localization plays a significant role in the production of ultrasound localization microscopy images. For instance, detecting more microbubbles reduces the time of acquisition, while localizing them more accurately im...
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localization plays a significant role in the production of ultrasound localization microscopy images. For instance, detecting more microbubbles reduces the time of acquisition, while localizing them more accurately improves the resolution of the images. Previous approaches to compare the multiple localization algorithms rely on numerical simulation of a single steady microbubble, with or without modeling its nonlinear response. In real-life situations, vessels have a nonconstant velocity profile, which creates relative movement, producing dynamically overlapped microbubbles even at low concentrations. These complexities deteriorate the behavior of the localization algorithms. To incorporate these effects on the characterization of the localization methods, we designed a virtual medium containing four microtubes of different inner diameters, where single-pixel microbubbles were allowed to flow within each microtube with a parabolic velocity profile. A finite difference method was used to simulate the propagation of ultrasound waves to obtain B-mode images that fed four direct microbubbles localization algorithms (i.e., weighted centroid, 2D-spline interpolation, parabolic fitting, and onset detection). The performance of these methods was quantified using the number of microbubbles detected, the microbubbles distribution, the full width at half maximum, the maximum velocity, and the computational time as metrics. Our simulation results suggest that 2D-spline and paraboloid fitting were the best methods, detecting 100% of the microbubbles with an error in their distribution of 249 and 244 microbubbles, respectively. Both methods with a computational time cost of 18% and 7% lower than weighted centroid, respectively. We also present an experimental comparison of these localization methods, finding results similar to the numerical ones.
In recent years, wireless sensor networks (WSNs) have attracted the attention of both the research community and the industry, and this has eventually lead to the widespread use of WSNs in various applications. The si...
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In recent years, wireless sensor networks (WSNs) have attracted the attention of both the research community and the industry, and this has eventually lead to the widespread use of WSNs in various applications. The significant advancements in WSNs and the advantages brought by WSNs have also enabled the rapid development of underwater acoustic sensor networks (UASNs). In UASNs, in addition to deployment, determining the locations of underwater sensor nodes after they have been deployed is important since it plays a critical role in many applications. Various localization techniques have been proposed for UASNs, and each one is suitable for specific scenarios and has unique challenges. In this paper, after presenting an overview of potential UASN applications, a survey of the deployment techniques and localization algorithms for UASNs has been presented based on their major advantages and disadvantages. Finally, research challenges and open research issues of UASNs have been discussed to provide an insight into future research opportunities.
We present a general scheme for analyzing the performance of a generic localization algorithm for multilateration (MLAT) systems (or for other distributed sensor, passive localization technology). MLAT systems are use...
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We present a general scheme for analyzing the performance of a generic localization algorithm for multilateration (MLAT) systems (or for other distributed sensor, passive localization technology). MLAT systems are used for airport surface surveillance and are based on time difference of arrival measurements of Mode S signals (replies and 1,090 MHz extended squitter, or 1090ES). In the paper, we propose to consider a localization algorithm as composed of two components: a data model and a numerical method, both being properly defined and described. In this way, the performance of the localization algorithm can be related to the proper combination of statistical and numerical performances. We present and review a set of data models and numerical methods that can describe most localization algorithms. We also select a set of existing localization algorithms that can be considered as the most relevant, and we describe them under the proposed classification. We show that the performance of any localization algorithm has two components, i.e., a statistical one and a numerical one. The statistical performance is related to providing unbiased and minimum variance solutions, while the numerical one is related to ensuring the convergence of the solution. Furthermore, we show that a robust localization (i.e., statistically and numerically efficient) strategy, for airport surface surveillance, has to be composed of two specific kind of algorithms. Finally, an accuracy analysis, by using real data, is performed for the analyzed algorithms;some general guidelines are drawn and conclusions are provided.
localization problem in mobile wireless sensor networks has drawn widely attention in recent years due to the rapid advances in mobile computing and improvements of wireless communication technologies. Indeed, mobile ...
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ISBN:
(纸本)9781467392655
localization problem in mobile wireless sensor networks has drawn widely attention in recent years due to the rapid advances in mobile computing and improvements of wireless communication technologies. Indeed, mobile sensor networks become a valuable asset for many applications, since sensors can be mobile or easily tethered to mobile entities such as robots, vehicles or human. Thereby, it is crucial to support mobility without compromising the quality of applications that require wireless communications. Although, numerous research activities have successfully addressed the localization problem in static networks, few studies deal with the localization issue in mobile sensor networks. In this paper, we present a survey on existing work in this area and we address the gaps for actual deployment of mobile wireless sensor networks. Following our analysis, we propose a classification of the most important algorithms and schemes that outcome from recent literature. We discuss their basic principles, characteristics and concepts. Finally, we point out open research issues, their challenges and some future directions for localization in mobile wireless sensor networks.
Recently there has been much focus on Wireless sensor networks (WSN). However Location information is one of the most important information in WSN. Many location estimation algorithms for WSNs have been used to determ...
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ISBN:
(纸本)9781479959792
Recently there has been much focus on Wireless sensor networks (WSN). However Location information is one of the most important information in WSN. Many location estimation algorithms for WSNs have been used to determine the sensor nodes position. In this paper, two different localization algorithms using Trilateration and Multilateration mathematical techniques have studied and compared according to the number of anchor nodes. Some conclusions that are extracted verified that increasing the Number of anchor nodes could enhance the accuracy of the mean location error of location algorithm based on Multilateration technique.
作者：
Nanthakumar, SathishP․, JothilakshmiAssistant Professor
Department of Electronics and Communication Engineering Sri Venkateswara College of Engineering Sriperumbudur India Associate Professor
Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Chennai India
The exploration of underwater surfaces and their monitoring has become an emerging field of interest for many researchers. localization is a crucial task in the Underwater Wireless Sensor Network (UWSN). In comparison...
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In Wireless Sensor Networks (WSNs), localization is one of the most important technologies since it plays a critical role in many applications, e.g., target tracking. If the users cannot obtain the accurate location i...
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In Wireless Sensor Networks (WSNs), localization is one of the most important technologies since it plays a critical role in many applications, e.g., target tracking. If the users cannot obtain the accurate location information, the related applications cannot be accomplished. The main idea in most localization methods is that some deployed nodes (landmarks) with known coordinates (e.g., GPS-equipped nodes) transmit beacons with their coordinates in order to help other nodes localize themselves. In general, the main localization algorithms are classified into two categories: range-based and range-free. In this paper, we reclassify the localization algorithms with a new perspective based on the mobility state of landmarks and unknown nodes, and present a detailed analysis of the representative localization algorithms. Moreover, we compare the existing localization algorithms and analyze the future research directions for the localization algorithms in WSNs.
We present a full-size engineering model of GALI - The GAmma-ray burst Localizing Instrument, composed of 362 CsI(Tl) small cubic scintillators, distributed within a small volume of similar to 2l, and read out by sili...
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ISBN:
(纸本)9781510675094;9781510675100
We present a full-size engineering model of GALI - The GAmma-ray burst Localizing Instrument, composed of 362 CsI(Tl) small cubic scintillators, distributed within a small volume of similar to 2l, and read out by silicon photomultipliers. GALI can provide directional information about GRBs with high angular accuracy from angle-dependent mutual obstruction between its scintillators. Here, we demonstrate GALI's laboratory experiments with an Am-241 source, which achieved directional reconstruction of <3 degrees accuracy, in agreement with our Monte-Carlo simulations. GALI has a wide field view of the unobstructed sky. With its current cubic configuration, GALI's effective area varies between 97 cm(2) (face on) and 138 cm(2) (from the corners at 45 degrees), which is verified in the current experiment.
Wireless sensor networks (WSNs) have wide applications in healthcare, environmental monitoring, and target tracking, relying on sensor nodes that are joined cooperatively. The research investigates localization algori...
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Wireless sensor networks (WSNs) have wide applications in healthcare, environmental monitoring, and target tracking, relying on sensor nodes that are joined cooperatively. The research investigates localization algorithms for both target and node in WSNs to enhance accuracy. An innovative localization algorithm characterized as an asynchronous time-of-arrival (TOA) target is proposed by implementing a differential evolution algorithm. Unlike available approaches, the proposed algorithm employs the least squares criterion to represent signal-sending time as a function of the target position. The target node's coordinates are estimated by utilizing a differential evolution algorithm with reverse learning and adaptive redirection. A hybrid received signal strength (RSS)-TOA target localization algorithm is introduced, addressing the challenge of unknown transmission parameters. This algorithm simultaneously estimates transmitted power, path loss index, and target position by employing the RSS and TOA measurements. These proposed algorithms improve the accuracy and efficiency of wireless sensor localization, boosting performance in various WSN applications.
In this paper, a non-statistical MATLAB-based software model for a bounded shallow underwater acoustic network comprising an arbitrary number of transmitters, receivers and the water channel is presented. The model is...
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ISBN:
(纸本)9781665439350
In this paper, a non-statistical MATLAB-based software model for a bounded shallow underwater acoustic network comprising an arbitrary number of transmitters, receivers and the water channel is presented. The model is constructed to highlight the non-ideal behavior of the underwater channel including attenuation, reflections, multipath effects, noise and phase-frequency shifts. To highlight the utility of the model, a Time-Difference-Of-Arrival (TDOA) algorithm to locate an underwater sound source using a hydrophone array is tested on the model and the received signals are observed to show realistic results as if they have actually propagated through the water channel. Such a network model can be used to test localization algorithms for any acoustic source, without the need to gather real data in the specific water reservoir.
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