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Permanent URI for this collectionhttps://hdl.handle.net/20.500.14288/6

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Indexed at: search.filters.indexed.PubMedSubject: search.filters.subject.Artificial intelligence

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    PublicationOpen Access
    End to end rate distortion optimized learned hierarchical bi-directional video compression
    (Institute of Electrical and Electronics Engineers (IEEE), 2022) Department of Electrical and Electronics Engineering; Tekalp, Ahmet Murat; Yılmaz, Mustafa Akın; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 26207; N/A
    Conventional video compression (VC) methods are based on motion compensated transform coding, and the steps of motion estimation, mode and quantization parameter selection, and entropy coding are optimized individually due to the combinatorial nature of the end-to-end optimization problem. Learned VC allows end-to-end rate-distortion (R-D) optimized training of nonlinear transform, motion and entropy model simultaneously. Most works on learned VC consider end-to-end optimization of a sequential video codec based on R-D loss averaged over pairs of successive frames. It is well-known in conventional VC that hierarchical, bi-directional coding outperforms sequential compression because of its ability to use both past and future reference frames. This paper proposes a learned hierarchical bi-directional video codec (LHBDC) that combines the benefits of hierarchical motion-compensated prediction and end-to-end optimization. Experimental results show that we achieve the best R-D results that are reported for learned VC schemes to date in both PSNR and MS-SSIM. Compared to conventional video codecs, the R-D performance of our end-to-end optimized codec outperforms those of both x265 and SVT-HEVC encoders ("veryslow" preset) in PSNR and MS-SSIM as well as HM 16.23 reference software in MS-SSIM. We present ablation studies showing performance gains due to proposed novel tools such as learned masking, flow-field subsampling, and temporal flow vector prediction. The models and instructions to reproduce our results can be found in https://github.com/makinyilmaz/LHBDC/.
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    PublicationOpen Access
    3D microprinting of iron platinum nanoparticle-based magnetic mobile microrobots
    (Wiley, 2021) Giltinan, Joshua; Sridhar, Varun; Bozüyük, Uğur; Sheehan, Devin; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; School of Medicine; College of Engineering; 297104
    Wireless magnetic microrobots are envisioned to revolutionize minimally invasive medicine. While many promising medical magnetic microrobots are proposed, the ones using hard magnetic materials are not mostly biocompatible, and the ones using biocompatible soft magnetic nanoparticles are magnetically very weak and, therefore, difficult to actuate. Thus, biocompatible hard magnetic micro/nanomaterials are essential toward easy-to-actuate and clinically viable 3D medical microrobots. To fill such crucial gap, this study proposes ferromagnetic and biocompatible iron platinum (FePt) nanoparticle-based 3D microprinting of microrobots using the two-photon polymerization technique. A modified one-pot synthesis method is presented for producing FePt nanoparticles in large volumes and 3D printing of helical microswimmers made from biocompatible trimethylolpropane ethoxylate triacrylate (PETA) polymer with embedded FePt nanoparticles. The 30 mu m long helical magnetic microswimmers are able to swim at speeds of over five body lengths per second at 200Hz, making them the fastest helical swimmer in the tens of micrometer length scale at the corresponding low-magnitude actuation fields of 5-10mT. It is also experimentally in vitro verified that the synthesized FePt nanoparticles are biocompatible. Thus, such 3D-printed microrobots are biocompatible and easy to actuate toward creating clinically viable future medical microrobots.
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    PublicationOpen Access
    The effects of odor and body posture on perceived duration
    (Frontiers, 2014) Schreuder, Eliane; Hoeksma, Marco R.; Smeets, Monique A. M.; Department of Psychology; Semin, Gün Refik; Researcher; Department of Psychology; College of Social Sciences and Humanities; 58066
    This study reports an examination of the internal clock model, according to which subjective time duration is influenced by attention and arousal state. In a time production task, we examine the hypothesis that an arousing odor and an upright body posture affect perceived duration. The experimental task was performed while participants were exposed to an odor and either sitting upright (arousing condition) or lying down in a relaxing chair (relaxing condition). They were allocated to one of three experimental odor conditions: rosemary (arousing condition), peppermint (relaxing condition), and no odor (control condition). The predicted effects of the odors were not borne out by the results. Self-reported arousal (SRA) and pleasure (PL) states were measured before, during (after each body posture condition) and postexperimentally. Heart rate (HR) and skin conductance were measured before and during the experiment. As expected, odor had an effect on perceived duration. When participants were exposed to rosemary odor, they produced significantly shorter time intervals than in the no odor condition. This effect, however, could not be explained by increased arousal. There was no effect of body posture on perceived duration, even though body posture did induce arousal. The results do not support the proposed arousal mechanism of the internal clock model.