Objective The mechanical properties of normal auricular cartilage provide a benchmark against which to characterize changes in auricular structure/function due to genetic defects creating phenotypic abnormalities in collage subtypes. Such properties also provide inputs/targets for auricular reconstruction scaffold design. Several studies report the biomechanical properties for septal, costal, and articular cartilage. However, analogous data for auricular cartilage is lacking. Therefore, our aim in this study was to characterize both whole ear and auricular cartilage mechanics by mechanically testing specimens and fitting the results to nonlinear constitutive models. Study Design Mechanical testing of whole ears and auricular cartilage punch biopsies. Methods Whole human cadaveric ear and auricular cartilage punch biopsies from both porcine and human cartilage were subjected to whole ear helix down compression and quasi-static unconfined compression tests. Common hyperelastic constitutive laws (widely used to characterize soft tissue mechanics) were evaluated for their ability to represent the stress-strain behavior of auricular cartilage. Results Load displacement curves for whole ear testing exhibited compliant linear behavior until after significant displacement where nonlinear stiffening occurred. All five commonly used 2-term hyperelastic soft tissue constitutive models successfully fit both human and porcine nonlinear elastic behavior (mean R2 fit greater than 0.95). Conclusion Auricular cartilage exhibits nonlinear strain stiffening elastic behavior that is similar to other soft tissues in the body. The whole ear exhibits compliant behavior with strain stiffening at high displacement. The constants from the hyperelastic model fits provide quantitative baselines for both human and porcine (a commonly used animal model for auricular tissue engineering) auricular mechanics.

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Device-to-device communication (D2D) is a key enabler for connecting devices together to form the Internet of Things (IoT). A growing issue with IoT networks is the increasing number of IoT devices congesting the spectral resources of the cellular bands. Operating D2D in unlicensed band alleviates this issue by offloading network traffic from the licensed bands, while also reducing the associated licensing costs. To this end, we present a new low-cost radio access technology (RAT) protocol, called Sidelink Communications on Unlicensed BAnds (SCUBA), which can be implemented on cellular devices such that it coexists with the legacy cellular protocol by operating as a secondary RAT in a time division duplex manner using the existing radio hardware. SCUBA is compatible on different types of cellular devices including the low-complexity halfduplex frequency division duplex machine type communication (MTC) user equipments. SCUBA provides flexible sidelink (SL) latency and battery life tradeoff using a discontinuous reception procedure, which ensures that it is applicable across a wide range of use cases. We prove the effectiveness of our protocol with analyses and simulation results of the medium access control layer of SCUBA using different types of MTC traffic for both SL and the underlying cellular communication.

This paper introduces a novel approach to address the problem of Physical Robot Interaction (PRI) during robot pushing tasks. The approach uses a data-driven forward model based on tactile predictions to inform the controller about potential future movements of the object being pushed, such as a strawberry stem, using a robot tactile finger. The model is integrated into a Deep Functional Predictive Control (d-FPC) system to control the displacement of the stem on the tactile finger during pushes. Pushing an object with a robot finger along a desired trajectory in 3D is a highly nonlinear and complex physical robot interaction, especially when the object is not stably grasped. The proposed approach controls the stem movements on the tactile finger in a prediction horizon. The effectiveness of the proposed FPC is demonstrated in a series of tests involving a real robot pushing a strawberry in a cluster. The results indicate that the d-FPC controller can successfully control PRI in robotic manipulation tasks beyond the handling of strawberries. The proposed approach offers a promising direction for addressing the challenging PRI problem in robotic manipulation tasks. Future work will explore the generalisation of the approach to other objects and tasks.

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