Coordinated Robotic System for Civil Structural Health Monitoring

With the recent advances in sensors, robotics, unmanned aerial vehicles, communication, and information technologies, it is now feasible to move towards the vision of ubiquitous cities, where virtually everything throughout the city is linked to an information system through technologies such as wireless networking and radio-frequency identification (RFID) tags, to provide systematic and more efficient management of urban systems, including civil and mechanical infrastructure monitoring, to achieve the goal of resilient and sustainable societies. In the proposed system, unmanned aerial vehicle (UAVs) is used to ascertain the coarse defect signature using panoramic imaging. This involves image stitching and registration so that a complete view of the surface is seen with reference to a common reference or origin point. Thereafter, crack verification and localization has been done using the magnetic flux leakage (MFL) approach which has been performed with the help of a coordinated robotic system. In which the first modular robot (FMR) is placed at the top of the structure whereas the second modular robot (SMR) is equipped with the designed MFL sensory system. With the initial findings, the proposed system identifies and localize the crack in the given structure. Research Methodology: The proposed approach used the advantages of the visual and MFL inspection approach to improve the efficiency of the SHM. Therefore, the usage of both approaches should be done in a way that the whole inspection is carried out in an optimal time period. Thus, due to the fast processing of visual inspection, it is done first followed by an MFL based verification approach. The visual inspection has been carried out such that the drone will take-off from a fixed point and take images at different heights without changing the GPS coordinate values of start point during flight. After completing the first scan, the coordinates of the GPS will be shifted and same procedure of taking images at different heights will be conducted. The process remain continue until the drone reaches to the starting GPS coordinates. The images which were taken at different heights for particular coordinates are considered as a single set. Thereafter, the image stitching (IS) is applied on individual sets. The process of IS involves a series of steps which were applied on the consecutive images of a particular set, such that one of the image is taken as a reference image (RI) whereas the other one is termed as the current image (CI). The resultant stitched image will be RI for the next consecutive image and then the whole stitching process is applied. The process remain continue for each set until a final stitched image has been obtained from them. The stitched result will be saved in the database with its corresponding GPS values. The same procedure of taking and stitching the images of the same structure will be repeated again after few months, depending upon the structural sensitivity as well as the severity of the weather condition around it. The current results will be compared with the stitched images present in the data base and if some anomaly is detected then the HP coordinates (i.e. the GPS coordinates) along with the estimated height for that particular location will be sent to the FMR to proceed the crack verification using MFL. The GPS module present in the FMR will guide the robot about its own location. As soon as Arduino Mega2560 Microcontroller receives the GPS coordinates from the system. It will translate them and compare them with its current location. The need of translation is because the FMR is present at the top of the building whereas the drone is flying at particular distance from the building. In order to obtain a correct translation the drone should remain at particular distance form in structure during the whole scanning process. The robot will take its direction based on the comparison result between its current GPS coordinates and the translated received GPS coordinates. As the robot moves it will keep checking the current GPS values and take decision accordingly. Since there might be some temporary or permanent obstacle present on the roof for decoration purpose. Therefore an ultrasonic range sensor has been used such that when the robot come close to an obstacle at defined distance the sensor will guide the robot to change its path and as soon as the obstacle is disappeared from the sensor range the robot will again start checking the GPS value to reach to its target destination. As it reaches to the target destination it will instruct the wrench motor to allow the SMR to reach to the location and obtain the current MFL reading of that place. These readings will be sent to the System. If an anomaly is detected then it is verified that the structure is having deformation at that particular location. If in vision based approach multiple anomalies have been detected then the robot will perform same procedure to determine the faults. Conclusion: With the initial findings, the proposed system appears to be a robust and inexpensive alternative to current approaches for automated inspection of civil/mechanical systems. The combination of VI and MFL approach provided the opportunity to detect, verify and localize the deformation in the structure.qscienc

A Prototype Of Virtually Interactive Hand Activating Devise-Low Cost Portable Head Mounted System (vihad Plus) For Neurological Rehabilitation

Background and Purpose: Restoring function in individuals who have severe paralysis of the upper extremity secondary to stroke is challenging. Recent technologies have made it possible to use robotic devices as novel tools for assisting the therapists to provide safe and intensive rehabilitation with repeated motions. However, most of the training robots are types of Continuous Passive Motion (CPM) devices that produce slower and stereotyped movement patterns. Earlier works have shown that passive or slow movements do not significantly benefit motor improvement. Several studies reveal that even the use of ipsilateral electromyographic (EMG) pattern recognition approaches might not be practical to decode movement intention and, may negatively affect re-mapping of the neural pathways in the brain. To have a successful hand rehabilitation system, the system should be able to produce a wide variety of unpredicted and challenging movement patterns of various degrees of speed and range of motion with increasing complexity, sufficient enough to produce the necessary neurological plasticity of the affected brain. Current rehabilitation devices are not sufficient to produce such a range of complex activities which enables maximum neurological plasticity. Objective: In this work, we describe a prototype of the contralateral EMG-based Interactive Hand Activating Devise for Stroke (IHADS) system that can detect a hemiplegic person's intention for bilaterally executed hand activities using his/her surface EMG signals from the non-affected side (contralateral). Furthermore, this system can assist in bilateral hand activities through an exoskeleton attached to the hemiplegic upper extremity to initiate progressively challenging and unpredicted type of activities in a virtual reality (VR) world to obtain optimum functional recovery by inducing maximum neurological plasticity. Design: The IHADS system is made up of an embedded controller and a robotic exoskeleton, contralateral EMG sensors and a VR interface with a semi-immersed VR system, where the patient will be seeing progressively impulsive activities that would force the brain to activate the affected extremity to manipulate through the remaining neural networks and mirror neuronal system which in turn will optimize the neurological recovery. This means that the paralyzed arm will be following the motion of the healthy arm whose motion is picked up by the EMG sensors and are translated as actuation signals for the exoskeleton to execute virtually created challenging activities. Conclusion: Contralateral EMG-based 'IHADS' system is a unique, cost effective, highly innovative and portable robotic device. If incorporated into the stroke rehabilitation, this system will be capable of autonomous guidance through the use of real-time feedback from the contralateral upper limb, integrated via the VR interface and the hand activating device to make rehabilitation more intense, functional, motivating, and capable of inducing maximum neurological plasticity

Design and implementation of a quadruped amphibious robot using duck feet

Roaming complexity in terrains and unexpected environments pose significant difficulties in robotic exploration of an area. In a broader sense, robots have to face two common tasks during exploration, namely, walking on the drylands and swimming through the water. This research aims to design and develop an amphibious robot, which incorporates a webbed duck feet design to walk on different terrains, swim in the water, and tackle obstructions on its way. The designed robot is compact, easy to use, and also has the abilities to work autonomously. Such a mechanism is implemented by designing a novel robotic webbed foot consisting of two hinged plates. Because of the design, the webbed feet are able to open and close with the help of water pressure. Klann linkages have been used to convert rotational motion to walking and swimming for the animal’s gait. Because of its amphibian nature, the designed robot can be used for exploring tight caves, closed spaces, and moving on uneven challenging terrains such as sand, mud, or water. It is envisaged that the proposed design will be appreciated in the industry to design amphibious robots in the near future

Adaptive Neuro-Fuzzy Inference System for Prediction of Surgery Time for Ischemic Stroke Patients

With the advent of machine learning techniques, creation and utilization of prediction models for different medical procedures including prediction of diagnosis, treatment and recovery of different medical conditions has become the norm. Recent studies focus on the automation of infarction volume growth rate prediction by the utilization of machine learning techniques. These techniques when effectively applied, could significantly help in reducing the time needed to attend to stroke patients. We propose, in this proposal, a Fuzzy Inference System that can determine when a stroke patient should undergo Decompressive Hemicraniectomy. The second infarction volume growth rate and the decision whether a patient needs to undergo this procedure, both predicted outputs of two trained models, act as inputs to this system. While the initial prediction model, that which predicts the second infarction volume growth rate is adopted from an earlier model, we propose the later model in this paper. Three Machine Learning techniques - Support Vector Machine, Artificial Neural Network and Adaptive Neuro Fuzzy Inference System with and without the feature reduction technique of Principle Component Analysis were modelled and evaluated, the best of which was selected to model the proposed prediction model. We also defined the structure of Fuzzy Inference System along with its rules and obtained an overall accuracy of 95.7% with a precision of 1 showing promising results from the use of fuzzy logic

Heart Rate as a Predictor of Challenging Behaviours among Children with Autism from Wearable Sensors in Social Robot Interactions

Children with autism face challenges in various skills (e.g., communication and social) and they exhibit challenging behaviours. These challenging behaviours represent a challenge to their families, therapists, and caregivers, especially during therapy sessions. In this study, we have investigated several machine learning techniques and data modalities acquired using wearable sensors from children with autism during their interactions with social robots and toys in their potential to detect challenging behaviours. Each child wore a wearable device that collected data. Video annotations of the sessions were used to identify the occurrence of challenging behaviours. Extracted time features (i.e., mean, standard deviation, min, and max) in conjunction with four machine learning techniques were considered to detect challenging behaviors. The heart rate variability (HRV) changes have also been investigated in this study. The XGBoost algorithm has achieved the best performance (i.e., an accuracy of 99%). Additionally, physiological features outperformed the kinetic ones, with the heart rate being the main contributing feature in the prediction performance. One HRV parameter (i.e., RMSSD) was found to correlate with the occurrence of challenging behaviours. This work highlights the importance of developing the tools and methods to detect challenging behaviors among children with autism during aided sessions with social robots

Defect Deconvolution using 4th Order Statistics for Ultrasonic Nondestructive Testing

Classification of defects using ultrasonic nondestructive testing (NDT) is primarily done in the field of industrial materials to provide useful information in order to assist in making administrative decisions in terms of maintenance and replacement. The technique presented in this paper utilizes the concept of defect induction as a convolution process between the clean sample and the defect signature. Hence, to identify the type of defect a deconvolution approach can be useful. Due to several similarities between the ultrasonic echoes and the usual modulated sinusoids, a motivation is present to use 4th order statistics for completely defining the waveform. Such a definition, when compared with standard defects, will provide useful insight in terms of defect classifications and understanding

Defect deconvolution using 3rd order statistics for Ultrasonic Nondestructive Testing

Ultrasonic nondestructive testing (NDT) is primarily based upon the detection and classification of a defect in the field of industrial materials. This information is useful in making administrative decisions in terms of maintenance and replacement. The technique presented in this paper utilizes the concept of defect induction as a convolution process between the clean sample and the defect signature. Hence, to identify the type of defect a deconvolution approach can be useful. Due to several similarities between the ultrasonic echoes and the usual modulated sinusoids, a motivation is present to use 2nd and higher order statistics for completely defining the waveform. Such a definition, when compared with standard defects, will provide useful insight in terms of defect classifications and understanding

Deep learning based identification of DDoS attacks in industrial application

Denial of Service (DoS) attacks are very common type of computer attack in the world of internet today. Automatically detecting such type of DDoS attack packets dropping them before passing through is the best prevention method. Conventional solution only monitors and provide the feedforward solution instead of the feedback machine-based learning. A Design of Deep neural network has been suggested in this paper. In this approach, high level features are extracted for representation and inference of the dataset. Experiment has been conducted based on the ISCX dataset for year 2017, 2018 and CICDDoS2019 and program has been developed in Matlab R17b using Wireshark. 2020 IEEE.This publication was made possible by NPRP11-S-1227-170135 grant from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

Monitoring DVT cuffs for long-term operation: A fuzzy approach

Stroke patients, as well as all those patients who are bed-bound for a long period of time are highly susceptible to deep vein thrombosis (DVT) as secondary complexity. DVT poses more dangers of a loose blood clot obstructing the blood flow to cardiac or cranial flow circuits and can cause further stroke or heart attack. As a typical clinical practice, a simple device called DVT Cuff is used on the leg which regulates the pressure using an air-pressure pump. This aids in blood circulation in extremities where most DVT happen. In this paper, A Fuzzy Inference System (FIS) has been proposed that can be used in long-term monitoring of the performance of the cuffs using human experience mapped into the logic hardware. Such monitoring applications are usually quite complicated and need large programs that might not fit into the small and limited memory of the embedded device. The proposed FIS converts heuristic knowledge of the healthcare experts, in solving this problem, into a hardware friendly matrix that has the possible input and output stored in it. A number of Flex sensors are the main sensing units that were used to detect the changes in the curvature of the surface of the DVT cuff, which accordingly provides the information related to the functionality of the cuff, i.e., if the cuff is inflated of deflated, etc?. 2016 IEEE.Scopu