Electrocardiogram Signal Denoising Using a Hybrid Technique

This paper presents an efficient method of electrocardiogram signal denoising based on a hybrid approach. Two techniques are brought together to create an efficient denoising process. The first is an Adaptive Dual Threshold Filter (ADTF) and the second is the Discrete Wavelet Transform (DWT). The presented approach is based on three steps of denoising, the DWT decomposition, the ADTF step and the highest peaks correction step. This paper presents some application of the approach on some electrocardiogram signals of the MIT-BIH database. The results of these applications are promising compared to other recently published techniques.

A Self Organized Map Method to Classify Auditory-Color Synesthesia from Frontal Lobe Brain Blood Volume

Absolute pitch is the ability to identify a musical note without a reference tone. Training for absolute pitch often occurs in preschool education. It is necessary to clarify how well the trainee can make use of synesthesia in order to evaluate the effect of the training. To the best of our knowledge, there are no existing methods for objectively confirming whether the subject is using synesthesia. Therefore, in this study, we present a method to distinguish the use of color-auditory synesthesia from the separate use of color and audition during absolute pitch training. This method measures blood volume in the prefrontal cortex using functional Near-infrared spectroscopy (fNIRS) and assumes that the cognitive step has two parts, a non-linear step and a linear step. For the linear step, we assume a second order ordinary differential equation. For the non-linear part, it is extremely difficult, if not impossible, to create an inverse filter of such a complex system as the brain. Therefore, we apply a method based on a self-organizing map (SOM) and are guided by the available data. The presented method was tested using 15 subjects, and the estimation accuracy is reported.

Online Battery Equivalent Circuit Model Estimation on Continuous-Time Domain Using Linear Integral Filter Method

Equivalent circuit models (ECMs) are widely used in battery management systems in electric vehicles and other battery energy storage systems. The battery dynamics and the model parameters vary under different working conditions, such as different temperature and state of charge (SOC) levels, and therefore online parameter identification can improve the modelling accuracy. This paper presents a way of online ECM parameter identification using a continuous time (CT) estimation method. The CT estimation method has several advantages over discrete time (DT) estimation methods for ECM parameter identification due to the widely separated battery dynamic modes and fast sampling. The presented method can be used for online SOC estimation. Test data are collected using a lithium ion cell, and the experimental results show that the presented CT method achieves better modelling accuracy compared with the conventional DT recursive least square method. The effectiveness of the presented method for online SOC estimation is also verified on test data.

Lithium-Ion Battery State of Charge Estimation Using One State Hysteresis Model with Nonlinear Estimation Strategies

Battery state of charge (SOC) estimation is an important parameter as it measures the total amount of electrical energy stored at a current time. The SOC percentage acts as a fuel gauge if it is compared with a conventional vehicle. Estimating the SOC is, therefore, essential for monitoring the amount of useful life remaining in the battery system. This paper looks at the implementation of three nonlinear estimation strategies for Li-Ion battery SOC estimation. One of the most common behavioral battery models is the one state hysteresis (OSH) model. The extended Kalman filter (EKF), the smooth variable structure filter (SVSF), and the time-varying smoothing boundary layer SVSF are applied on this model, and the results are compared.

Teaching Light Polarization by Putting Art and Physics Together

Light Polarization has many technological applications, and its discovery was crucial to reveal the transverse nature of the electromagnetic waves. However, despite its fundamental and practical importance, in high school, this property of light is often neglected. This is a pity not only for its conceptual relevance, but also because polarization gives the possibility to perform many brilliant experiments with low cost materials. Moreover, the treatment of this matter lends very well to an interdisciplinary approach between art, biology and technology, which usually makes things more interesting to students. For these reasons, we have developed, and in this work, we introduce a laboratory on light polarization for high school and undergraduate students. They can see beautiful pictures when birefringent materials are set between two crossed polarizing filters. Pupils are very fascinated and drawn into by what they observe. The colourful images remind them of those ones of abstract painting or alien landscapes. With this multidisciplinary teaching method, students are more engaged and participative, and also, the learning process of the respective physics concepts is more effective.

Experimental Study on Dehumidification Performance of Supersonic Nozzle

Supersonic nozzles are commonly used to purify natural gas in gas processing technology. As an innovated technology, it is employed to overcome the deficit of the traditional method, related to gas dynamics, thermodynamics and fluid dynamics theory. An indoor test rig is built to study the dehumidification process of moisture fluid. Humid air was chosen for the study. The working fluid was circulating in an open loop, which had provision for filtering, metering, and humidifying. A stainless steel supersonic separator is constructed together with the C-D nozzle system. The result shows that dehumidification enhances as NPR increases. This is due to the high intensity in the turbulence caused by the shock formation in the divergent section. Such disturbance strengthens the centrifugal force, pushing more particles toward the near-wall region. In return return, the pressure recovery factor, defined as the ratio of the outlet static pressure of the fluid to its inlet value, decreases with NPR.

PM10 Chemical Characteristics in a Background Site at the Universidad Libre Bogotá

One of the most important factors for air pollution is that the concentrations of PM10 maintain a constant trend, with the exception of some places where that frequently surpasses the allowed ranges established by Colombian legislation. The community that surrounds the Universidad Libre Bogotá is inhabited by a considerable number of students and workers, all of whom are possibly being exposed to PM10 for long periods of time while on campus. Thus, the chemical characterization of PM10 found in the ambient air at the Universidad Libre Bogotá was identified as a problem. A Hi-Vol sampler and EPA Test Method 5 were used to determine if the quality of air is adequate for the human respiratory system. Additionally, quartz fiber filters were utilized during sampling. Samples were taken three days a week during a dry period throughout the months of November and December 2015. The gravimetric analysis method was used to determine PM10 concentrations. The chemical characterization includes non-conventional carcinogenic pollutants. Atomic absorption spectrophotometry (AAS) was used for the determination of metals and VOCs were analyzed using the FTIR (Fourier transform infrared spectroscopy) method. In this way, concentrations of PM10, ranging from values of 13 µg/m3 to 66 µg/m3, were obtained; these values were below standard conditions. This evidence concludes that the PM10 concentrations during an exposure period of 24 hours are lower than the values established by Colombian law, Resolution 610 of 2010; however, when comparing these with the limits set by the World Health Organization (WHO), these concentrations could possibly exceed permissible levels.

E-Learning Recommender System Based on Collaborative Filtering and Ontology

In recent years, e-learning recommender systems has attracted great attention as a solution towards addressing the problem of information overload in e-learning environments and providing relevant recommendations to online learners. E-learning recommenders continue to play an increasing educational role in aiding learners to find appropriate learning materials to support the achievement of their learning goals. Although general recommender systems have recorded significant success in solving the problem of information overload in e-commerce domains and providing accurate recommendations, e-learning recommender systems on the other hand still face some issues arising from differences in learner characteristics such as learning style, skill level and study level. Conventional recommendation techniques such as collaborative filtering and content-based deal with only two types of entities namely users and items with their ratings. These conventional recommender systems do not take into account the learner characteristics in their recommendation process. Therefore, conventional recommendation techniques cannot make accurate and personalized recommendations in e-learning environment. In this paper, we propose a recommendation technique combining collaborative filtering and ontology to recommend personalized learning materials to online learners. Ontology is used to incorporate the learner characteristics into the recommendation process alongside the ratings while collaborate filtering predicts ratings and generate recommendations. Furthermore, ontological knowledge is used by the recommender system at the initial stages in the absence of ratings to alleviate the cold-start problem. Evaluation results show that our proposed recommendation technique outperforms collaborative filtering on its own in terms of personalization and recommendation accuracy.

Effective Dose and Size Specific Dose Estimation with and without Tube Current Modulation for Thoracic Computed Tomography Examinations: A Phantom Study

The purpose of this study is to reduce radiation dose for chest CT examination by including Tube Current Modulation (TCM) to a standard CT protocol. A scan of an anthropomorphic male Alderson phantom was performed on a 128-slice scanner. The estimation of effective dose (ED) in both scans with and without mAs modulation was done via multiplication of Dose Length Product (DLP) to a conversion factor. Results were compared to those measured with a CT-Expo software. The size specific dose estimation (SSDE) values were obtained by multiplication of the volume CT dose index (CTDIvol) with a conversion size factor related to the phantom’s effective diameter. Objective assessment of image quality was performed with Signal to Noise Ratio (SNR) measurements in phantom. SPSS software was used for data analysis. Results showed including CARE Dose 4D; ED was lowered by 48.35% and 51.51% using DLP and CT-expo, respectively. In addition, ED ranges between 7.01 mSv and 6.6 mSv in case of standard protocol, while it ranges between 3.62 mSv and 3.2 mSv with TCM. Similar results are found for SSDE; dose was higher without TCM of 16.25 mGy and was lower by 48.8% including TCM. The SNR values calculated were significantly different (p=0.03

Statistical Modeling of Mobile Fading Channels Based on Triply Stochastic Filtered Marked Poisson Point Processes

Understanding the statistics of non-isotropic scattering multipath channels that fade randomly with respect to time, frequency, and space in a mobile environment is very crucial for the accurate detection of received signals in wireless and cellular communication systems. In this paper, we derive stochastic models for the probability density function (PDF) of the shift in the carrier frequency caused by the Doppler Effect on the received illuminating signal in the presence of a dominant line of sight. Our derivation is based on a generalized Clarke’s and a two-wave partially developed scattering models, where the statistical distribution of the frequency shift is shown to be consistent with the power spectral density of the Doppler shifted signal.

Application of Single Tuned Passive Filters in Distribution Networks at the Point of Common Coupling

The harmonic distortion of voltage is important in relation to power quality due to the interaction between the large diffusion of non-linear and time-varying single-phase and three-phase loads with power supply systems. However, harmonic distortion levels can be reduced by improving the design of polluting loads or by applying arrangements and adding filters. The application of passive filters is an effective solution that can be used to achieve harmonic mitigation mainly because filters offer high efficiency, simplicity, and are economical. Additionally, possible different frequency response characteristics can work to achieve certain required harmonic filtering targets. With these ideas in mind, the objective of this paper is to determine what size single tuned passive filters work in distribution networks best, in order to economically limit violations caused at a given point of common coupling (PCC). This article suggests that a single tuned passive filter could be employed in typical industrial power systems. Furthermore, constrained optimization can be used to find the optimal sizing of the passive filter in order to reduce both harmonic voltage and harmonic currents in the power system to an acceptable level, and, thus, improve the load power factor. The optimization technique works to minimize voltage total harmonic distortions (VTHD) and current total harmonic distortions (ITHD), where maintaining a given power factor at a specified range is desired. According to the IEEE Standard 519, both indices are viewed as constraints for the optimal passive filter design problem. The performance of this technique will be discussed using numerical examples taken from previous publications.

Introduction of the Fluid-Structure Coupling into the Force Analysis Technique

This paper presents a method to take into account the fluid-structure coupling into an inverse method, the Force Analysis Technique (FAT). The FAT method, also called RIFF method (Filtered Windowed Inverse Resolution), allows to identify the force distribution from local vibration field. In order to only identify the external force applied on a structure, it is necessary to quantify the fluid-structure coupling, especially in naval application, where the fluid is heavy. This method can be decomposed in two parts, the first one consists in identifying the fluid-structure coupling and the second one to introduced it in the FAT method to reconstruct the external force. Results of simulations on a plate coupled with a cavity filled with water are presented.

H-Infinity and RST Position Controllers of Rotary Traveling Wave Ultrasonic Motor

Traveling Wave Ultrasonic Motor (TWUM) is a compact, precise, and silent actuator generating high torque at low speed without gears. Moreover, the TWUM has a high holding torque without supply, which makes this motor as an attractive solution for holding position of robotic arms. However, their nonlinear dynamics, and the presence of load-dependent dead zones often limit their use. Those issues can be overcome in closed loop with effective and precise controllers. In this paper, robust H-infinity (H∞) and discrete time RST position controllers are presented. The H∞ controller is designed in continuous time with additional weighting filters to ensure the robustness in the case of uncertain motor model and external disturbances. Robust RST controller based on the pole placement method is also designed and compared to the H∞. Simulink model of TWUM is used to validate the stability and the robustness of the two proposed controllers.

Cadmium Filter Cake of a Hydrometallurgical Zinc Smelter as a New Source for the Biological Synthesis of CdS Quantum Dots

The cadmium sulfide nanoparticles were synthesized from the nickel-cadmium cake of a hydrometallurgical zinc producing plant and sodium sulfide as Cd2+ and S-2 sources, respectively. Also, the synthesis process was performed by using the secretions of Bacillus licheniformis as bio-surfactant. Initially, in order to obtain a cadmium rich solution, two following steps were carried out: 1) Alkaline leaching for the removal of zinc oxide from the cake, and 2) acidic leaching to dissolve cadmium from the remained solid residue. Afterward, the obtained CdSO4 solution was used for the nanoparticle biosynthesis. Nanoparticles were characterized by the energy dispersive spectroscopy (EDS) and X-ray diffraction (XRD) to confirm the formation of CdS crystals with cubic structure. Also, transmission electron microscopy (TEM) was applied to determine the particle sizes which were in 2-10 nm range. Moreover, the presence of the protein containing bio-surfactants was approved by using infrared analysis (FTIR). In addition, the absorbance below 400 nm confirms quantum particles’ size. Finally, it was shown that valuable CdS quantum dots could be obtained from the industrial waste products via environment-friendly biological approaches.

Evaluation of Sensor Pattern Noise Estimators for Source Camera Identification

This paper presents a comprehensive survey of recent source camera identification (SCI) systems. Then, the performance of various sensor pattern noise (SPN) estimators was experimentally assessed, under common photo response non-uniformity (PRNU) frameworks. The experiments used 1350 natural and 900 flat-field images, captured by 18 individual cameras. 12 different experiments, grouped into three sets, were conducted. The results were analyzed using the receiver operator characteristic (ROC) curves. The experimental results demonstrated that combining the basic SPN estimator with a wavelet-based filtering scheme provides promising results. However, the phase SPN estimator fits better with both patch-based (BM3D) and anisotropic diffusion (AD) filtering schemes.

EEG Signal Processing Methods to Differentiate Mental States

EEG is a very complex signal with noises and other bio-potential interferences. EOG is the most distinct interfering signal when EEG signals are measured and analyzed. It is very important how to process raw EEG signals in order to obtain useful information. In this study, the EEG signal processing techniques such as EOG filtering and outlier removal were examined to minimize unwanted EOG signals and other noises. The two different mental states of resting and focusing were examined through EEG analysis. A focused state was induced by letting subjects to watch a red dot on the white screen. EEG data for 32 healthy subjects were measured. EEG data after 60-Hz notch filtering were processed by a commercially available EOG filtering and our presented algorithm based on the removal of outliers. The ratio of beta wave to theta wave was used as a parameter for determining the degree of focusing. The results show that our algorithm was more appropriate than the existing EOG filtering.

A Reconfigurable Microstrip Patch Antenna with Polyphase Filter for Polarization Diversity and Cross Polarization Filtering Operation

A reconfigurable microstrip patch antenna with polyphase filter for polarization diversity and cross polarization filtering operation is presented in this paper. In our approach, a polyphase filter is used to obtain the four 90° phase shift outputs to feed a square microstrip patch antenna. The antenna can be switched between four states of polarization in transmission as well as in receiving mode. Switches are interconnected with the polyphase filter network to produce left-hand circular polarization, right-hand circular polarization, horizontal linear polarization, and vertical linear polarization. Additional advantage of using polyphase filter is its filtering capability for cross polarization filtering in right-hand circular polarization and left-hand circular polarization operation. The theoretical and simulated results demonstrated that polyphase filter is a good candidate to drive microstrip patch antenna to accomplish polarization diversity and cross polarization filtering operation.

A Video Watermarking Algorithm Based on Chaotic and Wavelet Neural Network

This paper presented a video watermarking algorithm based on wavelet chaotic neural network. First, to enhance binary image’s security, the algorithm encrypted it with double chaotic based on Arnold and Logistic map, Then, the host video was divided into some equal frames and distilled the key frame through chaotic sequence which generated by Logistic. Meanwhile, we distilled the low frequency coefficients of luminance component and self-adaptively embedded the processed image watermark into the low frequency coefficients of the wavelet transformed luminance component with the wavelet neural network. The experimental result suggested that the presented algorithm has better invisibility and robustness against noise, Gaussian filter, rotation, frame loss and other attacks.

A Sentence-to-Sentence Relation Network for Recognizing Textual Entailment

Over the past decade, there have been promising developments in Natural Language Processing (NLP) with several investigations of approaches focusing on Recognizing Textual Entailment (RTE). These models include models based on lexical similarities, models based on formal reasoning, and most recently deep neural models. In this paper, we present a sentence encoding model that exploits the sentence-to-sentence relation information for RTE. In terms of sentence modeling, Convolutional neural network (CNN) and recurrent neural networks (RNNs) adopt different approaches. RNNs are known to be well suited for sequence modeling, whilst CNN is suited for the extraction of n-gram features through the filters and can learn ranges of relations via the pooling mechanism. We combine the strength of RNN and CNN as stated above to present a unified model for the RTE task. Our model basically combines relation vectors computed from the phrasal representation of each sentence and final encoded sentence representations. Firstly, we pass each sentence through a convolutional layer to extract a sequence of higher-level phrase representation for each sentence from which the first relation vector is computed. Secondly, the phrasal representation of each sentence from the convolutional layer is fed into a Bidirectional Long Short Term Memory (Bi-LSTM) to obtain the final sentence representations from which a second relation vector is computed. The relations vectors are combined and then used in then used in the same fashion as attention mechanism over the Bi-LSTM outputs to yield the final sentence representations for the classification. Experiment on the Stanford Natural Language Inference (SNLI) corpus suggests that this is a promising technique for RTE.

The Enhancement of Target Localization Using Ship-Borne Electro-Optical Stabilized Platform

Electro-optical (EO) stabilized platforms have been widely used for surveillance and reconnaissance on various types of vehicles, from surface ships to unmanned air vehicles (UAVs). EO stabilized platforms usually consist of an assembly of structure, bearings, and motors called gimbals in which a gyroscope is installed. EO elements such as a CCD camera and IR camera, are mounted to a gimbal, which has a range of motion in elevation and azimuth and can designate and track a target. In addition, a laser range finder (LRF) can be added to the gimbal in order to acquire the precise slant range from the platform to the target. Recently, a versatile functionality of target localization is needed in order to cooperate with the weapon systems that are mounted on the same platform. The target information, such as its location or velocity, needed to be more accurate. The accuracy of the target information depends on diverse component errors and alignment errors of each component. Specially, the type of moving platform can affect the accuracy of the target information. In the case of flying platforms, or UAVs, the target location error can be increased with altitude so it is important to measure altitude as precisely as possible. In the case of surface ships, target location error can be increased with obliqueness of the elevation angle of the gimbal since the altitude of the EO stabilized platform is supposed to be relatively low. The farther the slant ranges from the surface ship to the target, the more extreme the obliqueness of the elevation angle. This can hamper the precise acquisition of the target information. So far, there have been many studies on EO stabilized platforms of flying vehicles. However, few researchers have focused on ship-borne EO stabilized platforms of the surface ship. In this paper, we deal with a target localization method when an EO stabilized platform is located on the mast of a surface ship. Especially, we need to overcome the limitation caused by the obliqueness of the elevation angle of the gimbal. We introduce a well-known approach for target localization using Unscented Kalman Filter (UKF) and present the problem definition showing the above-mentioned limitation. Finally, we want to show the effectiveness of the approach that will be demonstrated through computer simulations.