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Abstract: The advanced smart home environment presents an important trend for the future of human wellbeing. One of the prerequisites for applying its rich functionality is the ability to differentiate between various user categories, such as gender, age, speakers, etc. We propose a model for an efficient acoustic gender and age classification system for human–computer interaction in a smart home. The objective was to improve acoustic classification without using high-complexity feature extraction. This was realized with pitch as an additional feature, combined with additional acoustic modeling approaches. In the first step, the classification is based on Gaussian mixture models. In thesecond step, two new procedures are introduced for gender and age classification. The first is based on the count of the frames with the speaker’s pitch values, and the second is based on the sum of the frames with pitch values belonging to a certain speaker. Since both procedures are based on pitch values, we have proposed a new, effective algorithm for pitch value calculation. In order to improve gender and age classification, we also incorporated speech segmentation with the proposed voice activity detection algorithm. We also propose a procedure that enables the quick adaptation of the classification algorithm to frequent smart home users. The proposed classification model with pitch values has improved the results in comparison with the baseline system.
Keywords: acoustic classification, acoustic signal processing, Gaussian mixture model, pitch analysis, smart home
Published in DKUM: 11.12.2023; Views: 471; Downloads: 21
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Abstract: In the master thesis, we have created an interrogation system for measuring the change of the optical path in the Fabry-Perot interferometer caused by the strain of the surface at which it was attached to. The change of strain can be calculated via a change in optical path length which is visible as a shift in phase angle. The system will be used as a system for measuring strain, although it can be used for measuring various physical parameters that can cause a change in optical path length, such as pressure, force, temperature, etc. Initially, the theoretical background of the system and project components are represented and explained, following the building process of the electronic and optical part of the project. Lastly, the working principle and programming algorithms of a system are presented and explained. Measurement results are shown at the end, as well as the conclusion that is drawn from the thesis.
Keywords: optical fibers, sensors, interferometer, signal processing
Published in DKUM: 04.11.2020; Views: 1244; Downloads: 116
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Abstract: Background and purpose: We explore the problem of License Plate Recognition (LPR) to highlight a number of algorithms that can be used in image analysis problems. In management support systems using image object recognition, the intelligence resides in the statistical algorithms that can be used in various LPR steps. We describe a number of solutions, from the initial thresholding step to localization and recognition of image elements. The objective of this paper is to present a number of probabilistic approaches in LPR steps, then combine these approaches together in one system. Most LPR approaches used deterministic models that are sensitive to many uncontrolled issues like illumination, distance of vehicles from camera, processing noise etc. The essence of our approaches resides in the statistical algorithms that can accurately localize and recognize license plate. Design/Methodology/Approach: We introduce simple and inexpensive methods to solve relatively important problems, using probabilistic approaches. In these approaches, we describe a number of statistical solutions, from the initial thresholding step to localization and recognition of image elements. In the localization step, we use frequency plate signals from the images which we analyze through the Discrete Fourier Transform. Also, a probabilistic model is adopted in the recognition of plate characters. Finally, we show how to combine results from bilingual license plates like Saudi Arabia plates. Results: The algorithms provide the effectiveness for an ever-prevalent form of vehicles, building and properties management. The result shows the advantage of using the probabilistic approached in all LPR steps. The averaged classification rates when using local dataset reached 79.13%. Conclusion: An improvement of recognition rate can be achieved when there are two source of information especially of license plates that have two independent texts.
Keywords: image analysis, probabilistic modeling, signal processing, license plate recognition
Published in DKUM: 28.11.2017; Views: 1501; Downloads: 366
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Abstract: This article introduces the use of the GNSS method for the load testings of bridge structures. We know that there area lot of methods by which you can determine vertical displacement. However, as new bridge constructions are constructed over almost impossible and inaccessible gorges and valleys, the classic measurements method do not allow us to determine vertical displacement so reliably any more or they are very time-consuming. The GNSS method is well known but there are difficulties when determining the altitude component of this method, which is rather questionable. For this purpose, the methods were tested as practical examples in which some difficulties were encountered, so subsequently another test recording was performed of altitude changes using this method.
Keywords: bridges, measurements, strain gauge, load test, deformation, strain, signal processing
Published in DKUM: 29.08.2016; Views: 2014; Downloads: 444
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