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  • Open access
  • 104 Reads
Smartphone-Based Optical Fiber Sensor for the Assessment of a Fed-Batch Bioreactor

Industry is currently in a period of great expansion, the so-called “Industry 4.0”. It relies on the development of new sensor technologies for the generation of systems capable of collecting, distributing and delivering information. Particularly, on Chemical and Biochemical industries, the development of portable monitoring devices can improve many process parameters, like safety and productivity. In this work, the design of a smartphone-based optical fiber sensing platform for the online assessment of fed-batch fermentation systems is reported. The setup is comprised of a smartphone equipped with a 3D-printed case and an application for analyzing the pixel intensity, which is correlated to the broth refraction index (function of sucrose concentration). A sensitivity of 85.83 RIU-1 (refractive index unit) was verified, and the sensor performance was compared to a handheld refractometer and to model predictions. It showed to be a reliable, portable and low-cost instrument for online monitoring bioreactors, easily reproducible on-site by simply printing it.

  • Open access
  • 73 Reads
Detection of Velocity Based on Change in the Apparent Size

This article discusses a concept for developing a vision-based sensing system for measuring the velocity of an object which is based on the concept of apparent size. Objects at a finite distance from the eye look smaller than their real dimension. Movement of the object causes to change in its apparent size. In this work a mathematical relation is obtained which relates infinitesimal change in apparent size to the infinitesimal change in spatial coordinates of the object in the form of an ordinary differential equation. A mechanical device is fabricated for measuring the apparent size. Then by knowing the change in apparent size due to motion, change in displacement is calculated. Experiments are conducted to measure the average velocity of a regular shaped object based on the change in its apparent size due to its motion. The average magnitude of error between average velocity calculated from the change in apparent size through the equation and from the actual displacement is about 2% and it is varying in between 0 and 5%. Results show the possibility to develop a vision sensor system to measure the velocity of objects by using high-speed cameras when the real size of the object is known and also it may be possible to develop vision velocity sensors for mobile robot applications

  • Open access
  • 93 Reads
Adaptation and Selection techniques based on Deep Learning for Human Activity Recognition using Inertial Sensors

Deep learning techniques have been widely applied to Human Activity Recognition (HAR), but a specific challenge appears when HAR systems are trained and tested with different subjects. Each user presents different patterns when performing several physical activities, so HAR systems should adapt the activity models trained with some users’ data to new subjects. This paper describes and evaluates several techniques based on deep learning algorithms for adapting and selecting the training data used to generate a HAR system using accelerometer recordings. This paper proposes two alternatives to adapt and select the training data: autoencoders and Generative Adversarial Networks (GANs). Both alternatives are based on deep neural networks including convolutional layers for feature extraction and fully-connected layers for classification. Fast Fourier Transform (FFT) is used as a transformation of acceleration data to provide an appropriate input data to the deep neural network. This study has used acceleration recordings from hand, chest and ankle sensors included in the PAMAP2 dataset. This is a public dataset including recordings from nine subjects while performing 12 activities such as walking, running, sitting, ascending stairs or ironing. The evaluation has been performed using a Leave-One-Subject-Out cross-validation: all recordings from a subject are used as testing subset and recordings from the rest subjects are used as training subset. The obtained results suggest that strategies using autoencoders to adapt training data to test data improve the general performance. Moreover, training data selection algorithms with autoencoders also provide improvements. The GAN approach, using the discriminator module, provides a significant improvement in adaptation experiments.

  • Open access
  • 115 Reads
Intelligent plant disease identification system using Machine Learning

Agriculture is the backbone of each and every country in the world. In India, most of the rural population still depends on agriculture. The agricultural sector provides major employment in rural areas. Further, it contributes a significant amount to India’s Gross Domestic Product (GDP). So, protecting and enhancing the agricultural sector helps in developing India’s economy. In this work, a real-time decision support system integrated with camera sensor module is designed and developed for identification of plant disease. Further, the performance of three machine learning algorithm such as Extreme Learning Machine (ELM), Support Vector Machine (SVM) with linear and polynomial kernels is analyzed. Results demonstrate that the performance of extreme learning machine is better when compared to the adopted support vector machine classifier. Also, it is observed that the sensitivity of support vector machine with polynomial kernel is better when compared to the other classifiers. This work appears to be of high social relevance since the developed real-time hardware is capable of detecting different plant diseases.

  • Open access
  • 67 Reads
Technical and Economic Viability Analysis of Optical Fiber Sensors for Monitoring Industrial Bioreactors

Bioreactors are employed in several industries, such as pharmaceutics, energy, biomedic and food. To ensure the proper operation of these bioreactors, Enzyme-Linked Immunosorbent Assay (ELISA) and High-Performance Liquid Chromatography (HPLC) systems are commonly used. Although ELISA and HPLC provide very precise results, they are incapable of real-time monitoring and present high operational costs. Given this context, in this work we discuss the technical and economic viability of implementing fiber optics based monitoring systems in lieu of traditional ELISA and HPLC systems. We have selected fed-batch ethanol fermentative systems for our analysis, as fermentative system are not only very prevalent in different industries, but ethanol production represents a major sector of the Brazilian economy, with an annual production in excess of 35 billion liters. A simple fiber sensing system for measuring the refractive index of the fermentation broth, capable of real-time monitoring the fermentation process, is proposed and the advantages of real-time process control are discussed. Afterwards, we present the long-term economic gains of implementing such a system. We estimate that, by using readily commercially available components, the typical Brazilian ethanol plant will see a return for their investment in a time as short as 50 days, and a 5-year Internal Rate of Return (IRR) of 742% by setting up a fiber optic monitoring system over HPLC. With the provided list of components, these numbers can be easily adjusted for industries worldwide, providing incredibly attractive economic prospects.

  • Open access
  • 108 Reads
Analysis of preload effect in the axisymmetric damped steel wire using ultrasonic guided wave monitoring

In this research paper, the guided ultrasonic wave propagation characteristics in the axisymmetric prestressed viscoelastic waveguide for acoustic emission (AE) monitoring using the semi-analytical finite element (SAFE) method is studied broadly. The formulation demonstrated in this work uses a cylindrical coordinate system to reduce the finite element discretization to one-dimensional mesh over the cross-section of the axisymmetric waveguide. For the numerical investigation, a single high strength steel wire is considered. Based on the axisymmetric SAFE method, a comprehensive and in-depth study on the propagation characteristics of the AE signal in a single wire is carried out. Both undamped and damped waveguides are considered for attaining SAFE solutions and presented in a detailed manner. The SAFE method for an axisymmetric cross-section in cylindrical coordinates is utilized to analyze the two main influencing factors of steel wire in a practical scenario, namely, material damping and initial tension. For steel wire of viscoelastic material, energy velocity and attenuation factor are indicators that are more suitable for describing their fluctuation characteristics. For the effect of initial stress, the calculation shows that the initial tensile stress can increase and decrease the energy velocity and attenuation factor of most modal waves above the cut-off frequency, and the effect is linear. Finally, a mode suitable for cable AE monitoring is carefully chosen. Some longitudinal wave modes in the high-frequency region show their potential for AE monitoring as these modes have a low attenuation factor and small external surface vibration. By considering various states of initial stress in a cylindrical damped waveguide, the effect of prestress on the dispersion characteristics is understood in a better manner. It showed potential for non-destructive evaluation and health monitoring application of overhead transmission line conductors.

  • Open access
  • 69 Reads
Multimodal stimulation system to control fibroblast proliferation using optical and ultrasonic stimulation

An optical stimulation shows various effects for skin regeneration and wound treatment by using different wavelength. Similarly, ultrasound stimulation can improve skin wrinkles and contours by inducing the contraction and synthesis of collagen to reduce local fat accumulation. In this study, using commercially available LEDs for skin regeneration masks (415nm, 630nm, 850nm), a single wavelength and multiple wavelengths were applied to fibroblast cells in various ways to control the proliferation effect of skin cells. In addition, ultrasonic stimulation was applied simultaneously to quantitatively evaluate the proliferation effect of fibroblasts. As a result, it was confirmed that there was an effect on fibroblast cell proliferation when the LED light stimulation of a specific wavelength was applied, and also the proliferation activity of skin cells increased even in the multimodal stimulation by applying a combination of LEDs and ultrasound.

  • Open access
  • 95 Reads
Acoustic Description of Bird Broiler Vocalisations in a Real-Life Intensive Farm and Its Impact on Animal Welfare: A Comparative Analysis from several Recording Campaigns

The poultry meat industry is one of the most efficient biological systems to transform cereal protein into high quality protein for human consumption at a low cost. However, intensive production generates stress to animals, which can be major sources of welfare problems. In this study, broiler bird welfare is measured by some indicators: CO2, temperature, humidity, weight, deaths, food and water intake. Additionally, we approach the acoustic analysis of the bird's vocalisations as a possible metric to add to the aforementioned parameters. For this purpose, an acoustic recording and analysis of an entire production cycle of an intensive broiler Ross 308 poultry farm in the Mediterranean area has been performed, and that data has been analysed. The following step to consolidate the analysis is to stablish a clear comparison among the performance of the indicators (Leq, PF, PF variation, death, CO2, weight, etc) in the conditions of three different recording campaigns corresponding to three different entire production cycles. The promising dependencies about peak frequency and Leq variability among the other parameters measured in the farm previously studied should be validated in an inter-campaign comparison, which may also arise the possibility of changes due to the season of the year.

  • Open access
  • 66 Reads
Effect of the different crystallinity of ionic liquid based solid polymer electrolyte on the performance of amperometric gas sensor

Disadvantages of classical liquid electrolytes are overcome by the usage of solid polymer electrolyte (SPE) which is usually based on organic ionic liquid immobilized in polymer matrix. Ionic liquids are currently widely used in various fields of electrochemistry and chemistry because of their unique properties, which are partially implemented in SPE. The advantage lays in the composition, which offers an opportunity to prepare SPE layers with different porosity and microstructure.

The experimental study was carried out on semi-planar three-electrodes amperometric sensor with the layer of SPE, that consists of 1-butyl-1-methylpyrrolidinium bis(trifluoromethylsulfonyl)imide[BMPYR][N(Tf)2], poly(vinylidene fluoride), and 1-methyl-2-pyrrolidone. The SPE layer was deposited by drop casting on alumina substrate with platinum electrodes. The substrate with SPE layer was placed on a hot plate where the sample was kept at an appropriate temperature for a specific time in order to achieve different crystalline forms of the polymer in the solvent. Afterwards, the working electrode was deposited by airbrushing of spherical glassy carbon powder. All electrical measurements were provided under equilibrium conditions when the sensor was being kept at particular NO2 concentration for the required amount of time to fulfill the memorylessness of current fluctuations.

The study shows how SPE of different crystallinity affects the performance of amperometric gas sensor from the point of view of current response (sensitivity), limit of detection and current fluctuations. The sensor with the SPE of the highest temperature and the longest interval of treatment after deposition showed the highest current fluctuations in the frequency range as well as the highest current response on change of detected gas concentration.

  • Open access
  • 87 Reads
Comprehensive optimization of the tripolar concentric ring electrode with respect to the accuracy of Laplacian estimation based on the finite dimensions model of the electrode

Optimization performed in this study is based on the finite dimensions model of the concentric ring electrode as opposed to the negligible dimensions model widely used in the past. This makes the optimization problem comprehensive since all of the electrode parameters including, for the first time, the radius of the central disc and individual widths of concentric rings are optimized simultaneously. The optimization criterion used is maximizing the accuracy of the surface Laplacian estimation since ability to estimate Laplacian at each electrode constitutes primary biomedical significance of concentric ring electrodes. Even though obtained results and derived principles defining optimal electrode configurations are illustrated on tripolar (2 concentric rings) electrodes, they were also confirmed for quadripolar (3 rings) and pentapolar (4 rings) electrodes and are likely to continue to hold for any higher number of concentric rings. For tripolar concentric ring electrodes, the optimal configuration was compared to previously proposed linearly increasing inter-ring distances and constant inter-ring distances configurations of the same size and also based on the finite dimensions model of the electrode. Obtained results suggest that previously proposed configurations correspond to an almost two-fold and more than three-fold increases in Laplacian estimation error respectively compared to the optimal configuration proposed in this study.