Search results for " Detection"
showing 10 items of 1676 documents
Modeling and Verification of Symbolic Distributed Applications Through an Intelligent Monitoring Agent
2022
Wireless Sensor Networks (WSNs) represent a key component in emerging distributed computing paradigms such as IoT, Ambient Intelligence, and Smart Cities. In these contexts, the difficulty of testing, verifying, and monitoring applications in their intended scenarios ranges from challenging to impractical. Current simulators can only be used to investigate correctness at source code level and with limited accuracy. This paper proposes a system and a methodology to model and verify symbolic distributed applications running on WSNs. The approach allows to complement the distributed application code at a high level of abstraction in order to test and reprogram it, directly, on deployed network…
A Novel Visual Interface to Foster Innovation in Mechanical Engineering and Protect from Patent Infringement
2018
One of the main time and money consuming tasks in the design of industrial devices and parts is the checking of possible patent infringements. Indeed, the great number of documents to be mined and the wide variety of technical language used to describe inventions are reasons why considerable amounts of time may be needed. On the other hand, the early detection of a possible patent conflict, in addition to reducing the risk of legal disputes, could stimulate a designers' creativity to overcome similarities in overlapping patents. For this reason, there are a lot of existing patent analysis systems, each with its own features and access modes. We have designed a visual interface providing an …
A Behavior-Based Intrusion Detection System Using Ensemble Learning Techniques
2022
Intrusion Detection Systems (IDSs) play a key role in modern ICT security. Attacks detected and reported by IDSs are often analyzed by administrators who are tasked with countering the attack and minimizing its damage. Consequently, it is important that the alerts generated by the IDS are as detailed as possible. In this paper, we present a multi-layered behavior-based IDS using ensemble learning techniques for the classification of network attacks. Three widely adopted and appreciated models, i.e., Decision Trees, Random Forests, and Artificial Neural Networks, have been chosen to build the ensemble. To reduce the system response time, our solution is designed to immediately filter out tra…
Probabilistic Anomaly Detection for Wireless Sensor Networks
2011
Wireless Sensor Networks (WSN) are increasingly gaining popularity as a tool for environmental monitoring, however ensuring the reliability of their operation is not trivial, and faulty sensors are not uncommon; moreover, the deployment environment may influence the correct functioning of a sensor node, which might thus be mistakenly classified as damaged. In this paper we propose a probabilistic algorithm to detect a faulty node considering its sensed data, and the surrounding environmental conditions. The algorithm was tested with a real dataset acquired in a work environment, characterized by the presence of actuators that also affect the actual trend of the monitored physical quantities.
Real-Time Object Detection in Embedded Video Surveillance Systems
2008
In this paper we report a new method to detect both moving objects and new stationary objects in video sequences. On the basis of temporal consideration we classify pixels into three classes: background, midground and foreground to distinguish between long-term, medium-term and short-term changes. The algorithm has been implemented on a hardware platform with limited resources and it could be used in a wider system like a wireless sensor networks. Particular care has been put in realizing the algorithm so that the limited available resources are used in an efficient way. Experiments have been conducted on publicly available datasets and performance measures are reported.
Pedestrian Tracking in 360 Video by Virtual PTZ Cameras
2018
Since the data acquired by a PTZ camera change while adjusting the pan, tilt and zoom parameters, the results of tracking algorithms are difficult to reproduce; such diffi- culty limits the development and the comparison of tracking algorithms with PTZ cameras. The recently introduced 360- degree cameras acquire spherical views of the environment, generally stored as equirectangular images. Each pixel of an equirectangular image corresponds to a point on the spherical surface. A gnomonic projection can be used to project the points on the spherical surface onto a plane tangent to the sphere. Such tangent plane can be interpreted as the image plane of a virtual PTZ camera oriented towards th…
A framework for real-time Twitter data analysis
2016
A framework for real-time Twitter data analysisWe propose improvements to the Soft Frequent Pattern Mining (SFPM) algorithmThe stream of tweets is organized in dynamic windows whose size depends both on the volume of tweets and timeThe set of keywords used to query Twitter is progressively refined to highlight the user's point of viewComparisons with two state of the art systems Twitter is a popular social network which allows millions of users to share their opinions on what happens all over the world. In this work we present a system for real-time Twitter data analysis in order to follow popular events from the user's perspective. The method we propose extends and improves the Soft Freque…
Detection of Points of Interest in a Smart Campus
2019
Understanding users' habits is a critical task in order to develop advanced services, such as personalized recommendation and virtual assistance. In this work, we propose a novel approach to detect Points of Interest visited by users of a campus, by using mobility traces collected through users' smartphones. Our method takes advantage of the intentional and recurrent nature of human movements to build up mobility profiles, and combines different machine learning methods to merge sensory information with the past users' behavior. The proposed approach has been validated on a synthetic dataset and the experimental results show its effectiveness.
A distributed Bayesian approach to fault detection in sensor networks
2012
Sensor networks are widely used in industrial and academic applications as the pervasive sensing module of an intelligent system. Sensor nodes may occasionally produce incorrect measurements due to battery depletion, dust on the sensor, manumissions and other causes. The aim of this paper is to develop a distributed Bayesian fault detection algorithm that classifies measurements coming from the network as corrupted or not. The computational complexity is polynomial so the algorithm scales well with the size of the network. We tested the approach on a synthetic dataset and obtained significant results in terms of correctly labeled measurements.
QoS-Aware Fault Detection in Wireless Sensor Networks
2013
Wireless sensor networks (WSNs) are a fundamental building block of many pervasive applications. Nevertheless the use of such technology raises new challenges regarding the development of reliable and fault-tolerant systems. One of the most critical issues is the detection of corrupted readings amidst the huge amount of gathered sensory data. Indeed, such readings could significantly affect the quality of service (QoS) of the WSN, and thus it is highly desirable to automatically discard them. This issue is usually addressed through “fault detection” algorithms that classify readings by exploiting temporal and spatial correlations. Generally, these algorithms do not take into account QoS re…