An infinite Max-Margin Linear Discriminant Projection (iMMLDP) model is developed to deal with the classification problem on multimodal distributed high-dimensional data. Different from global projection, iMMLDP divides the data into a set of local regions via Dirichlet Process (DP) mixture model and meanwhile learns a linear Max-Margin Linear Discriminant Projection (MMLDP) classifier in each local region. By assembling these local classifiers, a flexible nonlinear classifier is constructed. Under this framework, iMMLDP combines dimensionality reduction, clustering and supervised classification in a principled way, therefore, an underlying structure of the data could be uncovered. As a result, the model can handle the classification of data with global nonlinear structure, especially the data with multi-modally distributed structure. With the help of Bayesian nonparametric prior, the model selection problem (e.g. the number of local regions) can be avoided. The proposed model is implemented on synthesized and real-world data, including multi-modally distributed datasets and measured radar high range resolution profile (HRRP) data, to validate its efficiency and effectiveness.
Smoothed l0 (SL0) norm algorithm, using the steepest descent method and gradient projection principle, approaches to l0 norm with selected smooth function, so as to solve the optimization problem and achieve signals reconstruction. A reconstruction algorithmImproved Composite Trigonometric Function (ICTF-SL0) is proposed, researching approximation of smooth function, precision and calculation load of the algorithm. Firstly, composite trigonometric function is chosen as the smooth one, meanwhile constraint condition is designed by adding Total Variation (TV) as a weight value. And then, matrix decomposition is alternated by using Chaotic iteration to accomplish gradient projection. Finally, by contrast with origin SL0 algorithm and other improved algorithms, simulation results demonstrate that ICTF-SL0 algorithm can availably improve imaging precision, decrease calculation load and achieve signal snapshot imaging under sparse array MIMO radar.
When interferometric SAR is used to measure the terrain elevation, the dense interference fringes in the complex terrain will lead to phase unwrapping failure, so that the terrain elevation information can not be obtained accurately. In this paper, a new method to reduce the error rate of phase unwrapping is proposed. The interference fringe gradient is decreased by increasing the size of the interference complex image, and the difficulties of phase filtering and phase unwrapping is reduced. Based on the relationship between the complex image frequency and the Nyquist frequency, the adaptability of the proposed method to the terrain is analyzed. The effectiveness of the proposed method is verified by theoretical analysis and simulation. Especially for the large gradient region conforming Nyquist sampling, this method has good filtering and unwrapping effect. The method proposed does not need extra data, is simple to implement and fast in operation, and can be applied to the ground processing system of interferometric SAR.
In order to get high-precision interferogram of ocean surface current, static control points from land area are normally used to coregistrate ocean surface complex images of along-track interferometric SAR. When there is no control point in the image, ocean wave texture can only be used instead. Under the influence of stochastic movement and low signal-to-noise ratio of the ocean, the coregistration error tends to exceed one pixel, hence damages the quality of interferogram severely. Since the period of large-scale wave is much longer than the interferometric interval, large-scale wave can be treated as static during the interval. Based on this matter of fact, this paper proposes a coregistration method by reserving the spectrum of large-scale wave to improve the signal-to-noise ratio and correlation coefficient, further improving the coregistration precision. Ocean azimuth resolution is used as the criterion to decide which part of the spectrum should be reserved. Airborne along-track interferometric SAR data is demonstrated here, proving the proposed method can improve the coregistration precision of ocean surface complex images without control point.
In the small-aperture high-frequency surface-wave over-the-horizon radar system, the ionospheric clutter that covers a certain range bins, the Doppler bins and all the angle bins is generally existent. To restrain the clutter and ensure the submerged target easy detect, this paper presents an ionospheric clutter suppression algorithm based on spatial multi-beam. This method makes full use of the characteristics of the strong correlation between the beams in the wide beam and the characteristics of the number of range bins or Doppler bins covered by the clutter is far more than the number of bins covered by the target to achieve the clutter information estimation and suppression. The results of the simulation experiment and the measured data show that the method can effectively suppress the ionospheric clutter, and significantly improve the detection probability of the submerged target.
The spatially separated observation angles from the multiple inverse synthetic aperture radar sensors can be converted to the accumulation time of the same target, which can improve the cross-range resolution of ISAR image by the coherent fusion of raw echo signals collected from different sensors. To solve the issue of multiple radar sensors coherent fusion ISAR imaging of space target moving on orbit, the radar location optimal method based on the orbital prior of the space target is proposed to improve the efficiency of the echoes fusion, the spatial- variant property of the fusion imaging plane of space target is analyzed using orbital motion model to solve the range Migration Through Resolution Cells (MTRC) and time-varying Doppler. The simulation results based on real orbit of space station confirm the effectiveness of the proposed method.
The geometric characteristics (such as position, shape, size, etc.) of a large size target such as the broken or sinking subgrade are particularly important in engineering applications and municipal infrastructure maintenance. Due to the attenuation of the electromagnetic wave inside the target, the reflection from back surface of the target is too weak to be detected. In this paper, a target reconstruction algorithm for weak signal compensation based on internal resonances is proposed. Due to the limited target boundary, the electromagnetic wave will produce multiple reflections along the propagation direction inside the target. This phenomenon is reflected as periodic resonances in the recording signal. The relationship between the resonant period and the target width is analyzed and the position of the back surface of the target is estimated. The virtual image around the front surface of target is removed by means of phase difference. The whole target shape is reconstructed according to the front surface and back surface of the target. The experimental results verify the effectiveness of the proposed method and the robustness to noise.
Squint Synthetic Aperture Radar (SAR) can observe the side-front or side-rear scene of the platform. The squint mode improves the observation area and flexibility of SAR greatly. For subaperture imaging of squint SAR, a Local Optimal Matching Algorithm (LOMA) is proposed in this paper. In the algorithm, a new criterion is used in the presentation of the functions for range cell migration correction, secondary range compression and compensation in azimuth frequency domain. The criterion is that the target located at the azimuth frequency is matched optimally. It is different from the traditional algorithm, whose criterion is that the target at the azimuth center is matched optimally. Based on the new criterion, the proposed algorithm is able to avoid the mismatching and improve the focusing of the targets far from the azimuth center. The validity of the proposed algorithm is illustrated by the simulation results.
In dual pulse repetition frequencies radar systems, mathematical deduction of the number of pulse repetition intervals required to satisfy quantitative requirements is presented to resolve velocity ambiguity. Multiple layer systems to increase the maximum unambiguous velocity are proposed. A new model of quadruple pulse repetition frequencies signal is proposed. The lower bound of the number of pulse repetition intervals required to satisfy quantitative requirements is deduced and quadruple pulse repetition frequencies signal is simplified as triplet pulse repetition frequencies signal. Simulation results validate the accuracy of our deduction.
In order to solve the problem of low tracking performance for bistatic MIMO radar based on Adaptive Asymmetric Joint Diagonalization (AAJD) algorithm, an improved multi-target tracking method is proposed. The reason for the low tracking performance of AAJD algorithm is that the estimated angle of the previous time is reused for angle estimation. It is proved that each eigenvectors of the AAJD algorithm corresponds to a target. Improved AAJD algorithm can estimate the DOD and DOA directly, which improves tracking performance and can solve high maneuvering target tracking problems. And the ESPRIT algorithm is improved to realize the automatic matching and association of DOD and DOA. The simulation results show that the improved AAJD algorithm has higher tracking performance than AAJD algorithm, especially when the large maneuvering target is tracking. The efficiency of the proposed method is verified.
High Frequency Radar (HFR) works in the crowded high-frequency band (3~30 MHz) with limited continuous bandwidth. It affects the ability to distinguish the near targets. Therefore, this paper introduces a kind of synthesis bandwidth signal with a proposed method for estimating the target parameters in 1-D and 2-D based on Matrix Completion (MC). They are respectively named Matrix Completion Estimation for One Dimension (MCE-1D) and Matrix Completion Estimation for Two Dimensions (MCE-2D). The incomplete sampling set can be considered as low rank matrix, by constructing the two-fold Hankel matrix, this problem is transformed into a Semi-Definite Programming (SDP) problem. Using this new method to the high frequency radar, then the accurate estimation of the target position in the scene can be obtained in the background of the discontinuous spectrum, which solves the problem of base mismatch for off-the-grid targets in the traditional grid estimate method. It also has higher resolution and anti-noise performance. The simulation results demonstrate the effectiveness of this method.
As modulated by the ionosphere, the classical High-Frequency (HF) model of ocean clutter can not describe the HF scatterings from sea surface approvingly in view of skywave Over-The-Horizon Radar (OTHR). In this paper, ocean current is taken into consideration based on the Walsh HF sea clutter model. An equivalent ionospheric reflecting screen model is established which can be resolved into several sub screens, based on which the ionospheric modulation on frequency is discussed. Incorporating with Walsh model, a normalized sea clutter model for skywave OTHR is presented. Using the presented model, the spectrums in different cases involving Doppler shift, spectrum broadening, splitting and multipath propagation are simulated, and comparing with the measured data, the model’s validity and robustness are verified.
For non-cooperative ISAR targets, the rotation motion and the translation motion are coupled under wide-angle circumstance, and it is hard to compensate the translation motion precisely. A two dimensional autofocus translation compensation method for wide-angle ISAR imaging is proposed. Firstly, the translation motion is compensated coarsely; then, two dimensional coupling between rotation and translation is decoupled through Polar Format Algorithm (PFA); finally, the error phase is extracted by autofocus algorithm and then the residual translation is estimated from the error phase. The proposed method can correct the envelope and phase errors simultaneously, and compared with traditional translation compensation methods, range alignment combining with autofocus, the proposed method has a higher precision; compared with the methods of optimization for jointing alignment and phase, the proposed method is more efficient. The experimental results using both simulated data and measured data collected in microwave chamber confirm the validation and practicality of the proposed algorithm.
A new Constant False Alarm Rate (CFAR) target detection algorithm is proposed based on Compressive Sensing (CS). Firstly, the sparsity of target in the distance dimension is analyzed and the sparse dictionary is constructed for the echo signal. Secondly, a certain measurement matrix and CFAR detection structure are designed based on CS. The proposed detector can detect sparse signals directly with high accuracy without any signal reconstruction. The proposed algorithm has a good noise reduction performance, which can detect low SNR and low Signal-to-Interference Ratio (SIR) signals successfully. Finally, computer simulation results verify that when SNR is equal to -14 dB and SIR is equal to -10 dB, the proposed detector can reduce the half measurements via compared with classical Matched Filter (MF) algorithm. What’s more, the performance of the proposed detector is better than CS MF algorithm.
A fast algorithm based on the effective FrFT is proposed to realize the detection and parameter estimation of Linear Frequency Modulation (LFM) signal, since the traditional algorithms have a great computational burden. The effective FrFT is first analyzed, and pointed out to have problems in choosing the rotation angles, being easily affected by initial frequency, and poor anti-noise performance. Faced with the above problems, a modified power spectrum smooth filtering method is used to improve the effective FrFT algorithm. The theoretical analysis indicates that the proposed method based on effective FrFT can realize the detection and parameter estimation of LFM signal in low SNR condition with only three rotation angles. Furthermore, the computational cost is greatly reduced under the guarantee of the same parameter estimation accuracy compared to traditional FrFT. The simulation results verify the effectiveness of the proposed algorithm.
Coherent Change Detection (CCD) has good performance on detecting change regions with low coherence in the scene by using repeat-pass Synthetic Aperture Radar (SAR) data. However, some regions as vegetation, radar shadows, sidelobes of strong reflectivity and low reflectivity areas show low coherent character as well, which disturbs the result of change detection, especially in high frequency band SAR CCD with more evident disturbance. This paper proposes a multi-temporal CCD method by establishing a probabilistic graphical model using CCD images formed by multi-temporal SAR data. In this method, multi-temporal CCD images are used as observations to calculate a posterior probability of objective change region via choosing appropriate number of processing CCD images and optimizing the classification of change regions in the scene. The proposed method can reduce the disturbance of low coherence disturb regions effectively. The simulated and experimental results demonstrate the validity and effectiveness of the proposed method.
The determination of the scanning emitter position with known scan rate using Direction Of Arrival (DOA) and Time Difference Of Interception (TDOI) is investigated. The Cramér-Rao Lower Bound (CRLB) of the DOA and TDOI based localization regime is firstly derived. It demonstrates that the equivalent DOA measurements noise power ratio of the individual regime is closely related to the improvement of the combination regime. To exclusively determine the position of the scanning emitter, the TOI measurements are transformed to the corresponding DOA measurements and then a Weighted Pseudo-linear Least Square (WPLS) estimator is proposed. However, the WPLS is biased due to the noise correlation between the regressor and regressand of the pseudo-linear equation. The Instrumental Variable (IV) method is resorted to eliminate the bias caused by the WPLS, and a Weighted IV (WIV) estimator, at the cost of two times computational complexity of the WPLS, is proposed. Simulations show that the WIV performs approximately to the Maximum Likelihood (ML) estimator. It can reach the CRLB in one scan cycle, and is asymptotic unbiased within multiple cycles.
The trend of heterogeneous node and functional diversification is presented in the development of space information network. The synchronization of network nodes is the technical foundation of task coordination of network nodes. The characteristic of time synchronization service of space information network is analyzed in this paper and the error model of multi-hop relay time synchronization is given. The concept of clock offset relative invariance is proposed based on the characteristic of non-real-time time synchronization, and a novel space information network topology aggregation model is given for time synchronization service. Differing from normal communication-service-oriented network models, the constraint condition of the model establishment is the performance of node clock and the time synchronization link, and the characteristic and requirement of time synchronization service is concerned. Simulation of space information network time synchronization is made according to the model. In the process of multi-hop relay time synchronization based on the model, less routing and linking number is needed. The link resource consumption is reduced, and the time synchronization precision is improved.
Signal processing on graphs extends signal processing concepts and methodologies from the classical signal processing theory to data indexed by general graphs. For a band-limited graph signal, the unsampled data can be reconstructed from the sampled data by exploiting the relationship of the graph signals. This paper proposes a concept of graph diffusion operator for signal processing on graphs, and uses the operator to reconstruct band-limited graph signals from the sampled data. In each iteration, the residuals of the sampled vertices are propagated to all the unsampled vertices, and the known information and initial estimated results are further exploited via weighted process, aiming at accelerating the convergence. An analysis framework is proposed for the unsampled graph signals. The simulation results of synthetic data and real-world data demonstrate the wonderful effectiveness of the proposed reconstruction strategy.
Along with more and more important role of depth features played in computer saliency community, traditional RGB saliency models can not directly utilized for saliency detection on RGB-D domains. This paper proposes saliency center prior and Saliency-Depth (S-D) probability adjustment RGB-D saliency detection framework, making the depth and RGB features adaptively fuse and complementary to each other. First, the initial saliency maps of depth images are obtained according to three-dimension space weights and depth prior; second, the feature fused Manifold Ranking model with extracted depth features is utilized for RGB image saliency detection. Then, the saliency center prior based on depth is computed and this value is used as saliency weight to further improve the RGB image saliency detection results, obtaining the final RGB saliency map. After that, Saliency-Depth (S-D) rectify probability is also computed and the saliency results of depth images are corrected with this probability. Then the saliency center prior based on RGB is also computed and this value is used as saliency weights to further improve the depth image saliency detection results and to obtain the final depth saliency maps. Finally the optimization framework is utilized to optimize the depth image final saliency maps and to obtain the final RGB-D saliency map. All the experiments are executed on the public NLPR RGBD-1000 benchmark and extensive experiments demonstrate that the proposed algorithm achieves better performance compared with existing state-of-the-art approaches.
Person re-identification is the identification of the same pedestrian in a multi camera surveillance without overlapping views. Aiming at the problem of the existence of visual angle, illumination and scale change in pedestrian images which from different camera. An indirect person re-identification method is proposed based on the support samples. At first, the algorithm extracts the support samples from different cameras by the clustering method. When it comes to matching pedestrians from different cameras, the support samples are used to distinguish the pedestrians categories under the camera on the basis of the distance metric, by comparing the categories to determine whether the same pedestrian. The method avoids the direct matching of pedestrian images under different cameras, which effectively solve the problem of the existence of visual angle, illumination and scale change in different camera. The experimental results show that the algorithm has a high recognition rate, and on the data set VIPeR, CAVIAR4ReID and CUHK01the, Rank1 reaches 43.60%, 41.36% and 43.82% respectively.
The aim of this paper is to achieve a low-lighting image enhancement method by using the similarity between fog image and inverted low illumination image. The transmittance estimation of the pseudo-fog image is estimated by the improved boundary constraint, and then it is optimized. Based on the formation principle of pseudo fog, the light intensity of pseudo fog map is estimated by using the brightness component of low illumination image. The enhanced pseudo fog image is reversed to obtain the enhanced low illumination image. Extensive experimental results using natural low-lighting images indicate that the proposed method perform better than contemporary algorithms in terms of several metrics, including the intensity, the contrast. The proposed algorithm can effectively suppress the wrong phenomenon caused by enhanced with low complexity.
This paper presents an efficient algorithm to design M-channel oversampled graph filter banks with better overall performance. In the new algorithm, a two-step scheme is exploited to tackle the design task. Firstly, for controlling the spectral selectivity, the analysis filter is designed by solving a constraint optimization problem that minimizes the passband ripple and stopband energy subject to 3 dB constraint; secondly, by taking the Perfect Reconstruction (PR) condition into account, the design problem of synthesis filters is formulated into an optimization problem that minimizes the stopband energy subject to PR constraint. Both the optimization problems are Semi-Definite Programming (SDP), which can be efficiently solved. Since the proposed method fully considerate the spectral characteristic and PR condition, M-channel biorthogonal oversampled graph filter banks with better performance can be obtained. Numerical examples and comparison show that compared with the existing methods, the proposed method can lead to graph filter banks with smaller reconstruction error.
For two-way Multiple-Input Multiple-Output (MIMO) relay communication systems, the main challenge is to get full knowledge of all channel matrices with minimal cost of signal handling at the relay node. In this paper, a low-complexity joint channel estimation scheme for two-way MIMO relay communication systems is proposed. Both users transmit orthogonal channel training signals to the relay node simultaneously. Then the relay amplifies the received signals by using designed amplification factors, and forwards the amplified signals to both users. The received signals at each user is formulated as a PARAllel FACtor (PARAFAC) model, and then the iterative and non-iterative fitting algorithms are derived to estimate the Channel State Information (CSI) knowledge of all links involved. Compared with existing schemes, the proposed scheme has the advantages of design flexibility and low complexity, and has higher estimation accuracy with a few number of channel training signals.
Active reconfigurable Frequency Selective Surface (FSS) using pin diode for the Radar Cross Section (RCS) reduction of antenna is proposed. The reconfigurable technology is applied to the FSS design. The reconfigurable FSS reflector is able to perform switch between band-pass FSS and band-stop FSS. The active reconfigurable FSS with pin diodes applies to the antenna reflector for the antenna RCS reduction, and the radiation performance of the antenna is preserved. Through the diode is on or off, the reconfigurable FSS reflectors are different states. It can contribute to the reconfigurable RCS reduction of dipole antenna under different working conditions. The simulated and measured results show the largest RCS reduction is more than 20dB, and the RCS reduction region is -60°≤θ≤+60°. The radiation performance of the antenna is preserved when the diodes are ON-state. The active reconfigurable FSS provide a good method to solve the conflict between the gain enhancement and the RCS reduction. The reduction band and the state of the RCS can be switched by pin diodes.
In order to solve the limitation to use the traditional antenna selection algorithm in a massive antenna for fifth Generation (5G) system, a random antenna selection algorithm based on the dictionary of capacity correlation in multi-users scenario is proposed. Before the dictionary is built, the correlation characteristics among antenna units are abstracted by the interactions between the base station and users at different locations. It needs to search the antenna unit with optimal capacity to connect one Radio Frequency (RF) link, and the reminder RF links can select the antennas randomly which have the large correlation with the optimal one in the dictionary. The method is applicable to massive antennas system because of its low complexity and stability. Moreover, there is no need to renew the dictionary for coming new users. In addition, the massive antenna channel measurements in an open office including line-of-sight and none-line-of-sight at 26 GHz are carried out to analyze the capacity correlations among the antenna units located in different rows as well as to verify the system capacity performance with the random antenna selection algorithm proposed in this work.
With the actually used frequency band going higher and higher in wireless communications, the influence of phase noise induced by imperfect carrier on OFDM systems can no longer be ignored. At the same time, the restrictions on the antenna spacing may make it impossible for different antennas to share the same carrier clock. In a MIMO-OFDM system with independent phase noises on different antennas, the approximate expression of error covariance matrix for demodulated data streams is given, and it is shown that the influence of CPE, ICI and additive noise are additive and independent of each other; besides, for both CPE and ICI, the influence of phase noises induced by the transmitter or the receiver are additive and independent of each other too. The validity of the theoretical deduction is verified by numerical simulation.
Considering the modulation technique and multiple-access topology for indoor Visible Light Communication (VLC), two Multi-dimensional Carrierless Amplitude and Phase (Multi-CAP) modulation schemes are proposed in this paper, namely, DC biased Optical Multi-CAP (DCO-MCAP) and Unipolar optical Multi-CAP (U-MCAP). The design object is modeled as a mini-max optimization problem with a non-constraint condition named Perfect Reconstruction (PR), which can be solved by the quadratic programming algorithm. Only real and unipolar signals can be intensity modulated, therefore several modifications are performed in CAP VLC systems accordingly. A DC bias added to the signal and a pair of new samples including a zero padding to mark signal polarity are adopted to obtain unipolar signal respectively. According to Lambertian reflection model and considering light of sight link corrupted by Additive White Gaussian Noise (AWGN), the theoretical Symbol Error Rate (SER) of DCO-CAP/MCAP and U-CAP/MCAP are derived and corroborated by Monte Carlo simulations. Meanwhile, the performance of three kinds of dimensional (2D, 3D, 4D) optical CAP systems are analyzed and compared in the room in size of 5 m×5 m×3 m. The numerical results show that the error performance of optical Multi-CAP is superior to that of traditional two dimensional optical CAP with the same constellation, and the multi-CAP (3D or 4D) scheme can not only achieve approximate SER performance with respect to the same bandwidth efficiency, but also enable multiple access in VLC system. In addition, the effects of channel parameters on the system performance are evaluated which shows that the loss of SNR obviously presents positive relation with the increase of the radiation angle or the distance between the transmitter and the receiver.
Ciphertext-Policy Attribute-Based Encryption (CP-ABE), especially large universe CP-ABE that is not bounded with the attribute set, is getting the more and the more extensive application to the cloud storage. However, there exists an important challenge in original large universe CP-ABE, namely dynamic user and attribute revocation. In this paper, a large universe CP-ABE scheme with efficient attribute level user revocation is proposed, namely the revocation to an attribute of some user can not influence the common access of other legitimate attributes. To achieve the revocation, the master key is divided into two parts: delegation key and secret key, which are sent to the cloud provider and user separately. In this scheme proposed, if an attribute is revoked, then the ciphertext corresponding to this attribute should be updated so that only persons who are not revoked will be able to carry out key updating and decrypt the ciphertext successfully. Note that, the proposed scheme is proved selectively secure in the standard model under “q-type” assumption. Finally, the performance analysis and experimental verification are carried out in this paper, and the experimental results show that, compared with the existing revocation schemes, although the proposed scheme increases the Computational load of Storage service Provider (CSP) in order to achieve the attribute revocation, it does not need the participation of Attribute Authority (AA), which reduces the computational load of AA. Moreover, the user does not need any additional parameters to achieve the attribute revocation except of the private key, thus saving the storage space greatly.
According to QoS characteristics of network multimedia service, this paper proposes a algorithm of network multimedia QoS class recognition. This paper studies new multimedia traffic QoS class division mode. According to new QoS classes defined, Flow Aggregation (FA) can be formed by gathering multimedia traffic flows with similar QoS characteristics. Network multimedia QoS class recognition prefers fewer QoS features by FA, and it is possible to divide network multimedia traffics in suitable granularity based on FA. This paper analyzes the property of FA recognition from QoS perspective, uses improved K-SVD (Kernel Singular Value Decomposition) to learn dictionary by using the sparse representation of typical QoS characteristics of network multimedia traffics, and presents a network multimedia QoS class recognition method. Experiment results show that the proposed recognition method can achieve more accurate QoS class recognition than previous methods.
Aeromagnetic prospecting is an important method in geophysical prospecting for its high-efficiency and convenience. In the paper, a new kind of the three axis gradient system based on the unmanned helicopter is designed and an aeromagnetic compensation method based on the neural network is proposed. The system is equipped with four Optically Pumped Magnetometers (OPMs), from which the three axis gradient data can be achieved. In aeromagnetic compensation, the feedforward network is used. The Improvement Ratio (IR) of the three axis gradient data is 15.2, 4.7 and 5.9, respectively. The Peak Signal to Noise Ratio (PSNR) of the three axis gradient data is improved by 17.1 dB, 6.5 dB and 6.5 dB, respectively. Through the result of the Cross Calibration Index (CCI), the generalization ability of the method is good. The three axis gradient system and the aeromagnetic compensation are proven valid in aeromagnetic prospecting by an experiment.
A new absolute error function is presented in this paper. The function is applied to extract parameters of the nonlinear model, which can avoid the calculation error and reduce the inaccurate parameter extraction significantly. Nitride semiconductor devices are widely used, especially the AlGaN/GaN HEMT devices. The AlGaN/GaN HEMT model and parameters is very important to radio frequency, power electronic devices and circuit design. The new absolute error function is applied to extract the parameters of AlGaN/GaN HEMT nonlinear devices model. Through comparing three kinds of error function, the results show that the proposed error function is more accurate and effective. At the same time, a precise and effective method is provided to extract the parameters of electronic devices in the future.
Heterogeneous signcryption can be used to guarantee the confidentiality and unforgeability in the different cryptography. In 2016, between traditional public key cryptography and certificateless public key cryptography, the mutual signcryption schemes including PCHS and CPHS were proposed by Liu et al. However, via the security analysis, it is shown that the above schemes are not secure. Firstly, the processes of attack performed by the second type of adversary are described. Secondly, the possible reasons why the second type of adversary can perform these attacks are analyzed. In the end, the original schemes are improved. The improved schemes can overcome the security weakness of the original schemes, and can also ensure the security of data transmission between traditional public key cryptographic and certificateless public key cryptography.
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