scholarly journals Traffic Monitoring via Mobile Device Location

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4505 ◽  
Author(s):  
Juan Martín ◽  
Emil J. Khatib ◽  
Pedro Lázaro ◽  
Raquel Barco
Keyword(s):  
Mobile Devices ◽  
Mobile Device ◽  
Smart Cities ◽  
Computational Cost ◽  
Traffic Monitoring ◽  
Traffic Measurement ◽  
Other Information ◽  
Massive Number ◽  
Real Scenario ◽  
Low Computational Cost

Measuring traffic in real time is one of the main functionalities of Smart Cities. To reduce the costs of deployment and operation, traffic measurement with mobile devices has been widely studied. In this paper, a traffic monitoring system using mobile devices is proposed. The proposed algorithm has the advantage of having a very low computational cost, allowing most of the pre-processing to be done in the mobile device and therefore making possible the centralized collection of a massive number of measurements. The proposed system is composed of three algorithms; a map-matching algorithm to correct minor location errors, a Virtual Inductive Loop that estimates the traffic and a traffic data collector that aggregates the information from many devices and combines it with other information sources. The system has been tested in a real scenario, comparing its accuracy with a traditional traffic sensor, showing its accuracy.

scholarly journals Testing Off-the-Shelf Optical Wireless LANs for Smart City Environments

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5451
Author(s):  
Loreto Pescosolido ◽  
Emilio Ancillotti ◽  
Andrea Passarella
Keyword(s):  
Light Intensity ◽  
Mobile Devices ◽  
Mobile Device ◽  
Smart City ◽  
Smart Cities ◽  
Wireless Lans ◽  
Optical Wireless ◽  
Operational Conditions ◽  
Stable Throughput ◽  
Indoor Scenarios

Optical wireless LANs (OWLs) constitute an emerging networking paradigm for indoor scenarios’ fit to different smart cities’ fields of applications. Commercial products employing this technology have been made available on the market in recent years. In this work, we investigate, through a set of indoor communication experiments based on commercially available products, how different environmental and usage modes affect the performance of the system, addressing the presence of multiple users, the position and mobility of the mobile devices, the handover among adjacent cells and the effect of background lighting. Our finding shows that the system is quite robust with respect to the variation of operational conditions. We show that, in most conditions, the links can reliably sustain a stable throughput, achieving at least 50% of the throughput achieved with using the maximum light intensity of the transmitting lamp, while they are affected in a very mild way by factors like position and height of the mobile device, and virtually unaffected by variations in the background light.

An Advanced and Secure Symbian-Based Mobile Approach for Body Sensor Networks Interaction

2012 ◽  
pp. 1061-1077
Author(s):  
Orlando R. E. Pereira ◽  
João M. L. P. Caldeira ◽  
Joel J. P. C. Rodrigues
Keyword(s):  
Sensor Networks ◽  
Mobile Devices ◽  
Data Storage ◽  
Mobile Device ◽  
Health Monitoring ◽  
Data Gathering ◽  
Body Sensor Networks ◽  
Routine Tasks ◽  
Medical Teams ◽  
Real Scenario

The combination of body sensor networks (BSNs) and mobile devices brings a personalized health monitoring opportunity to patients and medical teams. Mobile devices may be used to process and present data collected by BSN sensors in an easy and meaningful way to users. The mobility of such systems improves patients’ quality of life, enabling continuous unobtrusive health monitoring during regular daily routine tasks. This paper presents a Symbian-powered smartphone based solution for BSN sensors data gathering, monitoring, and presentation. The systems’ sensor platform hardware provides an onboard long-term data storage module, enabling continuous data gathering even in the absence of the mobile device. The mobile device connects wirelessly to the BSN using Bluetooth technology, supporting interaction with multiple sinks. This system aims to help patients that need continuous monitoring of human bio-physiological parameters in a transparent and unobtrusive way. A case study is presented, based on a sensor for women’s core body temperature collection, enabling fertility follow up processing. The system was evaluated successfully, proving its usefulness in a real scenario. As a result, it is ready for regular use.

An Advanced and Secure Symbian-Based Mobile Approach for Body Sensor Networks Interaction

2012 ◽  
pp. 33-48
Author(s):  
Orlando R. E. Pereira ◽  
João M. L. P. Caldeira ◽  
Joel J. P. C. Rodrigues
Keyword(s):  
Sensor Networks ◽  
Mobile Devices ◽  
Data Storage ◽  
Mobile Device ◽  
Health Monitoring ◽  
Data Gathering ◽  
Body Sensor Networks ◽  
Routine Tasks ◽  
Medical Teams ◽  
Real Scenario

The combination of body sensor networks (BSNs) and mobile devices brings a personalized health monitoring opportunity to patients and medical teams. Mobile devices may be used to process and present data collected by BSN sensors in an easy and meaningful way to users. The mobility of such systems improves patients’ quality of life, enabling continuous unobtrusive health monitoring during regular daily routine tasks. This paper presents a Symbian-powered smartphone based solution for BSN sensors data gathering, monitoring, and presentation. The systems’ sensor platform hardware provides an onboard long-term data storage module, enabling continuous data gathering even in the absence of the mobile device. The mobile device connects wirelessly to the BSN using Bluetooth technology, supporting interaction with multiple sinks. This system aims to help patients that need continuous monitoring of human bio-physiological parameters in a transparent and unobtrusive way. A case study is presented, based on a sensor for women’s core body temperature collection, enabling fertility follow up processing. The system was evaluated successfully, proving its usefulness in a real scenario. As a result, it is ready for regular use.

scholarly journals Traffic Monitoring System Based on Deep Learning and Seismometer Data

2021 ◽  
Vol 11 (10) ◽  
pp. 4590
Author(s):  
Ahmad Bahaa Ahmad ◽  
Takeshi Tsuji
Keyword(s):  
Neural Network ◽  
Seismic Data ◽  
Computational Cost ◽  
Vertical Component ◽  
Traffic Monitoring ◽  
Vehicle Classification ◽  
Magnetic Characteristics ◽  
Seismic Records ◽  
The Neural Networks ◽  
Low Computational Cost

Currently, vehicle classification in roadway-based techniques depends mainly on photos/videos collected by an over-roadway camera or on the magnetic characteristics of vehicles. However, camera-based techniques are criticized for potentially violating the privacy of vehicle occupants and exposing their identity, and vehicles can evade detection when they are obscured by larger vehicles. Here, we evaluate methods of identifying and classifying vehicles on the basis of seismic data. Vehicle identification from seismic signals is considered a difficult task because of interference by various noise. By analogy with techniques used in speech recognition, we used different artificial intelligence techniques to extract features of three, different-sized vehicles (buses, cars, motorcycles) and seismic noise. We investigated the application of a deep neural network (DNN), a convolutional neural network (CNN), and a recurrent neural network (RNN) to classify vehicles on the basis of vertical-component seismic data recorded by geophones. The neural networks were trained on 5580 unprocessed seismic records and achieved excellent training accuracy (99%). They were also tested on large datasets representing periods as long as 1 month to check their stability. We found that CNN was the most satisfactory approach, reaching 96% accuracy and detecting multiple vehicle classes at the same time at a low computational cost. Our findings show that seismic methods can be used for traffic monitoring and security purposes without violating the privacy of vehicle occupants, offering greater efficiency and lower costs than current methods. A similar approach may be useful for other types of transportation, such as vessels and airplanes.

Sky Segmentation for Enhanced Depth Reconstruction and Bokeh Rendering with Efficient Architectures

2020 ◽  
Vol 2020 (14) ◽  
pp. 378-1-378-7
Author(s):  
Tyler Nuanes ◽  
Matt Elsey ◽  
Radek Grzeszczuk ◽  
John Paul Shen
Keyword(s):  
Real Time ◽  
Mobile Device ◽  
Computational Cost ◽  
False Positives ◽  
Compact Model ◽  
High Quality ◽  
False Negatives ◽  
Trade Off ◽  
Depth Reconstruction ◽  
Binary Classifiers

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.

scholarly journals iBikeSafe: A Multi-Parameter System for Monitoring, Evaluation and Visualization of Cycling Paths in Smart Cities Targeted at Cycling Adverse Conditions

Smart Cities ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 1058-1086
Author(s):  
Franklin Oliveira ◽  
Daniel G. Costa ◽  
Luciana Lima ◽  
Ivanovitch Silva
Keyword(s):  
Urban Areas ◽  
Smart Cities ◽  
Large City ◽  
Health And Safety ◽  
Parameter System ◽  
Urban Centers ◽  
Adverse Conditions ◽  
Last Mile ◽  
Real Scenario

The fast transformation of the urban centers, pushed by the impacts of climatic changes and the dramatic events of the COVID-19 Pandemic, will profoundly influence our daily mobility. This resulted scenario is expected to favor adopting cleaner and flexible modal solutions centered on bicycles and scooters, especially as last-mile options. However, as the use of bicycles has rapidly increased, cyclists have been subject to adverse conditions that may affect their health and safety when cycling in urban areas. Therefore, whereas cities should implement mechanisms to monitor and evaluate adverse conditions in cycling paths, cyclists should have some effective mechanism to visualize the indirect quality of cycling paths, eventually supporting choosing more appropriate routes. Therefore, this article proposes a comprehensive multi-parameter system based on multiple independent subsystems, covering all phases of data collecting, formatting, transmission, and processing related to the monitoring, evaluating, and visualizing the quality of cycling paths in the perspective of adverse conditions that affect cyclist. The formal interactions of all modules are carefully described, as well as implementation and deployment details. Additionally, a case study is considered for a large city in Brazil, demonstrating how the proposed system can be adopted in a real scenario.

scholarly journals Computing Expectiles Using k-Nearest Neighbours Approach

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 645
Author(s):  
Muhammad Farooq ◽  
Sehrish Sarfraz ◽  
Christophe Chesneau ◽  
Mahmood Ul Hassan ◽  
Muhammad Ali Raza ◽  
...  
Keyword(s):  
Computational Cost ◽  
Real Life ◽  
Distance Measures ◽  
Computational Time ◽  
High Dimensional ◽  
Test Error ◽  
Nearest Neighbours ◽  
Comparable Performance ◽  
Asymmetric Least Squares ◽  
Low Computational Cost

Expectiles have gained considerable attention in recent years due to wide applications in many areas. In this study, the k-nearest neighbours approach, together with the asymmetric least squares loss function, called ex-kNN, is proposed for computing expectiles. Firstly, the effect of various distance measures on ex-kNN in terms of test error and computational time is evaluated. It is found that Canberra, Lorentzian, and Soergel distance measures lead to minimum test error, whereas Euclidean, Canberra, and Average of (L1,L∞) lead to a low computational cost. Secondly, the performance of ex-kNN is compared with existing packages er-boost and ex-svm for computing expectiles that are based on nine real life examples. Depending on the nature of data, the ex-kNN showed two to 10 times better performance than er-boost and comparable performance with ex-svm regarding test error. Computationally, the ex-kNN is found two to five times faster than ex-svm and much faster than er-boost, particularly, in the case of high dimensional data.

scholarly journals Directional TGV-Based Image Restoration under Poisson Noise

2021 ◽  
Vol 7 (6) ◽  
pp. 99
Author(s):  
Daniela di Serafino ◽  
Germana Landi ◽  
Marco Viola
Keyword(s):  
Computed Tomography ◽  
Image Restoration ◽  
Gaussian Noise ◽  
Computational Cost ◽  
Data Fitting ◽  
Poisson Noise ◽  
Glass Fibres ◽  
Leibler Divergence ◽  
Generalized Variation ◽  
Low Computational Cost

We are interested in the restoration of noisy and blurry images where the texture mainly follows a single direction (i.e., directional images). Problems of this type arise, for example, in microscopy or computed tomography for carbon or glass fibres. In order to deal with these problems, the Directional Total Generalized Variation (DTGV) was developed by Kongskov et al. in 2017 and 2019, in the case of impulse and Gaussian noise. In this article we focus on images corrupted by Poisson noise, extending the DTGV regularization to image restoration models where the data fitting term is the generalized Kullback–Leibler divergence. We also propose a technique for the identification of the main texture direction, which improves upon the techniques used in the aforementioned work about DTGV. We solve the problem by an ADMM algorithm with proven convergence and subproblems that can be solved exactly at a low computational cost. Numerical results on both phantom and real images demonstrate the effectiveness of our approach.

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