Monocular Simultaneous Localization and Mapping (SLAM) is one of the most complex and well-known problems, affecting several scientific fields: robotics, computer vision, virtual reality. This paper aims to study the SLAM problem for the mobile device with a monocular camera and sensors: accelerometer, gyroscope and digital compass. The latter allow to obtain an additional estimation of a mobile device position and orientation. The aim is to assess the potential suitability and efficiency of using extra information from inertial sensors to improve the solution quality and to reduce the time to obtain the solution. The experimental part of the study, including both model and field experiments, allowed to determine the requirements for permissible errors introduced by the sensors of the mobile device. For a specific model of a mobile device, it is shown that the electronic compass meets these requirements, while the errors of the inertial sensors used to determine the movements are unacceptably large.

The paper presents an analysis of various approaches to constructing descriptions for the gradient fields of digital images. The analyzed approaches are based on the well-known methods for data dimensionality reduction, such as Principal (PCA) and Independent (ICA) Component Analysis, Linear Discriminant Analysis (LDA). We apply these methods not to the original image, represented as a two-dimensional field of brightness (a halftone image), but to its secondary representation in the form of a two-dimensional gradient field, that is, a complex-valued image. In this case, approaches based on using both the entire gradient field and only its phase component are considered. In addition, two independent ways of forming the final description of the original object are considered: using expansion coefficients of the gradient field in a derived basis and using an original authors' design that is called model-oriented descriptors. With the latter, the number of real coefficients used in the description of the original object can be halved. The studies are conducted via solving a face recognition problem. The effectiveness of the analyzed methods is demonstrated by applying them to images from Extended Yale Face Database B. The comparison is made using a nearest neighbor's classifier.

In this paper, we set forth a new longitudinal corpus and a toolset in an effort to address the influence of voice-aging on speaker verification. We have examined previous longitudinal research of agerelated voice changes as well as its applicability to real world use cases. Our findings reveal that scientists have treated agerelated voice changes as a hindrance instead of leveraging it to the advantage of the identity validator. Additionally, we found a significant dearth of publicly available corpora related to both the time span of and the number of participants in audio recordings. We also identified a significant bias toward the development of speaker recognition technologies applicable to government surveillance systems compared to speaker verification systems used in civilian IT security systems. To solve the aforementioned issues, we built an open project with the largest publicly available longitudinal speaker database, which includes 229 speakers with an average talking time exceeding 15 hours spanning across an average of 21 years per speaker. We assembled, cleaned, and normalized audio recordings and developed software tools for speech features extractions, all of which we are releasing to the public domain.