Two stabilization methods for an optoelectronic oscillator are presented. By using a frequency discriminator with a feedback control loop the long-term stabilization was improved. Our study revealed that a properly designed control loop does not increase the phase noise. The second stabilization method was used to increase the side-mode suppression ratio. This was achieved with an additional control loop in the form of an oscillator-loop phase modulation. The two methods were designed to operate simultaneously. This design provides both simplicity and cost effectiveness for the stabilized opto-electronic oscillator.
COBISS.SI-ID: 11249748
This paper presents a wearable training system designed to facilitate the learning process of proper movement patterns in sports training. The system implements a gesture user interface and real-time biofeedback. To demonstrate the concept of the proposed real-time biofeedback training system, an application for golf swing training is developed. The application implements the system using smartphone motion sensors and audio biofeedback and aids golfers in correcting unwanted head movements during a golf swing. The application is driven by a gesture user interface. During the golf swing, the application provides users with real-time audio feedback that signals head movement errors. The field test results show that the developed application can be used as an efficient tool in golf swing training.
COBISS.SI-ID: 11111764
In this study, we aim to determine the stress level in a non-invasive way to the maximum extent possible by analysing behavioural and contextual data received from the only source being a smartphone containing the data gathered in real-life situations. The information collected includes audio, gyroscope and accelerometer features, light condition, screen mode (on/off), current stress level self-assessment, and the current activity type. Three stress analysis models have been built: two with the consideration of current activities of a participant and one without those. Classification of low- and high-stress conditions, which was executed for a separate model for a certain kind of activity only, enabled us to achieve 3.9 % higher accuracy than that under the conditions when those activities were neglected. An Android application was developed as a means for the current activity-type identification
COBISS.SI-ID: 11118420
This paper describes a user study on the interaction with an in-vehicle information system (IVIS). The motivation for conducting this research was to investigate the subjectively and objectively measured impact of using a single- or multi-modal IVIS while driving. A hierarchical, list-based menu was presented using a windshield projection (head-up display), auditory display and a combination of both interfaces. The users were asked to navigate a vehicle in a driving simulator and simultaneously perform a set of tasks of varying complexity. The experiment showed that the interaction with visual and audiovisual head-up displays is faster and more efficient than with the audio-only display. All the interfaces had a similar impact on the overall driving performance. There was no significant difference between the visual only and audio-visual displays in terms of their efficiency and safety; however, the majority of test subjects clearly preferred to use the multi-modal interface while driving.
COBISS.SI-ID: 10729812
The paper introduces a novel approach for generating uniformly distributed search directions in higher dimensional search spaces based on uniformly distributed orthogonal matrices. The proposed approach is implemented in an optimization algorithm from the family of mesh adaptive direct search algorithms (MADS). The second directional derivative is numerically computed along uniformly distributed search directions and used for updating the Hessian approximation. A quasi-Newton step computed from the approximate Hessian speeds up the convergence towards the solution. The obtained algorithm was tested on a set of mathematical test functions. It outperformed the state of the art algorithm from the MADS family.
COBISS.SI-ID: 11235924