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Mechanical systems operating in noisy environments create a challenging signal processing and monitoring problem especially in real-time. To detect a particular type of subsystem from noisy vibration data, it is necessary to identify signatures or particular features that make it unique. Resonant (modal) frequencies emitted during its normal operation satisfy this constraint. Monitoring structural...
Focusing on detection technology for autonomous underwater detection device which found marine target which radiated noise signal submerged in background noise, chaotic detector based on Duffing oscillator is researched. In this paper, the Duffing oscillator and chaotic detector based on Duffing oscillator are described. Through numerical simulation and theoretical analysis, it is proved that the...
Traffic retention is an important factor of intelligent transportation system, which can vividly reflect current state of the road. Accurate estimation of traffic retention can provide support for the dynamic allocation of the traffic signal, thus alleviating the problem of urban traffic congestion. The traditional method can only estimate the long-term or red light traffic retention according to...
The application of machine learning to software fault injection data has been shown to be an effective approach for the generation of efficient error detection mechanisms (EDMs). However, such approaches to the design of EDMs have invariably adopted a fault model with a single-fault assumption, limiting the practical relevance of the detectors and their evaluation. Software containing more than a...
This paper is concerned with the problem of designing successful false data injection attacks in cyber-physical systems from the attacker's angle. A discrete linear time-invariant system is considered, which is equipped with a Kalman filter and a χ2 failure detector. It is assumed that the attacker cannot obtain the accurate model of the control system. Based on the inaccurate model, the method of...
In this paper, we present a non-parametric dataanalytic soft-error detector. Our detector uses the key properties of Gaussian process regression. First, because Gaussian process regression provides confidence on the prediction, this confidence can be used to automatize construction of the detection range. Second, because the correlation model of a Gaussian process captures the similarity among neighboring...
This paper presents an online multiple pedestrian detection and tracking method using unified multi-channel features. The proposed method efficiently utilizes the multi-channel features by sharing them in each module: pedestrian detection, visual tracking, and data association. The multi-channel features are originally generated from the pedestrian detection module, and they represent sufficiently...
Data acquisition systems for large-scale high-energy physics experiments have to handle hundreds of gigabytes per second of data, and are typically implemented as specialized data centers that connect a very large number of front-end electronics devices to an event detection and storage system. The design of such systems is often based on many assumptions, small-scale experiments and a substantial...
We have proposed a concept for classification interesting points in images by means of a machine learning approach. The basic idea is that each interesting point detected in an image is classified either as a point belonging to some trained model (e.g. corner of a license plate) or not. During the first stage, we detected interesting points in a set of images by the well-known SURF method. Then we...
The Schroedinger Eigenmaps (SE) embedding has been previously introduced and applied to spectral target detection problems in hyperspectral imagery (HSI). The proposed SE-based detection approach combines the spectral and spatial connectivity of target-like pixels into the Schroedinger operator by using a “knowledge propagation” scheme. Likewise, it has been noted the impact that the local data structure...
Traffic light detection (TLD) is a vital part of both intelligent vehicles and driving assistance systems (DAS). General for most TLDs is that they are evaluated on small and private datasets making it hard to determine the exact performance of a given method. In this paper we apply the state-of-the-art, real-time object detection system You Only Look Once, (YOLO) on the public LISA Traffic Light...
Detection of anomalous pixels within hyperspectral imagery is frequently used for purposes ranging from the location of invasive plant species to the detection of military targets. The task is unsupervised because no information about target or background spectra is known or assumed. Some of the most commonly used detection algorithms assume a statistical distribution for the background and rate spectral...
With the rise of end-to-end learning through deep learning, person detectors and re-identification (ReID) models have recently become very strong. Multi-target multicamera (MTMC) tracking has not fully gone through this transformation yet. We intend to take another step in this direction by presenting a theoretically principled way of integrating ReID with tracking formulated as an optimal Bayes filter...
This paper presents a data-driven model to estimate the traversal speed of public transport on urban arterials under non-recurring congestion. We group unidirectional links of an arterial and use the concept of Macroscopic Fundamental Diagram to achieve a smooth speed-density relationship along the arterial. The methodology comprises two main steps: first, developing the model and estimating fit parameters...
In this paper we investigate, through experimental measurements, a propagation model of the visible light. The scope is to come up with a fine tuned propagation model which also accounts for reflection from the optical bench. The experiments were conducted in the European Laboratory of Non Linear Spectroscopy (LENS). The proposed propagation model can be used to evaluate the performance of visible...
A novelty detection algorithm inspired by human audio pattern recognition is conceptualized and experimentally tested. Time-domain data obtained from a microphone is processed by applying a short-time FFT, which returns time-frequency patterns. Such patterns are fed to a probabilistic algorithm, which is designed to detect novel signals and identify windows in the frequency domain where such novelties...
In our previous work, we have applied ordinary linear regression equation to network anomaly detection. However, the performance of ordinary linear regression equation is susceptible to outliers. Unfortunately, it is almost impossible to obtain a “clean” traffic data set for ordinary regression model due to the burstiness of network traffic and the pervasive network attacks. In this paper, we make...
Multi-object tracking is often hindered by difficulties such as occlusion and illumination change. In this paper, we propose a novel multi-object tracking method based on main-parts model. Main-parts model is formulated by segmenting parts of object and accumulating variations of appearance from previous frames. We assume that parts with weaker appearance variations are main-parts of an object. By...
Image stitching technique is to integrate multiple images with overlapping regions into a complete image with a wide viewing angle, less distortion, and no obvious suture. Image stitching could be used for global positioning and robot autonomous navigation without changing the hardware. SIFT feature and SURF feature are the classical algorithm in the image stitching. But they have the long time-consuming...
Online changepoint detection is an important task for machine learning in changing environments, as it signals when the learning model needs to be updated. Presence of noise that can be mistaken for real changes makes it difficult to develop an effective approach that would have a low false alarm rate and being able to detect all the changes with a minimal delay. In this paper we study how performance...
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