Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Experiments of a Squid-Like Aquatic-Aerial Vehicle with Soft Morphing Fins and Arms

Aquatic-aerial multimodal vehicle is a new concept aircraft that can freely shuttle between water and air. Some of the natural organisms provide the inspiration to realize this multi-locomotion. Most of current prototypes use rigid link mechanisms or hinges to morph the structure thus to adapt to the aquatic-aerial environment, which is commonly complicated and bulky. In this paper, we present a novel prototype with pneumatically-driven soft fins and arms that can fold and spread just like the flying squid. The fins and arms can augment the lift force during flying by spreading and reduce drag force during swimming by folding. The performance of the morphable structures was investigated in wind and water tunnel. The results explain the tradeoff strategies of multimodal-locomotion between water and air, and verify the feasibility of the novel aquatic-aerial vehicle with soft morphable structures.
Single Supply Level Shifter Circuit using body-bias

A multi-VDD scheme using different supply voltages exists as a technique to realize low energy consumption. In this scheme, a circuit for converting voltage amplitude of the signal called level shifter is necessary between different power supply voltage areas. However, a level shifter requires two types of power supply voltage of a high supply voltage and a low supply voltage, and there is a disadvantage that it complicates the power routing in the layout and increases the area. In this paper, we propose a level shifter circuit that operates with a single power supply by applying body-bias and Silicon-on-Thin-BOX.
Wireless Power Sensors to Renovate Energy Metering in IIoT Converted Factories

The paper will stress the role of wireless current sensors in converting assessed factories and buildings, endowed with a reduced metering equipment, to the Industrial Internet-of-Things (IIoT) paradigm, by renovating and increasing at a reduced cost the installed metering devices and architecture. In this way a great number of algorithms and application are made available, from cloud and edge computing, to improve the general efficiency of the plant and the factory. An experimental setup, under test, will report significant results.
Fabrication and Characterization of Muscle Rings Using Circular Mould and Rotary Electrical Stimulation for Bio-Syncretic Robots

Bio-syncretic robots made up of living biological systems and electromechanical systems may have the potential excellent performance of natural biological entities. Therefore, the study of the bio-syncretic robots has got lots of attention in recent years. The 3D skeletal muscles have been used widely, due to the considerable contraction force and the controllability. However, the low differentiation quality of the C2C12 in the tissues hinders the broad application in the development of the skeleton muscle actuated bio-syncretic robots. In this work, an approach based on circular mould and rotary electrical stimulation to build high-quality muscle rings, which can be used to actuate various bio-syncretic robots, has been proposed. Firstly, the advantage of the proposed circular mould for the muscle rings culture has been shown by simulation. Then, the muscle rings have been fabricated with different moulds using the experiment-optimized compositions of the biological mixture. After that, the muscle rings in the circular moulds with different electrical stimulations have been cultured, to show the superiority of the proposed rotary electrical stimulation. Moreover, the contractility of the muscle rings have been measured under the different electrical pulses stimulation, for the study of the control property of the muscle rings. This work may be meaningful not only the development of bio-syncretic robots actuated by 3D muscle tissues but also the muscle tissue engineering.
Learning to Capture a Film-Look Video with a Camera Drone

The development of intelligent drones has simplified aerial filming and provided smarter assistant tools for users to capture a film-look footage. Existing methods of autonomous aerial filming either specify predefined camera movements for a drone to capture a footage, or employ heuristic approaches for camera motion planning. However, both predefined movements and heuristically planned motions are hardly able to provide cinematic footages for various dynamic scenarios. In this paper, we propose a data-driven learning-based approach, which can imitate a professional cameraman’s intention for capturing a film-look aerial footage of a single subject in real-time. We model the decision-making process of the cameraman with two steps: 1) we train a network to predict the future image composition and camera position, and 2) our system then generates control commands to achieve the desired shot framing. At the system level, we deploy our algorithm on the limited resources of a drone and demonstrate the feasibility of running automatic filming onboard in real-time. Our experiments show how our data-driven planning approach achieves film-look footages and successfully mimics the work of a professional cameraman.
A preliminary investigation of the effect of contact pressure on the accuracy of heart rate monitoring by wearable PPG wrist band

The increase of safety and health monitor of workers has become a crucial objective to prevent excessive physical workloads, injuries, accidents and errors. Heart rate (HR) is a very important physiological indicator which could properly describe the workers’ physical status. Recently, wearable photoplethysmographic (PPG) wristband trackers have been utilized to measure HR without hindering normal gesture of workers. However, the quality of PPG signals is highly affected by human physical motions, resulting in a poor reliable HR estimation. Specifically, during different activities and gestures, PPG sensor contact pressures may have an impact on the quality of the heart rate signal. To approach the problem, an instrumented adjustable bracelet equipped with a PPG sensor and a load cell has been designed. A simple test protocol consisting of three different activities (i.e. walking, jogging and running) has been realized to test PPG signal quality. PPG sensor has been subjected to different skin-sensor contact interface conditions to take into account the effect of contact pressure. Preliminary results showed that a weak contact pressure results in a poor quality of PPG signal as the activity becomes more and more intense. These results suggest that the contact pressure exerted by the sensor affects both the arterial compliance and the arterial volume, providing different results.
Sleeve Pneumatic Artificial Muscles for Antagonistically Actuated Joints

Pneumatic artificial muscles (PAMs) have been researched for applications in powered exoskeletons, orthosis and robotics. Their high force to mass ratio, low cost and inherent compliance are particularly advantageous for systems requiring physical interaction with humans.Sleeve PAMs, which introduce an internal structure to the actuator, offer improved force capacity, contraction ratio, efficiency and operating bandwidth. In this paper sleeve PAMs are applied to a popular muscle configuration; that of a joint operated antagonistically by two muscles. It is shown that the sleeve PAM can increases the range of joint rotation by 14% or load capacity by over 50% of that of a comparable joint actuated with traditional PAMs, depending on the joint configuration. The stiffness of joints actuated with both PAM types is also studied, particularly the case of closed system operation (mass of air in the PAMs is constant), where the reduced volume of the sleeve PAM significantly increases the observed stiffness. Finally energy consumption is considered, showing substantial savings in the case of joints actuated with sleeve PAMs.
Search Engine for the Internet of Things: Lessons From Web Search, Vision, and Opportunities

With the development of the Internet of Things (IoT), massive numbers of IoT devices (smart sensors, cameras, phones, and so on) have been deployed and utilized in various environments, supporting numerous smart-world applications. Those devices communicate with computing infrastructure servers over network infrastructures to send relevant collected data in order to link physical objects to the cyber world. As the number of IoT devices increases rapidly, the volume of collected data likewise increases prodigiously. Thus, how to search for and through specific IoT datasets among the enormous amount of data become critical issues for potential data consumers. Moreover, various IoT devices and applications establish IoT-based systems, also known as the smart-world systems or smart cyber–physical systems (CPS), such as smart grids, smart transportation, smart healthcare, smart cities, smart homes, and smart manufacturing systems, among others. However, the individual CPS are independently designed and deployed, such that they collect and analyze data independently, with no information sharing or interconnection, raising serious challenges in searching for valuable information. Thus, in order to efficiently and precisely utilize the IoT datasets, suitable search techniques designed for the IoT environments are fundamental. In this paper, we first summarize popular web search techniques and survey existing research on the search and analysis related to the IoT. We then outline the opportunities and challenges of the IoT search techniques. Furthermore, we propose a problem space for the IoT search techniques and provide a clear view of potential future research directions.
Large-Scale Multi-Object Rearrangement

This paper describes a new robotic tabletop rearrangement system, and presents experimental results. The tasks involve rearranging as many as 30 to 100 blocks, sometimes packed with a density of up to 40%. The high packing factor forces the system to push several objects at a time, making accurate simulation difficult, if not impossible. Nonetheless, the system achieves goals specifying the pose of every object, with an average precision of ± 1 mm and ± 2°. The system searches through policy rollouts of simulated pushing actions, using an Iterated Local Search technique to escape local minima. In real world execution, the system executes just one action from a policy, then uses a vision system to update the estimated task state, and replans. The system accepts a fully general description of task goals, which means it can solve the singulation and separation problems addressed in prior work, but can also solve sorting problems and spell out words, among other things. The paper includes examples of several solved problems, statistical analysis of the system’s behavior on different types of problems, and some discussion of limitations, insights, and future work.
A Coordinate-based Approach for Static Balancing and Walking Control of Compliantly Actuated Legged Robots

The paper addresses the static balancing and walking of elastically actuated legged robots. The control is realized by commanding the motor positions only and exploiting the bijective relation between motor and link positions at equilibrium under static external forces. The approach is formulated in a quite general framework first. The main implementation contribution is the definition of a body coordinate system and of an appropriate set of constraints, which leads to a fully determined system of equations. In addition to the desired COM and the vertical foot positions, which are defined by the walking task and the terrain, the imposed constraints are related to distances between individual legs. The controller is experimentally validated on a compliantly actuated quadruped.
Transfer Learning for Surgical Task Segmentation

In this paper, we present a novel approach for surgical task segmentation. A segmentation policy learns the correlations between features and segmentation points from manually labeled data. The most correlated features and rules for segmenting them are identified and learned. These form a complete set of segmentation policy. The proposed approach is developed to segment new but similar tasks through transfer learning. It is verified through applying the segmentation rule learned from the labeled data to segment other tasks. The performance of the proposed algorithm was evaluated by comparing the results against the ground truths. Experimental results demonstrate that our approach can achieve high segmentation rates with an accuracy of between 68.8% - 81.8%.
Ultra-short Pulse Generator with Back Edge Shaper

a high-performance ultra-short pulse generator based on a step recovery diode (SRD) has been proposed in the paper. The generator circuit solution utilizes the selection of a mode of operation, the optimization of passive component values and the back edge shaper to generate the output pulse signal with the high amplitude and the short width. According to the measurement results the pulse signal amplitude is 70V, the width is 230ps and the ringing level is -23.5dB. This performance has significant potential in applications where operating in thick and lossy medium is concerned.
Learning To Grasp Under Uncertainty Using POMDPs

Robust object grasping under uncertainty is an essential capability of service robots. Many existing approaches rely on far-field sensors, such as cameras, to compute a grasp pose and perform open-loop grasp after placing gripper under the pose. This often fails as a result of sensing or environment uncertainty. This paper presents a principled, general and efficient approach to adaptive grasping, using both tactile and visual sensing as feedback. We first model adaptive grasping as a partially observable Markov decision process (POMDP), which handles uncertainty naturally. We solve the POMDP for sampled objects from a set, in order to generate data for learning. Finally, we train a grasp policy, represented as a deep recurrent neural network (RNN), in simulation through imitation learning. By combining model-based POMDP planning and imitation learning, the proposed approach achieves robustness under uncertainty, generalization over many objects, and fast execution. In particular, we show that modeling only a small sample of objects enables us to learn a robust strategy to grasp previously unseen objects of varying shapes and recover from failure over multiple steps. Experiments on the G3DB object dataset in simulation and a smaller object set with a real robot indicate promising results.
Improved Orthogonal Space-Time Block Codes with Multiple Transmit Antennas Using Hadamard Transform

In this paper, imposing the Hadamard Transform (HDT) on Orthogonal Space-Time Block Codes (OSTBC) schemes using multiple transmit antennas is proposed in order to increase the reliability and improve the error performance of wireless communications. It is referred to as joint Hadamard transform and OSTBC schemes with multiple transmit antennas. However, HDT is used to change the location of constellation points by converting the transmit symbols of OSTBC system, and hence extending the Euclidean distance between transmit symbols for a precise detection. Simulation results demonstrated that increasing the number of transmit antennas in OSTBC systems employing 8-PSK improves the Bit Error Rate (BER) performance as in the case of OSTBC 2×2 scheme where an SNR of about 13.5 dB is required to achieve a BER of 10^-6, while only about 7 dB in OSTBC 8×8 scheme is required to obtain the same error probability. It is also demonstrated that imposing the Hadamard criteria on the OSTBC 2×2, 4×4, and 8×8 schemes has showed a performance improvement of about 3 dB, 6 dB and 9 dB, respectively, as compared to the conventional OSTBC systems.
Position control of medical cable-driven flexible instruments by combining machine learning and kinematic analysis

Non-linearities in cable transmissions are important limitations for the accurate control of flexible instruments used in medical endoscopic systems. Hysteresis effects greatly impact the accuracy of conventional kinematic models. This is especially critical for implementing automatic motions in flexible medical robotic systems. In this paper, we propose a method for improving open-loop accuracy of flexible instruments by implementing a Position Inverse Kinematic Model which is able to take into account hysteresis effects. In order to avoid complex physical modeling, the method relies on the off-line learning of the behavior of the instruments. Basic knowledge of the kinematic is also incorporated in the learning process in order to make it fast. The validity of the approach is demonstrated by the execution of 2D and 3D trajectories with the instruments of the STRAS medical robot. The accuracy is shown to be significantly improved with respect to other learning-based methods.
KO-Fusion: Dense Visual SLAM with Tightly-Coupled Kinematic and Odometric Tracking

Dense visual SLAM methods are able to estimate the 3D structure of an environment and locate the observer within them. They estimate the motion of a camera by matching visual information between consecutive frames, and are thus prone to failure under extreme motion conditions or when observing texture-poor regions. The integration of additional sensor modalities has shown great promise in improving the robustness and accuracy of such SLAM systems. In contrast to the popular use of inertial measurements we propose to tightly-couple a dense RGB-D SLAM system with kinematic and odometry measurements from a wheeled robot equipped with a manipulator. The system has real-time capability while running on GPU. It optimizes the camera pose by considering the geometric alignment of the map as well as kinematic and odometric data from the robot. Through experimentation in the real-world, we show that the system is more robust to challenging trajectories featuring fast and loopy motion than the equivalent system without the additional kinematic and odometric knowledge, whilst retaining comparable performance to the equivalent RGB-D only system on easy trajectories.
ScalableFusion: High-resolution Mesh-based Real-time 3D Reconstruction

Dense 3D reconstructions generate globally consisent data of the environment suitable for many robot applications. Current RGB-D based reconstructions, however, only maintain the color resolution equal to the depth resolution of the used sensor. This firmly limits the precision and realism of the generated reconstructions. In this paper we present a real-time approach for creating and maintaining a surface reconstruction in as high as possible geometrical fidelity with full sensor resolution for its colorization (or surface texture). A multi-scale memory management process and a Level of Detail scheme enable equally detailed reconstructions to be generated at small scales, such as objects, as well as large scales, such as rooms or buildings. We showcase the benefit of this novel pipeline with a PrimeSense RGB-D camera as well as combining the depth channel of this camera with a high resolution global shutter camera. Further experiments show that our memory management approach allows us to scale up to larger domains that are not achievable with current state-of-the-art methods.
Deep n-Shot Transfer Learning for Tactile Material Classification with a Flexible Pressure-Sensitive Skin

n-shot learning, i.e., learning a classifier from only few or even one training samples per class, is the ultimate goal in minimizing the cost of sample acquisition. This is esp. important for active sensing tasks like tactile material classification. Achieving high classification accuracy from only few samples is typically possible only when pre-knowledge is used. In n-shot transfer learning, knowledge from pre-training on a large knowledge set with many classes and samples per class has to be transferred to support the training for a given task set with only few samples per new class. In this paper, we show for the first time that deep end-to-end transfer learning is feasible for tactile material classification. Based on the previously presented (TactNet-II) [1], a deep convolutional neural network (CNN) which reaches superhuman tactile classification performance, we adapt state-of-the art deep transfer learning methods. We evaluate the resulting deep n-shot learning methods with a publicly available tactile material data set with 36 materials [1] in a 6-way n-shot learning task with 30 materials in the knowledge set. In 1-shot learning, our deep transfer learning method reaches 75.5% classification accuracy and in 10-shot more than 90%, outperforming classification without knowledge transfer by more than 40%. This results in an up to 15 time reduction in the number of samples needed to reach a desired accuracy level. We also provide insights of the inner workings of the derived deep transfer learning methods.
A Study on Low Level Quantizers for Block Turbo Decoding for Product Codes of Binary Linear Code

Block Turbo Decodings (BTDs) with Soft-In Soft-Out (SISO) decodings for two dimensional product codes of linear codes can achieve good error performance, however, large computational complexity of the BTDs can be a problem. Therefore, to reduce the computational complexity, quantization methods can be employed for the BTDs by sacrificing the error performance. In this paper, a study on design for low level quantizers for the BTD with S1SO Ordered Statistics Decoding for product codes of binary linear code is shown. From simulation results for a 4-level quantizer, the authors propose a new 5-level quantizer. The proposed 5-level quantizer achieves better error performance than 4-level quantizer.
Online adaptation of uncertain models using neural network priors and partially observable planning

One of the key challenges in realizing a robot that is capable of completing a variety of manipulation tasks in the real world is the need to utilize sufficiently compact and rich world models. If the assumed prediction model does not match real observations, planning systems are unable to perform properly. We propose a system that corrects the models based on information collected from the robot’s sensors. We encode prior experiences in a neural network to generate possible parameters of the models for a physics engine from real observations. An online POMDP solver is used to plan actions to complete the task while progressively validating and improving the models. We perform experiments in simulations and on a real robot. The results show that this approach appropriately clarifies observed environments, can handle dynamics with discontinuities, and with increasing domain complexity achieves a better success rate than baseline methods.
Resolved Viscoelasticity Control Considering Singularity for Knee-stretched Walking of a Humanoid

This paper describes a stable knee-stretched walking of a humanoid by the resolved viscoelasticity control (RVC). The RVC method resolves multiple viscoelasticities in task-space, including the center of mass viscoelasticity for balancing, into joint-space viscoelasticity. Although a robust and compliant motion was achieved by the RVC method in previous studies, the conventional knee-bent posture to avoid the kinematic singularity suffered large knee joint torque. In this study, we propose an extension of the RVC capable of the kinematic singularity. We demonstrate through simulations and experiments that the RVC method considering the singularity achieves a stable and human-like walking, reducing the knee joint torque and improving the energy efficiency.
Advantage of Stratton and Chu Formulas for Electromagnetic Field Reconstruction

In the article performance of different integral equation formulations for electromagnetic field source reconstruction is examined. Equivalence of equivalent source theorem and Stratton and Chu formulas from physical and computational mathematics points of view is discussed. It is shown that equivalent electromagnetic field source reconstruction through Stratton and Chu formulas provides more stable solution and reduces the errors up to 2 dB.