scholarly journals SSD Real-Time Illegal Parking Detection Based on Contextual Information Transmission

2020 ◽  
Vol 62 (1) ◽  
pp. 293-307
Author(s):  
Huanrong Tang ◽  
Aoming Peng ◽  
Dongming Zhang ◽  
Tianming Liu ◽  
Jianquan Ouyang
Keyword(s):  
Information Transmission ◽  
Real Time ◽  
Contextual Information

scholarly journals DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Suppawong Tuarob ◽  
Poom Wettayakorn ◽  
Ponpat Phetchai ◽  
Siripong Traivijitkhun ◽  
Sunghoon Lim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Data Collection ◽  
Stock Market ◽  
Real Time ◽  
Stock Prices ◽  
Contextual Information ◽  
Online News ◽  
Stock Prediction ◽  
Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

scholarly journals IoTSAS: An Integrated System for Real-Time Semantic Annotation and Interpretation of IoT Sensor Stream Data

Computers ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 127
Author(s):  
Besmir Sejdiu ◽  
Florije Ismaili ◽  
Lule Ahmedi
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Contextual Information ◽  
Semantic Annotation ◽  
Weather Conditions ◽  
Water Quality Monitoring ◽  
Integrated System ◽  
Quality Monitoring ◽  
Stream Data ◽  
Semantic Annotations

Sensors and other Internet of Things (IoT) technologies are increasingly finding application in various fields, such as air quality monitoring, weather alerts monitoring, water quality monitoring, healthcare monitoring, etc. IoT sensors continuously generate large volumes of observed stream data; therefore, processing requires a special approach. Extracting the contextual information essential for situational knowledge from sensor stream data is very difficult, especially when processing and interpretation of these data are required in real time. This paper focuses on processing and interpreting sensor stream data in real time by integrating different semantic annotations. In this context, a system named IoT Semantic Annotations System (IoTSAS) is developed. Furthermore, the performance of the IoTSAS System is presented by testing air quality and weather alerts monitoring IoT domains by extending the Open Geospatial Consortium (OGC) standards and the Sensor Observations Service (SOS) standards, respectively. The developed system provides information in real time to citizens about the health implications from air pollution and weather conditions, e.g., blizzard, flurry, etc.

scholarly journals Design and Implementation of a Connection between Augmented Reality and Sensors

Robotics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 3
Author(s):  
Marlon Aguero ◽  
Dilendra Maharjan ◽  
Maria del Pilar Rodriguez ◽  
David Dennis Lee Mascarenas ◽  
Fernando Moreu
Keyword(s):  
Augmented Reality ◽  
Data Collection ◽  
Real Time ◽  
Strain Gauge ◽  
Low Cost ◽  
Contextual Information ◽  
Wireless Sensor ◽  
Infrastructure Management ◽  
Data Collection And Analysis ◽  
The Way

Wireless sensor networks (WSN) are used by engineers to record the behavior of structures. The sensors provide data to be used by engineers to make informed choices and prioritize decisions concerning maintenance procedures, required repairs, and potential infrastructure replacements. However, reliable data collection in the field remains a challenge. The information obtained by the sensors in the field frequently needs further processing, either at the decision-making headquarters or in the office. Although WSN allows data collection and analysis, there is often a gap between WSN data analysis results and the way decisions are made in industry. The industry depends on inspectors’ decisions, so it is of vital necessity to improve the inspectors’ access in the field to data collected from sensors. This paper presents the results of an experiment that shows the way Augmented Reality (AR) may improve the availability of WSN data to inspectors. AR is a tool which overlays the known attributes of an object with the corresponding position on the headset screen. In this way, it allows the integration of reality with a virtual representation provided by a computer in real time. These additional synthetic overlays supply data that may be unavailable otherwise, but it may also display additional contextual information. The experiment reported in this paper involves the application of a smart Strain Gauge Platform, which automatically measures strain for different applications, using a wireless sensor. In this experiment, an AR headset was used to improve actionable data visualization. The results of the reported experiment indicate that since the AR headset makes it possible to visualize information collected from the sensors in a graphic form in real time, it enables automatic, effective, reliable, and instant communication from a smart low-cost sensor strain gauge to a database. Moreover, it allows inspectors to observe augmented data and compare it across time and space, which then leads to appropriate prioritization of infrastructure management decisions based on accurate observations.

Intelligent Traffic Control at Intersection Based upon Real-Time Vehicle Detection

2013 ◽  
Vol 313-314 ◽  
pp. 343-346
Author(s):  
Guo Zhong Yao
Keyword(s):  
Information Transmission ◽  
Real Time ◽  
Traffic Control ◽  
New Method ◽  
System Operation ◽  
Traffic Condition ◽  
Traffic Lights ◽  
Ism Band ◽  
Pass Through ◽  
Real Traffic

A new method of traffic control based upon real-time number of the vehicles at the intersection was pulled forward. Serial vehicle detectors were used to detect the number of the vehicle waiting to pass through the intersection. One or more control boxes were used to control the traffic lights according to real traffic condition and transreceived data between itself and the detectors. The information transmission between the detectors and the controllers is based upon 2.4G ISM band. Arithmetic on the system operation, which took the pedestrians and the situation of excessive vehicles into account, is introduced.

scholarly journals Movement Onset Detection and Target Estimation for Robot-Aided Arm Training

2015 ◽  
Vol 63 (4) ◽  
Author(s):  
Robert Riener ◽  
Domen Novak
Keyword(s):  
Eye Tracking ◽  
Real Time ◽  
Time Use ◽  
Healthy Subjects ◽  
Robot Control ◽  
Contextual Information ◽  
Estimation Algorithm ◽  
Movement Onset ◽  
Joint Torques ◽  
Arm Rehabilitation

AbstractThis paper presents a motion intention estimation algorithm that is based on the recordings of joint torques, joint positions, electromyography, eye tracking and contextual information. It is intended to be used to support a virtual-reality-based robotic arm rehabilitation training. The algorithm first detects the onset of a reaching motion using joint torques and electromyography. It then predicts the motion target using a combination of eye tracking and context, and activates robotic assistance toward the target. The algorithm was first validated offline with 12 healthy subjects, then in a real-time robot control setting with 3 healthy subjects. In offline crossvalidation, onset was detected using torques and electromyography 116 ms prior to detectable changes in joint positions. Furthermore, it was possible to successfully predict a majority of motion targets, with the accuracy increasing over the course of the motion. Results were slightly worse in online validation, but nonetheless show great potential for real-time use with stroke patients.

scholarly journals Situated Language Processing Across the Lifespan: A Review

2017 ◽  
Vol 7 (1) ◽  
pp. 1 ◽  
Author(s):  
Katja Münster ◽  
Pia Knoeferle
Keyword(s):  
Older Adults ◽  
Real Time ◽  
Language Processing ◽  
Time Course ◽  
Contextual Information ◽  
Psycholinguistic Research ◽  
Younger Adults ◽  
Real Time Processing ◽  
Age Related ◽  
Vision And Language

In this review we focus on the close interplay between visual contextual information and real-time language processing. Crucially, we are showing that not only college-aged adults but also children and older adults can profit from visual contextual information for language comprehension. Yet, given age-related biological and experiential changes, children and older adults might not always be able to link visual and linguistic information in the same way and with the same time course as younger adults in real-time language processing. Psycholinguistic research on visually situated real-time language processing in children and even more so older adults is still scarce compared to research in this domain using college-aged participants. In order to gain more comprehensive insights into the interplay between vision and language during real-time processing, we are arguing for a lifespan approach to situated language processing.

scholarly journals Real-time Factory Smoke Detection Based on Two-stage Relation-guided Algorithm

2021 ◽  
Author(s):  
Zhenyu Wang ◽  
Senrong Ji ◽  
Duokun Yin
Keyword(s):  
Real Time ◽  
Contextual Information ◽  
Search Space ◽  
Detection Algorithm ◽  
Image Resolution ◽  
Single Stage ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Smoke Detection ◽  
Two Stage

Abstract Recently, using image sensing devices to analyze air quality has attracted much attention of researchers. To keep real-time factory smoke under universal social supervision, this paper proposes a mobile-platform-running efficient smoke detection algorithm based on image analysis techniques. Since most smoke images in real scenes have challenging variances, it’s difficult for existing object detection methods. To this end, we introduce the two-stage smoke detection (TSSD) algorithm based on the lightweight framework, in which the prior knowledge and contextual information are modeled into the relation-guided module to reduce the smoke search space, which can therefore significantly improve the shortcomings of the single-stage method. Experimental results show that the TSSD algorithm can robustly improve the detection accuracy of the single-stage method and has good compatibility for different image resolution inputs. Compared with various state-of-the-art detection methods, the accuracy AP mean of the TSSD model reaches 59.24%, even surpassing the current detection model Faster R-CNN. In addition, the detection speed of our proposed model can reach 50 ms (20 FPS), which meets the real-time requirements, and can be deployed in the mobile terminal carrier. This model can be widely used in some scenes with smoke detection requirements, providing great potential for practical environmental applications.

Information Transmission with Quality of Service

2010 ◽  
pp. 262-275
Author(s):  
Manjunath Ramachandra
Keyword(s):  
Quality Of Service ◽  
Supply Chain ◽  
Information Transmission ◽  
Real Time ◽  
Data Transfer ◽  
Time Data ◽  
Real Time Data ◽  
Data Transfers ◽  
The Web

The data being transferred over the supply chain has to compete with the increasing applications around the web, throwing open the challenge of meeting the constraint of in-time data transfers with the available resources. It often leads to flooding of resources, resulting in the wastage of time and loss of data. Most of the applications around the customer require real time data transfer over the web to enable right decisions. To make it happen, stringent constraints are required to be imposed on the quality of the transfer. This chapter provides the mechanism for shaping of traffic flows towards sharing the existing infrastructure.

The Role of Augmented Reality within Ambient Intelligence

2014 ◽  
pp. 73-88
Author(s):  
Kevin Curran ◽  
Denis McFadden ◽  
Ryan Devlin
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Physical Environment ◽  
Intelligent Systems ◽  
Ambient Intelligence ◽  
Contextual Information ◽  
Ambient Intelligent

An Augmented Reality (AR) is a technology which provides the user with a real time 3D enhanced perception of a physical environment with addition virtual elements—either virtual scenery, information regarding surroundings, other contextual information—and is also capable of hiding or replacing real structures. With Augmented Reality applications becoming more advanced, the ways the technology can be viably used is increasing. Augmented Reality has been used for gaming several times with varying results. AR systems are seen by some as an important part of the ambient intelligence landscape. Therefore, the authors present several types of augmentation applications of AR in the domestic, industrial, scientific, medicinal, and military sectors which may benefit future ambient intelligent systems.

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