Emotion Detection Using Noninvasive Low Cost Sensors

Emotion recognition from biometrics is relevant to a wide range of application domains, including healthcare. Existing approaches usually adopt multi-electrodes sensors that could be expensive or uncomfortable to be used in real-life situations. In this study, we investigate whether we can reliably recognize high vs. low emotional valence and arousal by relying on noninvasive low cost EEG, EMG, and GSR sensors. We report the results of an empirical study involving 19 subjects. We achieve state-of-the- art classification performance for both valence and arousal even in a cross-subject classification setting, which eliminates the need for individual training and tuning of classification models.Comment: To appear in Proceedings of ACII 2017, the Seventh International Conference on Affective Computing and Intelligent Interaction, San Antonio, TX, USA, Oct. 23-26, 201

A Python-based Brain-Computer Interface Package for Neural Data Analysis

Anowar, Md Hasan, A Python-based Brain-Computer Interface Package for Neural Data Analysis. Master of Science (MS), December, 2020, 70 pp., 4 tables, 23 figures, 74 references. Although a growing amount of research has been dedicated to neural engineering, only a handful of software packages are available for brain signal processing. Popular brain-computer interface packages depend on commercial software products such as MATLAB. Moreover, almost every brain-computer interface software is designed for a specific neuro-biological signal; there is no single Python-based package that supports motor imagery, sleep, and stimulated brain signal analysis. The necessity to introduce a brain-computer interface package that can be a free alternative for commercial software has motivated me to develop a toolbox using the python platform. In this thesis, the structure of MEDUSA, a brain-computer interface toolbox, is presented. The features of the toolbox are demonstrated with publicly available data sources. The MEDUSA toolbox provides a valuable tool to biomedical engineers and computational neuroscience researchers

Real-time human ambulation, activity, and physiological monitoring:taxonomy of issues, techniques, applications, challenges and limitations

Automated methods of real-time, unobtrusive, human ambulation, activity, and wellness monitoring and data analysis using various algorithmic techniques have been subjects of intense research. The general aim is to devise effective means of addressing the demands of assisted living, rehabilitation, and clinical observation and assessment through sensor-based monitoring. The research studies have resulted in a large amount of literature. This paper presents a holistic articulation of the research studies and offers comprehensive insights along four main axes: distribution of existing studies; monitoring device framework and sensor types; data collection, processing and analysis; and applications, limitations and challenges. The aim is to present a systematic and most complete study of literature in the area in order to identify research gaps and prioritize future research directions

Ultrasonic signal detection and recognition using dynamic wavelet fingerprints

A novel ultrasonic signal detection and characterization technique is presented in this dissertation. The basic tool is a simplified time-frequency (scale) projection which is called a dynamic wavelet fingerprint. Take advantage of the matched filter and adaptive time-frequency analysis properties of the wavelet transform, the dynamic wavelet fingerprint is a coupled approach of detection and recognition. Different from traditional value-based approaches, the dynamic wavelet fingerprint based technique is pattern or knowledge based. It is intuitive and self-explanatory, which enables the direct observation of the variation of non-stationary ultrasonic signals, even in complex environments. Due to this transparent property, efficient detection and characterization algorithms can be customized to address specific problems. Furthermore, artificial intelligence can be integrated and expert systems can be developed based on it.;Several practical ultrasonic applications were used to evaluate the feasibility and performance of this technique. The first application was ultrasonic materials sorting. Dynamic wavelet fingerprints of echoes from the surface of different plates were generated and then used to successfully identify corresponding plates.;The second application was ultrasonic periodontal probing. The dynamic wavelet fingerprint technique was used to expose the hidden trend of the complex waveforms. Taking the manual probing data as gold standard , a 40% agreement ratio was achieved with a tolerance limit of 1mm. However, statistically, lack of agreement was found in terms of the limits of agreement of Bland and Altman.;The third application was multi-mode Lamb wave tomography. The dynamic wavelet fingerprint technique was used to extract arrival times of transmitted Lamb wave modes. The overall quality of the estimated arrival times was acceptable in terms of their regular distributions and discernable variation patterns that correspond to specific defects. The tomographic images generated from estimated arrival times were also fine enough to indicate different defects in aluminum plates.;The last application was ultrasonic thin multi-layers inspection. High precision and robustness of a dynamic wavelet fingerprint based algorithm was demonstrated by processing simulated ultrasonic signals. When applied to practical data obtained from a plastic encapsulated IC package, multiple interfaces in the package were successfully detected

A Neutral Network Based Vehicle Classification System for Pervasive Smart Road Security

Pervasive smart computing environments make people get accustomed to convenient and secure services. The overall goal of this research is to classify vehicles along the I215 freeway in Salt Lake City, USA. This information will be used to predict future roadway needs and the expected life of a roadway. The classification of vehicles will be performed by a synthesis of multiple sets of features. All feature sets have not yet been determined; however, one such set will be the reduced wavelet transform of the image of a vehicle. In order to use such a feature, it is necessary that the image be normalized with respect to size, position, and so on. For example, a car in the right most lane in an image will appear smaller than one in the left most lane, because the right most lane is closest to the camera. Likewise, a vehicle’s size will vary depending on where in a lane its image is captured. In our case, the image capture area for each lane is approximately 100 feet of roadway. A goal of this paper is to normalize the image of a vehicle so that regardless of its lane or position in a lane, the features will be approximately the same. The wavelet transform itself will not be used directly for recognition. Instead, it will be input to a neural network and the output of the neural network will be one element of the feature set used for recognition

Historical forest biomass dynamics modelled with Landsat spectral trajectories

Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin