Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource Allocation for Vehicular Communications With Low Latency and High Reliability

Proximity-based communications have been considered as a promising candidate for supporting vehicular communications. However, the high mobility in vehicular communications makes it hard to obtain accurate fast varying channel information, which poses significant challenges on meeting the requirements of high reliability and low latency. Based only on slowly varying large-scale fading channel information, this paper performs a reliability and latency aware resource allocation, which maximizes the throughput of vehicular-to-network (V2N) links while satisfying reliability and latency requirements of vehicular-to-vehicular (V2V) links. First, we obtain steady-state reliability and latency expressions based on queueing analysis for each possible spectrum reusing pair of a V2N link and a V2V link. Then, an optimal power allocation algorithm is developed for each possible spectrum reusing pair. Afterward, the spectrum reusing pattern is optimized by addressing a polynomial time solvable bipartite matching problem. The simulation results demonstrate the accuracy of the proposed queueing analysis and confirm the effectiveness of the proposed resource allocation comparing with available strategies.
Online Walking Pattern Generation for Humanoid Robot with Compliant Motion Control

The compliant motion of humanoid robots is one of their most important characteristics for interacting with humans and various environments in the real world. During walking, compliant motion ensures stable contact between the foot and ground, but walking stability is degraded by position tracking performance and unknown disturbances. To address the issue of instability of humanoid robot walking with compliant motion control, this paper proposes a model for real-time walking pattern generation considering the motion control performance of a robot. The dynamic model of a robot with a motion controller is described as a second-order system approximating position tracking performance with a linear inverted pendulum model to determine the relationship between the zero-moment point and center of mass (CoM). The CoM trajectory is calculated using preview control based on the dynamics model and current state of the robot. Therefore, even if the robot has the low tracking performance due to compliant motion control, the walking stability can be ensured. The proposed method was implemented on our humanoid robot, DYROS-JET, and its performance was demonstrated through improved stability during walking.
60 GHz Beam-Steering Antenna Array Using Liquid Crystal Phase Shifter

The applications of communication devices have extended widely especially with the revolution of 5G systems. The rapid progress in the material science and electronic technology, makes it possible to design tunable and flexible reconfigurable wireless devices. In this paper, the anisotropic properties of liquid crystals in their nematic phase are exploited for desigening tunable wireless devices at 60GHz millimeter wave band. Two different design approaches for 60GHz are presented, a liquid crystal tunable resonator, and beam-steering slotted patch antenna array. Simulations indicate around 5.04% of continuous frequency tuning with low insertion loss, with antenna array ability to steer its beam in the E-plane by a maximum of 20°.
Echinoderm Inspired Variable Stiffness Soft Actuator with Connected Ossicle Structure

An echinoderm can actively modulate the structural stiffness of its body wall by as much as 10 times, using the material and structural features that make up its body, including calcite ossicles, connective tissue and interossicular muscle. This capacity for variable stiffness makes it possible to adapt to the kinematics and dynamics required to perform a given task and the surrounding environment. This characteristic can improve the ability of soft material robots, which currently have limited application because of their low load-bearing capability. This paper presents a stiffness modulation method inspired by the connected ossicle structures of echinoderms. We introduce the mechanism, structure, and stiffness variation of the proposed design with respect to different ossicle shape, interval, and elastomer. Then we built a finger-shaped stiffening structure using the proposed design, measured its stiffness according to vacuum level, and showed its load-bearing capacity under control. The proposed design was then applied to a robotic gripper, a typical device that interacts with unpredictable environments and needs variable stiffening ability.
Scaling-up collaborative practices through mobile technology

The beginning of the 21st century is experiencing the advent of smart technologies, which are redefining a new space and time. Consequently, we are more and more witnessing emerging companies, which have partially dematerialised and deterritorialised their knowledge, and where collaboration happens in a "space in-between". This paper proposes to examine digital technologies, more specifically mobile technology with regards to the reorganisation of work and innovation in creative industries. The aim of this paper is to shed light on Nonaka and Konno’s concept of ba (1998) in relation to the new digital paradigm. Our key intention is to investigate how can mobile technology foster a co-creative space between people and processes.Using a broad literature, and a qualitative approach, we will examine how mobile technology, beyond just being a tool, enables a freshened consideration of the concept of ba, a new form of organisation of creative work.
Mobile Robotic Painting of Texture

Robotic painting is well-established in controlled factory environments, but there is now potential for mobile robots to do functional painting tasks around the everyday world. An obvious first target for such robots is painting a uniform single color. A step further is the painting of textured images. Texture involves a varying appearance, and requires that paint is delivered accurately onto the physical surface to produce the desired effect. Robotic painting of texture is relevant for architecture and in themed environments.A key challenge for robotic painting of texture is to take a desired image as input, and to generate the paint commands to as closely as possible create the desired appearance, according to the robotic capabilities. This paper describes a deep learning approach to take an input ink map of a desired texture, and infer robotic paint commands to produce that texture.We analyze the trade-offs between quality of reconstructed appearance and ease of execution. Our method is general for different kinds of robotic paint delivery systems, but the emphasis here is on spray painting. More generally, the framework can be viewed as an approach for solving a specific class of inverse imaging problems.
Metal-Coated 3D-Printed Waveguide Devices for mm-Wave Applications [Application Notes]

ï»¿Modern 3D printing technologies, also known as additive manufacturing, have evolved rapidly in recent years and are currently used in a wide range of engineering applications. In particular, the use of the new manufacturing techniques is leading to the development of very promising 3D electromagnetic structures for millimeter-wave (mm-wave) devices [1]. Thanks to the very compet it ive performance of 3D printing in electromagnetic structures, both the industrial and research communities are paying special attention to this manufacturing technique, a novel fabrication process for polymer, metal, and ceramic materials. The technique is an attractive option for innovative and complex part fabrication, providing improvements in customization, part compactness, and rapid prototyping as well as reduced weight, cost, and mass production lead time.
Easily Deployable Underwater Acoustic Navigation System for Multi-Vehicle Environmental Sampling Applications

Water as a medium poses a number of challenges for robots, limiting the progress of research in underwater robotics vis-á-vis ground or aerial robotics. The primary challenges are satellite based positioning and radio communication being unusable due to high attenuation of electromagnetic waves in water. We have developed miniature, agile, easy to carry and deploy Autonomous Underwater Vehicles (AUVs) equipped with a suite of sensors for underwater environmental sensing. We previously demonstrated adaptive sampling and feature tracking, and gathered data from a lake for limnological research, with the AUV performing inertial navigation. In this paper, we demonstrate a new underwater acoustic positioning system, which allows on-board estimation of AUV position. Our system uses absolute time information from GNSS for initial clock synchronization and uses one-way-travel-time for range measurements, which makes it scalable in the number of robots. It is easily deployable and does not rely on any installed infrastructure in the environment. We describe various hardware and software components of our system, and present results from experiments in Lake Geneva.
Modeling and Control of a Passively-Coupled Tilt-Rotor Vertical Takeoff and Landing Aircraft

This paper presents the modeling and control of a passively-coupled tilt-rotor vertical takeoff and landing aircraft. The aircraft consists of a quadrotor frame attached to a fixed-wing aircraft by an unactuated hinged mechanism. The platform is capable of smooth transitions from hover to forward flight without the use of tilting actuators. The transition from hover to forward flight is made possible by differential thrust between the fore and aft propellers of the quadrotor frame. In this paper, the coupled dynamics between the quadrotor frame and the aircraft frame are modeled as a constrained multi-body system. The equations of motion are established using a constrained Lagrangian approach and the model developed is used to build a realistic simulation environment for control design purpose. A cascaded control architecture based on P/PID controllers is proposed to achieve inner-loop attitude, height and forward velocity control. Simulated and experimental results are obtained with a close match for hover, transitions, forward flight, and banked turn maneuvers.
Web-Based Framework for Accessing Native Opportunistic Networking Applications

Opportunistic networking is one way to realize pervasive applications while placing little demand on network infrastructure, especially for operating in less well connected environments. In contrast to the ubiquitous network access model inherent to many cloud-based applications, for which the web browser forms the user front end, opportunistic applications require installing software on mobile devices. Even though app stores (when accessible)offer scalable distribution mechanisms for applications, a designer needs to support multiple OS platforms and only some of those are suitable for opportunistic operation to begin with. In this paper, we present a web-based framework that allows users to interact with opportunistic application content without installing the respective app and thus also includes users whose mobile OSes do not support opportunistic networking at all via minimal stand-alone infrastructure. We describe our system and protocol design, validate its operation using simulations and a testbed. We implement web versions of two existing native mobile opportunistic applications: PeopleFinder and Here & Now.
Tele-Echography using a Two-Layer Teleoperation Algorithm with Energy Scaling

Performing ultrasound procedures from a remote site is a challenging task since both a stable behavior, for the safety of the patient, and a high-level of usability, to exploit the sonographer’s expertise, need to be guaranteed. Furthermore, a teleoperation system that provides such requirements has to deal with communication delays as well. To address this issue, we use the two-layer algorithm: a passivity-based bilateral teleoperation architecture able to guarantee stability despite unknown and time-varying delay. Its flexibility allows to implement different kinds of control laws. In a Tele-Echography system, the slave manipulator has to apply significant forces needed by the procedure whereas the haptic device at the master side should be very light to avoid tiring the operator. Therefore, the energy needed by these two robots to perform their movements is very different and the energy injected into the system by the operator is often not sufficient to implement the desired action at the slave side. Methods to overcome this problem require to perfectly know the dynamical models of the robots. The solution proposed in this paper does not require such knowledge and is based on properly scaling the energy exchanged between the master and the slave side. We show the effectiveness of this approach in a real setup using a TOUCH haptic device and a WAM Barrett robot holding an ultrasound probe.
Joint kinematic configuration influence on the passivity of an impedance-controlled robotic leg

Although the design of legged robots may be dependent on the application, all of them share the need to safely deal with physical interaction with the environment in every step they take. Impedance controllers have been applied with success to handle contact, and some authors applied the concept of passivity to guarantee stability during the interaction. Whereas previous studies on the passivity of legged robots considered aspects such as inner force loop gains and actuation bandwidth influence on the Z-width (i.e. the range of renderable passive impedances), they did not take into account the role of the kinematic configuration of the leg on the stability of the interaction. Thus, in this work we present a systematic analysis of the effects of joint positions on the passivity conditions of a robotic leg and show that this is a very relevant aspect that may seriously affect the stability and passivity of an impedance controller. By analyzing a linearized model of the leg via its Nyquist plots and the respective Z-width diagrams, we were able to determine what joint configurations within the leg workspace are more suitable to physically interact with the environment or people.
Use of Machine Learning to Detect Causes of Unnecessary Active Scanning in WiFi Networks

We address the problem of automating the process of network troubleshooting for a large-scale WiFi network. Specifically, we target identifying the causes of unnecessary active scans in WiFi networks, that are known to degrade the WiFi performance. We collect 340 hours worth of data with several thousands of episodes of active scans to train various machine learning models. Data is collected with 27 devices across vendors in varied network setups under a controlled setting. We study unsupervised and supervised machine learning techniques to conclude that a multilayer perceptron is the best model to detect the causes of active scanning. Further, we perform an in-vivo model validation in an uncontrolled real-world WiFi network. We also compare our models with a static rule-based approach. Our model improves the mean F1-score accuracy of cause detection from 0.78 to 0.99. Our proposed mechanism has the potential of being incorporated in the existing WiFi controllers, such as that of Cisco and Aruba.
Singularity of Cable-Driven Parallel Robot With Sagging Cables: Preliminary Investigation

This paper addresses for the first time the singularity analysis of cable-driven parallel robot (CDPR) with sagging cables using the Irvine model. We present the mathematical framework of singularity analysis of CDPR using this cable model. We then show that, besides a cable model representation singularity, both the inverse and forward kinematics (IK and FK) have a singularity type, called parallel robot singularity, which correspond to the singularity of an equivalent parallel robot with rigid legs. We then show that both the IK and FK have also full singularities, that are not parallel robot singularity and are obtained when two of the IK or FK solution branches intersect. IK singularity will usually lie on the border of the CDPR workspace. We then exhibit an algorithm that allow one to prove that a singularity exist in the neighborhood of a given pose and to estimate its location with an arbitrary accuracy. Examples are provided for parallel robot, IK and FK singularities. However we have not been able to determine examples of combined singularity where both the IK and FK are singular (besides parallel robot singularity).
A preliminary study on topical model for multi-domain speech recognition via word embedding vector

In this paper, we suggest a basic topical model(TM) framework to adapt speech recognition system to multi-domain and prevent topical errors. This paper employs the cosine similarities between target and context words at a spoken utterance as the topical model parameters. The TM is applied to frames having a large number of candidate words at lattice network, and it adjusts the ranking of candidate words by adding it to total cost estimated from acoustic model(AM) and language model(LM). To cover multidomain, the word embedding was trained with 5.5 billion text corpus from multi-domain. As an acoustic model and a language model, DNN-HMM and $N$ - gram were selected. 501 sentences (10,054 words) includes 35 topics were used as an evaluation data set. As a result, the best performances were obtained by our approach, and the performance of WERR was increased up to about 4% compared with $N$-gram based model. The WERR increased above 10% when the word errors were correctly detected. The results show this suggestion has a possibility of adapting a model to multi-domains without sub-topic models.
A Benchmarking Framework for Systematic Evaluation of Robotic Pick-and-Place Systems in an Industrial Grocery Setting

Robotic manipulation is a very active field of research nowadays; however, pick-and-place operations constitute the majority of today’s industrial robotic applications. In order to adopt a robotic solution for an industrial setting, proper evaluation processes should be defined to assess the system’s performance. A number of benchmarks have been proposed in the literature focusing mainly on individual components needed to perform the task, like grasping, perception and motion planning; thus, they do not provide enough information on the performance of the entire robotic system. To address this, we propose a benchmarking framework for a pick-and-place task inspired by a use case for picking fruits and vegetables in an industrial setting. To foster reproducible research and comparison of different robotic systems, the benchmarking framework uses surrogate objects with instructions on how to build them, an easy-to-reproduce environment, and guidelines for object placement. The proposed benchmark is applied to evaluate the performance of two variants of a robotic system with different end-effectors.
Finding divers with SCUBANet

Robot-diver communication underwater is complicated by the attenuation of RF signals, the complexities of the environment in terms of deploying interaction devices, and issues related to the cognitive loading of human operators. Humans operating underwater have developed a simple yet effective strategy for diver-diver communication based on the visual recognition of gestures. Can a similar approach be effective for diver-robot communication? Here we present experiments with SCUBANet, an underwater detection dataset of body parts associated with diver-robot communication. Given the nature of standard diver gestures, here we concentrate on diver recognition and in particular on diver body-head-hand localization and examine the feasibility of using a CNN-based approach to address this problem. Such data-driven approaches typically require an appropriately annotated dataset. The SCUBANet dataset contains images of object classes commonly encountered during human-robot communication underwater. Object classes are labeled using per-instance bounding boxes. Annotations were created through crowd sourcing via a web-based interface to ease deployment. We provide baseline performance on diver and diver component recognition and localization using transfer learning on three widely available pre-trained models.
Dynamic Locomotion on Slippery Ground

Dynamic locomotion on unstructured and uneven terrain is a challenging task in legged robotics. Especially when it comes to slippery ground conditions, common state estimation and control algorithms suffer from the usual no-slip assumption. In fact, there has been only little research on this subject. This paper addresses the problem of slipping by treating slip detection and recovery tasks separately. Our contribution to the former is a probabilistic slip estimator based on a Hidden Markov Model. In the second part of this paper, we propose impedance control and friction modulation as useful tools to recover stability during traction loss. We demonstrate the success of our estimation/control architecture by enabling ANYmal, a quadrupedal torque-controllable robot, to dynamically walk over slippery terrain.
Project Management -Towards a More Complete Learning ecoSystem?

The current research shows, a learning organization can emerge. Based on the case of the Dutch Gateway Review Community, the current paper shows a learning organization can be stimulated and can become thriving. Improving the chances of success in project- and program and portfolio management even in typical large governmental change projects and programs can be facilitated. Based on ten years of history of conducting Gateway Reviews, it can be concluded that a conscious choice on embracing a helping and learning perspective is in itself a well-invested change program.
Using Augmentation to Improve the Robustness to Rotation of Deep Learning Segmentation in Robotic-Assisted Surgical Data

Robotic-Assisted Minimally Invasive Surgery allows for easy recording of kinematic data, and presents excellent opportunities for data-intensive approaches to assessment of surgical skill, system design, and automation of procedures. However, typical surgical cases result in long data streams, and therefore, automated segmentation into gestures is important. The public release of the JIGSAWS dataset allowed for developing and benchmarking data-intensive segmentation algorithms. However, this dataset is small and the gestures are similar in their structure and directions. This may limit the generalization of the algorithms to real surgical data that are characterized by movements in arbitrary directions. In this paper, we use a recurrent neural network to segment a suturing task, and demonstrate one such generalization problem-limited generalization to rotation. We propose a simple augmentation that can solve this problem without collecting new data, and demonstrate its benefit using: (1) the JIGSAWS dataset, and (2) a new dataset that we recorded with a da Vinci Research Kit. Our study highlights the prospect of using data augmentation in the analysis of kinematic data in surgical data science.
Performance Metrics for a Robotic Actuation System using Static and Mobile Electromagnets

Wireless magnetic microrobots can perform complex tasks for small-scale minimally invasive surgery (MIS) that requires high precision and dexterity. The choice of the configuration of the electromagnetic actuation (EMA) system is a key issue for reliable medical applications. This paper addresses the study of a robotic EMA platform firstly devoted to ophthalmic MIS, aiming at improving the manipulability and dexterity of the procedure. To this end, a robotic EMA system comprising four static and four mobile electromagnets is investigated. Evaluation of the magnetic force and torque, the manipulability and the dexterity indexes of EMA platforms are studied. The results demonstrate that a robotic EMA platform increases the versatility of the EMA system, and becomes resourceful to perform various tasks.