https://figshare.com/articles/preprint/Future_Smart_Cities_Requirements_Emerging_Technologies_Applications_Challenges_and_Future_Aspects/14722854/1/files/28285611.pdf

Future smart cities: requirements, emerging technologies, applications, challenges, and future aspects

A full understanding of somite development requires knowledge of the molecular genetic pathways for cell determination as well as the cellular behaviors that underlie segmentation, somite epithelialization, and somite patterning. The zebrafish has long been recognized as an ideal organism for cellular and histological studies of somite patterning. In recent years, genetics has proven to be a very powerful complementary approach to these embryological studies, as genetic screens for zebrafish mutants defective in somitogenesis have identified over 50 genes that are necessary for normal somite development. Zebrafish is thus an ideal system in which to analyze the role of specific gene products in regulating the cell behaviors that underlie somite development. We review what is currently known about zebrafish somite development and compare it where appropriate to somite development in chick and mouse. We discuss the processes of segmentation and somite epithelialization, and then review the patterning of cell types within the somite. We show directly, for the first time, that muscle cell and sclerotome migrations occur at the same time. We end with a look at the many questions about somitogenesis that are still unanswered.

https://anatomypubs.onlinelibrary.wiley.com/doi/pdf/10.1002/1097-0177%282000%299999%3A9999%3C%3A%3AAID-DVDY1065%3E3.0.CO%3B2-A

Somite Development in Zebrafish

Minimally invasive robotic interventions have highlighted the need to develop efficient techniques to measure forces applied to the soft tissues. Since the last decade, many scholars have focused on micro-scale and macro-scale robotic manipulations. Early articles used the model of soft tissue mathematically and tracked the displacement of the contour of the object in the vision system to provide the corresponding force to the user. Lack of knowledge of different materials and the computational complexity led to a transition from model-based to learning-based approaches to interpret the relation between object deformations, extracted from the vision system, and the real forces applied to the object. The dramatic growth of machine learning techniques and its integration with computer vision has brought novel learning-based visual data processing methods to the area. The application of the image-based force estimation methods in a controlled medical intervention has also received significant attention in the last five years. A decent number of surveys have been published on micromanipulation in recent years, especially for cell microinjection. However, the state of the art in meso- and macro-scale medical robotic interventions has not been reviewed. The aim and contribution of this paper are to fill the stated gap by reviewing the recent advances in image-based force estimation in robotic interventions. The survey shows that learning-based force estimation methods are growing significantly by using deep learning-based methods. The survey will encourage researchers and surgeons to apply learning-based algorithms to real-time medical and health-related operations.

Image-Based Force Estimation in Medical Applications: A Review

Accurate semantic image segmentation from medical imaging can enable intelligent vision-based assistance in robot-assisted minimally invasive surgery. The human body and surgical procedures are highly dynamic. While machine-vision presents a promising approach, sufficiently large training image sets for robust performance are either costly or unavailable. This work examines three novel generative adversarial network (GAN) methods of providing usable synthetic tool images using only surgical background images and a few real tool images. The best of these three novel approaches generates realistic tool textures while preserving local background content by incorporating both a style preservation and a content loss component into the proposed multi-level loss function. The approach is quantitatively evaluated, and results suggest that the synthetically generated training tool images enhance UNet tool segmentation performance. More specifically, with a random set of 100 cadaver and live endoscopic images from the University of Washington Sinus Dataset, the UNet trained with synthetically generated images using the presented method resulted in 35.7% and 30.6% improvement over using purely real images in mean Dice coefficient and Intersection over Union scores, respectively. This study is promising towards the use of more widely available and routine screening endoscopy to preoperatively generate synthetic training tool images for intraoperative UNet tool segmentation.

https://www.mdpi.com/1424-8220/21/15/5163/pdf

Local Style Preservation in Improved GAN-Driven Synthetic Image Generation for Endoscopic Tool Segmentation.

Teleoperated systems enable human control of robotic proxies and are particularly amenable to inaccessible environments unsuitable for autonomy. Examples include emergency response, underwater manipulation, and robot assisted minimally invasive surgery. However, teleoperation architectures have been predominantly employed in manipulation tasks, and are thus only useful when the robot is within reach of the task. This work introduces the idea of extending teleoperation to enable online human remote control of legged robots, or telelocomotion, to traverse challenging terrain. Traversing unpredictable terrain remains a challenge for autonomous legged locomotion, as demonstrated by robots commonly falling in high-profile robotics contests. Telelocomotion can reduce the risk of mission failure by leveraging the high-level understanding of human operators to command in real-time the gaits of legged robots. In this work, a haptic telelocomotion interface was developed. Two within-user studies validate the proof-of-concept interface: (i) The first compared basic interfaces with the haptic interface for control of a simulated hexapedal robot in various levels of traversal complexity; (ii) the second presents a physical implementation and investigated the efficacy of the proposed haptic virtual fixtures. Results are promising to the use of haptic feedback for telelocomotion for complex traversal tasks.

Telelocomotion—Remotely Operated Legged Robots

The problem of placing evenly-spaced stripes on a triangular mesh mirrors that of having evenly-spaced course rows and wale columns in a knit graph for a given geometry. This work presents strategies for producing helix-free stripe patterns and traces them to produce helix-free knit graphs suitable for machine knitting. We optimize directly for the discrete differential (1-form) of the stripe texture function, i.e., the spinning form, and demonstrate the knitting-specific advantages of this framework. In particular, we note how simple linear constraints allow us to place stitch irregularities, align course rows and wale columns to boundary/feature curves, and eliminate helical stripes. Two mixed-integer optimization strategies using these constraints are presented and applied to several mesh models. The results are smooth, globally-informed, helix-free stripe patterns that we trace to produce machine-knittable graphs. We further provide an explicit characterization of helical stripes and a theoretical analysis of their elimination constraints.

Helix-Free Stripes for Knit Graph Design

Haptic feedback can render real-time force interactions with computer simulated objects. In several telerobotic applications, it is desired that a haptic simulation reflects a physical task space or interaction accurately. This is particularly true when excessive applied force can result in disastrous consequences, as with the case of robot-assisted minimally invasive surgery (RMIS) and tissue damage. Since force cannot be directly measured in RMIS, non-contact methods are desired. A promising direction of non-contact force estimation involves the primary use of vision sensors to estimate deformation. However, the required fidelity of non-contact force rendering of deformable interaction to maintain surgical operator performance is not well established. This work attempts to empirically evaluate the degree to which haptic feedback may deviate from ground truth yet result in acceptable teleoperated performance in a simulated RMIS-based palpation task. A preliminary user-study is conducted to verify the utility of the simulation platform, and the results of this work have implications in haptic feedback for RMIS and inform guidelines for vision-based tool-tissue force estimation. An adaptive thresholding method is used to collect the minimum and maximum tolerable errors in force orientation and magnitude of presented haptic feedback to maintain sufficient performance.

Characterizing Limits of Vision-Based Force Feedback in Simulated Surgical Tool-Tissue Interaction

Collaborative robotic approaches seek to incorporate either direct human intervention in tasks previously suited for isolated robot devices, or to use precise machines (cobots) to assist in sensitive tasks. Cobots work alongside humans to extend the scope of robot assistance to spaces such as service and dynamic industrial or assembly tasks. With that said, the close proximity of humans with machines necessitates safe interaction, which can be achieved via lightweight materials and novel sensing capabilities. The inherent physical strength needed in some robot tasks, such as in assembly, make contact sensing of particular concern when introducing a human collaborator. A minimally intrusive method that can be seamlessly subsumed into extant devices is desired. In this paper, such a contact sensor is prototyped and tested. The sensor is bidirectional in that it actively provides an oscillatory actuation signal to a rigid link while simultaneously recording and analyzing the mechanical vibration of said link. Natural oscillation frequency shifts and energy concentration changes due to damping are congruent with different types of contact with the rigid link. The method is lightweight, low-cost and can be quickly incorporated into various manipulators. The developed configuration is advantageous as it does not require any delicate sensors on the robot body and relies primarily on actuated oscillations of the manipulator. Oscillatory acceleration data is collected and subsequently used to train and classify different contact locations using frequency-based features. Three separate classes are distinguished according to contact location. Results are promising and show excellent classification of both contact and contact location.

Contact Sensing via Active Oscillatory Actuation

Teleoperation of robotic proxies can extend human control to spaces, tasks and constraints that would otherwise be intractable or unattainable. The robustness to harsh environments, scalability, precision and repeatability of robots make them ideal for dangerous or difficult missions, and their use oftentimes reduces monetary and human cost. Maneuverable robot devices can provide more flexibility and articulation, but come at the cost of kinematic complexity. The kinematics and workspace of the remote device is oftentimes dissimilar to the input device, leading to potential confusion and frustration of the human operator. One solution is to constrain the input device motion to a scaled version of remote device joint ranges. This paper presents a method for doing so with 3 degree of freedom (DOF) manipulators and input devices with kinematic dissimilarities. The approach utilizes a simple tree structure, whereby a local Cartesian workspace limit is sampled and indexed by joint. This generates a locally sampled joint limit surface, represented as a point cloud. This local point cloud is then used to provide 3DOF haptic feedback to the operator as an indication that a joint limit has been reached, and provides kinesthetic force feedback to efficiently remove the operator from that joint limit. This work can improve usability of human-robot interfaces for teleoperation by allowing users to naturally intuit remote device joint limits and avoid confusion.

Sampling of 3DOF Robot Manipulator Joint-Limits for Haptic Feedback

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