Miracle-dz/UniFAR-FTUs huggingface dataset:
Lunar optical wireless communication and navigation network for robotic and human exploration

Exploration of the moon is a stepping stone for further research of our solar system, the galaxy and, ultimately, the universe. Many intriguing questions arise regarding the moon; what is the moon's composition and structure, what is the potential for settlement or colonization and how did our solar system evolve to name a few. New technologies are required in order to answer these questions. The main goal in our project is to develop technologies for optical wireless communication and navigation systems for use in robotic and in human exploration on the moon. These technologies facilitate the exploration of the moon surface by enabling placing scientific equipment at precise locations and subsequently transferring the acquired information at high data rates. The main advantages of optical technology in comparison with RF technology are: a) high data rate transmission, b) small size and weight of equipment, c) low power consumption, d) very high accuracy in measuring range and orientation and e) no contamination of the quiet electromagnetic (EM) environment on the dark side of the moon In this paper we present a mathematical model and an engineering implementation of a system that simultaneously communicates, and measures the location and orientation of a remote robot on the moon.
Mueller matrix by imaging polarimeter

Most of imaging polarimeters in the field measure only a few components of the Mueller matrix or their combinations such as Stokes vector, degree of linear polarization (DOLP) and degree of circular polarization (DOCP). Our imaging polarimeter was similar in that it produced two combinations of 16 Mueller components. We upgraded our polarimeter to acquire the Mueller matrix of a scene in the field (Mueller image). Scenes consisted of flat plates mounted on a large panel, a large cylinder, and natural background such as trees and grass. We established a formula to derive Mueller images from the measurements with our instrument. Mueller images provided comprehensive information about the polarization effect on any targets in the scene, which were useful in distinguishing man-made objects from natural background. In addition, Mueller images enabled us to emulate some images by imaging polarimeters with limited capability. Comparison of those images with Mueller images provided an insight on the effectiveness and shortcomings of the associated imaging polarimeters.
Semi-Automatic Infrared Calibration for Augmented Reality Systems in Surgery*

Augmented reality (AR) has the potential to improve the immersion and efficiency of computer-assisted orthopaedic surgery (CAOS) by allowing surgeons to maintain focus on the operating site rather than external displays in the operating theatre. Successful deployment of AR to CAOS requires a calibration that can accurately calculate the spatial relationship between real and holographic objects. Several studies attempt this calibration through manual alignment or with additional fiducial markers in the surgical scene. We propose a calibration system that offers a direct method for the calibration of AR head-mounted displays (HMDs) with CAOS systems, by using infrared-reflective marker-arrays widely used in CAOS. In our fast, user-agnostic setup, a HoloLens 2 detected the pose of marker arrays using infrared response and time-of-flight depth obtained through sensors onboard the HMD. Registration with a commercially available CAOS system was achieved when an IR marker-array was visible to both devices. Study tests found relative-tracking mean errors of 2.03 mm and 1.12° when calculating the relative pose between two static marker-arrays at short ranges. When using the calibration result to provide in-situ holographic guidance for a simulated wire- insertion task, a pre-clinical test reported mean errors of 2.07 mm and 1.54° when compared to a pre-planned trajectory.
Soft Tactile Contour Following for Robot-Assisted Wiping and Bathing

The automated cleaning of surfaces such as furniture, bathroom sinks, and even human bodies is challenging due to the three-dimensional nature of their geometries. Yet, enabling robots to effectively and safely perform these tasks would not only reduce user efforts spent on household cleaning chores, but would also alleviate the strenuous workload of caretakers as the elderly population continues to grow at an unprecedented rate. In this work, we unify the applications of wiping objects and bathing humans as a general contour-following problem. To this end, we utilize a depth camera-based soft tactile sensor to extract the contact geometries and force-correlated measures during interaction between the robot and the target object or body part, and design a general contour-following controller that not only maintains contact with the target throughout the cleaning process, but also regulates the amount of force applied. Our system enables successful cleaning of pipes, shelving, and even human limbs and torsos without the need for data-driven methods such as deep learning, upon which the majority of existing works have relied.
Magnetic microrobot control using an adaptive fuzzy sliding-mode method

The magnetic medical microrobots are influenced by diverse factors such as the medium, the geometry of the microrobot, and the imaging procedure. It is worth noting that the size limitations make it difficult or even impossible to obtain reliable physical properties of the system. In this research, to achieve a precise microrobot control using minimum knowledge about the system, an Adaptive Fuzzy Sliding-Mode Control (AFSMC) scheme is designed for the motion control problem of the magnetically actuated microrobots in presence of input saturation constraint. The AFSMC input consists of a fuzzy system designed to approximate an unknown nonlinear dynamical system and a robust term considered for mismatch compensation. According to the designed adaptation laws, the asymptotic stability is proved based on the Lyapunov theorem and Barbalat's lemma. In order to evaluate the effectiveness of the proposed method, a comparative simulation study is conducted.
A Survey on Intrusion Detection Systems: Types, Datasets, Machine Learning methods for NIDS and Challenges

The Internet of Things (IoT) has grown tremendously in the last few years that adequate measure concerning security needs to quickly keep up. IoT devices have limited computational capability, low power, and run low quality software making them highly susceptible to various cyber attacks. Security, confidentiality and privacy are considered the main matter of contention in any smart environment based on IoT model. As a result, there is a pressing need for IoT-specific intrusion detection systems (IDS) that can safeguard devices and networks while also reducing security assaults and vulnerabilities. In this paper we have discussed intrusion detection system for IoT encompassing details on detection, placement and deployment strategies, different datasets and various machine learning methods and the challenges posed for network IDS.
Robust H∞ infinity control in the presence of stochastic uncertainty

This paper considers a robust H infinity state feedback control problem for linear uncertain systems with stochastic uncertainty. The uncertainty class considered in the paper involves uncertain multiplicative white noise perturbations which satisfy a certain variance constraint. A state feedback controller is presented which guarantees a prescribed level of disturbance attenuation for all admissible stochastic uncertainties.
Idiopathic Tenosynovitis with Rice Bodies: Clinical Case Report

Introduction: Tenosynovitis is the main cause of pain in the upper limbs; it can be originated from infectious and non-infectious causes. 50% of reported cases are tuberculous tenosynovitis. When the rice body formation pathogenesis is uncertain after performing all extension studies, it is called idiopathic tenosynovitis. Clinical Case: A 79- year-old male patient with a pathological history of chronic ischemic heart disease treated with cardiac catheterization with stent placement comes to the Plastic Surgery Office due to the presence of multiple masses on the back of the right hand in zone V, VI and VII with 3 years of evolution, without significant accompanying symptoms. Physical examination revealed the presence of multiple non-painful mobile masses of approximately 4x5cm in the extensor zone V, VI and VII of the right hand. In the surgical exploration for biopsy, within the macroscopic characteristics, multiple converging and interconnected masses were found with granular content infiltrating sheath and tendon itself (II, III, IV and V fingers of the right hand). Posterior capsule of masses is in contact with intrinsic and interosseous muscles of the right hand with the appearance of rice bodies. At the moment, the patient shows a favorable evolution and continues in periodic controls. Conclusions: In this case, having no underlying diseases related to this pathology and having ruled out among the differential diagnoses, it was determined that the occurrence of rice body masses in an infiltrative manner in the tendons is of an idiopathic origin, so our patient will remain on long-term follow-up, but so far with favorable evolution.
Exploring the Themes of Focus for Change Management Applied to Multinational Corporations: A Scoping Review

Change management is fundamental for engineering management and creating a lasting change within organisations; without effective CM initiatives, improvements to optimise or enhance business value through change could be challenging. This article aims to identify the literature and themes that link Change Management (CM) and Multinational Corporations (MNCs). This article follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach supported by bibliometric analysis. Considering the vast amount of literature on CM and MNCs as individual topics, the number of publications regarding CM and MNC as a combined research focus is less researched. This article forms a foundation for identifying relevant thematic topics, trends, and patterns regarding CM for MNCs.
Strain effects in epitaxial GaN grown on AlN‐buffered Si(111)

Growth of GaN thin films on AlN‐buffered Si(111) by ultrahigh‐vacuum rf glow discharge reactive magnetron sputtering is reported. Epitaxy of GaN is established by x‐ray and electron diffraction. Raman scattering from the epitaxial films consistent with that of wurtzitic GaN is observed. The ion energies involved in the growth process are quantified by measuring the plasma potentials of the Ar/N2 glow discharge by an emissive Langmuir probe technique. As a function of increasing input power, a systematic increase in ion energies and a systematic straining of the GaN lattice are observed. Measured GaN phonon energy scales with lattice strain.
Human Resources Strategies in High-tech Startups during the Seed Phase: The Relationship between Recruitment, Career, and Tolerance of Uncertainty

In startup companies, it is important to understand and assess the abilities that are required to become the core competencies of the organization. However, startups have limited resources, so they need an easier technique to measure the match with the culture and traits of the company. To do this, it is first necessary to consider what characteristics of the organization’s core competencies. After conducting action research and analyzing meetings at a high-tech start-up company managed by Mr. Nakaya as CTO, we found that the ability to tolerate uncertainty, such as the proficiency to create hypotheses without being limited by immediate facts and methodologies, is a skill of great value, especially to the company. We also found the characteristics of those who have these abilities and those who do not appear in conversations by using correspondence analysis, or principal component analysis (PCA). Furthermore, using the PCA results, we aim to create a framework that can be used universally within the company that conducted the analysis.
An Automated Approach to Re-Hosting Embedded Firmware by Removing Hardware Dependencies

Firmware emulation is useful for finding vulnerabil-ities, performing debugging, and testing functionalities. However, the process of enabling firmware to execute in an emulator (i.e., re-hosting) is difficult. Each piece of the firmware may depend on hardware peripherals outside the microcontroller that are inaccessible during emulation. Current practices involve painstakingly disentangling these dependencies or replacing them with developed models that emulate functions interacting with hardware. Unfortunately, both are highly manual and error-prone. In this paper, we introduce a systematic graph-based approach to analyze firmware binaries and determine which functions need to be replaced. Our approach is customizable to balance the fidelity of the emulation and the amount of effort it would take to achieve the emulation by modeling functions. We run our algorithm across a number of firmware binaries and show its ability to capture and remove a large majority of hardware dependencies.
Semantic Scene Completion through Multi-Level Feature Fusion

Partial observation of indoor scenes (single-viewed RGB-D) carries insufficient spatial information for complex tasks such as autonomous navigation and virtual reality, thus many learning-based methods are proposed to realize semantic scene completion (SSC) from single-viewed input. However, most of them only extract scene-level features of input to generate output, which might lose details. In this paper, a new method that fully utilizes both instance-level and scene-level features is proposed. Firstly, an object detection module is pre-trained to localize indoor objects. Secondly, coarse completion result is obtained from scene-level feature using an encoder-decoder structure. Finally, based on the pre-trained bounding boxes, coarse completion result is refined using a geometric refinement module. Our network's performance is evaluated on both real and synthetic datasets. The results demonstrate that our network is able to reconstruct indoor scenes with more geometric details, get clearer boundaries between instances and outperform most existing SSC methods both intuitively and quantitatively.
A revised Gaussian pencil beam model for calculation of the in-water dose caused by clinical electron-beam irradiation

Purposes: The dose in water caused by clinical electron-beam irradiation is mainly composed of the doses due to the direct electrons, the indirect electrons, and the contaminant X-rays. In this paper, an analytical method for 3-dimensional (3D) dose calculation in water for the direct electrons and for the direct-plus-indirect electrons is proposed in light of the electron Monte Carlo (eMC) datasets. Methods: The dose calculation was performed for square fields, based on a revised Gaussian pencil beam model, where the parallel beam depth-dose dataset under an infinitely large field was reconstructed from the depth-dose dataset of a fan beam with a finite field. We used a semi-infinite water phantom, setting the beam incident surface on the isocenter plane that is perpendicular to the beam axis. The dose calculation model sets an effective field at each dose calculation depth, where the effective field is larger than the geometrical field that the electron applicator forms on the basis of its divergent spread under the effective source-surface distance (SSDeff). Results and conclusions: By comparing the calculated datasets of depth dose (DD) and off-axis dose (OAD) in water with the eMC datasets for electron beams of E=6, 12, and 18 MeV by Wieslander and Knöös (2006), it has been found that the revised Gaussian pencil beam model is of practical use and has a bright prospect to give almost the same calculation results as the eMC datasets.
Adaptive strategy with output constraints for multi-section web linkage system

Solving unpredictable fluctuations in tension and speed is essential for optimizing the material transportation process of web materials linkage systems. We develop an adaptive controller to control the output values and restraint tension values within a specific range of requirements. External disturbances existing in this nonlinear system are estimated by the radial basis function neural network (RBF NN), while tension output constraints maintenance and stability of object are both guaranteed by Barrier Lyapunov function (BLF) analysis. The superiority of the proposed controller compared with other nonlinear controllers is verified through simulation.
Torque Density Improvement of a Low-Speed High-Torque Swirling Actuator Driven by Electromagnetic Radial Force With Integrated Mechanical Gears

The torque density improvement of a low-speed high-torque swirling actuator is presented in this article. The swirling actuator is driven by the electromagnetic radial force and integrated mechanical gears. The electromagnetic radial force generates the circular motion of the swirler, which is converted into the low-speed rotor rotation by the mechanical gears. First, the dimensions of the swirler with 12-pole permanent magnets are optimized to enhance the electromagnetic radial force by analytical calculation and three-dimensional finite-element analysis. Second, an improved gear set with a reduced pressure angle and an increased transmission ratio is designed. The gear efficiency and torque are investigated analytically considering the friction loss. Two prototypes are designed, and the experimental results exhibit that the peak torque density is improved from 27 to 64 Nm/L with a small volume of 0.16 L.
Torque Control of Hydraulic Pressure Servo Valve Driven Actuator with Deep Neural Network

Non-linear dynamics, model uncertainties due to hydraulic fluid, and disturbances in hydraulic systems make it difficult to obtain accurate torque tracking performance. In this study, a learning-based torque-tracking method is proposed, which does not require approximating the torque dynamics. The proposed method can capture disturbances and model uncertainties of system. The applied neural network comprises a nonlinear autoregressive model with exogenous inputs (NARX) and a long shortterm neural network (LSTM). NARX is employed due to its ability to predict time series control input from the states of system, and LSTM is used to overcome the vanishing and exploding gradient, which causes long-term memory loss in NARX, leading to inaccurate torque tracking performance. LSTM with NARX achieved a better prediction performance with a mean square error and standard deviation of $0.0015 \pm 0.4\times 103$ compared to only NARX with a mean square error of $0.004 \pm 1.0\times 103$ at 10 K training data size.
Co-optimization of Acrobot Design and Controller for Increased Certifiable Stability

Unlike fully actuated systems, the control of underactuated robots necessitates the use of passive dynamics to fulfill control objectives. Hence, there is an increased interdependence between their design parameters and the closed loop performance. This paper proposes a novel approach for co-optimization of robot design and controller parameters for increased certifiable stability obtained with means of region of attraction analysis and gradient free optimization. In particular, it discusses the co-optimization problem of a gymnastic acrobot robot where the design and the controller are optimized to have a large region of attraction (ROA) taking into account the closed loop dynamics of the non-linear system stabilized by a linear quadratic regulator (LQR) controller. The results are validated by extensive simulation of the acrobot's closed loop dynamics.
MPC-PF: Social Interaction Aware Trajectory Prediction of Dynamic Objects for Autonomous Driving Using Potential Fields

Predicting object motion behaviour is a challenging but crucial task for safe decision making and path planning for an autonomous vehicle. It is challenging in large part due to the uncertain, multi-modal, and practically intractable set of possible agent-agent and agent-space interactions, especially in urban driving settings. Models solely based on constant velocity or social force have an inherent bias and may lead to inaccurate predictions across the prediction horizon whereas purely data driven approaches suffer from a lack of a holistic set of rules governing predictions. We tackle this problem by introducing MPC-PF: a novel potential field-based trajectory predictor that incorporates social interaction and is able to tradeoff between inherent model biases across the prediction horizon. Through evaluation on a variety of common urban driving scenarios, we show that our model is capable of producing accurate predictions for both short and long term timesteps. We also demonstrate the significance of our model architecture through an ablation study.
Modular robot networking: a novel schema and its performance assessment

Modular robots (MRs) consist of unique robots which interconnect and work as a collective to perform objectives. Coordinating these robots rely on robust communication, as modules moving independently can lead to damaging behaviour. We present a robust structure for modular robot communication, implemented and tested on a new MR. The structure has different communication protocols depending on the importance and bandwidth of the exchanged information, has fast error responses, and considerations which allow for two modules to actuate the same joint. We evaluate the wireless protocols, novel error response, and coordinated actuation empirically, validating the system on a new modular robot, the Mori3. We find two wireless protocols can be used to balance speed and reliability; transmitting errors through both wireless and serial is more consistent and faster; and sharing motor targets, control variables, and measurements allow for motors to operate a shared joint with equal efforts.
IMU Dead-Reckoning Localization with RNN-IEKF Algorithm

In complex urban environments, the Inertial Navigation System (INS) is important for navigating unmanned ground vehicles (UAVs) for its environment-independency and reliability of real-time localization. It is usually employed as the baseline in the case of other sensors failures, such as the GPS, Lidar, or Cameras. However, one problem for the INS is that its estimation error of localization accumulates over time, and thus the estimated trajectories of the UAVs continue to drift away from their ground truths. To solve this problem, this paper proposes an improved algorithm based on the Invariant Extended Kalman Filter (IEKF) for dead-reckoning of autonomous vehicles, which dynamically adjusts the process noise and the observation noise covariance matrixes through Attention mechanism and Recurrent Neural Network (RNN). The algorithm achieves more robust and accurate dead-reckoning localization in the experiments conducted on the KITTI dataset, reducing the translational error by about 45%compared to the baseline.