This paper proposes to apply the genetic algorithm and the firefly algorithm to enhance the estimation of the direction of arrival (DOA) angle of electromagnetic signals of a smart antenna array. This estimation is es...This paper proposes to apply the genetic algorithm and the firefly algorithm to enhance the estimation of the direction of arrival (DOA) angle of electromagnetic signals of a smart antenna array. This estimation is essential for beamforming, where the antenna array radiating pattern is steered to provide faster and reliable data transmission with increased coverage. This work proposes using metaheuristics to improve a maximum likelihood DOA estimator for an antenna array arranged in a uniform cuboidal geometry. The DOA estimation performance of the proposed algorithm was compared to that of MUSIC on different two dimensions scenarios. The metaheuristic algorithms present better performance than the well-known MUSIC algorithm.展开更多
Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear mode...Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.展开更多
针对矢量水听器阵列信号波达方向(Direction of Arrival,DOA)估计中存在的估计精度不高、运算量大的问题,提出了一种改进蛇鹫优化算法的最大似然DOA估计方法。采用了4种策略对蛇鹫优化算法进行改进,在种群初始化时利用佳点集策略进行改...针对矢量水听器阵列信号波达方向(Direction of Arrival,DOA)估计中存在的估计精度不高、运算量大的问题,提出了一种改进蛇鹫优化算法的最大似然DOA估计方法。采用了4种策略对蛇鹫优化算法进行改进,在种群初始化时利用佳点集策略进行改进,能够更加均匀地在搜索空间得到初始种群解,提高种群多样性;在进行位置更新时,引入减法平均优化器和柯西策略能够扩大搜索范围,从而跳出局部最优;引入黄金正弦策略,不断向最优位置进行搜索,提高了算法的估计精度。将所提算法应用于最大似然DOA估计,实验结果表明:与灰狼优化算法、粒子群算法、正弦余弦算法、光学显微镜算法、蛇鹫优化算法的最大似然DOA估计方法相比,所提算法的最大似然DOA估计具有收敛速度快、估计精度高、运算量小和稳健性更好的性能。展开更多
In a developing country like Ghana, the study of land use and land cover change(LULCC) based on satellite imageries still remains a challenge due to cost, resolution and availability with less skilled man power. Exist...In a developing country like Ghana, the study of land use and land cover change(LULCC) based on satellite imageries still remains a challenge due to cost, resolution and availability with less skilled man power. Existing researches are skewed towards the southerly part of Ghana thereby leaving the Northern sectors uncovered. The maximum likelihood classification(MLC) algorithm was employed for the LULCC between 2000 and 2014 in Nadowli: an area characterized by an upsurge in mining in the Northern belt of Ghana. A spatial-social approach was utilized combining both satellite imagery and socio economic data. Land use transition matrix, land use integrated index/degree indices was used to depict the characters of the change. A semi structured interview, pair wise ranking and key informant interviews were used to correlate the socio economic impact of the different LULC. Overall changes in the landscape showed an increase in bare ground by 19.22%, open savannah by 16.8% whereas closed savanna decreased by 50%. Land use change matrix showed increasing trends of bare ground at the expense of vegetation. The integrated land use index highlighted the bare ground and built up areas rising with a decreasing closed vegetation woodlot. Large farm size are shrinking whiles majority of the people view mining as the main socio economic activity affecting the environment and the reduction in vegetation. This study therefore provides a strategic guide and a baseline data for land use policy actors in the Northern belt of Ghana. This will aid in developing models for future land use change implications in surrounding areas where mining is on the rise.展开更多
摘要This paper proposes to apply the genetic algorithm and the firefly algorithm to enhance the estimation of the direction of arrival (DOA) angle of electromagnetic signals of a smart antenna array. This estimation is essential for beamforming, where the antenna array radiating pattern is steered to provide faster and reliable data transmission with increased coverage. This work proposes using metaheuristics to improve a maximum likelihood DOA estimator for an antenna array arranged in a uniform cuboidal geometry. The DOA estimation performance of the proposed algorithm was compared to that of MUSIC on different two dimensions scenarios. The metaheuristic algorithms present better performance than the well-known MUSIC algorithm.
摘要Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.
摘要针对矢量水听器阵列信号波达方向(Direction of Arrival,DOA)估计中存在的估计精度不高、运算量大的问题,提出了一种改进蛇鹫优化算法的最大似然DOA估计方法。采用了4种策略对蛇鹫优化算法进行改进,在种群初始化时利用佳点集策略进行改进,能够更加均匀地在搜索空间得到初始种群解,提高种群多样性;在进行位置更新时,引入减法平均优化器和柯西策略能够扩大搜索范围,从而跳出局部最优;引入黄金正弦策略,不断向最优位置进行搜索,提高了算法的估计精度。将所提算法应用于最大似然DOA估计,实验结果表明:与灰狼优化算法、粒子群算法、正弦余弦算法、光学显微镜算法、蛇鹫优化算法的最大似然DOA估计方法相比,所提算法的最大似然DOA估计具有收敛速度快、估计精度高、运算量小和稳健性更好的性能。
基金self-supported as part of the Ph D Program on CSC scholarship in the China University of Geosciences (Wuhan)
摘要In a developing country like Ghana, the study of land use and land cover change(LULCC) based on satellite imageries still remains a challenge due to cost, resolution and availability with less skilled man power. Existing researches are skewed towards the southerly part of Ghana thereby leaving the Northern sectors uncovered. The maximum likelihood classification(MLC) algorithm was employed for the LULCC between 2000 and 2014 in Nadowli: an area characterized by an upsurge in mining in the Northern belt of Ghana. A spatial-social approach was utilized combining both satellite imagery and socio economic data. Land use transition matrix, land use integrated index/degree indices was used to depict the characters of the change. A semi structured interview, pair wise ranking and key informant interviews were used to correlate the socio economic impact of the different LULC. Overall changes in the landscape showed an increase in bare ground by 19.22%, open savannah by 16.8% whereas closed savanna decreased by 50%. Land use change matrix showed increasing trends of bare ground at the expense of vegetation. The integrated land use index highlighted the bare ground and built up areas rising with a decreasing closed vegetation woodlot. Large farm size are shrinking whiles majority of the people view mining as the main socio economic activity affecting the environment and the reduction in vegetation. This study therefore provides a strategic guide and a baseline data for land use policy actors in the Northern belt of Ghana. This will aid in developing models for future land use change implications in surrounding areas where mining is on the rise.