Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Robot Region-of-Interest Reconstruction with Dec-MCTS

We consider the problem of reconstructing regions of interest of a scene using multiple robot arms and RGB-D sensors. This problem is motivated by a variety of applications, such as precision agriculture and infrastructure inspection. A viewpoint evaluation function is presented that exploits predicted observations and the geometry of the scene. A recently proposed non-myopic planning algorithm, Decentralised Monte Carlo tree search, is used to coordinate the actions of the robot arms. Motion planning is performed over a navigation graph that considers the high-dimensional configuration space of the robot arms. Extensive simulated experiments are carried out using real sensor data and then validated on hardware with two robot arms. Our proposed targeted information gain planner is compared to state-of-the-art baselines and outperforms them in every measured metric. The robots quickly observe and accurately detect fruit in a trellis structure, demonstrating the viability of the approach for real-world applications.
On Design for Additive Manufacturing: Review of Challenges and Opportunities utilising Visualisation Technologies

Design for additive manufacturing poses new challenges and opportunities for manufacturers to produce highly customised parts while reducing cost, production time and improving quality. Manufacturing constraints of conventional manufacturing methods, such as geometric complexity limitations and workpiece handling, have shaped the landscape of computer-aided design tools, which are therefore not suitably adapted to design for additive manufacturing. Furthermore, computer-aided design tools require a high level of training to produce appropriate models. Augmented reality and feedback technologies pose an interesting opportunity for design for additive manufacturing, whereby the interaction with 3D models in an augmented or virtual design space can provide intuitive feedback to engineers and designers, providing fast validation of designs, parametric modelling and opportunities for training and use in both professional and amateur designer communities. This paper will explore and review the opportunities this exciting new technology provides.
Speech Recognition using Deep Learning

Presently, computers have already replaced a tremendous number of humans in many creative professions. Therefore, Artificial Intelligence areas are composed of Machine Learning, Natural Language Processing, Computer Vision and Robotics. Similarly, speech recognition can be predicted by using computers. In audio files or video files that are large and have many minutes in length, many files have a variety of audio and audio files. This researcher chose to listen to the desired sound from a large file. In this research, deep learning was used to classify speech. Google corpus was used to train the model. We received 66.22% of accuracy.
Computation Rate Maximization for Wireless Powered Mobile Edge Computing with NOMA

In this paper, we consider a mobile edge computing (MEC)network, that is wirelessly powered. Each user harvests wireless energy and follows a binary computation offloading policy, i.e., it either executes the task locally or offloads it to the MEC as a whole. For the offloading users, non-orthogonal multiple access (NOMA)is adopted for information transmission. We consider rate-adaptive computational tasks and aim at maximizing the sum computation rate of all users by jointly optimizing the individual computing mode selection (local computing or offloading), the time allocations for energy transfer and for information transmission, together with the local computing speed or the transmission power level. The major difficulty of the rate maximization problem lies in the combinatorial nature of the multiuser computing mode selection and its involved coupling with the time allocation. We also study the case where the offloading users adopt time division multiple access (TDMA)as a benchmark, and derive the optimal time sharing among the users. We show that the maximum achievable rate is the same for the TDMA and the NOMA system, and in the case of NOMA it is independent from the decoding order, which can be exploited to improve system fairness. To maximize the sum computation rate, for the mode selection we propose a greedy solution based on the wireless channel gains, combined with the optimal allocation of energy transfer time. Numerical results show that the proposed solution maximizes the computation rate in homogeneous networks, and binary offloading leads to significant gains. Moreover, NOMA increases the fairness of rate distribution among the users significantly, when compared with TDMA.
Salty-A Domain Specific Language for GR(1) Specifications and Designs

Designing robot controllers that correctly react to changes in the environment is a time-consuming and error-prone process. An alternative is to use “correct-by-construction” synthesis approaches to automatically generate controller designs from high-level specifications. In particular, Generalized Reactivity(l) or GR(1) specifications are well-suited to express specifications for robots that must act in dynamic environments, and approaches to generate controller designs from GR(1) specifications are highly computationally efficient. Toward that end, this paper presents Salty, a domain-specific language for GR(1) specifications. While tools exist to synthesize system designs from GR(1) specifications, Salty makes such specifications easier to write and debug by supporting features such as richer input and output types, user-defined macros, common specification patterns, and specification optimization and sanity checking. Salty interfaces with the separately developed synthesis tool Slugs to produce a system or controller design, and Salty translates this design to a software implementation in a variety of languages. We demonstrate Salty on an application involving coordination of multiple unmanned air vehicles (UAVs) and provide a workflow for connecting synthesized UAV controllers to freely available UAV planning and simulation software suites UxAS and AMASE.
Fuzzy Entropy Metrics for the Analysis of Biomedical Signals: Assessment and Comparison

Fuzzy entropy (FuzEn) was introduced to alleviate limitations associated with sample entropy (SampEn) in the analysis of physiological signals. Over the past decade, FuzEn-based methods have been widely used in various real-world biomedical applications. Several fuzzy membership functions (MFs), including triangular, trapezoidal, Z-shaped, bell-shaped, Gaussian, constant-Gaussian, and exponential functions have been employed in FuzEn. However, these FuzEn-based metrics have not been systematically compared yet. Since the threshold value <inline-formula> <tex-math notation="LaTeX">${r}$ </tex-math></inline-formula> used in FuzEn is not directly comparable across different MFs, we here propose to apply a defuzzification approach using a surrogate parameter called ’center of gravity’ to re-enable a fair and direct comparison. To evaluate these MFs, we analyze several synthetic and three clinical datasets. FuzEn using the triangular, trapezoidal, and Z-shaped MFs may lead to undefined entropy values for short signals, thus providing a very limited advantage over SampEn. When dealing with an equal value of the center of gravity, the Gaussian MF, as the fastest algorithm, results in the highest Hedges’ <inline-formula> <tex-math notation="LaTeX">$g$ </tex-math></inline-formula> effect size for long signals. Our results also indicate that the FuzEn based on exponential MF of order four better distinguishes short white, pink, and brown noises, and yields more significant differences for the short real signals based on Hedges’ <inline-formula> <tex-math notation="LaTeX">$g$ </tex-math></inline-formula> effect size. The triangular, trapezoidal, and Z-shaped MFs are not recommended for short signals. We propose to use FuzEn with Gaussian and exponential MF of order four for characterization of short (around 50–400 sample points) and long data (longer than 500 sample points), respectively. We expect FuzEn with Gaussian and exponential MF as well as the concept of defuzzification to play prominent roles in the irregularity analysis of biomedical signals. The MATLAB codes for the FuzEn with different MFs are available at <uri>https://github.com/HamedAzami/FuzzyEntropy_Matlab</uri>.
Fast and Robust Initialization for Visual-Inertial SLAM

Visual-inertial SLAM (VI-SLAM) requires a good initial estimation of the initial velocity, orientation with respect to gravity and gyroscope and accelerometer biases. In this paper we build on the initialization method proposed by Martinelli [1] and extended by Kaiser et al. [2], modifying it to be more general and efficient. We improve accuracy with several rounds of visual-inertial bundle adjustment, and robustify the method with novel observability and consensus tests, that discard erroneous solutions. Our results on the EuRoC dataset show that, while the original method produces scale errors up to 156%, our method is able to consistently initialize in less than two seconds with scale errors around 5%, which can be further reduced to less than 1% performing visual-inertial bundle adjustment after ten seconds.
CNN-based user selection in MIMO broadcasting channel

In this paper, we apply the convolutional neural networks (CNN) to the user selection in MIMO BC channel. To verify feasibility of the CNN-based user selection, the sum rates of CNN-based user selection are evaluated according to various types of input data. The simulation results compare the CNN-based user selection with the conventional user selection schemes and show that the CNN-based user selection is feasible.
SAFFRON: A Fast, Efficient, and Robust Framework for Group Testing Based on Sparse-Graph Codes

Group testing is the problem of identifying <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> defective items among <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> items by <italic>pooling</italic> groups of items. In this paper, we design group testing algorithms for approximate recovery with order-optimal sample complexity by leveraging design and analysis tools from modern sparse-graph coding theory. Our algorithm, SAFFRON, recovers at least <inline-formula><tex-math notation="LaTeX">$(1-\varepsilon)K$</tex-math></inline-formula> defective items w.p. <inline-formula><tex-math notation="LaTeX">$1-K/n^r$</tex-math></inline-formula> with <inline-formula><tex-math notation="LaTeX">$m=2(1+r)C(\varepsilon)K\log _2{n}$</tex-math></inline-formula> tests, where <inline-formula><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> is an arbitrarily small constant, <inline-formula><tex-math notation="LaTeX">$C(\varepsilon)$</tex-math></inline-formula> is a precisely characterizable constant, and <inline-formula><tex-math notation="LaTeX">$r$</tex-math></inline-formula> is any positive integer. The decoding complexity is <inline-formula><tex-math notation="LaTeX">$\Theta (K\log n)$</tex-math></inline-formula>. We also propose variations of SAFFRON, which are robust to noise and unknown offsets. For example, for <inline-formula><tex-math notation="LaTeX">$n \simeq 4.3\times 10^9$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$K = 128$</tex-math></inline-formula>, our algorithm is observed to recover all defective items with <inline-formula><tex-math notation="LaTeX">$m \simeq 8.3\times 10^{5}$</tex-math></inline-formula> tests, even in the presence of noisy test results. Moreover, the decoding time takes less than 4 seconds on a laptop with a 2 GHz Intel Core i7 and 8 GB memory.
Simulated Annealing-optimized Trajectory Planning within Non-Collision Nominal Intervals for Highway Autonomous Driving

This article considers the problem of near-optimal trajectory generation for autonomous vehicles on highways. The goal is to select a predictive reference trajectory in the free evolution space, while avoiding both generating a pre-calculated set of candidate trajectories and decoupling path and velocity optimizations. Moreover, this trajectory aims at optimizing a decision process based on multi-criteria functions, which are not straightforward to design and can have a blackbox formulation. The main idea of this article is to use the decision evaluation function in the trajectory generator with a Simulated Annealing (SA) approach. The parameters of a sigmoid trajectory are optimized within Non-Collision Nominal Intervals (NCNI), which are defined as collision-free intervals under nominal conditions using a velocity-space representation.
Optical Force Sensing In Minimally Invasive Robotic Surgery

This paper evaluates the feasibility of a novel optical sensing concept to measure forces applied at the tip of daVinci EndoWrist instruments. An optical slit is clamped onto the instrument shaft, in-line with an infrared LED-bicell pair. Deflection of the shaft moves the slit with respect to the LED-bicell pair and modulates the light incident on each active element of the bicell. The differential photocurrent is conditioned and monitored to estimate the tip forces. The feasibility evaluation consists of a flexible beam model to quantify the required sensor performance, experimental results with a 3D printed prototype and estimation of the sensor limitations including the measurement bandwidth due to the structural dynamics. The proposed approach requires no modifications to the instrument, is adaptable to different instruments and robot platforms, and leads to high-resolution, high-dynamic range sensing without hysteresis.
Control of Delayed Bilateral Teleoperation System for Robotic Tele-Echography

This paper describes development of a controllerfor bilateral robotic system for tele-echography with delayed communications. The system comprises two identical 6DOF robotic devices based on Stewart-Gough mechanisms, which were developed especially for the sake of the considered application. Controller design is facilitated by a symmetry of the teleoperation setup, in which the master and slave devices have identical geometry, actuation and measurement arrays. To further simplify the analysis, we treat coupling between degrees of freedom as exogenous disturbances and this allows us to split the control problem into six independent IDOF settings. The IDOF problems are addressed then using a novel approach to bilateral teleoperation control, based on a complete parameterization of feasible teleoperators. It allows to intuitively shape teleoperator performance while guaranteeing passivity and thus coupled stability of the system. The potential of the proposed control method is demonstrated with experimental results.
Stiffness Bounds for Resilient and Stable Physical Interaction of Articulated Soft Robots

It is widely recognized that impedance modulation is a key aspect in applications in which robots significantly interact with the environment or humans. Either active impedance controllers or actuators with passive variable impedance can be exploited to modulate the impedance. However, methods capable of determining the right (constant or time-varying) impedance profile in order to guarantee task performance as well as resilience and stability are required. In this letter, we discuss how task-related aspects, such as uncertainties, contact surface shapes, and interaction forces, set bounds on the admissible Cartesian stiffness. We recall that, an upper bound on the stiffness is required to prevent high forces exchanged during the interaction to guarantee adaptability and safety. Despite this, however, there is also a lower bound to be considered in order to preserve stability during the interaction. To this purpose, we study the interaction of a robot, with controllable Cartesian impedance, with a curved surface. Thus, we provide an analytic lower bound for the Cartesian stiffness that guarantees stability of such interaction task, and we prove that this bound directly depends on task parameters, namely contact force and surface curvature. Theoretical results are experimentally validated on robots powered by variable stiffness actuators and compliance controlled industrial robots.
Improving Thai Herb Image Classification using Convolutional Neural Networks with Boost up Features

The use of herbs is incredibly important for human and animals. Herbs have high similarity in shape, color, and texture so that there are difficulties for normal people to classify herbs. Even though plant classification technology has been developed for years, this technology is still unsolved and considered as challenging problem. This research focuses on improving herbal leaves classification. The proposed methodologies include a simple CNN structure, preprocessing process and a boost up feature technique. The proposed technique was used to improve the performance of simple CNN model. The results show that the model trained by using boost up feature technique outperforms the model conventionally trained by approximately 10% on the average.
Whole-Body Active Compliance Control for Humanoid Robots with Robot Skin

Humanoid robots are expected to interact in human environments, where physical interactions are unavoidable. Therefore, whole-body control methods that include multi-contact interactions are required. The new emerging technologies in touch sensing are fundamental to acquire online and rich information about these physical interactions with the environment. These technologies lead to the design of novel control systems that can profit from the tactile sensor information in an efficient form, thus producing reactive and compliant robots capable of interacting with their environment. In this paper, we present a novel control framework to integrate the multi-modal tactile information of a robot skin with different control strategies, producing dynamic behaviours suitable for Human-Robot Interactions (HRI). The control framework was experimentally evaluated on a full-size humanoid robot covered with more than 1260 skin cells distributed in the whole robot body. The results show that multi-modal tactile information can be fused hierarchically with multiple control strategies, producing active compliance in a position-controlled stiff humanoid robot.
An Integrated Information Systems Adoption Model for Businesses in Kajiado, Kenya.

Business organizations in Kenya are classified as small, medium and large. Within these classifications, there exist structural and operational factors that influence the adoption of information systems (IS), and identifying these factors is crucial to IS implementation. The study conducted a survey of 50 such businesses within Kajiado, a County characterized by under-developed IS infrastructure and a resource scarcity that is typical of many peripheral counties in Kenya. Results are presented in the form of descriptive statistics and a hierarchical regression model. Some of the key variables studies during adoption and diffusion included characteristics of decision-makers, organization, the environment and the IS infrastructure. The results of this study can be used to identify the key variables that drive the adoption of IS in small- and medium-sized businesses. The study therefore provides parties involved with adoption process a practical synopsis of the IS adoption process which is believed to assist them with successful adoption.
Entrepreneurial Employees: A Review and Future Research Agenda

Volatile markets and accelerating innovation cycles progressively force established companies to adopt alternative innovation strategies such as entrepreneurship. Due to the key role entrepreneurial employees play for strengthening the company’s abilities for innovation and change, various concepts have emerged like corporate entrepreneurship or intrapreneurship. While the extant literature has increasingly examined only specific issues of entrepreneurial employees, an overall view on it lacks investigation. Therefore, the purpose of this paper is to structurally present current research on entrepreneurial employees by conducting a broad systematic literature review. The resulting research streams contribute to a clearer justification for future research and are a first step towards a comprehensive research view related to intrapreneurship.
On the role of wearable haptics for force feedback in teleimpedance control for dual-arm robotic teleoperation

Robotic teleoperation enables humans to safely complete exploratory procedures in remote locations for applications such as deep sea exploration or building assessments following natural disasters. Successful task completion requires meaningful dual arm robotic coordination and proper understanding of the environment. While these capabilities are inherent to humans via impedance regulation and haptic interactions, they can be challenging to achieve in telerobotic systems. Teleimpedance control has allowed impedance regulation in such applications, and bilateral teleoperation systems aim to restore haptic sensation to the operator, though often at the expense of stability or workspace size. Wearable haptic devices have the potential to apprise the operator of key forces during task completion while maintaining stability and transparency. In this paper, we evaluate the impact of wearable haptics for force feedback in teleimpedance control for dual-arm robotic teleoperation. Participants completed a peg-in-hole, box placement task, aiming to seat as many boxes as possible within the trial period. Experiments were conducted both transparent and opaque boxes. With the opaque box, participants achieved a higher number of successful placements with haptic feedback, and we saw higher mean interaction forces. Results suggest that the provision of wearable haptic feedback may increase confidence when visual cues are obscured.
Compact, Wideband, Planar Filtenna With Reconfigurable Tri-Polarization Diversity

A compact, wideband, planar filtenna with reconfigurable tri-polarization diversity is presented. The operating mechanisms of its bandpass filter and its bandwidth enhancement are explained through a step-by-step design evolution. Its feed network consists of three J-shaped probes, a Wilkinson power divider, and phase shifters. Reconfigurability is achieved by integrating four pairs of p-i-n (PIN) diodes into it. Three polarization-reconfigurable states: linear polarization (LP), left-hand circular polarization (LHCP), and right-hand circular polarization (RHCP), are dynamically controlled by those PIN diode switches. A prototype was fabricated, assembled, and tested, which has a compact size (<inline-formula> <tex-math notation="LaTeX">$0.48 \lambda _{0} \times 0.45 \lambda _{0}$ </tex-math></inline-formula>) and is low profile (<inline-formula> <tex-math notation="LaTeX">$0.096~\lambda _{0}$ </tex-math></inline-formula>). The measured results, in good agreement with their simulated values, demonstrate that the filtenna has a wide −10-dB impedance bandwidth (<inline-formula> <tex-math notation="LaTeX">$\vert \text{S}_{11}\vert \le -10$ </tex-math></inline-formula> dB) of 15.6%, which completely encompasses the axial ratio (AR) bandwidths (AR ≤ 3 dB) of the two circular polarization (CP) states. The realized gain values over this bandwidth: 7.7 ± 0.5 dBi, are very stable for all three dynamical states. Furthermore, the filtenna exhibits good out-of-band rejection performance characteristics.