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...     

An Experimental Evaluation of Link Outage Due to Beam Wander in a Turbulent FSO Link

Wireless Personal Communications - Wireless communication using free space optics (FSO) is becoming attractive for data transmission purposes. However, the system performance gets affected by...     

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.     

Learning with rationales for document classification

We present a simple and yet effective approach for document classification to incorporate rationales elicited from annotators into the training of any off-the-shelf classifier. We empirically show on several document classification datasets that our classifier-agnostic approach, which makes no assumptions about the underlying classifier, can effectively incorporate rationales into the training of multinomial naïve Bayes, logistic regression, and support vector machines. In addition to being classifier-agnostic, we show that our method has comparable performance to previous classifier-specific approaches developed for incorporating rationales and feature annotations. Additionally, we propose and evaluate an active learning method tailored specifically for the learning with rationales framework.     

Dual‐band circularly polarized dielectric resonator antenna for wireless applications

This letter investigates an integrated antenna configuration for WLAN/WiMAX applications. The proposed composite antenna configuration is simply the grouping of ring dielectric resonator along with reformed square‐shaped slot antenna. Three significant characteristics of proposed article are: (1) aperture act as magnetic dipole and excite HE_11δ mode in ring dielectric resonator antenna; (2) reforming of square aperture generates orthogonal modes in ring DRA and creates CP in lower frequency band; (3) annular‐shaped Microstrip line along with reformed square aperture creates CP wave in upper frequency band. With the purpose of certifying the simulated outcomes, prototype of proposed structure is fabricated and tested. Good settlement is to be got between experimental and software generated outcome. Experimental outcomes show that the proposed radiating structure is operating over 2 frequency bands that is, 2.88‐3.72 and 5.4‐5.95 GHz. Measured 3‐dB axial ratio bandwidth in lower and upper frequency band is approximately 9.52% (3.0‐3.4 GHz) and 5.85% (5.64‐5.98 GHz), respectively. These outcomes indicate that the proposed composite antenna structure is appropriate for WLAN and WiMAX applications.     

A policy based framework for quality of service management in software defined networks

Growth in multimedia traffic over the Internet increases congestion in the network architecture. Software-Defined Networking (SDN) is a novel paradigm that solves the congestion problem and allows the network to be dynamic, intelligent, and it centrally controls the network devices. SDN has many advantages in comparison to traditional networks, such as separation of forwarding and control plane from devices, global centralized control, management of network traffic. We design a policy-based framework to enhance the Quality of Service (QoS) of multimedia traffic flows in a potential SDN environment. We phrase a max-flow-min-cost routing problem to determine the routing paths and presented a heuristic method to route the traffic flows in the network in polynomial time. The framework monitors the QoS parameters of traffic flows and identifies policy violations due to link congestion in the network. The introduced approach dynamically implements policy rules to SDN switches upon detection of policy violations and reroutes the traffic flows. The results illustrate that the framework achieves a reduction in end-to-end delay, average jitter, and QoS violated flows by 24%, 37%, and 25%, respectively, as compared to the Delay Minimization method. Furthermore, the proposed approach has achieved better results when compared to SDN without policy-based framework and reduced end-to-end delay, average jitter, and QoS violated flows by 51%, 62%, and 28%, respectively.