Compact UWB-MIMO Triple Notched Antenna for Isolation Reduction

Wireless Personal Communications - In this article compact multiple input multiple output (MIMO) antenna with triple notch at the ultra-wideband application having two identical monopole antennas...     

SIP and SDP based content adaptation during real-time video streaming in Future Internets

Multimedia Tools and Applications - Future Internet technology supports content adaptation to improve the QoS / QoE in a heterogeneous environment. The adaptation process removes partial data to...     

Ear Field Adaptive Noise Control for Snoring: An Real-time Experimental Approach

In this paper, an active noise control (ANC) system is developed to provide an effective and non-intrusive solution for reducing loud snoring to provide a quiet environment for a snorer's bed partner. An adaptive least mean square (LMS) algorithm optimized for different kinds of snore signals is introduced and theoretically analyzed. Also, a residual noise masking approach is proposed to further reduce the effect of the snore noise without interfering with the LMS algorithm. Computer simulations followed by real-time experiments are conducted to demonstrate the feasibility of the snore ANC systems based on a pillow setup. For the optimum effect based on the characteristics of human hearing, the performance of the proposed approach is evaluated by using the multi-channel feedforward ANC systems based on the filtered-X least mean square (FXLMS) algorithm. Compared with a traditional headboard setup for snoring noise control, the proposed snore ANC systems optimized for ear field operation yield much higher noise reduction around the ears of the snorer's bed partner.      

Multi-characteristic optimization of wax patterns in the investment casting process using grey–fuzzy logic

The present investigation focuses on optimizing the injection process parameters with multiple performance characteristics in the investment casting process using the orthogonal array with grey–fuzzy logics. A grey–fuzzy reasoning grade obtained from the grey–fuzzy logics analysis is used as a performance index to determine the optimal injection process parameters. The selected injection process parameters are injection temperature, injection time, and injection pressure, while the considered performance characteristics are linear shrinkage and surface finish. The response table, response graph, and analysis of variance are used to find the optimal setting and the influence of injection process parameters on the multiple performance characteristics. The results of confirmation experiments reveal that grey–fuzzy logics can effectively acquire an optimal combination of the process parameters. Hence, quality of wax patterns in the investment casting process can be significantly improved through this approach.     

Experimental determination of dynamic parameters of robotic arms

This article presents a practical method for determining the dynamic parameters of robotic arms. The two unique features of the method are that it determines the friction parameters along with the inertia and mass parameters and that it does not require the measurement of joint accelerations. The method is demonstrated experimentally by determining the dynamic parameters of a closed-chain direct-drive arm. The precision of the determined parameter was verified in the context of computed torquebased controllers. Statistical analysis also shows that the estimated values are reliable. © 1993 John Wiley & Sons, Inc.     

Prevention of defects in castings using back propagation neural networks

Defects in castings often lead to rejection, which would ultimately result in loss of productivity for a foundry. Expert systems developed by some researchers mostly act as postmortem tools, discussing and analyzing a defect after it has occurred. Though some investigators have attempted to predict a few important defects, a tool that could predict all the possible defects just before the castings are made has not yet been developed. Hence in the present work, an attempt has been made to predict major casting defects like cracks, misruns, scabs, blowholes and air-locks using back-propagation neural networks from the data collected from a steel foundry. The neural network was trained with parameters like green compression strength (GCS), green shear strength (GSS), permeability, moisture percent, composition of the charge and melting conditions as inputs and the presence/absence of defects as outputs. After the training was over, the set of inputs of the casting that is going to be made was fed to the network and the network could predict whether the casting would be sound or defective. If defective, the nature of the defect was also specified by the neural network. The neural network could predict cracks, misruns and air-locks accurately in most of the cases. The neural network could also predict other defects successfully. Investigating the causes followed by altering the moulding parameters and appropriate treatment of the molten metal can prevent the defects that were predicted by the backpropagation neural network.     

Influence of MgO addition on mechanical and ablation characteristics of ZrB2–SiC composites developed through microwave sintering

Journal of Materials Science - Zirconium diboride (ZrB2) is a credible candidate for hypersonic aerospace applications owing to magnificent characteristics such as higher melting point temperature,...     

Motion-compensated temporal filtering with optimized motion estimation

The lifting-based discrete wavelet transforms applied temporally along the motion trajectory is a powerful tool in scalable video compression. The longer filter is used for motion-compensated temporal filtering (MCTF) for better de-correlation. The bidirectional motion estimation (ME) used for MCTF is the major speed bottleneck. This paper propose two stages motion predictive technique to optimize the ME. In the first stage, zero motion block prediction is carried out using the content of high frequency frame, obtained after TF without motion compensation. In the second stage, the inter-frame and inter-layer prediction technique is applied to skip the coarse search of any fast ME, except for the first pair of frames in a group of pictures. The proposed algorithm is implemented in MC-EZBC. The variations observed in objective or subjective quality of the decoded video is negligible compared to the speed gained.     

Expanding user’s query with tag-neighbors for effective medical information retrieval

Medical information is a natural human demand. Existing search engines on the Web often are unable to handle medical search well because they do not consider its special requirements. Often a medical information searcher is uncertain about his exact questions and unfamiliar with medical terminology. Under-specified queries often lead to undesirable search results that do not contain the information needed. To overcome the limitations of under-specified queries, we utilize tags to enhance information retrieval capabilities by expanding users’ original queries with context-relevant information. We compute a set of significant tag neighbor candidates based on the neighbor frequency and weight, and utilize the qualified tag neighbors to expand an entry query. The proposed approach is evaluated by using MedWorm medical article collection and results show considerable precision improvements over state-of-the-art approaches.