In this paper, we propose a controlled retransmission scheme in optical burst switching (OBS) networks. Different from previous works in the literature, we set a different value to retransmission probability at each contention and propose a retransmission analytical model for burst segmentation contention resolution scheme. In addition, we consider the effect of relevance in traffic come from multiple paths. We take into account the load at each link (include the given links and the other correlated links taking traffic) due to both the fresh and the retransmitted traffic and calculate the path blocking probability and the byte loss probability (ByLP) in cases of without and with full- wavelength conversion to evaluate the network performance. An extensive simulation is proposed to validate our analytical model, and results have shown that both path blocking probability and ByLP are affected by the load and the retransmission probability in each contention along a path and the correlated traffic carried links on the path.

Near-field beamforming has played an important role in many scenarios such as radar imaging and acoustic detection. In this paper, the near-field beamforming is implemented in the time modulated array with the harmonic. The beam pattern with a low sidelobe level in precise position is achieved by controlling the switching sequence in time modulated cross array. Numerical results verify the correctness of the proposed method.

In this paper, we study the fundamental combinatorial nonlinear resonances of a system consisting of two identical periodic forced circuits coupled by a linear resistor. The circuit equations are described by a system of coupled Duffing's equations. We discuss two cases of external periodic force, i.e., in-phase and anti-phase, and obtain the bifurcation diagram of each case. Periodic solutions are classified according to the symmetrical property of the circuit. Resonances in the coupled system are explained from the combinatorial standpoint. That is, we introduce the definition of combinatorial resonances and investigate the patterns of combinatorial solutions in this system.

This letter proposes a blind phase compensation method for the phase errors in the Multi-Carrier Multiple-input multiple-output (MIMO) radar, which decouples the range and DOA coupling. The phase errors under the Linear Frequency Modulated Continuous Waveform (LFMCW) scheme are firstly derived, followed with the signal processing steps. Further, multiple targets with certain velocities can be handled uniformly without pre-knowledge of the actual range information of the targets. The evaluations of the DOA estimation performance are carried out through simulations, which validate the effectiveness of the proposed method.

A predictive compact model of p-MOSFET negative bias temperature instability (NBTI) degradation for circuit simulation is reported with unified description of the interface-state-generation and hole-trapping mechanisms. It is found that the hole-trapping is responsible for the initial stage of the stress degradation, and the interface-state generation dominates the degradation afterwards, especially under high stress conditions. The predictive compact model with 8 parameters enables to reproduce the measurement results of the NBTI degradation under a wide range of stress bias conditions. Finally, the developed NBTI model is implemented into the compact MOSFET model HiSIM for circuit degradation simiulation.

This letter proposes the use of a novel time-modulated array structure to estimate the direction of arrival (DOA). Such a time-modulated coprime array (TMCA) is obtained by exchanging a coprime array's phase shifter for a radio frequency (RF) switch. Compared with a traditional coprime array, the TMCA's structure is much simpler, and it has a higher degree of freedom and resolution compared with a time-modulated uniform linear array (TMULA) due to its exploitation of the virtual array's equivalent signals. Theoretical analysis and experimental results have validated the effectiveness of the proposed structure and method and have confirmed that a TMCA's DOA performance is better than that of a TMULA using the same number of antennas.

Automatic modulation recognition(AMR) of radar signals is a currently active area, especially in electronic reconnaissance, where systems need to quickly identify the intercepted signal and formulate corresponding interference measures on computationally limited platforms. However, previous methods generally have high computational complexity and considerable network parameters, making the system unable to detect the signal timely in resource-constrained environments. This letter firstly proposes an efficient modulation recognition network(EMRNet) with tiny and low latency models to match the requirements for mobile reconnaissance equipments. One-dimensional residual depthwise separable convolutions block(1D-RDSB) with an adaptive size of receptive fields is developed in EMRNet to replace the traditional convolution block. With 1D-RDSB, EMRNet achieves a high classification accuracy and dramatically reduces computation cost and network paraments. The experiment results show that EMRNet can achieve higher precision than existing 2D-CNN methods, while the computational cost and parament amount of EMRNet are reduced by about 13.93× and 80.88×, respectively.

Previously, convolutional neural networks have made tremendous progress in target recognition based on micro-Doppler radar. However, these studies only considered the presence of one target at a time in the surveillance area. Simultaneous multi-targets recognition for surveillance radar remains a pretty challenging issue. To alleviate this issue, this letter develops a multi-instance multi-label (MIML) learning strategy, which can automatically locate the crucial input patterns that trigger the labels. Benefitting from its powerful target-label relation discovery ability, the proposed framework can be trained with limited supervision. We emphasize that only echoes from single targets are involved in training data, avoiding the preparation and annotation of multi-targets echo in the training stage. To verify the validity of the proposed method, we model two representative ground moving targets, i.e., person and wheeled vehicles, and carry out numerous comparative experiments. The result demonstrates that the developed framework can simultaneously recognize multiple targets and is also robust to variation of the signal-to-noise ratio (SNR), the initial position of targets, and the difference in scattering coefficient.

A nonlinear circuit described by the forced Duffing's equation is known to display a rich variety of dynamical behavior. Coupling two Duffing's circuits by a linear resistor, we conclude that combinatorial resonances occur on weak coupling condition. In a coupled system, although symmetrical properties are usually observed, breaking of symmetry can lead to much more complex nonlinear resonant phenomena. In this paper, we discuss asymmetry in four cases of perturbation on parameters. Many bifurcation diagrams are presented. Comparing with symmetrical cases, we analyze the combinatorial resonances in coupled Duffing's circuit completely.

Fault localization analyzes the runtime information of two classes of test cases (i.e., passing test cases and failing test cases) to identify suspicious statements potentially responsible for a failure. However, the failing test cases are always far fewer than passing test cases in reality, and the class imbalance problem will affect fault localization effectiveness. To address this issue, we propose a data augmentation approach using conditional variational auto-encoder to synthesize new failing test cases for FL. The experimental results show that our approach significantly improves six state-of-the-art fault localization techniques.