Miracle-dz/UniFAR-FTUs huggingface dataset:
Circle Formation with Computation-Free Robots Shows Emergent Behavioural Structure

In this paper, we demonstrate how behavioural structure, such as a finite state machine, can emerge in minimal robots without computation nor memory capabilities. As a case study we observe the ability of a group of non-holonomic robots to form robust, self-healing circle formations in a decentralized manner using only a limited frontal binary sensor. We present a grid-search method to find suitable parameters that promote the formation of a stable circle. We then examine how the parameters of the controllers affect the appearance of the behaviour, and provide theoretical proof for its emergence and self-healing properties. We validate the proposed model through a set of experiments with ten mobile real robots. Our results with real robots match the simulated experiments and provide insights on how a simple, computation-free behaviour can generate complex spatio-temporal dynamics.
Optimal ICU Admission Control With Premature Discharge

The intensive care unit (ICU) delivers care to critically ill patients with high resource intensity. Stochastic patient arrival and uncertain length of stay in ICUs present tremendous challenge to establish the admission and discharge policy to maximize the number of surviving patients and improve operational efficiency. In practice, ICU schedulers reserve some beds for potential patients with most critical conditions. Moreover, they prematurely discharge current ICU patients who are in stable status to accommodate for new and more urgent arrivals. We develop an analytical framework to quantify the impact of the number of reserved beds and suggest when to prematurely discharge current patients. A Markov decision process model is established to strike a balance between the rejection of incoming patient and the premature discharge in the near future. Monotonicity, concavity, and structural properties of the optimal control policy are presented. Using the inpatient record at a tertiary-level hospital in China, we conduct a case study and propose an effective threshold policy. Extensive numerical experiments are performed to analyze the effect of each parameter on the total survival benefits and compare different policies. Note to Practitioners—The intensive care unit (ICU) is the most resource intensive unit in the hospital, which has a significant impact on patient safety and care quality. Due to the increasing and aging population, the contradiction between the increasing demand and the insufficient ICU resources puts eligible ICU candidates at risk of lacking timely ICU care. Given the important role of the ICU in surviving lives of most critical patients, it is of paramount significance to improve ICU resource utilization and maximize the number of ICU survivors. Therefore, the objective of this paper is to develop a mathematical model to study ICU admission policies and premature discharge decisions. Based on our analytical framework, the ICU admission director can evaluate performance measures of ICU admission control system and investigate the impact of different policies on care quality of patients. By using an effective threshold admission policy proposed in this paper, the ICU admission director can be equipped with a decision support tool for the ICU admission control decisions with quantitative measures of potential impact.
Intelligent Fault Diagnosis of Rolling Element Bearings Based on HHT and CNN

Automatic and accurate identification of rolling bearings fault categories and fault severities is still a major challenge in fault diagnosis. In this paper, a deep learning based approach is presented to translate traditional diagnostic methods based on one-dimensional time-series analysis into graphical images for fault type and severity identification, with rolling bearing as a representative example. Specifically, time sequences of vibration signals are first converted by Hilbert-Huang transform (HHT) to time-frequency images. Next, a convolutional neural network (CNN) learns fault-sensitive features in the time-frequency domain from these images and performs fault classification. Experiments on bearing data demonstrates effectiveness and efficiency of the developed approach with a classification accuracy 95%.
Systematic Methodology for the Global Stability Analysis of Nonlinear Circuits

A new methodology for the detection of Hopf, flip, and turning-point bifurcations in nonlinear circuits analyzed with harmonic balance (HB) is presented. It enables a systematic determination of bifurcation loci in terms of relevant parameters, such as input power, input frequency, and bias voltages, for instance. It does not rely on the use of continuation techniques and is able to globally provide the entire loci, often containing multivalued sections and/or disconnected curves, in a single simulation. The calculation of Hopf and flip bifurcations is based on the extraction of a small-signal admittance/impedance function from HB and the calculation of its zeros through a geometrical procedure. The method is ideally suited for the investigation of the global stability properties of power amplifiers and other nonlinear circuits. The turning-point locus, associated with either jump phenomena or synchronization, is obtained by taking into account the annihilation/generation of steady-state solutions that is inherent to this type of bifurcation. A technique is also presented for the exhaustive calculation of oscillation modes in multidevice oscillators and oscillators loaded with multiresonance networks. The new methodologies are illustrated through their application to a power amplifier and a multimode oscillator.
Continuous Shape Changing Locomotion of 32-legged Spherical Robot

Shape changing robot is an approach towards locomotion on uncertain terrain due to its omni-directional features. However, the current locomotion method for such robots rely on discontinuous rolling. We propose a free form locomotion: an omni directional continuous crawling for deformable robots. This method introduce continuous shifting of contact surface similar to amoeba movement. A Mochibot that has thirty two telescopic legs is developed to verify the proposed locomotion method. Through the experiments, we have confirmed that the robot can track smooth paths: straight, smooth, and hand written curves. We also evaluate errors between desired and measured trajectories of the robot.
Natural Dynamics Exploitation of Dynamic Soaring: Towards Bio-Inspired and Energy Efficient Flying Locomotion

Albatross has an energy efficient flying pattern (dynamic soaring) among seabirds. This interesting point encourages us to exploit its flying natural dynamics so as to control the flying robots on energy efficient and robust gaits. In doing so, we study the albatross dynamic soaring from analytical and biological perspectives and realize that to generate the dynamic soaring instead of trajectory control, the mechanical energy should be regulated. Accordingly, the control objective is set to mechanical energy regulation, and the bank angle and lift coefficient are computed to satisfy this objective. The presented method is simulated on a standard albatross model and generates two different types of dynamic soaring; O-shaped and a-shaped patterns. In addition, by means of simulations, it is investigated that the presented method is robust in face of variations in initial conditions and unexpected disturbances in the environment's model; i.e., they cannot disturb the stability and cyclic behavior of the system. Moreover, the simulation results are compared with pieces of natural evidence from albatross and interesting similarities are observed.
Study of Corner Singularity in Conformal Structured Mesh Generation for the Finite-Difference Time-Domain Method Based on Ray Tracing

We investigate the problem of erroneous conformal area extracted by ray tracing in conformal structured mesh generation for the finite-difference time-domain method when tangential situations arise, referred to as corner singularity. The erroneous conformal area results from the fuzzification of intersecting length for tangential situations. Here, an effective mesh scheme to solve the corner singularity is proposed. In this scheme, additional degrees of freedom for mesh flags of vertexes and generalized consistency modification are proposed to solve the fuzzification of intersecting length, leading to accurate extraction of the conformal area. Furthermore, the scheme can tackle the convex and concave points uniformly without leading to possible breakdown of the conventional parity-number-based method. Rigorous and less rigorous staircase criteria are proposed to classify the cells accurately and effectively. The difficulties of concave point combined with the tangential situation are also successfully solved. Numerical examples of several microwave devices are given to show the validity of the proposed mesh scheme.
Real-Time Feature Depth Estimation for Image-Based Visual ServOing

Without the 3-D geometry of the target and robust to camera calibration error, image-based visual servoing schemes have gained a lot of attention. However, the depth of the selected feature, which is involved in the interaction matrix relating the time variation of the feature to the velocity twist of the camera, must be estimated correctly to guarantee the stability of the controller. To this end, this paper proposes a new nonlinear reduced-order observer structure to recover the feature depth in real time. Compared with the existing works, the proposed observer has a global asymptotic convergence property and fast convergence rate, and the convergence rate can be easily adjusted only using a single gain parameter. In addition, the proposed observer has a less restrictive observability condition and stronger robustness to noisy measurements. Extensive comparative numerical simulations are carried out to validate the effectiveness of the proposed depth observer.
Drivers' Manoeuvre Prediction for Safe HRI

Machines with high levels of autonomy such as robots and our growing need to interact with them creates challenges to ensure safe operation. The recent interest to create autonomous vehicles through the integration of control and decision-making systems makes such vehicles robots too. We therefore applied estimation and decision-making mechanisms currently investigated for human-robot interaction to human-vehicle interaction. In other words, we define the vehicle as an autonomous agent with which the human driver interacts, and focus on understanding the human intentions and decision-making processes. These are then integrated into the ro-bot‘s/vehicle's own control and decision-making system not only to understand human behaviour while it occurs but to predict the next actions. To obtain knowledge about the human's intentions, this work relies heavily on the use of motion tracking data (i.e. skeletal tracking, body posture)gathered from drivers whilst driving. We use a data-driven approach to both classify current driving manoeuvres and predict future manoeuvres, by using a fixed prediction window and augmenting a standard set of manoeuvres. Results are validated against drivers of different sizes, seat preferences and levels of driving expertise to evaluate the robustness of the methods; precision and recall metrics higher than 95% for manoeuvre classification and 90% for manoeuvre prediction with time-windows of up to 1.3 seconds are obtained. The idea of prediction adds a highly novel aspect to human-robot/human-vehicle interaction, allowing for decision and control at a later point.
A Five-Year Evaluation of SMOS Level 2 Soil Moisture in the Corn Belt of the United States

We compare Soil Moisture Ocean Salinity Level 2 Soil Moisture (L2SM) retrievals to the five-year <italic>in situ</italic> soil moisture dataset available for the predominately agricultural South Fork Iowa River (SFIR) watershed in the U. S. Corn Belt. SMOS L2SM is 0.039 m<inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula> m<inline-formula><tex-math notation="LaTeX">$^{-3}$</tex-math></inline-formula> drier than the SFIR network soil moisture and has an unbiased RMSE (ubRMSE) of 0.062 m<inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula> m<inline-formula><tex-math notation="LaTeX">$^{-3}$</tex-math></inline-formula> for the period of April 2013 to November 2017 (excluding DJF). The bias is 11% of the range of <italic>in situ</italic> soil moisture. The largest monthly dry biases occur in April (0.052 m<inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula> m<inline-formula><tex-math notation="LaTeX">$^{-3}$</tex-math></inline-formula>), July (0.072 m<inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula> m<inline-formula><tex-math notation="LaTeX">$^{-3}$</tex-math></inline-formula>), and November (0.061 m<inline-formula><tex-math notation="LaTeX">$^3$</tex-math></inline-formula> m<inline-formula><tex-math notation="LaTeX">$^{-3}$</tex-math></inline-formula>). Potential sources of the dry bias we discuss are: bias in auxiliary modeled temperatures; errors in soil texture maps; and nonrepresentative parameterizations of single scattering albedo and soil surface roughness. Auxiliary skin temperature was colder than expected and may explain why SMOS L2SM has a slightly drier bias for evening overpasses. Increasing the parameterized soil surface roughness produces wetter SMOS L2SM retrievals but also decreases the range of SMOS L2SM. Random error in auxiliary surface temperature, edge-of-field locations of <italic>in situ</italic> sensors, and differences in sensing volume between SMOS and <italic>in situ</italic> sensors contribute to the soil moisture ubRMSE. ubRMSE can be decreased by using a nonzero single scattering albedo more representative of a corn and soybean canopy at the cost of increasing the dry bias.
Gamification in m-Health Applications and its Effects on Health Behavioural Changes in Net Generation Students

This research examines Gamification in m-Health Applications and its Effects on Health Behavioural Changes in Students. The four central themes highlighted in this research are Gamification, m-Health Applications, Persuasive Computing, and the Net Generation. Gamification is a relatively new topic that is beginning to be looked seriously as a way to overcome problems and improve processes due to its characteristics. One of these characteristics is its ability to persuade and motivate its users, which can have positive effects on their behaviours and attitudes. The Net Generation has been shown to be particularly susceptible to Gamification. Therefore, it is hoped that Gamification, when combined with the ever-increasing access to and improvement of mobile technology, could provide a serious solution to some of the health problems and behaviours common to the Net Generation. This research surveyed members of the Net Generation from the University of Cape Town in order to determine how their use of Gamification within m-health applications had affected their health behaviours. The findings, while not statistically representative of the entire Net Generation, show that there is evidence that the inclusion of Gamification in m-health applications has a positive effect on the health behaviours of members of the Net Generation. Although more research needs to be conducted to confirm this, the findings of this research imply that Gamification, when combined with m-health application, could indeed provide a solution to some of the health problems and behaviours common to the Net Generation.
Semi-Supervised SLAM: Leveraging Low-Cost Sensors on Underground Autonomous Vehicles for Position Tracking

This work presents Semi-Supervised SLAM - a method for developing a map suitable for coarse localization within an underground environment with minimal human intervention, with system characteristics driven by real-world requirements of major mining companies. This work leverages existing information common within a mining environment - namely a surveyed mine map - which is used to sparsely ground map locations within the mine environment, increasing map accuracy and allowing localization within a global frame. Map creation utilizes a low cost camera sensor and minimal user information to produce a map which can be used for single camera localization within a mining environment. We evaluate the localization capabilities of the proposed approach in depth by performing data collection on operational underground mining vehicles within an active underground mine and by simulating occlusions common to the environment such as dust and water. The proposed system is capable of producing maps which have an average localization error 2.5 times smaller than the next best performing method ORB-SLAM2, comparable localization performance to a state-of-the-art deep learning approach (which is not a feasible solution due to both compute and training requirements) and is robust to simulated environmental obscurants.
Methods for Autonomous Wristband Placement with a Search-and-Rescue Aerial Manipulator

A new robotic system for Search And Rescue (SAR) operations based on the automatic wristband placement on the victims' arm, which may provide identification, beaconing and remote sensor readings for continuous health monitoring. This paper focuses on the development of the automatic target localization and the device placement using an unmanned aerial manipulator. The automatic wrist detection and localization system uses an RGB-D camera and a convolutional neural network based on the region faster method (Faster R-CNN). A lightweight parallel delta manipulator with a large workspace has been built, and a new design of a wristband in the form of a passive detachable gripper, is presented, which, under contact, automatically attaches to the human, while disengages from the manipulator. A new trajectory planning method has been used to minimize the torques caused by the external forces during contact, which cause attitude perturbations. Experiments have been done to evaluate the machine learning method for detection and location, and for the assessment of the performance of the trajectory planning method. The results show how the VGG-16 neural network provides a detection accuracy of 67.99%. Moreover, simulation experiments have been done to show that the new trajectories minimize the perturbations to the aerial platform.
Decentralized Motion Control in a Cabled-based Multi-drone Load Transport System

A provably stable decentralized control scheme is proposed to allow multiple conventional quadcopters carry a cable-suspended payload. The method exploits a fundamental energetic passivity property of the combined drones, cables, and payload system to stably move the payload from its origin to destination. This is achieved without making any assumption about the status of the cables tension during the flight, and any measurement from the payload. The controller is decentralized in the sense that inter-drone communication of feedback measurements is not required. Motion stability is demonstrated via a Lyapunov analysis. The proposed controller is successfully implemented on a three-drone payload transport system in an indoor environment, using measurement from an optical tracking systems and the drones on-board IMUs.
Band Dependent Spatial Details Injection Based on Collaborative Representation for Pansharpening

This paper proposes an improved version of the generalized band dependent spatial detail pansharpening method. The proposed method called collaborative representation based band dependent spatial detail (CR-BDSD) benefits the advantages of collaborative representation (CR) to increase the difference between the panchromatic image and the approximation image. Therefore, the highlighted detail image is provided. Then, by adding it to the upsampled multispectral image, the pansharpened image is generated. The use of local spectral-spatial information and also removing noise and redundant spatial features are other benefits of CR in the pansharpening product. According to the experimental results, the CR-BDSD method provides superior fusion results in terms of both quantative and qualitative measures.
Re-Establishing Communication in Teams of Mobile Robots

As communication is important for cooperation, teams of mobile robots need a way to re-establish a wireless connection if they get separated. We develop a method for mobile robots to maintain a belief of each other's positions using locally available information. They can use their belief to plan paths with high probabilities of reconnection. This approach also works for subteams cooperatively searching for a robot or group of robots that they would like to reconnect with. The problem is formulated as a constrained optimization problem which is solved using a branch-and-bound approach. We present simulation results showing the effectiveness of this strategy at reconnecting teams of up to five robots and compare the results to two other strategies.
On the Application of Massive MIMO Systems to Machine Type Communications

This paper evaluates the feasibility of applying massive multiple-input multiple-output (MIMO) to tackle the uplink mixed-service communication problem. Under the assumption of an available physical narrowband shared channel, devised to exclusively consume data traffic from machine type communications (MTC) devices, the capacity (i.e., number of connected devices) of MTC networks and, in turn, that of the whole system, can be increased by clustering such devices and letting each cluster share the same time–frequency physical resource blocks. Following this research line, we study the possibility of employing sub-optimal linear detectors to the problem and present a simple and practical channel estimator that works without the previous knowledge of the large-scale channel coefficients. Our simulation results suggest that the proposed channel estimator performs asymptotically, as well as the MMSE estimator, with respect to the number of antennas and the uplink transmission power. Furthermore, the results also indicate that, as the number of antennas is made progressively larger, the performance of the sub-optimal linear detection methods approaches the perfect interference-cancellation bound. The findings presented in this paper shed light on and motivate for new and exciting research lines toward a better understanding of the use of massive MIMO in MTC networks.
Quadrupedal walking motion and footstep placement through Linear Model Predictive Control

The present work addresses the generation of a walking gait with automatic footstep placement for a quadrupedal robot, within a Linear Model Predictive Control framework. Existing work has shown how this is only possible within a non-convex programming framework, finding a solution of which is well-known to be very hard. We propose a way to formulate the joint optimization problem as an approximate QP with linear constraints, whose global optimum can be quickly found with off-the-shelf solvers. More specifically, this is done by introducing auxiliary states and control inputs, each of which is subject to linear constraints that are inspired from the literature on bipedal locomotion. Finally, we validate our method on the CENTAURO robot, a hybrid wheeled-legged quadruped with a humanoid upper-body.