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Mobile technology is opening a wide range of opportunities for transforming the standard of care for chronic disorders. Using smartphones as tools for longitudinally tracking symptoms could enable personalization of drug regimens and improve patient monitoring. Parkinson's disease (PD) is an ideal candidate for these tools. At present, evaluation of PD signs requires trained experts to quantify motor...
Motivated by the fact that modeling and representation of multi-class signal patterns plays a critical role in Electroencephalogram (EEG)-based brain computer interface (BCI) systems, the paper proposes the coupling of error correction output coding (ECOC) with the common spatial pattern (CSP) analysis. Referred to as the ECO-CSP framework, the ECOC approach is applied to EEG motor imagery classification...
We consider the task of automatically predicting spirometry readings from cough and wheeze audio signals for asthma severity monitoring. Spirometry is a pulmonary function test used to measure forced expiratory volume in one second (FEV1) and forced vital capacity (FVC) when a subject exhales in the spirometry sensor after taking a deep breath. FEV1%, FVC% and their ratio are typically used to determine...
In this work, the problem of video orchestration performed by combining information extracted by multiple video sequences is considered. The novelty of the proposed approach relies on the use of aesthetic features and of cinematographic composition rules for automatically aggregating the inputs from different cameras in a unique video. While prior methodologies have separately addressed the issues...
Most activity-based person identity recognition methods operate on video data. Moreover, the vast majority of these methods focus on gait recognition. Obviously, recognition of a subject's identity using only gait imposes limitations to the applicability of the corresponding methods whereas a method capable of recognizing the subject's identity from various activities would be much more widely applicable...
The development of condition monitoring strategies is necessary to ensure the efficiency and reliability of the operation on electric machines. The feature calculation is an important signal processing step used to obtain a characterization related to the working condition of machinery. In order to address this issue, this work proposes a diagnosis methodology based on the calculation of a statistical-time...
Feature selection has been studied widely in the literature. However, the efficacy of the selection criteria for low sample size applications is neglected in most cases. Most of the existing feature selection criteria are based on the sample similarity. However, the distance measures become insignificant for high dimensional low sample size (HDLSS) data. Moreover, the variance of a feature with a...
We propose a method for optimizing an acoustic feature extractor for anomalous sound detection (ASD). Most ASD systems adopt outlier-detection techniques because it is difficult to collect a massive amount of anomalous sound data. To improve the performance of such outlier-detection-based ASD, it is essential to extract a set of efficient acoustic features that is suitable for identifying anomalous...
Security concerns increase as the technology for falsification advances and biometrics provides airtight security by identifying an individual based on the physiological and/or behavioral characteristics. Physiological hidden biometrics represented by ECG biomedical signal is highly confidential, sensitive, and hard to steal and replicate, and also hold great promise to provide a more secure biometric...
Detection of targets using low power embedded devices has important applications in border security and surveillance. In this paper, we build on recent algorithmic advances in sensor fusion, and present the design and implementation of a novel, multi-mode embedded signal processing system for detection of people and vehicles using acoustic and seismic sensors. Here, by "multi-mode", we mean...
The tracking and labeling of multiple objects in multiple cameras is a fundamental task in applications such as video surveillance, autonomous driving, and sports analysis. In an ad-hoc multi-camera network without a fusion center nodes can benefit from local cooperation to solve signal processing tasks, such as distributed image enhancement. A crucial first step for the successful cooperation of...
In this paper, we propose a novel kernel learning scheme for acoustic scene classification using multiple short-term observations. The method takes inspiration from the recent result of psychological research — "Humans use summary statistics to perceive auditory sequences" we endeavor to devise computational framework imitating such important auditory mechanism for acoustic scene parsing...
This paper investigates the problem of context incorporation into human language systems and particular in Sentiment Analysis (SA) systems. So far, the analysis of how different features, when incorporated into such systems, improve their performance, has been discussed in a number of studies. However, a complete picture of their effectiveness remains unexplored. With this work, we attempt to extend...
We consider the problem of fine-grained physical object recognition and introduce a dataset PharmaPack containing 1000 unique pharma packages enrolled in a controlled environment using consumer mobile phones as well as several recognition sets representing various scenarios. For performance evaluation, we extract two types of recently proposed local feature descriptors and aggregate them using popular...
Smartphone applications designed to track human motion in combination with wearable sensors, e.g., during physical exercising, raised huge attention recently. Commonly, they provide quantitative services, such as personalized training instructions or the counting of distances. But qualitative monitoring and assessment is still missing, e.g., to detect malpositions, to prevent injuries, or to optimize...
This paper aims to investigate the neural networking system. The signals to be studied have been taken from photonic sensors. For classification, a given signal is first transformed into different feature domains and then neural network is used to train the given dataset to form the network. Wavelet transform is used to extract the signal properties-skewness, kurtosis and entropy and Fourier Transform...
Visual attention networks are so pervasive in the human brain that eye movements carry a wealth of information that can be exploited for many purposes. In this paper, we present evidence that information derived from observers' gaze can be used to infer their age. This is the first study showing that simple features extracted from the ordered sequence of fixations and saccades allow us to predict...
We introduce a time-frequency scattering method using hyperbolic tangent function for vessel sound classification. The sound data is wavelet transformed using a two channel filter-bank and filter-bank outputs are scattered using tanh function. A feature vector similar to mel-scale cepstrum is obtained after a wavelet packed transform-like structure approximating the mel-frequency scale. Feature vectors...
To extract the impulsive and cyclostationary features of repetitive transients in bearing fault diagnosis simultaneously, an anti-symmetric real Laplace wavelet is optimized by two fitness functions. The first is to maximize kurtosis of the squared envelope of the filtered signal to capture the impulsiveness and the second is to maximize kurtosis of the squared envelope spectrum to capture the cyclostationarity...
Gearbox faults in wind turbines are one of the most important reasons for the failure of these machines which lead to the longest downtime and maintenance cost. While much attention has been given to detect faults in these mechanical devices, real-time fault diagnosis for streaming vibration data from turbine gearboxes still remains an outstanding problem. Moreover, monitoring gearboxes in a wind...
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