In control systems of power grids, conveying observations to controllers and obtaining control outputs depend greatly on communication and computation resources. Particularly for large-scale systems, the costs of computation and communication (cyber costs) should not be neglected. This paper proposes a self-triggered frequency control system for a power grid to reduce communication costs. An equation for obtaining the triggering time is derived, and an approximation method is proposed to reduce the computation cost of triggering time. In addition, the communication cost of frequency triggering is measured quantitatively and proportionally. The defined cost function considers both physical cost (electricity transmission cost) and communication cost (control signal transmission cost). The upper bound of cost is estimated. According to the estimated upper bound of cost, parameters of the controller are investigated by using the proposed optimization algorithm to guarantee the high performance of the system. Finally, the proposed self-triggered power system is simulated to verify its efficiency and effectiveness. Full article

Sparse-representation based approaches have been integrated into image fusion methods in the past few years and show great performance in image fusion. Training an informative and compact dictionary is a key step for a sparsity-based image fusion method. However, it is difficult to balance “informative” and “compact”. In order to obtain sufficient information for sparse representation in dictionary construction, this paper classifies image patches from source images into different groups based on morphological similarities. Stochastic coordinate coding (SCC) is used to extract corresponding image-patch information for dictionary construction. According to the constructed dictionary, image patches of source images are converted to sparse coefficients by the simultaneous orthogonal matching pursuit (SOMP) algorithm. At last, the sparse coefficients are fused by the Max-L1 fusion rule and inverted to a fused image. The comparison experimentations are simulated to evaluate the fused image in image features, information, structure similarity, and visual perception. The results confirm the feasibility and effectiveness of the proposed image fusion solution. Full article

In recent years, sparse representation approaches have been integrated into multi-focus image fusion methods. The fused images of sparse-representation-based image fusion methods show great performance. Constructing an informative dictionary is a key step for sparsity-based image fusion method. In order to ensure sufficient number of useful bases for sparse representation in the process of informative dictionary construction, image patches from all source images are classified into different groups based on geometric similarities. The key information of each image-patch group is extracted by principle component analysis (PCA) to build dictionary. According to the constructed dictionary, image patches are converted to sparse coefficients by simultaneous orthogonal matching pursuit (SOMP) algorithm for representing the source multi-focus images. At last the sparse coefficients are fused by Max-L1 fusion rule and inverted to fused image. Due to the limitation of microscope, the fluorescence image cannot be fully focused. The proposed multi-focus image fusion solution is applied to fluorescence imaging area for generating all-in-focus images. The comparison experimentation results confirm the feasibility and effectiveness of the proposed multi-focus image fusion solution. Full article

Endoplasmic reticulum (ER) stress-induced apoptosis plays an important role in a range of neurological disorders, such as neurodegenerative diseases, spinal cord injury, and diabetic neuropathy. Valproate (VPA), a typical antiepileptic drug, is commonly used in the treatment of bipolar disorder and epilepsy. Recently, VPA has been reported to exert neurotrophic effects and promote neurite outgrowth, but its molecular mechanism is still unclear. In the present study, we investigated whether VPA inhibited ER stress and promoted neuroprotection and neuronal restoration in SH-SY5Y cells and in primary rat cortical neurons, respectively, upon exposure to thapsigargin (TG). In SH-SY5Y cells, cell viability was detected by the 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay, and the expression of ER stress-related apoptotic proteins such as glucose‑regulated protein (GRP78), C/EBP homologous protein (CHOP), and cleaved caspase-12/-3 were analyzed with Western blot analyses and immunofluorescence assays. To explore the pathway involved in VPA-induced cell proliferation, we also examined p-AKT, GSK3β, p-JNK and MMP-9. Moreover, to detect the effect of VPA in primary cortical neurons, immunofluorescence staining of β-III tubulin and Anti-NeuN was analyzed in primary cultured neurons exposed to TG. Our results demonstrated that VPA administration improved cell viability in cells exposed to TG. In addition, VPA increased the levels of GRP78 and p-AKT and decreased the levels of ATF6, XBP-1, GSK3β, p-JNK and MMP-9. Furthermore, the levels of the ER stress-induced apoptosis response proteins CHOP, cleaved caspase-12 and cleaved caspase-3 were inhibited by VPA treatment. Meanwhile, VPA administration also increased the ratio of Bcl-2/Bax. Moreover, VPA can maintain neurite outgrowth of primary cortical neurons. Collectively, the neurotrophic effect of VPA is related to the inhibition of ER stress-induced apoptosis in SH-SY5Y cells and the maintenance of neuronal growth. Collectively, our results suggested a new approach for the therapeutic function of VPA in neurological disorders and neuroprotection. Full article

The multi-focus image fusion method is used in image processing to generate all-focus images that have large depth of field (DOF) based on original multi-focus images. Different approaches have been used in the spatial and transform domain to fuse multi-focus images. As one of the most popular image processing methods, dictionary-learning-based spare representation achieves great performance in multi-focus image fusion. Most of the existing dictionary-learning-based multi-focus image fusion methods directly use the whole source images for dictionary learning. However, it incurs a high error rate and high computation cost in dictionary learning process by using the whole source images. This paper proposes a novel stochastic coordinate coding-based image fusion framework integrated with local density peaks. The proposed multi-focus image fusion method consists of three steps. First, source images are split into small image patches, then the split image patches are classified into a few groups by local density peaks clustering. Next, the grouped image patches are used for sub-dictionary learning by stochastic coordinate coding. The trained sub-dictionaries are combined into a dictionary for sparse representation. Finally, the simultaneous orthogonal matching pursuit (SOMP) algorithm is used to carry out sparse representation. After the three steps, the obtained sparse coefficients are fused following the max L1-norm rule. The fused coefficients are inversely transformed to an image by using the learned dictionary. The results and analyses of comparison experiments demonstrate that fused images of the proposed method have higher qualities than existing state-of-the-art methods. Full article

The synchronization problem is studied in this paper for a class of fractional-order chaotic neural networks. By using the Mittag-Leffler function, M-matrix and linear feedback control, a sufficient condition is developed ensuring the synchronization of such neural models with the Caputo fractional derivatives. The synchronization condition is easy to verify, implement and only relies on system structure. Furthermore, the theoretical results are applied to a typical fractional-order chaotic Hopfield neural network, and numerical simulation demonstrates the effectiveness and feasibility of the proposed method. Full article

A wireless e-nose network system is developed for the special purpose of monitoring odorant gases and accurately estimating odor strength in and around livestock farms. This system is to simultaneously acquire accurate odor strength values remotely at various locations, where each node is an e-nose that includes four metal-oxide semiconductor (MOS) gas sensors. A modified Kalman filtering technique is proposed for collecting raw data and de-noising based on the output noise characteristics of those gas sensors. The measurement noise variance is obtained in real time by data analysis using the proposed slip windows average method. The optimal system noise variance of the filter is obtained by using the experiments data. The Kalman filter theory on how to acquire MOS gas sensors data is discussed. Simulation results demonstrate that the proposed method can adjust the Kalman filter parameters and significantly reduce the noise from the gas sensors. Full article