This work presents the proposal for a method of prediction of thermal currents by means of neural networks, to facilitate decision-making in the flight of autonomous drones that use this form of energy to fulfill their missions. It is based on the experimental measurement during the flight of environmental parameters, geographical location, time and speed.
This work aims to assess inter-seasonal changes of a supervised classification in San Juan city and surroundings, Argentina, focusing on urban land use. A land cover supervised classification was performed from Landsat 8 (OLI) and Sentinel-1 SAR images and a spectral indices set. This classification showed a right class differentiation (Short/medium vegetation, Medium/tall vegetation, Bare soil, Erodible sediment, Rocky Material, Urban and Peri-urban), however, variations of the Urban class area between winter and summer seasons were observed. Alternatively, a classification based on VIIRS (NOAA-20) night lights images was performed. Although this second classification shows significant inter-seasonal differences, these can be explained by the vegetation medium/tall (trees) contrast between both seasons. A combination of day and night images classification was carried out, which represents an improvement over previous work on the seasonal stability of the Urban and Peri-urban classes. The obtained map is suitable to be used in future studies on the region.
This project focuses on the development of a set of devices that allow remote monitoring and remote control of the cistern and drinking water pumping station, in the city of Morteros (Cordoba), to streamline the operation and decision-making before emergencies. Specifically, it is proposed to create an IoT network with ESP32 and Arduino DUE microcontrollers for taking a remote reading of the tank level and flow of entry to it, adapting digital communications with the plant's own equipment; this also includes bi-directional communication with the PID controller that drives the booster pumps. Finally, the inclusion of new equipment and sensors that can determine the magnitudes of water inlet pressure, cistern outlet flow, dosage and amount of available chlorine and determine possible flooding due to water leaks in the pits is projected that are below ground level. After the implementations and after several months of its start- up, the project was concluded favorably since it was possible to have the monitoring and control of the plant; allowing a better administration of this and a reduction in failures.
The Argentine agricultural sector is a relevant worldwide commodity producer, being the main exporter in the country. Conventional crop management systems lead to inefficient time and costs. In Argentina, the Ministry of Agriculture, Livestock and Fisheries is the main entity that collects and reports productive data, a laborious task with high costs, slow and error-prone. Technological improvement and competitiveness of the sector require a new perspective in the current data management, towards digital transformation, achieving more efficiently the capabilities of the agricultural sector. The aim of this article is to introduce an agricultural data management platform, which includes several information-based technologies for enhancing the management of agro-productive establishments data. This system has great potential in data collecting, integration and analytics from diverse sources, with the goal of providing users with consistent and precise access and delivery of relevant data in support of decision making, and to meet the information requirements of all processes. The system is currently in use in an extensive region of the Southwest of the Buenos Aires Province and East of the La Pampa Province, which are under the logistic influence of the Bahia Blanca Port. The platform offers unified access to datasets and satellite imagery, automates the most relevant users processes such as field information upload (i.e., during sowing and harvesting seasons), processing and image visualization. Furthermore, the system includes functionalities such as remote monitoring, tools for crop estimation, and direct access to weather records and forecasts from the National Weather Service (SMN). This development represents a strategic development which goes beyond the purely technological aspects. It is an open innovation tool, promoting the creation of value, aiming at the digital transformation of the agricultural productive system.
Respiratory rate (RR) is an useful vital sign to evaluate a patient’s clinical condition. For this reason, an algorithm based on a linear filter and photoplestimography (PPG) signal is presented. This signal is used by the commercial pulse oximeters and its record uses a non-invasive and low-cost optical technique. The algorithm is characterized by low computational cost, which is suitable to be implemented in devices that have batteries and low computational resources such as wereable devices. Its validation was performed using two public databases available on the web (BIDMC and CCSHS). In total, 541 records with PPG signal were tested. Furthermore, a spectral analisys method was applied to define the filter cuttoff frequency. On the other hand, the root mean square error (RMSE) was used for performance evaluation. Several statistics parameters based on this parameter were computed such as mean (Me), standard desviation (std), median (Med) and interquartile range (IQR). The results obtained in each database, BIDMC (3,07; 1,68; 3,03; 2,13) and CCSHS (3,18; 0,77; 3,10; 1,02), are comparable with other works that use more complex signal processing methods requiring more computational resources. We conclude that the proposed algorithm might be quite relevant for the design of portable monitoring systems, particularly those used in telehealth.
In this work, a waveform design using a combination of OFDM and companding technique is proposed to improve the wireless power transfer (WPT) efficiency without sacrificing the wireless information transfer performance. Employing a SimultaneousWireless Information and Power Transfer (SWIPT) scheme called Power Splitting (PS) protocol, the transfer of power and data is achieved simultaneously between a Power Beacon (PB) and a receiver node. Furthermore, in order to capture all important nonlinear behaviors of a real energy harvester rectifier circuit, a curve fitting-based nonlinear rectifier model is used. Results show that the use of this companding technique allows the power amplifier (PA) to generate and transmit signals for WPT in a more efficient way, improving also the conversion efficiency of the energy harvester.
In a brain-computer interface (BCI), the first module after the user, is the amplifier to acquire electroencephalography (EEG) signals. These are usually multichannel amplifiers which include pre-condition of the EEG signal for further processing. To develop BCI based on BCI2000, there are many data acquisition modules as contributions from users of BCI2000. These contributions support EEG amplifiers that are not easily available for Latin American countries, limiting their accessibility by patients and health institutions, which are BCI’s final users. This article presents the design and evaluation of a new signal source module for BCI2000 to connect a biopotential amplifier from the open-source BioAmp project. This new signal source module embedded in BCI2000 allows the acquisition, visualization of EEG signals and their storing for later processing. Its implementation in the clinical context will allow the advancement in translational research in the BCI field.
This work presents a differential drive mobile robot design for telepresence applications. It consists on two independent wheels, which are driven by a brushless motor equipped with Hall effect current sensors for low-level control. Each motor has an associated PID velocity controller. The robot also includes a lidar sensor and a RGB-D camera to improve the environment perception. Several algorithms are included in the main Raspberry Pi 4 central processor working under ROS framework. However, the main control level incorporates a solution for the person following task and a video streaming software to allow telepresence. The robot can be also teleoperated by means of an external joystick. The user interface is based on a touch screen to allow the control and configuration of the different modules. Regarding the mechanical design, the robot consists on several segments built on carbon steel, aluminum, and 3d printing.
The influence of design parameters at cell level on performance at battery pack level is analyzed, in order to find the main causes of cell voltage unbalances and the consequent loss of battery pack capacity. The study parameters are electrode thicknesses, electrode porosities and electrolyte salt concentration. Three battery packs are defined with a 20 percent range variation in the values of the named design parameters. The configuration of the analyzed pack consists of six lithium cells, consisting of graphite anode and manganese oxide cathode, connected in series. For this analysis, a mathematical model with physical and phenomenological basis is applied in an optimization environment. The proposed optimization framework consists of maximizing the pack capacity by operating in simple steady-state charge-discharge cycles. The limiting cells are easily identified from the active bounds in key variables defined by the model. The most influential design parameter at the pack level, for the considered cell chemistry, turns out to be electrode porosity, since the pack with cells of different values of this parameter presents a capacity reduction of up to 15 percent when is compared to the pack of uniform cell designs.
Mathematical models for the generation of the action potential can improve the understanding of physiological mechanisms that are consequence of the electrical activity in neurons. In such models, some equations involving empirically obtained functions of the membrane potential are usually defined. The best known of these models, the Hodgkin-Huxley model, is an example of this paradigm since it defines the conductances of ion channels in terms of the opening and closing rates of each type of gate present in the channels. These functions need to be derived from laboratory measurements that are often very expensive and produce little data because they involve a time-space-independent measurement of the voltage in a single channel of the cell membrane. In this work, we investigate the possibility of finding the Hodgkin-Huxley model’s parametric functions using only two simple measurements (the membrane voltage as a function of time and the injected current that triggered that voltage) and applying Deep Learning methods to estimate these functions. This would result in an hybrid model of the action potential generation composed by the original Hodgkin-Huxley equations and an Artificial Neural Network that requires a small set of easy-to-perform measurements to be trained. Experiments were carried out using data generated from the original Hodgkin-Huxley model, and results show that a simple two-layer artificial neural network (ANN) architecture trained on a minimal amount of data can learn to model some of the fundamental proprieties of the action potential generation by estimating the model’s rate functions.
Hospital facilities are considered to be some of the buildings with the highest consumption per unit of floor area, due to the use of the functions performed in each hospital area and the need to ensure strict conditions for the habitability of its users. On the other hand, it has been detected that in the hospital network there are different construction systems in terms of the characteristics of the building envelope for the same hospital areas. This is due to the fact that they have been designed in different time contexts, responding to different criteria and regulations. In the current context, which leads to the need to provide energy efficiency solutions in a strategic way, this work provides a tool for the detection of the situations that present greater heat loss by the building envelopes. Thus, by applying the calculator developed to the same hospital area distributed in different buildings, an indicator of heat loss through the building envelope per unit of floor area is obtained. The results achieved make it possible to direct specific measures to the most critical situations, which will make it possible to save resources for building envelope refurbishment when implementing alternative energy strategies.
In this work an Electronic Control and Communications Platform is presented whose function is to execute all the necessary control and communication actions to allow controlling the energy flow in a bidirectional way in a solid-state transformer, controlling the power factor in the interconnected networks, controlling the injection of reactive power, receive the control instructions from the load dispatch center, report its operating status remotely, and in this way, allow the solid-state transformer to function as an interface between distributed generation systems (solar panels, wind turbines, micro-turbines, etc.), electrical energy storage and prosumers.
The amplitude of steady-state evoked potentials are modulated by the attention-level payed to the stimuli. In this manuscript, the effects of the multisensory stimulation (auditory and visual) are evaluated for generation of commands in a brain-computer interface experiment. Electroencephalographic signals were acquired from disposable electrodes on non-hair positions along with occipital electrodes. Three visual stimuli were presented at 37, 38 and 39 Hz and two amplitude modulation auditory stimuli at 38 and 39 Hz were used as well. The carrier signal was 1000 Hz. Then, these stimuli were combined into four different modes. Six volunteers participated on the experiments. Multisensory stimulation allows to obtain an increased performance of 15% on the detection of evoked potentials.
Accurate information of solar radiation from satellites is crucial for many applications, mainly in regions with lack of ground-based measurements. In this sense, comparison with ground-based measurement is necessary to ensure the reliability of the information. In this work, the daily global solar irradiation data from NASA’s Prediction of Worldwide Energy Resources (NASA-POWER; power.larc.nasa.gov) were compared with ground-based measurements in the 8 stations of the Saver-Net solar irradiance network (http://www.savernet-satreps.org/en) installed in the south of South America. A linear regression analysis was performed to analyze the agreement between satellite data and ground-based measurements. The coefficient of determination shows very good correspondence with a mean value of 0.95. The mean absolute error (0.63 kWh/m2/d) and the root mean squared error (0.48 kWh/m2/d) reflect a low difference.
The global warming resulting from the use of fossil fuels is putting pressure on governments to formulate and adopt energy policies aimed at different sectors of the economy. One of the most significant measures in this direction is industrial energy efficiency, a subject that concerns SMEs. An SME was identified in the Valle del Cauca region that recognized the economic and environmental need to have a more efficient system, especially in energy consumption. A bakery company met the requirements, this company has whole wheat bread as its main product. Likewise, during its operating time, an energy performance study has not been carried out, which leads to not knowing if they make adequate use of energy, be it electrical or in the form of heat, which can cause inefficiencies in their processes.Keeping these aspects in mind, in this article an energy audit process was implemented in the bakery company, according to the guidelines of ISO 50002; structuring the stages of the audit process, using a flow diagram; identifying different opportunities for improving energy performance for equipment with higher consumption and therefore greater savings potential; evaluating these improvement opportunities, which are linked to the change of operation and maintenance; and generating recommendations that make it possible to make correct decisions regarding energy consumption and thus reduce costs of this nature.
One of the main sources of electromagnetic interference that affects the operation of weather radars is due to Wi-Fi networks that operate in the same frequency band.There are various signal processing strategies to mitigate the interference effect. One of these strategies is to detect the signal using the deterministic preamble of the Wi-Fi packets.This work presents a radar receiver identification technique that allows generating a reference signal that better assimilates the preamble received in the processing stage and therefore helps to improve the method for detecting these interfering signals.
This work proposes a null space-based controller for a multi-articulated robot vehicle (MARV) in backward movements, capable of avoiding collision with static obstacles during navigation. This can be the case of a multi-trailer truck moving backwards or even a tractor vehicle pushing passive trailers in agricultural applications. These articulated vehicles should also avoid possible collisions between the elements of the composition (situation known as jackknife), which would prevent the vehicle to continue in motion. In this context, the article discusses the application of null space-based control to a MARV, to manage the conflicting tasks of following a path and avoid obstacles surrounding it. The MARV modeling and the proposed controller are presented. Results obtained running simulations considering a MARV composed of a unicycle mobile robot pushing two trailers validate the approach adopted to guide the MARV navigation when there are obstacles nearby or on the path being followed.
The use of Unmanned Aerial Vehicles (UAV) and digital photogrammetry has evolved as a versatile and low cost solution for small-to-medium scale Digital Surface Models (DSM) generation. This can be done using two different software tools: preplanned mission control software, and post-acquisition photogrammetry software. Still, even modern photogrammetry software requires a proper parameter setting that can be optimized to enhance final DSM results. In this work, we present a parametric analysis of OpenSource software called OpenDroneMap (ODM). Several DSM reconstructions were done assessing different parameter settings, and evaluating the results against local measurements and building’s construction plans aiming at optimizing the results. Also, ODM software generated orthophotos were used as an additional evaluation of the process. Optimal values of the parameters with more impact on the resulting model were found, but other considerations, relative to weather conditions, are detailed as a guide to improve the whole DSM reconstruction process.
The aim for organic farming is obtaining food of the highest quality, avoiding synthetic chemicals, protecting the environment and preserving the fertility of the land. In this context, effective pest control allows to reduce yield loss and pesticides application producing pollution-free vegetables. In fruit crops, Carpocapsa is the main pest present in pear, apple, walnut and quince trees. This insect produces irreversible damage to the fruit, since the larvae feed the seeds inside the fruit. In this paper, we present automatic pest detection and classification in the context of fruit crops based on image processing and Deep Neural Networks, employing an image collection obtained from in-field traps. Due to the limited size of the data set, we perform data augmentation to increase the number of images for training, to prevent over-fitting and to improve the deep neural network learning rate. Results showed an overall accuracy of 94.8%, while precision and recall scores for the class related with the moth were around 97.2% and 93.6% respectively, demonstrating the efficacy of this type of classifier proposed for pest detection. An inference time of 40 ms per image for the deep neural network classifier has been reached.
Formation of new particles in the atmosphere is a phenomenon of great importance in the Earth’s climate system. To study this phenomenon, number concentration of particles of various sizes (even nanometric) must be measured over long periods of time. Traditionally the analysis of the data requires a manual visual inspection of the records following pre-established protocols. A critical step in the analysis of the measurements is to detect those moments where the new particle formation (NPF) events actually occurred. In addition, the number of formed new particles and their particle dynamics are typically investigated and quantified. Manual analysis of the measurements makes the obtained results strongly subjective, even if the established protocols are strictly followed. Therefore, obtained results, such as the frequency of occurrence of such events, or the average new particle formation rate, can be highly variable. To decrease these uncertainties, we have developed a new methodology to automatize the NPF analysis. In this work, we present a system based on Hidden Markov Models (HMM) to automatically detect in long data series the instants where a NPF event occurs. We show that the HMM can be used to detect NPF event in an objective and effective way, with low complexity either to create the automatic classification system or to use it.
