Priority-based rate control for service differentiation and congestion control in wireless multimedia sensor networks

In wireless multimedia sensor networks (WMSNs) a sensor node may have different types of sensor which gather different kinds of data. To support quality of service (QoS) requirements for multimedia applications having a reliable and fair transport protocol is necessary. One of the main objectives of the transport layer in WMSNs is congestion control. We observe that the information provided may have different levels of importance and argue that sensor networks should be willing to spend more resources in disseminating packets carrying more important information. Some applications of WMSNs may need to send real time traffic toward the sink node. This real time traffic requires low latency and high reliability so that immediate remedial and defensive actions can be taken when needed. Therefore, similar to wired networks, service differentiation in wireless sensor networks is also an important issue. We present a priority-based rate control mechanism for congestion control and service differentiation in WMSNs. We distinguish high priority real time traffic from low priority non-real time traffic, and service the input traffic based on its priority. Simulation results confirm the superior performance of the proposed model with respect to delays, delay variation and loss probability.     

Retention of Antioxidant Capacity of Vacuum Microwave Dried Cranberry

ABSTRACT: In this study, cranberries were dried by vacuum-microwave drying (VMD), freeze-drying (FD), or hot air-drying (AD), to compare the effects of different drying processes on both physical changes as well as the retention of bioactive components in dried samples. Total porosity (%) and average pore radius of dehydrated cranberries were greater using VMD compared to FD and AD (P < 0.05). Crude methanol cranberry powdered extracts were fractionated by solid phase extraction (SPE) into organic acid-, total phenolics-, anthocyanin-, or proanthocyanidin-enriched extracts, respectively. The chemical composition of the 60% acidified methanol fractions contained cyanidin-3-galactoside, cyanidin-3-arabinoside, peonidin-3-galactoside, and peonidin-3-arabinoside, as assessed by HPLC. Antioxidant activities of cranberry fractions were measured using chemical ORAC and ABTS methods. The 60% acidified methanol fraction had a significantly higher (P < 0.05) antioxidant potential than the other chemical fractions, which was largely attributed to the relatively higher anthocyanin content. In general, vacuum-microwave drying and freeze-drying resulted in similar retention of anthocyanins and antioxidant activity, which were both relatively higher (P < 0.05) than that recovered from cranberries dried by hot air drying.     

A review on the application of structured sparse representation at image annotation

The increasing number of images on the Web and other information environments, needs efficient management and suitable retrieval especially by computers. Image annotation is a process which produces words for a digital image based on its content. Users prefer an image search based on text queries and keywords which has increased the use of image annotation. In this paper, we discuss the applicability of structured sparse representations at image annotation. First the components of image annotation and sparse representation are reviewed. Then, we survey the structure of sparse representation based on the image annotation algorithms. Next, the comparison of algorithm has been presented. Finally the paper concludes with some major challenges and open issues in image annotation using structured sparse representations.     

Effect of polyethylenimine addition and washing on stability and electrophoretic deposition of Co3O4 nanoparticles

In this work, the parameters of cobalt oxide suspension such as conductivity, zeta potential, particle size, stability, and finally the electrophoretic behavior of particles in the absence and presence of polyethylenimine (PEI) in acetone medium were investigated. Also, the effects of washing on the stability and electrophoretic deposition of Co₃O₄ were studied. Characterization of the obtained layer by optical microscopy revealed that there was no deposition in the suspension without PEI, while a uniform layer was formed in the presence of PEI additive. Scanning electron microscopy (SEM) results confirmed the uniformity of layer obtained in acetone using PEI additive. Moreover, SEM results demonstrated that more porous microstructures were obtained at longer deposition durations. The difference in the porosity of the layers, as indicated by the SEM micrographs, is attributed to increase in the deposition time.     

An energy-aware distributed clustering protocol in wireless sensor networks using fuzzy logic

Clustering is an effective approach for organizing a network into a connected hierarchy, load balancing, and prolonging the network lifetime. On the other hand, fuzzy logic is capable of wisely blending different parameters. This paper proposes an energy-aware distributed dynamic clustering protocol (ECPF) which applies three techniques: (1) non-probabilistic cluster head (CH) elections, (2) fuzzy logic, and (3) on demand clustering. The remaining energy of the nodes is the primary parameter for electing tentative CHs via a non-probabilistic fashion. A non-probabilistic CH election is implemented by introducing a delay inversely proportional to the residual energy of each node. Therefore, tentative CHs are selected based on their remaining energy. In addition, fuzzy logic is employed to evaluate the fitness (cost) of a node in order to choose a final CH from the set of neighboring tentative CHs. On the other hand, every regular (non CH) node elects to connect to the CH with the least fuzzy cost in its neighborhood. Besides, in ECPF, CH elections are performed sporadically (in contrast to performing it every round). Simulation results demonstrate that our approach performs better than well known protocols (LEACH, HEED, and CHEF) in terms of extending network lifetime and saving energy.     

A new reduced-reference image quality assessment based on the SVD signal projection

Multimedia Tools and Applications - A new image quality metric is proposed in this paper based on the degradation of structural information. It uses the singular value decomposition (SVD) as a...     

Improving image inpainting quality by a new SVD-based decomposition

Multimedia Tools and Applications - In this paper, we present a new algorithm for image inpainting using structure and texture information. Our image decomposition to texture and structure is...     

Ensemble-based clustering of large probabilistic graphs using neighborhood and distance metric learning

Graphs are commonly used to express the communication of various data. Faced with uncertain data, we have probabilistic graphs. As a fundamental problem of such graphs, clustering has many applications in analyzing uncertain data. In this paper, we propose a novel method based on ensemble clustering for large probabilistic graphs. To generate ensemble clusters, we develop a set of probable possible worlds of the initial probabilistic graph. Then, we present a probabilistic co-association matrix as a consensus function to integrate base clustering results. It relies on co-occurrences of node pairs based on the probability of the corresponding common cluster graphs. Also, we apply two improvements in the steps before and after of ensembles generation. In the before step, we append neighborhood information based on node features to the initial graph to achieve a more accurate estimation of the probability between the nodes. In the after step, we use supervised metric learning-based Mahalanobis distance to automatically learn a metric from ensemble clusters. It aims to gain crucial features of the base clustering results. We evaluate our work using five real-world datasets and three clustering evaluation metrics, namely the Dunn index, Davies–Bouldin index, and Silhouette coefficient. The results show the impressive performance of clustering large probabilistic graphs.