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

A typical sensor data processing sequence uses a detection algorithm prior to tracking to extract point-measurements from the observed sensor data. Track-before-detect (TkBD) is a paradigm which combines target detection and estimation by removing the detection algorithm and supplying the sensor data directly to the tracker. Various different approaches exist for tackling the TkBD problem. This paper compares the ability of several different approaches to detect low amplitude targets. The following algorithms are considered in this comparison: Bayesian estimation over a discrete grid, Dynamic Programming, Particle Filtering methods, and the Histogram Probabilistic Multi-Hypothesis Tracker. Algorithms are compared on the basis of detection performance and computation resource requirements.

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A comparison of detection performance for several Track-Before-Detect algorithms

In this position paper, we investigate the use of wireless sensor network (WSN) technology for ground surveillance. The goal of our project is to develop a prototype of WSN for outdoor deployment. We aim to design a system, which can detect and classify multiple targets (e.g., vehicles and troop movements), using inexpensive off-the-shelf wireless sensor devices, capable of sensing acoustic and magnetic signals generated by different target objects. In order to archive our goals, we intend to design a system, which is capable of automatic selforganization and calibration. Such a system would need to be capable of performing detection and tracking of targets as well as sending the real time enemy mobility information to a command centre. Real-time tacking with WSN is extremely challenging since it requires high system robustness, real time decision making, high frequency sampling, multi-modality of sensing, complex signal processing and data fusion, distributed coordination and wide area coverage. We propose a Hybrid Sensor Network architecture (HSN), tailored specifically to meet these challenges. We investigate data fusion technologies such as particle filters, to handle both environmental and sensing noises of inexpensive sensors.

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Wireless Sensor Networks for Battlefield Surveillance

A particle-based track-before-detect filtering algorithm is presented. This algorithm incorporates the Swerling family of target amplitude fluctuation models in order to capture the effect of radar cross-section changes that a target would present to a sensor over time. The filter is designed with an existence variable, to determine the presence of a target in the data, and an efficient method of incorporating this variable in a particle filter scheme is developed. Results of the algorithm on simulated data show a significant gain in detection performance through accurately modelling the target amplitude fluctuations.

Recursive track-before-detect with target amplitude fluctuations

Track-before-detect is a powerful technique for detection and tracking of targets with low signal-to-noise ratio. This paper presents a performance comparison of two particle filters recently proposed for this application using several different particle proposal densities designed for track initiation. The first particle filter is a standard SIR particle filter, which treats the track-before-detect problem as a hybrid estimation problem by incorporating a discrete random variable, "target existence," into the state vector. The second particle filter formulates the probability of existence calculation in a different way, avoiding the need for hybrid estimation. Three different particle proposal densities are considered, which are designed to compare performance when the data is used to aid in particle proposal.

A comparison of particle filters for recursive track-before-detect

The work reported in this paper investigates the performance of the Particle Filter (PF) algorithm for tracking a moving object using a wireless sensor network (WSN). It is well known that the PF is particularly well suited for use in target tracking applications. However, a comprehensive analysis on the effect of various design and calibration parameters on the accuracy of the PF has been overlooked. This paper outlines the results from such a study. In particular, we evaluate the effect of various design parameters (such as the number of deployed nodes, number of generated particles, and sampling interval) and calibration parameters (such as the gain, path loss factor, noise variations, and nonlinearity constant) on the tracking accuracy and computation time of the particle-filter-based tracking system. Based on our analysis, we present recommendations on suitable values for these parameters, which provide a reasonable trade-off between accuracy and complexity. We also analyze the theoretical Cramer-Rao Bound as the benchmark for the best possible tracking performance and demonstrate that the results from our simulations closely match the theoretical bound. In this paper, we also propose a novel technique for calibrating off-the-shelf sensor devices. We implement the tracking system on a real sensor network and demonstrate its accuracy in detecting and tracking a moving object in a variety of scenarios. To the best of our knowledge, this is the first time that empirical results from a PF-based tracking system with off-the-shelf WSN devices have been reported. Finally, we also present simple albeit important building blocks that are essential for field deployment of such a system.

Mark Rutten

Papers

A comparison of detection performance for several Track-Before-Detect algorithms

Wireless Sensor Networks for Battlefield Surveillance

Recursive track-before-detect with target amplitude fluctuations

A comparison of particle filters for recursive track-before-detect

Detection and Tracking Using Particle-Filter-Based Wireless Sensor Networks