Search results for: ECG noise filtering

Authors: Aqsa Jamil, Tamura Hiroshi, Katsuchi Hiroshi, Wang Jiaqi

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The yield point represents the upper limit of forces which can be applied to a specimen without causing any permanent deformation. After yielding, the behavior of the specimen suddenly changes, including the possibility of cracking or buckling. So, the accumulation of damage or type of fracture changes depending on this condition. As it is difficult to accurately detect yield points of the several stress concentration points in structural steel specimens, an effort has been made in this research work to develop a convenient technique using thermography (temperature-based detection) during tensile tests for the precise detection of yield point initiation. To verify the applicability of thermography camera, tests were conducted under different loading conditions and measuring the deformation by installing various strain gauges and monitoring the surface temperature with the help of a thermography camera. The yield point of specimens was estimated with the help of temperature dip, which occurs due to the thermoelastic effect during the plastic deformation. The scattering of the data has been checked by performing a repeatability analysis. The effects of temperature imperfection and light source have been checked by carrying out the tests at daytime as well as midnight and by calculating the signal to noise ratio (SNR) of the noised data from the infrared thermography camera, it can be concluded that the camera is independent of testing time and the presence of a visible light source. Furthermore, a fully coupled thermal-stress analysis has been performed by using Abaqus/Standard exact implementation technique to validate the temperature profiles obtained from the thermography camera and to check the feasibility of numerical simulation for the prediction of results extracted with the help of the thermographic technique.

Keywords: signal to noise ratio, thermoelastic effect, thermography, yield point

Procedia PDF Downloads 87

Authors: Toru Shionoya, Yosuke Kurihara, Takashi Kaburagi, Kajiro Watanabe

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Mass-produced bidirectional microphones have attractive characteristics. They work as a microphone as well as a sensor with high gain over a wide frequency range; they are also highly reliable and economical. We present novel multiple functional uses of the microphones. A mathematical model for explaining the high-pass-filtering characteristics of bidirectional microphones was presented. Based on the model, the characteristics of the microphone were investigated, and a novel use for the microphone as a sensor with a wide frequency range was presented. In this study, applications for using the microphone as a security sensor and a human biosensor were introduced. The mathematical model was validated through experiments, and the feasibility of the abovementioned applications for security monitoring and the biosignal monitoring were examined through experiments.

Keywords: bidirectional microphone, low-frequency, mathematical model, frequency response

Procedia PDF Downloads 523

Authors: Pavel Prochazka

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The study deals with transducers for measuring vibration displacements of rotating blade tips in turbomachines. In order to prevent major accidents with extensive economic consequences, it shows an urgent need for every low-pressure steam turbine stage being equipped with modern non-contact measuring system providing information on blade loading, damage and residual lifetime under operation. The requirement of measuring vibration and static characteristics of steam turbine blades, therefore, calls for the development and operational verification of both new types of sensors and measuring principles and methods. The task is really demanding: to measure displacements of blade tips with a resolution of the order of 10 μm by speeds up to 750 m/s, humidity 100% and temperatures up to 200 °C. While in gas turbines are used primarily capacitive and optical transducers, these transducers cannot be used in steam turbines. The reason is moisture vapor, droplets of condensing water and dirt, which disable the function of sensors. Therefore, the most feasible approach was to focus on research of electromagnetic sensors featuring promising characteristics for given blade materials in a steam environment. Following types of sensors have been developed and both experimentally and theoretically studied in the Institute of Thermodynamics, Academy of Sciences of the Czech Republic: eddy-current, Hall effect, inductive and magnetoresistive. Eddy-current transducers demand a small distance of 1 to 2 mm and change properties in the harsh environment of steam turbines. Hall effect sensors have relatively low sensitivity, high values of offset, drift, and especially noise. Induction sensors do not require any supply current and have a simple construction. The magnitude of the sensors output voltage is dependent on the velocity of the measured body and concurrently on the varying magnetic induction, and they cannot be used statically. Magnetoresistive sensors are formed by magnetoresistors arranged into a Wheatstone bridge. Supplying the sensor from a current source provides better linearity. The MR sensors can be used permanently for temperatures up to 200 °C at lower values of the supply current of about 1 mA. The frequency range of 0 to 300 kHz is by an order higher comparing to the Hall effect and induction sensors. The frequency band starts at zero frequency, which is very important because the sensors can be calibrated statically. The MR sensors feature high sensitivity and low noise. The symmetry of the bridge arrangement leads to a high common mode rejection ratio and suppressing disturbances, which is important, especially in industrial applications. The MR sensors feature high sensitivity, high common mode rejection ratio, and low noise, which is important, especially in industrial applications. Magnetoresistive transducers provide a range of excellent properties indicating their priority for displacement measurements of rotating blades in turbomachines.

Keywords: turbines, blade vibration, blade tip timing, non-contact sensors, magnetoresistive sensors

Procedia PDF Downloads 106

Authors: Roswati Md. Amin, Nurul Asmera Mudiman, Muhammad Faisal Abd. Rahman, Md-Suffian Idris, Noor Hazwani Mohd Azmi

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Distribution of picophytoplankton from two genera, Synechococcus and Prochlorococcus at the surface water (0.5m) were observed from coastal to offshore area of the southeastern coast of Peninsular Malaysia, for a six day cruise in August 2014 during SouthWest monsoon. The picophytoplankton was divided into two different size fractions (0.7-2.7μm and <0.7 μm) by filtering through GF/D (2.7 μm) and GF/F (0.7 μm) filter papers and counted by using flow cytometer. Synechococcus and Prochlorococcus contribute higher at 0.7-2.7μm size range (ca. 90% and 95%, respectively) compared to <0.7 μm (ca. 10% and 5%, respectively). Synechococcus (>52%) dominated the total picophytoplankton compared to Prochlorococcus (<26%) for both size fractions in southeastern coast of Peninsular Malaysia. Total density (<2.7 μm) of Synechococcus was ranging between 1.72 x104 and 12.57 x104 cells ml-1, while Prochlorococcus varied from 1.50 x104 to 8.62 x104. Both Synechococcus and Prochlorococcus abundance showed a decreasing trend from coastal to offshore.

Keywords: Peninsular Malaysia, prochlorococcus, South China Sea, synechococcus

Procedia PDF Downloads 295

Authors: Hamad M. Alnagran, Mohammed S. Imbabi

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Particulate matter (PM) in the air we breathe is detrimental to health. Overcoming this problem has attracted interest and prompted research on the use of PM filtration in commercial buildings and homes to be carried out. The consensus is that tangible health benefits can result from the use of PM filters in most urban environments, to clean up the building’s fresh air supply and thereby reduce exposure of residents to airborne PM. The authors have investigated and are developing a new large-scale Permeodynamic Filtration Technology (PFT) capable of permanently filtering and removing airborne PMs from outdoor spaces, thus also benefiting internal spaces such as the interiors of buildings. Theoretical models were developed, and laboratory trials carried out to determine, and validate through measurement permeodynamic filtration efficiency and pressure drop as functions of PM particle size distributions. The conclusion is that PFT offers a potentially viable, cost effective end of pipe solution to the problem of airborne PM.

Keywords: air filtration, particulate matter, particle size distribution, permeodynamic

Procedia PDF Downloads 186

Authors: Teng Li, Kamran Mohseni

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This paper presents an inviscid regularization technique for the incompressible two-phase flow simulations. This technique is known as observable method due to the understanding of observability that any feature smaller than the actual resolution (physical or numerical), i.e., the size of wire in hotwire anemometry or the grid size in numerical simulations, is not able to be captured or observed. Differ from most regularization techniques that applies on the numerical discretization, the observable method is employed at PDE level during the derivation of equations. Difficulties in the simulation and analysis of realistic fluid flow often result from discontinuities (or near-discontinuities) in the calculated fluid properties or state. Accurately capturing these discontinuities is especially crucial when simulating flows involving shocks, turbulence or sharp interfaces. Over the past several years, the properties of this new regularization technique have been investigated that show the capability of simultaneously regularizing shocks and turbulence. The observable method has been performed on the direct numerical simulations of shocks and turbulence where the discontinuities are successfully regularized and flow features are well captured. In the current paper, the observable method will be extended to two-phase interfacial flows. Multiphase flows share the similar features with shocks and turbulence that is the nonlinear irregularity caused by the nonlinear terms in the governing equations, namely, Euler equations. In the direct numerical simulation of two-phase flows, the interfaces are usually treated as the smooth transition of the properties from one fluid phase to the other. However, in high Reynolds number or low viscosity flows, the nonlinear terms will generate smaller scales which will sharpen the interface, causing discontinuities. Many numerical methods for two-phase flows fail at high Reynolds number case while some others depend on the numerical diffusion from spatial discretization. The observable method regularizes this nonlinear mechanism by filtering the convective terms and this process is inviscid. The filtering effect is controlled by an observable scale which is usually about a grid length. Single rising bubble and Rayleigh-Taylor instability are studied, in particular, to examine the performance of the observable method. A pseudo-spectral method is used for spatial discretization which will not introduce numerical diffusion, and a Total Variation Diminishing (TVD) Runge Kutta method is applied for time integration. The observable incompressible Euler equations are solved for these two problems. In rising bubble problem, the terminal velocity and shape of the bubble are particularly examined and compared with experiments and other numerical results. In the Rayleigh-Taylor instability, the shape of the interface are studied for different observable scale and the spike and bubble velocities, as well as positions (under a proper observable scale), are compared with other simulation results. The results indicate that this regularization technique can potentially regularize the sharp interface in the two-phase flow simulations

Keywords: Euler equations, incompressible flow simulation, inviscid regularization technique, two-phase flow

Procedia PDF Downloads 482

Authors: Prarabdh Tiwari, M. Vidya Sagar, K. Bhima Raju, Joy Choudhury, Subash Chandra, E. Nagaiah, Shakeel Ahmed

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Passive seismic, a wavefield interferometric imaging, low cost and rapid tool for subsurface investigation is used for various geotechnical purposes such as hydrocarbon exploration, seismic microzonation, etc. With the recent advancement, its application has also been extended to groundwater exploration by means of finding the bedrock depth. Council of Scientific & Industrial Research (CSIR)-National Geophysical Research Institute (NGRI) has experimented passive seismic studies along with electrical resistivity tomography for groundwater in hard rock (Choutuppal, Hyderabad). Passive Seismic with Electrical Resistivity (ERT) can give more clear 2-D subsurface image for Groundwater Exploration in Hard Rock area. Passive seismic data were collected using a Tromino, a three-component broadband seismometer, to measure background ambient noise and processed using GRILLA software. The passive seismic results are found corroborating with ERT (Electrical Resistivity Tomography) results. For data acquisition purpose, Tromino was kept over 30 locations consist recording of 20 minutes at each station. These location shows strong resonance frequency peak, suggesting good impedance contrast between different subsurface layers (ex. Mica rich Laminated layer, Weathered layer, granite, etc.) This paper presents signature of passive seismic for hard rock terrain. It has been found that passive seismic has potential application for formation characterization and can be used as an alternative tool for delineating litho-stratification in an urban condition where electrical and electromagnetic tools cannot be applied due to high cultural noise. In addition to its general application in combination with electrical and electromagnetic methods can improve the interpreted subsurface model.

Keywords: passive seismic, resonant frequency, Tromino, GRILLA

Procedia PDF Downloads 170

Authors: Heiko Weiß, Andreas Meister, Christoph Ament, Nils Dreifke

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Linear actuators are deployed in a wide range of applications. This paper presents the modeling and system identification of a variable excited linear direct drive (LDD). The LDD is designed based on linear hybrid stepper technology exhibiting the characteristic tooth structure of mover and stator. A three-phase topology provides the thrust force caused by alternating strengthening and weakening of the flux of the legs. To achieve best possible synchronous operation, the phases are commutated sinusoidal. Despite the fact that these LDDs provide high dynamics and drive forces, noise emission limits their operation in calm workspaces. To overcome this drawback an additional excitation of the magnetic circuit is introduced to LDD using additional enabling coils instead of permanent magnets. The new degree of freedom can be used to reduce force variations and related noise by varying the excitation flux that is usually generated by permanent magnets. Hence, an identified simulation model is necessary to analyze the effects of this modification. Especially the force variations must be modeled well in order to reduce them sufficiently. The model can be divided into three parts: the current dynamics, the mechanics and the force functions. These subsystems are described with differential equations or nonlinear analytic functions, respectively. Ordinary nonlinear differential equations are derived and transformed into state space representation. Experiments have been carried out on a test rig to identify the system parameters of the complete model. Static and dynamic simulation based optimizations are utilized for identification. The results are verified in time and frequency domain. Finally, the identified model provides a basis for later design of control strategies to reduce existing force variations.

Keywords: force variations, linear direct drive, modeling and system identification, variable excitation flux

Procedia PDF Downloads 358

Authors: Sudhina H. K, Ravi M. Yadahalli, N. M. Shetti

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Reconfigurable antennas represent a recent innovation in antenna design that changes from classical fixed-form, Fixed function antennas to modifiable structures that can be adapted to fit the requirements of a time varying system. The ability to control the operating band of an antenna system can have many useful applications. Systems that operate in an acquire-and-track configuration would see a benefit from active bandwidth control. In such systems a wide band search mode is first employed to find a desired signal, Then a narrow band track mode is used to follow only that signal. Utilizing active antenna bandwidth control, A single antenna would function for both the wide band and narrow band configurations providing the rejection of unwanted signals with the antenna hardware. This ability to move a portion of the RF filtering out of the receiver and onto the antenna itself will also aid in reducing the complexity of the often expensive RF processing subsystems.

Keywords: designing methods, mems, stack, reconfigurable elements

Procedia PDF Downloads 255

Authors: El Harraj Abdeslam, Raissouni Naoufal

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The detection of moving objects from a video image sequences is very important for object tracking, activity recognition, and behavior understanding in video surveillance. The most used approach for moving objects detection / tracking is background subtraction algorithms. Many approaches have been suggested for background subtraction. But, these are illumination change sensitive and the solutions proposed to bypass this problem are time consuming. In this paper, we propose a robust yet computationally efficient background subtraction approach and, mainly, focus on the ability to detect moving objects on dynamic scenes, for possible applications in complex and restricted access areas monitoring, where moving and motionless persons must be reliably detected. It consists of three main phases, establishing illumination changes in variance, background/foreground modeling and morphological analysis for noise removing. We handle illumination changes using Contrast Limited Histogram Equalization (CLAHE), which limits the intensity of each pixel to user determined maximum. Thus, it mitigates the degradation due to scene illumination changes and improves the visibility of the video signal. Initially, the background and foreground images are extracted from the video sequence. Then, the background and foreground images are separately enhanced by applying CLAHE. In order to form multi-modal backgrounds we model each channel of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture Model (GMM). Finally, we post process the resulting binary foreground mask using morphological erosion and dilation transformations to remove possible noise. For experimental test, we used a standard dataset to challenge the efficiency and accuracy of the proposed method on a diverse set of dynamic scenes.

Keywords: video surveillance, background subtraction, contrast limited histogram equalization, illumination invariance, object tracking, object detection, behavior understanding, dynamic scenes

Procedia PDF Downloads 240

Authors: Ahmed Mohammed Alsalama

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Recommendation systems are widely used in e-commerce applications. The engine of a current recommendation system recommends items to a particular user based on user preferences and previous high ratings. Various recommendation schemes such as collaborative filtering and content-based approaches are used to build a recommendation system. Most of the current recommendation systems were developed to fit a certain domain such as books, articles, and movies. We propose a hybrid framework recommendation system to be applied on two-dimensional spaces (User x Item) with a large number of Users and a small number of Items. Moreover, our proposed framework makes use of both favorite and non-favorite items of a particular user. The proposed framework is built upon the integration of association rules mining and the content-based approach. The results of experiments show that our proposed framework can provide accurate recommendations to users.

Keywords: data mining, association rules, recommendation systems, hybrid systems

Procedia PDF Downloads 443

Authors: Ferdi Sönmez, Nihal Yorulmaz

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Decision making on the true planning of the public transportation systems to serve potential users is a must for metropolitan areas. To take attraction of travelers to projected modes of transport, adequately fair overall travel times should be provided. In this fashion, other benefits such as lower traffic congestion, road safety and lower noise and atmospheric pollution may be earned. The congestion which comes with increasing demand of public transportation is becoming a part of our lives and making residents’ life difficult. Hence, regulations should be done to reduce this congestion. To provide a constructive and balanced regulation in public transportation systems, right stations should be located in right places. In this study, it is aimed to design and implement a Decision Support System (DSS) Application to determine the optimal bus stop places for public transport in Istanbul which is one of the biggest and oldest cities in the world. Required information is gathered from IETT (Istanbul Electricity, Tram and Tunnel) Enterprises which manages all public transportation services in Istanbul Metropolitan Area. By using the most real-like values, cost assignments are made. The cost is calculated with the help of equations produced by bi-level optimization model. For this study, 300 buses, 300 drivers, 10 lines and 110 stops are used. The user cost of each station and the operator cost taken place in lines are calculated. Some components like cost, security and noise pollution are considered as significant factors affecting the solution of set covering problem which is mentioned for identifying and locating the minimum number of possible bus stops. Preliminary research and model development for this study refers to previously published article of the corresponding author. Model results are represented with the intent of decision support to the specialists on locating stops effectively.

Keywords: operator cost, bi-level optimization model, user cost, urban transportation

Procedia PDF Downloads 227

Authors: Sandabad Sara, Sayd Tahri Yassine, Hammouch Ahmed

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The development of science has allowed computer scientists to touch the medicine and bring aid to radiologists as we are presenting it in our article. Our work focuses on the detection and localization of tumors areas in the human brain; this will be a completely automatic without any human intervention. In front of the huge volume of MRI to be treated per day, the radiologist can spend hours and hours providing a tremendous effort. This burden has become less heavy with the automation of this step. In this article we present an automatic and effective tumor detection, this work consists of two steps: the first is the image filtering using the filter Nl-means, then applying the expectation maximization algorithm (EM) for retrieving the tumor mask from the brain MRI and extracting the tumor area using the mask obtained from the second step. To prove the effectiveness of this method multiple evaluation criteria will be used, so that we can compare our method to frequently extraction methods used in the literature.

Keywords: MRI, Em algorithm, brain, tumor, Nl-means

Procedia PDF Downloads 316

Authors: S. Jagadeesh Kumar

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Face detection and recognition is an authoritative technology for image database management, video surveillance, and human computer interface (HCI). Face recognition is a rapidly nascent method, which has been extensively discarded in forensics such as felonious identification, tenable entree, and custodial security. This paper recommends an erudite technique using curvature analysis (CA) that has less false positives incidence, operative in different light environments and confiscates the artifacts that are introduced during image acquisition by ring correction in polar coordinate (RCP) method. This technique affronts mean and median filtering technique to remove the artifacts but it works in polar coordinate during image acquisition. Investigational fallouts for face detection and recognition confirms decent recitation even in diagonal orientation and stance variation.

Keywords: curvature analysis, ring correction in polar coordinate method, face detection, face recognition, human computer interaction

Procedia PDF Downloads 268

Authors: Saad Bakkali

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Morocco is a major producer of phosphate, with an annual output of 19 million tons and reserves in excess of 35 billion cubic meters. This represents more than 75% of world reserves. Resistivity surveys have been successfully used in the Oulad Abdoun phosphate basin. A Schlumberger resistivity survey over an area of 50 hectares was carried out. A new field procedure based on analytic signal response of resistivity data was tested to deal with the presence of phosphate deposit disturbances. A resistivity map was expected to allow the electrical resistivity signal to be imaged in 2D. 2D wavelet is standard tool in the interpretation of geophysical potential field data. Wavelet transform is particularly suitable in denoising, filtering and analyzing geophysical data singularities. Wavelet transform tools are applied to analysis of a moroccan phosphate deposit ‘disturbances’. Wavelet approach applied to modeling surface phosphate “disturbances” was found to be consistently useful.

Keywords: resistivity, Schlumberger, phosphate, wavelet, Morocco

Procedia PDF Downloads 402

Authors: T. Bryan , V. Kepuska, I. Kostnaic

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A method of data compression and denoising is introduced that is based on atomic decomposition of audio data using “basis vectors” that are learned from the audio data itself. The basis vectors are shown to have higher data compression and better signal-to-noise enhancement than the Gabor and gammatone “seed atoms” that were used to generate them. The basis vectors are the input weights of a Sparse AutoEncoder (SAE) that is trained using “envelope samples” of windowed segments of the audio data. The envelope samples are extracted from the audio data by performing atomic decomposition with Gabor or gammatone seed atoms. This process identifies segments of audio data that are locally coherent with the seed atoms. Envelope samples are extracted by identifying locally coherent audio data segments with Gabor or gammatone seed atoms, found by matching pursuit. The envelope samples are formed by taking the kronecker products of the atomic envelopes with the locally coherent data segments. Oracle signal-to-noise ratio (SNR) verses data compression curves are generated for the seed atoms as well as the basis vectors learned from Gabor and gammatone seed atoms. SNR data compression curves are generated for speech signals as well as early American music recordings. The basis vectors are shown to have higher denoising capability for data compression rates ranging from 90% to 99.84% for speech as well as music. Envelope samples are displayed as images by folding the time series into column vectors. This display method is used to compare of the output of the SAE with the envelope samples that produced them. The basis vectors are also displayed as images. Sparsity is shown to play an important role in producing the highest denoising basis vectors.

Keywords: sparse dictionary learning, autoencoder, sparse autoencoder, basis vectors, atomic decomposition, envelope sampling, envelope samples, Gabor, gammatone, matching pursuit

Procedia PDF Downloads 235

Authors: J. M. Hernández-Ontiveros, E. E. García-Guerrero, E. Inzunza-González, O. R. López-Bonilla

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This paper presents the implementation of an algorithm that identifies and accounts different fish species: Catfish, Sea bream, Sawfish, Tilapia, and Totoaba. The main contribution of the method is the fusion of the characteristics of invariance to the position, rotation and scale of the Hu moments, with the proper counting of fish. The identification and counting is performed, from an image under different noise conditions. From the experimental results obtained, it is inferred the potentiality of the proposed algorithm to be applied in different scenarios of aquaculture production.

Keywords: counting fish, digital image processing, invariant moments, pattern recognition

Procedia PDF Downloads 393

Authors: C. Rana, S. K. Jain

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The enormous amount of Web data has challenged its usage in efficient manner in the past few years. As such, a range of techniques are applied to tackle this problem; prominent among them is personalization and recommender system. In fact, these are the tools that assist user in finding relevant information of web. Most of the e-commerce websites are applying such tools in one way or the other. In the past decade, a large number of recommendation algorithms have been proposed to tackle such problems. However, there have not been much research in the evaluation criteria for these algorithms. As such, the traditional accuracy and classification metrics are still used for the evaluation purpose that provides a static view. This paper studies how the evolution of user preference over a period of time can be mapped in a recommender system using a new evaluation methodology that explicitly using time dimension. We have also presented different types of experimental set up that are generally used for recommender system evaluation. Furthermore, an overview of major accuracy metrics and metrics that go beyond the scope of accuracy as researched in the past few years is also discussed in detail.

Keywords: collaborative filtering, data mining, evolutionary, clustering, algorithm, recommender systems

Procedia PDF Downloads 395

Authors: Dimitriya A. Mihaylova, Zlatka V. Valkova-Jarvis, Georgi L. Iliev

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One possible approach for maintaining the security of communication systems relies on Physical Layer Security mechanisms. However, in wireless time division duplex systems, where uplink and downlink channels are reciprocal, the channel estimate procedure is exposed to attacks known as pilot contamination, with the aim of having an enhanced data signal sent to the malicious user. The Shifted 2-N-PSK method involves two random legitimate pilots in the training phase, each of which belongs to a constellation, shifted from the original N-PSK symbols by certain degrees. In this paper, legitimate pilots’ offset values and their influence on the detection capabilities of the Shifted 2-N-PSK method are investigated. As the implementation of the technique depends on the relation between the shift angles rather than their specific values, the optimal interconnection between the two legitimate constellations is investigated. The results show that no regularity exists in the relation between the pilot contamination attacks (PCA) detection probability and the choice of offset values. Therefore, an adversary who aims to obtain the exact offset values can only employ a brute-force attack but the large number of possible combinations for the shifted constellations makes such a type of attack difficult to successfully mount. For this reason, the number of optimal shift value pairs is also studied for both 100% and 98% probabilities of detecting pilot contamination attacks. Although the Shifted 2-N-PSK method has been broadly studied in different signal-to-noise ratio scenarios, in multi-cell systems the interference from the signals in other cells should be also taken into account. Therefore, the inter-cell interference impact on the performance of the method is investigated by means of a large number of simulations. The results show that the detection probability of the Shifted 2-N-PSK decreases inversely to the signal-to-interference-plus-noise ratio.

Keywords: channel estimation, inter-cell interference, pilot contamination attacks, wireless communications

Procedia PDF Downloads 199

Authors: Animut Meseret Simachew

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Parallel Code Phase Search Acquisition (PCSA) Algorithm has been considered as a promising method in GPS software receivers for detection and estimation of the accurate correlation peak between the received Global Positioning System (GPS) signal and locally generated replicas. GPS signal acquisition in highly dense multipath environments is the main research challenge. In this work, we proposed a robust variable step-size (RVSS) PCSA algorithm based on fast frequency transform (FFT) filtering technique to mitigate short time delay multipath signals. Simulation results reveal the effectiveness of the proposed algorithm over the conventional PCSA algorithm. The proposed RVSS-PCSA algorithm equalizes the received carrier wiped-off signal with locally generated C/A code.

Keywords: adaptive PCSA, detection and estimation, GPS signal acquisition, GPS software receiver

Procedia PDF Downloads 106

Authors: Arda Ozkurt, Ozlem Bozkurt

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Brain-computer interfaces (BCIs) are devices that output commands by interpreting the data collected from the brain. Electroencephalography (EEG) is a non-invasive method to measure the brain's electrical activity. Since it was invented by Hans Berger in 1929, it has led to many neurological discoveries and has become one of the essential components of non-invasive measuring methods. Despite the fact that it has a low spatial resolution -meaning it is able to detect when a group of neurons fires at the same time-, it is a non-invasive method, making it easy to use without possessing any risks. In EEG, electrodes are placed on the scalp, and the voltage difference between a minimum of two electrodes is recorded, which is then used to accomplish the intended task. The recordings of EEGs include, but are not limited to, the currents along dendrites from synapses to the soma, the action potentials along the axons connecting neurons, and the currents through the synaptic clefts connecting axons with dendrites. However, there are some sources of noise that may affect the reliability of the EEG signals as it is a non-invasive method. For instance, the noise from the EEG equipment, the leads, and the signals coming from the subject -such as the activity of the heart or muscle movements- affect the signals detected by the electrodes of the EEG. However, new techniques have been developed to differentiate between those signals and the intended ones. Furthermore, an EEG device is not enough to analyze the data from the brain to be used by the BCI implication. Because the EEG signal is very complex, to analyze it, artificial intelligence algorithms are required. These algorithms convert complex data into meaningful and useful information for neuroscientists to use the data to design BCI devices. Even though for neurological diseases which require highly precise data, invasive BCIs are needed; non-invasive BCIs - such as EEGs - are used in many cases to help disabled people's lives or even to ease people's lives by helping them with basic tasks. For example, EEG is used to detect before a seizure occurs in epilepsy patients, which can then prevent the seizure with the help of a BCI device. Overall, EEG is a commonly used non-invasive BCI technique that has helped develop BCIs and will continue to be used to detect data to ease people's lives as more BCI techniques will be developed in the future.

Keywords: BCI, EEG, non-invasive, spatial resolution

Procedia PDF Downloads 57

Authors: Zhongzhen Luo, Saeid Habibi, Martin v. Mohrenschildt

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Self-driving vehicle require a high level of situational awareness in order to maneuver safely when driving in real world condition. This paper presents a LiDAR based real time perception system that is able to process sensor raw data for multiple target detection and tracking in dynamic environment. The proposed algorithm is nonparametric and deterministic that is no assumptions and priori knowledge are needed from the input data and no initializations are required. Additionally, the proposed method is working on the three-dimensional data directly generated by LiDAR while not scarifying the rich information contained in the domain of 3D. Moreover, a fast and efficient for real time clustering algorithm is applied based on a radially bounded nearest neighbor (RBNN). Hungarian algorithm procedure and adaptive Kalman filtering are used for data association and tracking algorithm. The proposed algorithm is able to run in real time with average run time of 70ms per frame.

Keywords: lidar, segmentation, clustering, tracking

Procedia PDF Downloads 398

Authors: Rahul Paul, Peter Mctaggart, Luke Skinner

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Maintaining public safety and network reliability are the core objectives of all electricity distributors globally. For many electricity distributors, managing vegetation clearances from their above ground assets (poles and conductors) is the most important and costly risk mitigation control employed to meet these objectives. Light Detection And Ranging (LiDAR) is widely used by utilities as a cost-effective method to inspect their spatially-distributed assets at scale, often captured using high powered LiDAR scanners attached to fixed wing or rotary aircraft. The resulting 3D point cloud model is used by these utilities to perform engineering grade measurements that guide the prioritisation of vegetation cutting programs. Advances in computer vision and machine-learning approaches are increasingly applied to increase automation and reduce inspection costs and time; however, real-world LiDAR capture variables (e.g., aircraft speed and height) create complexity, noise, and missing data, reducing the effectiveness of these approaches. This paper proposes a method for identifying each conductor from LiDAR data via clustering methods that can precisely reconstruct conductors in complex real-world configurations in the presence of high levels of noise. It proposes 3D catenary models for individual clusters fitted to the captured LiDAR data points using a least square method. An iterative learning process is used to identify potential conductor models between pole pairs. The proposed method identifies the optimum parameters of the catenary function and then fits the LiDAR points to reconstruct the conductors.

Keywords: point cloud, LİDAR data, machine learning, computer vision, catenary curve, vegetation management, utility industry

Procedia PDF Downloads 84

Authors: Randa Boukhris, Alima Damak, Dorra Sellami

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Fingervein biometric authentication is one of the most popular and accurate technologies. However, low cost embedded solution is still an open problem. In this paper, a real-time implementation of fingervein recognition process embedded in Raspberry-Pi has been proposed. The use of Raspberry-Pi reduces overall system cost and size while allowing an easy user interface. Implementation of a target technology has guided to opt some specific parallel and simple processing algorithms. In the proposed system, we use four structural directional kernel elements for filtering finger vein images. Then, a Top-Hat and Bottom-Hat kernel filters are used to enhance the visibility and the appearance of venous images. For feature extraction step, a simple Local Directional Code (LDC) descriptor is applied. The proposed system presents an Error Equal Rate (EER) and Identification Rate (IR), respectively, equal to 0.02 and 98%. Furthermore, experimental results show that real-time operations have good performance.

Keywords: biometric, Bottom-Hat, Fingervein, LDC, Rasberry-Pi, ROI, Top-Hat

Procedia PDF Downloads 192

Authors: Zhaoyang Liu

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Oil pollution is typically caused by oil and gas-related operations such as vessel accidents, which can pollute waterways as well as the environment and damage the ecosystem. Tanker ship cleaning contributes to oil spills, which have a negative impact on coastal countries due to protracted service disruption. It is critical for coastal countries to develop efficient oil taint cleanup technology. There are various oil/water separation technologies, such as gravity separation, hydrocyclone, air flotation, and membrane filtration, among others. Among these, membrane filtration has been shown to produce high-quality effluent. Commercial membranes, on the other hand, nevertheless face significant practical challenges, such as a high susceptibility for membrane fouling when dealing with greasy effluent. We developed a unique anti-fouling filtering membrane for oil/water separation in this work. The membrane was made of inorganic nanofibers, which possesses the advantages of low membrane fouling, high permeation flux and long-term durability. This results from this study could facilitate to pave a new way for membranes filtration’s practical applications in oil/gas industry.

Keywords: oil, contaminate, wastewater, removal

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Authors: Luis Alvarado, Victor Poblete, Isaac Gonzalez, Yetzabeth Gonzalez

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In MIREX 2016 (http://www.music-ir.org/mirex), the deep neural network (DNN)-Deep Chroma Extractor, proposed by Korzeniowski and Wiedmer, reached the highest score in an audio chord recognition task. In the present paper, this tool is assessed under acoustic reverberant environments and distinct source-microphone distances. The evaluation dataset comprises The Beatles and Queen datasets. These datasets are sequentially re-recorded with a single microphone in a real reverberant chamber at four reverberation times (0 -anechoic-, 1, 2, and 3 s, approximately), as well as four source-microphone distances (32, 64, 128, and 256 cm). It is expected that the performance of the trained DNN will dramatically decrease under these acoustic conditions with signals degraded by room reverberation and distance to the source. Recently, the effect of the bio-inspired Locally-Normalized Cepstral Coefficients (LNCC), has been assessed in a text independent speaker verification task using speech signals degraded by additive noise at different signal-to-noise ratios with variations of recording distance, and it has also been assessed under reverberant conditions with variations of recording distance. LNCC showed a performance so high as the state-of-the-art Mel Frequency Cepstral Coefficient filters. Based on these results, this paper proposes a variation of locally-normalized triangular filters called Locally-Normalized Quarter Tone (LNQT) filters. By using the LNQT spectrogram, robustness improvements of the trained Deep Chroma Extractor are expected, compared with classical triangular filters, and thus compensating the music signal degradation improving the accuracy of the chord recognition system.

Keywords: chord recognition, deep neural networks, feature extraction, music information retrieval

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Authors: Seyed-Yaser Nabavi-Chashmi, Davood Asadi, Karim Ahmadi, Eren Demir

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The landing phase of a UAV is very critical as there are many uncertainties in this phase, which can easily entail a hard landing or even a crash. In this paper, the estimation of relative distance and velocity to the ground, as one of the most important processes during the landing phase, is studied. Using accurate measurement sensors as an alternative approach can be very expensive for sensors like LIDAR, or with a limited operational range, for sensors like ultrasonic sensors. Additionally, absolute positioning systems like GPS or IMU cannot provide distance to the ground independently. The focus of this paper is to determine whether we can measure the relative distance and velocity of UAV and ground in the landing phase using just low-resolution images taken by a monocular camera. The Lucas-Konda feature detection technique is employed to extract the most suitable feature in a series of images taken during the UAV landing. Two different approaches based on Extended Kalman Filters (EKF) have been proposed, and their performance in estimation of the relative distance and velocity are compared. The first approach uses the kinematics of the UAV as the process and the calculated optical flow as the measurement; On the other hand, the second approach uses the feature’s projection on the camera plane (pixel position) as the measurement while employing both the kinematics of the UAV and the dynamics of variation of projected point as the process to estimate both relative distance and relative velocity. To verify the results, a sequence of low-quality images taken by a camera that is moving on a specifically developed testbed has been used to compare the performance of the proposed algorithm. The case studies show that the quality of images results in considerable noise, which reduces the performance of the first approach. On the other hand, using the projected feature position is much less sensitive to the noise and estimates the distance and velocity with relatively high accuracy. This approach also can be used to predict the future projected feature position, which can drastically decrease the computational workload, as an important criterion for real-time applications.

Keywords: altitude estimation, drone, image processing, trajectory planning

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Authors: Maryam Shahbazi, Amy B. Cerato

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The importance of a systems’ natural frequency (fn) emerges when the vibration force frequency is equivalent to foundation's fn which causes response amplitude (resonance) that may cause irreversible damage to the structure. Several factors such as pile geometry (e.g., length and diameter), soil density, load magnitude, pile condition, and physical structure affect the fn of a soil-pile system; some of these parameters are evaluated in this study. Although experimental and analytical studies have assessed the fn of a soil-pile system, few have included individual and grouped helical piles. Thus, the current study aims to provide quantitative data on dynamic characteristics of helical pile-soil systems from full-scale shake table tests that will allow engineers to predict more realistic dynamic response under motions with variable frequency ranges. To evaluate the fn of single and grouped helical piles in dry dense sand, full-scale shake table tests were conducted in a laminar box (6.7 m x 3.0 m with 4.6 m high). Two different diameters (8.8 cm and 14 cm) helical piles were embedded in the soil box with corresponding lengths of 3.66m (excluding one pile with length of 3.96) and 4.27m. Different configurations were implemented to evaluate conditions such as fixed and pinned connections. In the group configuration, all four piles with similar geometry were tied together. Simulated real earthquake motions, in addition to white noise, were applied to evaluate the wide range of soil-pile system behavior. The Fast Fourier Transform (FFT) of measured time history responses using installed strain gages and accelerometers were used to evaluate fn. Both time-history records using accelerometer or strain gages were found to be acceptable for calculating fn. In this study, the existence of a pile reduced the fn of the soil slightly. Greater fn occurred on single piles with larger l/d ratios (higher slenderness ratio). Also, regardless of the connection type, the more slender pile group which is obviously surrounded by more soil, yielded higher natural frequencies under white noise, which may be due to exhibiting more passive soil resistance around it. Relatively speaking, within both pile groups, a pinned connection led to a lower fn than a fixed connection (e.g., for the same pile group the fn’s are 5.23Hz and 4.65Hz for fixed and pinned connections, respectively). Generally speaking, a stronger motion causes nonlinear behavior and degrades stiffness which reduces a pile’s fn; even more, reduction occurs in soil with a lower density. Moreover, fn of dense sand under white noise signal was obtained 5.03 which is reduced by 44% when an earthquake with the acceleration of 0.5g was applied. By knowing the factors affecting fn, the designer can effectively match the properties of the soil to a type of pile and structure to attempt to avoid resonance. The quantitative results in this study assist engineers in predicting a probable range of fn for helical pile foundations under potential future earthquake, and machine loading applied forces.

Keywords: helical pile, natural frequency, pile group, shake table, stiffness

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Authors: Ni Wenli, He Jing

Abstract:

Repeat–Accumulate (RA) codes are subclass of LDPC codes with fast encoder structures. In this paper, we consider a nonbinary extension of binary LDPC codes over GF(q) and construct a non-binary RA code and a non-binary QC-LDPC code over GF(2^4), we construct non-binary RA codes with linear encoding method and non-binary QC-LDPC codes with algebraic constructions method. And the BER performance of RA and QC-LDPC codes over GF(q) are compared with BP decoding and by simulation over the Additive White Gaussian Noise (AWGN) channels.

Keywords: non-binary RA codes, QC-LDPC codes, performance comparison, BP algorithm

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Authors: Smail Boussaadi, Hassina Aliane

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With the rapid growth of scientific literature, it is becoming increasingly challenging for researchers to keep up with the latest findings in their fields. Academic, professional networks play an essential role in connecting researchers and disseminating knowledge. To improve the user experience within these networks, we need effective article recommendation systems that provide personalized content.Current recommendation systems often rely on collaborative filtering or content-based techniques. However, these methods have limitations, such as the cold start problem and difficulty in capturing semantic relationships between articles. To overcome these challenges, we propose a new approach that combines BERTopic (Bidirectional Encoder Representations from Transformers), a state-of-the-art topic modeling technique, with community detection algorithms in a academic, professional network. Experiences confirm our performance expectations by showing good relevance and objectivity in the results.

Keywords: scientific articles, community detection, academic social network, recommender systems, neural topic model

Procedia PDF Downloads 80