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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...
Prognostic diagnosis is desirable for commercial core router systems to ensure early failure prediction and fast error recovery. The effectiveness of prognostic diagnosis depends on whether anomalies can be accurately detected before a failure occurs. However, traditional anomaly detection techniques fail to detect “outliers” when the statistical properties of the monitored data change significantly...
To ensure high reliability and rapid error recovery in commercial core router systems, a health-status analyzer is essential to monitor the different features of core routers. However, traditional health analyzers need to store a large amount of historical data in order to identify health status. The storage requirement becomes prohibitively high when we attempt to carry out long-term health-status...
Specific characteristics of the functional near infrared spectroscopy (fNIRS) of the hemodynamic response may represent the brain cortical activity levels during mental arithmetic tasks. In this paper, we use hemodynamic response signals of the prefrontal cortex, acquired by a 4-channel fNIRS system to identify the difficulty level of an arithmetic task. To this end, twelve temporal features and several...
One of the most serious cyber-security threats is the botnet. The botnet runs in the background of the compromised machine and maintains the communication with the C&C server to receive malicious commands. Botnet master uses botnet to launch dangerous attacks. %such as Distributed Denial of Service (DDoS), data stealing and spamming. This paper addresses the problem of detecting P2P botnet flow...
During the last decades topics such as video analysis and image understanding techniques have experimented an important evolution due to its inclusion in applications such as surveillance, intelligent spaces and assisted living. In order to validate all related works different datasets have been distributed within the research community: CAVIAR, KTH, Weizmann, INRIA or MuHAVI are some of the most...
The ability to generate computationally compact ECG analysis algorithms is of interest in the field of wearable physiologic monitors. Such remote monitors necessarily have limited on-board energy storage and therefore lack the computational power and physical memory often required for academic study of physiologic waveforms. Herein we evaluate a set of algorithms with markedly different computation...
In many distributed sensing applications, continuous sensor monitoring requires processing with a significant energy footprint, which hinders autonomous operation and battery lifetime of sensor nodes. In our research we explore the power savings gained by splitting the hardware architecture for continuous monitoring into two stages: an always-on ultra-low-power mixed-signal wake-up circuit placed...
According to analysis reports on road accidents of recent years, it's renowned that the main cause of road accidents resulting in deaths, severe injuries and monetary losses, is due to a drowsy or a sleepy driver. Drowsy state may be caused by lack of sleep, medication, drugs or driving continuously for long time period. An increase rate of roadside accidents caused due to drowsiness during driving...
Event detection plays an important role in today's Non-Intrusive Load Monitoring (NILM) systems faced more and more with nonlinear and variable loads. For this purpose, the paper presents an unsupervised NILM event detector based on kernel Fisher discriminant analysis (KFDA) which provides accurate start and end times of so-called active sections. Active sections are an extension of classical NILM...
In this work we investigate car detection from aerial imagery and explore how it can be applied to urban understanding. To perform car detection we use the rotationally-invariant Fourier HOG detector. By adding incremental changes we are able to improve its detection probability by 10% for a range of false alarm rates. Further improvements can be made if we filter out cars that are not near known...
Attention is one of the brain’s ability to focus on one particular issue among many others. Attention plays a critical role in those tasks that involve detection of rare events such as air traffic control where any attention lapses may cause a disaster. So, measurement and quantification of attention workload levels is critical and can be used to improve one’s performance. In this article an effective...
Anomaly detection in a network is important for diagnosing attacks or failures that affect the performance and security of a network. Lately, many anomaly detection techniques have been proposed for detecting attacks whose nature is strange. A process for extracting useful features is implemented in the anomaly detection framework. Standard matrices are applied for measuring the operation of the anomaly...
This paper discusses the circuit comparison of the electrocardiogram (ECG) heart rate detector for wearable biomedical devices. In this work, the QRS complex is used to calculate heart rate, representing the main component of the ECG signal. In order to achieve a high level of accuracy by the detector, the measured ECG signal must be free of noise. Typically, such noise originates from power line...
This paper reports results from a set of experiments that evaluate an insider threat detection prototype on its ability to detect scenarios that have not previously been seen or contemplated by the developers of the system. We show the ability to detect a large variety of insider threat scenario instances imbedded in real data with no prior knowledge of what scenarios are present or when they occur...
High-resolution scale-space scanning is introduced as a feature-probing technique in difference-of-Gaussian detectors. Scans of the feature response are produced versus scale-space parameter σ for different window sizes, for a set of diverse images. Mean repeatability scans are used to select the filter parameters of a reliable Scale-Invariant Feature Transform (SIFT) detector. A simple and hardware-friendly...
Smartphone malwares are serious threat. Malware detector is the primary tool to protect Smartphones against malwares. The malware detector efficiency is based on the technique it uses. In this paper, we survey the current state of the art of Smartphone malware detection techniques. Those techniques have been classified into a structured taxonomy based on 3 rules. Those rules are inferred and compiled...
Event Extraction is a complex and interesting topic in Information Extraction that includes event extraction methods from free text or web data. The result of event extraction systems can be used in several fields such as risk analysis systems, online monitoring systems or decide support tools. In this paper, we introduce a method that combines lexico -- semantic and machine learning to extract event...
During training and classification, instances are drawn from the instance space and mapped to the feature space. We focus on the problem of detecting hidden changes in the functions that map instances to feature vectors during classification. We call such changes feature shift and introduce an on-line method for detecting it. Our method is based on a robust similarity measure that uses one-class SVM...
Our developed solution of intelligent audio surveillance systems is based on modified Viterbi decoder customized for long-term audio-events monitoring application. The classical Viterbi decoder evaluates the hypothesis after detecting the end of the event (utterance) in the front-end module. The end of the event is usually detected using UBM (Universal Background Model) or relative entropy or energy...
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