The present paper describes the development of a prosthetic hand based on human hand anatomy. The hand phalanges are printed with 3D printing with Polylactic Acid material. One of the main contributions is the investigation on the prosthetic hand joins; the proposed design enables one to create personalized joins that provide the prosthetic hand a high level of movement by increasing the degrees of freedom of the fingers. Moreover, the driven wire tendons show a progressive grasping movement, being the friction of the tendons with the phalanges very low. Another important point is the use of force sensitive resistors (FSR) for simulating the hand touch pressure. These are used for the grasping stop simulating touch pressure of the fingers. Surface Electromyogram (EMG) sensors allow the user to control the prosthetic hand-grasping start. Their use may provide the prosthetic hand the possibility of the classification of the hand movements. The practical results included in the paper prove the importance of the soft joins for the object manipulation and to get adapted to the object surface. Finally, the force sensitive sensors allow the prosthesis to actuate more naturally by adding conditions and classifications to the Electromyogram sensor.

This paper presents the development of a portable system with the aim of allowing blind people to detect and recognize Euro banknotes. The developed device is based on a Raspberry Pi electronic instrument and a Raspberry Pi camera, Pi NoIR (No Infrared filter) dotted with additional infrared light, which is embedded into a pair of sunglasses that permit blind and visually impaired people to independently handle Euro banknotes, especially when receiving their cash back when shopping. The banknote detection is based on the modified Viola and Jones algorithms, while the banknote value recognition relies on the Speed Up Robust Features (SURF) technique. The accuracies of banknote detection and banknote value recognition are 84% and 97.5%, respectively.

Software risk prediction is the most sensitive and crucial activity of Software Development Life Cycle (SDLC). It may lead to the success or failure of a project. The risk should be predicted earlier to make a software project successful. A model is proposed for the prediction of software requirement risks using requirement risk dataset and machine learning techniques. In addition, a comparison is made between multiple classifiers that are K-Nearest Neighbour (KNN), Average One Dependency Estimator (A1DE), Naïve Bayes (NB), Composite Hypercube on Iterated Random Projection (CHIRP), Decision Table (DT), Decision Table/Naïve Bayes Hybrid Classifier (DTNB), Credal Decision Trees (CDT), Cost-Sensitive Decision Forest (CS-Forest), J48 Decision Tree (J48), and Random Forest (RF) achieve the best suited technique for the model according to the nature of dataset. These techniques are evaluated using various evaluation metrics including CCI (correctly Classified Instances), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), precision, recall, F-measure, Matthew’s Correlation Coefficient (MCC), Receiver Operating Characteristic Area (ROC area), Precision-Recall Curves area (PRC area), and accuracy. The inclusive outcome of this study shows that in terms of reducing error rates, CDT outperforms other techniques achieving 0.013 for MAE, 0.089 for RMSE, 4.498% for RAE, and 23.741% for RRSE. However, in terms of increasing accuracy, DT, DTNB, and CDT achieve better results.

This paper presents a new Electronic Travel Aid (ETA) ‘Acoustic Prototype’ which is especially suited to facilitate the navigation of visually impaired users. The device consists of a set of 3-Dimensional Complementary Metal Oxide Semiconductor (3-D CMOS) image sensors based on the three-dimensional integration and Complementary Metal-Oxide Semiconductor (CMOS) processing techniques implemented into a pair of glasses, stereo headphones as well as a Field-Programmable Gate Array (FPGA) used as processing unit. The device is intended to be used as a complementary device to navigation through both open known and unknown environments. The FPGA and the 3D-CMOS image sensor electronics control object detection. Distance measurement is achieved by using chip-integrated technology based on the Multiple Short Time Integration method. The processed information of the object distance is presented to the user via acoustic sounds through stereophonic headphones. The user interprets the information as an acoustic image of the surrounding environment. The Acoustic Prototype transforms the surface of the objects of the real environment into acoustical sounds. The method used is similar to a bat's acoustic orientation. Having good hearing ability, with few weeks training the users are able to perceive not only the presence of an object but also the object form (that is, if the object is round, if it has corners, if it is a car or a box, if it is a cardboard object or if it is an iron or cement object, a tree, a person, a static or moving object). The information is continuously delivered to the user in a few nanoseconds until the device is shut down, helping the end user to perceive the information in real time.

The detection of rotor failures in synchronous motors is a matter of primordial interest in many industrial sites where these machines are critical assets. However, due to the particular operation of these motors, most conventional techniques relying on steady-state analysis, commonly used in other electric machines, are not applicable to such motors. In this context, it has been recently proven that the analysis of different quantities under transient operation of the motor and, more specifically, under motor starting can provide crucial information for the diagnosis of many faults. This work proposes the time-frequency analysis of stray fluxes and currents to detect field winding faults in synchronous motors. The potential consequences of this fault can be catastrophic for the motor integrity, so that the detection of its presence in its early stages can be of critical importance for the industry. The results included in this paper prove the usefulness of the transient analysis of such non-invasive quantities not only to detect the presence of the field winding fault but also to set a starting point to determine its severity.

Electric motors are elementary drivers for various processes in countless applications concerning several areas of modern societies. This relevance can be mainly attributed to their high efficiency and robustness. However, due to their operating conditions, electric motors are subjected to different stresses that may lead to electromechanical damages, which, if not correctly detected, can cause irreversible failures and high repair costs. Moreover, damages in the different motor components reduce the motor efficiency since they increase machine losses. In this regard, the analysis of different machine signals has aided in diagnosing these failures; however, few systems can merge the information from relevant signals to identify a wide range of faults under different operating conditions. This article presents recent advances in the electric motor condition monitoring area, which have led to the development of a proprietary data fusion system (DFS) for automatic fault diagnosis. The DFS relies on the combined analysis of currents, stray magnetic fluxes, and infrared data, which can be measured in a noninvasive way by using simple and low-cost primary sensors. The DFS integrates conventional techniques based on stationary analysis (e.g., motor current signature analysis or MCSA) and a modern, robust methodology relying on the analysis of transient currents and fluxes, which has proven to yield high reliability for the final diagnosis while requiring a low computational burden. The article shows some results obtained when applying the system to actual machines, proving the capabilities of the developed methodology.