Underwater acoustic modem with streaming video capabilities

Oceans have shown tremendous importance and impact on our lives. Thus the need for monitoring and protecting the oceans has grown exponentially in recent years. On the other hand, oceans have economical and industrial potential in areas such as pharmaceutical, oil, minerals and biodiversity. This demand is increasing and the need for high data rate and near real-time communications between submerged agents became of paramount importance. Among the needs for underwater communications, streaming video (e.g. for inspecting risers or hydrothermal vents) can be seen as the top challenge, which when solved will make all the other applications possible. Presently, the only reliable approach for underwater video streaming relies on wired connections or tethers (e.g. from ROVs to the surface) which presents severe operational constraints that makes acoustic links together with AUVs and sensor networks strongly appealing. Using new polymer-based acoustic transducers, which in very recent works have shown to have bandwidth and power efficiency much higher than the usual ceramics, this article proposes the development of a reprogrammable acoustic modem for operating in underwater communications with video streaming capabilities. The results have shown a maximum data-rate of 1Mbps with a simple modulation scheme such as OOK, at a distance of 20 m.

An integrated system for human action recognition from video using hidden Markov model

In this paper, we present an integrated system for real-time automatic detection of human actions from video. The proposed approach uses the boundary of humans as the main feature for recognizing actions. Background subtraction is performed using Gaussian mixture model. Then, features are extracted from silhouettes and Vector Quantization is used to map features into symbols (bag of words approach). Finally, actions are detected using the Hidden Markov Model. The proposed system was validated using a newly collected real- world dataset. The obtained results show that the system is capable of achieving robust human detection, in both indoor and outdoor environments. Moreover, promising classification results were achieved when detecting two basic human actions: walking and sitting.