MULTIMEDIA NETWORKING A Leading-Edge Communication Technology

The realization of digital multimedia's full business potential requires networking. This article discusses the value-added benefits to the corporation of networked multimedia as well as the choice of architectures and technologies needed to deliver multimedia networking solutions.     

Metallic Nanoparticles in Heterogeneous Catalysis

Catalysis Letters - Heterogeneous catalysis is a chemical process achieved at solid–gas or solid–liquid interfaces. Many factors including the particle size, shape and metal-support...     

Generalized Bonferroni Mean Operator for Fuzzy Number Intuitionistic Fuzzy Sets and its Application to Multiattribute Decision Making

The Bonferroni mean (BM) was originally introduced by Bonferroni in 1950. A prominent characteristic of BM is its capability to capture the interrelationship between input arguments. This makes BM useful in various application fields, such as decision making, information retrieval, pattern recognition, and data mining. In this paper, we examine the issue of fuzzy number intuitionistic fuzzy information fusion. We first propose a new generalized Bonferroni mean operator called generalized fuzzy number intuitionistic fuzzy weighted Bonferroni mean (GFNIFWBM) operator for aggregating fuzzy number intuitionistic fuzzy information. The properties of the new aggregation operator are studied and their special cases are examined. Furthermore, based on the GFNIFWBM operator, an approach to deal with multiattribute decision‐making problems under fuzzy number intuitionistic fuzzy environment is developed. Finally, a practical example is provided to illustrate the multiattribute decision‐making process.     

Investigation on the cutting quality characteristics of abrasive water jet machining of AA6061-B4C-hBN hybrid metal matrix composites

Aluminum metal matrix composites (AMMCs) explicitly show better physical and mechanical properties as compared to aluminum alloys and results in a more preferred material for a wide range of applications. The addition of reinforcements embargo AMMCs employment to industry requirements by increasing order of machining complexity. However, it can be machined with a high order of surface integrity by nonconventional approaches like abrasive water jet machining. Hybrid aluminum alloy composites were reinforced by B₄C (5–15?vol%) and solid lubricant hBN (15?vol%) particles and fabricated using a liquid metallurgy route. This research article deals with the experimental investigation on the effect of process parameters such as mesh size, abrasive flow rate, water pressure and work traverse speed of abrasive water jet machining on hybrid AA6061-B₄C-hBN composites. Water jet pressure and traverse speed have been proved to be the most significant parameters which influenced the responses like kerf taper angle and surface roughness. Increase in reinforcement particles affects both the kerf taper angle and surface roughness. SEM images of the machined surface show that cutting wear mechanism was largely operating in material removal.     

DanVideo: an MPEG-7 authoring and retrieval system for dance videos

A well-annotated dance media is an essential part of a nation’s identity, transcending cultural and language barriers. Many dance video archives suffer from problems concerning authoring and access, because of the complex spatio-temporal relationships that exist between the dancers in terms of movements of their body parts and the emotions expressed by them in a dance. This paper presents a system named DanVideo for semi-automatic authoring and access to dance archives. DanVideo provides methods of annotation and authoring and retrieval tools for choreographers, dancers, and students. We demonstrate how dance media can be semantically annotated and how this information can be used for the retrieval of the dance video semantics. In particular, DanVideo offers an MPEG-7 based semi-automatic authoring tool that takes dance video annotations generated by dance experts and produces MPEG-7 metadata. DanVideo also has a search engine that takes users’ queries and retrieves dance semantics from metadata arranged using tree-embedding technique and based on spatial, temporal and spatio-temporal features of dancers. The search engine also leverages a domain-specific ontology to process knowledge-based queries. We have assessed the dance-video queries and semantic annotations in terms of precision, recall, and fidelity.     

Performance analysis of allocation policies for interGrid resource provisioning

Several Grids have been established and used for varying science applications during the last years. Most of these Grids, however, work in isolation and with different utilisation levels. Previous work has introduced an architecture and a mechanism to enable resource sharing amongst Grids. It has demonstrated that there can be benefits for a Grid to offload requests or provide spare resources to another Grid. In this work, we address the problem of resource provisioning to Grid applications in multiple-Grid environments. The provisioning is carried out based on availability information obtained from queueing-based resource management systems deployed at the provider sites which are the participants of the Grids. We evaluate the performance of different allocation policies. In contrast to existing work on load sharing across Grids, the policies described here take into account the local load of resource providers, imprecise availability information and the compensation of providers for the resources offered to the Grid. In addition, we evaluate these policies along with a mechanism that allows resource sharing amongst Grids. Experimental results obtained through simulation show that the mechanism and policies are effective in redirecting requests thus improving the applications’ average weighted response time.     

FireFly: a cross-layer platform for real-time embedded wireless networks

We propose a cross-layer approach with tightly-coupled time synchronization for real-time support and predictable lifetime in battery-operated sensor networks. Our design spans a sensor hardware platform with hardware-based global time synchronization, a TDMA link layer protocol with collision-free multi-hop support and node scheduling algorithms for maximum concurrency and streaming. Our dual-radio sensor platform, FireFly, features an IEEE 802.15.4 transceiver and supports global time synchronization indoors by using an AM radio carrier-current method and an atomic clock receiver for outdoors. A TDMA-based link protocol, RT-Link, leverages the hardware for fixed and mobile nodes with a near-optimal and predictable node lifetime of over 2 years. It outperforms comparable sensor network link protocols such as B-MAC and S-MAC in terms of end-to-end latency and throughput and node lifetime across all duty cycle ratios. Operating over RT-Link is MAX, a scheduling framework which offers optimal transmission concurrency and bandwidth management for networks with regular structure. Through analysis and experiments we show that global time sync is a robust, economical and scalable alternative to in-band software-based techniques. To illustrate the capabilities and flexibility of our platform, we describe our experiences with two-way voice streaming over multiple hops. We have deployed a 42-node network with sub-100 μs synchronization accuracy in the NIOSH experimental coal mine for people-tracking and voice communication.

An enhanced Support Vector Machine classification framework by using Euclidean distance function for text document categorization

This paper presents the implementation of a new text document classification framework that uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean distance function in the classification phase, coined as Euclidean-SVM. The SVM constructs a classifier by generating a decision surface, namely the optimal separating hyper-plane, to partition different categories of data points in the vector space. The concept of the optimal separating hyper-plane can be generalized for the non-linearly separable cases by introducing kernel functions to map the data points from the input space into a high dimensional feature space so that they could be separated by a linear hyper-plane. This characteristic causes the implementation of different kernel functions to have a high impact on the classification accuracy of the SVM. Other than the kernel functions, the value of soft margin parameter, C is another critical component in determining the performance of the SVM classifier. Hence, one of the critical problems of the conventional SVM classification framework is the necessity of determining the appropriate kernel function and the appropriate value of parameter C for different datasets of varying characteristics, in order to guarantee high accuracy of the classifier. In this paper, we introduce a distance measurement technique, using the Euclidean distance function to replace the optimal separating hyper-plane as the classification decision making function in the SVM. In our approach, the support vectors for each category are identified from the training data points during training phase using the SVM. In the classification phase, when a new data point is mapped into the original vector space, the average distances between the new data point and the support vectors from different categories are measured using the Euclidean distance function. The classification decision is made based on the category of support vectors which has the lowest average distance with the new data point, and this makes the classification decision irrespective of the efficacy of hyper-plane formed by applying the particular kernel function and soft margin parameter. We tested our proposed framework using several text datasets. The experimental results show that this approach makes the accuracy of the Euclidean-SVM text classifier to have a low impact on the implementation of kernel functions and soft margin parameter C.