R. de Azambuja, V.J. Brusamarello, S. Haffner, and R. Wolff Porto. “Analysis and Optimization of an Inductive Power Transfer With a Randomized Method.” IEEE Transactions on Instrumentation and Measurement 63, no. 5 (May 2014): 1145–52. doi:10.1109/TIM.2013.2296397.
Abstract: This paper introduces the analysis of the efficiency and transferred power of an inductive link circuit with different network configurations of capacitors connected to primary and secondary coils. The best performance for both cited objective functions was observed with two capacitors connected to the input coil and two capacitors connected to the output coil. However, the output equations in this circuit configuration for both efficiency and output power are very complex and a numerical method had to be applied to compute the capacitors values. Since an exhaustive search would be long, some simplifications were assumed to reduce the search space and the processing time. Thus, a search algorithm based on a randomized method was developed and successfully applied. The results for both efficiency and output power of four capacitors configuration were compared with other usual approaches, such as the single and two capacitors compensation. Finally, a basic prototype was built and the theoretical results were validated. Both simulated and experimental results of the four capacitor configuration showed a significant improvement on the efficiency and output power of the inductive link.
V. J. Brusamarello, Y. B. Blauth, R. de Azambuja, I. Muller, and F. R. de Sousa. “Power Transfer With an Inductive Link and Wireless Tuning.” IEEE Transactions on Instrumentation and Measurement 62, no. 5 (May 2013): 924–31. doi:10.1109/TIM.2013.2245041.
Abstract: This paper presents the analysis of two air-coupled coils used to transfer energy to charge a battery. This battery is used to power an electronic device designed to monitor variables such as impact strength, range of temperature, and humidity associated with the transport of fruits. The device is inside a sealed enclosure that cannot be opened for recharging the battery. The study shows that the coupled coils need to work with a resonance capacitor, at least on the secondary coil. However, the resonance frequency also depends on the coupling factor k. Therefore, this work proposes a monitoring system with a closed loop for fine-tuning the resonance frequency of the secondary coil circuit. Before starting charging the battery, the system scans the resonance frequency on the primary coil and measures the output power on the secondary coil looking for the optimal point. This procedure reduces problems of coupling factor variations with positioning of the coils during the battery charging.
R. de Azambuja, F. B. Klein, M. F. Stoelen, S. V. Adams, and A. Cangelosi. “Graceful Degradation Under Noise on Brain Inspired Robot Controllers.” In Neural Information Processing, edited by Akira Hirose, Seiichi Ozawa, Kenji Doya, Kazushi Ikeda, Minho Lee, and Derong Liu, 195–204. Lecture Notes in Computer Science 9947. Springer International Publishing, 2016. doi:10.1007/978-3-319-46687-3_21.
Abstract: How can we build robot controllers that are able to work under harsh conditions, but without experiencing catastrophic failures? As seen on the recent Fukushima’s nuclear disaster, standard robots break down when exposed to high radiation environments. Here we present the results from two arrangements of Spiking Neural Networks, based on the Liquid State Machine (LSM) framework, that were able to gracefully degrade under the effects of a noisy current injected directly into each simulated neuron. These noisy currents could be seen, in a simplified way, as the consequences of exposition to non-destructive radiation. The results show that not only can the systems withstand noise, but one of the configurations, the Modular Parallel LSM, actually improved its results, in a certain range, when the noise levels were increased. Also, the robot controllers implemented in this work are suitable to run on a modern, power efficient neuromorphic hardware such as SpiNNaker.
R. de Azambuja, A. Cangelosi, and S.V. Adams. “Diverse, Noisy and Parallel: A New Spiking Neural Network Approach for Humanoid Robot Control.” In 2016 International Joint Conference on Neural Networks (IJCNN), 1134–42. Vancouver, 2016. doi:10.1109/IJCNN.2016.7727325.
Abstract: How exactly our brain works is still an open question, but one thing seems to be clear: biological neural systems are computationally powerful, robust and noisy. Using the Reservoir Computing paradigm based on Spiking Neural Networks, also known as Liquid State Machines, we present results from a novel approach where diverse and noisy parallel reservoirs, totalling 3,000 modelled neurons, work together receiving the same averaged feedback. Inspired by the ideas of action learning and embodiment we use the safe and flexible industrial robot BAXTER in our experiments. The robot was taught to draw three different 2D shapes on top of a desk using a total of four joints. Together with the parallel approach, the same basic system was implemented in a serial way to compare it with our new method. The results show our parallel approach enables BAXTER to produce the trajectories to draw the learned shapes more accurately than the traditional serial one.
R. W. Porto, V. J. Brusamarello, I. Muller, F. R. Sousa, and R. Azambuja. “Design and Optimization of a Power Inductive Link.” In Instrumentation and Measurement Technology Conference (I2MTC), 2014 IEEE International, 648–53. IEEE, 2014. doi:10.1109/I2MTC.2014.6860823.
Abstract: The wireless power transfer efficiency plays an important role in design of implantable biomedical devices. There are some important constraints in the design, such as the size of the implantable coil as well as the power delivered to the implant. In this context, this paper presents the design of two planar spiral coils coupled by air used to transferring energy wirelessly. The geometric constraints of the application are determined to define the dimensions of the coils. These parameters along with the working frequency of the system are applied to a procedure that results in the equivalent electrical model characterization of the coils, as well as mutual inductance. A capacitor matching network is then determined in order to optimize two objective functions: efficiency of the whole link and power transferred to the load. Since in a practical application the values of the capacitors are discrete and limited to a commercial series of values, we developed a numerical procedure to conduct a search of the best results of both objective functions. This network is characterized by being made up of a series-parallel network at both the input and output. The sequence of this work includes the development of an oscillator used as the primary voltage source and the experimental evaluation of the entire system.
R. Azambuja, V. J. Brusamarello, S. Haffner, and R. W. Porto. “Full Four Capacitor Circuit Compensation for Inductive Power Transfer.” In Instrumentation and Measurement Technology Conference (I2MTC), 2013 IEEE International, 183–187. IEEE, 2013. doi:10.1109/I2MTC.2013.6555406.
Abstract: A novel full four capacitor compensation method for inductive power transfer is introduced. To compute the capacitors values, a very simple search algorithm based on Monte Carlo is used. In addiction, some heuristic are used to reduce the size of the search space. The efficiency, output power and power efficiency are compared with some classical approach such as the two capacitors compensation and also with the basic circuit without compensation. The results showed a significant improvement on the efficiency and output power.
R. W. Porto, V. J. Brusamarello, R. Azambuja, and O. Frison. “Design and Analysis of a GMR Eddy Current Probe for NDT.” In Sensing Technology (ICST), 2013 Seventh International Conference on, 424–429. IEEE, 2013. doi:10.1109/ICSensT.2013.6727688.
Abstract: Defect detection in metallic plates represents an important issue in metal industry, because its potential use in quality control process. Eddy current testing is one of the most extensively used nondestructive techniques for inspecting electrically conductive materials. The purpose of this paper is to present an eddy current testing system for surface defect detection in conducting materials using a giant magnetoresistive (GMR) sensor. An alternate magnetic field is produced by a solenoid and eddy currents are generated in the material under test. The GMR sensor was mounted inside the coil and the arrangement was adapted in the axis of a vertical machining center. In order to validating the measurement device, defects were induced by cracks machined in workpieces made of aluminum. Thus, the parts were scanned with the sensor prototype and a method to estimate the width and depth of the induced defects was proposed after analyzing the output voltage signal.
V. J. Brusamarello, Y. B. Blauth, R. Azambuja, and I. Muller. “A Study on Inductive Power Transfer with Wireless Tuning.” In Instrumentation and Measurement Technology Conference (I2MTC), 2012 IEEE International, 1098–1103. IEEE, 2012. doi:10.1109/I2MTC.2012.6229372.
Abstract: This paper presents the analysis of two loosely coupled coils used to transfer energy to charge a battery. This battery is used to power an electronic device designed to monitor variables such as impact strength, range of temperature and humidity associated with the transport of fruits. The device is inside a sealed enclosure that cannot be opened for recharging the battery. The study shows the loosely coupled coils need to work with a resonance capacitor, at least on the secondary coil. However the resonance frequency also depends on the coupling factor k and the power delivered to the load. Therefore, this work proposes a monitoring system with closed loop for fine-tuning the resonance frequency of the secondary coil circuit. Before starting charging the battery the system scans the resonance frequency on the primary coil and measures the output power on the secondary coil looking for the optimal point. This procedure reduces problems of variation of coupling factor with positioning of the coils.
D. A. Sala, R. de Azambuja, V. J. Brusamarello and A. Cangelosi “Positioning Control on a Collaborative Robot by Sensor Fusion with Liquid State Machines.” In Instrumentation and Measurement Technology Conference (I2MTC), 2017 IEEE International (ACCEPTED FOR PRESENTATION).
Abstract: A positioning controller based on Spiking Neural Networks for sensor fusion suitable to run on a neuromorphic computer is presented in this work. The proposed framework uses the paradigm of reservoir computing to control the collaborative robot BAXTER. The system was designed to work in parallel with Liquid State Machines that performs trajectories in 2D closed shapes. In order to keep a felt pen touching a drawing surface, data from sensors of force and distance are fed to the controller. The system was trained using data from a Proportional Integral Derivative controller, merging the data from both sensors. The results show that the LSM can learn the behavior of a PID controller on different situations
R. de Azambuja, F.B. Klein, S.V. Adams, M.F. Stoelen and A. Cangelosi. “Short-Term Plasticity in a Liquid State Machine Biomimetic Robot Arm Controller.” In 2017 International Joint Conference on Neural Networks (IJCNN). Anchorage (ACCEPTED FOR POSTER PRESENTATION).
Abstract: Biological neural networks are able to control limbs in different scenarios, with high precision and robustness. As neural networks in living beings communicate through spikes, modern neuromorphic systems try to mimic them making use of spike-based neuron models. Liquid State Machines (LSM), a special type of Reservoir Computing system made of spiking units, when it was first introduced, had plasticity on an external layer and also through Short-Term Plasticity (STP) within the reservoir itself. However, most neuromorphic hardware currently available does not implement both Short-Term Depression and Facilitation and some of them don’t support STP at all. In this work, we test the impact of STP in an experimental way using a 2 degrees of freedom simulated robotic arm controlled by an LSM. Four trajectories are learned and their reproduction analysed with Dynamic Time Warping accumulated cost as the benchmark. The results from two different set-ups showed the use of STP in the reservoir was useful for one out of three tested trajectories, though not computationally cost-effective for this particular robotic task.
R. de Azambuja, D.H. García, M.F. Stoelen and A. Cangelosi. “Neurorobotic Simulations on the Degradation of Multiple Column Liquid State Machines.” In 2017 International Joint Conference on Neural Networks (IJCNN). Anchorage (ACCEPTED FOR ORAL PRESENTATION).
Abstract: Two different configurations of Liquid State Machine (LSM), a special type of Reservoir Computing with internal nodes modelled as spiking neurons, implementing multiple columns (Modular and Monolithic approaches) are tested against the decimation of neurons, connections and entire columns in order to verify which one can better withstand the damage. Based on the neurorobotics outlook, this work is part of a bigger project that aims to apply artificial neural networks to the control of humanoid robots. Therefore, as a benchmark, we made use of a robotic task where an LSM is trained to generate the joint angles needed to command a simulated version of the collaborative robot BAXTER to draw a square on top of a table. The final drawn shape is analysed through Dynamical Time Warping to generate a cost value based on how close the produced drawing is to the original shape. Our results show both approaches, Modular and Monolithic, had a similar behaviour, however the Modular was better at withstanding the decimation of neurons when it was concentrated in a single column.