Browsing by Type "Controlled Vocabulary for Resource Type Genres::other"
  • Institution Publication
    Angle-Based Parametrization with Evolutionary Optimization for OESCL-Band Y-Junction Splitters
    (MDPI AG, 2023-02-01)
    Armas Alvarado, María Elisia
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    Prosopio-Galarza, Roy
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    García-Gonzales, J. Leonidas
    ;
    Jara, Freddy
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    Gonzalez, Jorge
    ;
    Rubio-Noriega, Ruth E.
    The design of passive photonic devices based on geometry optimization can lead to energy-efficient, small-footprint, and fabrication-ready geometries. In this work, we propose an angle-based parametrization method to optimize Y-junction splitters based on multimode interferometers. The selected figure of merit was the transmittance in the SCL and OESCL optical fiber communication bands. The performances of three optimization methods were compared: (i) particle swarm optimization (PSO), (ii) genetic algorithm (GA), and (iii) the covariance matrix adaptation-evolution strategy (CMA-ES). The results show that CMA-ES parametrization produces similar transmittance results (≤1.5% of difference) to PSO in the first 40 generations. The CMA-ES results are identical in the SCL (1460–1625 nm) and OESCL (1260–1625 nm) bands, whereas the GA and PSO executions are slightly different in terms of the rate and similarity of the figure of merit.
  • Institution Publication
    Automatic Segmentation of Mauritia flexuosa in Unmanned Aerial Vehicle (UAV) Imagery Using Deep Learning
    (MDPI AG, 2018-11-26)
    Telles Castillo, Joel Enyelber
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    Morales, Giorgio
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    Kemper, Guillermo
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    Sevillano, Grace
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    Arteaga, Daniel
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    Ortega, Ivan
    One of the most important ecosystems in the Amazon rainforest is the Mauritia flexuosa swamp or “aguajal”. However, deforestation of its dominant species, the Mauritia flexuosa palm, also known as “aguaje”, is a common issue, and conservation is poorly monitored because of the difficult access to these swamps. The contribution of this paper is twofold: the presentation of a dataset called MauFlex, and the proposal of a segmentation and measurement method for areas covered in Mauritia flexuosa palms using high-resolution aerial images acquired by UAVs. The method performs a semantic segmentation of Mauritia flexuosa using an end-to-end trainable Convolutional Neural Network (CNN) based on the Deeplab v3+ architecture. Images were acquired under different environment and light conditions using three different RGB cameras. The MauFlex dataset was created from these images and it consists of 25,248 image patches of 512×512 pixels and their respective ground truth masks. The results over the test set achieved an accuracy of 98.143%, specificity of 96.599%, and sensitivity of 95.556%. It is shown that our method is able not only to detect full-grown isolated Mauritia flexuosa palms, but also young palms or palms partially covered by other types of vegetation.
  • Institution Publication
    Characterization of a low consumption wireless sensor node for the intensive transmission of physiological signals
    (Institute of Advanced Engineering and Science, 2019-05-01)
    Yauri, Ricardo
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    Rubiños, Santiago
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    Grados, Juan
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    Chauca, Mario
    This paper describes the development and implementation of low power consumption wireless sensor nodes for the periodic monitoring of physiological signals with intensive data transmission, using Wi-Fi and ZigBee wireless communication modules, obtaining operation characteristics from the energy point of view that allow to increase the life time of the sensor node. The sensor nodes are designed and built using low energy consumption electronic devices to evaluate their energy performance using current data, transmission time, data transmission period and the relationship with the sensor node's lifetime when transmitting electrocardiographic (ECG), temperature and pulse type physiological signals. The development of this work generates recommendations for the design, development and construction of sensor nodes where the energy consumption of the wireless communication modules is evaluated. In this way, results are obtained that can allow the data transmission period, current consumption and size of data sent to be related to the operating time, defining the operating conditions and wireless technologies that allow the optimization of energy consumption when data is sent to Internet monitoring applications.
  • Institution Publication
    Comparative Analysis of Three Types of VHF/UHF Antennas for GPR Array
    (IEEE, 2021-11-11)
    Guerra-Huaranga, Tanith
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    Rubio-Noriega, Ruth
    ;
    Clemente-Arenas, Mark
    This article presents the comparative analysis of antennas for Ground Penetration Radars (GPR) that operate in the VHF/UHF band around 200 MHz. Three types of antennas are optimized to improve bandwidth, obtain a gain greater than 5 dB while analyzing their Half Power Beam Width-HPBW, and front to back levels. These antennas are candidates to be implemented on a 2×2 array for a double polarized GPR to improve its resolution and range. This radar conducts non-invasive explorations to obtain data from Caral’s archaeological center in the city of Lima, Peru. The geometry design, simulation results, and optimization of the antennas were calculated using the ANSYS-HFSS software. The simulation results have indicated that the Antipodal Vivaldi antenna showed better results than the Log Periodic Dipole Arrays and the DGS Vivaldi antennas.
  • Institution Publication
    Deep Neural Network-Assisted Microfluidic pH Sensor
    (Institute of Electrical and Electronics Engineers (IEEE), 2025-04-15)
    Armas Alvarado, María Elisia
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    Ventura-Grandez, Henry E.
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    Quevedo, Jonathan
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    Salazar-Reque, Itamar
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    Adanaque-Infante, Luz
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    Rubio-Noriega, Ruth
    Water pH measurement is vital as it provides fundamental information about its quality and suitability for agriculture, aquatic ecosystems, industry, and human consumption. Each of these applications may require numerical readings of acidity or alkalinity, preferably using tools that are already ubiquitous, such as cellphones. This work presents a microfluidic lab-on-a-chip system to measure the pH of liquid samples. We used purple cabbage as the colorimetric reagent to produce a 2640-image dataset with pH levels in the range of [2–12] on a polydimethylsiloxane (PDMS) microfluidic recipient. We fed our dataset to our parameterized deep neural network (DNN) to classify our samples and found an accuracy of 99.7%. In addition, we developed a mobile application with an easy-to-use graphic user interface that recognizes the microfluidic device shape, classifies the image’s color, and returns the pH level.
  • Institution Publication
    Design and implementation of a low cost isotropic electric field sensor for SAR measurements
    (Institute of Electrical and Electronics Engineers (IEEE), )
    Quispe Choquehuanca, Marco Antonio
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    Samaniego Manrique, Javier Eulogio
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    Olivares Quispe, Jorge Armando
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    Adriano, Rolando
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    Perez, Brayan
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    Inca, Saúl
    Specific Absorption Rate (SAR) measurement systems are used to evaluate exposure to fields radiated by wireless devices placed close to people's bodies. One of the fundamental elements of these systems is an isotropic electric field sensor that is small relative to the radiated wavelength. The obtained SAR value must be compared with the basic restriction levels or exposure limits established by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) or similar international bodies. This work presents the design and implementation of a novel isotropic electric field sensor made with low-cost materials and processes. The response of the developed sensor was compared with the response of a commercial probe in order to perform calibration. The calibration methodology proposed in this work allows transforming voltage measured with the developed low-cost probe to electric field values measured with a calibrated commercial probe in order to calculate SAR values. The results show that the developed sensor can be used in SAR measurement processes.
  • Institution Publication
    Design and implementation of a low-cost prototype system for continuous monitoring of electric fields generated by mobile base stations in the 850 MHz and 1900 MHz frequency bands
    (Institute of Electrical and Electronics Engineers (IEEE), 2016-03-21)
    Chuchón Nuñez, Mariano
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    Samaniego Manrique, Javier Eulogio
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    Quispe Choquehuanca, Marco Antonio
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    Adriano, R.
    This paper describes the development of a scalable, autonomous and inexpensive monitoring system of electric fields generated by mobile phone base stations in the 850 MHz and 1900 MHz frequency bands, in Peru. The developed system is scalable because it consists of a ZigBee wireless network with electric field strength sensor and repeater nodes in a mesh topology with a dynamic routing mechanism. Sensor and repeater nodes are autonomous because they get their power from a small photovoltaic system. All electronic circuits were developed using low-cost technology. To manage the system elements and to store the information captured by the sensors, a web platform was developed. Tests in laboratory and field were performed to assess the accuracy of the sensor node. For this, a calibrated isotropic Narda SRM 3000 electric field meter was used. The results were very satisfactory for 1900 MHz frequency band but not for 850 MHz; some recommendations are given to improve the accuracy in the latter case. The performance of the sensor node's photovoltaic system was also assessed through the monitoring of the continuity of the system operation over time. It was verified that the sensor node was kept operating continuously without failures during a three-month test.
  • Institution Publication
    Design and implementation of an alternative measurement system for MOSFET-Like sensors characterization
    (Universidad Nacional Autonoma de Mexico, 2019-06-25)
    Acosta Jacinto, Rubén Eusebio
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    Zorrilla, Luighi Viton
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    Lezama, Jinmi
    This paper describes the design and implementation of an alternative system to measure electrical parameters (voltage and current) in order to characterize MOSFET-Like sensors. To design a signal conditioning circuit is necessary to understand the sensor behavior, therefore knowing its characteristics is essential. As sensor manufacturers do not usually provide the whole technical information about them, a measurement system is proposed to obtain those sensor characteristics with the aim of modeling the sensor device. This system is based on a MCU which generates voltages and measures currents via an external transimpedance amplifier, and it is supported by a software platform developed upon python based open source tools. Such combination offers a low cost system to stimulate and capture sensor responses which could be processed later to extract the characteristic parameters. The system was tested principally with an Ion Sensitive Field Effect Transistor (ISFET) and results show the VDS-IDS and VGS curves obtained with it.
  • Institution Publication
    Detection of Rust Emergence in Coffee Plantations using Data Mining: A Systematic Review
    (Science Publications, 2022-03-31)
    Acosta Jacinto, Rubén Eusebio
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    Ríos Julcapoma, Milton
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    Huatangari, Lenin Quiñones
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    Ocaña Zúñiga, Candy Lisbeth
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    Huaccha Castillo, Annick Estefany
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    Milla Pino, Manuel Emilio
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    Rodríguez, Ricardo Yauri
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    Villaizán, Eduardo Mendoza
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    Cabrera, Aladino Pérez
    Hemileia vastatrix is a fungus that causes coffee rust disease and, depending on the level of severity, reduces the photosynthetic capacity of the plant and of new shoots, leading to low coffee yields and even death; its symptoms are visible on the leaf. Systems based on computer algorithms have been developed to predict diseases and pests in coffee. The objective of the manuscript was to analyse the detection of rust occurrence in coffee plantations, through field determinations of climatological, agronomic and crop management variables using data mining algorithms. A systematic review of studies published from 2001 to 2021 was carried out in the Scopus, Ebsco Host and Scielo databases, considering as an inclusion criterion the works that used experimental design in data collection. The studies included in this review were 22, 64% of which came from the top two coffee-roducing countries in Latin America (Brazil and Colombia); the analysis of these studies revealed that the input variables were climatic, soil fertility properties, management and physical properties of the crops. In addition, they used supervised (decision tree, artificial neural networks, multiple linear regression, among others) and unsupervised (clustering) algorithms, with the support of experts in the study of the fungus and used statistics such as coefficient of determination, root mean square error, among others, to validate the proposals. Overall, this systematic review provides evidence of the effectiveness of data mining algorithms implemented to detect the occurrence of rust in coffee plantations.
  • Institution Publication
    Differentiating nutritional and water statuses in Hass avocado plantations through a temporal analysis of vegetation indices computed from aerial RGB images
    (Elsevier BV, 2023-09-22)
    Salazar-Reque, Itamar
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    Arteaga, Daniel
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    Mendoza, Fabiola
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    Elena Rojas, Maria
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    Soto, Jonell
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    Huaman, Samuel
    ;
    Kemper, Guillermo
    Maximizing crop production efficiently and sustainably through plant health monitoring is key for global food security. Monitoring large areas with remote sensing technologies such as unmanned aerial vehicles (UAVs) with sensors deals with time and money issues; however, the usage of advanced sensors such as hyperspectral, multispectral and thermal cameras limit their usage among all the stakeholders. In this study we explore different vegetation indices (VIs) extracted from aerial RGB images acquired in different flights to differentiate the nutritional and water statuses of Hass avocado plantations. We used an image processing workflow consisting of image selection through a convolutional neural network (CNN) model, tree crown segmentation, color correction and feature extraction to automate the computation of VIs from RGB images. To compare the performance of VIs in the differentiation of nutritional and water statuses, we proposed a comparison metric called Mean Distance between Vegetation Indices (MDVI), analyzed the evolution of the extracted features, and studied their relationships with gold standard Normalized Difference Vegetation Index (NDVI) measurements. Since the extracted features from each group vary from flight to flight due to multiple factors such as the light intensity of each season and the phenological stage of the plant, the proposed comparison metric leverages the differences between the features extracted from each group, thus reducing these temporal effects. We found that Modified Green Red Vegetation Index (MGRVI) allows a better differentiation of nutritional and water statuses. Furthermore, the correlation coefficients of this VI in the three statuses and NDVI for nitrogen group range between 0.63 and 0.85, indicating a positive strong relationship. The results of this work show that MGRVI has a potential to be used as a correlation variable in studies that only use RGB sensors in order to monitor the nutritional and water status of crops.
  • Institution Publication
    Evaluation of a wireless low-energy mote with fuzzy algorithms and neural networks for remote environmental monitoring
    (Institute of Advanced Engineering and Science, 2021-08-01)
    Ríos Julcapoma, Milton
    ;
    Yauri, Ricardo
    ;
    Lezama, Jinmi
    The devices developed for applications in the internet of things have evolved technologically in the improvement of hardware and software components, in the area of optimization of the life time and to increase the capacity to save energy. This paper shows the development of a fuzzy logic algorithm and a power propagation neural network algorithm in a wireless mote (IoT end device). The fuzzy algorithm changes the transmission frequency according to the battery voltage and solar cell voltage. Moreover,the implementation of algorithms based on neural networks, implied a challenge in the evaluation and study of the energy commitment for the implementation of the algorithm, memory space optimization and low energy consumption.
  • Institution Publication
    Exploring Traffic Patterns Through Net work Programmability: Introducing SDNFLlow, a Comprehensive Openflow- Based Statistics Dataset for Attack Detection
    (Institute of Electrical and Electronics Engineers (IEEE), )
    Quiroz Arroyo, José Luis
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    Buzzio-García, Jorge
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    Vergara, Jaime
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    Ríos-Guiral, Santiago
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    Garzón, Christian
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    Gutiérrez, Sergio
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    Botero, Juan F.
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    Pérez-Díaz, Jesús Arturo
    In the contemporary cybersecurity landscape, robust attack detection mechanisms are important for organizations. However, the current state of research in Software-Defined Networking (SDN) suffers from a notable lack of recent SDN-OpenFlow-based datasets. This study seeks to bridge this gap by introducing a novel dataset for intrusion detection in Software-Defined Networking named SDNFlow. The dataset, derived from OpenFlow statistics gathered from real traffic, integrates a comprehensive range of network activities. An empirical evaluation leveraging diverse Machine and deep Learning algorithms was performed. Namely, Logistic regression, decision tree, random forest, K-nearest neighbors, Support Vector Machines, and Multilayer Perceptron were tested getting pretty good results with a precision average of 98% to 99% in binary classification and from 97% to 99% in multiclass classification depending of the attack, we highlight the efficacy of K-Nearest Neighbors (KNN) for traffic classification, particularly in detecting DDoS attacks and port scanning. The dataset is valuable for evaluating intrusion detection systems within SDN environments and deepening the understanding of traffic patterns in Software Defined Networks.
  • Institution Publication
    Inter-Node Message Passing Through Optical Reconfigurable Memory Channel
    (Institute of Electrical and Electronics Engineers (IEEE), )
    Palma, Mauricio G.
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    Gonzalez, Jorge
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    Carrasco, Martin
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    Rubio-Noriega, Ruth
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    Bergman, Keren
    ;
    Azevedo, Rodolfo
    Efficient data movement between nodes in a data center is essential for optimal performance of distributed workloads. With advancements in computing interconnection and memory, new opportunities have emerged. We propose a novel inter-node architecture and protocol called Flexible Memory Units (FMU) that uses optically disaggregated memory. FMUs can be dynamically allocated to different nodes during runtime using optical switches. The primary objective of FMUs is to use the disaggregated memory as temporary buffers during inter-node communication. We have implemented Simplecomm, an open-source simulator, to evaluate real MPI benchmarks using FMU. Our evaluation demonstrates significant speedups of up to 5.18× in communication-bound applications and 1.22× on computing-intensive applications, compared to a 100 Gbps InfiniBand interconnect.
  • Institution Publication
    LSTM perfomance analysis for predictive models based on Covid-19 dataset
    (IEEE, 2020-10-12)
    Cruz-Mendoza, Isac
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    Quevedo-Pulido, Jonathan
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    Adanaque-Infante, Luz
    Within the large amount of data that can be processed with Neural Networks (NN), COVID-19 is leaving us a lot of information that is susceptible to be treated and set trends regarding the development of the disease in the country. The present work shows the implementation and the optimization of a Long Short-Term Memory (LSTM) Neural Network in two different simulation environments, with a dataset related to the number of infected people by COVID-19 in Peru, in order to optimize the prediction level on the number of infected people on following days.
  • Institution Publication
    Mutual coupling effects in 2×2 antenna array for ground penetrating radar on multilayered soil
    (Institute of Advanced Engineering and Science, 2024-02-01)
    Becerra Pérez, Marco Antonio
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    Armas Alvarado, María Elisia
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    Guerra-Huaranga, Tanith
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    Clemente-Arenas, Mark
    ;
    Esther Rubio-Noriega, Ruth
    Caral stands as the Americas’ oldest city, boasting a heritage spanning 5,000 years. Over time, various natural forces have woven a complex geological stratum. To gain a deeper understanding of the Caral civilization, non-intrusive exploration methodologies like ground penetrating radar (GPR) are beginning to be used. This method safeguards the integrity of ancient subterranean remains. A GPR system is in development, tailored to the [200-500] MHz range, employing a 2×2 antenna array with dual polarization. These features enhance resolution without compromising penetration depth. However, using multiple antennas within complex, multi-layered environments introduce impedance band constraints and exacerbates antenna coupling issues. This study assesses the coupling of two antenna candidates: the Vivaldi with defected ground structures (DGS) and the log periodic dipole array (LPDA). The scattering parameters show that the LPDA antenna performed better considering measured and simulated data. Cross-polarization exhibited a broader bandwidth in the LPDA antenna, evident in both simulated and measured data. Additionally, a comprehensive comparison of GPR simulations for each antenna type within an 11-level multilayer medium, with different electromagnetic properties, further highlights LPDA. This antenna boasts a 209 MHz bandwidth and a coupling better than -23 dB for the cross-polarization configuration, firmly showing its best performance.