Energy-Aware Adaptive Weighted Grid Clustering Algorithm for Renewable Wireless Sensor Networks

Wireless sensor networks (WSNs), built from many battery-operated sensor nodes are distributed in the environment for monitoring and data acquisition. Subsequent to the deployment of sensor nodes, the most challenging and daunting task is to enhance the energy resources for the lifetime performance of the entire WSN. In this study, we have attempted an approach based on the shortest path algorithm and grid clustering to save and renew power in a way that minimizes energy consumption and prolongs the overall network lifetime of WSNs. Initially, a wireless portable charging device (WPCD) is assumed which periodically travels on our proposed routing path among the nodes of the WSN to decrease their charge cycle time and recharge them with the help of wireless power transfer (WPT). Further, a scheduling scheme is proposed which creates clusters of WSNs. These clusters elect a cluster head among them based on the residual energy, buffer size, and distance of the head from each node of the cluster. The cluster head performs all data routing duties for all its member nodes to conserve the energy supposed to be consumed by member nodes. Furthermore, we compare our technique with the available literature by simulation, and the results showed a significant increase in the vacation time of the nodes of WSNs

{5-Chloro-2-[(2-hy­droxy­benzyl­idene)amino]­phen­yl}(phen­yl)methanone

The title Schiff base compound, C20H14ClNO2, adopts an E configuration about the azomethine bond. The phenol and chloro­benzene rings form dihedral angles of 84.71 (9) and 80.70 (8)°, respectively, with the phenyl ring and are twisted by 15.32 (8)° with respect to one another. The mol­ecular conformation is stabilized by an intra­molecular O—H⋯N hydrogen bond, which forms an S(6) ring motif. In the crystal, mol­ecules are linked by C—H⋯O hydrogen bonds, forming columns parallel to the a axis

Low-Rank Kernel-Based Semisupervised Discriminant Analysis

Semisupervised Discriminant Analysis (SDA) aims at dimensionality reduction with both limited labeled data and copious unlabeled data, but it may fail to discover the intrinsic geometry structure when the data manifold is highly nonlinear. The kernel trick is widely used to map the original nonlinearly separable problem to an intrinsically larger dimensionality space where the classes are linearly separable. Inspired by low-rank representation (LLR), we proposed a novel kernel SDA method called low-rank kernel-based SDA (LRKSDA) algorithm where the LRR is used as the kernel representation. Since LRR can capture the global data structures and get the lowest rank representation in a parameter-free way, the low-rank kernel method is extremely effective and robust for kinds of data. Extensive experiments on public databases show that the proposed LRKSDA dimensionality reduction algorithm can achieve better performance than other related kernel SDA methods

Semisupervised SVM Based on Cuckoo Search Algorithm and Its Application

Semisupervised support vector machine (S3VM) algorithm mainly depends on the predicted accuracy of unlabeled samples, if lots of misclassified unlabeled samples are added to the training will make the training model performance degrade. Thus, the cuckoo search algorithm (CS) is used to optimize the S3VM which also enhances the model performance of S3VM. Considering that the cuckoo search algorithm is limited to the local optimum problem, a new cuckoo search algorithm based on chaotic catfish effect optimization is proposed. First, use the chaotic mechanism with high randomness to initialize the nest for range expansion. Second, chaotic catfish nest is introduced into the effective competition coordination mechanism after falling into the local optimum, so that the candidate’s nest can jump out of the local optimal solution and accelerate the convergence ability. In the experiment, results show that the improved cuckoo search algorithm is effective and better than the particle swarm optimization (PSO) algorithm and the cuckoo search algorithm on the benchmark functions. In the end, the improved cuckoo search algorithm is used to optimize semisupervised SVM which is applied into oil layer recognition. Results show that this optimization model is superior to the semisupervised SVM in terms of recognition rate and time

{5-Chloro-2-[(4-nitrobenzylidene)amino]phenyl}(phenyl)methanone

The molecule of the title Schiff base compound, C20H13ClN2O3, assumes an E configuration about the C=N bond. The aromatic rings of the nitrobenzene and chlorobenzene groups are twisted by 13.89 (13)° and form dihedral angles of 76.38 (13) and 84.64 (13)°, respectively, with the phenyl ring. In the crystal, molecules are linked into chains parallel to the b axis by C—H...π interactions