The ISAR imaging technology with sparse Stepped-Frequency Chirp Signals (SFCS) based on Compressive Sensing (CS) theory can construct the target image from a few of measurements with high probability, where the measurement matrix optimization is an effective way of improving the imaging quality and reducing the measurements. However, most of the existing measurement matrix optimization methods do not utilize the target characteristic, which leads to low adaptive ability of target. Therefore, an adaptive measurement matrix optimization method for Inverse Synthetic Aperture Radar (ISAR) Imaging with sparse SFCS is proposed in this paper, where the actual physical observation process is considered and the target characteristics are utilized to optimize the measurement matrix. In the method, a parametric sparse representation model of ISAR imaging is established to solve the Doppler sensitivity firstly. On the basis, the measurement matrix is optimized with the goal of obtaining the best target image with the minimum measurements under a given image quality requirement. As a result, the expected imaging results can be obtained with minimum measurements by using the optimized measurement matrix. The effectiveness of the proposed method is demonstrated by experiments.
The multi-angle characteristic of netted radar is helpful in obtaining the actual space structure of ballistic targets. In this paper, the echo model for precession targets with vanes is established. Based on the model, the rate of change of micro-Doppler for different scattering centers are analyzed, and the micro-Doppler information is extracted. Then the nonlinear least-square fitting is introduced to obtain the amplitude and phase information of scattering centers. Amplitude information of scattering centers at the conical point obtained from radars is used to estimate micro-motion characteristics and parameters of coordinate transformation. According to the distinctions among different projection components of scattering centers under different perspectives, scattering centers of fins are matched, and the instantaneous space coordinates are calculated to reconstruct the targets. Finally, simulations verify the effectiveness of the proposed method, and the influence of micro-motion parameters and SNR (Signal to Noise Ratio) on reconstruction results are analyzed.
A novel coherent ladar system with high repetition and wide bandwidth is proposed. Based on the I&Q modulator from the fiber communication community and the bandwidth synthesis technique, the proposed system provides wide bandwidth with high repetition rate, which is difficult for conversional ladar system based on tuning the wavelength of a tunable laser. The proposed system is expected to be useful for high range resolution imaging, Inverse Synthetic Aperture Ladar (ISAL), range resolved vibration measurement. The operation principle, system description, and signal processing method for bandwidth synthesis are discussed in the paper. And experiments are performed both in fiber delay line and free space. As a demonstration, the system generates signal with 6 GHz at repetition rate of 16.7 kHz. The resolution of the obtained images is finer than 2.5 cm. The experimental results demonstrates the effectiveness of the proposed system and the processing method.
In view of the detection waveform design for cognitive radar with imprecise prior knowledge of target and clutter, while considering the demand of power amplifier on low Peak-to-Average power Ratio (PAR) waveform, a low-PAR robust waveform design method in presence of signal-dependent clutter is proposed. Firstly, the optimization model of radar’s output Signal-to-Interference-plus-Noise Ratio (SINR) is established within the uncertainty of target and clutter via Max-Min method. Secondly, the clutter covariance matrix and Toeplitz matrix of target corresponding to worst-case SINR is obtained. Since the optimization problem of waveform is non-convex, Semi-Definite Relaxation (SDR) is adopted to converse the non-convex problem into a convex problem, which is about the semi-definite matrix of waveform. Finally, the optimal vector solution of waveform can be extracted from the optimal matrix solution by the rank-one approximation method combined with the nearest neighbor method. Compared with the existing methods, the computational complexity of the proposed method is obviously reduced without losing robust performance according to the analysis. The simulation results demonstrate the effectiveness and robustness of the proposed method.
Maneuvering target detection is one of the focuses in skywave Over-The-Horizon Radar (OTHR). The performance of existing algorithms is good for single maneuvering target in the condition of high SNR. However, the performance for multiple maneuvering targets in low SNR needs to be improved. In this paper, a new maneuvering target detection algorithm is proposed based on Hankel matrix decomposition. The maneuvering target signal is constructed in the form of Hankel matrix. Then the time-frequency estimation of maneuvering target signal is transformed into a convex optimization problem based on linear Principal Component Analysis (PCA) by matrix decomposition, so as to realize the separation of uniform and maneuvering targets, and the simultaneous estimation of multiple targets. The simulation results show that the proposed algorithm has the advantages of high estimation accuracy, low SNR and multiple target detection.
To solve the moving target localization problem in distributed MIMO radar systems, with the Bistatic Range (BR) and Bistatic Range Rate (BRR) used as the measurements, an algebraic algorithm based on multi- stage Weighted Least Squares (WLS) is proposed. The proposed algorithm needs three WLS stages. In the first WLS stage, by introducing proper additional parameters, the BR and BRR measurement equations are linearized, and weighted least square estimator is used to produce a rough estimate of target position and velocity. Then in the latter two WLS stages, the relation between the target location parameters and additional parameters is utilized to refine the estimate. Finally, the theoretical error of the proposed algorithm is derived, and it is proved that the theoretical error attains the Cramer-Rao Lower Bound. Simulation results indicate that the proposed algorithm achieves a significant performance improvement over the existing algorithms.
In order to further improve the BOC (1,1) and its derived types modulated signal’s code discrimination quality and tracking ability of the satellite navigation receiver, especially in high dynamic. This paper proposes a method to determine the BOC (1,1) modulated signal code phase by partial correlation function interpolation. This method is based on the structure of correlators array, the approximate range of the correlation peak is determined by the correlators’ output. The generalized extended approximation method is used to estimate the code phase, and a virtual correlator is created in order to process the generalized extended approximation when the extended range does not exist. In this paper, the influence of the one side correlators’ number N on the linear pull-in region is analyzed in detail. On this basis, the computer simulation experiment for the proposed method is given. Theoretical and simulation results show: the proposed method can be used to enlarge the linear pull-in region of phase discrimination function without adding too much hardware resources, furthermore, it can improve the BOC modulated signals’ tracking accuracy for the receiver.
In the GPS/INS integrated navigation system, the filtering precision of Square-root Cubature Kalman Filter (SCKF) will decrease or even diverge resulting from the uncertainty of the measured noise statistics, therefore, a Weighting Aaptive Square-root Cubature Kalman Filter (WASCKF) method is proposed in this paper. Firstly, the moving window method is employed to conduct the maximum likelihood estimation of the covariance matrix of SCKF, in order to realize the on-line adjustment of the statistical characteristics of the measured noise. Then, the weighting theory is utilized to set the corresponding weights according to the usefulness of the information at different times in the window, thus it takes great use of effective information in the window. Finally, the WASCKF is applied to the GPS/INS integrated navigation system for simulation and verification, and comparing with the SCKF and ASCKF methods. The results indicate that the mean square root of velocity errors and position errors of the proposed method are less than SCKF and ASCKF, and it can effectively improve the adaptive capability and navigation performance of GPS/INS integrated navigation system with the measured noise uncertainty.
Characteristic basis function method is one of the effective methods to analyze wide-angle electromagnetic scattering characteristics of objects. However, the incident wave excitations used to construct the Characteristic Basis Functions (CBFs) contain large amount of redundant information, which greatly reduces the construction efficiency of the CBFs. Moreover, when the complex target is analyzed, the calculation accuracy can not be significantly improved only using the Primary CBFs (PCBFs) when the number of excitations is increased. To solve these problems, an improved CBFs construction method is presented in this paper. Firstly, the Singular Value Decomposition (SVD) technique is used to effectively compress the excitation matrix to remove the redundant information, which in turn reduces the number of solving the matrix equation. Then, the mutual interaction among subdomains is fully considered, the Improved PCBFs (IPCBFs) are obtained by merging the PCBFs and the Secondary CBFs (SCBFs). The numerical results show that the proposed method has higher computational efficiency and computational accuracy than the traditional method.
The sea under different wave levels has an strong impact on the ship target Radar Cross Section (RCS) analysis. The far-field single-station RCS analysis model is established for the ship under different sea conditions based on the Physical Optics with Method Of Moments (PO-MOM) hybrid algorithm. Then the impact of sea conditions on ship RCS results is studied. The ship RCS results are reduced with the sea wave level increasing. Finally, an optimization ship RCS compensation method is proposed under different sea conditions based on Cubic Spline Interpolation (CSI) algorithm. The results show that the average value error and maximum value error of ship RCS results are less than 0.38 dBsm and 0.05 dBsm, respectively by employing the proposed method, which can reduce the influence of sea conditions on ship RCS analysis effectively.
Tropospheric slant delay is a main error source in two way time transfer via tropospheric scatter communication. A method for real-time estimation of tropospheric slant in two way time transfer via tropospheric scatter communication is proposed. The meteorological data of the station are calculated by the GPT2w model to overcome the reliance on the real-time meteorological data in the estimation of tropospheric delay. In order to solve the problem of the fixed height of the top troposphere layer, the real height of the top troposphere layer is calculated by geometric method to solve the practical application. Three stations in Japan are selected and compared with each other. After verifying the accuracy of the Hopfield model, the tropospheric delay of the three groups at different angles and different time is calculated. The results show that the tropospheric slant delay in two-way troposphere time transfer increases with the increase of the distance, and decreases with the increase of the angle, and the variation characteristics of the four seasons are obvious. The tropospheric delay of the three stations is between 10~35 m, and the time delay after subtracting 90% is 3.5~11.8 ns.
To solve the problem of quantum color image filtering, this paper proposes a frequency domain filtering method based on Quantum Fourier Transform (QFT). Firstly, the classical color image is represented as a quantum image by Novel Enhanced Quantum Representation (NEQR) scheme, then the QFT is applied to the quantum image, and the transformed image is divided into different frequency images using quantum Oracle defined according to filter function. Finally, these quantum images of different frequencies are transformed into spatial domain by Inverter Quantum Fourier Transform (IQFT). Through the measurement, different frequency filtering images can be obtained. In this paper, a specific quantum filter circuit is given, and the correctness of the proposed scheme is verified by taking the color image smoothing, sharpening and periodic noise elimination as an example.
In the infrared object tracking, the single classifier is not enough to fit the multimodal data due to the complex background information of the target and the significant change in the appearance. In this paper, Kernelized Correlation Filters (KCF) tracking algorithm is used to integrate kernelized correlation classifiers into one framework through ensemble learning. It uses the KCF classifier that has analytical solutions to balance the contradiction between the robustness and instantaneity, thereby addressing the complex background and significant appearance changes, and consequently significantly improving the tracking performance and stability. To verify the effectiveness of the algorithm, this paper uses two kernelized correlation trackers to learn a strong classifier. The qualitative and quantitative experiments show that the proposed algorithm outperforms the traditional KCF algorithm, and the tracking speed is superior to most of the comparison algorithms.
Most previous weakly supervised semantic segmentation works utilize the labels of the whole training set and thereby need the construction of a relationship graph about image labels. This method lack of structure information in single image and suffer from enormous quantity parameters which result in expensive computation. In this study, a weakly-supervised semantic segmentation algorithm is proposed. Under Conditional Random Field (CRF) framework, an novel energy function expression is developed based on saliency priors as structure context relationship, which avoids the construction of a huge graph in whole training dataset. Specifically, a nonparametric random Semantic Texton Forest (STF) is obtained using weakly supervised training data and images saliency. Then STF feature is extracted from image superpixels and probability estimates of superpixels label is calculated by naive Bayesian method. Finally, a CRF based optimization algorithm is proposed which can efficiency solved by alpha expansion algorithm. Experiments on the MSRC-21 dataset show that the new algorithm outperforms some previous influential weakly-supervised segmentation algorithms with no building graph in whole training set.
Existing Candid Covariance-free Incremental PCA (CCIPCA) has the limitation of the stable image inherent covariance, and a Generalized CCIPCA (GCCIPCA) with an appended term of the mean difference vector is presented. It can be considered that the CCIPCA is only a special case of the GCCIPCA and can extend the scope of the algorithm. Then, the incremental learning of the proposed GCCIPCA is innovated to the existing Bi-Directional PCA (BDPCA), and the called Incremental BDPCA (IBDPCA) is used for the robot perceptual learning and it can be used to incrementally compute the principal components without estimating the similar scatter matrixes in the row and column directions, which can build up the real-time processing speed greatly. Finally, the blocks grasped by the robot are used as the perceptual objects, and the experimental results demonstrate that the proposed algorithm works well, and the convergence rate, the classification recognition rate, the computation time and the required memory are improved significantly.
For facial expression recognition based on video sequences, the changing information of facial regions along the time axis can be described by dynamic descriptors more effectively than static descriptors. This paper proposes an expression recognition method based on the dynamic texture and motion information, learning from the principle of Local Binary Pattern on Three Orthogonal Planes (LBP-TOP), Spatio-Temporal Weber Local Descriptor (STWLD) is proposed to describe the dynamic texture feature information of the facial expression sequence. Moreover, using Block-based Histogram of Optical Flow features (BHOF), the motion information can be described. Through the combination of the dynamic texture and motion information, and finally SVM is applied to complete the expression classification. The results of the cross experiments on the CK + and MMI expression database show that the method achieves better performance than methods using the single descriptors. The comparison experiments with other related methods also prove the superiority of the method.
A novel expression based on Binary Image Microstructure Pattern (BIMP) and Gray Image Micorstructure Maximum Response Pattern (GIMMRP) coding method is proposed. Through the binary coding of the 3×3 neighborhood structure of the image, the description of the microstructure of the image is obtained, and then selecting the important execution mode subset and the pooling operation to realize the representation of the whole image. In order to verify the effectiveness of the algorithm, experiments are carried out on the ORL, YALE two human face data set, MNIST, USPS two handwritten digital public data sets, as well as non-public vehicle standard data set. The results show the method has strong discriminative power and robustness and can achieve better performance than many of the latest algorithms.
In the data fusion system, sensor biases lead to systematic deviation of the position states of targets reported to the fusion center. If sensor biases could not be estimated and compensated correctly, the fusion system will fail to achieve the expected performance superiority. However, the starting point of sensor bias estimation is the overdetermined equations construted on the biasis of data association. In the complicated environment, with the presence of interference factors such as random errors, sensor biases, false alarms and missed detections, the data association module outputs some misassociations inevitably. In view of the multisensor bias estimation problem under nonideal association, the robust estimation approach based on the least trimmed squares is proposed. Furthermore, the reweighted least squares apporach through eliminating abnormal equations is presented. Compared with the least squares and the least median of squares, the proposed approaches can not only ensure the robust performance on bias estimation, but also are less sensitive to random errors. Simulation results verify the effectiveness of the proposed methods.
In view of the defect for large number of atoms in the over-complete dictionary during sparse decomposition, this paper presents a fast sparse decomposition algorithm for three-order polynomial phase signal based on subspace. According to the characteristic of three-order polynomial phase signal, the original signal is transformed into two subspace signals, then the atoms are structured based on the two subspace signals in the over-complete dictionary, and the two subspace signals are sparsely decomposed by using orthogonal matching pursuit algorithm. Finally, the sparse decomposition for the original signal is completed by using the theory of the sparse decomposition. In the algorithm, three-order polynomial phase signal is transformed into two subspace signals, and two over-complete dictionaries are structured based on the two subspace signals. Compared to one over-complete dictionary, the atoms are reduced enormously by using two over-complete dictionaries in the algorithm, and one matching atom can be obtained in one over-complete dictionary when another matching atom in another over-complete dictionary is obtained by using fast Fourier transform. Therefore the method can sparsely decompose three-order polynomial phase signal with low computational complexity by reducing the atoms and using fast Fourier transform. Simulation results show that the computational efficiency of the proposed method is better than that of using Gabor atoms, genetic algorithm and the algorithm based on modulation correlation partition, and the sparsity is better.
The bandwidth of each hop in frequency hopping signal is very narrow, and the accumulating between multiple hop is difficult, thus the accuracy of time delay estimation for frequency hopping is low. To deal with the problem, the potential of “wide band hopping” of frequency hopping signal is fully exploited. A multi-frequency phase delay estimation model is established, and the problem of time delay estimation is transformed into ambiguity resolution. Then, Chinese Remainder Theorem (CRT) is used to solve the ambiguity, but in the “non-cooperation” scene the module can not be chosen easily, thus an extrapolation method for interferometric phase based on “virtual frequency” is proposed to relax the constraint of module selection. Finally, the closed-form Robust Chinese Remainder Theorem (RCRT) is used to solve the ambiguity, and the phase delay is obtained with high accuracy. Compared with the conventional algorithm, the proposed algorithm has the advantages of high precision, low computation complexity and independence on the propagation characteristics of the channel. The simulation results verify the validity and correctness of the proposed model and algorithm.
In the case of parameter estimation of Frequency Hopping (FH) signal based on conventional time- frequency analysis, the suppression of cross-terms in Time-Frequency Distribution (TFD) by kernel function always leads to the decrease of time-frequency concentration, which is adverse to signal parameter extraction. To deal with this problem, a kind of Sparse TFD (STFD) based FH signals processing method is proposed. Based on the principle of Cohen's class of TFD and the ambiguity function characteristics of FH signals, a Rectangle-shaped Kernel Distribution (RKD) is constructed by choosing the rectangle function in ambiguity domain as its kernel function. RKD can suppress the cross-terms effectively but is followed by poor time-frequency resolution. In order to improve the performance of RKD, the TFD sparsity of FH signals is analyzed and utilized, and the optimal model of STFD is established by additional constraints to RKD under the Compressed Sensing (CS) frame. STFD can not only restrain cross-terms effectively, but also has a high time-frequency concentration. Simulation results show that proposed STFD based parameter estimation of FH signals has better performance compared with conventional ones.
Sparse reconstruction method suffers severe degradation in presence of impulsive noise. To deal with this problem, this paper proposed a DoA estimation method based on polynomial matrix preconditioning through sparse reconstruction. Based on the sparse reconstruction, multiply the covariance function and the direction vector by Polynomial preprocessing, which can reduce the distribution matrix of singular values, improve singular value ratio, and exhibit better sparsity. Simulation results demonstrate that the proposed algorithm achieves accurate DoA in coherent and incoherent signal sources under impulsive noise, especially have high accuracy and robustness in the heavy impulsive noise.
An electrical impedance image reconstruction algorithm based on adaptive block-sparse dictionary is proposed. A block-sparse dictionary is constructed creatively, which preferably preserves the details of reconstructed images. Meanwhile, the sparsifying dictionary optimization and image reconstruction are performed alternately, and the intermediate result of the iterative reconstruction is used as the training sample of the sparse dictionary, which can effectively improve the learning effect of the dictionary. The numerical simulation and experiment results show that the patch-based sparsity method for measure noise has excellent robustness and can accurately reconstruct the conductivity distribution image, especially the precise details of mutation.
Attribute-based proxy re-encryption mechanism can not only realize data sharing but also achieve data forwarding. However, this mechanism can not support the functionality of data retrieval, which hinders the applications of attribute-based proxy re-encryption. In order to solve the issue, this paper proposes a ciphertext- policy attribute-based proxy re-encryption scheme with keyword search. By dividing a secret key into an attribute key and a search key, the new scheme can not only achieve the keyword search, but also support proxy re- encryption. In the test phase, while conducting the keywords matching algorithm, the cloud server can do partial decryption of the original ciphertext and the re-encrypted ciphertext, which can reduce the computational burden for users. The security analysis indicates that the proposed scheme can achieve data security, hidden keywords, query isolation and collusion resistance.
Integrated Fiber-Wireless (FiWi) access network has the problems of low utilization rate of optical network unit and large control overhead during data transmission. In this paper, an energy saving mechanism with uplink data frame aggregation is proposed, the M/G/1 model is used to analyze the queue delay of the data frame in the wireless domain node and the optical domain node, by combining with the maximum tolerable delay of different priority, the optimal length of the aggregation frame is deduced in different network conditions. And then according to the optimal frame length to perform sleep scheduling for optical domain nodes, on the premise of guaranteeing the service delay, as much as possible to extend the length of node sleep time, and then the network energy efficiency is improved. The simulation results show that the proposed method can effectively reduce the energy consumption of the whole network and guarantee the delay performance.
In order to solve the problems arising from the 2-Staggered Symmetry connection pattern (2-SS) in Feedback mechanism based load balanced Two-stage Switch Architecture (FTSA), a Feedback and Reverse Transmission Mechanism based Two-stage Switch Architecture (FRTM-TSA) is proposed in this paper. A novel reverse transmission mechanism of crossbar is introduced so that any input port can obtain the scheduling results of its adjacent input port. Based on such scheduling results, the buffer status information of middle-ports that received one slot ahead can be corrected. The exact information obtained from preprocessing enables FRTM-TSA to avoid the cell-conflict and cell-disordering and thus make the re-sequencing buffers are no longer needed at the output ports. Theoretical analysis and simulation experiments show that FRTM-TSA can achieve a better delay performance with a simper switching fabric and process compared to existing schemes.
Multiple Zero Correlation Zone (ZCZ) sequence sets are sutilable to Quasi-Synchronous Code-Division Multiple-Access (QS_CDMA) systems to remove both the Multi-Path Interference (MPI) and the Inter-Cell Interference (ICI). In this paper, multiple binary ZCZ sequence sets with inter-set Zero Corss-Correlation Zone (ZCCZ) are constructed from aperiodic complementary sequence sets with ZCZ, the resultant binary ZCZ sequence sets are almost optimal.
Motion-Compensated Frame Rate Up-Conversion (MC-FRUC) is one of the common temporal-domain tampering methods of video. The existing methods recognize MC-FRUC tampering by passively analyzing statistical characteristics of video; however, the non-stationarity in statistics of video affects the stability of forensics. This paper proposes an active noise-mixed forensics algorithm. First, white Gaussian noises are produced using a pseudorandom sequence, and these noises are added into the original video sequence. Second, based on the median absolute deviation of wavelet coefficients, the standard deviation of mixed Gaussian noises in each video frame is estimated. Last, the periodicity of standard deviation varying in time domain is detected, and MC-FRUC tampering with a hard-thresholding operation is automatically identified. Experimental results indicate that the proposed algorithm presents better performance of forensics for various MC-FRUC methods, and can still ensure high detection accuracy especially after videos are denoised or compressed.
The potential of wireless In-Band Full-Duplex (IBFD) in doubling spectral efficiency raises a special issue in channel allocation for Multi-user applications in capacity improvement. This paper presents an analysis on mutual interferences, contention conflicting and allocation constraints for different modes of full-duplex transmission. The maximization number of transmission links is chosen as the objective of multi-user system optimization, and a mathematic programming problem of IBFD channel allocation for three transmission modes, including Bidirectional Full-Duplex (BFD), Full-Duplex Relay (FDR) and the mixed of two is proposed. The exact optimal solutions are researched for two kinds of typical topology to find the capacity improvement of IBFD to Half-Duplex (HD). The results show that BFD multi-user system has an ideal capacity gain 100% in relative to HD, while FDR reveals a wide range of gain with 25% in the lowest. In addition, the gain of one dimensional chain of stations is found significantly superior to that of two-dimensional distribution.
In multi-user MIMO systems, the accuracy of the Channel State Information (CSI) has a sufficient effect on performance of interference alignment scheme. To solve the problem of interference leakage caused by limited feedback of CSI, a novel method for interference alignment based on dynamic feedback and power allocation is proposed. First, the relationship between system sum rate and the allocation for channel state feedback bits and transmitting power is analyzed theoretically, and the formula of sum rate of the system considering dynamic feedback and power allocation is derived. Based on the above analysis, the model of optimizing allocation for feedback bits and power problem is set to improve the throughput of system. Then, a low-complexity solution to the problem is raised on the basis of channel quasi-static characteristic, and the feedback bit and power allocation scheme are obtained by the Karush-Kuhn-Tucker (KKT) condition. Simulation results show that the proposed method can effectively reduce the interference leakage and improve the system sum rate compared with the interference alignment scheme that just dynamically feedback’s channel state.
The accumulation of miss detection and false alarm in spectrum sensing leads to the persistently decreasing of prediction accuracy in spectrum prediction. This paper takes neural network based spectrum prediction for example, and presents a minimum Bayesian Risk based spectrum prediction to solve this problem. The distribution fitting shows that the prediction output follows the normal distribution. The expectation of prediction mean square error is defined as the Bayesian Risk, and the optimal detection threshold of the prediction output is derived through minimizing the Bayesian Risk. Through this method, the prediction accuracy is insensitive to the spectrum sensing errors. Compared with the traditional spectrum prediction with fixed detection thresholds, simulation results demonstrate the robust spectrum prediction keeps the prediction accuracy stable, and improve the performance in dynamic spectrum access.
Hardware Trojan is the malicious circuit modification which can disable the Integrated Circuit (IC) or leak confidential information covertly to the adversary, and brings potential safety hazard for ICs. In this paper, a new approach for hardware Trojan detection based on compare the temperature variation characteristics when IC starts working. Ring Oscillator (RO) is used as a detector to obtain the information about IC’s temperature variation characteristics. In order to describe temperature variation characteristics accuracy, a parameter about the D-value of RO’s oscillation cycle counts is presented, and parameters about the quality of the fitting curve are used to estimate the hardware Trojan’s effect on IC’s temperature characteristics. Results from ten chips show that the proposed approach is effective towards increasing successful detection ratio and can achieve better Trojan detection probability 100% on average over conventional patterns for Trojan which is 32 logic elements, and for Trojan which is 16 logic elements can also achieve Trojan detection probability 90%, besides the proposed approach locating the Trojan’s insertion place roughly.
Obfuscation is used to safeguard lawful rights and interests of developers and users in software security, by protecting critical information and algorithms with the system logic relation. Also, how to achieve obfuscation method to protect the hardware IP core is becoming an urgent problem. In this paper, a hardware obfuscation scheme based on deflection strategy is proposed by studying the obfuscation method and the AES algorithm. The deflection strategy with redundancy and black hole states are used to realize the Finite State Machine (FSM) obfuscation, and the bit flip method is used to realize the combinational logic obfuscation. Finally, the proposed hardware obfuscation AES algorithm is designed in SMIC 65 nm CMOS process. The parameters of toggle, data correlation and code coverage are selected to evaluate the efficiency and effectiveness of hardware confusion. Experimental results show that the area and power consumption of the hardware obfuscation AES algorithm is increased by 9% and 16% respectively, and the code coverage rate is over 93%.
In order to improve the efficiency of Fast Fourier Transform (FFT) and reduce the computation time, an algorithm of inversed order sequence in FFT is studied. It is revealed that the inversed order sequences with different length N are not independent but have a deep connection, that is, the inversed order sequence with length N can be produced by the one with length N/2 according to a specific schedule. Based on the interconnectedness, a new approach for calculating the inversed order sequence with length N is proposed and the corresponding procedure flow is shown. The algorithm is simulated and the correctness of the algorithm is verified. The algorithm not only can be realized simply, but also has high efficiency. Compared with the traditional method, the new algorithm can improve the computing efficiency by three orders of magnitude.
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