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

Target detection and tracking is a well-established area of research. However, a majority of proposed solutions in existing literature rely on expensive and specialized sensors, which often have limited coverage. Using low cost sensor nodes is an attractive and complementary approach to scalable target detection and tracking applications. However, tracking with low cost Wireless Sensor Network (WSN), presents its own challenges, namely real time decision making, high frequency sampling, multi-modal sensing, complex signal processing, and data fusion. In this work, we investigate the use of inexpensive off-the-shelf WSN devices for ground surveillance. Our system estimates and tracks a target based on the spatial differences of the target object's signal strength detected by the monitoring sensors at different locations.

Detection and tracking using wireless sensor networks

Over-the-horizon radar (OTHR) uses the refraction of high frequency radiation through the ionosphere in order to detect targets beyond the line-of-sight horizon. The complexities of the ionosphere can produce multipath propagation, which may result in multiple resolved detections for a single target. When there are multipath detections, an OTHR tracker will produce several spatially separated tracks for each target. Information conveying the state of the ionosphere is required in order to determine the true location of the target and is available in the form of a set of possible propagation paths, and a transformation from measured coordinates into ground coordinates for each path. Since there is no a-priori information as to how many targets are in the surveillance region, or which propagation path gave rise to which track, there is a joint target and propagation path association ambiguity which must be resolved using the available track and ionospheric information. The multipath track association problem has traditionally been solved using a multiple hypothesis technique, but a shortcoming of this method is that the number of possible association hypotheses increases exponentially with both the number of tracks and the number of possible propagation paths. This paper proposes an algorithm based on a combinatorial optimisation method to solve the multipath track association problem. The association is formulated as a two-dimensional assignment problem with additional constraints. The problem is then solved using Lagrangian relaxation, which is a technique familiar in the tracking literature for the multidimensional assignment problem arising in data association. It is argued that due to a fundamental property of relaxations convergence cannot be guaranteed for this problem. However, results show that a multipath track-to-track association algorithm based on Lagrangian relaxation, when compared with an exact algorithm, provides a large reduction in computational effort, without significantly degrading association accuracy.

Multipath track association for over-the-horizon radar using Lagrangian relaxation

A foundational aspect of space domain awareness is the ability to identify and track space objects, including space object discovery and custody. This paper demonstrates the power of combining an e...

/pdf/joint-probabilistic-data-association-and-smoothing-applied-15zhjvgegl.pdf

Joint probabilistic data association and smoothing applied to multiple space object tracking

The Event-Based Sensor (EBS) is a new class of imaging sensor where each pixel independently reports “events” in response to changes in log intensity, rather than outputting image frames containing the absolute intensity at each pixel Positive and negative events are emitted from the sensor when the change in log intensity exceeds certain controllable thresholds internal to the device For objects moving through the field of view, a change in intensity can be related to motion The sensor records events independently and asynchronously for each pixel with a very high temporal resolution, allowing the detection of objects moving very quickly through the field of view Recently this type of sensor has been applied to the detection of orbiting space objects using a ground-based telescope This paper describes a method to treat the data generated by the EBS as a classical detect-then-track problem by collating the events spatially and temporally to form target measurements An efficient multi-target tracking algorithm, the probabilistic multi-hypothesis tracker (PMHT) is then applied to the EBS measurements to produce tracks This method is demonstrated by automatically generating tracks on orbiting space objects from data collected by the EBS

Probabilistic Multi Hypothesis Tracker for an Event Based Sensor

Fireball observations from camera networks provide position and time information along the trajectory of a meteoroid that is transiting our atmosphere. The complete dynamical state of the meteoroid at each measured time can be estimated using Bayesian filtering techniques. A particle filter is a novel approach to modeling the uncertainty in meteoroid trajectories and incorporates errors in initial parameters, the dynamical model used, and observed position measurements. Unlike other stochastic approaches, a particle filter does not require predefined values for initial conditions or unobservable trajectory parameters. The Bunburra Rockhole fireball, observed by the Australian Desert Fireball Network (DFN) in 2007, is used to determine the effectiveness of a particle filter for use in fireball trajectory modeling. The final mass is determined to be with a final velocity of , similar to previously calculated values. The full automatability of this approach will allow an unbiased evaluation of all events observed by the DFN and lead to a better understanding of the dynamical state and size frequency distribution of asteroid and cometary debris in the inner solar system.

http://iopscience.iop.org/article/10.3847/1538-3881/153/2/87/pdf

Mark Rutten

Papers

Detection and tracking using wireless sensor networks

Multipath track association for over-the-horizon radar using Lagrangian relaxation

Joint probabilistic data association and smoothing applied to multiple space object tracking

Probabilistic Multi Hypothesis Tracker for an Event Based Sensor

Analyzing Meteoroid Flights Using Particle Filters