Mehmet Turan

Ph.D. Student

Physical Intelligence

Computer vision tasks in capsule robotics, including 3D map building of the intestinal organs using capsule endoscopic images Vision-based localization of capsule robots Disease detection using capsule endoscopic images.

MS: RWTH Aachen, Germany, 2012
BS: RWTH Aachen, Germany, 2008

15 results (View BibTeX file of all listed publications)

2018

Magnetic-Visual Sensor Fusion-based Dense 3D Reconstruction and Localization for Endoscopic Capsule Robots

Turan, M., Almalioglu, Y., Ornek, E. P., Araujo, H., Yanik, M. F., Sitti, M.

ArXiv e-prints, March 2018 (article)

Abstract

Reliable and real-time 3D reconstruction and localization functionality is a crucial prerequisite for the navigation of actively controlled capsule endoscopic robots as an emerging, minimally invasive diagnostic and therapeutic technology for use in the gastrointestinal (GI) tract. In this study, we propose a fully dense, non-rigidly deformable, strictly real-time, intraoperative map fusion approach for actively controlled endoscopic capsule robot applications which combines magnetic and vision-based localization, with non-rigid deformations based frame-to-model map fusion. The performance of the proposed method is demonstrated using four different ex-vivo porcine stomach models. Across different trajectories of varying speed and complexity, and four different endoscopic cameras, the root mean square surface reconstruction errors 1.58 to 2.17 cm.

link (url) [BibTex]

2018

pi Turan, M., Almalioglu, Y., Ornek, E. P., Araujo, H., Yanik, M. F., Sitti, M. Magnetic-Visual Sensor Fusion-based Dense 3D Reconstruction and Localization for Endoscopic Capsule Robots ArXiv e-prints, March 2018 (article)

link (url) [BibTex]

Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots

Turan, M., Ornek, E. P., Ibrahimli, N., Giracoglu, C., Almalioglu, Y., Yanik, M. F., Sitti, M.

ArXiv e-prints, March 2018 (article)

Abstract

In the last decade, many medical companies and research groups have tried to convert passive capsule endoscopes as an emerging and minimally invasive diagnostic technology into actively steerable endoscopic capsule robots which will provide more intuitive disease detection, targeted drug delivery and biopsy-like operations in the gastrointestinal(GI) tract. In this study, we introduce a fully unsupervised, real-time odometry and depth learner for monocular endoscopic capsule robots. We establish the supervision by warping view sequences and assigning the re-projection minimization to the loss function, which we adopt in multi-view pose estimation and single-view depth estimation network. Detailed quantitative and qualitative analyses of the proposed framework performed on non-rigidly deformable ex-vivo porcine stomach datasets proves the effectiveness of the method in terms of motion estimation and depth recovery.

link (url) [BibTex]

pi Turan, M., Ornek, E. P., Ibrahimli, N., Giracoglu, C., Almalioglu, Y., Yanik, M. F., Sitti, M. Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots ArXiv e-prints, March 2018 (article)

link (url) [BibTex]

Sparse-then-dense alignment-based 3D map reconstruction method for endoscopic capsule robots

Turan, M., Pilavci, Y. Y., Ganiyusufoglu, I., Araujo, H., Konukoglu, E., Sitti, M.

Machine Vision and Applications, 29(2):345-359, Febuary 2018 (article)

Abstract

Despite significant progress achieved in the last decade to convert passive capsule endoscopes to actively controllable robots, robotic capsule endoscopy still has some challenges. In particular, a fully dense three-dimensional (3D) map reconstruction of the explored organ remains an unsolved problem. Such a dense map would help doctors detect the locations and sizes of the diseased areas more reliably, resulting in more accurate diagnoses. In this study, we propose a comprehensive medical 3D reconstruction method for endoscopic capsule robots, which is built in a modular fashion including preprocessing, keyframe selection, sparse-then-dense alignment-based pose estimation, bundle fusion, and shading-based 3D reconstruction. A detailed quantitative analysis is performed using a non-rigid esophagus gastroduodenoscopy simulator, four different endoscopic cameras, a magnetically activated soft capsule robot, a sub-millimeter precise optical motion tracker, and a fine-scale 3D optical scanner, whereas qualitative ex-vivo experiments are performed on a porcine pig stomach. To the best of our knowledge, this study is the first complete endoscopic 3D map reconstruction approach containing all of the necessary functionalities for a therapeutically relevant 3D map reconstruction.

link (url) DOI [BibTex]

pi Turan, M., Pilavci, Y. Y., Ganiyusufoglu, I., Araujo, H., Konukoglu, E., Sitti, M. Sparse-then-dense alignment-based 3D map reconstruction method for endoscopic capsule robots Machine Vision and Applications, 29(2):345-359, Febuary 2018 (article)

link (url) DOI [BibTex]

Deep EndoVO: A recurrent convolutional neural network (RCNN) based visual odometry approach for endoscopic capsule robots

Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.

Neurocomputing, 275, pages: 1861 - 1870, January 2018 (article)

Abstract

Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Medical device companies and many research groups have recently made substantial progresses in converting passive capsule endoscopes to active capsule robots, enabling more accurate, precise, and intuitive detection of the location and size of the diseased areas. Since a reliable real time pose estimation functionality is crucial for actively controlled endoscopic capsule robots, in this study, we propose a monocular visual odometry (VO) method for endoscopic capsule robot operations. Our method lies on the application of the deep recurrent convolutional neural networks (RCNNs) for the visual odometry task, where convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are used for the feature extraction and inference of dynamics across the frames, respectively. Detailed analyses and evaluations made on a real pig stomach dataset proves that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories.

link (url) DOI [BibTex]

pi Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M. Deep EndoVO: A recurrent convolutional neural network (RCNN) based visual odometry approach for endoscopic capsule robots Neurocomputing, 275, pages: 1861 - 1870, January 2018 (article)

link (url) DOI [BibTex]

2017

A deep learning based fusion of RGB camera information and magnetic localization information for endoscopic capsule robots

Turan, M., Shabbir, J., Araujo, H., Konukoglu, E., Sitti, M.

International Journal of Intelligent Robotics and Applications, 1(4):442-450, December 2017 (article)

Abstract

A reliable, real time localization functionality is crutial for actively controlled capsule endoscopy robots, which are an emerging, minimally invasive diagnostic and therapeutic technology for the gastrointestinal (GI) tract. In this study, we extend the success of deep learning approaches from various research fields to the problem of sensor fusion for endoscopic capsule robots. We propose a multi-sensor fusion based localization approach which combines endoscopic camera information and magnetic sensor based localization information. The results performed on real pig stomach dataset show that our method achieves sub-millimeter precision for both translational and rotational movements.

link (url) DOI [BibTex]

2017

pi Turan, M., Shabbir, J., Araujo, H., Konukoglu, E., Sitti, M. A deep learning based fusion of RGB camera information and magnetic localization information for endoscopic capsule robots International Journal of Intelligent Robotics and Applications, 1(4):442-450, December 2017 (article)

link (url) DOI [BibTex]

EndoSensorFusion: Particle Filtering-Based Multi-sensory Data Fusion with Switching State-Space Model for Endoscopic Capsule Robots

Turan, M., Almalioglu, Y., Gilbert, H., Araujo, H., Cemgil, T., Sitti, M.

ArXiv e-prints, September 2017 (article)

Abstract

A reliable, real time multi-sensor fusion functionality is crucial for localization of actively controlled capsule endoscopy robots, which are an emerging, minimally invasive diagnostic and therapeutic technology for the gastrointestinal (GI) tract. In this study, we propose a novel multi-sensor fusion approach based on a particle filter that incorporates an online estimation of sensor reliability and a non-linear kinematic model learned by a recurrent neural network. Our method sequentially estimates the true robot pose from noisy pose observations delivered by multiple sensors. We experimentally test the method using 5 degree-of-freedom (5-DoF) absolute pose measurement by a magnetic localization system and a 6-DoF relative pose measurement by visual odometry. In addition, the proposed method is capable of detecting and handling sensor failures by ignoring corrupted data, providing the robustness expected of a medical device. Detailed analyses and evaluations are presented using ex-vivo experiments on a porcine stomach model prove that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories.

link (url) Project Page [BibTex]

pi Turan, M., Almalioglu, Y., Gilbert, H., Araujo, H., Cemgil, T., Sitti, M. EndoSensorFusion: Particle Filtering-Based Multi-sensory Data Fusion with Switching State-Space Model for Endoscopic Capsule Robots ArXiv e-prints, September 2017 (article)

link (url) Project Page [BibTex]

Endo-VMFuseNet: Deep Visual-Magnetic Sensor Fusion Approach for Uncalibrated, Unsynchronized and Asymmetric Endoscopic Capsule Robot Localization Data

Turan, M., Almalioglu, Y., Gilbert, H., Eren Sari, A., Soylu, U., Sitti, M.

ArXiv e-prints, September 2017 (article)

Abstract

In the last decade, researchers and medical device companies have made major advances towards transforming passive capsule endoscopes into active medical robots. One of the major challenges is to endow capsule robots with accurate perception of the environment inside the human body, which will provide necessary information and enable improved medical procedures. We extend the success of deep learning approaches from various research fields to the problem of uncalibrated, asynchronous, and asymmetric sensor fusion for endoscopic capsule robots. The results performed on real pig stomach datasets show that our method achieves sub-millimeter precision for both translational and rotational movements and contains various advantages over traditional sensor fusion techniques.

link (url) Project Page [BibTex]

pi Turan, M., Almalioglu, Y., Gilbert, H., Eren Sari, A., Soylu, U., Sitti, M. Endo-VMFuseNet: Deep Visual-Magnetic Sensor Fusion Approach for Uncalibrated, Unsynchronized and Asymmetric Endoscopic Capsule Robot Localization Data ArXiv e-prints, September 2017 (article)

link (url) Project Page [BibTex]

Sparse-then-Dense Alignment based 3D Map Reconstruction Method for Endoscopic Capsule Robots

Turan, M., Yigit Pilavci, Y., Ganiyusufoglu, I., Araujo, H., Konukoglu, E., Sitti, M.

ArXiv e-prints, August 2017 (article)

Abstract

Since the development of capsule endoscopcy technology, substantial progress were made in converting passive capsule endoscopes to robotic active capsule endoscopes which can be controlled by the doctor. However, robotic capsule endoscopy still has some challenges. In particular, the use of such devices to generate a precise and globally consistent three-dimensional (3D) map of the entire inner organ remains an unsolved problem. Such global 3D maps of inner organs would help doctors to detect the location and size of diseased areas more accurately, precisely, and intuitively, thus permitting more accurate and intuitive diagnoses. The proposed 3D reconstruction system is built in a modular fashion including preprocessing, frame stitching, and shading-based 3D reconstruction modules. We propose an efficient scheme to automatically select the key frames out of the huge quantity of raw endoscopic images. Together with a bundle fusion approach that aligns all the selected key frames jointly in a globally consistent way, a significant improvement of the mosaic and 3D map accuracy was reached. To the best of our knowledge, this framework is the first complete pipeline for an endoscopic capsule robot based 3D map reconstruction containing all of the necessary steps for a reliable and accurate endoscopic 3D map. For the qualitative evaluations, a real pig stomach is employed. Moreover, for the first time in literature, a detailed and comprehensive quantitative analysis of each proposed pipeline modules is performed using a non-rigid esophagus gastro duodenoscopy simulator, four different endoscopic cameras, a magnetically activated soft capsule robot (MASCE), a sub-millimeter precise optical motion tracker and a fine-scale 3D optical scanner.

link (url) Project Page [BibTex]

pi Turan, M., Yigit Pilavci, Y., Ganiyusufoglu, I., Araujo, H., Konukoglu, E., Sitti, M. Sparse-then-Dense Alignment based 3D Map Reconstruction Method for Endoscopic Capsule Robots ArXiv e-prints, August 2017 (article)

link (url) Project Page [BibTex]

Six Degree-of-Freedom Localization of Endoscopic Capsule Robots using Recurrent Neural Networks embedded into a Convolutional Neural Network

Turan, M., Abdullah, A., Jamiruddin, R., Araujo, H., Konukoglu, E., Sitti, M.

arXiv preprint arXiv:1705.06196, May 2017 (article)

Abstract

Since its development, ingestible wireless endoscopy is considered to be a painless diagnostic method to detect a number of diseases inside GI tract. Medical related engineering companies have made significant improvements in this technology in last decade; however, some major limitations still residue. Localization of the next generation steerable endoscopic capsule robot in six degreeof-freedom (DoF) and active motion control are some of these limitations. The significance of localization capability concerns with the doctors correct diagnosis of the disease area. This paper presents a very robust 6-DoF localization method based on supervised training of an architecture consisting of recurrent networks (RNN) embedded into a convolutional neural network (CNN) to make use of both just-in-moment information obtained by CNN and correlative information across frames obtained by RNN. To our knowledge, our idea of embedding RNNs into a CNN architecture is for the first time proposed in literature. The experimental results show that the proposed RNN-in-CNN architecture performs very well for endoscopic capsule robot localization in cases vignetting, reflection distortions, noise, sudden camera movements and lack of distinguishable features.

DOI Project Page [BibTex]

pi Turan, M., Abdullah, A., Jamiruddin, R., Araujo, H., Konukoglu, E., Sitti, M. Six Degree-of-Freedom Localization of Endoscopic Capsule Robots using Recurrent Neural Networks embedded into a Convolutional Neural Network arXiv preprint arXiv:1705.06196, May 2017 (article)

DOI Project Page [BibTex]

A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots

Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.

arXiv preprint arXiv:1705.05444, May 2017 (article)

Abstract

In the gastrointestinal (GI) tract endoscopy field, ingestible wireless capsule endoscopy is considered as a minimally invasive novel diagnostic technology to inspect the entire GI tract and to diagnose various diseases and pathologies. Since the development of this technology, medical device companies and many groups have made significant progress to turn such passive capsule endoscopes into robotic active capsule endoscopes to achieve almost all functions of current active flexible endoscopes. However, the use of robotic capsule endoscopy still has some challenges. One such challenge is the precise localization of such active devices in 3D world, which is essential for a precise three-dimensional (3D) mapping of the inner organ. A reliable 3D map of the explored inner organ could assist the doctors to make more intuitive and correct diagnosis. In this paper, we propose to our knowledge for the first time in literature a visual simultaneous localization and mapping (SLAM) method specifically developed for endoscopic capsule robots. The proposed RGB-Depth SLAM method is capable of capturing comprehensive dense globally consistent surfel-based maps of the inner organs explored by an endoscopic capsule robot in real time. This is achieved by using dense frame-to-model camera tracking and windowed surfelbased fusion coupled with frequent model refinement through non-rigid surface deformations.

link (url) Project Page [BibTex]

pi Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M. A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots arXiv preprint arXiv:1705.05444, May 2017 (article)

link (url) Project Page [BibTex]

A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot

Turan, M., Pilavci, Y. Y., Jamiruddin, R., Araujo, H., Konukoglu, E., Sitti, M.

arXiv preprint arXiv:1705.06524, 2017 (article)

Abstract

In the gastrointestinal (GI) tract endoscopy field, ingestible wireless capsule endoscopy is emerging as a novel, minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Since the development of this technology, medical device companies and many research groups have made substantial progress in converting passive capsule endoscopes to robotic active capsule endoscopes with most of the functionality of current active flexible endoscopes. However, robotic capsule endoscopy still has some challenges. In particular, the use of such devices to generate a precise three-dimensional (3D) mapping of the entire inner organ remains an unsolved problem. Such global 3D maps of inner organs would help doctors to detect the location and size of diseased areas more accurately and intuitively, thus permitting more reliable diagnoses. To our knowledge, this paper presents the first complete pipeline for a complete 3D visual map reconstruction of the stomach. The proposed pipeline is modular and includes a preprocessing module, an image registration module, and a final shape-from-shading-based 3D reconstruction module; the 3D map is primarily generated by a combination of image stitching and shape-from-shading techniques, and is updated in a frame-by-frame iterative fashion via capsule motion inside the stomach. A comprehensive quantitative analysis of the proposed 3D reconstruction method is performed using an esophagus gastro duodenoscopy simulator, three different endoscopic cameras, and a 3D optical scanner.

link (url) Project Page [BibTex]

pi Turan, M., Pilavci, Y. Y., Jamiruddin, R., Araujo, H., Konukoglu, E., Sitti, M. A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot arXiv preprint arXiv:1705.06524, 2017 (article)

link (url) Project Page [BibTex]

A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots

Turan, M., Almalioglu, Y., Konukoglu, E., Sitti, M.

arXiv preprint arXiv:1705.05435, 2017 (article)

Abstract

We present a robust deep learning based 6 degrees-of-freedom (DoF) localization system for endoscopic capsule robots. Our system mainly focuses on localization of endoscopic capsule robots inside the GI tract using only visual information captured by a mono camera integrated to the robot. The proposed system is a 23-layer deep convolutional neural network (CNN) that is capable to estimate the pose of the robot in real time using a standard CPU. The dataset for the evaluation of the system was recorded inside a surgical human stomach model with realistic surface texture, softness, and surface liquid properties so that the pre-trained CNN architecture can be transferred confidently into a real endoscopic scenario. An average error of 7.1% and 3.4% for translation and rotation has been obtained, respectively. The results accomplished from the experiments demonstrate that a CNN pre-trained with raw 2D endoscopic images performs accurately inside the GI tract and is robust to various challenges posed by reflection distortions, lens imperfections, vignetting, noise, motion blur, low resolution, and lack of unique landmarks to track.

link (url) Project Page [BibTex]

pi Turan, M., Almalioglu, Y., Konukoglu, E., Sitti, M. A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots arXiv preprint arXiv:1705.05435, 2017 (article)

link (url) Project Page [BibTex]

Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots

Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M.

ArXiv e-prints, 2017 (article)

Abstract

Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Medical device companies and many research groups have recently made substantial progresses in converting passive capsule endoscopes to active capsule robots, enabling more accurate, precise, and intuitive detection of the location and size of the diseased areas. Since a reliable real time pose estimation functionality is crucial for actively controlled endoscopic capsule robots, in this study, we propose a monocular visual odometry (VO) method for endoscopic capsule robot operations. Our method lies on the application of the deep Recurrent Convolutional Neural Networks (RCNNs) for the visual odometry task, where Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are used for the feature extraction and inference of dynamics across the frames, respectively. Detailed analyses and evaluations made on a real pig stomach dataset proves that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories.

link (url) Project Page [BibTex]

pi Turan, M., Almalioglu, Y., Araujo, H., Konukoglu, E., Sitti, M. Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots ArXiv e-prints, 2017 (article)

link (url) Project Page [BibTex]

2016

Parallel microcracks-based ultrasensitive and highly stretchable strain sensors

Amjadi, M., Turan, M., Clementson, C. P., Sitti, M.

ACS applied materials \& interfaces, 8(8):5618-5626, American Chemical Society, Febuary 2016 (article)

Abstract

There is an increasing demand for flexible, skin-attachable, and wearable strain sensors due to their various potential applications. However, achieving strain sensors with both high sensitivity and high stretchability is still a grand challenge. Here, we propose highly sensitive and stretchable strain sensors based on the reversible microcrack formation in composite thin films. Controllable parallel microcracks are generated in graphite thin films coated on elastomer films. Sensors made of graphite thin films with short microcracks possess high gauge factors (maximum value of 522.6) and stretchability (ε ≥ 50%), whereas sensors with long microcracks show ultrahigh sensitivity (maximum value of 11 344) with limited stretchability (ε ≤ 50%). We demonstrate the high performance strain sensing of our sensors in both small and large strain sensing applications such as human physiological activity recognition, human body large motion capturing, vibration detection, pressure sensing, and soft robotics.

DOI Project Page [BibTex]

2016

pi Amjadi, M., Turan, M., Clementson, C. P., Sitti, M. Parallel microcracks-based ultrasensitive and highly stretchable strain sensors ACS applied materials \& interfaces, 8(8):5618-5626, American Chemical Society, Febuary 2016 (article)

DOI Project Page [BibTex]

2015

Biomedical applications of untethered mobile milli/microrobots

Sitti, M., Ceylan, H., Hu, W., Giltinan, J., Turan, M., Yim, S., Diller, E.

Proceedings of the IEEE, 103(2):205-224, IEEE, March 2015 (article)

Abstract

Untethered robots miniaturized to the length scale of millimeter and below attract growing attention for the prospect of transforming many aspects of health care and bioengineering. As the robot size goes down to the order of a single cell, previously inaccessible body sites would become available for high-resolution in situ and in vivo manipulations. This unprecedented direct access would enable an extensive range of minimally invasive medical operations. Here, we provide a comprehensive review of the current advances in biomedical untethered mobile milli/microrobots. We put a special emphasis on the potential impacts of biomedical microrobots in the near future. Finally, we discuss the existing challenges and emerging concepts associated with designing such a miniaturized robot for operation inside a biological environment for biomedical applications.

DOI Project Page Project Page [BibTex]

2015

pi Sitti, M., Ceylan, H., Hu, W., Giltinan, J., Turan, M., Yim, S., Diller, E. Biomedical applications of untethered mobile milli/microrobots Proceedings of the IEEE, 103(2):205-224, IEEE, March 2015 (article)

DOI Project Page Project Page [BibTex]

Mehmet Turan

2018

Magnetic-Visual Sensor Fusion-based Dense 3D Reconstruction and Localization for Endoscopic Capsule Robots

2018

Unsupervised Odometry and Depth Learning for Endoscopic Capsule Robots

Sparse-then-dense alignment-based 3D map reconstruction method for endoscopic capsule robots

Deep EndoVO: A recurrent convolutional neural network (RCNN) based visual odometry approach for endoscopic capsule robots

2017

A deep learning based fusion of RGB camera information and magnetic localization information for endoscopic capsule robots

2017

EndoSensorFusion: Particle Filtering-Based Multi-sensory Data Fusion with Switching State-Space Model for Endoscopic Capsule Robots

Endo-VMFuseNet: Deep Visual-Magnetic Sensor Fusion Approach for Uncalibrated, Unsynchronized and Asymmetric Endoscopic Capsule Robot Localization Data

Sparse-then-Dense Alignment based 3D Map Reconstruction Method for Endoscopic Capsule Robots

Six Degree-of-Freedom Localization of Endoscopic Capsule Robots using Recurrent Neural Networks embedded into a Convolutional Neural Network

A Non-Rigid Map Fusion-Based RGB-Depth SLAM Method for Endoscopic Capsule Robots

A fully dense and globally consistent 3D map reconstruction approach for GI tract to enhance therapeutic relevance of the endoscopic capsule robot

A Deep Learning Based 6 Degree-of-Freedom Localization Method for Endoscopic Capsule Robots

Deep EndoVO: A Recurrent Convolutional Neural Network (RCNN) based Visual Odometry Approach for Endoscopic Capsule Robots

2016

Parallel microcracks-based ultrasensitive and highly stretchable strain sensors

2016

2015

Biomedical applications of untethered mobile milli/microrobots

2015

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