Iskanje po katalogu digitalne knjižnice

Opis: Namen te naloge je razvoj in testiranje sistema za generiranje in stabilizacijo 10 MHz referenčnega signala, ki je ključnega pomena za natančno delovanje komunikacijskih sistemov. Uporabljene metode vključujejo napetostno krmiljen kristalni oscilator, modul globalnega pozicijskega sistema za sinhronizacijo ter algoritme za merjenje in prilagajanje frekvence. Rezultati kažejo, da naš modul omogoča bolj natančne meritve in boljšo stabilnost frekvence v primerjavi s prejšnjim sistemom. Med testiranjem smo odkrili nekaj programskih hroščev, ki smo jih odpravili. V prihodnosti bi lahko uporaba umetne inteligence dodatno izboljšala naš algoritem.
Ključne besede: referenčni signal, stabilizacija frekvence, generiranje signala, kristalni oscilator, merjenje frekvence
Objavljeno v DKUM: 06.02.2025; Ogledov: 0; Prenosov: 0

Opis: Because of the prevalence of depression, its often-chronic course, relapse and associated disability, early detection and non-intrusive monitoring is a crucial tool for timely diagnosis and treatment, remission of depression and prevention of relapse. In this way, its impact on quality of life and well-being can be limited. Current attempts to use artificial intelligence for the early classification of depression are mostly data-driven and thus non-transparent and lack effective means to deal with uncertainties. Therefore, in this paper, we propose an end-to-end framework for extracting observable depression cues from diary recordings. Furthermore, we also explore its feasibility for automatic detection of depression symptoms using observable behavioural cues. The proposed end-to-end framework for extracting depression was used to evaluate 28 video recordings from the Symptom Media dataset and 27 recordings from the DAIC-WOZ dataset. We compared the presence of the extracted features between recordings of individuals with and without a depressive disorder. We identified several cues consistent with previous studies in terms of their differentiation between individuals with and without depressive disorder across both datasets among language (i.e., use of negatively valanced words, use of first-person singular pronouns, some features of language complexity, explicit mentions of treatment for depression), speech (i.e., monotonous speech, voiced speech and pauses, speaking rate, low articulation rate), and facial cues (i.e., rotational energy of head movements). The nature/context of the discourse, the impact of other disorders and physical/psychological stress, and the quality and resolution of the recordings all play an important role in matching the digital features to the relevant background. In this way, the work presented in this paper provides a novel approach to extracting a wide range of cues relevant to the classification of depression and opens up new opportunities for further research.
Ključne besede: digital biomarkers of depression, facial cues, speech cues, language cues, deep learning, end-to-end pipeline, artificial intelligence
Objavljeno v DKUM: 17.01.2025; Ogledov: 0; Prenosov: 3
Celotno besedilo (2,34 MB)

Opis: The importance and value of real-world data in healthcare cannot be overstated because it offers a valuable source of insights into patient experiences. Traditional patient-reported experience and outcomes measures (PREMs/PROMs) often fall short in addressing the complexities of these experiences due to subjectivity and their inability to precisely target the questions asked. In contrast, diary recordings offer a promising solution. They can provide a comprehensive picture of psychological well-being, encompassing both psychological and physiological symptoms. This study explores how using advanced digital technologies, i.e., automatic speech recognition and natural language processing, can efficiently capture patient insights in oncology settings. We introduce the MRAST framework, a simplified way to collect, structure, and understand patient data using questionnaires and diary recordings. The framework was validated in a prospective study with 81 colorectal and 85 breast cancer survivors, of whom 37 were male and 129 were female. Overall, the patients evaluated the solution as well made; they found it easy to use and integrate into their daily routine. The majority (75.3%) of the cancer survivors participating in the study were willing to engage in health monitoring activities using digital wearable devices daily for an extended period. Throughout the study, there was a noticeable increase in the number of participants who perceived the system as having excellent usability. Despite some negative feedback, 44.44% of patients still rated the app’s usability as above satisfactory (i.e., 7.9 on 1–10 scale) and the experience with diary recording as above satisfactory (i.e., 7.0 on 1–10 scale). Overall, these findings also underscore the significance of user testing and continuous improvement in enhancing the usability and user acceptance of solutions like the MRAST framework. Overall, the automated extraction of information from diaries represents a pivotal step toward a more patient-centered approach, where healthcare decisions are based on real-world experiences and tailored to individual needs. The potential usefulness of such data is enormous, as it enables better measurement of everyday experiences and opens new avenues for patient-centered care.
Ključne besede: multilingual framework, risk assessment, symptom tracking, chronic diseases, patient-centered care, real-world data
Objavljeno v DKUM: 12.08.2024; Ogledov: 74; Prenosov: 12
Celotno besedilo (5,29 MB)

Opis: As the market of electrified vehicles is rapidly growing, the requirements for new products are also increasing, meaning the required development and production times are getting increasingly shorter. Therefore, the major interest in current active research is how to optimize the development process. Within this master’s thesis, we developed the base framework for a digital twin for wiring harness development and cross section optimization. It allows engineering teams to develop a digital replica of the desired physical system, which can be the basis for further review and optimization analyses, already at the development stage.
Ključne besede: digitalni dvojček, kabelski snop, presek, kabel, konektor
Objavljeno v DKUM: 31.01.2024; Ogledov: 382; Prenosov: 0

Opis: Sodobna vozila postajajo vse bolj zmogljivi in zapleteni elektronski sistemi. Dandanes je zato eden od izzivov, s katerimi se moramo soočiti, ob taki kompleksnosti zagotoviti tudi varna, robustna, hitra in visoko zanesljiva vozila. V magistrski nalogi se osredotočamo predvsem na analizo izvajanja storitveno usmerjenih komunikacijskih protokolov v avtomobilski vmesni programski opremi, potrebni za visoko zmogljive računalniške krmilne enote. Naš cilj je med drugim tudi oceniti zmogljivosti dveh storitveno orientiranih komunikacijskih protokolov pod določenimi pogoji. Protokola DDS in SOME/IP se na splošno izvajata na visoko zmogljivem računalniku skupaj s prilagodljivo vmesno programsko opremo Adaptive AUTOSAR Platform Demonstrator.
Ključne besede: AUTOSAR, Yocto, vsomeip, OpenDDS, Cyclone DDS, Linux, kernel, Bash, R, C++
Objavljeno v DKUM: 08.11.2022; Ogledov: 524; Prenosov: 62
Celotno besedilo (13,38 MB)