Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-cost Sonar Navigation System

In this paper, we present a sonar-based navigation system, designed to deploy a fleet of autonomous mobile platforms at a reasonable cost. In educational and hobbyist contexts, a large number of robots is required. By means of classical navigation approaches, every robot should be provided with accurate vision or range sensors. This limits the maximum number of robots in the fleet, due to the unaffordable cost of these sensors. In contrast to that, our system requires a single platform equipped with a higher quality sensor, used to perform calibration and mapping tasks. The rest of the fleet, able to localize and navigate, is equipped solely with low-cost sonars, providing a notable reduction in the overall cost. We achieve this task by presenting a novel calibration procedure to estimate the sonars extrinsic and adapting a classical monte-carlo localization algorithm to the sonar model, focusing on efficiency. We release an open source implementation of the system to the community.
A Robust Laser-Inertial Odometry and Mapping Method for Large-Scale Highway Environments

In this paper, we propose a novel laser-inertial odometry and mapping method to achieve real-time, low-drift and robust pose estimation in large-scale highway environments. The proposed method is mainly composed of four sequential modules, namely scan pre-processing module, dynamic object detection module, laser-inertial odometry module and laser mapping module. Scan pre-processing module uses inertial measurements to compensate the motion distortion of each laser scan. Then, the dynamic object detection module is used to detect and remove dynamic objects from each laser scan by applying CNN segmentation network. After obtaining the undistorted point cloud without moving objects, the laser-inertial odometry module uses an Error State Kalman Filter to fuse the data of laser and IMU and output the coarse pose estimation at high frequency. Finally, the laser mapping module performs a fine processing step and the “Frame-to-Model’' scan matching strategy is used to create a static global map. We compare the performance of our method with two state-of-the-art methods, LOAM and SuMa, using KITTI dataset and real highway scene dataset. Experiment results show that our method performs better than the state-of-the-art methods in real highway environments and achieves competitive accuracy on the KITTI dataset.
A Unified Framework on Reading Variable Size Traffic Sign Boards using Deep Neural Network

With the explosive increase of vehicles in modern era and continuous interest in autonomous vehicles across the globe, need for automatic signs reading software has attained global attention. Recent research has focused on reading small sized sign boards whereas previous work was more related to understanding large sized sign boards. This work serves twofold- identifying small or large sized sign boards within a single platform. This model makes use of recent advancements in deep learning paradigm entitle Inception V3, where this model is modified to cater needs of reading variable sizes of sign boards installed at traffic roads. This modified inception model is fed into Faster RCNN for better features extraction. RPN is applied for region of interest extraction. Later on, sign board is identified into three major classes, i.e. prohibited, warning or mandatory using fully connected layers of Neural Network. The proposed model achieves an accuracy of around 92.99% on a standard dataset.
GRASPA 1.0: GRASPA is a Robot Arm graSping Performance BenchmArk

The use of benchmarks is a widespread and scientifically meaningful practice to validate performance of different approaches to the same task. In the context of robot grasping the use of common object sets has emerged in recent years, however no dominant protocols and metrics to test grasping pipelines have taken root yet. In this letter, we present version 1.0 of GRASPA, a benchmark to test effectiveness of grasping pipelines on physical robot setups. This approach tackles the complexity of such pipelines by proposing different metrics that account for the features and limits of the test platform. As an example application, we deploy GRASPA on the iCub humanoid robot and use it to benchmark our grasping pipeline. As closing remarks, we discuss how the GRASPA indicators we obtained as outcome can provide insight into how different steps of the pipeline affect the overall grasping performance.
Investigating ‘One-Day Flies’ Users in The StackOverflow: Why Do and Don’t People Participate?

The paper will mainly investigate factors causing the critical issue of inactive users in one of online community, the Stack Overflow. The focus of inactive user itself will be ‘one-day flies’, those who posted once in Stack Overflow, then never come back. To establish the investigation, the author uses the systematic literature review approach.A new user is a valuable asset for Stack Overflow to keep their pace in the growing area. However, the facts that numerous users from this category become lurkers can be a huge burden for the community. The threat of unanswered questions, negative feedbacks, and deleted questions create a massive discouraging impact for the Stack Overflow, and therefore, raising a question about the health of this community - persistently intimidate new users may lead this giant community to the shrinking phase. However, there is also proves that through the user reputation and badge, some of them has passed the ‘novice’ stage and become actively participate in the community.
Anonymous multi-source counting statistics New methodology applied to the health sector

We present a technique for estimating the size of a statistical population spread across multiple sources. Each sources can overlap others, possibly leading to over-counting individuals in naive approaches. The methodology we introduce further guarantees that the process is performed in an anonymous way, i.e. without explicitly sharing individual data. We propose to apply this approach to the medical sector, where the input data are particularly sensible, and where anonymity is usually a requirement. Our methodology applies well for coordinated counting estimates, but can be in principle extended to generate data sets.
Evaluation of EEG Connectivity Network Measures based Features in Schizophrenia Classification

In this paper an architecture for the classification of Schizophrenia using EEG-based brain connectivity is proposed. Functional and effective networks were constructed from the EEG using a variety of connectivity measures and with graph theory metrics complex network features were extracted. Several classification algorithms were used for the evaluation of the architecture. Promising results were observed when using connectivity measures that also capture directionality properties of the network. The best classification accuracy was 82.36% and was achieved by Random Forest classifier with Direct Transfer Function as a connectivity measure.
PCB distributed filters

In this paper, a new family of ceramic resonators and filters is introduced. The proposed resonators are enabled by the integration of distributed resonators into standard Printed Circuit Board (PCB) substrates. This integration caters for not only a tremendous reduction in the resonator profile, but it also enables utilization of the unused PCB real estate. As an example of the feasibility of the proposed idea, a 3-pole filter based on 3-by-3 distributed resonant elements was designed and fabricated. The filter was made to operate at a frequency of 1.12 GHz with a bandwidth of 20 MHz. The substrate used for this filer has the dielectric permittivity of 12.85 and a loss tangent of 0.0019. The measured results show a good agreement with simulations.
Right of Way, Assertiveness and Social Recognition in Human-Robot Doorway Interaction

We expand on previous work for negotiating human-robot navigation contention around doorways to produce a more socially-compliant autonomous robot behaviour. Our goal is to improve the integration of robots navigating in human environments by eliciting human recognition of the robot’s right of way. This is achieved by incorporating feedback from a user study of our previous system to create a more communicative, reciprocal, and assertive behaviour. Our contribution includes both the updated behaviour and a new user study that evaluates and compares the system to its predecessor. Results show that participants are more likely to respect the robot’s right of way given the new robot behaviour, but their responses also highlight the challenges of socially integrating robots into human spaces.
One-Shot Composition of Vision-Based Skills from Demonstration

We consider the problem of learning multi-stage vision-based tasks on a real robot from a single video of a human performing the task, while leveraging demonstration data of subtasks with other objects. This problem presents a number of major challenges. Video demonstrations without teleoperation are easy for humans to provide, but do not provide any direct supervision. Learning policies from raw pixels enables full generality but calls for large function approximators with many parameters to be learned. Finally, compound tasks can require impractical amounts of demonstration data, when treated as a monolithic skill. To address these challenges, we propose a method that learns both how to learn primitive behaviors from video demonstrations and how to dynamically compose these behaviors to perform multi-stage tasks by “watching” a human demonstrator. Our results on a simulated Sawyer robot and real PR2 robot illustrate our method for learning a variety of order fulfillment and kitchen serving tasks with novel objects and raw pixel inputs. Video results are linked at https://sites.google. com/view/one-shot-hil.
Data Based Parameter Setting Method for Adaptive Cruise Control

Recent years, advanced driver assistance system (ADAS) has been highly developed and widely used. Among various ADAS techniques adaptive cruise control (ACC) is the fundamental of longitudinal control. The tradeoffs among safety, comfort and traffic efficiency are the main issue in ACC design process. In this study, large amount of road tests was carried out to explore the circumstance characteristics in ACC usages scenarios. Based on statistical analysis of road test data, a method for ACC safety performance evaluation is proposed. Furthermore, the effects of parameters such as following distance, jerk limit and time delay to ACC safety performance are discussed. Finally, a way of ACC parameter designing is developed, this method can obtain best comfort and traffic efficiency after safety request is guaranteed. In this study, regulations and realistic constrains of production vehicles are considered. Different from theoretical simulation, production-oriented ACC system is restricted in maximum deceleration, maximum braking jerk, detection latency, actuation latency, calculation capacity etc. For this reason, all the analysis is based on statistical results and requests from actual products, no computationally expensive optimal method is implemented. The proposed method may not get optimal results in theoretical simulation but is very instructive in production-oriented ACC system design.
Optimal Trajectory Planning for 2-DOF Adaptive Transformable Wheel

Steps are frequently encountered while mobile robots move in indoor environments; thus, the ability to overcome a step is essential to indoor mobile robots. In this study, a new adaptive transformable wheel is conceptually proposed to effectively overcome steps of different sizes. The center trajectory of the proposed wheel is optimally designed to minimize its fluctuations while the robot overcomes a step. For this purpose, a kinematic analysis of the proposed wheel is performed to identify the points that its center must pass while the robot overcomes a step. The center trajectory is optimized with an objective function to evaluate the mobile stability of the proposed wheel under kinematic constraints to avoid undesired interferences between the wheel and the step. Extensive simulations with different steps verify that the optimal trajectory ensures stable and effective overcoming of steps. The experiment using the prototype of mobile robot equipped with the proposed wheel verifies the smoothness of resulting trajectory of wheel center.
Development of an Arm Curl Machine with Variable Resistance Using Pneumatic Artificial Rubber Muscle

A method to improve quality of life of elderly people is to increase muscle strength. The purpose of this study is to develop a machine to improve muscle strength effectively. In this paper, we proposed a muscle strength training machine that exerts variable resistance using pneumatic artificial rubber muscle. In addition, we observed the effect on muscle activity by using the proposed machine with predictable and unpredictable load change patterns. Experimental results showed that there was a difference between the rising time of muscle activity and the maximum amplitude of electromyogram depending on predictability of the load change and showed the possibility of effective muscle strength improvement against unpredictable load using the arm curl machine.
Stochastic Path Planning for Autonomous Underwater Gliders with Safety Constraints

Autonomous underwater gliders frequently execute extensive missions with high levels of uncertainty due to limitations of sensing, control and oceanic forecasting. Glider path planning seeks an optimal path with respect to conflicting objectives, such as travel cost and safety, that must be explicitly balanced subject to these uncertainties. In this paper, we derive a set of recursive equations for state probability and expected travel cost conditional on safety, and use them to implement a new stochastic variant of FMT$^{\ast }$ in the context of two types of objective functions that allow a glider to reach a destination region with minimum cost or maximum probability of arrival given a safety threshold. We demonstrate the framework using three simulated examples that illustrate how user-prescribed safety constraints affect the results.
Big Data Governance for Building A Smart Cities

There have been many studies conducted related to Smart City, IT Governance and Big Data. In this study aims to find out how the relationship between the three and how to form a framework to explain it. The methodology used is qualitative by looking for some literature on smart city framework, IT Governance framework, and a Big Data framework. From these results, an overall picture of the relationship between the three is concluded, where Big Data has a role in IT Governance. and also the relationship of IT Governance to the realization of Smart City. And the final results of this study produce a framework to explain the relationship between Smart City, IT Governance and Big Data.
Plant Stress Classification for Smart Agriculture utilizing Convolutional Neural Network - Support Vector Machine

Plant stresses considerably increasing due to changing environmental conditions. This study aims to classify plant stresses using a hybrid convolutional neural network and support vector machine. This study used soybean leaf images with identified plant stresses in model training, testing, and validation activities. The results show that the hybrid model achieves an overall accuracy of 98.02%. This study found that the model is suitable for plant stress classification. This work contributes by providing a hybrid model that can potentially perform in a smart agriculture environment. This study presents some studies to extend their contribution.
Cooperative Network-Coded Multicast for Layered Content Delivery in D2D-Enhanced HetNets

The rapid growth in multimedia applications over cellular networks calls for multicast services. Multicast is an efficient means of delivering contents to multiple users while efficiently utilizing network resources. Layered streaming, e.g., scalable video coding (SVC) provides an excellent solution to handle channel diversities in wireless multicast. This paper presents a cooperative multicast scheme for scalable video content delivery in D2D-enabled heterogeneous cellular networks (HetNets). To extend the multicast service beyond base stations (BSs), D2D links are used to help relay content for cellular multicast. Network coding (NC), implemented through random linear network coding (RLNC) with unequal error protection (UEP) is incorporated in layered contents to enhance reliability and throughput. This paper tries to optimize the multicast scheduling procedures, aiming to assign the optimal modulation and coding schemes (MCSs) for transmissions. The constraints on cache size, backhaul capacity and channel fading are comprehensively considered. Besides, this paper also presents a interference-aware approach for D2D link selection in order to further improve the cooperative multicast. Sufficient numerical results have demonstrated the improvement brought by the proposed scheme significantly on the content delivery efficiency as well as quality of experience (QoE).
Design and Development of Compactly Folding Parallel Open-Close Gripper with Wide Stroke

A novel compact parallel gripper with wide stroke is proposed in this paper. Conventional parallel grippers with linear guide mechanisms have been widely utilized as end effectors of robots, especially in industrial fields, because of its simple mechanism and low cost nature. However, the width of the gripper is larger than the stroke due to the mechanical structure of its linear guide mechanism. When objects with various dimensions need to be grasped by a gripper, the width of the gripper is determined by the largest distance between fingers necessary for grasping all of the objects. This makes the collision avoidance of robot difficult even when handling a small object compared to the width of the gripper. The novel compact gripper proposed in this article has three features: long finger stroke with compact dimensions, fingers’ parallel motion along a single linear trajectory, and driven by a single actuator. The finger backlash and the gripping force of the proposed mechanism are analyzed, and the parallel gripper with the mechanism is designed based on the analyses. A prototype gripper with minimum width 64 mm, whose maximum fingers stroke is 121 mm, is developed, and experimental results illustrate the performance of the developed gripper.
Periodic Trajectory Planning and Robust Output Zeroing Control for Underactuated Bipedal Robots with Predicted Disturbances

For underactuated bipedal robots, it is important to compensate disturbances which affects the zero dynamics to realize a stable locomotion when we use output zeroing controllers. In order to deal with such disturbances, this paper presents a framework of a periodic trajectory planning and a robust output zeroing controller using a disturbance model learned by Gaussian process regression (GPR). In particular, we propose a control method considering the modeling uncertainty of the disturbance model by using a variance information of GPR model. We show the effectiveness of the proposed method through a numerical simulation of walking control of an underactuated robot model affected by an external force.
Crisis Communication on Twitter: A Social Network Analysis of Christchurch Terrorist Attack in 2019

Christchurch terrorist attack is a recent tragedy that happened in New Zealand on 15 March 2019. There are total 50 deaths caused by the attack in Christchurch. In two days, at least, 722,295 tweets are created by users of Twitter, they tweet about their thoughts and prayers about the attack. That means that social media is very important to spread information throughout the world while in a crisis condition, which also able to provide and facilitate how the information flow. To understand the issue, this paper examines the use of Twitter during post-tragedy, which is 2 days after the tragedy happened in New Zealand. The network structures within the conversation about Christchurch attack must be concerned, because many communities and actors play a part in the flow of information about the attack on Twitter. Our findings show an individual might have more information-spreading power than authority or government institutions. The influence of non-authority actors in Twitter network highlights an apparent reality that an individual might be spread information wider than authority without knowing the righteousness.
Research on Position Reversal Solution Model of 6-UPU Parallel Electric Platform

According to the structural characteristics of 6-UPU parallel electric platform, the research on position reversal solution model is proposed without neglecting the factors of themselves rotation of electric cylinder and ball screw. In this paper, the position reversal solution model is established by using the method of vector analysis. According to the displacement performance index value of the parallel electric platform in the actual engineering project, the whole domain motion of the various degrees of freedom is analyzed. It is concluded that the maximum displacement of the electric cylinder is 0.9913m and occurs in the working condition of the longitudinal motion. This result provides a theoretical basis for the stroke selection of the electric cylinder and has the important value of engineering application.
Toward an Efficient Hybrid Interaction Paradigm for Object Manipulation in Optical See-Through Mixed Reality

Human-computer interaction (HCI) plays an important role in the near-field mixed reality, in which the hand-based interaction is one of the most widely-used interaction modes, especially in the applications based on optical see-through head-mounted displays (OST-HMDs). In this paper, such interaction modes as gesture-based interaction (GBI) and physics-based interaction (PBI) are developed to construct a mixed reality system to evaluate the advantages and disadvantages of different interaction modes. The ultimate goal is to find an efficient hybrid paradigm for mixed reality applications based on OST-HMDs to deal with the situations that a single interaction mode cannot handle. The results of the experiment, which compares GBI and PBI, show that PBI leads to a better performance of users regarding their work efficiency in the proposed two tasks. Some statistical tests, including T-test and one-way ANOVA, have also been adopted to prove that the difference regarding the efficiency between different interaction modes is significant. Experiments for combining both interaction modes are put forward in order to seek a good experience for manipulation, which proves that the partially-overlapping style would help to improve work efficiency for manipulation tasks. The experimental results of the proposed two hand-based interaction modes and their hybrid forms can provide some practical suggestions for the development of mixed reality systems based on OST-HMDs.