Miracle-dz/UniFAR-FTUs huggingface dataset:
Passivity based Control of Antagonistic Tendon-Driven Mechanism

The paper presents a passivity-based control law for an antagonistic tendon-driven mechanism. It is proven, by using the passivity theorem, that the proposed control law is able to achieve two properties such as the passivity of interconnected subsystems when the external torque is applied and the global asymptotic stability during free motion when the external force is absent. The proposed controller is simple to be implemented for a complex tendon-driven mechanism because it requires only gravity compensation. In addition, it brings a robustness to the entire control system. And finally, the control strategy can be treated as one of the impedance control schemes so as to achieve the desired performance efficiently.
EEG Control of a Bionic Hand With Imagination Based on Chaotic Approximation of Largest Lyapunov Exponent: A Single Trial BCI Application Study

This paper investigates a method for imaginary hand fisting pattern recognition based on the electroencephalography (EEG). The proposed method estimate the largest Lyapunov exponent (LLE) chaotic feature that is based on approximation of mutual information (MI) and false nearest neighbor (FNN) methods for reconstructing a phase space. The selected method for MI and FNN approximation approaches is a new version of Tug of War optimization algorithm. The new algorithm utilizes chaotic maps to update candidate solutions. The chaotic approximation of the LLE (CALLE) is the utilized method for extracting the chaotic features and then categorizing features by means of soft margin support vector machine with a generalized radial basis function kernel classifier. Accuracy and paired t-test values are obtained and compared with the traditional LLE method; 18 candidates were participated to record the EEG for imaginary right-hand fisting task. The results show improvements for the CALLE algorithm in comparison with the traditional LLE by achieving a higher accuracy of 68.25%. Feature changes between two imaginary statuses were significant for 17 subjects, and the paired t-test values were $(p < 0.05)$ . From the results, it is concluded that the Tug of War optimization method finds different values to reach a higher accuracy than the traditional LLE method, and the traditional methods for the LLE are not optimum.
Guaranteed Active Constraints Enforcement on Point Cloud-approximated Regions for Surgical Applications

In this work, a passive physical human-robot interaction (pHRI) controller is proposed to intraoperatively ensure that sensitive tissues will not be damaged by the robot’s tool. The proposed scheme uses the point cloud of the restricted region’s surface as constraint definition and Artificial Potential fields for constraint enforcement. The controller is proven to be passive with respect to the interaction force and to guarantee constraint satisfaction in all cases. The proposed methodology is experimentally validated by the kinesthetic guidance of a KUKA LWR4+ robot’s end-effector driving a virtual slave KUKA in the vicinity of a 3D point-cloud of a kidney and its adjacent vessels.
Incorporating Textual Similarity in Video Captioning Schemes

The problem of video captioning has been heavily investigated from the research community the last years and, especially, since Recurrent Neural Networks (RNNs) have been introduced. Aforementioned approaches of video captioning, are usually based on sequence-to-sequence models that aim to exploit the visual information by detecting events, objects, or via matching entities to words. However, the exploitation of the contextual information that can be extracted from the vocabulary has not been investigated yet, except from approaches that make use of parts of speech such as verbs, nouns, and adjectives. The proposed approach is based on the assumption that textually similar captions should represent similar visual content. Specifically, we propose a novel loss function that penalizes/rewards the wrong/correct predicted words based on the semantic cluster that they belong to. The proposed method is evaluated using two widely-known datasets in the video captioning domain, Microsoft Research - Video to Text (MSR-VTT) and Microsoft Research Video Description Corpus (MSVD). Finally, experimental analysis proves that the proposed method outperforms the baseline approach in most cases.
Practical guide to solve the minimum-effort problem with geometric algorithms and B-Splines

This paper focuses on important implementation issues of numerical optimal control that are often overlooked. In particular, transcription methods should be carefully implemented for obtaining a discrete representation of the problem. For this purpose, we explain the algorithms to solve the minimum-effort problem by applying a direct collocation method based on B-Splines. In addition, we describe how to compute the gradient of the objective function as well as the Jacobian of the constraints without the use of finite differences and automatic differentiation. Geometric algorithms based on Lie groups and Lie algebra are examined to efficiently compute the analytical derivatives of the equations of motion of articulated robots. These ingredients allow the fast computation of dynamically feasible robot motions. We provide numerical comparisons with a biped robot to validate our recipe against classical direct collocation methods.
Multiple Object Tracking With Attention to Appearance, Structure, Motion and Size

Objective of multiple object tracking (MOT) is to assign a unique track identity for all the objects of interest in a video, across the whole sequence. Tracking-by-detection is the most common approach used in addressing MOT problem. In this work, we propose a method to address MOT by defining a dissimilarity measure based on object motion, appearance, structure, and size. We calculate the appearance and structure-based dissimilarity measure by matching histograms following a grid architecture. Motion and size for each track are predicted using the information from track’s history. These dissimilarity values are then used in the Hungarian algorithm, in the data association step for track identity assignment. In addition, we introduce a method to address any false detection in stable tracks. The proposed method runs in real time following an online approach. We evaluate our method in both MOT17 benchmark data-set for pedestrian tracking and KITTI benchmark data-set for vehicle tracking using the same system parameters to verify the robustness of the proposed method. The method can achieve state-of-the-art results in both benchmarks.
Solving Methods for Multi-Robot Missions Planning with Energy Capacity Consideration

We consider a problem minimizing the total duration of accomplishing missions performed by heterogeneous vehicles. The problem respects constraints related to vehicles’ capabilities and energy capacities. The goal is to determine the best routes of each vehicle deployed by choosing which waypoints to pass and which observations to perform. Each vehicle has a particular distance matrix and a limited energy. In order to provide high quality solutions within reasonable computational time, two decomposition-based approximate methods were implemented: (i) the Multiphase heuristic, and (ii) the Two-Phase iterative heuristic. The performance of the methods is evaluated against the Branch-and-Cut algorithm using generated instances.
A Progressive Web Application Based on Microservices Combining Geospatial Data and the Internet of Things

Modern Web applications combine information from different sources, such as Web services, static resources, or real-time sensors data. The Internet of Things (IoT) is increasingly being used in these applications to show useful, updated information. However, the information related to the IoT devices is commonly displayed on dashboards for monitoring and control purposes and is not often combined with other types of data. In addition, it is important to base information on the location displayed in the user context. In this paper, we propose the use of a software architecture based on microservices and micro frontends for assisting the user in the friendly, seamless acquisition of geospatial data and information concerning the IoT. Our solution orchestrates those microservices and a component-based progressive Web application (PWA). The main microservice handles the creation of component configurations using a selection graph consisting of component tags and other descriptive properties and also contextual information about the application user. To demonstrate how the proposed architecture works, we present a scenario in which the Web application is dynamically built up by combining the geospatial information, the data acquired from the IoT sensors, and other complementary data.
Design and Fabrication of Transformable Head Structures for Endoscopic Catheters*

We present a transformable catheter head structure for endoscopic catheter allowing the simultaneous use of a camera module and a large tool channel introduced through a small incision. At the site of interest, the head with a camera can be expanded from the initial straight configuration, which opens a window for advancing a tool that is located behind the camera. Two different designs were proposed and prototyped. One option has flexure joints directly fabricated at the distal end of a polymer catheter by laser micro-machining, while another design employs a hinged metal head assembled at the tip of the same type of catheter. The kinematic behavior of each head was evaluated during the head-up and tip steering motions, and compared with each other to draw a selection guideline between them. Experimental results prove the feasibility of the proposed head structure for smarter endoscopic catheters.
Development of Integrated System of Divided Prediction Results for Disease Classification

The Integrated System of Divided Prediction Results is a system that combines the results using a multiple classifier. Divide the class that classifies per classifier into subsets to improve the accuracy of classification items and combine results of the classifiers. This provides an environment in which classification items can be easily expanded while securing higher reliability like an ensemble classifier [1]. Classification items are classified into duplicate items and unique items to obtain weights. In this process, we propose a weights combination method.
A Tunable Single-Feed Triple-Band LTE Antenna With Harmonic Suppression

This paper presents a single-feed planar inverted-F antenna (PIFA) that operates at three resonant frequencies within the LTE frequency range which would result in the enhancement of the antenna bandwidth and the increase of the data-rate speed. The antenna is designed to satisfy the requirements of carrier aggregation systems (intraband non-contagious type) cell-phone applications. The three resonant frequencies are independently tunable within the low-band (0.7–1 GHz), mid-band (1.6–2.1 GHz), and high-band (2.1–2.7 GHz), respectively, using varactor diodes as tuning devices. Each varactor diode represents a variable capacitor controlled by biasing dc voltage. A parallel LC harmonic-trap filter is built inside the antenna to suppress the harmonics generated by the low-band resonance, which enhances the tuning capabilities at the high-band. The antenna has a size of 375 mm<sup>2</sup>, which is considered to be a low-profile design and placed on a <inline-formula> <tex-math notation="LaTeX">$100\times 66\times3.154$ </tex-math></inline-formula> mm<sup>3</sup> RO4003C (<inline-formula> <tex-math notation="LaTeX">$\varepsilon _{\textrm {r}}$ </tex-math></inline-formula> = 3.55) printed circuit board. The measured antenna radiation patterns in each band are nearly isotropic as expected from the PIFA and the measured antenna radiation efficiency is 25–80% within the entire LTE frequency bands.
Comparing Physical and Simulated Performance of a Deterministic and a Bio-inspired Stochastic Foraging Strategy for Robot Swarms

Designing resource-collection algorithms for relatively simple robots that are effective given the noise and uncertainty of the real world is a challenge in swarm robotics. This paper describes the performance of two algorithms for collective robot foraging: the stochastic central-place foraging algorithm (CPFA) and the distributed deterministic spiral algorithm (DDSA). With the CPFA, robots mimic the foraging behaviors of ants; they stochastically search for targets and share information to recruit other robots to locations where they detect multiple targets. With the DDSA, robots travel along pre-planned spiral paths; robots detect the nearest targets first and, in theory, guarantee eventual complete coverage of the arena with minimal overlap. We implemented both algorithms and compared their performance in a Gazebo simulation and in physical robots in a large outdoor arena. In a realistic Gazebo simulation, the DDSA outperforms the CPFA. However, in real-world experiments with obstacles, collisions, and errors, the movement patterns of robots implementing the DDSA become visually indistinguishable from the CPFA. The CPFA is less affected by noise and error, and it performs as well as, or better than, the DDSA. Physical experiments change our conclusion about which algorithm has the best performance, emphasizing the importance of systematically comparing the performance of swarm robotic algorithms in the real world.
Encoding and Decoding for mmWave Massive MIMO Systems

Millimeter-wave (mmWave) and massive multipleinput multiple-output (MIMO) are novel technologies to achieve a satisfactory data rate (up to Giga bits per second) needed by fifth generation (5G) communication systems. There are differences between the signal processing required in high and low frequencies such as hardware limitations and channel models. This paper presents the potential mmWave massive MIMO architecture. It also reviews the up-to-date receivers' structure proposed in mmWave massive MIMO systems. In addition, the paper presents the limitations and differences between the existing algorithms like the maximum ratio combining (MRC), the equal gain combining (EGC) and the iterative block decision feedback equalization (IB-DFE).
A system dynamics approach to modelling the management of the increased prediabetic prevalence of the South African population

The increasing prevalence of prediabetes in South Africa is of great concern. Due to the complex nature and nonlinear interactions that exist within the health care system to address prediabetic health care, a system dynamics-based approach is suggested as an appropriate analysis tool to evaluate and gain insight into the dominant factors that influence the effective management of prediabetes. This is intended to subsequently identify more effective and efficient prediabetic policy and intervention strategies. A six-dynamic hypothesis is proposed in the form of a causal loop diagram that was used in the development of a system dynamics model. In the developed system dynamics model, five interventions strategies, identified as scenarios, were tested. Finally, the increased screening of high-risk individuals for prediabetes was identified as the most effective intervention strategy for reducing the prediabetic population.
LocalVLC: Augmenting Smart IoT Services with Practical Visible Light Communication

Visible Light Communication (VLC)emerges as a communication technology for Internet of Things (IoT)services with appealing benefits not present in existing radio-based communication. However, current VLC designs commonly require dedicated LED lights to emit modulated light beams which entail high energy overhead and unpleasant visual experiences due to the perceptible light blinking effects for end users. This greatly limits the deployment and applicable scenarios of VLC. In this paper, we design and develop LocalVLC, a practical and low-cost VLC system that can be used as a standard light source to augment smart IoT services. LocalVLC introduces a novel Morse-code inspired modulation scheme that can operate on off-the-shelf LEDs with low energy overhead. It can effectively overcome the light flickering by encoding data into high frequency light pulses without requiring extra processing hardware such as FPGA or micro-controller. We have implemented and evaluated a full-fledged system prototype based on LocalVLC design. Under practical settings, our LocalVLC prototype can support up to 10 meters of range, and attain reasonable throughput (up to 1.4 Kbps)with low error rate and energy consumption. Comparing with the widely adopted Manchester encoding, LocalVLC yields 8x improvement on both throughput and energy consumption. In addition, we demonstrate the practicality of LocalVLC through two IoT use cases where we developed two lightweight LocalVLC-based solutions using low-cost off-the-shelf hardware to exemplify the usage of LocalVLC for indoor service discovery and smart home key management.
The Mechanics and Control of Leaning to Lift Heavy Objects with a Dynamically Stable Mobile Robot

A control algorithm is developed to enable dynamically stable spherical-wheel robots (ballbots) with arms to detect a heavy object of unknown mass, navigate to it, lift it, transport it, and place it in a desired location semi-autonomously. Previous work has successfully demonstrated two-wheeled dynamically stable mobile manipulator robots transporting heavy objects. We report here the first ballbot to reliably achieve such a task. A successful semi-autonomous lift and transport of a 15 kg heavy box whose actual mass was unknown was achieved using a combination of feedforward and feedback control laws based on a quasi-static center of mass computation. The ballbot’s pan and tilt sensor turret tracked fiducial markers on the box. Ballbot-to-human and human-to-ballbot exchanges of a 10 kg heavy object was achieved while dynamically balancing.
Joint Learning of Instance and Semantic Segmentation for Robotic Pick-and-Place with Heavy Occlusions in Clutter

We present joint learning of instance and semantic segmentation for visible and occluded region masks. Sharing the feature extractor with instance occlusion segmentation, we introduce semantic occlusion segmentation into the instance segmentation model. This joint learning fuses the instance-and image-level reasoning of the mask prediction on the different segmentation tasks, which was missing in the previous work of learning instance segmentation only (instance-only). In the experiments, we evaluated the proposed joint learning comparing the instance-only learning on the test dataset. We also applied the joint learning model to 2 different types of robotic pick-and-place tasks (random and target picking) and evaluated its effectiveness to achieve real-world robotic tasks.
Towards Fully Dense Direct Filter-Based Monocular Visual-Inertial Odometry

We propose a fully dense direct filter-based visual-inertial odometry method estimating both pixel depth for all pixels and robot state simultaneously, having all uncertainties in the same state vector. Due to the fully dense method, our approach works even in low-textured areas with very low, smooth gradients (i.e. scenes where feature based or semi-dense approaches fail). Our algorithm performs in real-time on a CPU with a time complexity linearly dependent on the amount of pixels in the provided image. To achieve this, we propose complexity reduction methods for fast matrix inversion, exploiting specific structures of the covariance matrix. We provide both simulated and real-world results in low-textured areas with a smooth gradient.
Hierarchical Depthwise Graph Convolutional Neural Network for 3D Semantic Segmentation of Point Clouds

This paper proposes a hierarchical depthwise graph convolutional neural network (HDGCN) for point cloud semantic segmentation. The main chanllenge for learning on point clouds is to capture local structures or relationships. Graph convolution has the strong ability to extract local shape information from neighbors. Inspired by depthwise convolution, we propose a depthwise graph convolution which requires less memory consumption compared with the previous graph convolution. While depthwise graph convolution aggregates features channel-wisely, pointwise convolution is used to learn features across different channels. A customized block called DGConv is specially designed for local feature extraction based on depthwise graph convolution and pointwise convolution. The DGConv block can extract features from points and transfer features to neighbors while being invariant to different point orders. HDGCN is constructed by a series of DGConv blocks using a hierarchical structure which can extract both local and global features of point clouds. Experiments show that HDGCN achieves the state-of-the-art performance in the indoor dataset S3DIS and the outdoor dataset Paris-Lille-3D.
Optimal Leg Sequencing for a Hexapod Subject to External Forces and Slopes

An optimal leg sequence selection method is developed, which maximizes hexapod robot stability, considering feasible gaits, motion modes, and terrain slope. A novel and fast search method is employed to find the most stable leg sequence for a given gait; if no such sequence exists, the next fastest stable gait is chosen and the most stable leg sequence for this gait is selected. The method can be based on any stability measure; here the Force-Angle Stability Margin criterion is employed that is sensitive to top-heaviness, and inertial and external forces. Results show that the developed method senses instabilities accurately and selects the best leg sequence for maximum stability far faster than exhaustive searches, offering distinct advantages when varied external forces are applied.
A UHF Broadband Low-Noise Amplifier for Active Digital TV Antenna

A broadband low-noise amplifier has been developed for an active digital TV antenna to operate in the frequency range of 470 - 800 MHz in the UHF band. The amplifier employs a SiGe:C Heterojunction Bipolar Transistor (HBT) and uses shunt negative feedback to achieve relatively flat gain and low return loss over a broad bandwidth. The amplifier prototype is fabricated on the 0.4-mm FR4 substrate. The measured results show that the gain of about 24 dB, the input return loss of better than 11 dB, and the noise figure of less than 1.4 dB are achieved throughout the desired frequency band.
Cyber-Physical Manufacturing Systems for Industry 4.0: Architectural Approach and Pilot Case

The pillars of Industry 4.0 require a modern smart factory to be integrated, store data into the Cloud, access the Cloud for data analytics and share information at software level for simulation and Hardware-In-the-Loop capabilities. The resulting Cyber-Physical System is often called Cyber-Physical Manufacturing System, and it becomes fundamental to cope with the increased system complexity and the desired performances. However, since a lot of old production systems are based on monolitic architectures, with limited external communication ports and reduced local computational capabilities, it is very difficult to make such production lines compliant to Industry 4.0 pillars. Wireless Sensor Network is a solution for the smart connection of a production line to a Cyber-Physical System architecture, data processing through Cloud Computing. The scope of this research work is to propose an intermediate layer within the architecture that allows each device, production line and machine to be independently connected despite the adopted protocol. The solution is based on OSGi Framework, which is able to seamlessly integrate both hardware and software wireless sensors, send data into the Cloud for further data analysis, and grant both Hardware-In-the-Loop and Cloud Computing capabilities. A general description of the architecture is here proposed, together with preliminary results on a real manufacturing line for data collection and analysis over a period of two months.