A novel and efficient coding method is proposed to improve person re-identification in the XQDA subspace. Traditional CRC (Collaborative Representation based Classification) conducts independent dictionary coding for each image and can not guarantee improved results over conventional euclidian distance. In this letter, however, a specific model is separately constructed for each probe image and each gallery image, i.e. in probe-galley pairwise manner. The proposed pairwise-specific CRC method can excavate extra discriminative information by enforcing a similarity item to pull similar sample-pairs closer. The approach has been evaluated against current methods on two benchmark datasets, achieving considerable improvement and outstanding performance.

Broadband satellites, operating at Ka band and above, are playing more and more important roles in future satellite networks. Meanwhile, rain attenuation is the dominant impairment in these bands. In this context, a dynamic power allocation scheme based on rain attenuation prediction is proposed. By this scheme, the system can dynamically adjust the allocated power according to the time-varying predicted rain attenuation. Extensive simulation results demonstrate the improvement of the dynamic scheme over the static allocation. It can be concluded that the allocated capacities match the traffic demands better by introducing such dynamic power allocation scheme and the waste of power resources is also avoided.

In this paper, a spectrum efficient spatial polarized quadrature amplitude modulation (SPQM) scheme for physical layer security in dual-polarized satellite systems is proposed, which uses the carrier's polarization state, amplitude, phase and the polarization characteristics of the transmitting beams as information bearing parameters, which can improve the transmission efficiency and enhance the transmission security at the same time. As we know, the depolarization effect is the main drawback that affects the symbol error rate performance when polarization states are used to carry information. To solve the problem, we exploit an additional degree of freedom, time, in the proposed scheme, which means that two components of the polarized signal are transmitted in turn in two symbol periods, thus they can be recovered without mutual interference. Furthermore, orthogonal polarizations of the transmitting beam are used as spatial modulation for further increasing the throughput. In addition, in order to improve the transmission security, two transmitting beams are designed to transmit the two components of the polarized signal respectively. In this way, a secure transmission link is formed from the transmitter to the receiver to prevent eavesdropping. Finally, superiorities of SPQM are validated by the theoretical analysis and simulation results in dual-polarized satellite systems.

This paper proposes a matched myriad filter based detector for MSK signal under symmetric alpha-stable (SαS) noise. As shown in the previous literatures, SαS distribution is more accurate to characterize the atmospheric noise, which is the main interference in VLF communication. MSK modulation is widely used in VLF communication for its high spectral efficiency and constant envelope properties. However, the optimal detector for MSK under SαS noise is rarely reported due to its memory modulation characteristic. As MSK signal can be viewed as a sinusoidal pulse weighted offset QPSK (OQPSK), a matched myriad filter is proposed to derive a near-optimal detection performance for the in-phase and quadrature components, respectively. Simulations for MSK demodulation under SαS noise with different α validate the effectiveness of the proposed method.

Bandwidth reservation is an important way to guarantee deterministic end-to-end service quality. However, with the traditional bandwidth reservation mechanism, the allocated bandwidth at each link is by default the same without considering the available resource of each link, which may lead to unbalanced resource utilization and limit the number of user connections that network can accommodate. In this paper, we propose a non-uniform bandwidth reservation method, which can further balance the resource utilization of network by optimizing the reserved bandwidth at each link according to its link load. Furthermore, to implement the proposed method, we devise a flexible and automatic bandwidth reservation mechanism based on meter table of Openflow. Through simulations, it is showed that our method can achieve better load balancing performance and make network accommodate more user connections comparing with the traditional methods in most application scenarios.

Excitation of Extremely Low Frequency (ELF)/Very Low Frequency (VLF) from ionosphere,which is artificial modulated by High Frequency (HF) waves can provide a way of antenna generation for deep submarine communication. In this paper, based on plasma energy conservation equation, the theoretical model of amplitude modulation HF pump heating low ionosphere for ELF/VLF generation is established. The linear frequency modulation technique of up-chirp and down-chirp have good self-correlation and cross-correlation, by which information can be transmitted by up-chirp and down-chirp. Thus, the linear frequency modulation technique can be applied to the ionosphere ELF/VLF communication. Based on this, a Chirp-BOK (Binary Orthogonal Keying) communication scheme is proposed. Indeed the Chirp-BOK amplitude and power modulation function are designed by combining the linear frequency modulation technique with the square wave amplitude modulation technique. The simulation results show in the condition that the ionosphere is heated by the Chirp-BOK power modulation HF waves, the temperature of ionospheric electronic and the variations of conductivity have obvious frequency modulation characteristics which are the same as that of power modulation, so does the variation of ionospheric current. Thus, when the ionosphere is heated by Chirp-BOK power modulation HF waves, the up-chirp (symbol ‘0’) and down-chirp (symbol ‘1’) ELF/VLF signals can be generated.

To improve the robustness of the polarization modulation (PM) technique applied in dual-polarized satellite systems, a zero-forcing aided demodulation (ZFAD) method is proposed to eliminate the impairment to the PM from the depolarization effect (DE). The DE elimination is traditionally dependent on the pre-compensation method, which is based on the channel state information (CSI). While the distance between communication partners in satellite systems is so long that the CSI can not be always updated in time at the transmitter side. Therefore, the pre-compensation methods may not perform well. In the ZFAD method, the CSI is estimated at the receiver side and the zero forcing matrix is constructed to process the received signal before demodulating the PM signal. In this way, the DE is eliminated. In addition, we derive the received signal-to-noise ratio expression of the PC and ZFAD methods with the statistical channel model for a better comparison. Theoretical analysis and simulation results demonstrate the ZFAD method can eliminate the DE effect effectively and achieve a better symbol error rate performance than the pre-compensation method.

In this letter, a polarization filtering based transmission (PFBT) scheme is proposed to enhance the spectrum efficiency in wireless communications. In such scheme, the information is divided into several parts and each is conveyed by a polarized signal with a unique polarization state (PS). Then, the polarized signals are added up and transmitted by the dual-polarized antenna. At the receiver side, the oblique projection polarization filters (OPPFs) are adopted to separate each polarized signal. Thus, they can be demodulated separately. We mainly focus on the construction methods of the OPPF matrix when the number of the separate parts is 2 and 3 and evaluate the performance in terms of the capacity and the bit error rate. In addition, we also discuss the probability of the signal separation when the number of the separate parts is equal or greater than 4. Theoretical results and simulation results demonstrate the performance of the proposed scheme.

In this letter, a novel mainlobe anti-jamming method via eigen-projection processing and covariance matrix reconstruction is proposed. The present work mainly focuses on two aspects: the first aspect is to obtain the eigenvector of the mainlobe interference accurately in order to form the eigen-projection matrix to suppress the mainlobe interference. The second aspect is to reconstruct the covariance matrix which is uesd to calculate the adaptive weight vector for forming an ideal beam pattern. Additionally, the self-null effect caused by the signal of interest and the sidelobe interferences elimination are also considered in the proposed method. Theoretical analysis and simulation results demonstrate that the proposed method can suppress the mainlobe interference effectively and achieve a superior performance.

Parameter estimation theorems for LFM signals have been developed due to the advantages of fractional Fourier transform (FrFT). The traditional estimation methods in the fractional Fourier domain (FrFD) are almost based on two-dimensional search which have the contradiction between estimation performance and complexity. In order to solve this problem, we introduce the orthogonal matching pursuit (OMP) into the FrFD, propose a modified optimization method to estimate initial frequency and final frequency of fractional bandlimited LFM signals. In this algorithm, the differentiation fractional spectrum which is used to form observation matrix in OMP is derived from the spectrum analytical formulations of the LFM signal, and then, based on that the LFM signal has approximate rectangular spectrum in the FrFD and the correlation between the LFM signal and observation matrix yields a maximal value at the edge of the spectrum (see Sect.3.3 for details), the edge spectrum information can be extracted by OMP. Finally, the estimations of initial frequency and final frequency are obtained through multiplying the edge information by the sampling frequency resolution. The proposed method avoids reconstruction and the traditional peak-searching procedure, and the iterations are needed only twice. Thus, the computational complexity is much lower than that of the existing methods. Meanwhile, Since the vectors at the initial frequency and final frequency points both have larger modulus, so that the estimations are closer to the actual values, better normalized root mean squared error (NRMSE) performance can be achieved. Both theoretical analysis and simulation results demonstrate that the proposed algorithm bears a relatively low complexity and its estimation precision is higher than search-based and reconstruction-based algorithms.

In this letter, a novel transmission scheme is proposed to eliminate the polarization dependent loss (PDL) effect in dual-polarized satellite systems. In fact, the PDL effect is the key problem that limits the performance of the systems based on the PM technique, while it is naturally eliminated in the proposed scheme since we transmit the two components of the polarized signal in turn in two symbol periods. Moreover, a simple and effective detection method based on the signal's power is proposed to distinguish the polarization characteristic of the transmit antenna. In addition, there is no requirement on the channel state information at the transmitter, which is popular in satellite systems. Finally, superiorities are validated by the theoretical analysis and simulation results in the dual-polarized satellite systems.

In this paper, a dual-polarized phased array based polarization state modulation method is proposed to enhance the physical-layer security in millimeter-wave (mm-wave) communication systems. Indeed, we utilize two polarized beams to transmit the two components of the polarized signal, respectively. By randomly selecting the transmitting antennas, both the amplitude and the phase of two beams vary randomly in undesired directions, which lead to the PM constellation structure distortion in side lobes, thus the transmission security is enhanced since the symbol error rate increases at the eavesdropper side. To enhance the security performance when the eavesdropper is close to the legitimate receiver and located in main beam, the artificial noise based on the orthogonal vector approach is inserted randomly between two polarized beams, which can further distort the constellation structure in undesired directions and improve the secrecy capacity in main beam as well. Finally, theoretical analysis and simulation results demonstrate the proposed method can improve the transmission security in mm-wave communication systems.

Coprime (pair of) DFT filter banks (coprime DFTFB), which process signals like a spectral analyzer in time domain, divides the power spectrum equally into MN bands by employing two DFT filter banks (DFTFBs) of size only M and N respectively, where M and N are coprime integers. With coprime DFTFB, frequencies in wide sense stationary (WSS) signals can be effectively estimated with a much lower sampling rates than the Nyquist rates. However, the imperfection of practical FIR filter and the correlation based detection mode give rise to two kinds of spurious peaks in power spectrum estimation, that greatly limit the application of coprime DFTFB. Through detailed analysis of the spurious peaks, this paper proposes a modified spectral analyzer based on dual coprime DFTFBs and sub-decimation, which not only depresses the spurious peaks, but also improves the frequency estimation accuracy. The mathematical principle proof of the proposed spectral analyzer is also provided. In discussion of simultaneous signals detection, an O-extended MN-band coprime DFTFB (OExt M-N coprime DFTFB) structure is naturally deduced, where M, N, and O are coprime with each other. The original MN-band coprime DFTFB (M-N coprime DFTFB) can be seen a special case of the OExt M-N coprime DFTFB with extending factor O equals ‘1’. In the numerical simulation section, BPSK signals with random carrier frequencies are employed to test the proposed spectral analyzer. The results of detection probability versus SNR curves through 1000 Monte Carlo experiments verify the effectiveness of the proposed spectrum analyzer.

This paper proposes a chirp-BOK modulation scheme for VLF (Very low frequency, 3-30kHz) communication under symmetric alpha-stable (SαS) noise. The atmospheric noise which is the main interference in VLF communication is more accurately characterized as SαS distribution in the previous literatures. Chirp-BOK, one of the chirp spread spectrum (CSS) technologies is widely used for its anti-interference performance and constant envelope properties. However, up-chirp and down-chirp are not strictly orthogonal, the bit error rate (BER) performance of chirp-BOK system is no longer improved with the increase of time-bandwidth product. So in this paper, the influence of non-orthogonal modulation waveform on the system is considered, and the model of the optimal parameters for chirp-BOK is derived from the perspective of minimum BER under gaussian noise and SαS noise respectively. Simulations for chirp-BOK scheme under gaussian noise and SαS noise with different α validate the effectiveness of the proposed method.

Encrypted traffic identification is to predict traffic types of encrypted traffic. A deep residual convolution network is proposed for this task. The Softmax classifier is fused with its angular variant, which sets an angular margin to achieve better discrimination. The proposed method improves representation learning and reaches excellent results on the public dataset.

This paper presents a novel model in the field of image co-saliency detection. Previous works simply design low level handcrafted features or extract deep features based on image patches for co-saliency calculation, which neglect the entire object perception properties. Besides, they also neglect the problem of visual similar region's mismatching when designing co-saliency calculation model. To solve these problems, we propose a novel strategy by considering both local prediction and global refinement (LPGR). In the local prediction stage, we train a deep convolutional saliency detection network in an end-to-end manner which only use the fully convolutional layers for saliency map prediction to capture the entire object perception properties and reduce feature redundancy. In the global refinement stage, we construct a unified co-saliency refinement model by integrating global appearance similarity into a co-saliency diffusion function, realizing the propagation and optimization of local saliency values in the context of entire image group. To overcome the adverse effects of visual similar regions' mismatching, we innovatively incorporates the inter-images saliency spread constraint (ISC) term into our co-saliency calculation function. Experimental results on public datasets demonstrate consistent performance gains of the proposed model over the state-of-the-art methods.

As known to us all, L-shaped co-prime array (LCA) is a recently introduced two-dimensional (2-D) sparse array structure, which is extended from linear co-prime array (CA). Such sparse array geometry can be used for 2-D parameters estimation with higher degrees-of-freedom (DOF). However, in the scenario where several narrowband transmissions spread over a wide spectrum, existing technique based on LCA with Nyquist sampling may encounter a bottleneck for both analog and digital processing. To alleviate the burden of high-rate Nyquist sampling, a method of joint wideband spectrum and direction-of-arrival (DOA) estimation with compressed sampling based on LCA, which is recognized as LCA-based modulated wideband converter (MWC), is presented in this work. First, the received signal along each antenna is mixed to basebands, low-pass filtered and down-sampled to get the compressed sampling data. Then by constructing the virtual received data of 2-D difference coarray, we estimate the wideband spectrum and DOA jointly using two recovery methods where the first is a joint ESPRIT method and the other is a joint CS method. Numerical simulations illustrate the validity of the proposed LCA based MWC system and show the superiority.

Traffic categorization aims to classify network traffic into major service types. A modern deep neural network based on temporal sequence modeling is proposed for encrypted traffic categorization. The contemporary techniques such as dilated convolution and residual connection are adopted as the basic building block. The raw traffic files are pre-processed to generate 1-dimensional flow byte sequences and are feed into our specially-devised network. The proposed approach outperforms other existing methods greatly on a public traffic dataset.