Safe Autonomous Agent Formation Operations Via Obstacle Collision Avoidance

In this paper, we consider the problem of flocking and shape‐orientation control of multi‐agent systems with inter‐agent and obstacle collision avoidance. We first consider the problem of forcing a set of autonomous agents to form a desired formation shape and orientation while avoiding inter‐agent collision and collision with convex obstacles, and following a trajectory known to only one of the agents, namely the leader of the formation. Then we build upon the solution given to this problem and solve the problem of guaranteeing obstacle collision avoidance by changing the size and the orientation of the formation. Changing the size and the orientation of the formation is helpful when the agents want to go through a narrow passage while the existing size or orientation of the formation does not allow this. We also propose collision avoidance algorithms that temporarily change the shape of the formation to avoid collision with stationary or moving nonconvex obstacles. Simulation results are presented to show the performance of the proposed control laws.     

Short‐Time Linear Quadratic Form Technique for Estimating Fast‐Varying Parameters in Feedback Loops

The precision of a closed‐loop controller system designed for an uncertain plant depends strongly upon the maximum extent to which it is possible to track the trend of time‐varying parameters of the plant. The aim of this study is to describe a new parameter estimation algorithm that is able to follow fast‐varying parameters in closed‐loop systems. The short‐time linear quadratic form (STLQF) estimation algorithm introduced in this paper is a technique for tracking time‐varying parameters based on short‐time analysis of the regressing variables in order to minimize locally a linear quadratic form cost function. The established cost function produces a linear combination of errors with several delays. To meet this objective, mathematical development of the STLQF estimation algorithm is described. To implement the STLQF algorithm, the algorithm is applied to a planar mobile robot with fast‐varying parameters of inertia and viscous and coulomb frictions. Next, performance of the proposed algorithm is assessed against noise effects and variation in the type of parameters.     

Sound source localization using compressive sensing-based feature extraction and spatial sparsity

In this paper, we propose a source localization algorithm based on a sparse Fast Fourier Transform (FFT)-based feature extraction method and spatial sparsity. We represent the sound source positions as a sparse vector by discretely segmenting the space with a circular grid. The location vector is related to microphone measurements through a linear equation, which can be estimated at each microphone. For this linear dimensionality reduction, we have utilized a Compressive Sensing (CS) and two-level FFT-based feature extraction method which combines two sets of audio signal features and covers both short-time and long-time properties of the signal. The proposed feature extraction method leads to a sparse representation of audio signals. As a result, a significant reduction in the dimensionality of the signals is achieved. In comparison to the state-of-the-art methods, the proposed method improves the accuracy while the complexity is reduced in some cases.     

An enhanced motion estimation approach using a genetic trail bounded approximation for H.264/AVC codecs

Genetic algorithm-based motion estimation schemes play a significant role in improving the results of H.264/AVC standardization efforts when addressing conversational and non-conversational video applications. In this paper, we present a robust motion estimation scheme that uses a noble genetic trail bounded approximation (GTBA) approach to speed up the encoding process of H.264/AVC video compression and to reduce the number of bits required to code frame. The proposed algorithm is utilized to enhance the fitness function strength by integrating trail information of motion vector and sum of absolute difference (SAD) information into a fitness function. Experimental results reveal that the proposed GTBA resolves conflict obstacles with respect to both the number of bits required to code frames and the execution time for estimation.     

Behavioral Attestation for Web Services using access policies

Service Oriented Architecture with underlying technologies like web services and web service orchestration opens new vistas for integration among business processes operating in heterogeneous environments. However, such dynamic collaborations require a highly secure environment at each respective business partner site. Existing web services standards address the issue of security only on the service provider platform. The partner platforms to which sensitive information is released have till now been neglected. Remote Attestation is a relatively new field of research which enables an authorized party to verify that a trusted environment actually exists on a partner platform. To incorporate this novel concept in to the web services realm, a new mechanism called WS-Attestation has been proposed. This mechanism provides a structural paradigm upon which more fine-grained solutions can be built. In this paper, we present a novel framework, Behavioral Attestation for Web Services, in which XACML is built on top of WS-Attestation in order to enable more flexible remote attestation at the web services level. We propose a new type of XACML policy called XACML behavior policy, which defines the expected behavior of a partner platform. Existing web service standards are used to incorporate remote attestation at the web services level and a prototype is presented, which implements XACML behavior policy using low-level attestation techniques.     

An enhanced fuzzy c-means algorithm for audio segmentation and classification

Automated audio segmentation and classification play important roles in multimedia content analysis. In this paper, we propose an enhanced approach, called the correlation intensive fuzzy c-means (CIFCM) algorithm, to audio segmentation and classification that is based on audio content analysis. While conventional methods work by considering the attributes of only the current frame or segment, the proposed CIFCM algorithm efficiently incorporates the influence of neighboring frames or segments in the audio stream. With this method, audio-cuts can be detected efficiently even when the signal contains audio effects such as fade-in, fade-out, and cross-fade. A number of audio features are analyzed in this paper to explore the differences between various types of audio data. The proposed CIFCM algorithm works by detecting the boundaries between different kinds of sounds and classifying them into clusters such as silence, speech, music, speech with music, and speech with noise. Our experimental results indicate that the proposed method outperforms the state-of-the-art FCM approach in terms of audio segmentation and classification.     

A low‐profile,low‐cost antenna system with improved gain for DSRC vehicle‐to‐vehicle communications

A novel wire antenna for future dedicated short range communications vehicle‐to‐vehicle communications is introduced. The proposed antenna carries low‐profile and low‐cost features, and possesses an improved gain performance. This article also includes a specific feed network design for the proposed antenna to meet the mechanical and manufacturing requirements. Two different numerical techniques using CST Microwave Studio and HFSS have been applied for evaluating the performance of the proposed antenna. The whole system including the feed network and the antenna elements is integrated, and its performance is also assessed. © 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2013.     

MC-MLAS: Multi-channel Minimum Latency Aggregation Scheduling in Wireless Sensor Networks

Data aggregation is a key, yet time-consuming functionality in wireless sensor networks (WSNs). Multi-channel design is a promising technique to alleviate interference as a primary reason for long latency of TDMA aggregation scheduling. Indeed, it provides more potential of parallel transmissions over different frequency channels, thus minimizing time latency. In this paper, we focus on designing a multi-channel minimum latency aggregation scheduling protocol, named MC-MLAS, using a new joint approach for tree construction, channel assignment, and transmission scheduling. To our best knowledge, this is the first work in the literature which combines orthogonal channels and partially overlapping channels to consider the total latency involved in data aggregation. Extensive simulations verify the superiority of MC-MLAS in WSNs.