scholarly journals A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Ling Pei ◽  
Min Zhang ◽  
Danping Zou ◽  
Ruizhi Chen ◽  
Yuwei Chen
Keyword(s):  
Indoor Localization ◽  
State Of The Art ◽  
Low Cost ◽  
Academic Community ◽  
Future Trends ◽  
Crowd Sensing ◽  
Practical System ◽  
Novel Approach ◽  
Fingerprint Database

Sensor-rich smartphone enables a novel approach to training the fingerprint database for mobile indoor localization via crowd sensing. In this survey, we discuss the crowd sensing based mobile indoor localization in terms of foundational knowledge, signals of fingerprints, trajectory of obtaining fingerprints, indoor maps, evolution of a fingerprint database, positioning algorithms, state-of-the-art solutions, and challenges. The survey concludes that the crowd sensing is a low cost solution of generating and updating an organic fingerprint database. Although the crowd sensing concept is widely accepted by the academic community in these years, there are a lot of unsolved problems which hinder the concept of transferring into a practical system. We address the challenges and predict future trends in the end.

Wireless Indoor Localization Systems and Techniques: Survey and Comparative Study

2016 ◽  
Vol 3 (2) ◽  
pp. 392 ◽  
Author(s):  
Ahmed Azeez Khudhair ◽  
Saba Qasim Jabbar ◽  
Mohammed Qasim Sulttan ◽  
Desheng Wang
Keyword(s):  
Indoor Localization ◽  
State Of The Art ◽  
Great Influence ◽  
Location Estimation ◽  
Future Trends ◽  
Wireless Localization ◽  
Promising Direction ◽  
New Research ◽  
Wireless Indoor Localization ◽  
Localization Systems

<p>The popularity, great influence and huge importance made wireless indoor localization has a unique touch, as well its wide successful on positioning and tracking systems for both human and assists also contributing to take the lead from outdoor systems in the scope of the recent research works. In this work, we will attempt to provide a survey of the existing indoor positioning solutions and attempt to classify different its techniques and systems. Five typical location predication approaches (triangulation, fingerprinting, proximity, vision analysis and trilateration) are considered here in order to analysis and provide the reader a review of the recent advances in wireless indoor localization techniques and systems to have a good understanding of state of the art technologies and motivate new research efforts in this promising direction. For these reasons, existing wireless localization position systems and location estimation schemes are reviewed. We also made a comparison among the related techniques and systems along with conclusions and future trends to identify some possible areas of enhancements. </p>

scholarly journals Fingerprint Database Reconstruction Based on Robust PCA for Indoor Localization

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2537 ◽  
Author(s):  
Lingwen Zhang ◽  
Teng Tan ◽  
Yafan Gong ◽  
Wenkao Yang
Keyword(s):  
Indoor Localization ◽  
Low Cost ◽  
Low Rank ◽  
Temporal Correlations ◽  
Propagation Behavior ◽  
Rank Matrix ◽  
Analysis Technique ◽  
Radio Signals ◽  
Fingerprint Database ◽  
Low Rank Matrix

The indoor localization method based on the Received Signal Strength (RSS) fingerprint is widely used for its high positioning accuracy and low cost. However, the propagation behavior of radio signals in an indoor environment is complicated and always leads to the existence of outliers and noises that deviate from a normal RSS value in the database. The fingerprint database containing outliers and noises will severely degrade the performance of an indoor localization system. In this paper, an approach to reconstruct the fingerprint database is proposed with the purpose of mitigating the influences of outliers. More specifically, by exploiting the spatial and temporal correlations of RSS data, the database can be transformed into a low-rank matrix. Therefore, the RPCA (Robust Principle Component Analysis) technique can be applied to recover the low-rank matrix from a noisy matrix. In addition, we propose an improved RPCA model which takes advantage of the prior knowledge of a singular value and could remove outliers and structured noise simultaneously. The experimental results show that the proposed method can eliminate outliers and structured noise efficiently.

scholarly journals Rescuing Low Frequency Variants within Intra-Host Viral Populations directly from Oxford Nanopore sequencing data

2021 ◽  
Author(s):  
Yunxi Liu ◽  
Joshua Kearney ◽  
Medhat Mahmoud ◽  
Bryce Kille ◽  
Fritz J Sedlazeck ◽  
...  
Keyword(s):  
State Of The Art ◽  
Low Cost ◽  
Low Frequency ◽  
Viral Population ◽  
Disease Monitoring ◽  
Sequencing Data ◽  
Accurate Identification ◽  
Novel Approach ◽  
Oxford Nanopore ◽  
Oxford Nanopore Technologies

Infectious disease monitoring on Oxford Nanopore Technologies (ONT) platforms offers rapid turnaround times and low cost, exemplified by well over a half of million ONT SARS-COV-2 datasets. Tracking low frequency intra-host variants has provided important insights with respect to elucidating within host viral population dynamics and transmission. However, given the higher error rate of ONT, accurate identification of intra-host variants with low allele frequencies remains an open challenge with no viable solutions available. In response to this need, we present Variabel, a novel approach and first method designed for rescuing low frequency intra-host variants from ONT data alone. We evaluated Variabel on both within patient and across patient paired Illumina and ONT datasets; our results show that Variabel can accurately identify low frequency variants below 0.5 allele frequency, outperforming existing state-of-the-art ONT variant callers for this task. Variabel is open-source and available for download at: www.gitlab.com/treangenlab/variabel.

scholarly journals Cost-effective Indoor Localization for Autonomous Robots using Kinect and WiFi Sensors

2020 ◽  
Vol 23 (65) ◽  
pp. 33-55 ◽  
Author(s):  
Raul Cesar Alves ◽  
Josué Silva de Morais ◽  
Keiji Yamanaka
Keyword(s):  
Indoor Localization ◽  
Autonomous Robots ◽  
Fundamental Problem ◽  
Low Cost ◽  
Cost Effective ◽  
Access Point ◽  
New Methods ◽  
Novel Approach ◽  
Separate Particle ◽  
Different Parts

Indoor localization has been considered to be the most fundamental problem when it comes to providing a robot with autonomous capabilities. Although many algorithms and sensors have been proposed, none have proven to work perfectly under all situations. Also, in order to improve the localization quality, some approaches use expensive devices either mounted on the robots or attached to the environment that don't naturally belong to human environments. This paper presents a novel approach that combines the benefits of two localization techniques, WiFi and Kinect, into a single algorithm using low-cost sensors. It uses separate Particle Filters (PFs). The WiFi PF gives the global location of the robot using signals of Access Point devices from different parts of the environment while it bounds particles of the Kinect PF, which determines the robot's pose locally. Our algorithm also tackles the Initialization/Kidnapped Robot Problem by detecting divergence on WiFi signals, which starts a localization recovering process. Furthermore, new methods for WiFi mapping and localization are introduced.

scholarly journals Deep Learning for Transient Image Reconstruction from ToF Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1962
Author(s):  
Enrico Buratto ◽  
Adriano Simonetto ◽  
Gianluca Agresti ◽  
Henrik Schäfer ◽  
Pietro Zanuttigh
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Light Response ◽  
Real Data ◽  
Depth Image ◽  
Learning Approach ◽  
Multiple Reflections ◽  
Noisy Input ◽  
Novel Approach ◽  
Incoming Light

In this work, we propose a novel approach for correcting multi-path interference (MPI) in Time-of-Flight (ToF) cameras by estimating the direct and global components of the incoming light. MPI is an error source linked to the multiple reflections of light inside a scene; each sensor pixel receives information coming from different light paths which generally leads to an overestimation of the depth. We introduce a novel deep learning approach, which estimates the structure of the time-dependent scene impulse response and from it recovers a depth image with a reduced amount of MPI. The model consists of two main blocks: a predictive model that learns a compact encoded representation of the backscattering vector from the noisy input data and a fixed backscattering model which translates the encoded representation into the high dimensional light response. Experimental results on real data show the effectiveness of the proposed approach, which reaches state-of-the-art performances.

scholarly journals A New Multi-Person Pose Estimation Method Using the Partitioned CenterPose Network

2021 ◽  
Vol 11 (9) ◽  
pp. 4241
Author(s):  
Jiahua Wu ◽  
Hyo Jong Lee
Keyword(s):  
Pose Estimation ◽  
Human Body ◽  
State Of The Art ◽  
Estimation Method ◽  
Bottom Up ◽  
Center Point ◽  
Novel Approach ◽  
Body Joints

In bottom-up multi-person pose estimation, grouping joint candidates into the appropriately structured corresponding instance of a person is challenging. In this paper, a new bottom-up method, the Partitioned CenterPose (PCP) Network, is proposed to better cluster the detected joints. To achieve this goal, we propose a novel approach called Partition Pose Representation (PPR) which integrates the instance of a person and its body joints based on joint offset. PPR leverages information about the center of the human body and the offsets between that center point and the positions of the body’s joints to encode human poses accurately. To enhance the relationships between body joints, we divide the human body into five parts, and then, we generate a sub-PPR for each part. Based on this PPR, the PCP Network can detect people and their body joints simultaneously, then group all body joints according to joint offset. Moreover, an improved l1 loss is designed to more accurately measure joint offset. Using the COCO keypoints and CrowdPose datasets for testing, it was found that the performance of the proposed method is on par with that of existing state-of-the-art bottom-up methods in terms of accuracy and speed.

scholarly journals UV Inscription and Pressure Induced Long-Period Gratings through 3D Printed Amplitude Masks

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1977
Author(s):  
Ricardo Oliveira ◽  
Liliana M. Sousa ◽  
Ana M. Rocha ◽  
Rogério Nogueira ◽  
Lúcia Bilro
Keyword(s):  
State Of The Art ◽  
Low Cost ◽  
Resonance Wavelength ◽  
Complex Structures ◽  
Pressing Method ◽  
Long Period ◽  
Long Period Gratings ◽  
3D Printed ◽  
Mechanical Pressing ◽  
First Time

In this work, we demonstrate for the first time the capability to inscribe long-period gratings (LPGs) with UV radiation using simple and low cost amplitude masks fabricated with a consumer grade 3D printer. The spectrum obtained for a grating with 690 µm period and 38 mm length presented good quality, showing sharp resonances (i.e., 3 dB bandwidth < 3 nm), low out-of-band loss (~0.2 dB), and dip losses up to 18 dB. Furthermore, the capability to select the resonance wavelength has been demonstrated using different amplitude mask periods. The customization of the masks makes it possible to fabricate gratings with complex structures. Additionally, the simplicity in 3D printing an amplitude mask solves the problem of the lack of amplitude masks on the market and avoids the use of high resolution motorized stages, as is the case of the point-by-point technique. Finally, the 3D printed masks were also used to induce LPGs using the mechanical pressing method. Due to the better resolution of these masks compared to ones described on the state of the art, we were able to induce gratings with higher quality, such as low out-of-band loss (0.6 dB), reduced spectral ripples, and narrow bandwidths (~3 nm).

Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Majid Yekkehfallah ◽  
Ming Yang ◽  
Zhiao Cai ◽  
Liang Li ◽  
Chuanxiang Wang
Keyword(s):  
Mobile Robots ◽  
Inertial Measurement Unit ◽  
Indoor Environment ◽  
State Of The Art ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Measurement Unit ◽  
3D Localization ◽  
Fast Motion ◽  
Tof Camera

SUMMARY Localization based on visual natural landmarks is one of the state-of-the-art localization methods for automated vehicles that is, however, limited in fast motion and low-texture environments, which can lead to failure. This paper proposes an approach to solve these limitations with an extended Kalman filter (EKF) based on a state estimation algorithm that fuses information from a low-cost MEMS Inertial Measurement Unit and a Time-of-Flight camera. We demonstrate our results in an indoor environment. We show that the proposed approach does not require any global reflective landmark for localization and is fast, accurate, and easy to use with mobile robots.

scholarly journals Application of Microextraction-Based Techniques for Screening-Controlled Drugs in Forensic Context—A Review

Molecules ◽  
2021 ◽  
Vol 26 (8) ◽  
pp. 2168
Author(s):  
Samir M. Ahmad ◽  
Oriana C. Gonçalves ◽  
Mariana N. Oliveira ◽  
Nuno R. Neng ◽  
José M. F. Nogueira
Keyword(s):  
Environmental Sustainability ◽  
Illicit Drugs ◽  
State Of The Art ◽  
Cost Benefit ◽  
Future Trends ◽  
Present Contribution ◽  
Controlled Drugs ◽  
Related Compounds ◽  
Innovative Techniques ◽  
Great Simplicity

The analysis of controlled drugs in forensic matrices, i.e., urine, blood, plasma, saliva, and hair, is one of the current hot topics in the clinical and toxicological context. The use of microextraction-based approaches has gained considerable notoriety, mainly due to the great simplicity, cost-benefit, and environmental sustainability. For this reason, the application of these innovative techniques has become more relevant than ever in programs for monitoring priority substances such as the main illicit drugs, e.g., opioids, stimulants, cannabinoids, hallucinogens, dissociative drugs, and related compounds. The present contribution aims to make a comprehensive review on the state-of-the art advantages and future trends on the application of microextraction-based techniques for screening-controlled drugs in the forensic context.

Sign in / Sign up

Export Citation Format

Share Document