Optimizing the sensor energy is one of the most important concern in Three-Dimensional(3D)Wireless Sensor Networks(WSNs).An improved dynamic hierarchical clustering has been used in previous works that computes optimu...Optimizing the sensor energy is one of the most important concern in Three-Dimensional(3D)Wireless Sensor Networks(WSNs).An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus,the total consumption of energy is optimal.However,the computational complexity will be increased due to data dimension,and this leads to increase in delay in network data transmission and reception.For solving the above-mentioned issues,an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis(ILDA)is proposed for 3D hierarchical clustering WSNs.The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs.This ILDA approach consists of four major steps such as data dimension reduction,distance similarity index introduction,double cluster head technique and node dormancy approach.This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head(CH)selection technique.According to node’s position and residual energy,optimal cluster-head function is generated,and every CH is elected by this formulation.For a 3D spherical structure,under the same network condition,the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering(IDHC)is compared with Distributed Energy-Efficient Clustering(DEEC),Hybrid Energy Efficient Distributed(HEED)and Stable Election Protocol(SEP)techniques.It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput,network residual energy,network lifetime and first node death round.展开更多
An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-neares...An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-nearest neighbor samples. The experimental results show our algorithm is effective.展开更多
The EMG signal which is generated by the muscles activity diffuses to the skin surface of human body. This paper presents a pattern recognition system based on Linear Discriminant Analysis (LDA) algorithm for the clas...The EMG signal which is generated by the muscles activity diffuses to the skin surface of human body. This paper presents a pattern recognition system based on Linear Discriminant Analysis (LDA) algorithm for the classification of upper arm motions;where this algorithm was mainly used in face recognition and voice recognition. Also a comparison between the Linear Discriminant Analysis (LDA) and k-Nearest Neighbor (k-NN) algorithm is made for the classification of upper arm motions. The obtained results demonstrate superior performance of LDA to k-NN. The classification results give very accurate classification with very small classification errors. This paper is organized as follows: Muscle Anatomy, Data Classification Methods, Theory of Linear Discriminant Analysis, k-Nearest Neighbor (kNN) Algorithm, Modeling of EMG Pattern Recognition, EMG Data Generator, Electromyography Feature Extraction, Implemented System Results and Discussions, and finally, Conclusions. The proposed structure is simulated using MATLAB.展开更多
文摘Optimizing the sensor energy is one of the most important concern in Three-Dimensional(3D)Wireless Sensor Networks(WSNs).An improved dynamic hierarchical clustering has been used in previous works that computes optimum clusters count and thus,the total consumption of energy is optimal.However,the computational complexity will be increased due to data dimension,and this leads to increase in delay in network data transmission and reception.For solving the above-mentioned issues,an efficient dimensionality reduction model based on Incremental Linear Discriminant Analysis(ILDA)is proposed for 3D hierarchical clustering WSNs.The major objective of the proposed work is to design an efficient dimensionality reduction and energy efficient clustering algorithm in 3D hierarchical clustering WSNs.This ILDA approach consists of four major steps such as data dimension reduction,distance similarity index introduction,double cluster head technique and node dormancy approach.This protocol differs from normal hierarchical routing protocols in formulating the Cluster Head(CH)selection technique.According to node’s position and residual energy,optimal cluster-head function is generated,and every CH is elected by this formulation.For a 3D spherical structure,under the same network condition,the performance of the proposed ILDA with Improved Dynamic Hierarchical Clustering(IDHC)is compared with Distributed Energy-Efficient Clustering(DEEC),Hybrid Energy Efficient Distributed(HEED)and Stable Election Protocol(SEP)techniques.It is observed that the proposed ILDA based IDHC approach provides better results with respect to Throughput,network residual energy,network lifetime and first node death round.
文摘An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-nearest neighbor samples. The experimental results show our algorithm is effective.
文摘The EMG signal which is generated by the muscles activity diffuses to the skin surface of human body. This paper presents a pattern recognition system based on Linear Discriminant Analysis (LDA) algorithm for the classification of upper arm motions;where this algorithm was mainly used in face recognition and voice recognition. Also a comparison between the Linear Discriminant Analysis (LDA) and k-Nearest Neighbor (k-NN) algorithm is made for the classification of upper arm motions. The obtained results demonstrate superior performance of LDA to k-NN. The classification results give very accurate classification with very small classification errors. This paper is organized as follows: Muscle Anatomy, Data Classification Methods, Theory of Linear Discriminant Analysis, k-Nearest Neighbor (kNN) Algorithm, Modeling of EMG Pattern Recognition, EMG Data Generator, Electromyography Feature Extraction, Implemented System Results and Discussions, and finally, Conclusions. The proposed structure is simulated using MATLAB.
