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

Standard target state estimation schemes typically use detections as their source of measurements, which are pro- duced by thresholding the output of a sensor's signal processing stage. This work exploits a track-before-detect (TBD) technique, which simultaneously detects and tracks a target without needing to threshold the sensor data. By removing the need for threshold- ing, TBD can potentially detect and track targets with a much lower signal-to-noise ratio (SNR) than conventional systems. The signal processing that is modelled in this work is designed to match that which might be found in a sensor such as radar. In these systems, the data used by a tracking filter is the magnitude of a complex spectrum. This gives rise to a signal in Rayleigh distributed noise. This paper presents a particle-based TBD fil- ter, operating on the output of this signal processing stage, which estimates the target state incorporating target existence, position, velocity and target signal strength. The main contributions of this work are the development of an efficient method of calculating the probability of target existence and the derivation of a TBD filter which operates in Rayleigh noise. It is shown through simulation that a target can be detected and tracked with an SNR as low as 3dB. The work of Boers and Driessen (4) develops a TBD al- gorithm similar in structure to Ristic but extended to con- sider multiple targets. The algorithm presented in this paper is based heavily on that of Ristic, with the main differences being that the likelihood function is modified to more ac- curately match radar signal processing and a more efficient method for calculating the probability of target existence is developed. In this work the simulated data used as the input to the tracker is a uniform grid, with intensities defined for each bin. The intensities are modelled as the magnitude of a complex spectrum, which corresponds to the output of sig- nal processing stages in operational sensors such as radar. The envelope of complex Gaussian noise is Rayleigh dis- tributed and the magnitude of a signal in complex Gaussian noise is Ricean distributed (5). These densities are properly incorporated into the likelihood function of the TBD filter. This paper introduces a TBD algorithm for processing data with an efficient method for calculating the probability of target existence. The algorithm simultaneously estim- ates target parameters including existence, position, velo- city and signal intensity. Section 2 sets the problem math- ematically, with a summary of the models used for target motion and the assumed signal processing model. Section 3 derives the filter, incorporating target existence, while Sec- tion 4 details the implementation of this filter using sequen- tial Monte-Carlo techniques. Section 5 gives details of the simulation used to test the filter and shows results gained from repeated simulation. The final section summarises the key contributions of this paper and offers further opportun- ities for research.

Efficient Particle-Based Track-Before-Detect in Rayleigh Noise

This book demonstrates how nonlinear/non-Gaussian Bayesian time series estimation methods were used to produce a probability distribution of potential MH370 flight paths. It provides details of how the probabilistic models of aircraft flight dynamics, satellite communication system measurements, environmental effects and radar data were constructed and calibrated. The probability distribution was used to define the search zone in the southern Indian Ocean. The book describes particle-filter based numerical calculation of the aircraft flight-path probability distribution and validates the method using data from several of the involved aircrafts previous flights. Finally it is shown how the Reunion Island flaperon debris find affects the search probability distribution.

Bayesian Methods in the Search for MH370

3D meteoroid trajectories

Over-the-horizon Radar (OTHR) uses the ionosphere as a propagation medium to detect targets beyond the line-of-sight horizon. The layered structure of the ionosphere can support several signal propagation paths between the radar site and detected targets, often giving rise to multiple radar tracks for a single target. A multi-hypothesis multipath track fusion (MPTF) algorithm for OTHR has been developed and reported in earlier publications. In this paper, the MPTF formalism is developed from first principles to explicitly explore sources of track dependence which arise in OTHR track fusion. In particular, a solution is proposed which accounts for track-to-track dependencies arising from common target ionospheric dynamic processes. The algorithm is applied to the simplest nontrivial case, where the ionosphere is modeled as two spherically-symmetric reflecting layers, and two radar tracks are observed.

Mark Rutten

Papers

Efficient Particle-Based Track-Before-Detect in Rayleigh Noise

Bayesian Methods in the Search for MH370

3D meteoroid trajectories

Joint ionospheric and target state estimation for multipath OTHR track fusion

Track‐Before‐Detect Techniques