Miracle-dz/UniFAR-FTUs huggingface dataset:
Multirotor dynamics based online scale estimation for monocular SLAM

This paper proposes a novel method to estimate the scale online for monocular SLAM. Unlike conventional scale estimation methods that require a metric sensor such as an IMU and apriori knowledge of its biases, this approach estimates the scale online by solely using the monocular camera and multirotor dynamics model in an extended Kalman Filter framework. Further, we discuss the observability of scale and theoretically show that the scale becomes observable when multirotor dynamics model and monocular vision are used in conjunction. We validate our proposition with extensive experimentation on the local as well as on the standard datasets and compare the same with other state of the art methods.
Pose and Posture Estimation of Aerial Skeleton Systems for Outdoor Flying

We present a novel pose and posture estimation framework of aerial skeleton system for outdoor flying. To exploit redundant/independent sensing while rendering the system “modular”, we attach an IMU (inertial measurement unit) sensor and a GNSS (global navigation satellite system) module on each link and perform SE(3)-motion EKF (extended Kalman filtering). We then apply the kinematic constraints of the aerial skeleton system to these EKF estimates of all the links through SCKF (smoothly constrained Kalman filtering), thereby, enforcing the kinematic coherency of the skeleton system and, consequently, significantly enhancing the estimation accuracy and the control performance/stability of the aerial skeleton system. A semi-distributed version of the obtained estimation framework is also presented to address the issue of scalability. The theory is then verified/demonstrated with real outdoor flying experiments and simulation studies of a three-link aerial skeleton system.
General Sequence Teacher–Student Learning

In automatic speech recognition, performance gains can often be obtained by combining an ensemble of multiple models. However, this can be computationally expensive when performing recognition. Teacher–student learning alleviates this cost by training a single student model to emulate the combined ensemble behaviour. Only this student needs to be used for recognition. Previously investigated teacher–student criteria often limit the forms of diversity allowed in the ensemble, and only propagate information from the teachers to the student at the frame level. This paper addresses both of these issues by examining teacher–student learning within a sequence-level framework, and assessing the flexibility that these approaches offer. Various sequence-level teacher–student criteria are examined in this work, to propagate sequence posterior information. A training criterion based on the Kullback–Leibler (KL)-divergence between context-dependent state sequence posteriors is proposed that allows for a diversity of state cluster sets to be present in the ensemble. This criterion is shown to be an upper bound to a more general KL-divergence between word sequence posteriors, which places even fewer restrictions on the ensemble diversity, but whose gradient can be expensive to compute. These methods are evaluated on the augmented multi-party interaction (AMI) meeting transcription and MGB-3 television broadcast audio tasks.
Autonomous Cooperative Flight of Rigidly Attached Quadcopters

In this paper, a method for online parameter estimation and automatic control of a system of rigidly attached quadcopters is introduced. First, the method performs an estimation of the physical structure attaching the quadcopters by relying solely on information from the quadcopters’ Inertial Measurement Units (IMU). This information is obtained via simple and short online experiments, allowing their plug and play assembly without any human intervention. Then, given the estimated physical attachment’s parameters, a stable operation of the quadcopters is achieved via an adaptive controller architecture, where the controller parameters are obtained using Reinforcement Learning. Finally, experimental results validate the proposed method, showing that a correct estimation of the physical structure is obtained allowing the autonomous flight of a pair of attached quadcopters.
Designing a Personality-Driven Robot for a Human-Robot Interaction Scenario

In this paper, we present an autonomous AI system designed for a Human-Robot Interaction (HRI) study, set around a dice game scenario. We conduct a case study to answer our research question: Does a robot with a socially engaged personality lead to a higher acceptance than a competitive personality? The flexibility of our proposed system allows us to construct and attribute two different personalities to a humanoid robot: a socially engaged personality that maximizes its user interaction and a competitive personality that is focused on playing and winning the game. We evaluate both personalities in a user study, in which the participants play a turn-taking dice game with the robot. Each personality is assessed with four different evaluation tools: 1) the Godspeed Questionnaire, 2) the Mind Perception Questionnaire, 3) a custom questionnaire concerning the overall HRI experience, and 4) a Convolutional Neural Network analyzing the emotions on the participants’ facial feedback throughout the game. Our results show that the socially engaged personality evokes stronger emotions among the participants and is rated higher in likability and animacy than the competitive one. We conclude that designing the robot with a socially engaged personality contributes to a higher acceptance within an HRI scenario.
A Comparison of CNN-Based and Hand-Crafted Keypoint Descriptors

Keypoint matching is an important operation in computer vision and its applications such as visual simultaneous localization and mapping (SLAM) in robotics. This matching operation heavily depends on the descriptors of the keypoints, and it must be performed reliably when images undergo condition changes such as those in illumination and viewpoint. Previous research in keypoint description has pursued three classes of descriptors: hand-crafted, those from trained convolutional neural networks (CNN), and those from pre-trained CNNs. This paper provides a comparative study of the three classes of keypoint descriptors, in terms of their ability to handle conditional changes. The study is conducted on the latest benchmark datasets in computer vision with challenging conditional changes. Our study finds that (a) in general CNN-based descriptors outperform hand-crafted descriptors, (b) the trained CNN descriptors perform better than pre-trained CNN descriptors with respect to viewpoint changes, and (c) pre-trained CNN descriptors perform better than trained CNN descriptors with respect to illumination changes. These findings can serve as a basis for selecting appropriate keypoint descriptors for various applications.
3D Printed Ferrofluid Based Soft Actuators

This work demonstrates 3D printed soft actuators with complex shapes and remote actuation using an external magnetic field. Instead of embedding magnetic particles in a polymeric matrix, we fabricated a novel ferrofluid-based actuator, in which the fluid can be moved to different locations in the actuator to affect actuator response. We studied the effect of both the ferrofluid and the 3D printed material on the motion of simple actuators using 3D printed tubes. In addition, we 3D printed more complex actuators mimicking a human hand and a worm to demonstrate more complex motion.
Autonomous Household Energy Management Using Deep Reinforcement Learning

With the massive growth of smart meters and availability of data, there is a unique opportunity to offer novel services to utility customers. The emergence of new technologies can be used to learn a customer's electrical consumption patterns and to offer customized solutions for minimizing electricity cost. One such technology is deep reinforcement learning (DRL), which is explored in this paper. The proposed approach for autonomous household energy management utilizes deep Q-learning with a novel method to deal with potential scalability issues that involves grouping dependent electrical devices. The defined reward function addresses the problems of previous research by adding more scenarios for the penalty and reward. Transfer learning using an adviser agent is also utilized to improve training time. By grouping dependent devices, we are able to resolve the problem of scalability. The results of these experiments demonstrate that DRL can be utilized to autonomously control household energy consumption.
Kinematic Analysis of a 4-DOF Parallel Mechanism with Large Translational and Orientational Workspace

This paper introduces a novel four-degree-of-freedom (4-DOF) parallel mechanism having 3 translational DOFs and 1 rotational DOF. The mechanism comprises 2 sets of parallelogram linkages, which constrain two of the rotational DOFs of the mechanism. An interesting feature of the mechanism is that it can be driven using 4 parallel sliders mounted on its base. As a result, one of the translational DOFs can be infinitely large. Also, the architecture of the mechanism provides a large rotational DOF in one direction. The kinematic equations of the mechanism are derived and the Jacobian matrices are obtained. The mathematical conditions that lead to singularities are also found. Moreover, a geometric description of the boundaries of the workspace is given, which can be expressed using simple equations. Finally, some design examples are proposed and a prototype is presented.
RESLAM: A real-time robust edge-based SLAM system

Simultaneous Localization and Mapping is a key requirement for many practical applications in robotics. In this work, we present RESLAM, a novel edge-based SLAM system for RGBD sensors. Due to their sparse representation, larger convergence basin and stability under illumination changes, edges are a promising alternative to feature-based or other direct approaches. We build a complete SLAM pipeline with camera pose estimation, sliding window optimization, loop closure and relocalisation capabilities that utilizes edges throughout all steps. In our system, we additionally refine the initial depth from the sensor, the camera poses and the camera intrinsics in a sliding window to increase accuracy. Further, we introduce an edge-based verification for loop closures that can also be applied for relocalisation. We evaluate RESLAM on wide variety of benchmark datasets that include difficult scenes and camera motions and also present qualitative results. We show that this novel edge-based SLAM system performs comparable to state-of-the-art methods, while running in real-time on a CPU. RESLAM is available as open-source software1.1Code is available: https://github.com/fabianschenk/RESLAM
Unsupervised Learning of Signal Strength Models for Device-Free Localization

RSS-based device-free localization (DFL) systems make use of the received signal strength (RSS) changes in a network of static wireless nodes to locate and track people. Current DFL systems require calibration, which depending on the method and required accuracy, can be very expensive in terms of time and effort, making DFL system deployment and maintenance challenging. This paper implements unsupervised learning of signal strength models (UnLeSS), a Baum-Welch based method to learn the parameters of a hidden Markov model (HMM) for each link, including the RSS distribution during the no-crossing state and the crossing state. The system uses the HMM to estimate the probability of each link being in the crossed state. As a demonstration of its effectiveness, the per-link probability is used in a radio tomographic imaging algorithm to track the location of a person. Experiments are conducted in two different homes to determine the performance of UnLeSS. We demonstrate that our system is capable of estimating the crossing/no-crossing distribution with Kullback-Leibler divergence maximum of 1.43. UnLeSS is capable of tracking a person with high accuracy (0.66 m) without a calibration period.
Positioning Accuracy of a Pipeline Surveying System Based on MEMS IMU and Odometer: Case Study

An inertial navigation system (INS) aided by odometers and a series of reference points has been extensively used to determine the location of pipeline inspection gauges (PIGs) in long-range gas and oil pipeline inspection. However, little attention has been paid to short-range urban underground pipeline surveying and the use of a low-cost inertial measurement unit (IMU) for location determination. The positioning performance of PIGs using low-cost microelectromechanical systems (MEMS) INS for short-range urban underground pipeline surveying applications has seldom been comprehensively evaluated. In this paper, we analyzed the positioning accuracy of a PIG based on a MEMS IMU and odometer through a real pipeline surveying case study. Numerous pipeline surveying cases, covering different pipeline lengths, pipe conditions, and surveying times, are included to achieve a practical conclusion with respect to the surveying accuracy. The results demonstrated that the maximum measurement repeatability errors of PIGs based on MEMS IMU and odometer do not exceed 0.25% and 0.1% of the pipeline length in the horizontal and vertical directions, respectively. These results not only provide powerful support for the PIG developers but also provide the expected PIG accuracy in urban underground pipeline surveying by using low-cost IMUs.
Bayesian Optimisation for Neuroimaging Pre-processing in Brain Age Prediction

Neuroimaging-based age predictions using machine learning have been shown to relate to cognitive performance, health outcomes and progression of neurodegenerative disease. However, even leading age-prediction algorithms contain measurement error, motivating efforts to improve experimental pipelines. T1-weighted MRI is commonly used for age prediction, and the pre-processing of these scans involves normalisation to a common template and resampling to a common voxel size, followed by spatial smoothing. Resampling parameters are often selected arbitrarily. Here, we sought to improve brain-age prediction accuracy by optimising resampling parameters using Bayesian optimisation. Using data on N=2001 healthy individuals (aged 16-90 years) we trained support vector machines to i) distinguish between young (<50 years) and old (>50 years) brains and ii) predict chronological age, with accuracy assessed using cross-validation. We also evaluated model generalisability to the Cam-CAN dataset (N=648, aged 18-88 years). Bayesian optimisation was used to identify optimal voxel size and smoothing kernel size for each task. This procedure adaptively samples the parameter space to evaluate accuracy across a range of possible parameters, using independent sub-samples to iteratively assess different parameter combinations to arrive at optimal values. When distinguishing between young and old brains a classification accuracy of 96.25% was achieved, with voxel size = 11.5mm3 and smoothing kernel = 2.3mm. For predicting chronological age, a mean absolute error (MAE) of 5.08 years was achieved, with voxel size = 3.73mm3 and smoothing kernel = 3.68mm. This was compared to performance using default values of 1.5mm3 and 4mm respectively, which gave a MAE = 5.48 years, a 7.3% improvement. When assessing generalisability, best performance was achieved when applying the entire Bayesian optimisation framework to the new dataset, out-performing the parameters optimised for the initial training dataset. Our study demonstrates the proof-of-principle that neuroimaging models for brain age prediction can be improved by using Bayesian optimisation to select more appropriate pre-processing parameters. Our results suggest that different parameters are selected and performance improves when optimisation is conducted in specific contexts. This motivates use of optimisation techniques at many different points during the experimental process, which may result in improved statistical sensitivity and reduce opportunities for experimenter-led bias.
Robotic Orientation Control of Deformable Cells

Robotic manipulation of deformable objects (vs. rigid objects) has been a classic topic in robotics. Compared to deformable synthetic objects such as rubber balls and clothes, biological cells are highly deformable and more prone to damage. This paper presents robotic manipulation of deformable cells for orientation control (both out-of-plane and in-plane), which is required in both clinical (e.g., in vitro fertilization) and biomedical (e.g., clone) applications. Compared to manual cell rotation control based on empirical experience, the robotic approach, based on mathematical modeling and path planning, effectively rotates a cell while consistently maintaining minimal cell deformation to avoid cell damage. A force model is established to determine the minimal force applied by the micropipette to rotate a spherical or more generally, an ellipsoidal mouse oocyte. The force information is translated into indentation through a contact mechanics model, and the manipulation path of the micropipette is formed by connecting the indentation positions on the oocyte. A compensation controller is designed to compensate for the variations of mechanical properties across cells. The polar body of an oocyte is detected by deep neural networks with robustness to shape and size differences. Experimental results demonstrate that the system achieved an accuracy of 97.6% in polar body detection and an accuracy of $0.7{^\circ}$ in oocyte orientation control with maximum oocyte deformation of $2.69 \mu \mathrm{m}$.
100 MHz Low-Power Modulator/Demodulator for Signal Isolation of SiC Gate Driver IC

This paper presents a low-power modulator and demodulator circuits for silicon carbide (SiC) MOSFET gate driver integrated circuit (IC). They have a very simple structure and reduce the pulse-width modulation (PWM) signal error from the external noise such as an electro-magnetic interference (EMI) and supply noise. The modulator adopts a current-starved ring oscillator with a beta-multiplier achieving 1.1/0.54% frequency variation with 150 °C temperature and 100 mV supply voltage change. The demodulator core part consists of only twelve MOSFETs and whole circuits consume 1 mW because of the leakage based operation algorithm. The proposed SiC gate driver is fabricated with $0.35 \mu\mathrm{m}$ BCDMOS process and total power consumption of the mod/demodulator circuit is 0.825/1 mW with 100 MHz carrier frequency.
Expressive Data-Centric Multicast on RPL in Low-Power Networks

Applications for the Internet of Things (IoT)are often data-centric. Data-centric routing then enables messages to reach relevant consumers while avoiding flooding and explicit resource discovery. This kind of routing thus provides energy savings as well as a convenient programming abstraction: messages can be addressed to nodes that advertise features matching a constraint. In low-power wireless mesh networks, such feature-oriented routing traditionally relies on costly and inflexible network overlays. Recent work establishes lightweight support for diverse data-centric traffic patterns, but sacrifices expressiveness of feature-oriented functionality and hence applicability. It is also unclear whether the energy consumption advantages offered by data-centric routing extend to this new lightweight approach. To address these concerns, this paper introduces the SMRFET system. SMRFET improves the expressiveness of state-of-the-art feature-oriented routing by supporting numeric rather than binary features. The system integrates data-centric functionality into group-based multicast and thus adds only a small amount of overhead: experiments show that SMRFET significantly reduces the required amount of message passing relative to alternative systems for group communication. Additionally, SMRFET can be reconfigured to handle memory constraints: its performance degrades gracefully as the amount of memory allocated to it decreases. SMRFET therefore brings lightweight and expressive group communication to the wireless IoT.
3D Printed Soft Pneumatic Actuators with Intent Sensing for Hand Rehabilitative Exoskeletons*

Loss of functional motor skills are common and often require patients to undergo rehabilitation so that they have a chance at motor recovery. Advancement in technology has seen to a rise in the use of robotic technology in conducting rehabilitative exercises that are traditionally carried out by physiotherapists. In recent years, soft robotic exoskeletons, using pneumatic-based actuation in particular, have gained much interest due to their compliant characteristics and safe operating conditions. In order to carry out complex task-based rehabilitative exercises, these soft pneumatic actuators must ideally be able to move with multiple degrees of freedom or minimally, in a bidirectional motion. Majority of the research covering soft actuators can only achieve finger flexion with some providing passive finger extension. Non-invasive intent detection in the control of these exoskeletons is also lacking in sensing both finger flexion and extension. In this paper we present our work on a fold-based bidirectional 3D printed intent-sensing soft pneumatic actuator (ISPA) that can achieve bidirectional motion and provide intent detection for finger flexion and extension for application in upper limb rehabilitative exoskeletons.
Developing private innovation scoreboard and its application in South Korea

The private sector is considered as a core driver of national innovation activities. From the perspective of national innovation system, it is the key sub-system in which various innovation activities are triggered by profit maximization motives. Currently, in South Korea, the research and development (R&D) investment in private sector accounts for about 75% of the national R&D investment. Considering the increasing role of the private sector in the national innovation system, it is essential for policy makers to make innovation activities in each domain to be complementary with each other. From the perspective of the national innovation system the primary objective of this study is to develop the innovation scoreboard that enables us to monitor the status of the innovation activities in private sector. Furthermore, we seek to ensure the comparability of all indicators so that we can easily identify one nation’s area of relative strength and comparative advantage.The private sector innovation scoreboard is composed of four sub-categories: innovation infrastructure, institutions, innovation activities and impact. The innovation activities are divided further onto knowledge activities, organizational activities and innovation output. By doing so, the part of innovation activities model the sequential process of input-output flow of innovation. In total, 60 indicators are included in the private innovation scoreboard.Among the 60 indicators, South Korea has relative strength in science and technology human resources, market size, private R&D support system, start-up system, and the number of start-ups. However, it shows relative weakness in indicators such as financial infrastructure, legal system for promoting science and technology, financial system, labor market efficiency, regulation and output. Scores on science and technology manpower, start-ups, and input into knowledge activities, which can be regarded as input, were relatively good. However, the output and impact sectors of innovation, which can be considered as performance areas, seem relatively vulnerable. Compared to input, the causes of low performance can be explained with lack of financial infrastructure, financial and scientific technology promotion system, and industry-academic cooperation.By creating a private innovation scoreboard this study contributes to the understanding of current status of overall private sector innovation system in Korea. Therefore, we can compare the current South Korea system with that of advanced countries and identify its strengths and weaknesses. As a result, we can contribute to the development of strategic policymaking, such as the supplementation of relatively vulnerable areas.