INANOE Repositorio (Inst- Nacional de Astrofísica, Óptica y Electrónica
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On an external memory scheme for processor arrays
Get PDFThe problem of generating memory interfaces between loop-based accelerators and external memory is gaining the attention from the high-level synthesis research community. This paper presents an external memory system for inserting/extracting data to/from a loop-based accelerator derived by a high-level synthesis approach. The memory system is composed by four architectural cases which could occur during hardware synthesis. The memory system is based on a global asynchronous local synchronous approach and the use of dualport memory banks. FPGA-based implementation results show that the proposed memory system is technologically achievable and provides a high-bandwidth without introducing communication overhead
Determination of heavy metals contamination using a silicon sensor with extended responsive to the UV
No full textDue to its potential risk to human health and ecology, the presence of heavy metals in water demands of techniques to determine them in a simple and economical way. Currently, new developments of light emitters and detectors open a window of opportunities to use optical properties to analyze contaminated water. In this paper, a silicon sensor developed to extend its sensitivity up to the UV range is used to determine heavy metals in water. Cadmium, Zinc, Lead, Copper and Manganese mixed in pure water at different concentrations were used as test samples. The photocurrent obtained by the light that passes through the samples was used to determine the optical transmittance of pure and contaminated water. Preliminary results show a good separability between samples, which can be used for qualitative and quantitative detection of such heavy metals in water
Novel microstrip diplexer for ultra-wide-band (UWB) and wireless LAN (WLAN) bands
Get PDFA new microstrip diplexer for ultra-wide-band (UWB) and wireless LAN (WLAN) bands is presented in this paper. The proposed structure integrates a multipole directional filter operating at WLAN band coupled to a UWB filter. The prototype is capable of processing the whole UWB region (from 3.1 to 10.6 GHz) with the WLAN band notch in one channel, and the passband WLAN band in other channel with a good selectivity due to the presence of transmission zeros at both sides of the WLAN band. The proposed diplexer is simple to fabricate, as it is designed using planar microstrip technology, and presents compact dimensions, making use of a low number of elements. Moreover, it presents the capacity of processing a narrow band (WLAN with 8% bandwidth) with a wide band (UWB). Simulated and measured results are presented with good agreement and the diplexer presents a good performance up to 15 GHz
Identifying the Diffusion and Drift Conduction Regions in MOSFETs Through S-Parameters
Get PDFA method for characterizing low-voltage-operating MOSFETs through small-signal S-parameters is introduced. The method allows extracting the drain-to-source channel resistance at zero drain-to-source voltage, which is not feasible with dc conventional methods. Furthermore, this zero drain-to-source voltage RF method identifies the gate voltage where the diffusion and drift conduction mechanisms overlap. This is really helpful in defining the appropriate subthreshold drain current model and its corresponding impact at RF operating conditions. The proposed experimental RF method is validated and compared with a dc-based method for an 80-nm-channel-length nMOSFET
Improved spherical continuation algorithm with application to the double-bounded homotopy (DBH)
No full textThe homotopy continuationmethods are useful tools for finding multiple solutionsof nonlinear problems.Animportant issue of this kind ofmethod is the correct implementationof the path-following techniques used to trace the homotopy trajectory. Therefore, in thiswork we propose a modification of the spherical algorithm to successfully trace the closedpaths of a DBH homotopy. The proposed methodology is depicted with three examples.Finally, a comparison of the results with a standard path-following technique is presentedand discussed
Brain Computer Interface Development Based on Recurrent Neural Networks and ANFIS Systems
Get PDFBrain Computer Interfaces (BCI) is the generic denomination of systems aiming to establish communication between a human being and an automated system, based on the electric brain signals detected through a variety of modalities. Among these, electroencephalographic signals (EEG) have received considerable attention due to several factors arising on practical scenarios, such as noninvasiveness, portability, and relative cost, without lost on accuracy and generalization. In this chapter we discuss the characteristics of a typical phenomenon associated to motor imagery and mental tasks experiments, known as event related synchronization and desynchronization (ERD/ERS), as well as its energy distribution in the time-frequency space. The typical behavior of ERD/ERS phenomenon has led proposal of different approaches oriented to the solution of the identification problem. In this work, an architecture based on adaptive neurofuzzy inference systems (ANFIS) assembled to a recurrent neural network, applied to the problem of mental tasks temporal classification, is presented. The electroencephalographic signals (EEG) are pre-processed through band-pass filtering in order to separate the set of energy signals in alpha and beta bands. The energy in each band is represented by fuzzy sets obtained through an ANFIS system, and the temporal sequence corresponding to the combination to be detected, associated to the specific mental task, is entered into a recurrent neural network. Experimentation using EEG signals corresponding to mental tasks exercises, obtained from a database available to the international community for research purposes, is reported. Two recurrent neural networks are used for comparison purposes: Elman network, and a fully connected recurrent neural network (FCRNN) trained by RTRL-EKF (real time recurrent learning – extended Kalman filter). A classification rate of 88.12 % in average was obtained through the FCRNN during the generalization stage
Models of performance of time series forecasters
Get PDFOne of the first steps when approaching any machine learning task is to select, among all the available procedures, which one is the most adequate to solve a particular problem; in automated problem solving this is known as the algorithm selection problem. Of course, this problem is also present in the field of time series forecasting, there, one needs to select the forecaster that makes the most accurate predictions. Generally, this selection task is manually performed by analyzing the characteristics of the time series, thus relying on the expertise that one has on the available forecasters. In this paper, we propose an automatic procedure to choose a forecaster given a set of candidates, i.e., to solve the algorithm selection problem on this domain. To do so, we follow two paths. Firstly, we propose to model the performance of the forecasters using a linear combination of features that were previously used to assess the problem difficulty of evolutionary algorithms, together with a set of features we propose in this paper. Then, this model is used to predict the performance of the forecasters and based on these predictions the forecaster is selected. Our second approach is to treat this algorithm selection process as a classification task where the descriptors of each time series are the proposed features. To show the capabilities of our approach, we test the forecasters on the time series of the M1 and M3 time series competitions and used three different forecasters. In all the cases tested, our proposals outperform the performance of the three forecasters indicating the viability of our approach
A Homotopy Continuation Approach for Testing a Basic Analog Circuit
Get PDFThe increase of complexity on integrated circuits has also raised the demand for new testing methodologies capable to detect functional failures within circuits before they reach the market. Hence, this work proposes to explore the use of homotopy as a tool for testing a basic analog circuit. The homotopy path is influenced by nonlinearities from the equilibrium equation of the circuit; this situation can be used to infer faults by detecting changes on the homotopy path. The concept was explored using numerical simulation of a simple test circuit; then comparing results for the circuit with and without faults, obtaining modifications on the homotopy path like: the final point, number of iterations, and the number of turning points
OClustR: A new graph-based algorithm for overlapping clustering
Get PDFClustering is a Data Mining technique, which has been widely used in many practical applications. From these applications, there are some, like social network analysis, topic detection and tracking, information retrieval, categorization of digital libraries, among others, where objects may belong to more than one cluster; however, most clustering algorithms build disjoint clusters. In this work, we introduce OClustR, a new graph-based clustering algorithm for building overlapping clusters. The proposed algorithm introduces a new graph-covering strategy and a new filtering strategy, which together allow to build overlapping clusterings more accurately than those built by previous algorithms. The experimental evaluation, conducted over several standard collections, showed that our proposed algorithm builds less clusters than those built by the previous related algorithms. Additionally, OClustR builds clusters with overlapping closer to the real overlapping in the collections than the overlapping generated by other clustering algorithms
Análisis de Señales Electroencefalográficas para la Clasificación de Habla Imaginada
Get PDFEl presente trabajo tiene como objetivo interpretar las señales de EEG registradas durante la pronunciación imaginada de palabras de un vocabulario reducido, sin emitir sonidos ni articular movimientos (habla imaginada o no pronunciada) con la intención de controlar un dispositivo. Específicamente, el vocabulario permitiría controlar el cursor de la computadora, y consta de las palabras del lenguaje español: “arriba”, “abajo”, “izquierda”, “derecha”, y “seleccionar”. Para ello, se registraron las señales de EEG de 27 individuos utilizando un protocolo básico para saber a priori en qué segmentos de la señal la persona imagina la pronunciación de la palabra indicada. Posteriormente, se utiliza la transformada wavelet discreta (DWT) para extraer características de los segmentos que son usados para calcular la energía relativa wavelet (RWE) en cada una de los niveles en los que la señal es descompuesta, y se selecciona un subconjunto de valores RWE provenientes de los rangos de frecuencia menores a 32 Hz. Enseguida, éstas se concatenan en dos configuraciones distintas: 14 canales (completa) y 4 canales (los más cercanos a las áreas de Broca y Wernicke). Para ambas configuraciones se entrenan tres clasificadores: Naive Bayes (NB), Random Forest (RF) y Máquina de vectores de soporte (SVM). Los mejores porcentajes de exactitud se obtuvieron con RF cuyos promedios fueron 60.11 % y 47.93 % usando las configuraciones de 14 canales y 4 canales, respectivamente. A pesar de que los resultados aún son preliminares, éstos están arriba del 20 %, es decir, arriba del azar para cinco clases. Con lo que se puede conjeturar que las señales de EEG podrían contener información que hace posible la clasificación de las pronunciaciones imaginadas de las palabras del vocabulario reducido.This work aims to interpret the EEG signals associated with actions to imagine the pronunciation of words that belong to a reduced vocabulary without moving the articulatory muscles and without uttering any audible sound (imagined or unspoken speech). Specifically, the vocabulary reflects movements to control the cursor on the computer, and consists of the Spanish language words: “arriba”, “abajo”, “izquierda”, “derecha”, and “seleccionar”. To do this, we have recorded EEG signals from 27 subjects using a basic protocol to know a priori in what segments of the signal a subject imagines the pronunciation of the indicated word. Subsequently, discrete wavelet transform (DWT) is used to extract features from the segments. These are used to compute relative wavelet energy (RWE) in each of the levels in that EEG signal is decomposed and, it is selected a RWE values subset with the frequencies smaller than 32 Hz. Then, these are concatenated in two different configurations: 14 channels (full) and 4 channels (the channels nearest to the brain areas of Wernicke and Broca). The following three classifiers were trained using both configurations: Naive Bayes (NB), Random Forest (RF) and support vector machines (SVM). The best accuracies were obtained by RF whose averages were 60.11 % and 47.93 % using both configurations, respectively. Even though, the results are still preliminary, these are above 20 %, this means they are more accurate than chance for five classes. Based on them, we can conjecture that the EEG signals could contain information needed for the classification of the imagined pronunciations of the words belonging to a reduced vocabulary