Blind reconstruction of convolutional code based on segmented Walsh-Hadamard transform

Walsh-Hadamard transform （WriT） can solve linear error equations on Field F2, and the method can be used to recover the parameters of convolutional code. However, solving the equations with many unknowns needs enormous computer memory which limits the application of WriT. In order to solve this problem, a method based on segmented WriT is proposed in this paper. The coefficient vector of high dimension is reshaped and two vectors of lower dimension are obtained. Then the WriT is operated and the requirement for computer memory is much reduced. The code rate and the constraint length of convolutional code are detected from the Walsh spectrum. And the check vector is recovered from the peak position. The validity of the method is verified by the simulation result, and the performance is proved to be optimal.

Underdetermined DOA estimation and blind separation of non-disjoint sources in time-frequency domain based on sparse representation method

This paper deals with the blind separation of nonstation-ary sources and direction-of-arrival （DOA） estimation in the under-determined case, when there are more sources than sensors. We assume the sources to be time-frequency （TF） disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspace-based algorithm under the same sparseness assumption, which suffers from the extra noise effect since it can-not estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their cor-responding TF values in any TF neighborhood simultaneously. An-other contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Sim-ulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation （BSS） and DOA estimation.

Blind reconstruction of linear scrambler

An algorithm based on eigenanalysis technique and Walsh-Hadamard transform （WriT） is proposed. The algorithm contains two steps. Firstly, the received sequence is divided into temporal windows, and a covariance matrix is computed. The linear feedback shift register （LFSR） sequence is reconstructed from the first eigenvector of this matrix. Secondly, equations according to the recovered LFSR sequence are constructed, and the Walsh spectrum corresponding to the equations is computed. The feedback polynomial of LFSR is estimated from the Walsh spectrum. The validity of the algorithm is verified by the simulation result. Finally, case studies are presented to illustrate the performance of the blind reconstruction method.

Modulation Index Estimation of Frequency and Phase Modulated Signals

Modulation index estimation is important in the demodulation and recognition of Angle Modulation (AM) signals which include Frequency Modulation (FM) and Phase Modulation (PM) signals. In this paper, we firstly analyzed the AM signals with baseband modulation types, such as monotone, PSK, FSK, in the time and frequency domain. Then, we established a unified mathematical representation for the AM signals. Finally, we derived a blind estimation algorithm for the modulation index without using any prior knowledge. Simulation results verify the capabilities of the proposed algorithm.

Dissociation of singlet excitons dominates photocurrent improvement in high-efficiency non-fullerene organic solar cells

In organic solar cells,the singlet and triplet excitons dissociate into free charge carriers with different mechanisms due to their opposite spin state.Therefore,the ratio of the singlet and triplet excitons directly affects the photocurrent.Many methods were used to optimize the performance of the low-efficiency solar cell by improving the ratio of triplet excitons,which shows a long diffusion length.Here we observed that in high-efficiency systems,the proportion of singlet excitons under linearly polarized light excitation is higher than that of circularly polarized light.Since the singlet charge transfer state has lower binding energy than the triplet state,it makes a significant contribution to the charge carrier generation and enhancement of the photocurrent.Further,the positive magnetic field effect reflects that singlet excitons dissociation plays a major role in the photocurrent,which is opposite to the case of low-efficiency devices where triplet excitons dominate the photocurrent.

Submicrometer optical frequency combs based on SPPs metallic multi-ring resonators

Optical frequency combs(OFCs)have great potential in communications,especially in dense wavelength-division multiplexing.However,the size of traditional OFCs based on conventional optical microcavities or dispersion fibers is at least tens of micrometers,far larger than that of nanoscale electronic chips.Therefore,reducing the size of OFCs to match electronic chips is of necessity.Here,for the first time to our knowledge,we introduce surface plasmon polaritons(SPPs)to the construction of OFCs to realize a miniature device.The thickness of our device is reduced below 1μm.Though the presence of SPPs may induce ohmic and scattering loss,the threshold of the device is obtained as 9μW,comparable to the conventional device.Interestingly,the response time is 13.2 ps,much faster than the optical counterparts.This work provides a feasible strategy for the miniaturization of OFCs.

Synchronized perturbation elimination and DOA estimation via signal selection mechanism and parallel deep capsule networks in multipath environment

State-of-the-art model-driven Direction-Of-Arrival(DOA)estimation methods for multipath signals face great challenges in practical application because of the dependence on the precise multipath model.In this paper,we introduce a framework,based on deep learning,for synchronizing perturbation auto-elimination with effective DOA estimation in multipath environment.Firstly,a signal selection mechanism is introduced to roughly locate specific signals to spatial subregion via frequency domain filters and compressive sensing-based method.Then,we set the mean of the correlation matrix’s row vectors as the input feature to construct the spatial spectrum by the corresponding single network within the parallel deep capsule networks.The proposed method enhances the generalization capability to untrained scenarios and the adaptability to non-ideal conditions,e.g.,lower SNRs,smaller snapshots,unknown reflection coefficients and perturbational steering vectors,which make up for the defects of the previous model-driven methods.Simulations are carried out to demonstrate the superiority of the proposed method.

Unintentional modulation evaluation in time domain and frequency domain

With the development of wireless communication technology, the electromagnetic environment has become more and more complex. Conventional signal identification methods are difficult to accurately identify illegal devices. However, electromagnetic signals have an unavoidable device-specific characteristic unintentionally generated by a transmitter, appearing in the form of an Un Intentional Modulation(UIM), namely Radio Frequency Fingerprint(RFF). RFFs can be used to uniquely identify an emitter to match a received signal with its source. In this paper, the authors propose a novel RFF scheme to separate UIM part from the original signals from the time and frequency domain, and then utilize non-Gaussian measuring tools to extract a set of dimensionreduced secondary features. Additionally, Singular Value Reconstruction(SVR) is developed to extract UIM in the frequency spectrum. In time domain, a curve-fitting residual method is proposed to extract the UIM on the estimated instantaneous phase based on Maximum Likelihood Estimator(MLE). Various aspects of the proposed method are evaluated, including identification accuracy under various Signal-to-Noise Ratio(SNR) conditions, energy relationships between the UIM and the whole signal, and sensitivity to training set size. Compared with other methods, experimental results based on real-world signals prove that the proposed method has remarkable performance and high practicability.

Strengthening mechanism of Fe-Cu alloy during aging

To give a further research on strengthening mechanism of binary Fe-Cu alloys dur- ing the aging process, the compositions, size distributions and volume fractions of precipitates were investigated by carbon extraction replicas. Both the size and the volume fraction of precipitates increase with the aging time prolonged. The precip- itate sizes are corresponding with Weibull distribution. The stability of the sample aged for 35 rain is worse than that aged for 1800 min and 18000 min. The precipi- tate is Cu-rich phase in the early aging process, and Cu content increases with the prolonging aging time. Before aging peak, the precipitation of Cu-rich phase plays a dominant role in strengthening. The growth and the change in crystal structure of the precipitate particles are the main factors leading to the decrease in strengthening after the aging peak.