This paper starts with the discussion of the principle of Reduced-Rank (RR) Space-Time Adaptive Processing (STAP). It is followed by a dedication of the upper bound performance of all eigen-based RR methods provided b...This paper starts with the discussion of the principle of Reduced-Rank (RR) Space-Time Adaptive Processing (STAP). It is followed by a dedication of the upper bound performance of all eigen-based RR methods provided by Cross Spectral Method (CSM) under the condition of a given processor rank and an identical secondary sample size. A performance comparison between two RR STAP processors with prefixed structure and CSM is performed by the means of simulations. It is shown that the performance of time pre-filtering followed by jointly localized STAP structure (i.e. 3DT-SAP) is very close to the upper bound and thereby it is an effective RR approach.展开更多
针对蚁群算法在图像边缘提取中经常出现收敛速度慢、检测精度低、停滞等问题,提出一种结合Powell法的排序加权蚁群(rank weighted ant colony optimization,RWACO)图像边缘提取算法。该算法将RWACO与Powell法相结合,利用RWACO算法进行...针对蚁群算法在图像边缘提取中经常出现收敛速度慢、检测精度低、停滞等问题,提出一种结合Powell法的排序加权蚁群(rank weighted ant colony optimization,RWACO)图像边缘提取算法。该算法将RWACO与Powell法相结合,利用RWACO算法进行全局优化,然后将全局最优值作为Powell法的初始点进行局部优化。实验结果表明,该算法兼顾了全局优化和局部优化的优点,与蚁群算法和Canny算法相比,明显提高了图像边缘精度,计算效率比蚁群算法提高了两倍多,并克服了其停滞等缺点,能够高效地检测出图像的边缘,从而验证了该算法的可行性,对今后的图像边缘检测具有参考价值。展开更多
针对传统低秩稀疏分解(low rank and sparse decomposition,LRSD)用于视频运动目标检测时检测精度较低的问题,提出了一种鲁棒非凸运动辅助LRSD(robust nonconvex motion-assisted LRSD,RNMALRSD)的运动目标检测算法。该算法首先考虑到...针对传统低秩稀疏分解(low rank and sparse decomposition,LRSD)用于视频运动目标检测时检测精度较低的问题,提出了一种鲁棒非凸运动辅助LRSD(robust nonconvex motion-assisted LRSD,RNMALRSD)的运动目标检测算法。该算法首先考虑到视频背景的低秩特性,采用非凸γ范数对秩函数进行逼近,考虑视频背景在变换域上仍然具有稀疏性,引入背景在变换域的稀疏先验。其次,引入运动辅助信息矩阵,使其融入前景的运动信息,表示每个像素属于背景的可能性,提高视频运动目标检测的准确度。然后,采用交替方向乘子法(alternating direction method of multipliers,ADMM)对提出的模型进行求解。最后,将提出的方法应用到视频运动目标检测上进行仿真实验。对实验结果的分析表明,提出的RNMALRSD方法比其他基于LRSD的运动目标检测方法具有更高的检测精度。展开更多
Climate change and global warming are widely recognized as the most significant environmental dilemma the world is experiencing today. Recent studies have shown that the Earth’s surface air temperature has increased ...Climate change and global warming are widely recognized as the most significant environmental dilemma the world is experiencing today. Recent studies have shown that the Earth’s surface air temperature has increased by 0.6°C - 0.8°C during the 20th century, along with changes in the hydrological cycle. This has alerted the international community and brought great interest to climate scientists leading to several studies on climate trend detection at various scales. This paper examines the long-term modification of the near surface air temperature in Rwanda. Time series of near surface air temperature data for the period ranging from 1958 to 2010 for five weather observatories were collected from the Rwanda National Meteorological Service. Variations and trends of annual mean temperature time series were examined. The cumulative sum charts (CUSUM) and bootstrapping and the sequential version of the Mann Kendall Rank Statistic were used for the detection of abrupt changes. Regression analysis was performed for the trends and the Mann-Kendall Rank Statistic Test was used for the examination of their significance. Statistically significant abrupt changes and trends have been detected. The major change point in the annual mean temperature occurred around 1977-1979. The analysis of the annual mean temperature showed for all observatories a not very significant cooling trend during the period ranging from 1958 to 1977-1979 while a significant warming trend was furthermore observed for the period after the 1977-1979 where Kigali, the Capital of Rwanda, presented the highest values of the slope (0.0455/year) with high value of coefficient of determination (R2 = 0.6798), the Kendall’s tau statistic (M-K = 0.62), the Kendall Score (S = 328) with a two-sided p-value far less than the confidence level α of 5%). This is most likely explained by the growing population and increasing urbanization and industrialization the country has experienced, especially the Capital City Kigali, during the last decades.展开更多
We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation,...We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation, and functionality, and robustness refers to the ability to handle incomplete and/or corrupt adversarial information, on one side, and image and or device variability, on the other side. The proposed methodology is model-free and non-parametric. It draws support from discriminative methods using likelihood ratios to link at the conceptual level biometrics and forensics. It further links, at the modeling and implementation level, the Bayesian framework, statistical learning theory (SLT) using transduction and semi-supervised lea- rning, and Information Theory (IY) using mutual information. The key concepts supporting the proposed methodology are a) local estimation to facilitate learning and prediction using both labeled and unlabeled data;b) similarity metrics using regularity of patterns, randomness deficiency, and Kolmogorov complexity (similar to MDL) using strangeness/typicality and ranking p-values;and c) the Cover – Hart theorem on the asymptotical performance of k-nearest neighbors approaching the optimal Bayes error. Several topics on biometric inference and prediction related to 1) multi-level and multi-layer data fusion including quality and multi-modal biometrics;2) score normalization and revision theory;3) face selection and tracking;and 4) identity management, are described here using an integrated approach that includes transduction and boosting for ranking and sequential fusion/aggregation, respectively, on one side, and active learning and change/ outlier/intrusion detection realized using information gain and martingale, respectively, on the other side. The methodology proposed can be mapped to additional types of information beyond biometrics.展开更多
文摘This paper starts with the discussion of the principle of Reduced-Rank (RR) Space-Time Adaptive Processing (STAP). It is followed by a dedication of the upper bound performance of all eigen-based RR methods provided by Cross Spectral Method (CSM) under the condition of a given processor rank and an identical secondary sample size. A performance comparison between two RR STAP processors with prefixed structure and CSM is performed by the means of simulations. It is shown that the performance of time pre-filtering followed by jointly localized STAP structure (i.e. 3DT-SAP) is very close to the upper bound and thereby it is an effective RR approach.
文摘针对蚁群算法在图像边缘提取中经常出现收敛速度慢、检测精度低、停滞等问题,提出一种结合Powell法的排序加权蚁群(rank weighted ant colony optimization,RWACO)图像边缘提取算法。该算法将RWACO与Powell法相结合,利用RWACO算法进行全局优化,然后将全局最优值作为Powell法的初始点进行局部优化。实验结果表明,该算法兼顾了全局优化和局部优化的优点,与蚁群算法和Canny算法相比,明显提高了图像边缘精度,计算效率比蚁群算法提高了两倍多,并克服了其停滞等缺点,能够高效地检测出图像的边缘,从而验证了该算法的可行性,对今后的图像边缘检测具有参考价值。
文摘针对传统低秩稀疏分解(low rank and sparse decomposition,LRSD)用于视频运动目标检测时检测精度较低的问题,提出了一种鲁棒非凸运动辅助LRSD(robust nonconvex motion-assisted LRSD,RNMALRSD)的运动目标检测算法。该算法首先考虑到视频背景的低秩特性,采用非凸γ范数对秩函数进行逼近,考虑视频背景在变换域上仍然具有稀疏性,引入背景在变换域的稀疏先验。其次,引入运动辅助信息矩阵,使其融入前景的运动信息,表示每个像素属于背景的可能性,提高视频运动目标检测的准确度。然后,采用交替方向乘子法(alternating direction method of multipliers,ADMM)对提出的模型进行求解。最后,将提出的方法应用到视频运动目标检测上进行仿真实验。对实验结果的分析表明,提出的RNMALRSD方法比其他基于LRSD的运动目标检测方法具有更高的检测精度。
文摘Climate change and global warming are widely recognized as the most significant environmental dilemma the world is experiencing today. Recent studies have shown that the Earth’s surface air temperature has increased by 0.6°C - 0.8°C during the 20th century, along with changes in the hydrological cycle. This has alerted the international community and brought great interest to climate scientists leading to several studies on climate trend detection at various scales. This paper examines the long-term modification of the near surface air temperature in Rwanda. Time series of near surface air temperature data for the period ranging from 1958 to 2010 for five weather observatories were collected from the Rwanda National Meteorological Service. Variations and trends of annual mean temperature time series were examined. The cumulative sum charts (CUSUM) and bootstrapping and the sequential version of the Mann Kendall Rank Statistic were used for the detection of abrupt changes. Regression analysis was performed for the trends and the Mann-Kendall Rank Statistic Test was used for the examination of their significance. Statistically significant abrupt changes and trends have been detected. The major change point in the annual mean temperature occurred around 1977-1979. The analysis of the annual mean temperature showed for all observatories a not very significant cooling trend during the period ranging from 1958 to 1977-1979 while a significant warming trend was furthermore observed for the period after the 1977-1979 where Kigali, the Capital of Rwanda, presented the highest values of the slope (0.0455/year) with high value of coefficient of determination (R2 = 0.6798), the Kendall’s tau statistic (M-K = 0.62), the Kendall Score (S = 328) with a two-sided p-value far less than the confidence level α of 5%). This is most likely explained by the growing population and increasing urbanization and industrialization the country has experienced, especially the Capital City Kigali, during the last decades.
文摘We advance here a novel methodology for robust intelligent biometric information management with inferences and predictions made using randomness and complexity concepts. Intelligence refers to learning, adap- tation, and functionality, and robustness refers to the ability to handle incomplete and/or corrupt adversarial information, on one side, and image and or device variability, on the other side. The proposed methodology is model-free and non-parametric. It draws support from discriminative methods using likelihood ratios to link at the conceptual level biometrics and forensics. It further links, at the modeling and implementation level, the Bayesian framework, statistical learning theory (SLT) using transduction and semi-supervised lea- rning, and Information Theory (IY) using mutual information. The key concepts supporting the proposed methodology are a) local estimation to facilitate learning and prediction using both labeled and unlabeled data;b) similarity metrics using regularity of patterns, randomness deficiency, and Kolmogorov complexity (similar to MDL) using strangeness/typicality and ranking p-values;and c) the Cover – Hart theorem on the asymptotical performance of k-nearest neighbors approaching the optimal Bayes error. Several topics on biometric inference and prediction related to 1) multi-level and multi-layer data fusion including quality and multi-modal biometrics;2) score normalization and revision theory;3) face selection and tracking;and 4) identity management, are described here using an integrated approach that includes transduction and boosting for ranking and sequential fusion/aggregation, respectively, on one side, and active learning and change/ outlier/intrusion detection realized using information gain and martingale, respectively, on the other side. The methodology proposed can be mapped to additional types of information beyond biometrics.
