A review on empirical mode decomposition in fault diagnosis of rotating machinery

Wireless sensor network (WSN) has emerged as one of the most promising technologies for the future. This has been enabled by advances in technology and availability of small, inexpensive, and smart sensors resulting in cost effective and easily deployable WSNs. However, researchers must address a variety of challenges to facilitate the widespread deployment of WSN technology in real-world domains. In this survey, we give an overview of wireless sensor networks and their application domains including the challenges that should be addressed in order to push the technology further. Then we review the recent technologies and testbeds for WSNs. Finally, we identify several open research issues that need to be investigated in future.

Our survey is different from existing surveys in that we focus on recent developments in wireless sensor network technologies. We review the leading research projects, standards and technologies, and platforms. Moreover, we highlight a recent phenomenon in WSN research that is to explore synergy between sensor networks and other technologies and explain how this can help sensor networks achieve their full potential. This paper intends to help new researchers entering the domain of WSNs by providing a comprehensive survey on recent developments.

Wireless sensor networks: a survey on recent developments and potential synergies

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of is), very high dynamic range (140dB vs. 60dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world.

Event-based Vision: A Survey

The problem of jointly detecting multiple objects and estimating their states from image observations is formulated in a Bayesian framework by modeling the collection of states as a random finite set. Analytic characterizations of the posterior distribution of this random finite set are derived for various prior distributions under the assumption that the regions of the observation influenced by individual objects do not overlap. These results provide tractable means to jointly estimate the number of states and their values from image observations. As an application, we develop a multi-object filter suitable for image observations with low signal-to-noise ratio (SNR). A particle implementation of the multi-object filter is proposed and demonstrated via simulations.

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Joint Detection and Estimation of Multiple Objects From Image Observations

Energy efficient and QoS based routing protocol for wireless sensor networks

Malaysian Airlines flight MH370 from Kuala Lumpur, Malaysia, to Beijing, China, departed on 8 March 2014 carrying 239 passengers and crew. Communications with the aircraft were lost as it transitioned from Malaysian to Vietnamese airspace, and the subsequent search and rescue efforts were unable to find the missing aircraft. Radar data indicates that the aircraft turned to the southwest and crossed Malaysia and then f lew northwest along the Malacca Strait. When the aircraft moved beyond radar coverage, it was still carrying enough fuel to fly for several hours, and the size of the reachable area is almost 20% of the surface of the Earth. Although communications were lost early in flight, there was an active communications link between the aircraft and a satellite; metadata from signaling messages on the link provide information about where the aircraft was, even though no user data was communicated.

How Statistical Signal Processing Was Used to Define the MH370 Search Zone

Fireball trails recorded on long exposure, Desert Fireball Network images are encoded with a unique time sequence that is synchronised between systems to 0.4 ms. This allows triangulation of individual positions along a meteoroid path as it passes through the atmosphere. Using this technique on the >21 second long fireball caught by the DFN in 2015 shows a distinctly non-linear path. A 3D particle filter, using raw astrometric observations is best suited to characterise the meteoroid and its trajectory.

Mark Rutten

Papers

How Statistical Signal Processing Was Used to Define the MH370 Search Zone

Investigating fireball flight in three dimensions