Linearly tapered meander line broadband monopole tag antenna with T‐match for Ultra High Frequency Radio Frequency Identification applications

In this paper, a compact, broadband linearly tapered meandered monopole tag antenna for UHF‐RFID is designed and optimized using particle swarm optimization (PSO) algorithm. An inductive T‐match network is utilized for impedance matching with capacitive Higgs‐4 chip. The optimization goal of PSO was conjugate matching and in consequence the maximization of read range. Equivalent circuit of the proposed tag antenna is derived using ADS software to validate its impedance characteristics. The performance of the proposed tag in terms of tag power sensitivity, read range, realized gain, and differential radar cross section has been experimentally characterized. To check the tolerance of the designed tag to various object platforms, its read range performance is also verified on objects like wood, fiber, plastic, and so forth. Furthermore, read pattern of the proposed tag has been measured and found to have figure of eight in E‐plane and omnidirectional in H‐plane. Experimental results reveal that the proposed tag covers 865‐867 MHz (ETSI band, Europe) and 902‐928 MHz (FCC band, United States) both major RFID bands with a read range of 10 and 12 m, respectively. The proposed tag has 2060 mm³ of volumetric size with maximum measured readable distance of 12 m with EIRP of 3.28 W.     

An efficient method for PET image denoising by combining multi-scale transform and non-local means

Multimedia Tools and Applications - The diagnosis of dementia, particularly in the early stages is very much helpful with Positron emission tomography (PET) image processing. The most important...     

Graph-Based Transfer Function for Volume Rendering

A good transfer function in volume rendering requires careful consideration of the materials present in a volume. A manual creation is tedious and prone to errors. Furthermore, the user interaction to design a higher dimensional transfer function gets complicated. In this work, we present a graph-based approach to design a transfer function that takes volumetric structures into account. Our novel contribution is in proposing an algorithm for robust deduction of a material graph from a set of disconnected edges. We incorporate stable graph creation under varying noise levels in the volume. We show that the deduced material graph can be used to automatically create a transfer function using the occlusion spectrum of the input volume. Since we compute material topology of the objects, an enhanced rendering is possible with our method. This also allows us to selectively render objects and depict adjacent materials in a volume. Our method considerably reduces manual effort required in designing a transfer function and provides an easy interface for interaction with the volume.     

Design and Analysis of MIMO Antenna with High Isolation and Dual Notched Band Characteristics for Wireless Applications

A very compact Superwideband multiple-input–multiple-output antenna with dual notched band characteristics is presented. Superwideband characteristics is obtained by means of radiating patch and high isolation between two input ports are obtained by using T-shaped stub in ground plane. Two rejection bands (wireless interoperability for microwave access (WiMAX)/C-band and wireless local area network) are obtained by etching two elliptical slots on radiating patch. Antenna offers large measured useable bandwidth of 2.60–20.04 GHz. Diversity performance is studied in terms of envelope correlation coefficient, diversity gain and total active reflection coefficient. Antenna also offers desirable radiation pattern, gain and radiation efficiency which makes proposed antenna quite suitable for different wireless applications.

     

Mobility-Aware and Radio Characteristics Enabled Routing for Video Streaming over MANETs

Wireless Personal Communications - Providing an adequate level of quality-of-experience (QoE) for multimedia applications in mobile ad-hoc networks (MANETs) is a challenging task due to its...     

Visual Speech Recognition Using Optical Flow and Hidden Markov Model

Wireless Personal Communications - The present work proposes audio-visual speech recognition with the use of Gammatone frequency cepstral coefficient (GFCC) and optical flow (OF) features with...     

Improved visual SLAM: a novel approach to mapping and localization using visual landmarks in consecutive frames

Pathfinding is becoming more and more common in autonomous vehicle navigation, robot localization, and other computer vision applications. In this paper, a novel approach to mapping and localization is presented that extracts visual landmarks from a robot dataset acquired by a Kinect sensor. The visual landmarks are detected and recognized using the improved scale-invariant feature transform (I-SIFT) method. The methodology is based on detecting stable and invariant landmarks in consecutive (red-green-blue depth) RGB-D frames of the robot dataset. These landmarks are then used to determine the robot path, and a map is constructed by using the visual landmarks. A number of experiments were performed on various datasets in an indoor environment. The proposed method performs efficient landmark detection in various environments, which includes changes in rotation and illumination. The experimental results show that the proposed method can solve the simultaneous localization and mapping (SLAM) problem using stable visual landmarks, but with less computation time.     

Classifying and Summarizing Information from Microblogs During Epidemics

During a new disease outbreak, frustration and uncertainties among affected and vulnerable population increase. Affected communities look for known symptoms, prevention measures, and treatment strategies. On the other hand, health organizations try to get situational updates to assess the severity of the outbreak, known affected cases, and other details. Recent emergence of social media platforms such as Twitter provide convenient ways and fast access to disseminate and consume information to/from a wider audience. Research studies have shown potential of this online information to address information needs of concerned authorities during outbreaks, epidemics, and pandemics. In this work, we target three types of end-users (i) vulnerable population—people who are not yet affected and are looking for prevention related information (ii) affected population—people who are affected and looking for treatment related information, and (iii) health organizations—like WHO, who are interested in gaining situational awareness to make timely decisions. We use Twitter data from two recent outbreaks (Ebola and MERS) to build an automatic classification approach useful to categorize tweets into different disease related categories. Moreover, the classified messages are used to generate different kinds of summaries useful for affected and vulnerable communities as well as health organizations. Results obtained from extensive experimentation show the effectiveness of the proposed approach.     

Community Trolling: An Active Learning Approach for Topic Based Community Detection in Big Data

Community detection plays an important role in creation and transfer of information. Active learning has been employed recently to improve the performance of community detection techniques. Active learning provides a semi-automatic approach in a selective sampling of data. Based on this, a community trolling approach for topic based community detection in big data is proposed. Community trolling selectively samples the data relevant to the current context from polluted big data using active learning. Fine-tuned data is then used to study community and its sub-communities. Community trolling as a precursor to community detection leads to a reduction of the huge unreliable dataset into a reliable dataset and results in the better prediction of community elements such as important topics and important entities. Finally, the effectiveness of approach was evaluated by implementing it on a real world Tumbler dataset. The results illustrate that community trolling provides a richer dataset resulting in more appropriate communities.