Miracle-dz/UniFAR-FTUs huggingface dataset:
Fluid Independent Thermal Flow Sensor using Constant-Temperature Anemometry and the 3ω-Method

A new method for the implementation of a fluid-independent thermal flowmeter is presented, combining the 3ω-method and constant temperature anemometry. It is shown that a single calibration with a reference fluid (water in this case) leads to a high accuracy (< 4%). The measurement method was verified for water, isopropanol, and an isopropanol-water mixture. The deviation between measured flow rate value and reference flow rate value is less than 4% with respect to the full scale value. The findings may play a key role for next generation thermal flow meters.
Amplification of Clamping Mechanism Using Internally-Balanced Magnetic Unit

Machines tend to use powerful actuators and large gearboxes to bear large loads, which are inconvenient in terms of responsiveness as they affect the duration of operations. Thus, to compensate the force to grasp an object, we propose a clamping mechanism implementing the internally-balanced magnetic unit (IB Magnet) as a force amplifier, which is a mechanism able to switch attached and detached states of a permanent magnet with an external force considerably smaller than its original attractive force. To realize the bi-parting constitution of fingers, a new compensation method using conical coil springs was designed to provide both precision and miniaturization. Relative to the constitution with a single motored screw, the prototype gripper for proof of concept successfully amplified the grasping force at most to 292.2% assisted by the magnetic attraction, while keeping the increase in power consumption of a DC motor only by 11.8%, making the force-energy efficiency 2.6 times larger. Thus, it was verified that the proposed gripper enables the use of actuators and current supplies that require less power.
A Novel Method for Online Elimination of Position Estimation Error Caused by Cross-Coupling Effect in High Frequency Square-Wave Voltage Injection Based IPMSM Sensorless Control System

In order to eliminate the position estimation error caused by cross-coupling inductance in high frequency square wave injection (HFSI) based sensorless schemes, an online dual-heterodyne position demodulation (ODHPD) method is proposed in this paper. Compared with the traditional position demodulation method, the proposed method can effectively track the real rotor position without any compensation algorithm. Moreover, it boasts simple implementation as well as high estimation accuracy under heavy load condition. Simulation and experimental results are presented to validate the effectiveness and feasibility of the proposed method.
Fully-Online Always-Adaptation of Transfer Functions and Its Application to Sound Source Localization and Separation

This paper addresses fully-online always-adaptation of a transfer function for robot audition systems based on microphone array processing. The transfer function represents signal propagation characteristics between a microphone and a sound source, which provides essential information for real-world scene analysis, such as sound source localization and separation for robots. Although it is commonly defined as a stationary function, it should be considered together with room acoustics and their environmental changes for practical use, that is, it should be defined as a dynamically-changing function. To fulfill this requirement, we propose a fully-online always-adaptation method for a transfer function, by continuously estimating the transfer function from the observed signals in a passive manner, while performing sound source localization and separation. The proposed method was implemented on open source robot audition software HARK as modules which works online. These modules are applied to sound source localization and separation which are primary functions in robot audition. Experimental results showed that the proposed method successfully adapted to an office environment and improved the performance of sound source localization and separation at a close level to the transfer function recorded in the room.
Estimation of Leak Time Parameter Based on Fusion of Leak Behavior and Data Distribution Characteristic from Gas Flow Sensor Data

The accurately estimation of leak parameter is an essential part of pipeline leakage risk assessment and guarantee of the quality of leak samples, while today’s estimation methods leverage only coarse time estimation and rely on expert experience. This paper presents a leak time parameter estimation framework fusing leak behavior and data distribution characteristic from gas flow sensor data. Under the proposed framework, a leak behavior and a data distribution characteristic extraction modules are established for guaranteeing automatic estimation of leakage time parameters in a fine-grained time range. The estimation of leak starting time for 69 different leak events are implemented based on gas flow sensors data. The estimation accuracy of 94.2% demonstrates the effectiveness of the proposed method.
Development of SOI pixel detector in Cracow

This paper presents the design of a new monolithic Silicon-On-Insulator pixel sensor in $200~nm$ SOI CMOS technology. The main application of the proposed pixel detector is the spectroscopy, but it can also be used for the minimum ionizing particle (MIP) tracking in particle physics experiments. For this reason few different versions of pixel cells are developed: a source-follower based pixel for tracking, a low noise pixel with preamplifier for spectroscopy, and a self-triggering pixel for time and amplitude measurements. In addition the design of a Successive Approximation Register Analog-to-Digital Converter (SAR ADC) is also presented. A 10-bit SAR ADC is developed for spectroscopic measurements and a lower resolution 6-bit SAR ADC is integrated in the pixel matrix as a column ADC, for tracking applications.
Kernel Regression based Sparse Metric Learning for Extensive Classification of Visual Art Images

In recent decades, the collection of visual art paintings is in a large number which are digitized and available for public uses that are rapidly growing. The development of multi-media systems is very much needed due to the huge amount of digitized artwork collections for retrieving and archiving this large-scale data. This multimedia system is getting benefit from high-level tasks and has an essential step for measuringsimilarity of visual between the artistic items. For modeling the similarities between the artworks or paintings, it is essential to extract useful features of visual paintings and propose a best approach for learning this similarity metrics. In this paper, the detailed visual features are listed and similarity measurement between the paintings is optimized by the Sparse Metric Learning based Kernel Regression (KR-SML) and classification model is developed using hybrid SVM-ANN for semantic-level understanding for prediction of painting's genre, artist, and style. Furthermore, the Human-Computer Interaction (HCI) based formulation model is built to analyse the proposed technique. The simulations results are showing that the proposed model proves to be better in terms of performance when compared with other existing techniques.
Generative Adversarial Networks for Localized Vibrotactile Feedback in Haptic Surfaces

Touch-screens are the most relevant interface in the context of human-computer interaction. Moreover, they are widely used as interaction means for digital musical instruments, where a complex action-perception loop is involved in the user experience. This is why reestablishing a rich vibrotactile feedback is of key importance for improving the quality of the user's interaction. To the knowledge of the authors, this paper presents the first experiments with Generative Adversarial Networks (GANs) to generate time-reversed signals that can be used to create localized vibrotactile feedback over a rigid surface. The generated signals are sent into an experimental setup and a vibration scan is carried out. A localized peak generated with a signal synthesized by the trained GAN model is observed and studied. Later, different metrics are proposed to evaluate the quality of the generated samples and the obtained localized peak. Finally, a preliminary evaluation of the feasibility of this approach to generate localized vibrations in the range of 200 - 300 Hz for touch -screen applications is discussed.
Adjustment of Transmitter Coil Current Ratio in Two Transmitter and Two Receiver Wireless Power Transfer System

When one or two mobile devices are placed on a wireless charger consisting of two transmitter coils, a power transfer efficiency varies with a ratio of the current of each transmitter coil. Therefore, this paper proposes a transmitter coil current ratio for high power transfer efficiency in the wireless power transfer system with two transmitter coils and two receiver coils. The proposed coil current ratio is calculated using mutual inductance values between each transmitter coil and each receiver coil. Moreover, the mutual inductance extraction method is also proposed by analyzing the input current. The power transfer efficiency increases up to 8 % by applying the proposed transmitter coil current ratio.
A Novel Method of High-Speed PMSM Drive Based on LCL Filter

The performance of the current loop in the highspeed permanent magnet synchronous motor deteriorates due to the characteristics of the small inductance and low carrier ratio. On the one hand, the control accuracy and the efficiency of the current loop are reduced because of the current harmonics caused by the small inductance. On the other hand, the impact of cross-coupling is increased as a result of the low carrier ratio. In order to reduce the current harmonics and enhance the performance of linear control in the high-speed PMSM control system, a three-phase LCL filter method is proposed in this paper. First of all, the mathematical model of the LCL filter containing the stator inductance is built and quantitatively analyzed for parameter selection of the filter. Secondly, the parameters are optimized by analyzing the magnitude-frequency characteristic of the LCL filter. A passive damping strategy is implemented to suppress the resonance which is introduced by the LCL filter. Thirdly, aiming at the problem that the impact of cross-coupling is increased under low carrier ratio of the high-speed PMSM, a method of complex vector PI controller is presented to realize the current decoupling, and a virtual resistor strategy is implemented to increase the stability of the current loop. The simulation results are finally carried out to verify the effectiveness and correctness.
Integrated Position and Speed Control of Rail Transit Door Based on Speed-Current Loop Structure

The control quality of the servo system is mainly characterized by high control precision and fast response speed, so the structure of the outer control loop of the permanent magnet synchronous motor as the position loop can meet this demand. However, when the outer position loop meets the requirements of high precision and fast response, there are problems of uncontrollable speed or rough speed control. To solve this problem, this paper simplifies the control structure of the servo system, and proposes a position control strategy applied to the rail transit door based on the traditional speed current loop structure. The speed can be controlled by setting the outer loop speed, and the position servo can be realized according to the calculated absolute position. For the problem that the position servo accuracy of the structure is not high, the running track of the Transit Door is divided into position intervals. In the final stage of servo braking, the chopper regulator is applied to ensure the accuracy of position control at low speed. Simulation and experimental results show that the proposed method is correct and easy to implement in engineering practice.
Learning to Optimize Control Policies and Evaluate Reproduction Performance from Human Demonstrations

We are interested in learning from demonstration (LfD) that can both learn and execute a trajectory and evaluate the quality of a previously unseen trajectory in the domain of assistive robotics. To this end, we propose a novel continuous inverse optimal control (IOC) formulation that simultaneously learns an optimal time-invariant controller and an evaluation metric from human demonstrations. We assume that the expert’s objective function is a weighted combination of physically meaningful basis objective functions. The evaluation metric is derived from the learned expert’s objective function. The benefit of this approach is twofold: 1) the controller can be optimized with respect to the learned evaluation metric and subject to the robot’s dynamic limitations and 2) the evaluation metric can evaluate the quality of a demonstrated trajectory. We validate our approach with two experiments in a robot guided therapy setting: 1) evaluating demonstrated exercises with the learned metric and 2) reproducing both unconstrained trajectories and trajectories subject to the robot’s dynamic constraints.
Learning to Play Pursuit-Evasion with Visibility Constraints

We study the problem of pursuit-evasion for a single pursuer and an evader in polygonal environments where the players have visibility constraints. The pursuer is tasked with catching the evader as quickly as possible while the evader tries to avoid being captured. We formalize this problem as a zero-sum game where the players have private observations and conflicting objectives.One of the challenging aspects of this game is due to limited visibility. When a player, for example, the pursuer does not see the evader, it needs to reason about all possible locations of the evader. This causes an exponential increase in the size of the state space as compared to the arena size. To overcome the challenges associated with large state spaces, we introduce a new learning-based method that compresses the game state and uses it to plan actions for the players. The results indicate that our method outperforms the existing reinforcement learning methods, and performs competitively against the current state-of-the-art randomized strategy in complex environments.
Marine Autonomous Navigation for Biomimetic Underwater Robots Based on Deep Stereo Attention Network

This paper proposes a multi-objective visionbased navigation network for biomimetic underwater robots to cope with scientific observation, target selection, and obstacle avoidance in marine missions. Structurally, a stereo block attention module is first constructed to serially extract the channel and spatial attention portion of the real-time visual feedback. Next, the parallax attention mechanism is introduced to enable the network to excavate implicit parallax information in stereo pairs, effectively eliminating the oscillation of the network output in the presence of ambiguous visual input. Further, with the assistance of other low-cost sensors, the proposed navigation network can be expanded in some largescale application scenarios, such as sparse coral observation. Finally, underwater simulations reveal that the proposed method obtains significantly improved control effect and real-time ability, compared with other related works. In particular, based on a self-developed biomimetic robotic dolphin, collision-free simulations with a cumulative distance beyond 1000 m were carried out and validated the effectiveness and the superiority of the navigation network, where both dense and sparse targets were fully tested. The robotic dolphin can not only successfully conduct accurate coral observation without collision, but also quest the observation targets as much as possible in the area where the observation targets are concentrated. The proposed network provides an intelligent and efficient navigation scheme for autonomous underwater operation of small-size underwater robots.
Research on a Miniature Optical Force Accelerometer

The accelerometer is one of the vital core components in inertial navigation and guidance systems, which can greatly affect the performance of the whole system. With the development of optical levitation technology recently, the optical force accelerometer based on the latest principle is gradually developing and improving. However, limited by the large volume of the whole system, almost the optical force accelerometers reported cannot be used in a variety of miniature inertial and mechanical sensors. Hence, a miniature optical force levitation system is proposed to reduce the size as much as possible. The optical force levitation system includes one particle cavity fabricated by Micro-Electro-Mechanical System(MEMS) process and an optical system consists of one laser diode and a pair of lenses. The volume of the system is Φ 10 mm × 33 mm. Then the optical force accelerometer including the optical force levitation system and the fourQuadrant Photoelectric Detector (QPD) is built up. The volume of the system is 200 mm × 100 mm × 100 mm. By using one SiO2 whose diameter is 10 μm trapped by the optical trap in the air as the sensing unit, the stability of this accelerometer is tested and evaluated at last. The result shows that the bias stability of the accelerometer in an hour is 3.2 mg. Based on these consequences, this optical force accelerometer can be a new style of acceleration system which may be used in the navigation system and other sensing fields in the future.
Development of a 3D Printed Gap Gauge with Embedded Force Sensor for Balancing Unicompartmental Knee Arthroplasty.

A novel gap-gauge with embedded sensing is developed for assisting in Oxford Unicompartmental Knee Arthroplasty (OUKA). A fully 3D printed differential capacitive sensor for normal forces is embedded into the Fused Deposition Modelling (FDM) fabricated body of the gap-gauge. The sensor consists of three conductive parallel-plates realizing two capacitive sensors with a common ground plate. It is demonstrated that for the set of specifications the 3D printed device is potentially suitable for the application. Minimal drift, weak non-linearity and hysteresis in the response are present. There is good agreement between the designed and true sensitivity.
"Sugar-Cube" PLT: A Real-time Pedestrian Localization Testbed Utilizing Foot-mounted IMU/Barometer/Ultrasonic Sensors

In this paper, we propose a flexible multi-sensor hardware platform, "Sugar-Cube" Pedestrian Localization Testbed (PLT), for pedestrian navigation in GPS-challenged environments. The Sugar-Cube platform is the next generation of the Lab-On-Shoe platform [1] and aims to provide a flexible and easy-to-use development environment with a compact form factor. The proposed platform includes a micro-controller to collect measurements from a consumer-grade 9-axis Inertial Measurement Unit (IMU), a barometer, and an ultrasonic sensor and performs localization of a user in real-time. All source codes, hardware designs, and documentation related to the Sugar-Cube PLT will be available at [2]. We conducted a series of indoor walking experiments to verify the navigation accuracy of the Sugar-Cube platform and compared the navigation results with the Lab-On-Shoe system, which was equipped with near-tactical-grade IMUs. The experimental results showed that the Sugar-Cube platform could localize its user in real-time with a horizontal position Circular Error Probable (CEP) of 0.76 m and a vertical displacement Root-Mean-Square-Error (RMSE) of 0.24 after navigating for 60 s on mixed terrains, including flat planes, ramps, and stairs.
Wing Fold and Twist Greatly Improves Flight Efficiency for Bat-Scale Flapping Wing Robots

Inspired by bat flight performance, we explore the advantages of wing twist and fold for flapping wing robots. For this purpose, we develop a dynamical model that incorporates these two degrees of freedom to the wing. The twist is assumed to be linearly-increasing along the wing, while the wing fold is modeled as a relative rotation of the handwing with respect to the armwing. An optimization scheme parameterizes the wing kinematics for 2, 5 and 8 m/s forward flight velocities. The intricate interplay between wing orientation, effective angle of attack and the ensuing lift and thrust generation are discussed. The results show that wing twist and fold alleviate negative lift and thrust in the upstroke, and in some cases producing persistent positive thrust throughout cycle for handwing. As a result, power consumption drops precipitously compared to the base case of a rigid flat plate. Another crucial realization is the relative importance of wing twist and fold in achieving efficient flight strongly depends on speeds. At slow flight, twist is significantly more effective in minimizing the power, but becomes energetically inefficient for fast speeds. The results also show that a 45° wing fold during upstroke is energetically beneficial for all speeds. The synergy of wing twist and fold are most prominent at slow flight. These findings provides useful guidelines for designing flapping wing robots.
Fuzzy PID Controller Based on Yaw Angle Prediction of a Spherical Robot*

In this paper, a fuzzy PID controller based on yaw angle prediction is applied to design an attitude controller for a spherical rolling robot. The robot consists of a 2-DOF pendulum located inside a spherical shell with freedom to rotate about the transversal and longitudinal axis. The proposed controller allows the robot to autonomously change its parameters to adapt to different environments based on current state. The past researches on the motion of spherical robots mostly focused on simulation or ideal experimental environment. But in this paper, a physical system is built and experiments are carried out to demonstrate the effectiveness, robustness and adaptability of the controller.
Characterization of 3D printed sheets using multi-frequency scanning impedance microscopy

In this work we present a multi-frequency scanning impedance microscopy technique suitable for 3D printed structures. In this technique, a ball-head pogo pin is used to measure the voltage distribution in 3D printed samples by rolling it over the sample. It is shown that this technique can be used to measure the complex electric field distribution at varying frequencies in a single layer 3D printed conductor.
Human Motion Imitation using Optimal Control with Time-Varying Weights

Research in biomechanics hypothesizes that human motion is optimal with respect to an unknown cost function that varies depending on the action and/or task. This unknown cost function is often approximated as the weighted sum of a set of features or basis cost functions. As a person performs a sequence of actions, the weights associated to each of these basis functions are likely to vary over time. Given a human demonstration and the corresponding cost weight trajectory recovered via inverse optimal control (IOC), this paper proposes an optimal control (OC) method that can generate robot motion based on human movement using time-varying cost function weights. By using time-varying weights, the proposed optimal control method can handle changing optimization criteria without segmentation. The method is evaluated both in simulation and with recorded human data. Using human demonstration data, we demonstrate the reproduction of pick-and-place motions with an average end-effector error at the pick place location within 0.82 cm, which is significantly lower than the average trajectory error, indicating that the approach correctly prioritizes reaching the pick and place locations without manual segmentation.