资源列表
API-burning_A2
- 4S16P Battery 2000 cycles Life Test Program for VB
Aloha2
- 对射频识别中aloha算法的吞吐率、时延性能进行了仿真，并与帧时隙aloha算法进行了对比。-The throughput and delay performance of Aloha algorithm in radio frequency identification are simulated and compared with frame time slot Aloha algorithm.
vmware_vxworks6.6
- VMware下建VxWorks6.6虚拟机安装教程-VMware built under VxWorks6.6 virtual machine installation tutorial
SVM-parameter-selection
- 支持向量机SVM参数的设置，采用网格搜索法对C和G两个参数进行粗选和精选。-Support vector machine SVM parameter setting, using the grid search method for C and G for roughing and two parameters chosen.
infrared-basic-theory
- 红外成像基本理轮、红外传递函数计算、红外制导、红外性能评估理轮模型 -Infrared imaging basic processing wheel, infrared transfer function calculation, infrared guidance, infrared performance uation
infrared-seeker-simulation
- 红外成像导引头仿真及红外场景生成用于伪装及干扰的评估 -infrared seeker simulation and infrared scense generation to uate decoy effectiveness
Infrared-System-Test
- 红外成像系统性能测试及评估、红外成像导引头性能测试及半实物仿真 -Infrared System Test and Evaluation at APL
Hyperspectral
- 高光谱遥感技术、高光谱信息处理、高光谱数据反yan6-Hyperspectral remote sensing hyperspectral information processing
target-detection
- 目标检测识别算法、光谱检测识别算法 、目标检测及跟踪算法-Target detection and recognition algorithm, spectral detection and recognition algorithm, target detection and tracking algorithm
Structured-Sparsity-Models
- 用于混响背景语音分离的结构稀疏模型(Strutured sparisty model)方法-To further tackle the ambiguity of the reflection ratios, we propose a novel formulation of the reverberation model and estimate the absorption coefficients through a convex optimization exploiting
Subspace-Methods-for-Joint
- 基于子空间方法的联合稀疏恢复，通过MUSIC算法进行结合测试，给出了测试结果-We propose subspace-augmented MUSIC (SA-MUSIC), which improves on MUSIC such that the support is reliably recovered under such unfavorable conditions. Combined with a subspace-based greedy algorithm, known
A-Robust-Algorithm-for-Joint-Sparse
- 脉冲噪声背景下的联合稀疏恢复方法， 在不同背景下给出了测试结果-presents a robust solution for joint sparse recovery (JSR) under impulsive noise. The unknown measurement noise is endowed with the Student-t distribution, then a novel Bayesian probabilistic model is proposed to