Miracle-dz/UniFAR-FTUs huggingface dataset:
Waste Foundry Sand/Bauxite Residue for Enhanced NOx Reduction in Diesel Exhaust Pretreated With Plasma/O3 Injection

The increased usage of diesel fuel of late has led the researchers to pay more attention toward abatement of increased emission of oxides of nitrogen (NOx) in the engine exhaust. The focus is, therefore, lies in providing a technique that is economical, feasible, and long lasting. The combination of electric discharge plasma with adsorbent/catalyst derived out of solid waste can be one such possible technique. In this paper, waste foundry sand from foundry industry and red mud or bauxite residue from aluminum industry have been used. Diesel engine exhaust under dry condition was exposed to an oxidizing discharge plasma environment. The plasma-treated exhaust was subjected to either adsorption over waste foundry sand or catalysis over red mud. Studies were carried out in the laboratory environment with 20% loading of diesel generator. The oxidizing plasma environment was either with direct plasma or with indirect plasma in which case ozone injection into the exhaust was accomplished. The ozone-adsorbent cascading system has resulted in about 87% NOx reduction in the dry exhaust case. The effect of O3:NO ratio was discussed from the point of NO/NO2.
Finding Research Areas of Academicians using Clique Percolation

Identifying research areas of academicians is a challenging task. Most of the researchers have used supervised machine learning to handle this task which requires labelled data. In this paper, we propose to use clique percolation (an overlapping community detection algorithm) to identify research areas of academicians. We propose a framework to collect data from digital libraries and websites of selected universities in Pakistan. We then identify researchers working in multiple research areas using clique percolation and present results of our analyses.
Detection of Plagiarism in Urdu Text Documents

Plagiarism, intellectual theft, and copyright violation are the most important problems for researchers and academic organizations such as universities. The famous publicly available Plagiarism Detection (PD) tools are Turnitin, APlagramme, Plagscan, and Aplag and these tools use to overcome plagiarism problems. However, these tools mainly work for English, Persian and Arabic languages. Copyright and intellectual document have written in every language of the world and many South Asian countries including Pakistan and India, a huge amount of academic content is available in the Urdu language. Unfortunately, due to resources scarcity and less concentration of researcher There is no enough work has been done in Urdu PD. Capturing of plagiarism in Urdu is presented in this paper. Most existing Urdu PD systems fail to identify paraphrase plagiarism in comparison between suspicious and source text document. However, the proposed system is able to identify different types of plagiarism like sentence reordering, inert/delete inter-textual similarity and near copy similarity. The proposed system is based on a distance measuring method, structural alignment algorithm, and vector space model. The system performance is evaluated using machine learning classifiers i.e. Support Vector Machine and Naïve Bayes. The experimental results demonstrated that performance of the proposed method is improved as compared to other existing model i.e. cosine method, simple Jaccard measure.
A Frequency Selective Surface Based Reconfigurable Rasorber With Switchable Transmission/Reflection Band

In this letter, a reconfigurable rasorber has been designed based on switching activity of frequency selective surface (FSS) and resistive FSS. The proposed design consists of two periodic layers separated from each other by an air spacer. The upper layer comprising lumped resistors assists wide absorption band (above 90% absorption) from 3.8 to 10.3 GHz (entire C and partial X Band), whereas PIN diodes are mounted in the bottom layer to enable the switching operation of the rasorber at S-Band. The novelty of the proposed design lies in its reconfigurable transmission/reflection band, which switches from a bandpass response (at around 2.88 GHz) to perfect reflection, with the control of the biasing states of the diode. A sample prototype is also fabricated and experimentally verified. Wide absorption band along with switchable transmission/reflection band characteristics have made the proposed rasorber an excellent choice for various radar cross section reduction applications.
Dynamic Modelling and Motion Planning for the Nonprehensile Manipulation and Locomotion Tasks of the Quadruped Rsbot*This work is supported by the project of Robotics Innovation Based on Advanced Materials under Ritsumeikan Global Innovation Research Organization (R-GIRO)

This paper presents the dynamic modelling and motion planning method for a quadruped robot that uses its legs for nonprehensile manipulation as well as locomotion. Three different working modes named Drive Mode, Inchworm Mode and Scoot Mode are proposed to enable the robot to move forward together with the object. We firstly introduce a universal model for these modes and deduce its dynamic equation. Then the contact force constraints are combined and mapped to the system state variables. Based on the acquired state acceleration constraints, the motion planning problem can be solved by designing system state paths in the phase space. After that, we described the mathematical problems within the three working modes and generate the robot motions accordingly. Finally, experimental results obtained through simulations and physical tests are reported to demonstrate the effectiveness of our method.
The Propagation Model of Modulated Subterahertz Signals in the RoF Communication Systems

Radio-over-Fiber system scheme is described in the paper, the radio domain of this system is performed in the range 75–110 GHz (W-band). Optical signal with given orbital angular momentum (OAM signal) generation method in RoF fiber optic domain is suggested. The method is based on applying two independent lasers, performing at close wavelengths and the device for spatial phase modulation. For simulation and system parameters calculating, mathematical model of OAM signal envelope is suggested based on simplified wave equations system. Computer simulation of these signals envelope during its transmission was performed. It is shown that distortion of transmitted signals arises because of wavelengths difference. Recommendations were developed for constructing fiber-optic components of the system (including a radio-optical converter) designed to minimize this distortion.
Weighted Hybrid Admittance-Impedance Control with Human Intention Based Stiffness Estimation for Human-Robot Interaction

In a human-robot interaction (HRI)device that performs physical collaboration operations in constant contact with the user, admittance control and impedance control are generally used. Since the two controllers exhibit opposite performances depending on the stiffness condition, controllers capable of dealing with various magnitudes of stiffness are required. As such, this study proposes hybrid control that adjusts the control distribution ratios of admittance control and impedance control based on the operating frequency analysis to react to the user intention and various stiffness conditions in real time. The proposed controller algorithm exhibited lower overshoot than impedance control in the step input response simulation, faster response speed compared to admittance control in the response simulation for 0–5 Hz input frequencies, and the smallest vibration magnitude and number of vibrations in the case of a virtual wall collision, resulting in improved performance compared to existing control methods.
Design and Implementation of a Monitoring System for Decision Support in a Micro-Business Based on Solar Energy Microgrid in Rural Colombia

Rural electrification schemes are necessary for social and economic development. In several countries of Africa, Asia and Latin America this issue is fundamental and some governments have adopted new policies to increase service coverage, a case that has been studied with interest is Colombia. In 2014, the Government presented the Law 1715 for Regulating the Integration of Non Conventional Renewable Energies into the National Energy System. This Law aims to increase penetration of renewable energy in the country; and in 2016 the Peace Treaty between the Colombian Government and the Fuerzas Armadas Revolucionarias de Colombia (FARC-EP) was signed. These two events have paved the way towards a new country in which rural development based on technological advances and renewable energy becomes trend. This paper presents a case study that illustrates how technology has transformed the life of one community in the rural area in Colombia by triggering economic development. The technological proposal is the design of a monitoring system for a refrigeration center. The processes of design, implementation and validation with the rural community are presented.
Minimization of the High-Level Fault Model for Microprocessor Control Parts

The paper presents a method for representing the instruction set truth tables of microprocessors with High-Level Decision Diagrams (HLDD). A behavior level fault model is defined for the microprocessor control parts on the basis of instruction level truth tables (TT). Two methods are proposed for creating HLDDs from TTs with minimization of the edges on graphs: greedy algorithm, and branch and bound algorithm (B&B). A simple and fast computable lower bound is proposed to be used for pruning the search space of the B&B algorithm. Experimental data of using the fault model for several microprocessors and comparison data are provided to show the efficiency of the proposed high-level fault model over the gate-level Stuck-at-Fault (SAF) model.
Performance and Stability Analysis of Hydraulically Driven Synchronous Generators in Power Engineering

Nowadays, hydraulic drive trains play an important role in different industries. They are employed in power engineering as drive trains in some types of synchronous generation units. This research investigates an experimental setup based on a variable speed hydraulic drive train that rapidly accelerates a synchronous generator for frequency support in power grids. The prototype is composed of a 100kW hydraulic motor, a synchronous generator and a current driven proportional throttle valve for flow control. To safely perform the experimental testing of the designed electro-hydraulic system, a feasible mathematical model must be developed. This requires the full characterisation of the hydraulic drive train. The research presented here shows a simple, yet efficient data acquisition system for the experimental validation of electro-hydraulic systems. Followed by the design of an accurate mathematical model of the complicated hydraulic system.
Scale Correct Monocular Visual Odometry Using a LiDAR Altimeter

The inherent scale ambiguity in monocular vision is a well known issue that forces the integration of other sensory sources to obtain metric references. However, 2D or 3D LiDARs and RGB-D sensors, while guaranteeing metrological accuracy, impose a non negligible burden both in terms of computational load and power requirements limiting the feasibility of being implemented on small exploration vehicles. This paper presents a scale aware monocular Visual Odometry framework that fuses range data from a laser altimeter in order to recover and maintain a correct metric scale. The proposed Visual Odometry method consists of a keyframe based tracking and mapping algorithm using optical flow where range data serves as a scale constraint on a keyframe to keyframe basis. An optimization backend based on iSAM2 is employed in order to refine the trajectory and map estimates eliminating the scale drift without the need of performing loop closures. We demonstrate that our algorithm can obtain very similar performances to state of the art stereo visual SLAM and RGB-D methods.
Incremental Skill Learning of Stable Dynamical Systems

Efficient skill acquisition, representation, and online adaptation to different scenarios has become of fundamental importance for assistive robotic applications. In the past decade, dynamical systems (DS) have arisen as a flexible and robust tool to represent learned skills and to generate motion trajectories. This work presents a novel approach to incrementally modify the dynamics of a generic autonomous DS when new demonstrations of a task are provided. A control input is learned from demonstrations to modify the trajectory of the system while preserving the stability properties of the reshaped DS. Learning is performed incrementally through Gaussian process regression, increasing the robot's knowledge of the skill every time a new demonstration is provided. The effectiveness of the proposed approach is demonstrated with experiments on a publicly available dataset of complex motions.
A Novel Joint Torque Estimation Method and Sensory System for Assistive Lower Limb Exoskeletons

This work presents a novel method for estimating online the torques at the ankle, knee and hip of a user with the goal of generating reference signals for torque controlled lower limb exoskeletons. In particular, this approach attempts to address difficulties arising in real life scenarios when noncyclic locomotion activity, unexpected terrain or unpredicted interactions with the surroundings occur. An advantage of the proposed method is that it does not require any information on the user's upper body (i.e. pose, weight and center of mass location)or on any interaction of the user's upper body with the environment (i.e. payload handling or pushing and pulling task). By monitoring the interaction of the user's feet with the ground through a novel sensorized shoe sensing system, the method applies an inverse static analysis on the user's lower limbs to estimate in real time the torque at each leg joint. The system is fully wearable, ergonomic and portable and uses a reduced number of body posture sensors. The design of the sensorized shoes permits plantar flexion, while measuring the toe and heel orientation and the interaction loads. This allows walking on irregular terrains and natural feet postures in different tasks. Trials were performed to validate the proposed approach under different tasks and terrains. Finally, the knee torques estimated online by the proposed strategy were used as reference signals to drive the iT-Knee Bipedal System in an assistive task.
Urban Area PM2.5 Prediction with Machine Methods: An On-Board Monitoring System

Recently, particulate matter 2.5 (PM2.5) has drawn more and more attention due to the pursuit of life quality. In Taiwan, PM2.5 concentration data mostly come from the limited static stations, and their locations are far from streets where people walk or drive by. This might cause the underestimation of PM2.5 concentration. In this paper, an on-board monitoring system is established to record the PM2.5 concentration in the surrounding areas where people live. The recorded PM2.5 concentration data are more accurate than the data from the static stations. In order to get the future PM2.5 concentration trend, the prediction model is established in this study. A long short-term memory (LSTM) and a gated recurrent unit (GRU) are used as the core of the prediction model due to their ability to analyze time series data such as the PM2.5 concentration data. And the research results show that the root mean square error (RMSE) of the prediction model using LSTM and GRU is 3.57 and 3.67 in the testing set. The prediction results can provide important air pollutant information to the public and can be used to make better air pollution control policies.
Multi-Stage Learning of Selective Dual-Arm Grasping Based on Obtaining and Pruning Grasping Points Through the Robot Experience in the Real World

Recently, self-supervised approach is common for robot grasping. Although this approach improves success rate, it requires a long time to execute a number of grasp trials, and single-arm grasping is only considered. However, robots can grasp more various objects with two arms, and dual-arm robots such as humanoid robots are expected to execute dual-arm manipulation and overcome the single-arm limitation. In this paper, we introduce dual-arm grasping as another possible strategy and propose a multi-stage learning method for selective dual-arm grasping using Convolutional Neural Networks (CNN)for grasping point prediction and semantic segmentation. In the first stage, the network learns grasping points with the automatic annotation. Although a robot learns both single-arm and dual-arm grasping efficiently with the annotation, the robot may not be able to grasp it because the annotation algorithm is designed by human. Therefore, for the second stage, the robot samples various grasping points with both grasping strategies and learns how to grasp in the real world. In this stage, the robot obtains new possible grasping points and prunes unsuccessful ones for both grasping strategies through the robot experience. In the experiments in the real world, the adapted network achieved high success rate 76.7% in 90 trials. Since the network trained with no adaptation stage resulted in lower success rate 56.7%, this result also shows the network was refined with less than 250 times of grasp sampling. As an application of our method, we demonstrated that our system worked well in warehouse picking task.
An Actuator Design Criterion to Maximize Physical Balance Recovery

This paper first presents a formula to predict the largest balance disturbance from which a legged robot can recover without taking a step. It then presents an actuator design criterion derived from this formula that maximizes the robot's ability to recover. In this study, it is assumed that the robot is using a single major joint (e.g, a hip joint) to perform its balance recovery movement, and that the actuator consists of an electric motor and reduction gear. It is also assumed that the robot's support polygon is sufficiently small that it can be approximated as a point, and that the balance recovery motion is essentially planar, so that a 2-D analysis remains valid in 3-D. Finally, it is assumed that, for the purpose of studying balance recovery motion, the robot can be approximated by a reaction wheel pendulum. The theory has been tested experimentally on a robot designed to be good at balancing, and was found to agree closely with experimental results.
Rule-based Control Strategy for a River-based Grid-connected Hydrokinetic System

This paper presents an optimal control algorithm for improving the reliability of a river-based grid-interactive hydrokinetic system. The aim is to guarantee a reliable system operation during load demand uncertainties. The proposed system consists of a micro-hydrokinetic pumped-hydro storage (MHK-PHS) system, modelled to supply electricity to a commercial load profile. The open-loop optimization-based approach resulted in an unmet load demand and excessive energy supply, due to load demand uncertainty. Hence, a rule-based control algorithm is presented to ensure that the load demand is reliably met at all times. Thus, the results prove that the developed rule-based control algorithm, allowed the commercial load demand to be reliably met by solving both the unmet load demand and excessive energy supply challenges.
Distributed Reinforcement Learning Framework for Resource Allocation in Disaster Response

Making decisions during a disaster can be challenging when human lives and infrastructures are exposed. An important factor to consider when allocating resources, in these situations, is the critical infrastructures' interdependencies. i2Sim, the Infrastructures' Interdependencies Simulator, is a tool build for that purpose. As a layered architecture, i2Sim includes a dedicated decision-making layer. The use of Reinforcement Learning (RL), a machine learning approach based on an agent learning from experience, has been successfully tested with some dimensionality constraints. This paper introduces two improvements to our previous tests aiming at increasing speed and allowing larger dimensionality problems. The first addition is an improved reward scheme for speeding up convergence while guaranteeing it. The correct application of shaping rewards requires a deep understanding of the problem and extensive convergence tests. The second improvement added is the implementation of a scheduler programmed to trigger multiple instances of the same model using different parameters. This scheduler partitions the state space, enabling the agent's training to be done in parallel via a distributed RL algorithm. With this idea, the state/action matrix representing knowledge is partitioned for training, assigned to computing nodes, populated with knowledge (trained) and collected/reconstructed for use. This work has tested on an IBM cluster with 24 computing nodes. The test model is an aggregated model of the City of Vancouver configured for a disaster with numerous consequences over the different critical infrastructures. Based on the model's configuration, 24 scenarios were identified, created and solved simultaneously. The scheduler automates the training by setting up model and learning parameters, looping execution of instances and gathering results from all nodes. The results verify a proof of concept and enable applicability to new models with highly increased dimensionalities.
BigDataNetSim: A Simulator for Data and Process Placement in Large Big Data Platforms

Big Data platforms are convoluted distributed systems which commonly comprise skill- and labour-intensive solution development to treat inherent Big Data application challenges. Several tools have been proposed to help developers and engineers to overcome the involved complexities in coordinating the execution of plenty processes/threads on multiple machines. However, no work so far has been able to combine both an accurate representation of Big Data jobs and realistic modeling of the behaviour of Big Data platforms at scale, including networking elements and data and job placement. In this paper, we propose BigDataNetSim, the first simulator which models accurately all the main components of the data movements in Big Data platforms (e.g., HDFS, YARN/MapReduce, network topologies, switching/routing protocols) in a large scale system. BigDataNetSim can serve as a valuable tool for engineering Big Data solutions, which includes set-up of systems, prototyping of jobs, and improvement of components/algorithms for Big Data platforms. We also demonstrate that BigDataNetSim can simulate a real Hadoop cluster with a high degree of accuracy in terms of data and job placements, being able to scale up to very large systems.
Multiple Access With Polarity Division Sparse Code for Visible Light Communication

The increasing demand of mobile internet and Internet of Things poses challenging requirements for indoor wireless communication. Visible light communication (VLC) is a promising technology to alleviate the growing traffic problem. In addition, nonlinear distortion of light emitting diodes (LEDs) results in a poor link quality. In this paper, we propose multiple access with polarity division sparse code (PDSC) for VLC, which is based on nonorthogonal multiple access and spatial characteristic of LEDs. The experimental results indicate that the PDSC significantly enhances capacity and spectral efficiency of VLC network compared with orthogonal frequency division multiple access. We also demonstrate that the PDSC can optimize the problem of nonlinear distortion with polarity division. The experimental results reveal that PDSC is a promising multiple access scheme for the indoor VLC networks.