Continuous Signal Acquisition Research Articles

In this work, we have used several configurations of single-component seismic sensors array for the detection and analysis of the signals produced by lahars at the Cotopaxi Volcano, Ecuador. We have developed a compact, flexible, and reliable prototype enough to be used in volcanic monitoring that includes a new generation Field Programmable Gate Arrays (FPGA) as the basis of the

In this work, we have used several configurations of single-component seismic sensors array for the detection and analysis of the signals produced by lahars at the Cotopaxi Volcano, Ecuador. We have developed a compact, flexible, and reliable prototype enough to be used in volcanic monitoring that includes a new generation Field Programmable Gate Arrays (FPGA) as the basis of the embedded system for continuous signal acquisition, digitalization and storage. We have used an array of twelve single-component seismic sensors in linear geometric configuration. The information is also sent to the central base (Instituto Geofísico) located in the city of Quito through radiofrequency communication. Subsequently, the information is analyzed by means of a mathematical model developed during the time of execution of this research as a result, we have obtained a quick lahar detection including calculation of the speed with which the lahar descends, providing reliable information from the moment the event arises, generating an early warning to the affected population.

Robot Assisted THz Imaging with a Time Domain Spectrometer

THz-Time domain spectroscopic imaging is demonstrated combining a robotic scanning method with continuous signal acquisition and holographic reconstruction of the object to improve the imaging resolution. We apply the method to a metallic Siemens star in order to quantify resolution and to wood samples to demonstrate the technique on a non-metallic object with an unknown structure.

Internet of things-assisted architecture for QRS complex detection in real time

This paper presents the development of an Internet of Things-assisted architecture for QRS complex detection in an electrocardiogram regardless of the age and physiological characteristics of the patient. Detection of the QRS complex is affected by the abnormalities and quality in electrocardiogram recordings; the proposed method can detect QRS complex despite these challenges. Electrocardiogram continuous signal acquisition is performed with the BITalino biomedical data acquisition card. Electrocardiogram signals typically suffer from (a) premature atrial complexes, (b) premature ventricular complexes, (c) low signal-to-noise ratio, (d) right bundle branch blocks, (e) left bundle branch blocks, and (f) non-stationary effects. Interestingly, the signal processing is implemented by means of a bandpass filter, followed by a numerical derivative. Next, the Hilbert transform and the adaptive threshold technique are implemented to detect the QRS complex. Tests are performed to evaluate the Internet of Things-assisted architecture using the obtained signal in real time. Results, and the simplicity of the architecture, demonstrate that it is suitable for wearable, portable, and battery-operated electrocardiogram acquisition card.

Mid-infrared continuous-filtering Vernier spectroscopy using a doubly resonant optical parametric oscillator

We present a continuous-filtering Vernier spectrometer operating in the 3.15–3.4 µm range, based on a femtosecond doubly resonant optical parametric oscillator, a cavity with a finesse of 340, a grating mounted on a galvo scanner, and two photodiodes. The spectrometer allows acquisition of one spectrum spanning 250 nm of bandwidth in 25 ms with 8 GHz resolution, sufficient to detect molecular lines at atmospheric pressure. An active lock ensures good frequency and intensity stability of the consecutive spectra and enables continuous signal acquisition and efficient averaging. The relative frequency scale is calibrated using a Fabry–Perot etalon or, alternatively, the galvo scanner position signal. We measure spectra of a calibrated CH4 gas sample as well as dry and laboratory air and extract CH4 and H2O concentrations by multiline fitting of model spectra. The figure of merit of the spectrometer is 1.7 × 10−9 cm−1 Hz−1/2 per spectral element and the minimum detectable concentration of CH4 is 360 ppt Hz−1/2, averaging down to 90 ppt after 16 s.

Smart road that warns its network manager when it begins cracking

One of the components of the fifth generation road is its capacity to communicate with its network manager in order to improve maintenance. The aim of the experimental instrumentation presented in this study is to monitor the evolution of characteristic damage in mixed structures: nonvisible cracks rising from the hydraulic base towards the coated pavement surface. The objective of the sensors is to be able to anticipate damage before it reaches the surface. As temperature is also being measured, it is possible to use this data to communicate with users concerning the potential risk of ice forming on the road. The first measurements have been compared with a theoretical model of the structure, and the continuous acquisition of signals will enable the network manager to monitor the behaviour of the road.

Comparison of dimensionality reduction techniques for the fault diagnosis of mono block centrifugal pump using vibration signals

Bearing fault, Impeller fault, seal fault and cavitation are the main causes of breakdown in a mono block centrifugal pump and hence, the detection and diagnosis of these mechanical faults in a mono block centrifugal pump is very crucial for its reliable operation. Based on a continuous acquisition of signals with a data acquisition system, it is possible to classify the faults. This is achieved by the extraction of features from the measured data and employing data mining approaches to explore the structural information hidden in the signals acquired. In the present study, statistical features derived from the vibration data are used as the features. In order to increase the robustness of the classifier and to reduce the data processing load, dimensionality reduction is necessary. In this paper dimensionality reduction is performed using traditional dimensionality reduction techniques and nonlinear dimensionality reduction techniques. The effectiveness of each dimensionality reduction technique is also verified using visual analysis. The reduced feature set is then classified using a decision tree. The results obtained are compared with those generated by classifiers such as Naïve Bayes, Bayes Net and kNN. The effort is to bring out the better dimensionality reduction technique–classifier combination.

TOOL CONDITION MONITORING USING ACOUSTIC EMISSION, SURFACE ROUGHNESS AND GROWING CELL STRUCTURES NEURAL NETWORK

The monitoring of tool wear is a most difficult task in the case of various metal-cutting processes. Artificial Neural Networks (ANN) has been used to estimate or classify certain wear parameters, using continuous acquisition of signals from multi-sensor systems. Most of the research has been concentrated on the use of supervised neural network types like multi-layer perceptron (MLP), using back-propagation algorithm and Radial Basis Function (RBF) network. In this article, a new constructive learning algorithm proposed by Fritzke, namely Growing Cell Structures (GCS) has been used for tool wear estimation in face milling operations, thereby monitoring the condition of the tool. GCS generates compact network architecture in less training time and performs well on new untrained data. The performance of this network has been compared with that of another constructive learning algorithm-based neural network, namely the Resource Allocation Network (RAN). For the sake of establishing the effectiveness of GCS, results obtained have been compared with those obtained using Multi Layer Perceptron (MLP), which is a standard and widely used neural network.

Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool

The studies on tool condition monitoring along with digital signal processing can be used to prevent damages on cutting tools and workpieces when the tool conditions become faulty. These studies have become more relevant in today’s context where the order realization dates are crunched and deadlines are to be met in order to catch up with the competition. Based on a continuous acquisition of signals with sensor systems it is possible to classify certain wear parameters by the extraction of features. Data mining approach is extensively used to probe into structural health of the tool and the process. This paper discusses condition monitoring of carbide tipped tool using Support Vector Machine and compares the classification efficiency between C-SVC and ν-SVC. It further analyses the results with other classifiers like Decision Tree and Naïve Bayes and Bayes Net. The vibration signals are acquired for various tool conditions like tool-good condition, tip-breakage, etc. The effort is to bring out the better features-classifier combination.

CellTracks TDI: An image cytometer for cell characterization

Characterization of rare cells usually requires high sensitivity quantification of multiple parameters. Detection of morphological features of these cells is highly desired when routinely identifying circulating tumor cells (CTC) in blood of patients. We have designed an image cytometer intended for fast and sensitive routine analysis of CTC. After an initial scan, prospective events can be revisited for more detailed analysis. The image cytometer features: 375, 491, and 639 nm laser lines, a 40×/0.6NA objective, a CCD camera operating in TDI mode, servo stages to move the sample in two dimensions and a piëzo microscope objective positioner to move the objective in the third dimension. ImageJ is used for dedicated image analysis. A homogeneous illumination area, measuring 180 × 180 μm(2) , was created by the use of a rotating diffuser in combination with two micro-lens arrays. For feed-forward automatic focusing of the sample during a scan, a 3D spline was fitted through 30 predetermined focus positions before scanning the sample. Continuous signal acquisition is made possible by using a CCD operating in TDI mode synchronized to the movement of two servo scan stages. The limit of fluorescence sensitivity is 120 PE molecules on a bead with a diameter of 6.8 μm, at a scanning speed of 1.0 mm s(-1) . The resolution of the imaging system is 0.76 μm in the TDI scan direction at a wavelength of 580 nm. Identification of cells is facilitated by scatter plots of the fluorescent parameters in which each individual event can be viewed for its morphological features by fluorescence as well as bright field. The image cytometer measures quantitative fluorescence and morphological features at a high sensitivity, high resolution, and with minimal overhead time. It has the ability torelocate events of interest for further detailed analysis. The system can be used for routine identification and characterization of rare cells.

Optimizing the calibration and interpretation of dynamic ocular force measurements

Ophthalmodynamometric studies can provide useful clinical information regarding glaucoma, cerebrospinal fluid pressure, and vascular disease but are affected by variable reproducibility and unknown calibration of force in terms of intraocular pressure (IOP). The aim of this study was to calculate calibration factors and identify key design principles using three different types of ophthalmodynamometers. We constructed a modified ophthalmodynamometer named OcuDyn using a large contact lens inside a ring force transducer with continuous signal acquisition and averaging. OcuDyn and Sisler ophthalmodynamometers were applied to isolated pig eyes which were cannulated and connected to a fluid reservoir and a pressure transducer to measure induced IOP at increasing force application. Using these two devices and a Meditron ophthalmodynamometer, we measured the minimum ophthalmodynamometric force (ODF) required to induce pulsation in the hemi-veins and diastolic retinal artery of each eye in glaucoma patient volunteers and family members. Blood pressure was measured with the sphygmomanometer cuff held at eye level. In pigs, the relationship between induced IOP and ODF was strong (minimum r > 0.98, p < 0.001). In humans, the pressure increment (blood pressure - baseline IOP) correlated highly with human arterial ODF (r = 0.83, n = 75, p < 0.001, mean slope 0.32). Mean coefficients of variation were 12.4% (n = 66) for veins and 5.6% (n = 39) for arteries in diastole; these results did not differ significantly with those from the Meditron but were significantly lower than results with the Sisler (20%, p = 0.0014). Linearity between induced IOP and ODF is strong, suggesting that calibration of 0.32 mmHg/g for OcuDyn or using two point blood pressure measures allows for valid interpolation. The better optics of the Meditron and OcuDyn result in more repeatable end-point determination and outweigh benefits conferred by signal averaging or slit lamp mounting. We calculate calibration factors of 0.89 mmHg/arbitrary unit for Meditron and 0.82 mmHg/g for Sisler. These factors may be useful in the estimation of ocular vascular, orbital, and cerebrospinal fluid pressures.

Studies on Bayes classifier for condition monitoring of single point carbide tipped tool based on statistical and histogram features

Various methods of tool condition monitoring techniques are used to control the tool wear during machining in CNC machine tools. Based on a continuous acquisition of signals with sensor systems it is possible to classify certain wear parameters by the extraction of features. Data mining approach is used to probe into the structural information hidden in the signals acquired. This paper discusses machine tool condition monitoring of carbide tipped tool by using Naïve Bayes and Bayes Net classifiers and compares the results of histogram features with the statistical features to establish better classification among the two. The vibration signals are acquired for various tool conditions like tool-good condition, tip-breakage, etc. The effort is to bring out the better feature–classifier combine. The results are discussed.

Thermal properties of fruit juices as a function of concentration and temperature determined using the photopyroelectric (PPE) method

The photopyroelectric (PPE) method is proposed as a new technique for thermal characterisation of fruit juices. By combining different geometries and experimental conditions, the PPE method was shown capable of providing temperature-dependent (between 0 and 70 °C) values of thermophysical parameters (thermal diffusivity, thermal effusivity, thermal conductivity and volume specific heat) for juices having different Brix index. The computer-controlled PPE cell allowed stable temperature variation rates and continuous signal acquisition. The experimental data were used to develop empirical models and hence prediction equations for all the thermal parameters. © 1999 Society of Chemical Industry