Oil and gas pipeline networks require the periodic inspection of their infrastructure, which can cause gas and oil leakage with several damages to the environment and human health. For this, non-destructive testing (NDT) techniques of low-cost and easy implementation are required. An option is the metal magnetic memory (MMM) method, which could be used for real-time monitoring defects of ferromagnetic structures based on the analysis of self-magnetic leakage fields distribution around each defect. This method only requires magnetic sensors with high resolution and a data processing system. We present a measurement system of tangential and normal MMM signals of three rectangular defects of an ASTM A-36 steel pipe. This system is formed by a magnetoresistive sensor, an Arduino nano and a virtual instrumentation. The measured magnetic signals have non-uniform distributions around the rectangular defects, which have small differences with respect to the results obtained of a 2D magnetic dipole model. The size of each rectangular defect is related to the amplitude and shape of its tangential and normal MMM signals. The proposed system could be used for real-time monitoring of the size and location of rectangular defects of ferromagnetic pipes. This system does not require expensive equipment, operators with high skill level or a special treatment of the ferromagnetic samples.

We present a signal processing system with virtual instrumentation of a MEMS sensor to detect magnetic flux density for biomedical applications. This system consists of a magnetic field sensor, electronic components implemented on a printed circuit board (PCB), a data acquisition (DAQ) card, and a virtual instrument. It allows the development of a semi-portable prototype with the capacity to filter small electromagnetic interference signals through digital signal processing. The virtual instrument includes an algorithm to implement different configurations of infinite impulse response (IIR) filters. The PCB contains a precision instrumentation amplifier, a demodulator, a low-pass filter (LPF) and a buffer with operational amplifier. The proposed prototype is used for real-time non-invasive monitoring of magnetic flux density in the thoracic cage of rats. The response of the rat respiratory magnetogram displays a similar behavior as the rat electromyogram (EMG).

Several resonant magnetic field sensors based on microelectromechanical systems (MEMS) technology use piezoresistive detection techniques to convert the magnetic field signal into an electrical signal. We present a signal conditioning system implemented in a printed circuit board (PCB) for a resonant magnetic field sensor based on MEMS technology. This sensor is formed by a resonant structure of thin silicon beams (5 μm thick), an aluminum loop (1 μm thick), and a Wheatstone bridge with four p-type piezoresistors. The Wheatstone bridge is biased with an alternating voltage of 2 Vpp at 1 kHz and the aluminum loop is supplied using an alternating current with a root-mean-square (RMS) value of 20 mA. This current is applied to the resonant frequency of the sensor (14.38 kHz) through an oscillator that has a frequency stability of ± 100 ppm at atmospheric temperature and a resolution of 1 Hz. The proposed system obtains the sensor's electrical response in voltage or current mode, which presents an approximately linear behavior for a range of magnetic field density from -150 to +150 μT. This system minimizes the offset of the sensor's electrical response and allows the detection of the polarity and magnitude of the magnetic field density. A virtual instrument is designed using Lab VIEW software to visualize the 4-20 mA output of the sensor. The designed system can help the development of portable measurement equipment to detect (at pressure atmospheric) low magnetic field densities with a sensitivity and resolution of 4 V · T^-1 and 1 μT, respectively.

This study aimed to present a model of post-scratching locomotion with two intermixed central pattern generator (CPG) networks, one for scratching and another for locomotion. We hypothesized that the rhythm generator layers for each CPG are different, with the condition that both CPGs share their supraspinal circuits and their motor outputs at the level of their pattern formation networks. We show that the model reproduces the post-scratching locomotion latency of 6.2 ± 3.5 s, and the mean cycle durations for scratching and post-scratching locomotion of 0.3 ± 0.09 s and 1.7 ± 0.6 s, respectively, which were observed in a previous experimental study. Our findings show how the transition of two rhythmic movements could be mediated by information exchanged between their CPG circuits through routes converging in a common pattern formation layer. This integrated organization may provide flexible and effective connectivity despite the rigidity of the anatomical connections in the spinal cord circuitry.

The paper presents a novel algorithm for detection and measurement of indentations in Vickers hardness testing images, within a specific case of applied research on material quality evaluation based on image processing. The algorithm performs image segmentation by binarization, morphological filtering, and region growing, where the binarization threshold is automatically obtained from the input image. After identification of the rhombus shaped indentation footprint, its four vertices are determined using corner detection, which are used to calculate the diagonal lengths and the Vickers hardness number. The proposed procedure has been tested on 185 images of real data obtained by the micro hardness machine Mitutoyo HM-124, mostly from Steel-316 specimens, but also from Hafnium Nitride. Test images include specular-polished and rough surfaces, specimen with artifacts or imperfections, indentations with deformed or damaged edges, and low contrast images. Ground true diagonal lengths obtained in the conventional manual manner by an expert were compared with the results determined by our method. The proposed method achieves competitive accuracy compared to the best known methods, but it is simpler and hence more efficient.