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    PublicationOpen Access
    A gated fusion network for dynamic saliency prediction
    (Institute of Electrical and Electronics Engineers (IEEE), 2022) Kocak, Aysun; Erdem, Erkut; Department of Computer Engineering; Erdem, Aykut; Faculty Member; Department of Computer Engineering; College of Engineering; 20331
    Predicting saliency in videos is a challenging problem due to complex modeling of interactions between spatial and temporal information, especially when ever-changing, dynamic nature of videos is considered. Recently, researchers have proposed large-scale data sets and models that take advantage of deep learning as a way to understand what is important for video saliency. These approaches, however, learn to combine spatial and temporal features in a static manner and do not adapt themselves much to the changes in the video content. In this article, we introduce the gated fusion network for dynamic saliency (GFSalNet), the first deep saliency model capable of making predictions in a dynamic way via the gated fusion mechanism. Moreover, our model also exploits spatial and channelwise attention within a multiscale architecture that further allows for highly accurate predictions. We evaluate the proposed approach on a number of data sets, and our experimental analysis demonstrates that it outperforms or is highly competitive with the state of the art. Importantly, we show that it has a good generalization ability, and moreover, exploits temporal information more effectively via its adaptive fusion scheme.
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    A realistic simulation environment for mri-based robust control of untethered magnetic robots with intra-operational imaging
    (IEEE-Inst Electrical Electronics Engineers Inc, 2020) Tiryaki, Mehmet Efe; Erin, Önder; N/A; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; School of Medicine; College of Engineering; 297104
    Dual-use of magnetic resonance imaging (MRI) devices for robot tracking and actuation has transformed them into potential medical robotics platforms for targeted therapies and minimally invasive surgeries. In this letter, we present the dynamic simulations of anMRI-based tracking and actuation scheme, which performs intra-operational imaging while controlling untethered magnetic robots. In our realistic rigid-body simulation, we show that the robot could be controlled with a 1D projection-based position feedback while performing intra-operational echo-planar imaging (EPI). From the simulations, we observe that the velocity estimation error is the main source of the controller instability for low MRI sequence frequencies. To minimize the velocity estimation errors, we constrain the controller gains according to maximum closed-loop rates achievable for different sequence durations. Using the constrained controller in simulations, we confirm that EPI imaging could be introduced to the sequence as an intra-operational imaging method. Although the intro-operational imaging increases the position estimation error to 2.0 mm for a simulated MRI-based position sensing with a 0.6 mm Gaussian noise, it does not cause controller instability up to 128 k-space lines.With the presented approach, continuous physiological images could be acquired during medical operations while a magnetic robot is actuated and tracked inside an MRI device.
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    BirdBot achieves energy-efficient gait with minimal control using avian-inspired leg clutching
    (American Association for the Advancement of Science (AAAS), 2022) Badri-Sprowitz, Alexander; Sarvestani, Alborz Aghamaleki; Daley, Monica A.; N/A; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; School of Medicine; College of Engineering; 297104
    Designers of legged robots are challenged with creating mechanisms that allow energy-efficient locomotion with robust and minimalistic control. Sources of high energy costs in legged robots include the rapid loading and high forces required to support the robot's mass during stance and the rapid cycling of the leg's state between stance and swing phases. Here, we demonstrate an avian-inspired robot leg design, BirdBot, that challenges the reliance on rapid feedback control for joint coordination and replaces active control with intrinsic, mechanical coupling, reminiscent of a self-engaging and disengaging clutch. A spring tendon network rapidly switches the leg's slack segments into a loadable state at touchdown, distributes load among joints, enables rapid disengagement at toe-off through elastically stored energy, and coordinates swing leg flexion. A bistable joint mediates the spring tendon network's disengagement at the end of stance, powered by stance phase leg angle progression. We show reduced knee-flexing torque to a 10th of what is required for a nonclutching, parallel-elastic leg design with the same kinematics, whereas spring-based compliance extends the leg in stance phase. These mechanisms enable bipedal locomotion with four robot actuators under feedforward control, with high energy efficiency. The robot offers a physical model demonstration of an avian-inspired, multiarticular elastic coupling mechanism that can achieve self-stable, robust, and economic legged locomotion with simple control and no sensory feedback. The proposed design is scalable, allowing the design of large legged robots. BirdBot demonstrates a mechanism for self-engaging and disengaging parallel elastic legs that are contact-triggered by the foot's own lever-arm action.
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    PublicationOpen Access
    Control and transport of passive particles using self-organized spinning micro-disks
    (Institute of Electrical and Electronics Engineers (IEEE), 2022) Basualdo, Franco N. Pinan; Gardi, Gaurav; Wang, Wendong; Demir, Sinan O.; Bolopion, Aude; Gauthier, Michael; Lambert, Pierre; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; College of Engineering; School of Medicine; 297104
    Traditional robotic systems have proven to be instrumental in object manipulation tasks for automated manufacturing processes. Object manipulation in such cases typically involves transport, pick-and-place and assembly of objects using automated conveyors and robotic arms. However, the forces at microscopic scales (e.g., surface tension, Van der Waals, electrostatic) can be qualitatively and quantitatively different from those at macroscopic scales. These forces render the release of objects difficult, and hence, traditional systems cannot be directly transferred to small scales (below a few millimeters). Consequently, novel micro-robotic manipulation systems have to be designed to take into account these scaling effects. Such systems could be beneficial for micro-fabrication processes and for biological studies. Here, we show autonomous position control of passive particles floating at the air-water interface using a collective of self-organized spinning micro-disks with a diameter of 300 mu m. First, we show that the spinning micro-disks collectives generate azimuthal flows that cause passive particles to orbit around them. We then develop a closed-loop controller to demonstrate autonomous position control of passive particles without physical contact. Finally, we showcase the capability of our system to split from an expanded to several circular collectives while holding the particle at a fixed target. Our system's contact-free object manipulation capability could be used for transporting delicate biological objects and for guiding self-assembly of passive objects for micro-fabrication.
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    PublicationOpen Access
    Deep learning-based 3D magnetic microrobot tracking using 2D MR images
    (Institute of Electrical and Electronics Engineers (IEEE), 2022) Tiryaki, Mehmet Efe; Demir, Sinan Özgün; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; College of Engineering; School of Medicine; 297104
    Magnetic resonance imaging (MRI)-guided robots emerged as a promising tool for minimally invasive medical operations. Recently, MRI scanners have been proposed for actuating and localizing magnetic microrobots in the patient's body using two-dimensional (2D) MR images. However, three-dimensional (3D) magnetic microrobots tracking during motion is still an untackled issue in MRI-powered microrobotics. Here, we present a deep learning-based 3D magnetic microrobot tracking method using 2D MR images during microrobot motion. The proposed method comprises a convolutional neural network (CNN) and complementary particle filter for 3D microrobot tracking. The CNN localizes the microrobot position relative to the 2D MRI slice and classifies the microrobot visibility in the MR images. First, we create an ultrasound (US) imaging-mentored MRI-based microrobot imaging and actuation system to train the CNN. Then, we trained the CNN using the MRI data generated by automated experiments using US image-based visual servoing of a microrobot with a 500 mu m-diameter magnetic core. We showed that the proposed CNN can localize the microrobot and classified its visibility in an in vitro environment with +/- 0.56 mm and 87.5% accuracy in 2D MR images, respectively. Furthermore, we demonstrated ex-vivo 3D microrobot tracking with +/- 1.43 mm accuracy, improving tracking accuracy by 60% compared to the previous studies. The presented tracking strategy will enable MRI-powered microrobots to be used in high-precision targeted medical applications in the future.
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    Droeye: introducing a social eye prototype for drones
    (Association for Computing Machinery (ACM), 2020) Obaid, Mohammad; Mubin, Omar; Brown, Scott Andrew; Otsuki, Mai; Kuzuoka, Hideaki; Department of Media and Visual Arts; Yantaç, Asım Evren; Faculty Member; Department of Media and Visual Arts; College of Social Sciences and Humanities; 52621
    A drone agent can benefit from exhibiting social cues, as introducing behavioral cues in robotic agents can enhance interaction trust and comfort with users. In this work, we introduce the development and setup of a responsive eye prototype (DroEye) mounted on a drone to demonstrate prominent social cues in Human-Drone Interaction. We describe possible attributes associated with the DroEye prototype and our future research directions to enhance the overall experience with social drones in our environment.
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    Effect of Al2O3 and ZrO2 filler material on the microstructural, thermal and dielectric properties of borosilicate glass-ceramics
    (Multidisciplinary Digital Publishing Institute (MDPI), 2023) Karaahmet, Oğuz; Çiçek, Buğra; N/A; Department of Chemistry; Arıbuğa, Dilara; Balcı, Özge; PhD Student; Researcher; Department of Chemistry; Graduate School of Sciences and Engineering; College of Sciences; N/A; 295531
    Various glass-ceramics are widely used or considered for use as components of microelectronic materials due to their promising properties. In this study, borosilicate glass was prepared using the powder metallurgical route and then mixed with different amounts of Al2O3 and ZrO2 filler materials. Glass-ceramics are produced by high-energy ball milling and conventional sintering process under Ar or air. In this study, the effects of different filler materials and different atmospheres on the microstructural, thermal and dielectric properties were investigated. The data showed that ZrO2 filler material led to better results than Al2O3 under identical working conditions and similar composite structures. ZrO2 filler material significantly enhanced the densification process of glass-ceramics (100% relative density) and led to a thermal conductivity of 2.904 W/K.m, a dielectric constant of 3.97 (at 5 MHz) and a dielectric loss of 0.0340 (at 5 MHz) for the glass with 30 wt.% ZrO2 sample. This paper suggests that prepared borosilicate glass-ceramics have strong sinterability, high thermal conductivity, and low dielectric constants, making them promising candidates for microelectronic devices.
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    Elucidating the interaction dynamics between microswimmer body and immune system for medical microrobots
    (Amer Assoc Advancement Science, 2020) Yasa, Immihan Ceren; Ceylan, Hakan; Bozuyuk, Ugur; Wild, Anna-Maria; N/A; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; School of Medicine; College of Engineering; 297104
    The structural design parameters of a medical microrobot, such as the morphology and surface chemistry, should aim to minimize any physical interactions with the cells of the immune system. However, the same surface-borne design parameters are also critical for the locomotion performance of the microrobots. Understanding the interplay of such parameters targeting high locomotion performance and low immunogenicity at the same time is of paramount importance yet has so far been overlooked. Here, we investigated the interactions of magnetically steerable double-helical microswimmers with mouse macrophage cell lines and splenocytes, freshly harvested from mouse spleens, by systematically changing their helical morphology. We found that the macrophages and splenocytes can recognize and differentially elicit an immune response to helix turn numbers of the microswimmers that otherwise have the same size, bulk physical properties, and surface chemistries. Our findings suggest that the structural optimization of medical microrobots for the locomotion performance and interactions with the immune cells should be considered simultaneously because they are highly entangled and can demand a substantial design compromise from one another. Furthermore, we show that morphology-dependent interactions between macrophages and microswimmers can further present engineering opportunities for biohybrid microrobot designs. We demonstrate immunobots that can combine the steerable mobility of synthetic microswimmers and the immunoregulatory capability of macrophages for potential targeted immunotherapeutic applications.
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    PublicationOpen Access
    Envisioning social drones in education
    (Frontiers, 2022) Johal, W.; Obaid, M.; Department of Media and Visual Arts; N/A; Yantaç, Asım Evren; Gatos, Doğa Çorlu; Faculty Member; Department of Media and Visual Arts; College of Social Sciences and Humanities; Graduate School of Social Sciences and Humanities; 52621; N/A
    Education is one of the major application fields in social Human-Robot Interaction. Several forms of social robots have been explored to engage and assist students in the classroom environment, from full-bodied humanoid robots to tabletop robot companions, but flying robots have been left unexplored in this context. In this paper, we present seven online remote workshops conducted with 20 participants to investigate the application area of Education in the Human-Drone Interaction domain; particularly focusing on what roles a social drone could fulfill in a classroom, how it would interact with students, teachers and its environment, what it could look like, and what would specifically differ from other types of social robots used in education. In the workshops we used online collaboration tools, supported by a sketch artist, to help envision a social drone in a classroom. The results revealed several design implications for the roles and capabilities of a social drone, in addition to promising research directions for the development and design in the novel area of drones in education.
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    Generating robot/agent backchannels during a storytelling experiment
    (Institute of Electrical and Electronics Engineers (IEEE), 2009) Al Moubayed, S.; Baklouti, M.; Chetouani, M.; Dutoit, T.; Mahdhaoui, A.; Martin, J. -C.; Ondas, S.; Pelachaud, C.; Urbain, J.; Department of Mechanical Engineering; Yılmaz, Mustafa Akın; Tekalp, Ahmet Murat; PhD Student; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; N/A
    This work presents the development of a real-time framework for the research of Multimodal Feedback of Robots/Talking Agents in the context of Human Robot Interaction (HRI) and Human Computer Interaction (HCI). For evaluating the framework, a Multimodal corpus is built (ENTERFACE_STEAD), and a study on the important multimodal features was done for building an active Robot/Agent listener of a storytelling experience with Humans. The experiments show that even when building the same reactive behavior models for Robot and Talking Agents, the interpretation and the realization of the behavior communicated is different due to the different communicative channels Robots/Agents offer be it physical but less-human-like in Robots, and virtual but more expressive and human-like in Talking agents.