Abstract-Mobile robots in real-life settings would benefit from being able to localize sound sources. Such a capability can nicely complement vision to help localize a person or an interesting event in the environment, and also to provide enhanced processing for other capabilities such as speech recognition. In this paper we present a robust sound source localization method in three-dimensional space using an array of 8 microphones. The method is based on a frequency-domain implementation of a steered beamformer along with a probabilistic post-processor. Results show that a mobile robot can localize in real time multiple moving sources of different types over a range of 5 meters with a response time of 200 ms.
With the advent of visual sensor networks (VSNs), energy-aware compression algorithms have gained wide attention. That is, new strategies and mechanisms for power-efficient image compression algorithms are developed, since the application of the conventional methods is not always energy beneficial. In this paper, we provide a survey of image compression algorithms for visual sensor networks, ranging from the conventional standards such as JPEG and JPEG2000 to a new compression method, for example, compressive sensing. We provide the advantages and shortcomings of the application of these algorithms in VSN, a literature review of their application in VSN, as well as an open research issue for each compression standard/method. Moreover, factors influencing the design of compression algorithms in the context of VSN are presented. We conclude by some guidelines which concern the design of a compression method for VSN.
While designing a wavelet-based coder (WBC) in the context of Visual Sensor Network (VSN), engineers and designers must respect their strict constraint on power consumption. This makes the selection of the appropriate wavelet, among many existing competitors, not an easy task. A set of wavelet filters are evaluated and the best of them are selected. The comparison is performed in terms of the quality of the reconstructed image at the base station and the power consumption of a visual sensor (VS) processing the wavelet. Image quality is measured objectively using PSNR and SSIM, and subjectively using the mean opinion score of many viewers. For power consumption, we have developed a power model based on the number of times basic operations are performed by the filters. Moreover, we show and discuss some factors, such as decomposition level and filter length, influencing the power dissipation of a given VS while executing a given wavelet under evaluation. Our results provide a good reference for designers of WBC for power-constrained applications such as VSN.
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