Using mobile node speed changes for movement direction change prediction in a realistic category of mobility models

In order to evaluate performance of protocols for ad hoc networks, the protocols have to be tested under realistic conditions. These conditions may include a reasonable transmission range, a limited buffer size, and realistic movement of mobile users (mobility models). In this paper, we propose a new and realistic type of random mobility models in which the mobile node has to decelerate to reach the point of direction change and accelerates with a defined acceleration to reach its intended speed. This realistic mobility model is proposed based on random mobility models. In reality, mobile objects tend to change their speed when they are going to change their direction, i.e. decelerate when approaching a direction change point and accelerate when they start their movement in a new direction. Therefore, in this paper, we implement this behavior in random mobility models which lack such specification. In fact, this paper represents our effort to use this accelerated movement to anticipate a probable direction change of a mobile node with reasonable confidence. The simulation type of this paper is based on traces produced by a mobility trace generator tool. We use a data mining concept called association rule mining to find any possible correlations between accelerated movement of mobile node and the probability that mobile node wants to change its direction. We calculate confidence and lift parameters for this matter, and simulate this mobility model based on random mobility models. These simulations show a meaningful correlation between occurrence of an accelerated movement and event of mobile node's direction change.     

Anticipating infectious disease re-emergence and elimination: a test of early warning signals using empirically based models

Timely forecasts of the emergence, re-emergence and elimination of human infectious diseases allow for proactive, rather than reactive, decisions that save lives. Recent theory suggests that a generic feature of dynamical systems approaching a tipping point—early warning signals (EWS) due to critical slowing down (CSD)—can anticipate disease emergence and elimination. Empirical studies documenting CSD in observed disease dynamics are scarce, but such demonstration of concept is essential to the further development of model-independent outbreak detection systems. Here, we use fitted, mechanistic models of measles transmission in four cities in Niger to detect CSD through statistical EWS. We find that several EWS accurately anticipate measles re-emergence and elimination, suggesting that CSD should be detectable before disease transmission systems cross key tipping points. These findings support the idea that statistical signals based on CSD, coupled with decision-support algorithms and expert judgement, could provide the basis for early warning systems of disease outbreaks.     

Integration of beamforming and uncertainty-of-observation techniques for robust ASR in multi-source environments

This paper presents a new approach for increasing the robustness of multi-channel automatic speech recognition in noisy and reverberant multi-source environments. The proposed method uses uncertainty propagation techniques to dynamically compensate the speech features and the acoustic models for the observation uncertainty determined at the beamforming stage. We present and analyze two methods that allow integrating classical multi-channel signal processing approaches like delay and sum beamformers or Zelinski-type Wiener filters, with uncertainty-of-observation techniques like uncertainty decoding or modified imputation. An analysis of the results on the PASCAL-CHiME task shows that this approach consistently outperforms conventional beamformers with a minimal increase in computational complexity. The use of dynamic compensation based on observation uncertainty also outperforms conventional static adaptation with no need of adaptation data.     

A novel frequency-selective metamaterial to improve helix antenna

A novel frequency-selective metamaterial with negative permittivity and permeability for improving directivity and gain of a helix antenna is presented in this paper.The proposed metamaterial is composed of two Z-shape resonators printed on opposite sides of a dielectric substrate.Two forms of multilayered cells are found to be suitable for antennas and waveguides applications.In addition,a new method of designing a metamaterial-based helix antenna is presented with high directivity and gain.A comparison on radiation properties is given between the conventional and the new metamaterial-based helix antennas.Two comparisons on radiation properties are performed:(1) the effect of proposed Z-structure on monopole,dipole,and helix antennas;(2) the effect of OE3,split-ring resonator (SRR),and proposed Z-structure unit cells on the performance of helix antennas.The results show improvement of parameters such as directivity,gain,and radiation power of the new metamaterial-based helix antenna.Therefore,the combination of Z-structure with the helix antenna shows the best performance.     

Compact bilayer substrate integrated waveguide leaky wave antenna with dumbbell‐shaped slot based on the TE20 mode

In this article, a compact dual layer leaky wave antenna array is simulated and constructed using the substrate integrated waveguide (SIW) based on the TE₂₀ mode at the X‐ and Ku‐bands. The proposed antenna is designed by creating dumbbell‐shaped slots on the upper layer of the SIW. These slots have increased the antenna bandwidth so that the proposed antenna has a bandwidth of 9.5 to 13.7 GHz and a fractional bandwidth of 36%. In addition, to excite the TE₂₀ mode, an SIW power divider is used in the feeding network of the antenna located in the bottom layer. Moreover, the gain and directivity are other advantages of the proposed antenna so that at 12.5 GHz the antenna peak gain reaches to 15.7 dB. Antenna beam scanning angle is from 5° to 81°. This antenna is simulated and analyzed by the CST Microwave Studio software. The obtained results from the antenna test lab confirm the simulation results.     

Experimental optimization studies on steel core lengths in buckling restrained braces

Concentric braced frames are commonly used in steel structures to withstand lateral forces. However, brace hysteretic behavior is asymmetric in tension and compression. These frames typically exhibit substantial strength deterioration when loaded in compression. Consequently, the concept of Buckling Restrained Brace (BRB) has been introduced to overcome this deficiency.The length of a BRB steel core could have a significant effect on its overall behavior since it directly influences the energy dissipation of the member. Furthermore, BRB could be effectively utilized as a damper to dissipate seismic input energy, particularly when used as a fuse within the brace in a frame. Modified BRBs with shorter lengths may be called damper BRBs, because they function as if a typical damper is utilized along any brace member. These damper BRBs are fabricated and tested in this study both experimentally and analytically. Useful insights into local retrofitting of core plates and connections are also provided.     

SIW Corporate-Feed Network for Circular Polarization Slot Array Antenna

Wireless Personal Communications - In this paper, multilayer feeding network for linear slot array antenna is studied. A substrate integrated waveguide (SIW) corporate-feed network for linear slot...     

Comparative evaluation of back-propagation neural network learning algorithms and empirical correlations for prediction of oil PVT properties in Iran oilfields

This paper presents a new approach to improve the performance of neural network method to PVT oil properties prediction. The true value of PVT properties which is determined based on the accurate data is a challenge of the petroleum industry. The main goal of the following investigation would be the performance comparison of various back-propagation learning algorithms in neural network that could be applied for PVT prediction. Up to now, no procedure has been presented to determine the network structure for some complicated cases, therefore; design and production of neural network would be almost dependent on the user's experience. To prevent this problem, neural network based recommended procedure in this study was applied to present the advantages. To show the performance of this procedure, several learning algorithms were investigated for comparison. One of the most common problems in neural network design is the topology and the parameter value accuracy that if those elements selection was correctly and optimally, the designer would achieve better results. Since, fluids of different regions have varying hydrocarbon properties, therefore, the empirical correlations in different hydrocarbon systems should be investigated to find their accuracies and limitations. In this study, an investigation of different empirical correlations along with the artificial neural networks in Iran oilfields has been presented. Then, the new model of artificial neural network for prediction of PVT oil properties in Iran crude oil presented. To test this new method, it was evaluated by collecting dataset from 23 different oilfields in Iran (south, central, western and continental shelf). In this study, two networks for prediction of bubble point pressure values (P_b) and the oil formation volume factor at bubble point (B_ob) were designed. The parameters and topology of the optimum neural networks were determined and in order to consider the effect of these networks designing on results, their performances were compared with various empirical correlations. According to comparison between the obtained results, it shows that the improved method presented has better performance rather than empirical and current methods in neural network designing in petroleum applications for these predictions.     

Fuzzy automated visual broken edge detection

This paper describes an automated machine vision-based inspection method for fast and accurate detection of broken edges on the machined surface of water pumps which are formed during casting. The paper proposes a collection of steps to inspect images taken in a noisy environment to identify broken edges. By developing three broken edge verification features and using fuzzy C-means clustering, we provide a fuzzy broken edge inspection model that classifies water pumps into three classes: pumps with significant (major) broken edges, pumps with insignificant (minor) broken edges, and pumps with no broken edges. The devised machine vision-based inspection method is efficient in terms of processing time and accuracy to recognize the parts with broken edges and to make decisions consistent with human knowledge. The fuzzy broken edge detection was carried out on a database of 150 gray-level images. The developed method properly identifies about 95 % of the broken edges in the entire database.     

A CPW-fed wearable antenna at ISM band for biomedical and WBAN applications

Wireless Networks - In this paper, a Mercedes-Benz logo antenna with a metal plate located at an optimized distance from the proposed antenna is introduced as a wearable antenna to operate in the...