Joint 2D DOA and Doppler frequency estimation for L-shaped array using compressive sensing

A joint two-dimensional(2D)direction-of-arrival(DOA)and radial Doppler frequency estimation method for the L-shaped array is proposed in this paper based on the compressive sensing(CS)framework.Revised from the conventional CS-based methods where the joint spatial-temporal parameters are characterized in one large scale matrix,three smaller scale matrices with independent azimuth,elevation and Doppler frequency are introduced adopting a separable observation model.Afterwards,the estimation is achieved by L1-norm minimization and the Bayesian CS algorithm.In addition,under the L-shaped array topology,the azimuth and elevation are separated yet coupled to the same radial Doppler frequency.Hence,the pair matching problem is solved with the aid of the radial Doppler frequency.Finally,numerical simulations corroborate the feasibility and validity of the proposed algorithm.

Polynomial Root Finding on Frequency Estimation with Sub-Nyquist Temporal Sampling

This paper addresses an algebraic approach for wideband frequency estimation with sub-Nyquist temporal sampling. Firstly, an algorithm based on double polynomial root finding procedure to estimate aliasing frequencies and joint aliasing frequencies-time delay phases in multi-signal situation is presentcd. Since the sum of time delay phases determined from the least squares estimation shows the characteristics of the corre- sponding parameters pairs, then the pairmatching method is conducted by combining it with estimated parameters mentioned above. Although the proposed method is computationally simpler than the conventional schemes, simulation results show that it can approach optimum estimation performance.

Estimation of 2-D Angles of Arrival Based on Joint Diagonalization of Two Spatio-Temporal Correlation Matrices

In this paper, we propose a two-dimensional （2-D） angles of arrival （AOAs） estimation method based on a joint diagonalization of two spatio-temporal （ST） correlation matrices. The mathematical manipulations proposed in this paper take the structure of the array that enable estimating 2-D AOAs simultaneously without 2-D searching or pairing. The performance comparison shows that the proposed method is better than ST-DOA matrix method.

An improved probabilistic relaxation method for matching multi-scale road networks

Matching multi-scale road networks in the same area is the first step in merging two road networks or updating one based upon the other.The quality of the merge or update depends greatly on the matching accuracy of the two road networks.We propose an improved probabilistic relaxation method,considering both local and global optimizations for matching multi-scale of road networks.The aim is to achieve local optimization,as well as to address the identification of the M:N matching pattern by means of inserting virtual nodes to achieve global optimization effects.Then,by adding two attribute-related evaluation indicators,we developed four evaluation indicators to evaluate the matching accuracy,considering both geographic and attribute information.This paper also provides instructions on how to identify the proper buffer threshold during matching procedures.Extensive experiments were conducted to compare the proposed method with the traditional approach.The results indicate that:(1)the overall matching accuracy of each evaluation indicator exceeds 90%;(2)the overall matching accuracy increases by 6–12%after an M:N matching pattern is added,and by 4–6%following the addition of topology indicators;and(3)the proper buffer threshold is about twice the average value of the closest distance from all nodes.

Molecular Generation and Optimization of Molecular Properties Using a Transformer Model

Generating novel molecules to satisfy specific properties is a challenging task in modern drug discovery,which requires the optimization of a specific objective based on satisfying chemical rules.Herein,we aim to optimize the properties of a specific molecule to satisfy the specific properties of the generated molecule.The Matched Molecular Pairs(MMPs),which contain the source and target molecules,are used herein,and logD and solubility are selected as the optimization properties.The main innovative work lies in the calculation related to a specific transformer from the perspective of a matrix dimension.Threshold intervals and state changes are then used to encode logD and solubility for subsequent tests.During the experiments,we screen the data based on the proportion of heavy atoms to all atoms in the groups and select 12365,1503,and 1570 MMPs as the training,validation,and test sets,respectively.Transformer models are compared with the baseline models with respect to their abilities to generate molecules with specific properties.Results show that the transformer model can accurately optimize the source molecules to satisfy specific properties.

Discriminative Histogram Intersection Metric Learning and Its Applications

In this paper, a novel method called discriminative histogram intersection metric learning （DHIML） is proposed for pair matching and classification. Specifically, we introduce a discrimination term for learning a metric from binary infor-mation such as same/not-same or similar/dissimilar, and then combine it with the classification error for the discrimination in classifier construction. Compared with conventional approaches, the proposed method has several advantages. 1） The histogram intersection strategy is adopted into metric learning to deal with the widely used histogram features effectively. 2） By introducing discriminative term and classification error term into metric learning, a more discriminative distance metric and a classifier can be learned together. 3） The objective function is robust to outliers and noises for both features and labels in the training. The performance of the proposed method is tested on four applications： face verification, face-track identification, face-track clustering, and image classification. Evaluations on the challenging restricted protocol of Labeled Faces in the Wild （LFW） benchmark, a dataset with more than 7000 face-tracks, and Caltech-101 dataset validate the robustness and discriminability of the proposed metric learning, compared with the recent state-of-the-art approaches.

Joint diagonalization DOA matrix method

A novel joint diagonalization （DOA） matrix method is proposed to estimate the two-dimensional （2-D） DOAs of uncorrelated narrowband signals. The method constructs three subarrays by exploiting the special structure of the array, thereby obtaining the 2-D DOAs of the array based on joint diagonalization directly with neither peak search nor pair matching. The new method can handle sources with common 1-D angles. Simulation results show the effectiveness of the method.

Nonabelian omni-Lie algebroids

In this paper,we study the structure of nonabelian omni-Lie algebroids.Firstly,taking Lie algebroid(E,[·,·]_(E,ρE))as the starting point,a nonabelian omni-Lie algebroid is defined on direct sum bundle DE⊕JE,where DE and JE are,respectively,the gauge Lie algebroid and the jet bundle of vector bundle E,and study its properties.Furthermore,it is concluded that the nonabelian omni-Lie algebroid is a trivial deformation of the omni-Lie algebroid,and the nonabelian omni-Lie algebroid is a matched pair of Leibniz algebroids.