FPGA-Microprocessor Based Sensor for Faults Detection in Induction Motors Using Time-Frequency and Machine Learning Methods.

Induction motors (IM) play a fundamental role in the industrial sector because they are robust, efficient, and low-cost machines. Changes in the environment, installation errors, or modifications to working conditions can generate faults in induction motors. The trend on IM fault detection is focused on the design techniques and sensors capable of evaluating multiple faults with various signals using non-invasive analysis. The methodology is based on processing electric current signals by applying the short-time Fourier transform (STFT). Additionally, the computation of the mean and standard deviation of infrared thermograms is proposed as main indicators. The proposed system combines both parameters by means of Support Vector Machine and k-nearest-neighbor classifiers. The development of the diagnostic system was done with digital hardware implementations using a Xilinx PYNQ Z2 card that integrates an FPGA with a microprocessor, thus taking advantage of the acquisition and processing of digital signals and images in hardware. The proposed method has proved to be effective for the classification of healthy (HLT), misalignment (MAMT), unbalance (UNB), damaged bearing (BDF), and broken rotor bar (BRB) faults with an accuracy close to 99%.

Human Hand Anatomy-Based Prosthetic Hand.

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.

Euro Banknote Recognition System for Blind People.

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.

Interaural time difference thresholds as a function of frequency.

Different models of the binaural system make different predictions for the just-detectable interaural time difference (ITD) for sine tones. To test these models, ITD thresholds were measured for human listeners focusing on high- and low-frequency regions. The measured thresholds exhibited a minimum between 700 and 1,000 Hz. As the frequency increased above 1,000 Hz, thresholds rose faster than exponentially. Although finite thresholds could be measured at 1,400 Hz, experiments did not converge at 1,450 Hz and higher. A centroid computation along the interaural delay axis, within the context of the Jeffress model, can successfully simulate the minimum and the high-frequency dependence. In the limit of medium-low frequencies (f), where f . ITD << 1, mathematical approximations predict low-­ frequency slopes for the centroid model and for a rate-code model. It was found that measured thresholds were approximately inversely proportional to the frequency (slope = ­1) in agreement with a rate-code model. However, the centroid model is capable of a wide range of predictions (slopes from 0 to ­2).

Human interaural time difference thresholds for sine tones: the high-frequency limit.

The smallest detectable interaural time difference (ITD) for sine tones was measured for four human listeners to determine the dependence on tone frequency. At low frequencies, 250-700 Hz, threshold ITDs were approximately inversely proportional to tone frequency. At mid-frequencies, 700-1000 Hz, threshold ITDs were smallest. At high frequencies, above 1000 Hz, thresholds increased faster than exponentially with increasing frequency becoming unmeasurably high just above 1400 Hz. A model for ITD detection began with a biophysically based computational model for a medial superior olive (MSO) neuron that produced robust ITD responses up to 1000 Hz, and demonstrated a dramatic reduction in ITD-dependence from 1000 to 1500 Hz. Rate-ITD functions from the MSO model became inputs to binaural display models-both place based and rate-difference based. A place-based, centroid model with a rigid internal threshold reproduced almost all features of the human data. A signal-detection version of this model reproduced the high-frequency divergence but badly underestimated low-frequency thresholds. A rate-difference model incorporating fast contralateral inhibition reproduced the major features of the human threshold data except for the divergence. A combined, hybrid model could reproduce all the threshold data.