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

Track-Before-Detect (TkBD) is a paradigm that combines the target detection and estimation processes that are usually sequentially applied to sensor data in a conventional system. Existing literature uses only the envelope of complex data; this article presents an approach that also includes the phase information. The inclusion of phase is shown to both improve the discrimination of targets from noise and reduce the computation overhead, with improved performance demonstrated using three representative algorithms.

Using Phase to Improve Track-Before-Detect

Track-before-detect (TBD) refers to a tracking scheme where detection of a target is not made by placing a threshold on the sensor data. Rather, the complete sensor data is used to detect and track a target in the absence of a data threshold. By using all of the sensor data a TBD algorithm can detect and track targets which have a lower signal power than could be detected by using a standard detection and tracking scheme. This paper presents an efficient particle filter TBD algorithm, which models the signal processing stages which may be found in a sensor such as radar. In this type of sensor the noise is modelled as the magnitude of a complex Gaussian process, which is Rayleigh distributed. This noise model and the model of the sensor signal processing is incorporated into the filter derivation. It is shown that in a simple simulation the algorithm can detect and track targets with a signal-to-noise ratio as low as 3dB.© (2004) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Particle-based track-before-detect in Rayleigh noise

Event-based Sensing for Space Situational Awareness

Tracking of dim, or low signal-to-noise ratio (SNR), targets is commonly achieved using track-before-detect (TBD) techniques. While traditional tracking algorithms operate on detections, typically formed by applying an intensity threshold to the sensor data, TBD algorithms operate directly on un-thresholded sensor data. Increasing the information available to the tracker in this way potentially allows tracking of lower SNR targets compared to trackers using detections. This paper compares three algorithms on simulated data containing a single dim target: a histogram PMHT algorithm and a particle filter, both of which could be classified as track-before-detect algorithms, and a PDA algorithm which operates on detections. The algorithms are compared in terms of detection performance, false track rate and RMS error.

A Comparison of Three Algorithms for Tracking Dim Targets

A revolutionary type of imaging device, known as a silicon retina or event-based sensor, has recently been developed and is gaining in popularity in the field of artificial vision systems. These devices are inspired by a biological retina and operate in a significantly different way to traditional CCD-based imaging sensors. While a CCD produces frames of pixel intensities, an event-based sensor produces a continuous stream of events, each of which is generated when a pixel detects a change in log light intensity. These pixels operate asynchronously and independently, producing an event-based output with high temporal resolution. There are also no fixed exposure times, allowing these devices to offer a very high dynamic range independently for each pixel. Additionally, these devices offer high-speed, low power operation and a sparse spatio-temporal output. As a consequence, the data from these sensors must be interpreted in a significantly different way to traditional imaging sensors and this paper explores the advantages this technology provides for space imaging. The applicability and capabilities of event-based sensors for SSA applications are demonstrated through telescope field trials. Trial results have confirmed that the devices are capable of observing resident space objects from LEO through to GEO orbital regimes. Significantly, observations of RSOs were made during both day-time and night-time (terminator) conditions without modification to the camera or optics. The event based sensor’s ability to image stars and satellites during day-time hours offers a dramatic capability increase for terrestrial optical sensors. This paper shows the field testing and validation of two different architectures of event-based imaging sensors. An event-based sensor’s asynchronous output has an intrinsically low data-rate. In addition to low-bandwidth communications requirements, the low weight, low-power and high-speed make them ideally suitable to meeting the demanding challenges required by space-based SSA systems. Results from these experiments and the systems developed highlight the applicability of event-based sensors to ground and space-based SSA tasks.

Mark Rutten

Papers

Using Phase to Improve Track-Before-Detect

Particle-based track-before-detect in Rayleigh noise

Event-based Sensing for Space Situational Awareness

A Comparison of Three Algorithms for Tracking Dim Targets

Event-based Sensing for Space Situational Awareness