Abdilla, A. & Fitch, R. 2017, 'FCJ-209 Indigenous Knowledge Systems and Pattern Thinking: An Expanded Analysis of the First Indigenous Robotics Prototype Workshop', The Fibreculture Journal.
Abdilla, A. & Fitch, R. 2017, 'Indigenous Knowledge Systems and Pattern Thinking: An Expanded Analysis of the First Indigenous Robotics Prototype Workshop', Fibreculture Journal: internet theory criticism research, no. 28, pp. 1-14.
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In November 2014, the lead researcher’s interest in the conceptual development of digital
technology and her cultural connection to Indigenous Knowledge Systems created an opportunity to
explore a culturally relevant use of technology with urban Indigenous youth: the Indigenous Robotics
Prototype Workshop. The workshop achieved a sense of cultural pride and confidence in Indigenous
traditional knowledge while inspiring the youth to continue with their engagement in coding and
programming through building robots. Yet, the outcomes from the prototype workshop further revealed a
need to investigate how Indigenous Knowledge Systems, and particularly Pattern Thinking, might hint
toward a possible paradigm shift for the ethical and advanced design of new technologies. This article
examines the implications of such a hypothetical shift in autonomous systems in robotics and artificial
intelligence (AI), using the Indigenous Robotics Prototype Workshop as a case study and springboard.
Carmichael, M.G., Liu, D. & Waldron, K.J. 2017, 'A framework for singularity-robust manipulator control during physical human-robot interaction', The International Journal of Robotics Research, vol. 36, no. 5-7, pp. 861-876.
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Fitch, R., Best, G. & Martens, W. 2017, 'Path Planning With Spatiotemporal Optimal Stopping for Stochastic Mission Monitoring', IEEE Transactions on Robotics.
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Hassan, M. & Liu, D. 2017, 'Simultaneous area partitioning and allocation for complete coverage by multiple autonomous industrial robots', Autonomous Robots, vol. 41, no. 8, pp. 1609-1628.
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© 2017, Springer Science+Business Media New York. For tasks that require complete coverage of surfaces by multiple autonomous industrial robots, it is important that the robots collaborate to appropriately partition and allocate the surface areas amongst themselves such that the robot team’s objectives are optimized. An approach to this problem is presented, which takes into account unstructured and complex 3D environments, and robots with different capabilities. The proposed area partitioning and allocation approach utilizes Voronoi partitioning to partition objects’ surfaces, and multi-objective optimization to allocate the partitioned areas to the robots whilst optimizing robot team’s objectives. In addition to minimizing the overall completion time and achieving complete coverage, which are objectives particularly useful for applications such as surface cleaning, manipulability measure and joint’s torque are also optimized so as to help autonomous industrial robots to operate better in applications such as spray painting and grit-blasting. The approach is validated using six case studies that consist of comparative studies, complex simulated scenarios as well as real scenarios using data obtained from real objects and applications.
hassan, M., liu, D. & Paul, G. 2017, 'Collaboration of Multiple Autonomous Industrial Robots through Optimal Base Placements', Journal of Intelligent and Robotic Systems.
Li, Q., Xiong, R. & Vidal-Calleja, T. 2017, 'A GMM based uncertainty model for point clouds registration', Robotics and Autonomous Systems, vol. 91, pp. 349-362.
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© 2016 Elsevier B.V.The existing methods for the registration of point clouds acquired by laser scanners have some limitations. Firstly, as some samples of surface, a point cloud acquired by the laser scanner, which normally works in a spherical fashion, has very limited density when the surface is far away from the laser scanner and the density varies a lot at different ranges. Current registration methods cannot accurately model the surface uncertainty for such kind of point clouds of limited and large varying density. Secondly, when the point cloud is acquired while the platform is simultaneously moving, the estimation error of the platform motion makes the acquired point cloud distorted. To deal with these problems, in this paper, we propose an uncertainty model based on the Gaussian Mixture Model (GMM) to represent the point cloud. Specifically, we construct the GMM piece-wisely on the underlying surface of point cloud, which will accurately model the surface uncertainty. Also a hierarchical structure is employed to increase the robustness of the registration. Furthermore, by assigning each Gaussian component with a pose, a probabilistic graph can be constructed to tackle the problem of registration when the platform is moving while scanning. In this way the distorted point cloud, caused by the estimation error of the platform's motion, can be corrected by performing graph optimization. Simulation and real world experimental results show that our method leads to better convergence than the state-of-the-art methods due to the accurate modeling of the surface uncertainty and the hierarchical structure, and it also enables us to correct the distorted point clouds.
Martens, W., Poffet, Y., Soria, P.R., Fitch, R. & Sukkarieh, S. 2017, 'Geometric Priors for Gaussian Process Implicit Surfaces', IEEE Robotics and Automation Letters, vol. 2, no. 2, pp. 373-380.
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Nguyen, L.V., Kodagoda, S., Ranasinghe, R. & Dissanayake, G. 2017, 'Adaptive Placement for Mobile Sensors in Spatial Prediction under Locational Errors', IEEE Sensors Journal, vol. 17, no. 3, pp. 794-802.
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Norouzi, M., Miro, J.V. & Dissanayake, G. 2017, 'Planning Stable and Efficient Paths for Reconfigurable Robots On Uneven Terrain', Journal of Intelligent and Robotic Systems: Theory and Applications, vol. 87, no. 2, pp. 291-312.
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© 2017, Springer Science+Business Media Dordrecht. An analytical strategy to generate stable paths for reconfigurable mobile robots such as those equipped with manipulator arms and/or flippers, operating in an uneven environment whilst also meeting additional navigational objectives is hereby proposed. The suggested solution looks at minimising the length of the traversed path and the energy expenditure in changing postures, and also accounts for additional constraints in terms of sensor visibility and traction. This is particularly applicable to operations such as search and rescue where observing the environment for locating victims is the major objective, although this technique can be generalised to incorporate other potentially conflicting objectives (e.g. maximising ground clearance for a legged robot). The validity of the proposed approach is evaluated with two popular graph-based planners (A* and RRT) on a multi-tracked robot fitted with a manipulator arm and a range camera. Two challenging 3D terrain data sets have been employed: one obtained whilst operating the robot in a mock-up urban search and rescue (USAR) arena, and a second one, a reference on-line data set acquired on the quasi-outdoor rover testing facility at the University of Toronto Institute for Aerospace Studies (UTIAS).
Pagano, D. & Liu, D. 2017, 'An approach for real-time motion planning of an inchworm robot in complex steel bridge environments', Robotica, vol. 35, no. 6, pp. 1280-1309.
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Copyright © Cambridge University Press 2016 Path planning can be difficult and time consuming for inchworm robots especially when operating in complex 3D environments such as steel bridges. Confined areas may prevent a robot from extensively searching the environment by limiting its mobility. An approach for real-time path planning is presented. This approach first uses the concept of line-of-sight (LoS) to find waypoints from the start pose to the end node. It then plans smooth, collision-free motion for a robot to move between waypoints using a 3D-F2 algorithm. Extensive simulations and experiments are conducted in 2D and 3D scenarios to verify the approach.
Patten, T., Martens, W. & Fitch, R. 2017, 'Monte Carlo planning for active object classification', Autonomous Robots, pp. 1-31.
Quin, P., Paul, G. & Liu, D. 2017, 'Experimental Evaluation of Nearest Neighbour Exploration Approach in Field Environments', IEEE Transactions on Automation Science and Engineering.
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Inspecting surface conditions in 3-D environments such as steel bridges is a complex, time-consuming, and often hazardous undertaking that is an essential part of tasks such as bridge maintenance. Developing an autonomous exploration strategy for a mobile climbing robot would allow for such tasks to be completed more quickly and more safely than is possible with human inspectors. The exploration strategy tested in this paper, called the nearest neighbors exploration approach (NNEA), aims to reduce the overall exploration time by reducing the number of sensor position evaluations that need to be performed. NNEA achieves this by first considering at each time step only a small set of poses near to the current robot as candidates for the next best view. This approach is compared with another exploration strategy for similar robots performing the same task. The improvements between the new and previous strategy are demonstrated through trials on a test rig, and also in field trials on a ferromagnetic bridge structure.
Skinner, B., McPhee, M.J., Walmsley, B.J., Littler, B., Siddell, J., M.Cafe, L., Wilkins, J.F., Oddy, V.H. & Alempijevic, A. 2017, 'Live animal assessments of rump fat and muscle score in Angus cows and steers using 3-dimensional imaging', Journal of Animal Science, vol. 95, no. 4, pp. 1847-1857.
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The objective of this study was to develop a proof of concept for using off-the-shelf Red Green Blue-Depth (RGB-D) Microsoft Kinect cameras to objectively assess P8 rump fat (P8 fat; mm) and muscle score (MS) traits in Angus cows and steers. Data from low and high muscled cattle (156 cows and 79 steers) were collected at multiple locations and time points. The following steps were required for the 3-dimensional (3D) image data and subsequent machine learning techniques to learn the traits: 1) reduce the high dimensionality of the point cloud data by extracting features from the input signals to produce a compact and representative feature vector, 2) perform global optimization of the signatures using machine learning algorithms and a parallel genetic algorithm, and 3) train a sensor model using regression-supervised learning techniques on the ultrasound P8 fat and the classified learning techniques for the assessed MS for each animal in the data set. The correlation of estimating hip height (cm) between visually measured and assessed 3D data from RGB-D cameras on cows and steers was 0.75 and 0.90, respectively. The supervised machine learning and global optimization approach correctly classified MS (mean [SD]) 80 (4.7) and 83% [6.6%] for cows and steers, respectively. Kappa tests of MS were 0.74 and 0.79 in cows and steers, respectively, indicating substantial agreement between visual assessment and the learning approaches of RGB-D camera images. A stratified 10-fold cross-validation for P8 fat did not find any differences in the mean bias ( = 0.62 and = 0.42 for cows and steers, respectively). The root mean square error of P8 fat was 1.54 and 1.00 mm for cows and steers, respectively. Additional data is required to strengthen the capacity of machine learning to estimate measured P8 fat and assessed MS. Data sets for and continental cattle are also required to broaden the use of 3D cameras to assess cattle. The results demonstrate the importance of capturing curv...
To, W., paul, G. & liu, D. 2017, 'A comprehensive approach to real-time fault diagnosis during automatic grit-blasting operation by autonomous industrial robots', Robotics and Computer-Integrated Manufacturing.
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This paper presents a comprehensive approach to diagnose for faults that may occur during a robotic grit-blasting operation. The approach proposes the use of information collected from multiple sensors (RGB-D camera, audio and pressure transducers) to detect for 1) the real-time position of the grit-blasting spot and 2) the real-time state within the lasting line (i.e. compressed air only). The outcome of this approach will enable a grit-blasting robot to autonomous diagnose for faults and take corrective actions during the blasting operation. Experiments are conducted in a laboratory and in a grit-blasting chamber during real grit-blasting to demonstrate the proposed approach. Accuracy of 95% and above has been achieved in the experiments.
Wang, S., Kodagoda, S., Shi, L. & Xu, N. 2017, 'Lidar-based road terrain recognition for passenger vehicles', International Journal of Vehicle Design, vol. 74, no. 2, pp. 153-165.
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Copyright © 2017 Inderscience Enterprises Ltd. The road terrain type is important information about a passenger vehicle’s surroundings. It suggests an appropriate control algorithm and driving strategy. In this paper, a Lidar sensor is employed to reconstruct the road surface and extract features for terrain classification. The experiment vehicle was driven on four specific road terrains at a variety of speeds. The speed dependency and the effect of using principal component analysis were investigated. The simulation experimental results show that this Lidar sensor-based approach is feasible and robust for passenger vehicles in a range of outdoor scenarios.
Wang, X. & Wang, J.G. 2017, 'Detecting glass in Simultaneous Localisation and Mapping', Robotics and Autonomous Systems, vol. 88, pp. 97-103.
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© 2016 Simultaneous Localisation and Mapping (SLAM) has become one of key technologies used in advanced robot platform. The current state-of-art indoor SLAM with laser scanning rangefinders can provide accurate realtime localisation and mapping service to mobile robotic platforms such as PR2 robot. In recent years, many modern building designs feature large glass panels as one of the key interior fitting elements, e.g. large glass walls. Due to the transparent nature of glass panels, laser rangefinders are unable to produce accurate readings which causes SLAM functioning incorrectly in these environments. In this paper, we propose a simple and effective solution to identify glass panels based on the specular reflection of laser beams from the glass. Specifically, we use a simple technique to detect the reflected light intensity profile around the normal incident angle to the glass panel. Integrating this glass detection method with an existing SLAM algorithm, our SLAM system is able to detect and localise glass obstacles in realtime. Furthermore, the tests we conducted in two office buildings with a PR2 robot show the proposed method can detect ∼95% of all glass panels with no false positive detection. The source code of the modified SLAM with glass detection is released as a open source ROS package along with this paper.
Zhang, J., Xiao, W., Zhang, S. & Huang, S. 2017, 'Device-Free Localization via an Extreme Learning Machine with Parameterized Geometrical Feature Extraction.', Sensors (Basel), vol. 17, no. 4.
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Device-free localization (DFL) is becoming one of the new technologies in wireless localization field, due to its advantage that the target to be localized does not need to be attached to any electronic device. In the radio-frequency (RF) DFL system, radio transmitters (RTs) and radio receivers (RXs) are used to sense the target collaboratively, and the location of the target can be estimated by fusing the changes of the received signal strength (RSS) measurements associated with the wireless links. In this paper, we will propose an extreme learning machine (ELM) approach for DFL, to improve the efficiency and the accuracy of the localization algorithm. Different from the conventional machine learning approaches for wireless localization, in which the above differential RSS measurements are trivially used as the only input features, we introduce the parameterized geometrical representation for an affected link, which consists of its geometrical intercepts and differential RSS measurement. Parameterized geometrical feature extraction (PGFE) is performed for the affected links and the features are used as the inputs of ELM. The proposed PGFE-ELM for DFL is trained in the offline phase and performed for real-time localization in the online phase, where the estimated location of the target is obtained through the created ELM. PGFE-ELM has the advantages that the affected links used by ELM in the online phase can be different from those used for training in the offline phase, and can be more robust to deal with the uncertain combination of the detectable wireless links. Experimental results show that the proposed PGFE-ELM can improve the localization accuracy and learning speed significantly compared with a number of the existing machine learning and DFL approaches, including the weighted K-nearest neighbor (WKNN), support vector machine (SVM), back propagation neural network (BPNN), as well as the well-known radio tomographic imaging (RTI) DFL approach.
Falque, R., Vidal-Calleja, T., Dissanayake, G. & Miro, J.V. 2017, 'From the skin-depth equation to the inverse RFEC sensor model', 2016 14th International Conference on Control, Automation, Robotics and Vision, ICARCV 2016.
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© 2016 IEEE. In this paper, we tackle the direct and inverse problems for the Remote-Field Eddy-Current (RFEC) technology. The direct problem is the sensor model, where given the geometry the measurements are obtained. Conversely, the inverse problem is where the geometry needs to be estimated given the field measurements. These problems are particularly important in the field of Non-Destructive Testing (NDT) because they allow assessing the quality of the structure monitored. We solve the direct problem in a parametric fashion using Least Absolute Shrinkage and Selection Operation (LASSO). The proposed inverse model uses the parameters from the direct model to recover the thickness using least squares producing the optimal solution given the direct model. This study is restricted to the 2D axisymmetric scenario. Both, direct and inverse models, are validated using a Finite Element Analysis (FEA) environment with realistic pipe profiles.
Huang, S., Wang, J., Zhao, L., Ge, X., Zhang, C., He, X. & Wang, X. 2017, 'High Quality 3D Reconstruction of Indoor Environments using RGB-D Sensors', IEEE 12th Conference on Industrial Electronics and Applications (ICIEA), Siem Reap, Cambodia, pp. 1736-1741.
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Liu, L., Wang, Y., Zhao, L. & Huang, S. 2017, 'Evaluation of Different SLAM Algorithms usingGoogle Tangle Data', IEEE Conference on Industrial Electronics and Applications, Siem Reap, Cambodia..
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Munoz, F., Valls Miro, J., Dissanayake, G., Ulapane, N. & Nguyen, L.V. 2017, 'Design of a Lock-in Amplifier Integrated with a Coil System for Eddy-Current Non-Destructive Inspection', 12th IEEE Conference on Industrial Electronics and Applications, 12th IEEE Conference on Industrial Electronics and Applications, IEEE, Siem Reap, Cambodia.
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Nguyen, L.V., Ulapane, N., Valls Miro, J., Dissanayake, G. & Munoz, F. 2017, 'Improved Signal Interpretation for Cast Iron Thickness Assessment based on Pulsed Eddy Current Sensing', 12th IEEE Conference on Industrial Electronics and Applications, 12th IEEE Conference on Industrial Electronics and Applications, Siem Reap, Cambodia.
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This paper presents a novel signal processing approach for computing thickness of ferromagnetic cast iron material, widely employed in older infrastructure such as water mains or bridges. Measurements are gathered from a Pulsed Eddy Current (PEC) based sensor placed on top of the material, with unknown lift-off, as commonly used during non-destructive testing (NDT). The approach takes advantage of an analytical
logarithmic model proposed in the literature for the decaying voltage induced at the PEC sensor pick-up coil. An increasingly more accurate and robust algorithm is proven here by means of an Adaptive Least Square Fitting Line (ALSFL) recursive strategy, suitable to recognize the most linear part of the sensor’s logarithmic output voltage for subsequent gradient computation, from which thickness is then derived. Moreover, efficiency is also gained as processing can be carried out on only one decaying voltage signal, unlike averaging over multiple measurements as
is usually done in the literature. Importantly, the new signal processing methodology demonstrates highest accuracy at the lower thicknesses, a circumstance most relevant to NDT evaluation. Experiments that verify the proposed method in real-world thickness assessment of cast iron material are presented and compared with current practices, showing promising results.
Perera, K., Ranasignhe, R. & Dissanayake, G. 2017, 'A Neural Network Based Place Recognition Technique for a Crowded Indoor Environment', The 13th IEEE conference on industrial electronics and applications, Siem Reap, Cambodia.
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Poon, J.T., Cui, Y., Valls Miro, J., Matsubara, T. & Sugimoto, K. 2017, 'Local Driving Assistance from Demonstration for Mobility Aids', International Conference on Robotics and Automation (ICRA), Singapore.
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Shakor, P., Renneberg, J., Nejadi, S. & Paul, G. 2017, 'Optimisation of Different Concrete Mix Designs for 3D Printing by Utilising 6DOF Industrial Robot', 34th International Symposium on Automation and Robotics in Construction, Taipei, Taiwan.
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Additive Manufacturing (AM) technologies are becoming increasingly viable for commercial and research implementation into various applications. AM refers to the process of forming structures layer upon layer and finds application in prototyping and manufacturing for building construction. It has recently begun to be considered as a viable and attractive alternative in certain circumstances in the construction industry. This paper focuses on the utilisation of different concrete mixtures paired with extrusion techniques facilitated by a six Degree of Freedom (DOF) industrial robot. Using methods of Damp Least Squares (DLS) in conjunction with Resolved Motion Rate Control (RMRC), it is possible to plan stable transitions between several waypoints representing the various print cross-sections. Calculated paths are projected via ‘spline’ interpolation into the manipulator controlled by custom software. This article demonstrates the properties of different concrete mixture designs, showing their performance when used as a filament in 3D Printing and representing a comparison of the results that were found. In this study, the prepared materials consist of ordinary Portland cement, fine sand between (425~150) micron, coarse aggregate ranges (3) mm and chemical admixtures which have been used to accelerate setting times and reduce water content. Numerous tests were performed to check the buildability, flowability, extrudability and moldability of the concrete mixtures. The horizontal test was used to determine the flowability and consistency, while the vertical and squeeze-flow tests were used to determine the buildability of the layers. The extrudability and moldability of the concrete mixtures were controlled by the robot and associated extruder speeds.
Shi, L., Valls Miro, J., Vidal Calleja, T., Vitanage, D. & Rajalingam, J. 2017, 'Innovative Data-driven “along-the-pipe” Condition Assessment for Critical Water Mains', OZWATER’17 Australia’s International Water Conference & Exhibition, OZWATER’17 Australia’s International Water Conference & Exhibition, Australian Water Association, Sydney.
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Recent research findings on remaining life prediction for older Cast Iron critical water mains suggest increasing reliability by calculating stress concentration factors from the corrosion patch geometries expected to be present in the asset, not just extreme pitting as is generally carried out within the industry. This study proposes an innovative data-driven “along-the-pipe” framework able to utilise local inspection results further by capturing data correlations present in the remaining wall thickness measurement. This knowledge can in turn be utilised to produce estimates for “along-the-pipe” patch geometry predictions, hence remaining life. Results from inspections in a real pipeline in the Sydney Water network are compared to conventional Extreme Value Analysis (EVA) to validate the improvements of the proposed strategy.
Su, D., Vidal Calleja, T.A. & Valls Miro, J. 2017, 'Towards Real-Time 3D Sound Sources Mapping with Linear Microphone Arrays', IEEE International Conference on Robotics and Automation : ICRA : [proceedings] IEEE International Conference on Robotics and Automation, IEEE International Conference on Robotics and Automation, Singapore.
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Sun, L., Vidal Calleja, T.A. & Valls Miro, J.A.I.M.E. 2017, 'Coupling Conditionally Independent Submaps for Large-Scale 2.5D Mapping with Gaussian Markov Random Fields', IEEE International Conference on Robotics and Automation : ICRA : [proceedings] IEEE International Conference on Robotics and Automation, IEEE International Conference on Robotics and Automation, Singapore.
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Ulapane, N., Nguyen, L.V., Valls Miro, J., Alempijevic, A. & Dissanayake, G. 2017, 'Designing A Pulsed Eddy Current Sensing Set-up for Cast Iron Thickness Assessment', 12th IEEE Conference on Industrial Electronics and Applications, 12th IEEE Conference on Industrial Electronics and Applications, Siem Reap, Cambodia.
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Pulsed Eddy Current (PEC) sensors possess proven functionality in measuring ferromagnetic material thickness. However, most commercial PEC service providers as well as researchers have investigated and claim functionality of sensors on homogeneous structural steels (steel grade Q235 for example). In this paper, we present design steps for a PEC sensing set-up to measure thickness of cast iron, which is unlike steel, is a highly inhomogeneous and non-linear ferromagnetic material. The setup
includes a PEC sensor, sensor excitation and reception circuits, and a unique signal processing method. The signal processing method yields a signal feature which behaves as a function of thickness. The signal feature has a desirable characteristic of being lowly influenced by lift-off. Experimental results show that the set-up is usable for Non-destructive Evaluation (NDE) applications such as cast iron water pipe assessment.