Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on Stumbles of the Elderly from a Depth Perception Dependency Test

In this paper, we investigate the relationship between the depth perception and an approaching motion toward an object. We propose the depth perception dependency test, which is the combination of tests of a motion and depth perception based on the situation that an object is placed on the human’s pathway. Firstly, as the motion test, we set the ball approach motion and asked elderly participants to approach and contact a ball by their foot, because this motion is easy to measure and requires localization skill and motion planning skill. The swing/support legs positions at the toeoff time of the swing leg just before contacting the ball was analyzed. Secondly, as the depth perception test, the pseudo3D image test was proposed. The coordinate transformation model for the calculation of the depth perception ability was also proposed. Through the proposed test and the proposed model, it was clarified that the participants who are regarded as perceiving the strong visual illusion perceive the objects closer than its real position, and their swing leg toe off positions in the ball approaching motion were significantly farther than other participants. Thus, it can be concluded that there was a relationship between the depth perception and the approaching motion that is thought to be a higher risk of stumbles.
A Multimodal Soft Crawling-Climbing Robot with the Controllable Horizontal Plane to Slope Transition*

Most of the existing soft locomotive robots are capable of moving on horizontal planes and small-angled slopes, but few of them can accomplish the large-angled slope climbing or wall climbing. We introduce an inchworm inspired soft crawling-climbing robot capable of continuous motion from a horizontal plane to a slope of up to 75 degrees and it can perform multiple locomotion including crawling, climbing, and the transition between them. This soft crawling-climbing robot is powered by three pneumatic actuators connected in series that deforms with two degrees of freedom, negative pressure sucker feet that generate periodical adhesion, and a semi-automatic controlling system synchronizing the body shape and anchoring of the feet. The robot mimics the body deformation and feet anchoring pattern exhibited by inchworms and can be applied in inspection, surveillance, and rescuing.
Unified Human-Robot Shared Control with Application to Haptic Telemanipulation

Human-robot shared control (SC) has largely been studied in two complementary forms, namely divisible shared control (DSC) and interactive shared control (ISC). DSC enables clean division of the human and the robot subtasks, thus enabling them to work independently, while ISC allows for flexible intervention to improve the collaborative performance or experience. This paper presents a unified scheme that combines both forms of SCs to attain the benefits of flexibility as well as ease of use when human and robot jointly work on a task together. Based on the idea that flexibility should be embedded in every task constraint that the robot is controlling, we connect ISC into the robot subtask providing a soft boundary between the divided orthogonal subspaces allowing human to access the robot subtask and intervene whenever necessary. We also propose a new simple yet effective Cartesian stiffness adaptation law that enables the robot to modify its endpoint stiffness in the robot’s control subspace in the presence of disagreement from the human. Simulations and real robot studies for a teleoperated path-following scenario were performed to demonstrate the flexibility of the unified shared control (USC), which allows the robot to dynamically adapt its task based on the operator’s intentions.
Decentralized Coordination Mechanism between Neck and Limbs for Efficient Quadrupedal Walking

An optional robotic arm unit, like animals’ neck, allows quadruped robot to achieve multifunctional tasks such as manipulation and patrol. However, increases in body weight due to the neck unit will make energetic issues in legged robots’ locomotion more serious. This study presents a minimal decentralized coordination mechanism between a neck and limbs, especially focusing on quadruped mammals’ nodding behaviors for efficient walking. The results of two dimensional (2D) simulation suggest that bilateral sensory feedback mechanism between neck and limbs plays essential roles for the quadruped robot to coordinate neck and limbs in response to the physical situation of the body parts, achieving efficient quadrupedal walking.
A Novel Quantization Method for Deep Learning-Based Massive MIMO CSI Feedback

In massive multiple-input multiple-output (MIMO) systems, channel state information (CSI) needs feeding back to the base station (BS) by user equipment (UE) to attain the potential benefits of massive MIMO. But the large number of antennas at the BS causes a huge feedback overhead, thereby making it prohibitive to realize CSI feedback in massive MIMO. Deep leaning-based (DL) compressive sensing methods for CSI feedback can potentially reduce the overhead significantly. However, without quantization, a data-bearing bitstream for transmission cannot be produced at the UE. In this paper, we propose a novel quantization framework and training strategy for DL-based CSI feedback, which not only makes the current CSI feedback network applicable in real communication systems but also minimizes the introduced quantization distortion to improve the reconstruction quality. Experimental results demonstrate that the proposed quantization method performs well and is robust to quantization errors.
Generative-Discriminative Crop Type Identification using Satellite Images

Crop type identification refers to distinguishing certain crop from other landcovers, which is an essential and crucial task in agricultural monitoring. Satellite images is good data input for identifying different crops since satellites capture relatively wider area and more spectral information. Based on prior knowledge of crop’s phenology, multi-temporal images are stacked to extract growth pattern of varied crops. In this paper, we proposed a machine learning model which combines generative and discriminative models and achieved averaged AP score of 0.903 over all tested crops and regions under the limitation of small dataset and label noise using satellite images taken at different times.
Calibration of Motional Frequency Spread for Wide-Band FMCW Automotive Millimeter-Wave Radar

As a key module for self-driving cars and advanced driver assistant systems (ADAS), frequency modulation continuous wave (FMCW) automotive millimeter-wave (mmwave) radar has a good market prospect. Due to the relative motion of target and radar in the detection window, the motional frequency spread is inevitable especially for wide-band FMCW automotive mmwave radar systems with large bandwidth and possible high-speed motion. In this paper, for wide-band FMCW automotive mmwave radar systems, we first derive analytical expressions of the motional frequency interference term and phase interference term in the intermediate frequency (IF) signal. Then, the effect of frequency spread is comprehensively analyzed with the help of numerical results. Motivated by the mechanism and influence of frequency spread, we propose three schemes for motional frequency spread calibration with different performance-complexity tradeoff. Finally, simulations verify that the proposed schemes can effectively compensate the degradation of system resolution and parameter estimation accuracy due to frequency spread.
A Convolutional Network for Joint Deraining and Dehazing from A Single Image for Autonomous Driving in Rain

In this paper, we focus on a rain removal task from a single image of the urban street scene for autonomous driving in rain. We develop a Convolutional Neural Network which takes a rainy image as input, and directly recovers a clean image in the presence of rain streaks, atmospheric veiling effect (haze, fog, mist) caused by distant rain streak accumulation. We propose a synthetic dataset containing images of urban street scenes with different rain intensities, orientations and haziness levels for training and evaluation. We evaluate our method quantitatively and qualitatively on the synthetic data. Experiments show that our model outperforms state-of-the-art methods. We also test our method qualitatively on the real-world data. Our model is fast and it takes 0.05s for an image of $1024 \times 512$. Our model can be seamlessly integrated with existing image-based high-level perception algorithms for autonomous driving in rain. Experiment results show that our deraining method improves semantic segmentation and object detection largely for autonomous driving in rain.
Trial on the Possibility to Assess the Static and Dynamic Balance in the Elderly with Diseases on the Level of the Supporting Joints

The study aims to identify human walking disorders, proposing a model to solve these disorders, regardless of what caused them. Material and method Clinical and somatometric tests, pedometric investigations and sensors were used. An outpatient trial was conducted for a period of 6 weeks with a frequency of application of the recovery program of 3 times / week. For this study, there were 21 patients over the age of 65 years, which were divided into two groups: the witness group in which 11 patients had upper limb and / or spine disorders, and the control group with 10 patients with degenerative diseases in the knees and ankles. The tests were applied to both groups following the demographic data, the evaluation of the static and dynamic balance and the parameters of the gait. Results and discussions In the witness group, the results were significant because there were no problems in the bearing joints, the balance was not affected, whereas in the study group the main problem was that these joints with a role in achieving stability and balance, were affected. Conclusions: The application of wireless sensors at key points at the articular level (pelvis, coxofemorals, knees, ankles, toes) would allow a better data recording and faster finding of walking and balance deficiencies in the elderly patients, reducing the duration of the recovery program.
Task-oriented Grasping in Object Stacking Scenes with CRF-based Semantic Model

In task-oriented grasping, the robot is supposed to manipulate the objects in a task-compatible manner, which is more important but more challenging than just stably grasping. However, most of existing works perform task-oriented grasping only in single object scenes. This greatly limits their practical application in real world scenes, in which there are usually multiple stacked objects with serious overlaps and occlusions. To perform task-oriented grasping in object stacking scenes, in this paper, we firstly build a synthetic dataset named Object Stacking Grasping Dataset (OSGD) for task-oriented grasping in object stacking scenes. Secondly, a Conditional Random Field (CRF) is constructed to model the semantic contents in object regions. The modelled semantic contents can be illustrated as incompatibility of task labels and continuity of task regions. This proposed approach can greatly reduce the interference of overlaps and occlusions in object stacking scenes. To embed the CRF-based semantic model into our grasp detection network, we implement the inference process of CRFs as a RNN so that the whole model, Task-oriented Grasping CRFs (TOG-CRFs) can be trained end to end. Finally, in object stacking scenes, the constructed model can help robot achieve 69.4% success rate for task-oriented grasping.
Multi-Contact Heavy Object Pushing With a Centaur-Type Humanoid Robot: Planning and Control for a Real Demonstrator

Performing a demanding manipulation task with a multi-legged loco-manipulation platform may require the exploitation of multiple external contacts with different environment surfaces for counteracting the manipulation forces. This is the case of the pushing task of a heavy object, where the grip forces at ground may not be adequate and establishing leg contacts against a wall turns out to be an effective solution to the manipulation problem. In order to produce such behaviour, this letter presents a control architecture that is able to freely exploit the environment complexity to perform loco-manipulation actions, e.g. pushing a heavy object, while meeting the implementation requirements to achieve a real demonstrator. The proposed approach, conceived for torque-controlled platforms, combines the planning capabilities of nonlinear optimization over the robot centroidal statics, based on a continuous description of the environment through superquadric functions, with the instantaneous capabilities of hierarchical inverse kinematics and reactive contact force distribution. Experimental validation has been performed on the pushing task of a wooden cabinet loaded with bricks, using the CENTAURO robot developed at Istituto Italiano di Tecnologia (IIT).
Domain-Independent Unsupervised Detection of Grasp Regions to grasp Novel Objects

One of the main challenges in the vision-based grasping is the selection of feasible grasp regions while interacting with novel objects. Recent approaches exploit the power of convolutional neural network (CNN) to achieve accurate grasping at the cost of high computational power and time. In this paper, we present a novel unsupervised learning based algorithm for the selection of feasible grasp regions. Unsupervised learning infers the pattern in dataset without any external labels. We applied k-means clustering at every sampling stage in image plane to identify the grasp regions, followed by axes assignment method. We define a novel concept of Grasp Decide Index (GDI) to select the best grasp pose in the image plane. We have conducted several experiments in clutter or isolated environment on standard objects of Amazon Robotics Challenge 2017 and Amazon Picking Challenge 2016. We compared the results with prior learning based approaches to validate the robustness and adaptive nature of our algorithm for a variety of novel objects in different domains.
Mobile Robot Learning from Human Demonstrations with Nonlinear Model Predictive Control

Learning by imitation is a powerful way that can reduce the complexly in searching space. It could help the mobile robot to acquire new skills from interaction with a human-being in natural way. In this paper, the dynamic movement primitives (DMPs) is utilized to imitate the trajectory from human walking. DMPs is a modified formulation of virtual spring-dampers (VSD) system that enjoys better fitting performance in learning. Further, while dealing with the trajectory tracking problem of mobile robots, a novel nonlinear model predictive control (MPC) approach is proposed for motion control. The nonlinear MPC scheme applies a new neural network named Varying-parameter Lagrangian Neural Network (VP-LNN) to solve a Quadratic Programming (QP) problem by iterating over a finite receding horizon. The new network of VP-LNN can converge to the global optimal solution. Thus, a new human-robot interaction (HRI) scheme for mobile robot is proposed, which can reduce the complexity in motion planning in various applications.
Unified Balance Control for Biped Robots Including Modification of Footsteps with Angular Momentum and Falling Detection Based on Capturability

In this paper, we propose walking balance control based on Caputurability. The proposed method consists of five strategies: (i) moving Zero Moment Point (ZMP) in the support polygon (ii) landing position modification (iii) landing timing modification (iv) angular momentum control (v) falling detection and fall control. Walking pattern generation calculates the ZMP so that the Capture Point (CP) reaches the position of the supporting foot at the end of the double support phase. Owing to the asymmetry of the reachable landing region, landing timing modification is different in the sagittal and lateral planes, and the step time is extended in the lateral plane depending on the direction of disturbances. The torque around the center of gravity to avoid falling is realized through whole-body inverse kinematics with constraints on the angular momentum. In addition, we propose falling detection considering the reachable landing region. We verified the effectiveness of the proposed method through experiments in which the biped robot was disturbed by pushing during tether-free walking. The robot could prevent breakdown by detecting possible falling and performed knee bending motions to suppress damage.
Computer Analysis of Hidden Layers of Sleep EEG: Dreams Correlates

The dream is an autonomous activity of the brain. The background EEG is distinguishable in the frequency domain. To better understand the mechanism of dreams, it is necessary to extract more information from the hidden layers of the EEG. One possible approach is to analyze the microstates of dreams, which we present in this paper. Our results show that microstate maps differ in dreamless sleep. Activation of different brain centers within dreaming is propagated to the surface and is evident in two microstates. So within the dream, other centers are activated and we can notice their propagation on the surface of the scalp.
Tunable Hydrogels based on Chitosan, Collagen and Poly(Acrylic Acid) for Regenerative Medicine

Hydrogels based on natural polymers are of great interest for the engineering of soft tissues because of their hydrophilicity and softness, which minimizes the damage to the surrounding tissue during their application and using. Biopolymers like chitosan and collagen ensure a good biological response but their week mechanical properties require the use of crosslinking methods (small molecules or networking with synthetic polymers). In this paper new hydrogels based on chitosan, collagen and poly(acrylic acid) as natural-synthetic networks have been prepared in the aim to create tunable materials for the regenerative medicine The FTIR-ATR spectra confirmed the expected structure and the scanning electron microscopy images recorded revealed a porous morphology with interconnected pores. In addition, the swelling degree assays indicated a tunable behavior correlated with the composition in natural polymers. Finally, the in vitro biodegradation and biocompatibility tests revealed good material response in simulated biological environment.
A Novel Design of Single Band 4X4 Butler Matrix using Branch Line Couplers without a Crossover and a Phase Shifter

A single band $4 \times 4$ Butler matrix (BM) without a crossover and a phase shifter has been proposed, fabricated and tested in this paper. By employing two modifies BLCs and two 90° BLCs, the 4 x 4 BM has been developed. In order to justify the design approach, a single band $4 \times 4$ BM has been designed and implemented on Rogers5870 and then tested. Compared to the BMs existing in the literature, the presented BM has been developed with a lesser number of components. As a result, the presented $4 \times 4$ BM has a simpler and compact design.
Multimedia Forensic: An Approach for Splicing Detection based on Deep Visual Features

Fabrication of an image by utilizing a wide range of image altering tasks can lead to fraud. Since a measurable inspector must consider each of these. numerous techniques have emerged for calculation to identifying picture tampering activities and algorithms. In this paper, we propose a convolution neural network based network to deal with tampering and segmentation of visual data. Network is designed to preserve local and global characteristics of data to detect tampering. Inception module based CNN network is proposed intended to identify a picture’s content and tampered part using mask R-CNN. Through a progression of examinations, we showed that our proposed methodology can consequently figure out how to distinguish numerous picture tampering methods without depending on pre-chosen highlights or any pre-preparing and segment out tampered region. Evaluation results demonstrate that our proposed methodology can distinguish tampering over CASIA v1.0 and v2.0 with an accuracy of 98.76% and 97.92%, respectively.
RONet: Real-time Range-only Indoor Localization via Stacked Bidirectional LSTM with Residual Attention

In this study, a three-layered bidirectional Long Short-term Memory (Bi–LSTM) with residual attention, named as RONet, is proposed to achieve localization using range measurements. Accordingly, we acquired our own datasets and tested RONet using realistic conditions. It is shown that the RONet can estimate the position of the mobile robot in real time using the Nvidia Jetson AGX Xavier based only on range measurements. We also analyzed the sequence length of LSTM as a type of hyperparameters. We found that optimal sequence length is eight for more than eight anchors and twelve for fewer anchors compared to sequences with different lengths, given that construction of the network with the optimal sequence length estimates the position precisely and accounts for uncertainties. As verified experimentally, RONet yields more precise performance and results in increased robustness against outliers compared to a conventional range-only approach based on a particle filtering and the other conventional deep-learning-based approaches. We set three cases, reduced the number of anchors, and verified that the RONet was a robust solution. We also confirmed that it is the best solution that yields the smallest Root-Mean-Square-Error (RMSE) values, equal to 4.466 cm, 3.210 cm, and 3.090 cm, in the cases where three, five, and eight anchors were deployed, respectively.
Learning Virtual Borders through Semantic Scene Understanding and Augmented Reality

Virtual borders are an opportunity to allow users the interactive restriction of their mobile robots’ workspaces, e.g. to avoid navigation errors or to exclude certain areas from working. Currently, works in this field have focused on human-robot interaction (HRI) methods to restrict the workspace. However, recent trends towards smart environments and the tremendous progress in semantic scene understanding give new opportunities to enhance the HRI-based methods. Therefore, we propose a novel learning and support system (LSS) to support users during teaching of virtual borders. Our LSS learns from user interactions employing methods from visual scene understanding and supports users through recommendations for interactions. The bidirectional interaction between the user and system is realized using augmented reality. A validation of the approach shows that the LSS robustly recognizes a limited set of typical areas for virtual borders based on previous user interactions (F 1 - Score= 91.5%) while preserving the high accuracy of standard HRI-based methods with a median of Mdn= 84.6%. Moreover, this approach allows the reduction of the interaction time to a constant mean value of M =2 seconds making it independent of the border length. This avoids a linear interaction time of standard HRI-based methods.
Robotic Orthosis for Recovery of People with Different Trauma

This paper presents the design of a robotic orthotic device used to evaluate and improve the parameters of the neuro-musculoskeletal system for patients recovering after trauma. The design of this medical device is specially created to be easily handled. The location of the sensors does not require special preparations, being very easy to use and the cost of maintaining the equipment is low. In addition, this robotic orthotic device constantly assists the healthcare professionals by informing them in real time about the patient’s condition and the evolution of the treatment. By permanently monitoring the body’s parameters and storing information in a database, the performance of physicians in treating and improving the health of patients with different conditions is considerably improved.