scholarly journals Collaborative Filtering to Predict Sensor Array Values in Large IoT Networks

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4628
Author(s):  
Fernando Ortega ◽  
Ángel González-Prieto ◽  
Jesús Bobadilla ◽  
Abraham Gutiérrez
Keyword(s):  
Collaborative Filtering ◽  
Failure Rate ◽  
Sensor Array ◽  
Sensor Arrays ◽  
Weather Conditions ◽  
Sensor Data ◽  
Population Movements ◽  
Use Of The Internet ◽  
Rate Threshold

Internet of Things (IoT) projects are increasing in size over time, and some of them are growing to reach the whole world. Sensor arrays are deployed world-wide and their data is sent to the cloud, making use of the Internet. These huge networks can be used to improve the quality of life of the humanity by continuously monitoring many useful indicators, like the health of the users, the air quality or the population movements. Nevertheless, in this scalable context, a percentage of the sensor data readings can fail due to several reasons like sensor reliabilities, network quality of service or extreme weather conditions, among others. Moreover, sensors are not homogeneously replaced and readings from some areas can be more precise than others. In order to address this problem, in this paper we propose to use collaborative filtering techniques to predict missing readings, by making use of the whole set of collected data from the IoT network. State of the art recommender systems methods have been chosen to accomplish this task, and two real sensor array datasets and a synthetic dataset have been used to test this idea. Experiments have been carried out varying the percentage of failed sensors. Results show a good level of prediction accuracy which, as expected, decreases as the failure rate increases. Results also point out a failure rate threshold below which is better to make use of memory-based approaches, and above which is better to choose model-based methods.

Utilizing Sensor Arrays to Attain High-Operating Bandwidth

2004 ◽  
Vol 126 (2) ◽  
pp. 294-302
Author(s):  
Sugathevan Suranthiran ◽  
Suhada Jayasuriya
Keyword(s):  
Sensor Array ◽  
Sensor Arrays ◽  
Cost Effective ◽  
Sensor Data ◽  
Pass Band ◽  
Multiple Sensors ◽  
High Bandwidth ◽  
Small Bandwidth ◽  
Operating Bandwidth ◽  
Single Sensor

Proposed in this paper is a methodology for the design of a sensor array of small bandwidth passband sensors (sensors with small bandwidth but different bandwidths) to attain a high operating bandwidth. In certain control applications, it is necessary that a high bandwidth sensor be used for feedback efficiency. The design of a single sensor with the desired high bandwidth may not be easy and economically feasible. A new approach, which recommends the use of an array of small bandwidth pass-band sensors in place of a single sensor of high bandwidth is proposed. It is shown that the idea of sensor arrays can be utilized to obtain a cost effective and efficient solution to the problem posed. The proposed sensor array that consists of multiple sensors with possible overlapping operating regions as defined by their pass-bands requires that an effective fusion technique be used to unite multi-sensor data. A multi-sensor data fusion scheme using Frequency Response Methods is developed to facilitate the possible implementation of proposed sensor arrays.

Water quality of Tampa Bay, Florida : wet-weather conditions, October 1971

1975 ◽  
Author(s):  
Carl R. Goodwin ◽  
Joseph S. Rosenshein ◽  
D.M. Michaelis
Keyword(s):  
Water Quality ◽  
Weather Conditions ◽  
Tampa Bay ◽  
Wet Weather

Measuring patient engagement among cancer patients in eHealth and mHealth interventions on symptom management: an integrated review (Preprint)

2020 ◽  
Author(s):  
Juqing Zhao ◽  
Pei Chen ◽  
Guangming Wan
Keyword(s):  
Cancer Patients ◽  
Patient Engagement ◽  
Symptom Management ◽  
Subjective Experience ◽  
Participation Rate ◽  
Sensor Data ◽  
Quality Of Data ◽  
Mhealth Intervention

BACKGROUND There has been an increase number of eHealth and mHealth interventions aimed to support symptoms among cancer survivors. However, patient engagement has not been guaranteed and standardized in these interventions. OBJECTIVE The objective of this review was to address how patient engagement has been defined and measured in eHealth and mHealth interventions designed to improve symptoms and quality of life for cancer patients. METHODS Searches were performed in MEDLINE, PsychINFO, Web of Science, and Google Scholar to identify eHealth and mHealth interventions designed specifically to improve symptom management for cancer patients. Definition and measurement of engagement and engagement related outcomes of each intervention were synthesized. This integrated review was conducted using Critical Interpretive Synthesis to ensure the quality of data synthesis. RESULTS A total of 792 intervention studies were identified through the searches; 10 research papers met the inclusion criteria. Most of them (6/10) were randomized trial, 2 were one group trail, 1 was qualitative design, and 1 paper used mixed method. Majority of identified papers defined patient engagement as the usage of an eHealth and mHealth intervention by using different variables (e.g., usage time, log in times, participation rate). Engagement has also been described as subjective experience about the interaction with the intervention. The measurement of engagement is in accordance with the definition of engagement and can be categorized as objective and subjective measures. Among identified papers, 5 used system usage data, 2 used self-reported questionnaire, 1 used sensor data and 3 used qualitative method. Almost all studies reported engagement at a moment to moment level, but there is a lack of measurement of engagement for the long term. CONCLUSIONS There have been calls to develop standard definition and measurement of patient engagement in eHealth and mHealth interventions. Besides, it is important to provide cancer patients with more tailored and engaging eHealth and mHealth interventions for long term engagement.

scholarly journals How Good Is the STW Sensor? An Account from a Larger Shipping Company

2021 ◽  
Vol 9 (5) ◽  
pp. 465
Author(s):  
Angelos Ikonomakis ◽  
Ulrik Dam Nielsen ◽  
Klaus Kähler Holst ◽  
Jesper Dietz ◽  
Roberto Galeazzi
Keyword(s):  
High Frequency ◽  
Additional Data ◽  
Empirical Method ◽  
Influential Factor ◽  
Sensor Data ◽  
Sea State ◽  
Shipping Company ◽  
Frequency Sensor ◽  
Semi Empirical

This paper examines the statistical properties and the quality of the speed through water (STW) measurement based on data extracted from almost 200 container ships of Maersk Line’s fleet for 3 years of operation. The analysis uses high-frequency sensor data along with additional data sources derived from external providers. The interest of the study has its background in the accuracy of STW measurement as the most important parameter in the assessment of a ship’s performance analysis. The paper contains a thorough analysis of the measurements assumed to be related with the STW error, along with a descriptive decomposition of the main variables by sea region including sea state, vessel class, vessel IMO number and manufacturer of the speed-log installed in each ship. The paper suggests a semi-empirical method using a threshold to identify potential error in a ship’s STW measurement. The study revealed that the sea region is the most influential factor for the STW accuracy and that 26% of the ships of the dataset’s fleet warrant further investigation.

Robust Environmental Sensing Using UAVs

2021 ◽  
Vol 2 (4) ◽  
pp. 1-20
Author(s):  
Ahmed Boubrima ◽  
Edward W. Knightly
Keyword(s):  
Air Pollution ◽  
Gas Sensors ◽  
High Performance ◽  
Weather Conditions ◽  
Ambient Air ◽  
Mission Planning ◽  
Pollution Monitoring ◽  
Compound Pollution ◽  
Planning Algorithm

In this article, we first investigate the quality of aerial air pollution measurements and characterize the main error sources of drone-mounted gas sensors. To that end, we build ASTRO+, an aerial-ground pollution monitoring platform, and use it to collect a comprehensive dataset of both aerial and reference air pollution measurements. We show that the dynamic airflow caused by drones affects temperature and humidity levels of the ambient air, which then affect the measurement quality of gas sensors. Then, in the second part of this article, we leverage the effects of weather conditions on pollution measurements’ quality in order to design an unmanned aerial vehicle mission planning algorithm that adapts the trajectory of the drones while taking into account the quality of aerial measurements. We evaluate our mission planning approach based on a Volatile Organic Compound pollution dataset and show a high-performance improvement that is maintained even when pollution dynamics are high.

scholarly journals Economic Analysis of a Transport Company in the Aspect of Car Vehicle Operation

2021 ◽  
Vol 13 (1) ◽  
pp. 427
Author(s):  
Magdalena Rykała ◽  
Łukasz Rykała
Keyword(s):  
Weather Conditions ◽  
Neural Model ◽  
Motor Vehicles ◽  
Bulk Materials ◽  
The Sustainable Development ◽  
Average Temperature ◽  
Transport Services ◽  
Vehicle Operation ◽  
The Impact

The article describes the issues of transport of bulk materials. The knowledge of this process has a key impact on the rational planning of transport tasks. It is necessary to have knowledge about the transport services market and the competition that exists in it. In order to achieve a competitive advantage on the market, enterprises should analyze data on the implementation of transport tasks on an ongoing basis. It is also important that the costs incurred from the conducted activity are minimized, while increasing the quality of services and taking into account the sustainable development of the enterprise. The study analyzes data from a few selected motor vehicles in the period of 3 years of operation, coming from an enterprise specializing in the transport of bulk materials. Moreover, a global sensitivity analysis was performed based on a neural model describing the impact of the analyzed factors on the company’s profit. The results show that the most important factors influencing the company’s profit are the fuel consumption of individual vehicles, the driver (driving style) and the month (average temperature, weather conditions).

scholarly journals Modeling of Yield Losses Caused by Potato Late Blight on Eight Cultivars with Different Levels of Resistance to Phytophthora infestans

Plant Disease ◽  
2012 ◽  
Vol 96 (7) ◽  
pp. 935-942 ◽  
Author(s):  
Toky Rakotonindraina ◽  
Jean-Éric Chauvin ◽  
Roland Pellé ◽  
Robert Faivre ◽  
Catherine Chatot ◽  
...  
Keyword(s):  
Late Blight ◽  
Phytophthora Infestans ◽  
Relative Yield ◽  
Weather Conditions ◽  
Potato Late Blight ◽  
Yield Losses ◽  
Predictive Quality ◽  
Wide Range ◽  
Cultivar Susceptibility

The Shtienberg model for predicting yield loss caused by Phytophthora infestans in potato was developed and parameterized in the 1990s in North America. The predictive quality of this model was evaluated in France for a wide range of epidemics under different soil and weather conditions and on cultivars different than those used to estimate its parameters. A field experiment was carried out in 2006, 2007, 2008, and 2009 in Brittany, western France to assess late blight severity and yield losses. The dynamics of late blight were monitored on eight cultivars with varying types and levels of resistance. The model correctly predicted relative yield losses (efficiency = 0.80, root mean square error of prediction = 13.25%, and bias = –0.36%) as a function of weather and the observed disease dynamics for a wide range of late blight epidemics. In addition to the evaluation of the predictive quality of the model, this article provides a dataset that describes the development of various late blight epidemics on potato as a function of weather conditions, fungicide regimes, and cultivar susceptibility. Following this evaluation, the Shtienberg model can be used with confidence in research and development programs to better manage potato late blight in France.

Optimized Collaborative Filtering Recommendation Based on User’s Social Relationships

2014 ◽  
Vol 1044-1045 ◽  
pp. 1484-1488
Author(s):  
Yue Kun Fan ◽  
Xin Ye Li ◽  
Meng Meng Cao
Keyword(s):  
Collaborative Filtering ◽  
Social Relationships ◽  
Digital Libraries ◽  
Social Relationship ◽  
Nearest Neighbor ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Personalized Recommendation ◽  
Recommendation Algorithm

Currently collaborative filtering is widely used in e-commerce, digital libraries and other areas of personalized recommendation service system. Nearest-neighbor algorithm is the earliest proposed and the main collaborative filtering recommendation algorithm, but the data sparsity and cold-start problems seriously affect the recommendation quality. To solve these problems, A collaborative filtering recommendation algorithm based on users' social relationships is proposed. 0n the basis of traditional filtering recommendation technology, it combines with the interested objects of user's social relationship and takes the advantage of the tags to projects marked by users and their interested objects to improve the methods of recommendation. The experimental results of MAE ((Mean Absolute Error)) verify that this method can get better quality of recommendation.

scholarly journals A Review of Detection and Removal of Raindrops in Automotive Vision Systems

2021 ◽  
Vol 7 (3) ◽  
pp. 52
Author(s):  
Yazan Hamzeh ◽  
Samir A. Rawashdeh
Keyword(s):  
Detrimental Effect ◽  
Weather Conditions ◽  
Traffic Sign ◽  
Survey Paper ◽  
Sign Recognition ◽  
Adverse Weather Conditions ◽  
Wide Range ◽  
Adverse Weather ◽  
Protective Cover

Research on the effect of adverse weather conditions on the performance of vision-based algorithms for automotive tasks has had significant interest. It is generally accepted that adverse weather conditions reduce the quality of captured images and have a detrimental effect on the performance of algorithms that rely on these images. Rain is a common and significant source of image quality degradation. Adherent rain on a vehicle’s windshield in the camera’s field of view causes distortion that affects a wide range of essential automotive perception tasks, such as object recognition, traffic sign recognition, localization, mapping, and other advanced driver assist systems (ADAS) and self-driving features. As rain is a common occurrence and as these systems are safety-critical, algorithm reliability in the presence of rain and potential countermeasures must be well understood. This survey paper describes the main techniques for detecting and removing adherent raindrops from images that accumulate on the protective cover of cameras.

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