Instantaneous Localization

Jaulin, Luc

doi:10.1016/b978-1-78548-048-5.50005-x

Cited by 5 publications

(5 citation statements)

References 10 publications

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“…LAWS is based on a widely used algorithm in GPS navigation called multilateration 28 to track CWS relative to nearby protein atoms in an MD trajectory. LAWS has several advantages: (i) it reduces alignment errors compared to traditional global alignment approaches, (ii) it quantifies the local structural perturbations in the vicinity of CWS, and (iii) it provides local density information for each CWS.…”

Section: Laws Methodsmentioning

confidence: 99%

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LAWS: Local alignment for water sites—Tracking ordered water in simulations

Klyshko

Kim

Rauscher

2023

Biophysical Journal

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Section: Laws Methodsmentioning

confidence: 99%

“…This problem is formulated in the literature as multilateration, 28 which is commonly encountered in navigation and surveillance. For example, a GPS device calculates distances di by measuring the times required for a signal to travel from a set of satellites.…”

Section: Methodsmentioning

confidence: 99%

LAWS: Local alignment for water sites—Tracking ordered water in simulations

Klyshko

Kim

Rauscher

2023

Biophysical Journal

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“…The set of fixed distances to the nearby protein atoms can then be used to find the unknown coordinates of the WS while the protein atom positions x i , y i , z i change at every frame. In other words, for every frame we are aiming to find the position x, y, z of the WS that satisfies the n distance equations: This problem is formulated in the literature as multilateration, 28 which is commonly encountered in navigation and surveillance. For example, a GPS device calculates distances by measuring the times required for a signal to travel from a set of satellites.…”

Section: Methodsmentioning

confidence: 99%

“…Here, we propose a method to analyze CWS called local alignment for water sites (LAWS). LAWS is based on a widely used algorithm in GPS navigation called multilateration 28 to track CWS relative to nearby protein atoms in an MD trajectory. LAWS has several advantages: (i) it reduces alignment errors compared to traditional global alignment approaches, (ii) it quantifies the local structural perturbations in the vicinity of CWS, and (iii) it provides local density information for each CWS.…”

Section: Introductionmentioning

confidence: 99%

LAWS: Local Alignment for Water Sites — tracking ordered water in simulations

Klyshko

Kim

Rauscher

2022

Preprint

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Protein solvation plays a crucial role in protein function; accurate modeling of protein-water interactions is important for understanding the molecular basis of protein function. In addition to protein structure, x-ray crystallography provides positions of crystallographic water sites (CWS) coordinated by the protein, which can serve as a strong benchmark for modeling accuracy. Such comparison requires special method-ological considerations that take into account the dynamic nature of proteins. However, existing methods for analyzing CWS in MD simulations rely on global alignment of the protein onto the crystal structure, which introduces substantial errors in the case of significant structural deviations. Here, we propose a method called local alignment for water sites (LAWS), which is based on multilateration — an algorithm widely used in GPS tracking. LAWS considers the contacts formed by CWS and protein atoms in the crystal structure and uses these interaction distances to track the CWS in a simulation. LAWS provides a framework to quantify how perturbations of the local protein environment affect the preservation of crystallographic waters in simulations. We applied our method to a 1 µs simulation of a protein crystal and demonstrate that 76% of CWS are preserved. Compared to existing methods, LAWS identifies more high-confidence (low B-factor) CWS, which are characterized by more prominent water density peaks and a less-perturbed protein environment.

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“…In the latter, given a known propagation speed, the distance between a receiver and a group of transmitters is measured using techniques such as Direction of Arrival (DoA), Time of Arrival (TOA)/Time of Flight (TOF), Angle of Arrival (AoA), Time Difference of Arrival (TDOA) and Return Time of Flight (RTOF). These techniques are commonly used in Global Navigation Satellite Systems (GNSS) [ 13 ], such as Global Positioning System (GPS) and Galileo, but surprisingly they are also found in IoT indoor navigation solutions [ 14 ]. However, multilateration relies mainly on the travelling time or the direction of the signal rays.…”

Section: Related Workmentioning

confidence: 99%

Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture)

Boudani

Kanaris

Kokkinis

et al. 2020

Sensors

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In the near future, the fifth-generation wireless technology is expected to be rolled out, offering low latency, high bandwidth and multiple antennas deployed in a single access point. This ecosystem will help further enhance various location-based scenarios such as assets tracking in smart factories, precise smart management of hydroponic indoor vertical farms and indoor way-finding in smart hospitals. Such a system will also integrate existing technologies like the Internet of Things (IoT), WiFi and other network infrastructures. In this respect, 5G precise indoor localization using heterogeneous IoT technologies (Zigbee, Raspberry Pi, Arduino, BLE, etc.) is a challenging research area. In this work, an experimental 5G testbed has been designed integrating C-RAN and IoT networks. This testbed is used to improve both vertical and horizontal localization (3D Localization) in a 5G IoT environment. To achieve this, we propose the DEep Learning-based co-operaTive Architecture (DELTA) machine learning model implemented on a 3D multi-layered fingerprint radiomap. The DELTA begins by estimating the 2D location. Then, the output is recursively used to predict the 3D location of a mobile station. This approach is going to benefit use cases such as 3D indoor navigation in multi-floor smart factories or in large complex buildings. Finally, we have observed that the proposed model has outperformed traditional algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN).

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Instantaneous Localization

Cited by 5 publications

References 10 publications

LAWS: Local alignment for water sites—Tracking ordered water in simulations

LAWS: Local alignment for water sites—Tracking ordered water in simulations

LAWS: Local Alignment for Water Sites — tracking ordered water in simulations

Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture)

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