonstrate the effectiveness of the proposed algorithm.

With regarding to the issue of the accuracy of the acquisition is worse under the noise in the low Signal to Noise Ratio (SNR) environment, a two-dimensional refinement adaptive algorithm based on the pseudo-code phase and the carrier frequency is proposed. The coarse acquisition of the pseudo-code phase and the carrier frequency are completed first under larger adaptive acquisition step and threshold. On the basis of the results of acquisition, the pseudo-code phase grid and carrier frequency grid are refined for the second precise acquisition, which can improve the accuracy of the pseudo-code phase and the carrier frequency. Theoretical analysis and simulation show that the algorithm is able to enhance the resolution of captured the pseudo-code phase and the carrier frequency and effectively improve the detection probability, and can be edge option in the low SNR and high precision requirement for quick acquisition.

In order to identify the netting type of active radar networks, the Dempster-Shapher (D-S) evidence theory is applied to solve the problem of sequential information fusion, and a sequential method for netting type recognition of active radars is presented based on multi-source information fusion. Firstly, features are extracted from the information of active radars and communication transmitters, which is obtained by reconnaissance equipments. Then the matching degrees between feature vector and different radar netting types are calculated combining with expert system. Finally, identification of 4 ordinary netting types of active radars is implemented by evidence modification and fusion using an improved mutual weight combination algorithm. Simulation results show that the proposed method is applicable and effective.

The narrow beam and flat earth hypothesis is a widely used assumption in Synthetic Aperture Radar (SAR) motion compensation. It proposes an approach in dealing with the space variance of the motion errors of the moving platform relative to arbitrary point targets in the scene, which is approximately considered as the space variance of the associated distance of the same range gate in the direction of radar beam center. Based on the narrow beam and flat earth hypothesis, this paper derives an implied conclusion that the optimal direction of motion compensation is perpendicular to the line of sight transmitted from the radar beam center only when the height error exists. This analysis is suitable for both broadside and squinted motion compensation models. Then, the reason of the hypothesis in carrying out motion compensation is presented in detail and its validity is confirmed by means of simulated experiments.

A two-dimensional Chirp-Z Transform (CZT) imaging algorithm for general bistatic high squint SAR is proposed. To deal with the serious range-azimuth cross coupling of echo signal in bistatic high squint SAR, Linear Range Walk Correction (LRWC) is performed in range frequency-azimuth time domain to correct the large LRW induced by the high squint model of platforms, and then the expression of a modified bistatic point target reference spectrum is derived. Reference Function Multiplication (RFM) is firstly performed to finish the bulk focusing. With the track decoupling formulas, phase terms of spectrum are decomposed into two independent phase terms as range-variant phase terms and azimuth-variant phase terms, and their space variances are eliminated by CZT respectively to get the focusing result. The simulation tests validate the effectiveness of the proposed imaging algorithm to focus the data of general airborne bistatic high squint SAR.

Multistage Wiener filter is currently one of the reduced rank space-time adaptive processing algorithms with the fastest convergent speed, but it is only suitable for stationary stochastic signal, so it is not able to directly suppress non-homogeneous clutter of airborne conformal array radar. Based on the characteristic analysis of conformal array radar clutter, a spatial filter in elevation is proposed to overcome the non-homogeneity of the clutter, the traditional covariance matrix taper algorithm is improved and finally a novel algorithm of suppressing clutter of airborne conformal array radar, named spatial filter in Elevation-based New Covariance Matrix Taper MultiStage Wiener Filter algorithm (ENCMT-MSWF) is constructed. Simulation results show that the proposed method has better performance in suppression of conformal array radar clutter than other existing methods, and it has the advantages of small amount of computation and fast convergence speed. It is easy to be realized in engineering and has practical application prospects in conformal array radar clutter suppression.

Using Multiple Kernel Learning (MKL) algorithms, which have the function of multi-source information fusion, this paper presents a method for specific radar emitter identification based on kernel-level information fusion. For various feature representations of radar emitter signals, the corresponding kernel functions or kernel matrices are constructed respectively, then their combination coefficients are calculated according to some criteria and the classification hyperplane of Support Vector Machines (SVM) is obtained simultaneously or independently, finally the identification of different emitters is realized. Especially, the proposed methods can effectively fuse the near-zero-doppler slices of Ambiguity Function (AF) of radar signals, getting better performance than the representative-doppler-slice of AF. The experimental results on three real radar data demonstrate the validity of the proposed methods.

This paper proposes two novel objective functions and a new heuristic??optimization algorithm for rotating synthetic aperture passive imaging system which has non-uniform sampling scheme. Firstly, this paper introduces two objective functions named modified minimum electric charge energy and minimum error gridding, while the mostly used maximum baselines distance product objective function introduced by Cornwell will results in dense baseline distribution in centric and boundary area but sparse in the middle. To overcome the problem of updating global best position slowly, rising the risk of being trapped in local extremum by standard Particle Swarm Optimization (PSO), this paper introduces the novel Quantum-Goose Particle Swarm Optimization (QGPSO), which outperforms the existing method of global exploration efficiency and accuracy. Numerical simulations validate that these two functions provide more uniform radial baseline distribution and minimum error gridding objective function provides the most accurate reconstructed image. This method proposes reference for practical design and application of rotating thinned array.

In order to research the signal preprocessing and the inversion method of the ocean wave, the simulation model of a space-borne real aperture radar spectrometer based on signal flow is established. The block diagrams for the modulation spectrum and directional spectrum simulation are provided. Using the simulation model, different ocean wave spectrum model and ocean conditons are simulated. The results show that the retrieved modulation spectrum and referenced modulation spectrum are highly related. What is more, a method used to correct the influence of the ocean anisotropy is proposed. The ocean anisotropy can be corrected by using the normalized modulation coefficient. The simulation results show that the directional spectrum retrieval error is significantly reduced after modulation coefficient correction, especially in the conditon that wind wave and swell wave exist simultaneously and are vertical to each other. The energy integral error of directional spectrum reduces to 2% from 20%.

To deal with the issue of coupling between three components of electric field in measuring, this paper presents a new 3-D testing method based on coplanar decoupling structure. Different from the conventional testing method, which places sensing elements in three orthogonal directions of X, Y, Z, in the proposed method sensing elements are arranged un-collinearly in the same plane, and the demodulation of X, Y, Z components from three sensing elements is realized by utilizing an electric field coupling sensitivity matrix. A three dimensional electric field sample sensor is developed using MEMS chips as sensing elements. Furthermore, an innovative fixture is designed, which can realize the sensor rotation of any angle, and the corresponding calibration and testing system is built up. Experiments show that the proposed 3-D electric field sensor can realize the accurate measurement, and it has a simple structure. The coupling interferences among three electric field components are eliminated, which improves the measurement accuracy.

Ultra-WideBand (UWB) feeds play critical roles in communications and radio astronomy. Quad-ridged horns in general have the performance of poor return loss, severe fluctuation with frequency of the beam patterns and relatively small illumination angle. Based on the latest developments, this paper proposes a dual-polarized large-subtended-angle quadruple-ridged UWB feed working from 1.2~6.8 GHz. By adopting “chamfering”, “profiled thickness” and “over-extension” to the ridge structure, the return loss at the lower frequency is significantly improved and the illumination angle is broadened to 68° so that it is more suitable for the primary-focus reflector systems. Measurements of the manufactured feed show that the bandwidth and return loss of this feed are slightly better than that of previously published foreign prototype in literature. With a large edge illumination angle of about 68°, the feed has a return loss of better than -10 dB from 1.2~6.8 GHz (the relative bandwidth is 5.67:1), and even better than -15 dB from 2.5~5.3 GHz. The fluctuation of the edge illumination angles in E-plane and D-plane is less than 20° with an edge taper of -12 dB.

With the development of communication technology, the requirements of power amplifier linearity are increasing. This paper presents a kind of two-branch predistortion circuit which contains the main branch and the auxiliary branch. The main branch is a diode based nonlinear generator and the auxiliary branch consists of variable attenuator and nonlinear generator. Compared with single nonlinear generator, this two-branch predistortion circuit increases the slope of amplitude characteristic. The ADS co-simulation of Traveling Wave Tube Amplifier (TWTA) and new predistorion circuit show the improvement of linearity. Based on the analysis and simulation, a Ku-band practically predistortion cuicuit is designed and tested with the TWTA. The results of experiments show that, the Carrier to Intermodulation (C/IM3) at TWTA Input Power Back Off (IPBO) of 3 dB can reach 12.92 dB, C/IM3 at TWTA IPBO of 6 dB can reach 22.8 dB. With the linearizer, the linearity of TWTA is clearly improved.

The Rs&Rp model is an efficient method which analyzes the characteristic of PhotoVoltaic (PV) device, and it gives consideration to both computational complexity and accuracy. In Rs&Rp model, five parameters should be determined, and they are photo-current  I_PV, diode saturation current I_o, diode ideality constant a, series resistance R_s and parallel resistance R_p, respectively. In order to find accurate parameters of PV models, a Modified Differential Evolution (MDE) algorithm is proposed in this paper. The MDE uses different scale factors in mutation, which is beneficial to improving the diversity of population. In addition, it also adopts different crossover rates in crossover operation, which enables the proposed algorithm itself to get rid of local optimums. Experimental results show that the MDE can find accurate parameters of Rs&Rp model within a short period of time, which enables the experimental datasets to be well matched by the I-V curves based on the MDE and the Rs&Rp model.

In order to increase the phase measurement accuracy of a given fixed amount of samples, this paper proposes a novel bi-directional DFT symmetric compensation measuring phase method. Through implementing forward DFT and backward DFT on the given samples respectively, the accurate phase information can be extracted from the symmetric peak spectral locations of these forward and backward DFT. Based on this, the theoretic closed-form expression of phase estimation variance of this novel estimator, which is verified by simulation. Moreover, the simulation results verify that the desired phase estimation variance is between all-phase FFT (apFFT) and Cramer-Rao Lower Bound (CRLB) of 2 parameters, thus the proposed method has high accuracy  and wide application prospects.

In this paper, an input coupler of W-band TE₀₁ mode gyroklystron is designed. Structure dimensions of the input coupler are obtained through the theory analysis. Results from 3-D electromagnetic simulation software CST, HFSS and FEKO are applied to its verification and optimization. The effects on the coupling frequency and quality factor from some dimensions, including dimensions of the apertures and the radius of the cylindrical cavity, are also researched. Besides, the mode purity in the cylindrical cavity is calculated. The results show that the TE₀₁ mode is obtained in the cylindrical cavity, with the coupling frequency of 93.55 GHz, external quality factor of 145, and the mode purity of 99%.

As the number of modules in System-on-Chip (SoC) increases, the topology is more likely to suffer from excessive end-to-end hop-counts, causing an increase of power consumption and area overhead. Concerning this issue, a novel Mesh-based Hierarchical topology called CHMesh is proposed, which is divided into two levels. The bottom level is interconnected with Mesh and divided into several regions, so as to guarantee communications of adjacent nodes, and the upper level employs intermediate nodes to promote the interconnection among different bottom routing regions with CMesh, so as to decrease the network diameter. Correspondingly, this article elaborates on a shortest-path CHXY routing algorithm, which has a low complexity and can realize deadlock avoidance. Performance analysis and experimental results demonstrate that, compared with traditional Mesh and Ref-Mesh, the CHMesh can increase the average throughput by about 60% and 10% respectively under non-uniform traffic patterns, presenting more advantages on large-scale NoC.