Miracle-dz/UniFAR-FTUs huggingface dataset:
Printed Single Layer Transreflector for Millimeter-Wave Wireless Link Antennas

A new type of single layer transreflector structure based on microstrip reflective antenna array is described. The developed device is a single layer printed circuit board where a system of printed reflectors is arranged on front side and a polarization structure comprised by parallel metal conductors is arranged on other side. The shape and geometrical dimensions of printed reflectors, arranged along the rectangular or hexagonal (triangular) pattern, are chosen in such a way that they convert a spherical wavefront of incidence wave to a planar wavefront of reflected wave. The polarizing structure of the developed transreflector makes minimal electromagnetic energy loss for incident wave with a horizontal polarization when the wave passes thorough the structure. The characteristics modeling results, including element phase curves of the reflective lattice for different angles of the incidence wave on the studied planar structure in the W-band are given. Based on the results the sizes of the reflective elements of the transreflector, which provide for the correction of the incident wave with the necessary phase discrete, are determined and an electrodynamic model of the transreflector antenna was built. The simulation of the main radiation characteristics of the antenna with the developed single layer transreflector was carried out.
MaplDroid: Malicious Android Application Detection based on Naive Bayes using Multiple

Android is currently the most popular operating system for mobile devices in the market. Android device is being used by every other person for everyday life activities and it has become a centre for storing personal information. Because of these reasons it attracts many hackers, who develop malicious software for attacking the platform; thus a technique that can effectively prevent the system from malware attacks is required. In this paper, an malware detection technique, MaplDroid has been proposed for detecting malware applications on Android platform. The proposed technique statically analyses the application files using features which are extracted from the manifest file. A supervised learning model based on Naive Bayes is used to classify the application as benign or malicious. MaplDroid achieved Recall score 99.12% and F1 score 83.45%.
Neural Control with an Artificial Hormone System for Energy-Efficient Compliant Terrain Locomotion and Adaptation of Walking Robots

In order to use walking robots for exploration in a real complex environment, an adaptive control system is required to allow them to successfully and efficiently traverse the terrains. To achieve this, we propose here our adaptive locomotion control technique of a walking robot. It is based on a modular structure, combining neural control with an artificial hormone system. The neural control coordinates all leg joints of the robot and generates its locomotion with various insect-like gaits. In parallel, the artificial hormone system uses the motor commands from the neural control and foot contact feedback to estimate the walking state and automatically adapt the joint movements with respect to the terrain. The adaptability is quickly achieved in an online manner within a few seconds. Robot walking experiments show that this adaptive control technique enables a six-legged robot to adapt to various difficult terrains with energy efficiency. Such terrains include sand (loose ground), sponge with different softness levels (soft/compliant ground), grass (vegetated ground), and floor/pavement (hard ground). The technique does not require robot kinematics or an environmental model; and can, therefore, be potentially applied to different legged robots to achieve stable online adaptation and energy-efficient locomotion on unpredictable (compliant) terrains.
A QoE-Based Alarm Model for Terminal Video Quality

With the advent of the 5G era, quality of experience (QoE) has become one of the most important indicators to measure mobile video quality. If QoE degradation events can be accurately predicted, it will be beneficial for operators to provide better services. However, many factors could affect QoE, including users’ subjective factors, network and users’ terminal parameters. This paper proposes a model for QoE degradation alarm in the mobile video stream. Specifically, a large-scale dataset is collected in the practical environment by a self-developed application. The dataset is cleaned to reduce the influence of redundancy and outliers firstly. Then, a QoE degradation alarm model is proposed based on data-driven approaches. The random forest is chosen as the core of this model which integrates the advantages of several classification tree and can deal with the high-dimensional data. In order to have a better prediction performance, Synthetic Minority Oversampling Technique (SMOTE) is used for solving the imbalance problem of the train set. As is shown by the experimental results, this alarm model can strike a balance between precision and false alarm rate and performance outperforms the state-of-the-art methods. This result means the proposed model can save the operational cost while increasing users’ satisfaction with the mobile video quality.
Planar Dual-Band ISM Antenna for Wireless Sensors

A planar dual-band circular antenna for a chemical wireless sensor network is described in this paper. Four antennas compose the wireless sensor package and one antenna is selected at a given time using an SP4T switch. This work describes the unit antenna used to transmit sensor information to a nearby hub that operates in the ISM bands. The antenna is composed of concentric shorted annular rings using substrate integrated waveguides and includes a circular patch at its center. The antenna presents two types of radiation patterns, one omnidirectional in the azimuth plane at 2.4 GHz, and one unidirectional in the elevation plane at 5.8 GHz. The simulated gains obtained at 2.4 GHz and 5.8 GHz are 2 dB and 7 dB, respectively.
CoMon-DAS: A Framework for Efficient and Robust Dynamic Adaptive Streaming over NDN

Implementing DASH, the most popular method for multimedia streaming, over NDN, a potential future Internet architecture, can substantially increase the network bandwidth utilization. However, inherent features of NDN can create new security risks for adaptive multimedia streaming. We propose a novel attack called Bitrate Oscillation Attack (BOA), which adversely exploits NDN’s autonomous on-path caching and interest aggregation to unsettle DASH functionality. BOA forces the resolution and quality of video received by the attacked client to oscillate with high frequency and amplitude. Subsequently, we present CoMon-DAS, a framework for lightweight coordination that mitigates BOA and other attacks in NDN. Through extensive simulations, we demonstrate that BOA is very harmful for DAS over NDN, but can be significantly mitigated by CoMon-DAS.
6-Axis Hybrid Sensing and Estimation of Tip Forces/Torques on a Hyper-Redundant Robotic Surgical Instrument

In this paper a hybrid method for estimation of 6 degree-of-freedom (DOF) laparoscopic instrument tip force/torques in robotic-assisted minimally invasive surgery systems is proposed. The method is implemented on an in-house developed hyper-redundant (11-DOF) surgical robotic forceps prototype. This surgical robot is composed of two modules with a 7-DOF Kuka IIWA manipulator for performing 4-DOF Remote Centre of Motion (RCM) about the trocar and an articulated 4-DOF parallel wrist/gripper mechanism attached to the distal end of the Kuka robot for intra-corporeal manipulation. The hybrid sensor-based/sensorless method fuses torque estimates from the parallel wrist mechanism with measurements from a force sensor attached between the wrist base and the Kuka tool frame. With this approach it is possible to obtain force/torque estimates on all Cartesian axes (x, y, z, yaw, pitch, roll) of the forceps tip. The prototype is one of the first systems designed for robotic surgery that can achieve accurate force estimation on all 6 axes of the instrument tip. Experiment validation results with a force sensor verify the efficacy of the proposed method.
ML-Based Block Sparse Recovery for distributed MIMO Radars in Clutter Environments

In distributed MIMO radars, block sparse recovery methods can be used to estimate the multiple targets parameters while the sampling rate at each receiver is reduced by exploiting Compressive Sensing (CS). However, the performances of these methods severely degrade in strong clutter environments. In this paper, we propose a Maximum Likelihood (ML) based block sparse recovery scheme called ML-BSR for target localization in these environments. The ML optimization requires a very high computational complexity particularly for the localization of an unknown number of targets. However, the complexity of the proposed scheme is acceptable. The proposed scheme has an accurate performance in clutter environments even with reduced the sampling rate at the receivers.
Towards Ergonomic Control of Collaborative Effort in Multi-human Mobile-robot Teams

In this paper, we propose a control framework for a multi-human and mobile-robot collaborative team, that takes into account the co-workers’ ergonomic requirements as well as the demand for high flexibility in the manufacturing industries. The new MObile Collaborative robotic Assistant (MOCA), which is composed of a lightweight manipulator arm, an underactuated hand, and a mobile platform driven by four omni-directional wheels enabling mobility in the workspace, is able to accomplish multiple tasks in a wide area with a high level of adaptability. In addition, an ergonomics module to anticipate and mitigate the human risk factors by means of a multi-object optimisation is integrated into the framework to ensure human safety and improvement of working conditions. The main advantage of this approach is that MOCA can assist multiple human operators, reducing their physical risks, with fast-adaptive capacities due to agile mobility and advanced interaction and manipulation. We validated the proposed method with an experiment simulating a simple manufacturing line which involves two subjects and the MOCA. The results demonstrate that the proposed framework is able to address multi-workers’ ergonomics with a high level of flexibility in the workplace.
DeepControl: Energy-Efficient Control of a Quadrotor using a Deep Neural Network

Synthesis of a feedback controller for nonlinear dynamical systems like a quadrotor requires to deal with the trade-off between performance and online computation requirement of the controller. Model predictive controllers (MPC) provide excellent control performance, but at the cost of high online computation. In this paper, we present our experience in approximating the behavior of an MPC for a quadrotor with a feed-forward neural network. To facilitate the collection of training data, we create a faithful model of the quadrotor and use Gazebo simulator to collect sufficient training data. The deep neural network (DNN) controller learned from the training data has been tested on various trajectories to compare its performance with a model-predictive controller. Our experimental results show that our DNN controller can provide almost similar trajectory tracking performance at a lower control computation cost, which helps in increasing the flight time of the quadrotor. Moreover, the hardware requirement for our DNN controller is significantly less than that for the MPC controller. Thus, the use of DNN based controller also helps in reducing the overall price of a quadrotor.
Social-aware Mobile Road Side Unit for Content Distribution in Vehicular Social Networks

Network operators are more than ever overwhelmed by increased capital investments and operational costs incurred due to the explosive mobile data traffic growth. An efficient strategy in managing operational expenditure within limits without sacrificing end-user satisfaction is to offload the traffic of content dissemination from the network backbone to local road-side units (RSUs). Allocating suitable subsets of contents to each RSU cache so as to maximize the hit ratio of requests made by vehicular entities is an optimization problem of paramount value. In this paper, we address the issue of content dissemination in a socially-aware hybrid environment with mobile end-users, wherein an overlay network comprising of a social network over a cellular vehicular network is considered. In this setting, vehicles represent the mobile nodes that obtain requested contents either from base station or from local RSUs, if available. By employing a mobile RSU that accounts for the social characteristics of the underlying network, we introduce a novel approach for content distribution in an urban environment. Results from simulation experiments reveal an average improvement of 6% in network throughput for the proposed method as compared to conventional content dissemination counterparts.
Visual-Inertial Odometry Tightly Coupled with Wheel Encoder Adopting Robust Initialization and Online Extrinsic Calibration

Combining camera, IMU and wheel encoder is a wise choice for car positioning because of the low cost and complementarity of the sensors. We propose a novel extended visual-inertial odometry algorithm tightly fusing data from the above three sensors. Firstly we propose an IMU-odometer pre-integration approach utilizing complete IMU measurements and wheel encoder readings, to make scale estimation more accurate in subsequent 4-degrees of freedom (DoF) optimization. Secondly we develop an original initialization module where encoder readings are fully utilized to refine gravity direction and provide an initial value for camera pose in real scale. Thirdly, we design a computationally efficient online extrinsic calibration method by fixing the linearization point for the rotational component of IMU-odometer extrinsic parameters, which is deployed depending on the convergence of accelerometer bias. Experimental results prove the robustness of our initialization module and the accuracy of the whole trajectory, as well as the improvement brought about by online extrinsic calibration. Our program can also run on an Nvidia Jetson TX2 module in real time.
Introducing a Scalable and Modular Control Framework for Low-cost Monocular Robots in Hazardous Environments

Robotics for hazardous environments is currently an important area of research, with the ambition of reducing human risk in potentially devastating situations. Here, we are presenting a Modular Control Framework (MCF) for a low-cost robot with limited sensory resources to address this issue. As a proof of concept, we emulate 3 scenarios – (1) adaptive planning for obstruction avoidance (road block), (2) object identification and support-case-based behaviour adjustment (search and rescue) and (3) autonomous navigation through the environment with reporting of structural status (patrol and monitoring). These were implemented and validated using a Cozmo robot in a small-scale Lego environment. We found that our system can reroute in 90%, can help an injured person 80% and report about failing equipment in 80% of all tested cases, where most of the fails were caused by the object detection used. Our MCF is implemented using ROS, making it easy to use and adjust for other robotic platforms.
ESKO6d - A Binocular and RGB-D Dataset of Stored Kitchen Objects with 6d Poses*

We present a new dataset with the goal of advancing the state-of-the-art in object pose estimation especially for stored porcelain and glass crockery in kitchen scenes. Specifically the ESKO 6d (EASE Stored Kitchen Objects with 6d poses) dataset features texture-less, glossy or glassy ordinary used objects which were naturally stored in a cupboard, drawer or dishwasher. There is a large degree of occlusion being the specific challenge in these scenes. Each scene was recorded in video sequences by two cameras (RGB-D (Kinect) and binocular) within multiple setup stages. The dataset contains an RGB-D image or binocular RGB image plus stereo-matched depth image as well as 6d pose ground truth and instance segmentation. Our dataset contains twelve stored object scenes with a combined amount of 47 video sequences captured by each camera, resulting in over 17k annotated Kinect images and more than 42k annotated stereo images showing around 50 different objects. The ground truth annotation is precise to 3. 5mm ADD (details see paper). The dataset can be accessed under http://www.informatik. uni-bremen.de/esko6d-dataset. Besides the concrete dataset we propose a method of ground truth pose measurement based on an external 3d tracking system that allows on the one hand to precisely measure the object’s pose inside a tight packed storage and on the other hand to obtain the object pose in several images with just one manual measurement.
Local Pose optimization with an Attention-based Neural Network

In this paper, we propose a novel pose optimizer which can be inserted into either supervised or unsupervised end-to-end visual odometry for the purpose of local pose optimization. The pose optimizer is an analogue of the pose graph optimization used in traditional VSLAM algorithms. Local pose optimization is performed by an attention-based neural network which iteratively refines the predicted pose estimates of an image snippet. Instead of complicated graph convolutional network, the attention mechanism based on geometric consistency of trajectory constraint is utilized because pose features whose spatial distribution is not important can be flattened to vectors and then processed. The pose optimizer is aimed at improving pose estimation accuracy by redistributing errors of pose estimates. Quantitative and qualitative evaluation of the proposed approach on the KITTI Odometry dataset [1] is presented to demonstrate its effectiveness in improving pose estimation accuracy and minimizing pose drift.
Sample Efficient Interactive End-to-End Deep Learning for Self-Driving Cars with Selective Multi-Class Safe Dataset Aggregation

The objective of this paper is to develop a sample efficient end-to-end deep learning method for self-driving cars, where we attempt to increase the value of the information extracted from samples, through careful analysis obtained from each call to expert driver’s policy. End-to-end imitation learning is a popular method for computing self-driving car policies. The standard approach relies on collecting pairs of inputs (camera images) and outputs (steering angle, etc.) from an expert policy and fitting a deep neural network to this data to learn the driving policy. Although this approach had some successful demonstrations in the past, learning a good policy might require a lot of samples from the expert driver, which might be resource-consuming. In this work, we develop a novel framework based on the Safe Dataset Aggregation (safe DAgger) approach, where the current learned policy is automatically segmented into different trajectory classes, and the algorithm identifies trajectory segments/classes with the weak performance at each step. Once the trajectory segments with weak performance identified, the sampling algorithm focuses on calling the expert policy only on these segments, which improves the convergence rate. The presented simulation results show that the proposed approach can yield significantly better performance compared to the standard Safe DAgger algorithm while using the same amount of samples from the expert.
On Model-based Adhesion Control of a Vortex Climbing Robot

In this article, the adhesion modeling and control case of a Vortex Climbing Robot (VCR) is investigated against a surface of variable orientations. The critical adhesion force exerted from the implemented Vortex Actuator (VA) and the VCR’s achievable payload are analyzed under 3-DOF rotations of the test surface, while extracted from both geometrical analysis and dynamically-simulated numerical results. A model-based control scheme is later proposed, with the goal of achieving adhesion while the VCR remains immobilized, limiting the power consumption and compensating for disturbances (e.g. moving cables) leading to Center-of-Mass (CoM) changes. Finally, the model-based control scheme is experimentally evaluated, with the VCR prototype on a rotating and moving flat surface. The presented results support the use of the proposed methodology in climbing robots targeting inspection and maintenance of stationary surfaces (flat, curved etc.), as well as future robotic solutions operating on moving structures (e.g. ships, cranes, folding bridges).
Modelling of Uniaxial EGaIn-Based Strain Sensors for Proprioceptive Sensing of Soft Robots

Soft strain resistive sensors based on eutectic gallium-indium liquid metal can play an important role in proprioceptive sensing for soft robots. However, there are no available mathematical models to accurately estimate the strain as a function of the measured resistance. Furthermore, non-uniform strain in the microchannels has not been analysed yet. In this paper, we introduce a new model to estimate the strain or elongation in sub-millimetre scale, and analyse its accuracy through a customised testing set-up and procedure. The effect of strain rate on the measurement accuracy is also studied. We compare existing theoretical models with our experimental results, and discuss the differences between them. Moreover, we analyse the effect of strain rate on hysteresis caused by the viscoelastic behaviour and introduce a new model for it to be potentially used for future work. This paper demonstrates, among other things, that rational models could provide high accuracy in strain estimation, and might help to enhance proprioceptive sensing and state control of soft robots.
DDDT_A_231958 309..329

Hassan Dana* Ghanbar Mahmoodi Chalbatani* Elahe Gharagouzloo Seyed Rouhollah Miri Fereidoon Memari Reza Rasoolzadeh Mohammad Reza Zinatizadeh Peyman Kheirandish Zarandi Vahid Marmari 1Cancer Research Center, Cancer Institute of Iran, Tehran University of Medical Science, Tehran, Iran; 2Department of Biology, Damghan Branch, Islamic Azad University, Damghan, Iran; 3Department of Biology, Science and Research Branch, Islamic Azad University, Tehran, Iran
CORROSION INHIBITION PERFORMANCE OF PINK, ORANGE AND WHITE COLOURED BOUGAINVILLEA GLABRA BRACT EXTRACT ON MILD STEEL IN 1N HCl

Gunavathy, N. * and M. Sangeetha Department of Chemistry, Nirmala College for Women, Coimbatore. *Email: gunavathykrishnaprakash@yahoo.co.in The present study was undertaken to find out the phytochemicals present in Bougainvillea glabra bracts extract and to compare the corrosion inhibition efficiency between B.glabra Pink, Orange and White coloured bract extract on mild steel in 1N HCl. Phyto chemical analysis showed the presence of alkaloids, flavonoids, phenolic compounds and tannins in the extract as confirmed by implying different qualitative tests specified for these phytochemical. Corrosion rate, inhibition efficiency, FTIR analysis was determined. The corrosion of mild steel in 1N HCl acid media was significantly reduced upon the additions of BG bract extracts. The inhibition efficiency increased with the increasing concentration of the inhibitor. Maximum inhibition efficiency was observed at an optimum concentration of 2 % v/v.