198 research outputs found
Integrating Information Visualization and Dimensionality Reduction: A pathway to Bridge the Gap between Natural and Artificial Intelligence
By importing some natural abilities from human thinking into the design of computerized decision support systems, a cross-cutting trend of intelligent systems has emerged, namely, the synergetic integration between natural and artificial intelligence. While natural intelligence provides creative, parallel, and holistic thinking, its artificial counterpart is logical, accurate, able to perform complex and extensive calculations, and tireless. In the light of such integration, two concepts are important: controllability and interpretability. The former is defined as the ability of computerized systems to receive feedback and follow users’ instructions, while the latter refers to human-machine communication. A suitable alternative to simultaneously involve these two concepts—and then bridging the gap between natural and artificial intelligence—is bringing together the fields of dimensionality reduction (DimRed) and information visualization (InfoVis).By importing some natural abilities from human thinking into the design of computerized decision support systems, a cross-cutting trend of intelligent systems has emerged, namely, the synergetic integration between natural and artificial intelligence. While natural intelligence provides creative, parallel, and holistic thinking, its artificial counterpart is logical, accurate, able to perform complex and extensive calculations, and tireless. In the light of such integration, two concepts are important: controllability and interpretability. The former is defined as the ability of computerized systems to receive feedback and follow users’ instructions, while the latter refers to human-machine communication. A suitable alternative to simultaneously involve these two concepts—and then bridging the gap between natural and artificial intelligence—is bringing together the fields of dimensionality reduction (DimRed) and information visualization (InfoVis)
Spectral analysis of electric current in LEDs Lamps
This work presents an analysis of electric current
signal in LEDs lamps. Electrical signals are measured in two circuits, one that corresponds to the commercial LEDs lamp connected to AC source and another one incorporating a control system into the LEDs lamp. Such control system works as a power factor correction (PFC) and is designed by using a boost converter and a current controller. Signals are analyzed in terms of frequency-based representations oriented to estimate the power spectral density (PSD). In this study, two approaches are used:
Discrete Fourier transform and periodogram. The goal of this work is to show that more complex PSD estimation methods can provide useful information for studying the quality energy in electric power systems, which is comparable with that provided by traditional approaches. In particular, periodogram shows to be a suitable alternative exhibiting meaningful changes along its spectral power plotting when analyzing the circuit without applying PFC. As a result of this work, a set of LEDs lamps characteristics is introduced, including a novel periodicity factor
Educación y formación cultural en fuentes de energía alternativa para Departamento de Nariño
Actualmente, en el departamento de Nariño se está ejecutando proyectos de investigación en el campo de las energías alternativas con alto impacto social, liderados por el Departamento de Electrónica de la Universidad de Nariño, como institución comprometida con el desarrollo de la región. Como resultado de estos proyectos de investigación, se ha identificado zonas rurales aisladas que no tienen acceso a la energía eléctrica, lo que les impide acceder a los nuevos desarrollos tecnológicos. En el caso de las instituciones educativas en dichas zonas, algunas cuentan con equipos de cómputo que los energizan por pocas horas, dependiendo del combustible que dispongan y otros, simplemente los guardan para utilizarlos cuando tengan un eventual suministro de energía en el futuro
Bridging the gap between human knowledge and machine learning
Nowadays, great amount of data is being created by several sources from academic, scientific, business and industrial activities. Such data intrinsically contains meaningful information allowing for developing techniques, and have scientific validity to explore the information thereof. In this connection, the aim of artificial intelligence (AI) is getting new knowledge to make decisions properly. AI has taken an important place in scientific and technology development communities, and recently develops computer-based processing devices for modern machines. Under the premise, the premise that the feedback provided by human reasoning -which is holistic, flexible and parallel- may enhance the data analysis, the need for the integration of natural and artificial intelligence has emerged. Such an integration makes the process of knowledge discovery more effective, providing the ability to easily find hidden trends and patterns belonging to the database predictive model. As well, allowing for new observations and considerations from beforehand known data by using both data analysis methods and knowledge and skills from human reasoning. In this work, we review main basics and recent works on artificial and natural intelligence integration in order to introduce users and researchers on this emergent field. As well, key aspects to conceptually compare them are provided
Electromiographic Signal Processing Using Embedded Artificial Intelligence: An Adaptive Filtering Approach
In recent times, Artificial Intelligence (AI) has become ubiquitous in technological fields, mainly due to its ability to perform computations in distributed systems or the cloud. Nevertheless, for some applications -as the case of EMG signal processing- it may be highly advisable or even mandatory an on-the-edge processing, i.e., an embedded processing methodology. On the other hand, sEMG signals have been traditionally processed using LTI techniques for simplicity in computing. However, making this strong assumption leads to information loss and spurious results. Considering the current advances in silicon technology and increasing computer power, it is possible to process these biosignals with AI-based techniques correctly. This paper presents an embedded-processing-based adaptive filtering system (here termed edge AI) being an outstanding alternative in contrast to a sensor-computer- actuator system and a classical digital signal processor (DSP) device. Specifically, a PYNQ-Z1 embedded system is used. For experimental purposes, three methodologies on similar processing scenarios are compared. The results show that the edge AI methodology is superior to benchmark approaches by reducing the processing time compared to classical DSPs and general standards while maintaining the signal integrity and processing it, considering that the EMG system is not LTI. Likewise, due to the nature of the proposed architecture, handling information exhibits no leakages. Findings suggest that edge computing is suitable for EMG signal processing when an on-device analysis is required
Analysis of Sorting Algorithms Using a WSN and Environmental Pollution Data based on FPGA
Wireless Snesor Network (WSN) based sys-
tems with a focus on Internet of Things (IoT) applications
generate a large amount of data. Many applications
that need to process data in real time make use of
microcontroller-based architectures with sequential pro-
gramming. Systems based on sequential programming
can emulate parallelism up to a certain number of in-
structions, which is not the case with Field Programmable
Gate Array (FPGA). The main objective of this work is
to monitor a network of 40 CO2 sensors and to perform
real-time sorting of all data. In addition, the run time
analysis of two sorting algorithms is performed: bubble
sort and insertion sort. For this purpose, an FPGA-
based architecture is implemented, controlled by a finite
state machine(FSM), which executes each of the sorting
algorithms. The results show that the insertion sort
algorithm is faster than the burbble sort, but consumes
more hardware resources in the FPG
Estudio Comparativo de Métodos de Selección de Características de Inferencia Supervisada y No Supervisada
In this work, a comparative study of feature selection methods for supervised and unsupervised inference obtained from classical PCA is presented. We deduce an expression for the cost function of PCA based on the mean square error of data and its orthonormal projection, and then this concept is extended to obtain an expression for general WPCA. Additionally, we study the supervised and unsupervised Q – α algorithm and its relation with PCA. At the end, we present results employing two data sets: A low-dimensional data set to analyze the effects of orthonormal rotation, and a highdimensional data set to assess the classification performance. The feature selection methods were assessed taking into account the number of relevant features, computational cost and classification performance. The classification was carried out using a partitional clustering algorithm.En este trabajo se presenta un estudio comparativo de algunos métodos de selección de características de inferencia supervisada y no supervisada derivados del algoritmo PCA clásico. Se deduce una función objetivo de PCA a partir del error cuadrático medio de los datos y su proyección sobre una base ortonormal, y se extiende este concepto para derivar una expresión asociada al algoritmo fundamental de WPCA. Adicionalmente, se estudian los algoritmos Q - α supervisado y no supervisado y se explica su relación con PCA. Se presentan resultados empleando dos conjuntos de datos: Uno de baja dimensión para estudiar los efectos de la rotación ortogonal y la dirección de los componentes principales y otro de alta dimensión para evaluar los resultados de clasificación. Los métodos de selección de características fueron evaluados teniendo en cuenta la cantidad de características relevantes obtenidas, costo computacional y resultados de clasificación. La clasificación se realizó con un algoritmo particional de agrupamiento no supervisado
- …