Connectivity-aware minimum-delay geographic routing with vehicle tracking in VANETs

In this paper, we propose the connectivity-aware minimum-delay geographic routing (CMGR) protocol for vehicular ad hoc networks (VANETs), which adapts well to continuously changing network status in such networks. When the network is sparse, CMGR takes the connectivity of routes into consideration in its route selection logic to maximize the chance of packet reception. On the other hand, in situations with dense network nodes, CMGR determines the routes with adequate connectivity and selects among them the route with the minimum delay. The performance limitations of CMGR in special vehicular networking situations are studied and addressed. These situations, which include the case where the target vehicle has moved away from its expected location and the case where traffic in a road junction is so sparse that no next-hop vehicle can be found on the intended out-going road, are also problematic in most routing protocols for VANETs. Finally, the proposed protocol is compared with two plausible geographic connectivity-aware routing protocols for VANETs, A-STAR and VADD. The obtained results show that CMGR outperforms A-STAR and VADD in terms of both packet delivery ratio and ratio of dropped data packets. For example, under the specific conditions considered in the simulations, when the maximum allowable one-way transmission delay is 1 min and one gateway is deployed in the network, the packet delivery ratio of CMGR is approximately 25% better than VADD and A-STAR for high vehicle densities and goes up to 900% better for low vehicle densities.     

In situ preparation and property investigation of polypropylene/fumed silica nanocomposites

We present the preparation of polypropylene (PP)/fumed silica (FS) nanocomposites via in situ polymerization in this article. The approach includes preparation and utilization of a bisupported Ziegler–Natta catalytic system in which magnesium ethoxide and FS are used as conjugate supports of the catalyst. Catalyst preparation and polymerization processes are carried out in the slurry phase and under argon atmosphere. Scanning electron microscopy images show a good dispersion of the FS throughout the PP matrix. Results from differential scanning calorimetry reveal that the crystallization temperature of prepared nanocomposites increases by increasing FS loading. Also, crystal content of nanocomposites increases as the FS concentration increases up to 3.48 wt%. Nanocomposites containing <3.14 wt% of nanoparticles do not show considerable change in their melting point where with more increment in filler concentration, melting temperature slightly increases. Thermogravimetric analysis shows a considerable improvement in the thermal stability of PP/FS nanocomposites compared to pure PP. Rheological studies indicate that the incorporation of FS into PP matrix results in increment in storage modulus, loss modulus, and complex viscosity of polymeric matrix, particularly in low frequency region. By increasing FS loading, the PP/FS nanocomposites show a transition from liquid‐like to solid‐like viscoelasticity behavior depicting microstructural changes in their structures. POLYM. COMPOS., 35:37–44, 2014. © 2013 Society of Plastics Engineers     

Comparative Performance Evaluation of RF Localization for Wireless Capsule Endoscopy Applications

This paper presents an assessment of the accuracy of cooperative localization of a wireless capsule endoscope (WCE) using radio frequency (RF) signals with particular emphasis on localization inside the small intestine. We derive the Cramer–Rao lower bound (CRLB) for cooperative location estimators using the received signal strength (RSS) or the time of arrival (TOA) of the RF signal. Our derivations are based on a three-dimension human body model, an existing model for RSS propagation from implanted organs to the body surface and a new TOA ranging error model for the effects of non-homogeneity of the human body on TOA of the RF signals. Using models for RSS and TOA errors, we first calculate the 3D CRLB bounds for cooperative localization of the WCE in three major digestive organs in the path of GI tract: the stomach, the small intestine and the large intestine. Then we analyze the performance of localization techniques on a typical path inside the small intestine. Our analysis includes the effects of the number of external sensors, the external sensor array topology, number of WCEs used in cooperation and the random variations in the transmitted power from the capsule.     

Color classification using margin-setting with ellipsoids

This paper presents a robust and computationally efficient genetic algorithm for color classification. It designs well-fitted color space prolate spheroids (ellipsoids) that envelop the training pixels. The ellipsoids are then used to classify unlabeled image pixels in accordance with their color, in order to partition the image. The color classification algorithm described here has very low error rates, boasts very high operational speed, and permits trading higher indecision rates for lower rates of misclassification. The performance of the color classifier developed in this paper is compared with those of the support vector machine (SVM) and the nearest-neighbor (kNN) classifiers. It has been shown that our color classifier outperforms SVM and kNN for partitioning of color images that contain several closely spaced color classes. It has higher correct classification, lower misclassification, and significantly reduced operational latency in comparison with color classifiers based on kNN and SVM.     

Synthesis of System Dynamics Tools for Holistic Conceptualization of Water Resources Problems

Out-of-context analysis of water resources systems can result in unsustainable management strategies. To address this problem, systems thinking seeks to understand interactions among the subsystems driving a system??s overall behavior. System dynamics, a method for operationalizing systems thinking, facilitates holistic understanding of water resources systems, and strategic decision making. The approach also facilitates participatory modeling, and analysis of the system??s behavioral trends, essential to sustainable management. The field of water resources has not utilized the full capacity of system dynamics in the thinking phase of integrated water resources studies. We advocate that the thinking phase of modeling applications is critically important, and that system dynamics offers unique qualitative tools that improve understanding of complex problems. Thus, this paper describes the utility of system dynamics for holistic water resources planning and management by illustrating the fundamentals of the approach. Using tangible examples, we provide an overview of Causal Loop and Stock and Flow Diagrams, reference modes of dynamic behavior, and system archetypes to demonstrate the use of these qualitative tools for holistic conceptualization of water resources problems. Finally, we present a summary of the potential benefits as well as caveats of qualitative system dynamics for water resources decision making.     

A CDMA interference canceling receiver with an adaptive blind array

Interference cancelling receivers have been suggested as low complexity multiuser receivers for code division multiple access (CDMA) systems. A multi-element interference cancelling receiver is proposed, and it is demonstrated that using spatial information about the users will improve the performance of the receiver. Two blind algorithms are suggested to adaptively combine the outputs of the antenna elements. The performances of these algorithms are compared, and it is shown that without requiring any additional information, the receiver can spatially discriminate between the users and improve the error performance     

Combinatorial Methods for the Formation of Sparse Flexibility Matrices

Two algorithms are designed for the formation of cycle bases of the graph models of rigid–jointed skeletal structures leading to sparse flexibility matrices. These combinatorial methods employ the Greedy Algorithm for the formation of suboptimal cycle bases with the least possible lengths.     

Development of an artificial neural network model for prediction of cell voltage and current efficiency in a chlor-alkali membrane cell

This paper presents the development of an artificial neural network (ANN) model for the prediction of cell voltage and caustic current efficiency (CCE) versus various operating parameters in a lab scale chlor-alkali membrane cell. In order to validate the model predictions, the effects of various operating parameters on the cell voltage and current efficiency of the membrane cell were experimentally studied. The membrane cell incorporated a standard DSA/Cl₂ electrode as the anode, a nickel electrode as the cathode and a Flemion 892 polymer film as the membrane. Each of the six process parameters including anolyte pH (2–5), operating temperature (25–90 °C), electrolyte velocity (2.2–5.9 cm/s), brine concentration (200–300 g/L), current density (1–4 kA/m²), and run time were thoroughly studied at four levels and low caustic concentrations (5–22 g/L). The predictions of ANN model as well as those from other statistical methods were evaluated versus the measured values of cell voltages.

The developed ANN model is not only capable to predict the cell voltage and caustic current efficiency but also to reflect the impacts of process parameters on the same functions. The predicted cell voltages and current efficiencies using ANN modeling were found to be close to the measured values, particularly at higher current densities.