Neodymium iron boron magnets (NdFeB) play a critical role in various technological applications due to their outstanding magnetic properties, such as high maximum energy product, high remanence and high coercivity. Production of NdFeB is expected to rise significantly in the coming years, for this reason, demand for the rare earth elements (REE) will not only remain high but it also will increase even more. The recovery of rare earth elements has become essential to satisfy this demand in recent years. In the present study rare earth elements recovery from NdFeB magnets as new promising process flowsheet is proposed as follows; (1) acid baking process is performed to decompose the NdFeB magnet to increase in the extraction efficiency for Nd, Pr, and Dy. (2) Iron was removed from the leach liquor during hydrolysis. (3) The production of REE-oxide from leach liquor using ultrasonic spray pyrolysis method. Recovery of mixed REE-oxide from NdFeB magnets via ultrasonic spray pyrolysis method between 700 °C and 1000 °C is a new innovative step in comparison to traditional combination of precipitation with sodium carbonate and thermal decomposition of rare earth carbonate at 850 °C. The synthesized mixed REE- oxide powders were characterized by X-ray diffraction analysis (XRD). Morphological properties and phase content of mixed REE- oxide were revealed by scanning electron microscopy (SEM) and Energy-dispersive X-ray (EDX) analysis. To obtain the size and particle size distribution of REE-oxide, a search algorithm based on an image-processing technique was executed in MATLAB. The obtained particles are spherical with sizes between 362 and 540 nm. The experimental values of the particle sizes of REE- oxide were compared with theoretically predicted ones.

Nanoparticle properties are correlated to their size, size distribution, and shape; it is essential to accurately measure these features in the field of nanoscience. In this study, silver nanoparticles (AgNPs) were synthesized with the ultrasonic-spray-pyrolysis (USP) method from a water solution of silver nitrate. The synthesized AgNPs were characterized by Dynamic Light Scattering (DLS) analysis and Scanning Electron Microscopy (SEM) to reveal their size and size distribution. A search algorithm based on an image-processing technique to obtain particle size and particle-size distribution from SEM micrographs is proposed. In order to obtain more quantitative information and data with respect to the morphology of particles synthesized under different process parameters, SEM micrographs with a nonhomogeneous background contrast were examined via image-processing techniques in MATLAB. Due to the inhomogeneous contrast of SEM micrographs, defining an overall threshold value was insufficient in the detection of whole nanoparticles. Thus, subimages were directly created according to the maximum and minimum particle size specified by the user to determine local threshold values. The obtained results were automatically combined to represent both particle dimension and location in the SEM micrographs. We confirmed that the results of our DLS analysis, theoretical calculation, and image-processing technique were correlated with our expected results.

Efficient action prediction is of central importance for the fluent workflow between humans and equally so for human-robot interaction. To achieve prediction, actions can be algorithmically encoded by a series of events, where every event corresponds to a change in a (static or dynamic) relation between some of the objects in the scene. These structures are similar to a context-free grammar and, importantly, within this framework the actual objects are irrelevant for prediction, only their relational changes matter. Manipulation actions and others can be uniquely encoded this way. Using a virtual reality setup and testing several different manipulation actions, here we show that humans predict actions in an event-based manner following the sequence of relational changes. Testing this with chained actions, we measure the percentage predictive temporal gain for humans and compare it to action-chains performed by robots showing that the gain is approximately equal. Event-based and, thus, object independent action recognition and prediction may be important for cognitively deducing properties of unknown objects seen in action, helping to address bootstrapping of object knowledge especially in infants.

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In recent years, robotic applications have been improved for better object manipulation and collaboration with human. With this motivation, the detection of objects has been studied with serial elastic parallel gripper by simple touching in case of no visual data available. A series elastic gripper, capable of detecting geometric properties of objects is designed using only elastic elements and absolute encoders instead of tactile or force/torque sensors. The external force calculation is achieved by employing an estimation algorithm. Different objects are selected for trials for recognition. A Deep Neural Network model is trained by synthetic data extracted from STL file of selected objects . For experimental set-up, the series elastic parallel gripper is mounted on a Staubli RX160 robot arm and objects are placed in pre-determined locations in the workspace. All objects are successfully recognized using the gripper, force estimation and the DNN model. The best DNN model capable of recognizing different objects with the average prediction value ranging from 71% to 98%. Hence the proposed design of gripper and the algorithm achieved the recognition of selected objects without need for additional force/torque or tactile sensors.