Search results for "Detection"
showing 10 items of 2543 documents
Mixed l-/l1 fault detection observer design for positive switched systems with time-varying delay via delta operator approach
2014
Published version of an article in the journal: International Journal of Control, Automation and Systems. Also available from the publisher at: http://dx.doi.org/10.1007/s12555-013-0466-1 This paper investigates the problem of fault detection observer design for positive switched systems with time-varying delay via delta operator approach. A new fault sensitivity measure, called l-index, is proposed. The l- fault detection observer design and multi-objective l -/l1 fault detection observer design problems are addressed. Based on the average dwell time approach and the piecewise copositive type Lyapunov-Krasovskii functional method in delta domain, sufficient conditions for the existence of …
Anomaly Detection in Traffic Surveillance Videos Using Deep Learning
2022
In the recent past, a huge number of cameras have been placed in a variety of public and private areas for the purposes of surveillance, the monitoring of abnormal human actions, and traffic surveillance. The detection and recognition of abnormal activity in a real-world environment is a big challenge, as there can be many types of alarming and abnormal activities, such as theft, violence, and accidents. This research deals with accidents in traffic videos. In the modern world, video traffic surveillance cameras (VTSS) are used for traffic surveillance and monitoring. As the population is increasing drastically, the likelihood of accidents is also increasing. The VTSS is used to detect abno…
Automatic Relative Radiometric Normalization of Bi-Temporal Satellite Images Using a Coarse-to-Fine Pseudo-Invariant Features Selection and Fuzzy Int…
2022
Relative radiometric normalization (RRN) is important for pre-processing and analyzing multitemporal remote sensing (RS) images. Multitemporal RS images usually include different land use/land cover (LULC) types; therefore, considering an identical linear relationship during RRN modeling may result in potential errors in the RRN results. To resolve this issue, we proposed a new automatic RRN technique that efficiently selects the clustered pseudo-invariant features (PIFs) through a coarse-to-fine strategy and uses them in a fusion-based RRN modeling approach. In the coarse stage, an efficient difference index was first generated from the down-sampled reference and target images by combining…
Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian b…
2023
Numerous buildings fall short of expectations regarding occupant satisfaction, sustainability, or energy efficiency. In this paper, the performance of buildings in terms of occupant comfort is evaluated using a probabilistic model based on Bayesian networks (BNs). The BN model is founded on an in-depth anal- ysis of satisfaction survey responses and a thorough study of building performance parameters. This study also presents a user-friendly visualization compatible with BIM to simplify data collecting in two case studies from Norway with data from 2019 to 2022. This paper proposes a novel Digital Twin approach for incorporating building information modeling (BIM) with real-time sensor data…
Malware Detection in Internet of Things (IoT) Devices Using Deep Learning
2022
Internet of Things (IoT) devices usage is increasing exponentially with the spread of the internet. With the increasing capacity of data on IoT devices, these devices are becoming venerable to malware attacks; therefore, malware detection becomes an important issue in IoT devices. An effective, reliable, and time-efficient mechanism is required for the identification of sophisticated malware. Researchers have proposed multiple methods for malware detection in recent years, however, accurate detection remains a challenge. We propose a deep learning-based ensemble classification method for the detection of malware in IoT devices. It uses a three steps approach; in the first step, data is prep…
Automatic defect localization in VLSI circuits: A fusion approach based on the Dempster-Shafer theory
2017
Defect localization in Very Large Integration Cir-cuits (VLSI) requires to use multi-sensor information such aselectrical waveforms, emission microscopy images and frequencymapping in order to detect, localize and identify the failure. Eachsensor provides a specific kind of feature modeling the evidence.Thus, the defect localization in VLSI can be summarized asa problem of data fusion with heterogeneous and impreciseinformation. This study illustrates how to reproduce the humandecision for modeling and fusing the different multi-sensorfeatures by using the Demspter-Shafer theory. We propose notonly an automatic decision rule for mass functions computingbut also confidence intervals to quantif…
Josephson Traveling Wave Parametric Amplifiers as Non-Classical Light Source for Microwave Quantum Illumination
2021
Abstract Detection of low-reflectivity objects can be enriched via the so-called quantum illumination procedure. In order that this quantum procedure outperforms classical detection protocols, entangled states of microwave radiation are initially required. In this paper, we discuss the role of Josephson Traveling Wave Parametric Amplifiers (JTWPAs), based on circuit-QED components, as suitable sources of a two-mode squeezed vacuum state, a special signal-idler entangled state. The obtained wide bandwidth makes the JTWPA an ideal candidate for generating quantum radiation in quantum metrology and information processing applications.
An accurate and efficient collaborative intrusion detection framework to secure vehicular networks
2015
Display Omitted We design and implement an accurate and lightweight intrusion detection framework, called AECFV.AECFV aims to protect the vehicular ad hoc networks (VANETs) against the most dangerous attacks that could occurred on this network.AECFV take into account the VANET's characteristics such as high node's mobility and rapid topology change.AECFV exhibits a high detection rate, low false positive rate, faster attack detection, and lower communication overhead. The advancement of wireless communication leads researchers to develop and conceive the idea of vehicular networks, also known as vehicular ad hoc networks (VANETs). Security in such network is mandatory due to a vital informa…
VIGIL System: A Computer Vision-Based AID System. Evaluation in a Ring Motorway Section in Madrid
1994
Abstract The VIGIL system is an Automatic Incident and Congestion Detection System based on computer vision technology tor motorway applications. VIGIL is constituted by two main modules, the Local Sensor Module and the Central System module. The objective of this paper is to describe the VIGIL Central System functions, mainly the alarm filtering and management capabilities, and to present the field trial currently carried out in Madrid within the ARTIS project (Advanced RTI in Spain) supported by the Tranport Telematic Programme of the EC.
An Embedded Real-Time Lane-Keeper for Automatic Vehicle Driving
2008
Automatic vehicle driving involves several issues, such as the capability to follow the road and keep the right lane, to maintain the distance between vehicles, to regulate vehiclepsilas speed, to find the shortest route to a destination. In this paper a real-time automatic lane-keeper is proposed. The main features of the system are the lane markers location process as well as the computation of the vehiclepsilas steering lock. The above techniques require high elaboration speed to execute, check and complete an operation before a prearranged time. Clearly if system processing exceeds the deadline, the whole operation became meaningless or, in the meantime, the vehicle can reach a critical…