Sökning: "Memory strategy"

Visar resultat 1 - 5 av 106 uppsatser innehållade orden Memory strategy.

  1. 1. DIFFERENTIAL OUTCOME TRAINING AND HUMANOID ROBOT FEEDBACK ON A VISUOSPATIAL GAMIFIED TASK: An experimental study investigating learning, affective social engagement cues and cognitive learning strategies

    Kandidat-uppsats, Institutionen för tillämpad informationsteknologi

    Författare :Alva Markelius; Sofia Sjöberg; [2023-02-01]
    Nyckelord :Human-robot interaction; gamified memory task; dementia; differential outcome theory; Furhat; engagement; digitalised treatments; robot assistance;

    Sammanfattning : By combining Differential Outcome Training (DOT), the usage of unique stimulus-response pairings, and feedback from a humanoid simulated robot (SDK) this study aims to improve learning performance of subjects on a visuospatial gamified memory task. This is achieved by using the SDK as an interface for an algorithm that provides audiovisual, reinforcing feedback promoting engangement in a new dyadic setup for a gamified task. LÄS MER

  3. 2. Traduire la mémoire : La mémoire du Chambon-sur-Lignon entre 'foreignization' & 'domestication'

    Magister-uppsats, Linnéuniversitetet/Institutionen för språk (SPR)

    Författare :Julia Lindholm; [2023]
    Nyckelord :historical memory; translation strategies; world war two; translation of historical memory; mémoire historique; stratégies de traduction; seconde guerre mondiale; traduction de mémoire historique;

    Sammanfattning : This paper examines how to translate the historical memory of Chambon-sur-Lignon in France, by analyzing the translation procedures used to transfer proper nouns (personal names, place names, names of organizations) and cultural references (historical, theological) from French to Swedish, on a scale between the strategies foreignization and domestication. The analysis is based on our own translation of three source texts from the book La Montagne refuge. LÄS MER

  4. 3. Unsupervised Online Anomaly Detection in Multivariate Time-Series

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Datorteknik

    Författare :Ludvig Segerholm; [2023]
    Nyckelord :unsupervised; online; anomaly detection; explainable ai; machine learning; mahalanobis distance;

    Sammanfattning : This research aims to identify a method for unsupervised online anomaly detection in multivariate time series in dynamic systems in general and on the case study of Devwards IoT-system in particular. A requirement of the solution is its explainability, online learning and low computational expense. LÄS MER

  5. 4. A Conjugate Residual Solver with Kernel Fusion for massive MIMO Detection

    Master-uppsats, Högskolan i Halmstad/Centrum för forskning om tillämpade intelligenta system (CAISR)

    Författare :Ioannis Broumas; [2023]
    Nyckelord :MIMO; massive MIMO; GPU; CUDA; Software Defined Radio; SDR; MMSE; ZF; zero-forcing; parallel detection; iterative methods; conjugate residual; parallel computing; kernel fusion;

    Sammanfattning : This thesis presents a comparison of a GPU implementation of the Conjugate Residual method as a sequence of generic library kernels against implementations ofthe method with custom kernels to expose the performance gains of a keyoptimization strategy, kernel fusion, for memory-bound operations which is to makeefficient reuse of the processed data. For massive MIMO the iterative solver is to be employed at the linear detection stageto overcome the computational bottleneck of the matrix inversion required in theequalization process, which is 𝒪(𝑛3) for direct solvers. LÄS MER

  7. 5. RocksDB Read Optimization Strategies for Streaming Applications

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Björkman Fredrik; [2023]
    Nyckelord :RocksDB; Data streams; Micro-batching; Data stream processing; read operation benchmark; Data stream workload simulation; RocksDB; Dataströmmar; Mikro-batching; Dataströmsprocessering; Läsoperationsmätresultat; Dataströmsarbetsbelastningssimulation;

    Sammanfattning : Modern stream processors rely on embedded key-value stores to manage state that accumulates over long-running computations and exceeds the available memory size. One of these key-value stores is RocksDB, which is widely used in many applications requiring high-performing storage with low latency. LÄS MER