Radar imaging with 1-bit data is attractive thanks to its low storage and transmission burden. Existing 1-bit radar imaging methods cannot satisfactorily suppress the artifacts in the imaging result induced by 1-bit quantization error and noise. In this article, we propose a new 1-bit compressive sensing (CS) based algorithm, i.e., the adversarial-sample-based binary iterative hard thresholding (AS-BIHT) algorithm, to improve the 1-bit radar imaging performance. First, we formulate a parametric model for 1-bit radar imaging with a new adjustable quantization level parameter. The parametric 1-bit radar imaging model updates the imaging scene and the quantization level parameter in an iterative fashion based on adversarial samples. Then, we design a mechanism to generate adversarial samples by attacking the 1-bit radar imaging model to resist the quantization consistency condition, such that forcing quantization consistent reconstruction on adversarial samples mitigates the quantization error and noise. The quantization level parameter is then tuned based on the adversarial samples. In this way, the ability of the model to adapt to echo data contaminated by noise and quantization error is enhanced, and the artifacts are well suppressed. Simulation and experimental results on real radar data demonstrate the effectiveness of the proposed AS-BIHT algorithm in 1-bit radar imaging.

Correlation filters (CFs) have been applied to visual tracking with success providing excellent performance in terms of accuracy and efficiency. The underlying periodic assumption of the training samples results in their great efficiency when using the fast Fourier transform (FFT), yet it also brings unwanted boundary effects. To address this issue, the recently proposed spatially-regularized discriminative CF (SRDCF) method introduces a Gaussian weight function to regularize the learning filter, yielding favorable performances in accuracy but high computational complexity because the objective of the SRDCF cannot achieve a closed solution via the FFT. Motivated by SRDCF, we present an efficient and effective CF-based tracker using center-biased constraint weights (CBCWs), which improve simultaneously speed and accuracy. Specifically, we first construct a CBCW function by exploiting the symmetry of the Fourier transform. The values of the constraint weights are real in both time and frequency domains, so that the optimization can be directly solved in the frequency domain without any data transformation, thereby greatly reducing its computational complexity. Moreover, according to the average peak-tocorrelation energy value of the CF response, we propose an efficient and effective filter update strategy to handle occlusions during tracking. Extensive experiments on the OTB-2013, OTB- 2015, and VOT2016 benchmarks demonstrate that the proposed tracker significantly outperforms the baseline SRDCF in terms of accuracy and efficiency. Moreover, the proposed method performs favorably against 16 other representative state-of-the-art methods regarding robustness and success rate.

The life of many wireless sensor networks is limited by their battery-based energy source. Recharging batteries from a distance by the wireless energy transferring technique could lift this restriction. However, how to deploy the mobile charging device requires further research. In this paper, we take the energy constraint of mobile wireless charger (MWC) into consideration and aim at minimizing the total energy consumption by it in recharging cycles. After formulating the optimization problem, we present the MMES-LME method based on the modified MAXMIN Ant System and equalization strategy with the constraint of MWC limited energy. The equalization strategy is presented to equalize the lifespan of all sensor nodes to avoid the untimely death of WSN and balance the consumption of MWC travelling energy and recharging energy. Our experimental results demonstrate improved performance in comparison to the greedy method and MM-LME method, which is based on MAX-MIN Ant System.

Sparse representation has been applied to visual tracking. The visual tracking models based on sparse representation use a template set as dictionary atoms to reconstruct candidate samples without considering similarity among atoms. In this paper, we present a robust tracking method based on the inverse sparse group lasso model. Our method exploits both the group structure of similar candidate samples and the local structure between templates and samples. Unlike the conventional sparse representation, the templates are encoded by the candidate samples, and similar samples are selected to reconstruct the template at the group level, which facilitates inter-group sparsity. Every sample group achieves the intra-group sparsity so that the information between the related dictionary atoms is taken into account. Moreover, the local structure between templates and samples is considered to build the reconstruction model, which ensures that the computed coefficients similarity is consistent with the similarity between templates and samples. A gradient descent-based optimization method is employed and a sparse mapping table is obtained using the coefficient matrix and hash-distance weight matrix. Experiments were conducted with publicly available datasets and a comparison study was performed against 20 state-of-the-art methods. Both qualitative and quantitative results are reported. The proposed method demonstrated improved robustness and accuracy and exhibited comparable computational complexity.

Interference management is an important problem in wireless networks. In this paper, we focus on the successive interference cancellation (SIC) technique, and aim to design an efficient cross-layer solution to increase throughput for multi-hop wireless networks with SIC. We realize that the challenge of this problem is its mixed integer linear programming formulation, which has bunches of integer variables. In order to solve this problem efficiently, we propose an iterative framework to improve the solution for integer variables and use a linear programming to solve the problem for other variables. Our analysis indicates that the proposed algorithm is with polynomial-time complexity. Simulation results show that SIC can increase throughput of a multi-hop wireless network by around 300%.