The Internet of Things (IoT) is becoming a hot research area, and tens of billions of devices are being connected to the Internet which are advancing on the sensor search service. IoT features (searches are strong spatiotemporal variability, limited resources of the sensor, and mass heterogeneous dynamic data) raise a challenge to the search engines for efficiently and effectively searching and selecting the sensors. In this paper, Piecewise-Linear fitting Sensor Similarity (PLSS) search method is proposed. Based on the content values, PLSS calculates the sensor similarity models to search most similarity sensors. PLSS improves the accuracy and efficiency of search compared with FUZZY set algorithm (FUZZY) and least squares method. PLSS storage costs are at least two order of magnitude less than raw data.
When evaluating the enhancement quality of a whole image set, the existing average score criterion will vary inconsistently with different image sets and produce a large evaluation quality fluctuation. Therefore, this paper proposes a consistency enhancement quality assessment criterion in confidence interval for any image set. By setting application parameters and using confidence interval to screen data, the proposed criterion compares the quality score difference before and after enhancing each image, and evaluates the consistency of image quality enhancement, and then calculates the effective value of consistency enhancement quality scores. Among many image enhancement algorithms, the proposed criterion can select the high-reliability enhancement algorithm for a specific application. The experimental results show that the proposed criterion has good subjective and objective consistency and outperforms the existing average score criterion, which provides an evaluation criterion for those image enhancement algorithms applied to any image set.
A new robust Generalized Synchrosqueezing S-Transform(GSST) is proposed to solve the distortion problem of SynchroSqueezing S-Transform(SSST) in mixture noise. Firstly, the method improves the Viterbi algorithm for improving the Time-Frequency(TF) analysis performance of S-transform in alpha-gaussian mixture noise. After acquiring the phase locus information of the FM signal, the synchrosqueezing is used to improve the time-frequency aggregation. The simulation results show that the proposed method can accurately obtain the time-frequency information of FM signal under the background of Alpha-Gaussian mixture noise in low SNR, and has a better robustness and applicability than the SST.
In order to improve the robustness of MLAPG algorithm, a person re-identification algorithm, called Equid-MLAPG algorithm, is proposed which is based on the equidistance measurement learning strategy. Due to the imbalanced distribution of positive and negative sample pairs in the mapping space, sample spacing hyper-parameter of MLAPG algorithm is more affected by the distance of negative sample pairs. Therefore, Equid-MLAPG algorithm tends to map the positive sample pair to be a point in the transform space. That is, the distance of a positive sample pair in the transform space is mapped to be zero, resulting in no intersection in the distribution of positive and negative sample pairs in the transform space when algorithm convergences. Experiments show that the Equid-MLAPG algorithm can achieve better experimental results on commonly used person re-identification datasets with better recognition rate and wide applicability.
Marine magnetic anomaly detection is one of the basic means of marine scientific observation, exploration of undersea resources, national defense and security. However, the complexity of the magnetic field noise increases the difficulty of the magnetic detection. It is of great significance to study various magnetic field noise mechanisms and suppression methods for the improvement of measurement accuracy. In this paper, the wave magnetic field model under general and infinite depth conditions is used to estimate the noise induced by sea waves respectively. The wave and geomagnetic noise in the magnetic anomaly signal is filtered out by the combination of spectral subtraction and wavelet. In order to verify the validity of the algorithm, the ocean magnetic field in a sea area of South China Sea in August 2015 is observed. The results show that this method can filter out most of the wave and geomagnetic field noise. The wave distribution in the frequency range of 0.4～0.8 Hz is obviously reduced, the waveform in the time domain is greatly improved, the magnetic anomaly signal of the target is highlighted. Signal to noise ratio can be increased by nearly 11 dB. The proposed method has the advantages of low computational complexity, strong real-time performance and easy implementation, which can provide an effective measure for noise suppression of marine magnetic anomaly detection.
Considering the difficulty of neighbor discovery in underwater acoustic communication networks, a neighbor discovery mechanism is presented based on directional transmission and reception. In this mechanism, the nodes only send and receive signals directionally, which can avoid the hidden terminal problem caused by asymmetric gain and increase the network coverage. Time is divided into neighbor discovery time slot and listening & reply time slot. In neighbor discovery time slot, the node sends the HELLO signal, and then waits to receive the REPLY signal sent by its neighbor node. In listening & reply time slot, the node listens the channel for the HELLO signal sent by the source node, then replies REPLY signal to the source node. The node can discover its neighbor through HELLO/REPLY two-way handshake based on competition and direct & indirect discovery, which can overcome the " deaf” nodes problem and improve the efficiency of neighbor discovery. Compared with the randomized two-way neighbor discovery mechanism, simulation tests show that the proposed mechanism has the shorter average discovery latency and the higher average discovery ratio in various network density and number of antenna sectors.
Spread Doppler Clutter (SDC) caused by multi-mode propagation restrains the detection performance of Over-The-Horizon Radar (OTHR) for low detectable targets, such as slow ships. To solve this problem, a bi-iterative Minimum Variance Distortionless Response (MVDR) beamformer is proposed to suppress multi-mode SDC for MIMO OTHR system. As it is difficult to obtain the signal-free training data and enough sample support in MIMO-OTHR with time-staggered linear frequency modulated continuous wave or slow time phase-coded waveforms, the block matrix is used for data preprocessing to reduce the effect of expected signal component in the training data, then multi-mode SDC could be suppressed by the LN-variate MVDR beamformer which is restored through bi-iterative calculation with an L-variate transmit and an N-variate receive beamformer. This algorithm improves the convergence of MVDR beamformer, while reducing the computational load and the requirement of sample support. Theoretical analysis and simulation experiment are presented to verify the effectiveness of this algorithm.
A multiuser Differential Chaos Shift Keying (DCSK) communication system based on Hilbert transform is proposed (HMU-DCSK), to solve the problem of low transmission rate of DCSK. Under the condition of fixed-order Walsh codes, the set of orthogonally-based signals is doubled by the Hilbert transform and the carrier signals assigned to each user are guaranteed to be orthogonal. The Bit-Error-Rate (BER) formula in Rayleigh fading channel is derived and numerous simulations are conducted. The simulation results show that the transmission rate of HMU-DCSK system is twice that of traditional multiuser DCSK system under the same N value, meanwhile, the BER performance of HMU-DCSK system is obviously better than the traditional multi-user DCSK system under the same transmission rate.
Under the condition of lack of echo data and low SNR, the ISAR imaging performance greatly reduced by using Random Chirp Frequency-Stepped (RCFS) signal. To solve the above problems, based on fully analyzing the echo characteristics of the random chirp frequency-stepped signal, a new method of obtaining high quality ISAR images is proposed using the joint sparse feature of the target range dimension. First, a joint block sparse imaging model of the target echo signal under the condition of random chirp frequency-stepped signal is derived and the characteristics of the model are analyzed. Secondly, a Joint Block sparse Orthogonal Matching Pursuit (JBOMP) algorithm is proposed for solving the model. The algorithm utilizes the sparse information and the joint sparse information of the ISAR echo. Therefore, the ISAR imaging performance is enhanced under the condition of low measurement and low SNR. The proposed algorithm also can achieve joint processing of multidimensional signals and has a faster operation speed. Both theoretical analysis and simulation experiments verify the effectiveness of the proposed method.
For the problem of without accurate acquisition of Double Binary Offset Carrier (DBOC) modulated signal for high dynamic environment, a method which is based on Partial Matched Filtering (PMF) - Fast Fourier Transform (FFT) is proposed. According to the problem of low detection performance caused by the related loss and scallop loss, a new improved acquisition scheme is proposed. Firstly, the Discrete Polynomial phase Transform (DPT) is used to remove the high order dynamic term of the received signal, and then the PMF-FFT algorithm is redesigned for the DBOC signal. Finally, the spectrum correction method is used to correct the maximum power spectrum after FFT. Simulation results show that, under the same conditions, the proposed scheme improves the detection probability by about 2 dB, and shortens effectively the acquisition time.
Digital BeamForming (DBF) in elevation plays a crucial role for spaceborne Multiple Elevation Beam (MEB) SAR realizing the High-Resolution Wide-Swath (HRWS) imaging mode. However, due to the influence of satellite attitude error, the deviation of the DBF receiving beam direction always arises in such system. This leads to ghost targets appearing in the SAR image, when mapping the scenes (such as the seaport areas) with strong scatterers. To address the problem, a matrix pencil method based DBF processing approach in elevation is presented. Firstly, according to the given threshold, the peak position of the strong scatterer is found from the range-compressed signals. Then, the direction of arrival angle of the strong scatterer is estimated using the matrix pencil method. Finally, based on the imaging geometry model, the DBF weighting vector is adjusted to ensure the receiving beam to precisely point to the signal sources. Thereby, the interferences of ghost targets in SAR image can be eliminated effectively. The theoretical analysis is derived in detail, then it is validated by simulation experiments.
By using the characteristic of matrix eigenvalues, this paper proposes a new secret sharing scheme without trusted center. The scheme does not require a trusted center,and each participant provides the same secret share (column vector) and generates its own secret share in the black box, thus avoiding the authority deception of the trusted center. Reversible matrix P consisting of column vectors provided by all participants,and diagonal matrix
Aircraft detection is a hot issue in the field of remote sensing image analysis. There exist many problems in current detection methods, such as complex detection procedure, low accuracy in complex background and dense aircraft area. To solve these problems, an end-to-end aircraft detection method named MDSSD is proposed in this paper. Based on Single Shot multibox Detector (SSD), a Densely connected convolutional Network (DenseNet) is used as the base network to extract features for its powerful ability in feature extraction, then an extra sub-network consisting of several feature layers is appended to detect and locate aircrafts. In order to locate aircrafts of various scales more accurately, a series of aspect ratios of default boxes are set to better match aircraft shapes and combine predictions deduced from feature maps of different layers. The method is more brief and efficient than methods that require object proposals, because it eliminates proposal generation completely and encapsulates all computation in a single network. Experiments demonstrate that this approach achieves better performance in many complex scenes.
The traditional bistatic equivalent range model has low accuracy and make the traditional Polar Format Algorithm (PFA) are inapplicable in missile-borne bistatic Synthetic Aperture Radar (SAR) imaging with curved track due to the existing of three-axis velocity and acceleration. In addition, due to the existing of space-variant motion error introduced by acceleration, the traditional 2-D sub-block compensation method will cause the discontinuities between the image sub-blocks, and thus affecting the subsequent image matching application. In view of these problems, this paper proposes a Back-Filtering PFA algorithm (BFPFA) which is based on the Improved Generalized Bistatic Equivalent Range Model (IGBERM). Constructing the combined compensating filter of space-variant phase error and geometric distortion, as well as reverse mapping interpolation, can realize the combined compensation of motion error, wavefront bending and geometric distortion in the process of oblique conversion, and obtain the SAR distance map without distortion, which is more conducive to the subsequent image matching applications. Finally, the simulations validate the effectiveness of the proposed algorithm.
The performance of trajectory based user identification is poor since the existing methods ignore the order feature of location sequence. To solve this problem, a Cross Domain Trajectory matching algorithm based on Paragraph2vec (CDTraj2vec) is proposed. Firstly, the user trajectory is transformed to the grid representation which is easy to handle. And the PV-DM model in the Paragraph2vec algorithm is utilized for extracting order feature of location sequence in trajectory. Then the original user trajectories are divided by a certain time size and distance scale to construct a training sample suitable for training PV-DM model. The PV-DM model is trained by different types of training samples, and the vector representation of the user trajectories is obtained. Finally, the matching of the trajectory is determined by the user trajectory vector. Experimental results on BrightKite shows that the F-measure is improved by 2%～4% compared with the existing frequency based and distance based algorithm. The proposed algorithm can effectively extract the order feature of location sequence, and realize the trajectory based user identification across social networks.
The diving SAR usually adopts the highly squinted mode and sub-aperture to satisfy the maneuvering and real-time processing. However, the existence of severe range-azimuth coupling, range-dependent squint angle and three-dimension velocity and acceleration leads to the space variance of range envelope and azimuth phase, which makes imagery unfocused seriously. To solve these problems, a Two-stage Frequency Filtering Algorithm (TsFFA) is proposed. After preprocessing, the First-stage Frequency Filtering (FsFF) factor is first introduced to correct azimuth-dependent Range Cell Migration (RCM) and realize the unified RCM correction. Furthermore, the Second-stage Frequency Filtering (SsFF) factor is adopted to equalize azimuth-dependent Doppler parameters and realize unified azimuth phase focused. Simulation results are presented to validate the effectiveness of the proposed approach.
Continuous monitoring of IntraOcular Pressure (IOP) plays an important role in the diagnosis and treatment of the glaucoma. Existing IOP sensors have some problems, such as low sensitivities, high central resonant frequencies and difficult fabrication. In order to solve the aforementioned problems, this paper presents a wireless, passive and non-invasive IOP sensor based on MEMS technology. The sensor contains five stacked layers, where Parylene, copper and PDMS are adopted as the functional materials within two flexible substrate layers, two electrode layers, and a dielectric layer, respectively. The electrode layers and the dielectric layer consist of two inductors and two capacitors to form a resonant circuit in C-L-C-L series. In the term of fabrication, a MEMS planar process followed by thermally shaping is proposed to fit curved surfaces of the eyeballs, and then this design scheme can effectively solve such issues as the difficulty in making the sensor and so on. Experimental results show that the central resonant frequency is decreased to 40 MHz, relative sensitivity is quantified as 1028.57 ppm/kPa, and resolution reached up to 50 Pa (0.375 mmHg). This study can be used for long-term, continuous monitoring of IOP.
Clutter of airborne bistatic radar is related to configuration and has serious range dependence characteristic, therefore the clutter ridge is complex and variable, and few Independent and Identically Distributed (IID) samples exist. As the result, the traditional Space-Time Adaptive Processing (STAP) has a degraded suppression performance for airborne bistatic radar clutter. Based on the sparsity of airborne radar clutter in the angle-Doppler domain and the advantages of Sparse Bayesian Learning (SBL) in sparse signal reconstruction, SBL algorithm is applied to the more complex airborne bistatic radar with both transmitter and receiver moving. The method can estimate the Clutter Covariance Matrix (CCM) of the unit under test with very few training samples, then perform space-time adaptive processing. Since the method does not need independent and identically distributed samples, it has better performance of clutter suppression in the airborne bistatic radar with both transmitter and receiver moving. Simulation results verify the effectiveness of the algorithm.
When using Inverse Synthetic Aperture Radar (ISAR) to observe the spinning targets, the range-Doppler time-varying characteristics of spinning target echo would lead to the inefficiency of traditional imaging methods. To solve this problem, a fast high-resolution imaging method based on distributed matching sparse representation model is proposed for wideband spinning targets imaging. Firstly, a distributed matching sparse representation model is constructed based on the sparsity of spinning target echo. Secondly, a Fast Distributed Simultaneous Multiple Orthogonal Matching Pursuit (FDSMOMP) algorithm is proposed for achieving the fast robust imaging of the spinning parts. The proposed algorithm can significantly improve the reconstruction efficiency by reducing the iteration times and computational complexity of each iteration. Additionally, in order to enhance the robustness of FDSMOMP, a related threshold is designed to suppress the false reconstruction. Finally, the mechanism of the presented method is analyzed theoretically, and it is proved that the high quality imaging result can still be obtained under the conditions of sub-Nyquist sampling and lower SNR (Signal Noise Ratio). Simulation results show the validation of the proposed method.
Due to the distortions of the broadcasted satellite signals and the inconsistencies of parameter settings for different receivers, the single difference or double difference of pseudo-ranges between two receivers are different for two pair of different receivers. Bias inconsistencies will lead to adverse effects for pseudo-range-based positioning applications. Pseudo-range biases can also hinder carrier-phase ambiguity resolution. However, fewer articles deal with pseudo-range biases for BeiDou navigation satellite System (BDS). In order to mitigate the impact of biases on BDS to the greatest extent, the generation mechanisms and characteristics of pseudo-range biases are studied in detail firstly. Then based on this, experimental verification methods are designed using Haoping Radio Observatory (HRO) of Chinese Academy of Sciences to observe BDS signals. Pseudo-range biases of all visible BDS satellites are measured and evaluated with high accuracy, using the 40 meters dish antenna and modern equipment of HRO. Finally, some important parameters of BDS receivers, such as the correlator spacing and front-end bandwidth, are suggested to mitigate the ranging errors and positioning errors result from pseudo-range biases. The achievements of this paper can provide a worthy reference for GNSS signal designers, GNSS monitoring and assessment and GNSS receiver designers.
The micro Synthetic Aperture Radar (SAR) system based on the traditional GaAs and GaN devices is not conducive to the monolithic integration, and the development bottleneck of volume, power consumption, weight and cost is becoming increasingly apparent, which is impossible to meet the needs of the miniaturized and ubiquitous unmanned platforms in the future. A new scheme for the design of a fully coherent Frequency Modulated Continuous Wave (FMCW) SAR with high resolution is proposed. The design method of high pulse phase stability and high isolation is studied and realized. The prototype of micro SAR is developed based on silicon chip and experimentally demonstrated. The micro SAR operates at K band, producing a signal bandwidth of wider than 2 GHz, enabling a range resolution of 7.5 cm. The system has made remarkable progress in terms of size, weight, power consumption and lay technical foundation for the monolithic integration of micro SAR system in a silicon chip.
The resource allocation for Cloud Radio Access Network (C-RAN) is investigated. The max-min fairness criterion is used as the optimization criterion and the Energy Efficiency (EE) of C-RAN users is taken as the optimization objective function, by maximizing the EE of the worst link under the constraints of maximum transmit power and minimum transmit rate, the user transmit power and Remote Radio Heads (RRHs) beamforming vectors are jointly optimized. The above optimization problem belongs to the nonlinear and fractional programming problem. First, the original nonconvex optimization problem is transformed into an equivalent optimization problem in subtractive form. Then, by introducing a new variable, non-smooth equivalent optimization problem is transformed into a smooth optimization problem. Finally, a two-layer iterative power allocation and beamforming algorithm is proposed. The proposed algorithm is compared with traditional non-EE resource allocation algorithm and EE maximization algorithm. The experimental results show that the proposed algorithm is effective in improving the EE and the fairness of resource allocation.
Detection of stationary little targets in heavy ground clutter is the key problem facing the millimeter wave airport runway Foreign Object Debris (FOD) detection radar. This paper proposes a hierarchical FOD detection algorithm based on power spectrum feature extraction and Support Vector Domain Description (SVDD) classifier. The clutter map Constant False Alarm Rate (CFAR) detection algorithm is first utilized to suppress the complex background clutter. In order to solve the high false alarm problem after the clutter suppression, the power spectrum features are extracted to transform the radar returns into the feature domain where the FOD and false alarm are more distinguishable. Finally, the one-class SVDD classifier is utilized to categorize the FOD and false alarm into different kinds so as to reduce the false alarm rate. Experimental results based on measured data show that the proposed method can achieve good detection performance.
Protocol Oblivious Forwarding (POF) supports the arbitrary protocol processing, enhancing the programmability of Software Defined Networking (SDN). In order to improve the forwarding performance, a flow caching method is proposed. To parse the packet in advance, absolute positions of matching fields are obtained by identifying the dependency of matching and actions. To guarantee the acceleration effect of flow caching, flow tables are selected according to their matching types and number of entries. In addition, the single-flow table cache and multi-flow table cache are compared and an adaptive switching strategy is proposed based on the actual situation of network traffic. The POFSwitch is extended to implement the proposed method and it is validated under the real rules and backbone traces. The switch packet forwarding rate is increased by 220% after applying flow caching. Flow caching can provide higher forwarding performance for programmable data planes.
Focusing on the problem of reducing the large computation cost of traditional antenna design methods, a new surrogate model based on Back Propagation Neural Networks (BPNN) is constructed. In order to solve the problem of easily falling into local optimum in BPNN, a PSO-BPNN surrogate model is developed by improving initial structural parameters of neural networks and applied to fast multi-objective optimization design of multi-parameter antenna structures. The design results show that the proposed PSO-BPNN outperforms other existing antenna surrogate models in terms of prediction accuracy and prediction speed. The proposed method is of value in dealing with complex antenna designs with high-dimensional parameter space.
Estimation of Direction Of Arrival (DOA) with scanned beams of single rotational antenna is meaningful. To obtain precise estimation with low computation burden, a closed-form estimator is proposed based on estimating the mode component. Firstly, the problem can be transformed into the estimation of mode component when antenna pattern is expressed with a formula of exponential sums, thus DOA can be induced from each mode. Considering the estimation error, a multi-mode estimator with its theoretical error is derived. Non-ideal observing conditions result in an ill-determined problem for the estimation of mode component. A modified method is proposed by reconstructing the antenna pattern. By calculating cross-correlation of the observed amplitude trains with the antenna pattern samples, a coarse estimation of DOA is obtained to determine the angle range under the matched reconstruction. Then, ill-determined problem can be avoided if the converted mode component is calculated with the new pattern. Both theoretical and simulation results demonstrate that the proposed method can obtain high precise estimation with low computation cost, and the proposed matched reconstruction approach extends the adaptability of the method.
This paper proposes a threat assessment based sensor control by using multi-target filter with random finite set. First, the general sensor control approach based on information theory is presented in the framework of Partially Observable Markov Decision Process (POMDP). Meanwhile, combined with target movement situation, the factors that affect the target threat degree are analyzed. Then, the multi-target state is estimated based on the particle multi-target filter, the multi-target threat level is established according to the multi-target motion situation, and the maximum threat target distribution characteristic is analyzed and extracted from the multi-target distribution characteristic. Finally, the Rényi divergence is used as the evaluation index in sensor control, and the final control policy is solved with the maximum information gain as the criterion. The simulation results verify the feasibility and effectiveness of the proposed method.
Mobile Edge Computing (MEC) draws much attention in the next generation of mobile networks with high bandwidth and low latency by enabling the IT and cloud computation capacity at the Radio Access Network (RAN). Matching problem between requesting nodes and servicing nodes is studied when a vehicle wants to offload tasks, a MEC-based offloading framework in vehicular networks is proposed, Vehicle can either offload task to MEC sever as V2I link or neighboring vehicle as V2V link. Taking into account the limited and heterogeneous resources, and the diversity of tasks, offloading framework is established as combination auction model, and a multi-round sequential combination auction mechanism is proposed, which consists of Analytic Hierarchy Process (AHP) ranking, task bidding and winners decision. Simulation results show that the proposed mechanism can maximize the efficiency of service nodes while increasing the efficiency of requesting vehicles under the constraints of the delay and the capacity.
A novel broadband circularly polarized monopole antenna is proposed by microstrip feed line. The antenna is composed of C-shaped patch and an improved ground plane with the overall size of 25×25×1 mm3. The impedance bandwidth and axial ratio bandwidth of the antenna can be effectively widened by cutting the corner on the C-shaped patch and adding triangular stubs on the ground plane. The design procedure of the antenna is given, and the working mechanism of the circularly polarized antenna is analyzed from the surface current distributions. Besides, the antenna is fabricated and measured. Simulated and measured results show that the antenna has ultra wide impedance bandwidth and axial ratio bandwidth. The operating bandwidth of the antenna is 4.35～12 GHz (relative bandwidth 93.6%), and the 3 dB axial ratio bandwidth is 4.15～11.8 GHz (relative bandwidth 95.9%). At the same time, the radiation performance and gain characteristics of the antenna are measured and the measured results are in good agreement with the simulated results, which proves the effectiveness of the antenna. The antenna can be applied to Ultra-WideBand (UWB) wireless communication systems and satellite communication systems.
Continuous Phase Frequency Shift Keying (CPFSK) is widely adopted as a standard by the telemetry community. The Multi-Symbol Detection (MSD) technique can increase channel gain for the CPFSK telemetry system. Therefore, the timing synchronization method for CPFSK signal needs to adapt to the scenario with lower SNR. According to existing timing synchronization methods’ poor performance in low SNR, a novel timing synchronization method for CPFSK signal based on MSD is proposed, which is suitable to variable rate. The simulation results show that, when Eb/N0 is 0 dB and symbol rate is 2 Mbps, the proposed method achieves 2 dB more channel gain than the single symbol likelihood decision method, and has similar performance to the early-late gate code synchronization method with reduced hardware resource by 60%. Finally, the validity of the numerical simulation and resource evaluation is verified by principle prototype realization.
Wireless powered technology is an effective way to extend the lifetime of wireless network nodes. A wireless powered hybrid multiple access system is studied that is consist of a base station and multiple users in clusters. The transmission of the system is divided into two phases. The base station broadcasts energy to the users in the first phase. The users transmit information to the base station in the second phase. The users among different clusters transmit in the time division multiple access manner, while the users in the same cluster transmit in the non-orthogonal multiple access manner. Joint phase time duration allocation and power allocation are investigated at the base station and the users in order to improve the spectrum efficiency and user fairness, respectively. Two algorithms are proposed, which maximize the system throughput and the minimum throughput of the clusters, respectively. Simulation results show that the two proposed algorithms can effectively increase spectral efficiency and guarantee fairness of user clusters, respectively.
Focusing on the problem of adaptive beamformer performance decreasing due to target steering vector constraint errors, an algorithm for robust beamforming with joint iterative estimations of steering vector and covariance matrix is proposed. First, the initial value of target steering vector is obtained by sparse reconstruction, following eliminating the target signal estimation in the sampling covariance matrix, the initialization of the covariance matrix is completed; Then, basing on the steering vector error optimization model, this algorithm adopts the convex optimization to estimate joint-iteratively target steering vector and interference plus noise covariance matrix. Finally, the adaptive weight vector is obtained with the steady estimations of steering vector and covariance matrix. Simulation results show output signal to interference and noise ratio is improved in the situation of target steering vector constraint errors.
The structured random sampling strategy adopted in array diagnosis has negative influence on the performance of measurement matrix. Therefore, a compressed sensing based deterministic sampling strategy to diagnose defective array elements using far-field measurements is investigated in this paper. In the case of the number of failed elements satisfies sparsity, the sparse vector is constructed by subtracting incentives of reference array without failures and the array under test. Deterministic Partial Fourier Matrix (DPFM) is then formulated by the proposed strategy as the measurement matrix. Finally, accurate diagnosis with high probability is achieved by l1 norm minimization. Theoretical analysis and simulation results demonstrate that the proposed method can avoid the adverse impact on the performance of measurement matrix effectively arising from the random distribution of sampling positions, simplify the sampling procedure and improve the probability of success rate of diagnosis.
Complicated underwater environment puts forward high requirements on the fault-tolerant and reliability of underwater acoustic localization systems. An anti-outlier localization method based on K-Means Clustering and Decision Fusion (KMCDF) is proposed for integrated Long baseline/Ultra-Short BaseLine (L/USBL) systems. Firstly, the target position is preliminarily estimated by the multi-parameter redundant information measured by the integrated system. Then, the clustering degree of the preliminary coordinates is analyzed by k-means clustering. According to the incompatibility between outliers and normal parameters, the outliers are identified by the decision fusion method. Furthermore, the impact of outliers on positioning is eliminated. Simulation analysis shows that the proposed method fully incorporates the multi-parameter information, and the tolerance of outliers is better than the existing anti-outlier positioning methods based on the time-delay parameter. Lake trial results demonstrate further the effectiveness of the proposed method.
Blind separation performance bound of Paired Carrier Multiple Access (PCMA) mixed signal is a measure of the separability of mixed signals and the performance of the separation algorithm. For the PCMA mixed signal, the spatial mapping of the modulation signal bits and symbols is constructed from the transmit signal model. The maximum likelihood criterion is used to derive the lower bound expression of separation performance independent of the separation algorithm. Numerical results agree well with the Viterbi simulation results under ideal conditions, which verify the rationality of the derived performance boundaries.
After analyzing the features of three measured data from the low-resolution radar system, corresponding to the helicopter, the propeller, and the turbojet, an algorithm is proposed by using multiple features to classify and recognize the aircraft targets. First, multiple features are extracted, including Doppler frequency shift, relative magnitude, waveform entropy of time and frequency domain, and time-frequency domain features from the measured data. Then, these features are utilized for classification purpose by means of the Support Vector Machine (SVM). Finally, owing to the symmetry and the width of time-frequency distributions of the returned signals between the helicopters with odd and even blades, a method is proposed to recognize of helicopter. The experimental results of measured data verify the effectivity of the proposed algorithms.
Face detection is finding and locating all faces in the input image, and then returning the position and size of the faces. It is an important direction of target detection. In order to solve the problem which is caused by the diversity of face size, a new single shot multiscale face algorithm is presented based on feature fusion. This method combines predictions from multiple feature maps with different resolutions to handle faces of various sizes, and the fusion of the feature maps in the shallow layers can improve the detection accuracy of the small size face by introducing the contextual information. Experimental results on the FDDB and WIDERFACE datasets confirm that the proposed method has competitive accuracy. Additionally, the object proposal step is removed, which makes the method fast. The proposed model achieves 87.9%, 93.2% and 93.4% MAP (Mean Average Precision) on the WIDERFACE sub-datasets respectively, at 35 fps. The proposed method outperforms a comparable state-of-the-art HR model, and at the same time improves the speed while ensuring the accuracy.
Based on the theory of Galois rings of characteristic 4, a new class of quaternary sequences with period 2p2 is established over Z4 using generated cyclotomy, where p is an odd prime. The linear complexity of the new sequences is determined. Results show that the sequences have larger linear complexity and resist the attack by Berlekamp-Massey (B-M) algorithm. It is a good sequence from the viewpoint of cryptography.
Traditional saliency object detection methods, assuming that there is only one salient object, is not conductive to practical application. Their effects are dependent on saliency threshold. Object detection model provides a kind of new solutions. SSD can accurately detect multi-objects with different scales simultaneously, except for small objects. To overcome this drawback, this paper presents a new multi- saliency objects detection model, DAR-SSD, appending a deconvolution module embedded with an attention residual module. Experiments show that DAR-SSD achieves a higher detection accuracy than SOD. Also, it improves detection performance for multi- saliency objects on small scales, compared with original SSD, and it has an advantage over complicated background, compared with MDF and DCL, which also are deep model based methods.
The traditional Total Variation (TV) model based on local operators for texture image colorization has some problems, such as inhomogeneous color diffusion, small coloring ranges and so on. In order to solve these problems, a coupled total variation model based on nonlocal operators is presented for image colorization, and the correspond numerical algorithm is designed to solve the model by incorporating the Alternating Direction Method of Multipliers (ADMM), and the convergence result of the algorithm is given. The proposed model makes full use of the similarity between the brightness of the pixel areas to perform color diffusion, which can effectively avoid the problem of inhomogeneous color diffusion due to local diffusion only using the brightness edge information. The experimental results are given to show that the model can effectively solve the problem of inhomogeneous color diffusion at textures and other details while fast colorizing.
To solve the problem of the line spectrum estimation under colored noise background, a subband line spectrum estimation method using sparse reconstruction is proposed. Firstly, the input signal is divided into several subbands by a multi-rate cosine modulated filter bank. The subband signal has the flatter power spectrum. The sparse learning via iterative minimization method is utilized on each subband to estimate the line spectrum signal. Then, the results of line spectrum estimation on each subband are processed by frequency domain synthesis filtering and threshold decision. Finally, the line spectrum signal under colored noise background is identified. Theoretical derivation and simulation experiments show that the proposed method has better line spectrum estimation performance under colored noise background. The colored noise background can be removed, and the advantage of high frequency resolution of sparse reconstruction method is retained.
A method based on Gaussianization and generalized matching, called Gaussianization-Generalized Matching (GGM) method is proposed, for nonlinear processing in impulsive noise. The GGM method can be designed based on noise samples, aided by nonparametric probability density estimation. Thus the GGM design is suitable for nonlinear processing in unknown noise models. The GGM method in the
The Compressed Sensing (CS) Multiple Measurement Vector (MMV) model is used to solve multiple snapshots problem with the same sparse structure. MUltiple SIgnal Classification (MUSIC) is a common method in traditional array signal processing applications. However, when the number of snapshots is below sparsity performance will be dramatically deteriorated. Kim et al. derive a modified MUSIC spectral method and propose a Compressed Sensing MUSIC method (CS-MUSIC) combining the compression reconstruction method and the MUSIC algorithm, which can effectively overcome the problem of insufficient snapshot number. In this paper, Kim et al.’s conclusion is extended to the general case, and a Modified MUSIC (MMUSIC) algorithm is proposed based on the traditional MUSIC method and the CS-MUSIC method. The simulation results show that the proposed algorithm can effectively overcome the shortage of snapshots and has a higher reconstruction probability than the CS-MUSIC algorithm and the compressed sensing greedy algorithm.
To solve the common problem of classification performance restriction caused by big intra-class variations and inter-class similarities in video classification domain, this paper proposes a deep metric learning based video classification method. The proposed method designs a deep network which contains three parts: feature learning, deep metric learning based similarity measure as well as classification. The principle of similarity measure is: Firstly, the Euclidean distance between features is calculated as the semantic distance between samples. Secondly, a margin distributing function is designed to dynamically allocate margin in the basis of the semantic distances. Finally, the difference of the sample semantic distance can be learned by calculating the loss and propagating it backwards so as to the network can automatically focus on the hard negative samples and more fully learn the characteristic of them. With a multi-task learning training method in the training stage, the similarity measure and classification can be learned jointly. Experimental results on UCF101 and HMDB51 show that the proposed method can effectively improve the classification precision.
Recent researches show great interests in localizing dynamic objects through cost-effective technologies. Laser or visual-based approaches have to solve the singularity and occlusion problem from the environment. Radio Frequency IDentification (RFID) is used as a preferred technology to address these issues, due to the unique identification and the communication without line of sight. In this paper, an innovative method is proposed to localize precisely a dynamic object equipped with an RFID tag by fusing laser information RFID information. A particle filter is used to fuse RFID signal strength, phase information, and laser ranging data. Particularly, a pre-trained signal strength-based model is used to incorporate the signal strength information. Then, the laser ranging data is divided into different clusters and the velocities of these clusters are compared with the RFID phase velocity. Matching results of both velocities are used to confine the locations of the particles during the update stage of the particle filtering. The proposed approach is verified by several experiments on a SCITOS service robot and results show that the proposed approach provides better localization accuracy when compared with laser-based approach and the signal strength-based approach.
In recent years, searchable encryption technology and fine-grained access control attribute encryption is widely used in cloud storage environment. Considering that the existing searchable attribute-based encryption schemes have some flaws: It only support single-keyword search without attribute revocation. The single-keyword search may result in the waste of computing and broadband resources due to the partial retrieval from search results. A verifiable multi-keyword search encryption scheme that supports revocation of attributes is proposed. The scheme allows users to detect the correctness of cloud server search results while supporting the revocation of user attributes in a fine-grained access control structure without updating the key or re-encrypting the ciphertext during revocation stage. The aforementioned scheme is proved by the deterministic linearity hypothesis, and the relevant analysis results indicate that it can resist the attacks of keyword selection and the privacy of keywords in the random oracle model with high computational efficiency and storage effectiveness.
To solve the problem of network structure change and route failure caused by random movement of network nodes, a relative mobility prediction based k-hop clustering algorithm is proposed, the movement of nodes are analyzed and predicted, the cluster structure is adjusted adaptively, the stability of cluster structure is improved. First, the Doppler shift is used to calculate the relative moving speed and obtain the link expiration time between nodes. Then, during the cluster formation stage, the MAX-MIN heuristic algorithm is used to select the cluster head according to the average link expiration time of the node. Furthermore, during the cluster maintenance stage, a network adaptive adjustment method is proposed based on node motion. On the one hand, the node information transmission cycle is adjusted to balance the data overhead and accuracy; On the other hand, the cluster structure is adjusted by predicting the link disconnection to reduce link reconstruction time and improve the quality of network operation. Simulation results show that the proposed algorithm can effectively prolong the duration of cluster head and improve the stability of cluster structure in dynamic environment.
Wi-Fi indoor localization technique is one of the current research hotspots in the field of mobile computing, however, the conventional location fingerprinting based localization scheme does not consider the diversity of Wi-Fi signal distribution in the complicated indoor environment, resulting in the low robustness of indoor localization system. To address this problem, a new hybrid hypothesis test of signal distribution for Wi-Fi indoor localization is proposed. Specifically, the Jarque-Bera (JB) test is conducted to examine the normality of Wi-Fi signal distribution at each Reference Point (RP). Then, according to the different Wi-Fi signal distributions, the hybrid Mann-Whitney U test and T test approaches are used to construct the set of matching reference points with the purpose of realizing the area localization. Finally, by calculating the K-Nearest Neighbor (KNN) of matching reference points in the located area, the location coordinate of the target is obtained. The experimental results indicate that the proposed approach is featured with higher localization accuracy as well as stronger system robustness compared with the conventional Wi-Fi indoor localization approaches.
Lai-Massey structure is a block cipher structure developed from IDEA algorithm. FOX is the representative of this cipher structure. In this paper, the keys are assumed to be generated independently and uniform randomly, and then the provable security against differential and linear cryptanalysis of Lai-Massey structure is studied from two aspects: the upper bound of the average differential probability and the upper bound of the average linear chains probability with the given starting and ending points. This paper proves that when
To link better scattering centers with target structures, a forward method is presented to deduce the component-level 3-D scattering center position of radar target under the mechanisms of single and double scattering based on target geometric model. Under the mechanism of double scattering, the principle and method for determining the ray equivalent position is introduced especially under the situation of strong scattering. As for other weak scattering situations, the equivalent transformation is used to transform the weak scattering situations to the strong one. Finally, this position derivation method is applied to the models of right dihedral angle, obtuse dihedral angle, SLICY and T72 tank to deduce and analyze their component-level scattering center positions. The corresponding simulated or actual SAR images are used for contrast to validate the accuracy of the position derivation method.
Firefly Algorithm (FA) may suffer from the defect of low convergence accuracy depending on the complexity of the optimization problem. To overcome the drawback, a novel learning strategy named Orthogonal Opposition Based Learning (OOBL) is proposed and integrated into FA. In OOBL, first, the opposite is calculated by the centroid opposition, making full use of the population search experience and avoiding depending on the system of coordinates. Second, the orthogonal opposite candidate solutions are constructed by orthogonal experiment design, combining the useful information from the individual and its opposite. The proposed algorithm is tested on the standard benchmark suite and compared with some recently introduced FA variants. The experimental results verify the effectiveness of OOBL and show the outstanding convergence accuracy of the proposed algorithm on most of the test functions.
Heavy computational burden, or complex training procedure and poor universality caused by the manual setting of the fixed thresholds are the main issues associated with most of the noise image quality evaluation algorithms using domain transformation or machine learning. As an attempt for solution, an improved spatial noisy image quality evaluation algorithm based on the masking effect is presented. Firstly, according to the layer-layer progressive rule based on Hosaka principle, an image is divided into sub-blocks with different sizes that match the frequency distribution of its content, and a masking weight is assigned to each sub-block correspondingly. Then noise in the image is detected through the pixel gradient information extraction, via a two-step strategy. Following that, the preliminary evaluation value is obtained by using the masking weights to weighting the noise pollution index of all the sub-blocks. Finally, the correction and normalization are carried out to generate the whole image quality evaluation parameter——i.e. Modified No-Reference Peak Signal to Noise Ratio (MNRPSNR). Such an algorithm is tested on LIVE and TID2008 image quality assessment database, covering a variety of noise types. The results indicate that compared with the current mainstream evaluation algorithms, it has strong competitiveness, and also has the significant effects in improving the traditional algorithm. Moreover, the high degree of consistency to the human subjective feelings and the applicability to multiple noise types are well demonstrated.
To improve the resolution of the SAR system, radar bandwidth should be improved. By means of synthetic bandwidth, wide bandwidth can be achieved with less hardware complexity. For frequency band synthesis SAR system, frequency difference should be accurately known. However, in the real measurement situation, the frequency difference may drift and should be estimated based on the raw data. In this manuscript, an effective method is proposed to estimate the frequency difference error and compensate the phase error. Based on the relation between the interferometric phase of subband echoes and frequency difference, the frequency difference drift is estimated. The interferometry between subband images yields the interferometric image. It is observed that in the yielded image, phase varies with range and the slope is proportional to the frequency difference. Also, the phase is redundant along azimuth. Based on the redundancy along azimuth, a new vector is formed. The vector is a sinusoidal signal with the frequency value corresponding to the relative range shift. Frequency analysis yields the value of the frequency difference error. Based on the proposed method, the SAR image is improved. The effectiveness of the method is verified by processing the real SAR data.
As to the problem of sound event detection in low Signal-Noise-Ratio (SNR) noise environments, a method is proposed based on discrete cosine transform coefficients extracted from multi-band power distribution image. First, by using gammatone spectrogram analysis, sound signal is transformed into multi-band power distribution image. Next, 8×8 size blocking and discrete cosine transform are applied to analyze the multi-band power distribution image. Based on the main Zigzag coefficients which are scanned from the discrete cosine transform coefficients, features of sound event are constructed. Finally, features are modeled and detected through random forests classifier. The results show that the proposed method achieves a better detection performance in low SNR comparing to other methods.
There are a large number of indoor WiFi signals which can be used for indoor positioning. Although many WiFi indoor positioning technology is proposed, it's positioning accuracy still does not meet the actual application requirements. For this problem, an Adaptive Affinity Propagation Clustering (AAPC) algorithm is proposed to improve the clustering quality of WiFi fingerprint, thus improving the positioning accuracy. The AAPC algorithm generates different clustering results by dynamically adjusting parameters, then cluster validity indices are used to select the best ones. A large number of real environmental data are collected and tested. The experimental results show that the clustering results generated by AAPC algorithm have higher positioning accuracy.
An adaptive virtual resource allocation algorithm is proposed based on Constrained Markov Decision Process (CMDP) for wireless access network slice virtual resource allocation. First of all, this algorithm in the Non-Orthogonal Multiple Access (NOMA) system, uses the user outage probability and the slice queues as constraints, uses the total rate of slices as a reward to build a resource adaptive problem using the CMDP theory. Secondly, the post-decision state is defined to avoid the expectation operation in the optimal value function. Furthermore, aiming at the problem of " dimensionality disaster” of MDP, based on the approximate dynamic programming theory, a basis function for the assignment behavior is designed to replace the post-decision state space and to reduce the computational dimension. Finally, an adaptive virtual resource allocation algorithm is designed to optimize the slicing performance. The simulation results show that the algorithm can improve the performance of the system and meet the service requirements of slicing.
Firewall policy is defined as access control rules in Software Definition Network (SDN), and distributing these ACL (Access Control List) rules across the networks, it can improve the quality of service. In order to reduce the number of rules placed in the network, the Heuristic Algorithm of Rules Allocation (HARA) of rule multiplexing and merging is proposed in this paper. Considering TCAM storage space of commodity switches and connected link traffic load of endpoint switches, a mixed integer linear programming model which minimize the number of rules placed in the network is established, and the algorithm solves the rules placement problem of multiple routing unicast sessions of different throughputs. Compared with the nonRM-CP algorithms, simulations show that HARA can save 18% TCAM at most and reduce the bandwidth utilization rate of 13.1% at average.
Vehicle detection is one of the hotspots in the field of remote sensing image analysis. The intelligent extraction and identification of vehicles are of great significance to traffic management and urban construction. In remote sensing field, the existing methods of vehicle detection based on Convolution Neural Network (CNN) are complicated and most of these methods have poor performance for dense areas. To solve above problems, an end-to-end neural network model named DF-RCNN is presented to solve the detecting difficulty in dense areas. Firstly, the model unifies the resolution of the deep and shallow feature maps and combines them. After that, the deformable convolution and RoI pooling are used to study the geometrical deformation of the target by adding a small number of parameters and calculations. Experimental results show that the proposed model has good detection performance for vehicle targets in dense areas.
A novel power frequency electric field measurement system based on high-performance MEMS electric field sensing chips is developed. Based on cross-correlation detection principle, a power frequency electric field demodulation algorithm of MEMS sensing chips that can inhibit background interference noise is proposed. And a small-scale, high-resolution electric field measuring probe is designed. Moreover, the system overall structure scheme is designed for implementation of high-accuracy demodulation electric field signals. The test result under power lines shows that the plotted curves of the developed MEMS system are consistent with Narda EFA-300.
Based on interference cancellation method, a low complexity Iterative Parallel Interference Cancellation (IPIC) algorithm is proposed for the uplink of massive MIMO systems. The proposed algorithm avoids the high complexity matrix inversion required by the linear detection algorithm, and hence the complexity is maintained only at
The Coherent Plane-Wave Compounding (CPWC) algorithm is based on the recombination of several plane-waves with different steering angles, which can achieve high-quality images with high frame rate. However, CPWC ignores the coherence between the plane-wave imaging results. Coherence Factor (CF) weighted algorithm can effectively improve the imaging contrast and resolution, while it degrades the background speckle quality. A Short-Lag Coherence Factor (SLCF) algorithm for CPWC is proposed. SLCF uses the angular difference parameter to ascertain the order of the coherence factor and calculates the coherence factor for the plane-waves with small angular difference. Then, SLCF is utilized to weight CPWC to obtain the final images. Simulated and experimental results show that SLCF-weighted algorithm can improve the imaging quality in terms of lateral resolution and Contrast Ratio (CR), compared with CPWC. In addition, in comparison with CF and Generalized Coherence Factor (GCF) weighted algorithm, SLCF can achieve better background speckle quality and it has lower computational complexity.
The azimuth resolution of traditional synthetic aperture radar is only provided by synthetic aperture. However, in the forward looking area, the Doppler diversity is limited, so the imaging performance declines rapidly. And forward looking imaging also has the Doppler ambiguity problem. In this paper, an adaptive beam forming method with spatial confinement under ideal line track is proposed. The imaging quality of the positive forward region is improved effectively by combining the array of real aperture and synthetic aperture, and the Doppler solution is blurred by using the array space domain. First, the echo data is processed by High Squint SAR imaging to obtain the blurred image. Then the beam-forming is performed, weighted and coherent accumulated with each channel image, so as to resolve Doppler ambiguity and enhance the azimuth resolution. Simulation confirms the validity of the proposed approach.
Single beacon location algorithm based on additive noise model can not accurately represent the actual characteristics of distance measurement, leading to a problem of model mismatch. A two step location algorithm considering the multiplicative noise characteristics is presented, which combines least squares algorithm and nonlinear fading filter. A range error model in the background of multiplicative noise is established baed on the analysis of the effective sound velocity error. The nonlinear fading filtering algorithm with single fading factor under multiplicative noise background is improved by introducing the attenuation factor which increases the track continuity. Using the least squares based pre-location process to solve the problem that the improved algorithm is sensitive to the initial value. The simulation and experimental data show that the location precision of the proposed algorithm is obviously better than the extended Kalman filtering algorithm under the additive noise background.
In the advanced applications of real-time radar imaging and high-precision scientific computing systems, the design of high throughput and reconfigurable Floating-Point (FP) FFT accelerator is significant. Achieving high throughput FP FFT with low area and power cost poses a greater challenge due to high complexity of FP operations in comparison to fixed-point implementations. To address these issues, a serial of mixed-radix algorithms for 128/256/512/1024/2048-point FFT are proposed by decomposing long FFT into short implementations with cascaded radix-2k stages so that the complexity of multiplications can be significantly reduced. Besides, two novel fused FP add-subtract and dot-product units for dual-mode functionality are proposed, which can either compute on a pair of double precision operands or on two pairs of single precision operands in parallel. Thus, a high throughput dual-mode floating-point variable length FFT is designed. The proposed processor is implemented based on SMIC 28 nm CMOS technology. Simulation results show that the throughput and Signal-to-Quantization Noise Ratio (SQNR) in single-channel single precision and dual-channel half precision floating-point mode are 3.478 GSample/s, 135 dB and 6.957 GSample/s, 60 dB respectively. Compare to the other FP FFT, this processor can achieve 12 times improvement of normalized throughput-area ratio.
Under the present network architecture, it is disadvantageous for scalability and service performance of server cluster to adopt hardware systems to realize load balancing of server cluster, because there are some restriction factors in such a method, including the difficulty of acquiring load nodes status and the complexity of redirecting traffic, etc. To solve the problem, a Load Balancing mechanism based on Software-Defined Networking (SDNLB) is proposed. With superiorities of SDN such as centralized control and flexible traffic scheduling, SDNLB monitors run states of servers and overall network load information by means of SNMP protocol and OpenFlow protocol in real time, and chooses the highest weight server as target server aiming for processing coming flows through the way of weight value calculation. On this basis, SDNLB takes full advantage of the optimal forwarding path algorithm to carry on traffic scheduling, and achieves the goal that raises utilization rate and processing performance of server cluster. An experiment platform is built to carry out simulation tests for overall performance of SDNLB, and the experiment results show that under the same network load conditions, SDNLB lowers effectively loads of server cluster, noticeably raises network throughput and bandwidth utilization, and reduces finish time and average latency of flows, compared with other load balancing algorithms.
Attribute based encryption can provide data confidentiality protection and fine-grained access control for fog-cloud computing, however mobile devices in fog cloud computing system are difficult to bear the burdensome computing burden of attribute based encryption. In order to address this problem, an offline/online ciphertext-plicy attribute-based encryption scheme is presented with verifiable outsourced decryption based on the bilinear group of prime order. It can realize the offline/online key generation and data encryption. Simultaneously, it supports the verifiable outsourced decryption. Then, the formal security proofs of its selective chosen plaintext attack security and verifiability are provided. After that, the improved offline/online ciphertext-plicy attribute-based encryption scheme with verifiable outsourced decryption is presented, which reduces the number of bilinear pairings from linear to constant in the transformation phase. Finally, the efficiency of the proposed scheme is analyzed and verified through theoretical analysis and experimental simulation. The experimental results show that the proposed scheme is efficient and practical.
In passive bistatic radar systems, there exists the zero and non-zero Doppler shift multipath clutter in the surveillance channel. The multipath clutter affects the target detection. Temporal adaptive iterative filter such as Least Mean Square (LMS), Normalized Least Mean Square (NLMS) and Recursive Least Square (RLS) are often used to reject multipath clutter in passive bistatic radar, but these methods are only applicable to reject zero Doppler shift multipath clutter. To solve the problem of zero and non-zero Doppler shift multipath clutter, combined with the orthogonal frequency division multiplexing waveform features of digital broadcasting television signals, a clutter rejection algorithm is proposed based on carrier domain adaptive iterative filter. The algorithm utilizes the correlation of multipath clutter with the same Doppler shift at the same carrier frequency in subcarrier domain to reject the zero and non-zero Doppler shift multipath clutter. Simulation and experiment data processing results show the superiority of the proposed algorithm.
Magnetic Anomaly Detection (MAD) is a widely used passive target detection method. Its applications include surface warship target monitoring, underwater moving targets, and land target detection and identification. It is of great significance to research on the reliability detection method of weak magnetic anomaly signals based on geomagnetic background. This paper proposes a single sensor detection method based on the fractal characteristics of target magnetic anomaly signal based on the study of the differences in geomagnetic background and fractal characteristics of magnetic anomaly signals and conducts actual field test verification. The experimental results show that the method can accurately distinguish the geomagnetic background interference and magnetic anomaly signals, and can detect the weak magnetic anomaly signals in the geomagnetic background noise.
To reduce the beamforming training cost and network delay, make the best of Beacon and S-CAP sub-period in the existing Terahertz Wireless Personal Access Network (TWPAN) directional MAC protocols, an Adaptive Directional MAC (AD-MAC) protocol for TWPAN is proposed. AD-MAC adaptively uses the entire network cooperative beam training in a static scenario, and makes network nodes quickly respond to beam training frames based on historical information in a dynamic scenario. The reverse listening strategy is used to reduce the collision probability of same sector nodes. The control frame and data frame are transmitted simultaneously in the Beacon and S-CAP slot using time-slot reuse. Theoretical analysis verifies the effectiveness of AD-MAC. Also, simulation results show that, comparing with ENLBT-MAC, AD-MAC reduces about 21.84% of beamforming training cost and 22.70% of the average network delay in static scene, and reduces about 18.7% of beamforming training cost and 13.07% of the average network delay in dynamic scene.
The Digital Video Broadcasting-Common Scrambling Algorithm (DVB-CSA) is a hybrid symmetric cipher. It is made up of the block cipher encryption and the stream cipher encryption. DVB-CSA is often used to protect MPEG-2 signal streams. This paper focuses on impossible differential cryptanalysis of the block cipher in DVB-CSA called CSA-BC. By exploiting the details of the S-box, a 22-round impossible differential is constructed, which is two rounds more than the previous best result. Furthermore, a 25-round impossible differential attack on CSA-BC is presented, which can recover 24 bit key. For the attack, the data complexity, the computational complexity and the memory complexity are 253.3 chosen plaintexts, 232.5 encryptions and 224 units, respectively. For impossible differential cryptanalysis of CSA-BC, the previous best result can attack 21-round CSA-BC and recover 16 bit key. In terms of the round number and the recovered key, the result significantly improves the previous best result.
The signal source position can only be estimated by passive monitoring of the signal in terms of that the signal monitored by the spectrum monitoring system can not be controlled and there is no prior knowledge. To address this issue, based on Received Signal Strength Indication Difference (RSSID) and using Kalman filtering, a location algorithm is proposed to improve its localization accuracy. The proposed algorithm transforms the RSSID between two base stations into the ratio of the distance from the location of the signal source to the two base stations, and the distances to constructs the matrix of location equations is obtained according to the ratio, and then the least square method to find the signal source position is obtained. The simulation results show that the proposed algorithm has better performance than the classical RSSI localization algorithm, reducing the impact of environmental factors on the positioning accuracy, and better meet the positioning service needing fewer parameters. This algorithm can be effectively applied to the spectrum monitoring system. In addition, Kalman algorithm can effectively improve the system's positioning accuracy, and achieve the expected positioning effect.
The separable probability is a significant criterion to evaluate the resolution characteristics of SAR distribution targets. On the basis of refining the separable condition of targets and taking the statistic characteristic of SAR distribution targets into consideration, a new separable judgment criterion for targets is proposed, and a precise calculation method of the separable probability is deduced. Besides, in order to simplify the calculation, the approximate calculation method with less computational complexity is presented. It is shown in the simulation results that the proposed method is in accordance with the actual situation, which can reflect the effect of the statistic characteristic of SAR distribution target on the resolution characteristic, and can provide theoretical support for the SAR image quality evaluation and system parameter design.
This paper presents a novel unsupervised image classification method for Polarimetric Synthetic Aperture Radar (PolSAR) data. The proposed method is based on a discriminative clustering framework that explicitly relies on a discriminative supervised classification technique to perform unsupervised clustering. To implement this idea, an energy function is designed for unsupervised PolSAR image classification by combining a supervised Softmax Regression (SR) model with a Markov Random Field (MRF) smoothness constraint. In this model, both the pixelwise class labels and classifiers are taken as unknown variables to be optimized. Starting from the initialized class labels generated by Cloude-Pottier decomposition and K-Wishart distribution hypothesis, the classifiers and class labels are iteratively optimized by alternately minimizing the energy function with respect to them. Finally, the optimized class labels are taken as the classification result, and the classifiers for different classes are also derived as a side effect. This approach is applied to real PolSAR benchmark data. Extensive experiments justify that the proposed approach can effectively classify the PolSAR image in an unsupervised way and produce higher accuracies than the compared state-of-the-art methods.
For the fact that current gridless Direction Of Arrival (DOA) estimation methods with two-dimensional array suffer from unsatisfactory performance, a novel girdless DOA estimation method is proposed in this paper. For two-dimensional array, the atomic L0-norm is proved to be the solution of a Semi-Definite Programming (SDP) problem, whose cost function is the rank of a Hermitian matrix, which is constructed by finite order of Bessel functions of the first kind. According to low rank matrix recovery theorems, the cost function of the SDP problem is replaced by the log-det function, and the SDP problem is solved by Majorization-Minimization (MM) method. At last, the gridless DOA estimation is achieved by Vandermonde decomposition method of semidefinite Toeplitz matrix built by the solutions of above SDP problem. Sample covariance matrix is used to form the initial optimization problem in MM method, which can reduce the iterations. Simulation results show that, compared with on-grid MUSIC and other gridless methods, the proposed method has better Root-Mean-Square Error (RMSE) performance and identifiability to adjacent sources; When snapshots are enough and Signal-Noise-Ratio (SNR) is high, proper choice of the order of Bessel functions of the first kind can achieve approximate RMSE performance as that of higher order ones, and can reduce the running time.
Virtualization is a new technology that can effectively solve the low resource utilization and service inflexibility problem in the current Wireless Sensor Network (WSN). For the resource competition problem in virtualized WSN, a multi-task resource allocation strategy based on Stackelberg game is proposed. According to the different Quality of Service (QoS) requirements of the business carried by Virtual Sensor Network Request (VSNR), the importance of multiple VSNRs is quantified. Then, the optimal price of WSN and the optimal resource requirements of VSNRs are obtained by using distributed iteration method. Finally, the resource corresponding to multiple VSNRs is acquired according to optimal price and optimal resource allocation determined by Nash equilibrium. The simulation results show that the proposed strategy can not only meet the diversified needs of users, but also improve the resource utilization of nodes and links.
To address track-to-track association problem of radar and Electronic Support Measurements (ESM) in the presence of sensor biases and different targets reported by different sensors, an anti-bias track-to-track association algorithm based on track vectors detection is proposed according to the statistical characteristics of Gaussian random vectors. The state estimation decomposition equation is firstly derived in the Modified Polar Coordinates (MPC). The track vectors are obtained by the real state cancellation method. Second, In order to eliminate most non-homologous target tracks, the rough association is performed according to the features of the azimuthal rate and Inverse-Time-to-Go (ITG). Finally, the track-to-track association of radar and ESM is extracted based on track vectors chi-square distribution. The effectiveness of the proposed algorithm are verified by Monte Carlo simulation experiments in the presence of sensor biases, targets densities and detection probabilities.
The deep learning model based on the residual network and the spectrogram are used to recognize infant crying in this paper. The corpus has balanced proportion of infant crying and non-crying samples. Finally, through the 5-fold cross validation, compared with three models of Support Vector Machine (SVM), Convolutional Neural Network (CNN) and the cochleagram residual network based on Gammatone filters (GT-Resnet), the spectrogram based residual network gets the best F1-score of 0.9965 and satisfies requirements of real time. This paper proves that the spectrogram could react acoustics features intuitively and comprehensively in the recognition of infant crying. The residual network based on spectrogram is a good solution to infant crying recognition problem.
To solve the problems in current co-saliency detection algorithms, a novel co-saliency detection algorithm is proposed which applies fully convolution neural network and global optimization model. First, a fully convolution saliency detection network is built based on VGG16Net. The network can simulate the human visual attention mechanism and extract the saliency region in an image from the semantic level. Second, based on the traditional saliency optimization model, the global co-saliency optimization model is constructed, which realizes the transmission and sharing of the current superpixel saliency value in inter-images and intra-image through superpixel matching, making the final saliency map has better co-saliency value. Third, the inter-image saliency value propagation constraint parameter is innovatively introduced to overcome the disadvantages of superpixel mismatching. Experimental results on public test datasets show that the proposed algorithm is superior over current state-of-the-art methods in terms of detection accuracy and detection efficiency, and has strong robustness.
For Network Function Virtualization (NFV) environment, the existing placement methods can not guarantee the mapping cost while optimizing the network delay, a service function chaining optimal placement algorithm is proposed based on the IQGA-Viterbi learning algorithm. In the training process of Hidden Markov Model (HMM) parameters, the traditional Baum-Welch algorithm is easy to fall into the local optimum, so the quantum genetic algorithm is proposed which can better optimize the model parameters. In each iteration, the improved algorithm maintains the diversity of feasible solutions and expands the scope of the spatial search by replicating the best fitness population with equal proportion, thus improving the accuracy of the model parameters. In the process of solving Hidden Markov chain, to overcome the problem that can not be directly observed for hidden sequences, Viterbi algorithm can solve the implicit sequences exactly and solve the problem of optimal service paths in the directed graph. Experimental results show that the network delay and mapping costs are lower compared with the existing algorithms. In addition, the acceptance ratio of requests is raised.
For the Inverse Synthetic Aperture Radar (ISAR) imaging, the ISAR image obtained by the Range-Doppler (RD) or time-frequency analysis methods can not display the target's real shape due to its azimuth relating to the target Doppler frequency, thus the cross-range scaling is required for ISAR image. In this paper, a fast cross-range scaling method for ISAR is proposed to estimate the Rotational Angular Velocity (RAV). Firstly, the proposed method utilizes efficient Pseudo Polar Fast Fourier Transform (PPFFT) to transform the rotational motion of two ISAR images from two different instant time into translation in the polar angle direction. Then, a new cost function called integrated correction is defined to obtain the RAV coarse estimation. Finally, the optimal RAV can be estimated using the Bisection method to realize the cross-range scaling. Compared with the available algorithms, the proposed method avoids the problems of precision loss and high computational complexity caused by interpolation operation. The results of computer simulation and real data experiments are provided to demonstrate the validity of the proposed method.
In order to improve the fusion quality of panchromatic image and multi-spectral image, a remote sensing image fusion method based on optimized dictionary learning is proposed. Firstly, K-means cluster is applied to image blocks in the image database, and then image blocks with high similarity are removed partly in order to improve the training efficiency. While obtaining a universal dictionary, the similar dictionary atoms and less used dictionary atoms are marked for further research. Secondly, similar dictionary atoms and less used dictionary atoms are replaced by panchromatic image blocks with the largest difference from the original sparse model to obtain an adaptive dictionary. Furthermore the adaptive dictionary is used to sparse represent the intensity component and panchromatic image, the modulus maxima coefficients in the sparse coefficients of each image blocks are separated to obtain maximal sparse coefficients, and the remaining sparse coefficients are called residual sparse coefficients. Then, each part is fused by different fusion rules to preserve more spectral and spatial detail information. Finally, inverse IHS transform is employed to obtain the fused image. Experiments demonstrate that the proposed method provides better spectral quality and superior spatial information in the fused image than its counterparts.
For qualitative and quantitative complex evaluation problem of electromagnetic environment. This paper proposes a novel electromagnetic environment complex evaluation algorithm based on fast S-transform and time-frequency space model, which can count time-complex, frequency-complex and energy-complex simultaneously. At the same time, the computation methods and concept of qualitative and quantitative evaluation degree are introduced in this paper. To overcome the limitations of the traditional, F-norm and root-mean-square are selected as two important evaluation indicators, which have the advantage in accurate evaluation. Simulation results show that the proposed method is accurate and effective to reflect the intensity degree of electromagnetic interference; Meanwhile, the interference experiment of bus card confirms the correctness of the time-frequency space model. The experimental test results verify the correctness of the evaluators mentioned in this paper.
To solve problem of the high delay caused by the change of physical network topology under the 5G access network C-RAN architecture, this paper proposes a scheme about dynamic deployment of Service Function Chain (SFC) in access network based on Partial Observation Markov Decision Process (POMDP). In this scheme, the system observes changes of the underlying physical network topology through the heartbeat packet observation mechanism. Due to the observation errors, it is impossible to obtain all the real topological conditions. Therefore, by the partial awareness and stochastic learning of POMDP, the system dynamically adjust the deployment of the SFC in the slice of the access network when topology changes, so as to optimize the delay. Finally, point-based hybrid heuristic value iteration algorithm is used to find SFC deployment strategy. The simulation results show that this model can support to optimize the deployment of SFC in the access network side and improve the access network’s throughput and resource utilization.
Chroma extensions video coding is a hot topic in the field of video coding. Chroma extensions video coding scheme based on AVS2 platform is proposed. The most direct solution is pseudo444/422 coding. In this method, chroma component in the input image is down sampled by averaging adjacent samples. The core coding modules are still 420 coding. Further, this paper seamlessly extends intra prediction and loop filter to the 444/422 chroma format to implement 444/422 intra prediction coding. The experimental results show that compared with pseudo444/422 coding, in the case of high bit rate, the average U/V BD-rate saving is 31.44%/31.72% and 18.85%/19.30% for 444 and 422 test sequences respectively, with negligible increase of Y BD-rate (0.5% on average). The modification of the 422 chroma intra prediction algorithm achieves up to 5.66% Y/U/V BD-rate reduction. 444/422 intra prediction coding provides similar or better coding performance than HEVC RExt coding at low bitrates.
Incoherent scatter spectrum plays an important role in studying the physical parameters of the ionosphere. The conventional theoretical model of incoherent scatter spectrum for derivation and calculation is extremely complicated and the model of the autocorrelation function can not be obtained . In this paper, the simplified model of ionospheric incoherent scatter spectrum is re-derived and the corresponding autocorrelation function is proposed. In the procedure of traditional incoherent scattering radar signal processing, the autocorrelation function is imbalance at different delays. This is mainly because the range resolution of zero-lag is very low, which affects the estimated performance of ionospheric scatter spectrum. Focus on this problem, a method based on data fitting is proposed to estimate the autocorrelation at zero-lag. Considering the computational complexity, a fast implementation method by polynomial functions is proposed to approach the autocorrelation function. Finally, experimental results on real echo data demonstrate the correctness and efficiency of the proposed method, which is of great significance for ionospheric detection.
A multi-parameter convolutional neural network method is proposed for gesture recognition based on Frequency Modulated Continuous Wave (FMCW) radar. A multidimensional parameter dataset is constructed for gestures by performing time-frequency analysis of the radar signal to estimate the distance, Doppler and angle parameters of the gesture target. To realize feature extraction and classification accurately, an end-to-end structured Range-Doppler-Angle of Time (RDA-T) multi-dimensional parameter convolutional neural network scheme is further proposed using multi-branch network structure and high-dimensional feature fusion. The experimental results reveal that using the combined gestures information of distance, Doppler and angle for multi-parameter learning, the proposed scheme resolves the problem of low information quantity of single-dimensional gesture recognition methods, and its accuracy outperforms the single-dimensional methods in terms of gesture recognition by 5%～8%.
To improve accuracy and reliability of the traditional turbine-vital capacity meter, a novel four-line turbine-detection method is presented for the high precision and high reliability Chronic Obstructive Pulmonary Disease (COPD) monitoring system. On the hardware, a four-line breath signal acquisition circuit is designed following the four-line turbine-type detection method, which improves the resolution of the optical path through reasonable components arrangement. On the software, a linear regression algorithm is used to obtain early screening and diagnostic indicators such as Forced Vital Capacity (FVC), Peak Expiratory Flow (PEF) and so on. The standard Fluke air flow analyzer is used for data calibration, compared with the traditional medical turbine-type lung function meter: FVC average relative error is reduced from 1.98% to 1.47% and PEF average relative error is reduced from 2.04% to 1.02%. It is showed that the expiratory parameters of the four-line turbine-type COPD monitoring system is more accurate and reliable than that of the traditional COPD system which is suitable for early screening and accurate diagnosis of COPD. Combined with pulse oxygen saturation, End-tidal CO2, it can be used to achieve the medical care for COPD and play an important role to early detect and control of disease for moderate or severe COPD patients.
Due to the probabilistic failure of the optical fiber of the underlying network in the virtual environment, traditional full protection configures one protection path at least which leads to high resource redundancy and low acceptance rate of the virtual network. In this paper, a Security Awareness-based Diverse Virtual Network Mapping (SA-DVNM) strategy is proposed to provide security guarantee in the event of failures. In SA-DVNM, the physical node weight formula is designed by considering the hops between nodes and the bandwidth of adjacent links, besides, a path-balanced link mapping mechanism is proposed to minimize the overloaded link. For improving the acceptability of virtual network, SA-DVNM strategy designs a resource allocation mechanism that allows path cut when a single path is unavailable for low security. Considering the difference of time delay to ensure the security of delay-sensitive services, a multipath routing spectrum allocation method based on delay difference is designed to optimize the routing and spectrum allocation for SA-DVNM strategy. The simulation results show that the proposed SA-DVNM strategy can improve the spectrum utilization and virtual optical network acceptance rate in the probabilistic fault environment, and reduce the bandwidth blocking probability.
The privacy preserving aggregate signcryption for heterogeneous systems can ensure the confidentiality and unforgeability of the data between heterogeneous cryptosystems, it also can provide multi-ciphertext batch verification. This paper analyzes the security of a scheme with privacy-preserving aggregate signcryption heterogeneous, and points out that the scheme can not resist the attack of malicious Key Generating Center (KGC), it can forge a valid ciphertext. In order to improve the security of the original scheme, a new heterogeneous aggregation signature scheme with privacy protection function is proposed.The new scheme overcomes the security problems existing in the original scheme and ensures the data transmission between the certificateless public key cryptography and the identity-based public key cryptographic, and the security of the new scheme is proved under the random oracle model. Efficiency analysis shows that the new program is equivalent to the original one.
Monthly Journal Founded in 1979
The Source Journal of EI Compendex The Source Journal of ESCI Database
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ISSN 1009-5896 CN 11-4494/TN
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