Trepo
Trepo on Tampereen yliopiston avoin julkaisuarkisto. Trepo sisältää Tampereen yliopiston avoimia julkaisuja: yliopiston henkilökunnan kirjoittamien tieteellisten artikkeleiden rinnakkaistallenteita ja yliopiston muita avoimia julkaisuja sekä opinnäytteitä. Tampere University Pressin Open Access -kirjat ovat Trepossa omana TUP OA Books -kokoelmanaan.
Kokoelmat
Trepo [78528]
Artikkelit [6140]
TUNICRIS-julkaisut [17159]
TUP OA Books [255]
Väitöskirjat [4793]
Kandidaatintutkielmat [7111]
Viimeksi lisätty
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Making sense of robots together: Examining group interactions with Pepper
(2022)
conferenceObjectHuman-Robot Interaction (HRI) is traditionally addressed as interaction of one human user with a single robot. In this workshop contribution we reflect upon HRI in group interactions. Our approach follows ethnomethodological ... -
Laterally Bound Co Porphyrin on CdTe QD : A Long-Lived Charge-Separated Nanocomposite
(2023)
articleCobalt porphyrin (CoP) derivatives are potential compounds for photocatalytic CO2 reduction which must be activated by photoinduced electron transfer from a suitable electron donor. Herein, we have prepared and studied the ... -
Full-Duplex Multifunction Transceivers : Combining Radar, Communication and Security
Tampere University Dissertations - Tampereen yliopiston väitöskirjat : 1037 (Tampere University, 2024)
ArtikkeliväitöskirjaKaksisuuntainen (engl. full-duplex, FD) lähetin-vastaanotin on laite, joka pystyy samanaikaisesti lähettämään ja vastaanottamaan samalla taajuuskaistalla. Perinteisesti tällaisten laitteiden ongelma on ollut voimakas ... -
State-Conditioned Adversarial Subgoal Generation
(AAAI PRESS, 27.06.2023)
conferenceObjectHierarchical reinforcement learning (HRL) proposes to solve difficult tasks by performing decision-making and control at successively higher levels of temporal abstraction. However, off-policy HRL often suffers from the ... -
TAU-Indoors Dataset for Visual and LiDAR Place Recognition
(Springer, 2023)
conferenceObjectThere is a growing number of autonomous driving datasets that can be used to benchmark vision and LiDAR based place recognition and localization methods. The same sensor modalities, vision and depth, are important for ...