Search results for: PQ signal filtering
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 1910

Search results for: PQ signal filtering

1340 Geographical Information System-Based Approach for Vertical Takeoff and Landing Takeoff and Landing Site Selection

Authors: Chamnan Kumsap, Somsarit Sinnung, Suriyawate Boonthalarath, Teeranai Srithamarong

Abstract:

This research paper addresses the GIS analysis approach to the investigation of suitable sites for a vertical takeoff and landing drone. The study manipulated GIS and terrain layers into a proper input before the spatial analysis that included slope, reclassify, classify, and buffer was applied to the individual layers. The output layers were weighted, and multi-criteria analyzed before those patches failing to comply with filtering out criteria were discarded. Field survey for each suitable candidate site was conducted to cross-check the proposed approach with the real world. Conclusion was extracted for the VTOL takeoff and landing sites, and discussion was provided with further study being suggested on the mission simulation of selected takeoff and landing sites.

Keywords: GIS approach, site selection, VTOL, takeoff and landing

Procedia PDF Downloads 99
1339 Experimental Study on Dehumidification Performance of Supersonic Nozzle

Authors: Esam Jassim

Abstract:

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.

Keywords: supersonic nozzle, dehumidification, particle separation, nozzle geometry

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1338 A Versatile Data Processing Package for Ground-Based Synthetic Aperture Radar Deformation Monitoring

Authors: Zheng Wang, Zhenhong Li, Jon Mills

Abstract:

Ground-based synthetic aperture radar (GBSAR) represents a powerful remote sensing tool for deformation monitoring towards various geohazards, e.g. landslides, mudflows, avalanches, infrastructure failures, and the subsidence of residential areas. Unlike spaceborne SAR with a fixed revisit period, GBSAR data can be acquired with an adjustable temporal resolution through either continuous or discontinuous operation. However, challenges arise from processing high temporal-resolution continuous GBSAR data, including the extreme cost of computational random-access-memory (RAM), the delay of displacement maps, and the loss of temporal evolution. Moreover, repositioning errors between discontinuous campaigns impede the accurate measurement of surface displacements. Therefore, a versatile package with two complete chains is developed in this study in order to process both continuous and discontinuous GBSAR data and address the aforementioned issues. The first chain is based on a small-baseline subset concept and it processes continuous GBSAR images unit by unit. Images within a window form a basic unit. By taking this strategy, the RAM requirement is reduced to only one unit of images and the chain can theoretically process an infinite number of images. The evolution of surface displacements can be detected as it keeps temporarily-coherent pixels which are present only in some certain units but not in the whole observation period. The chain supports real-time processing of the continuous data and the delay of creating displacement maps can be shortened without waiting for the entire dataset. The other chain aims to measure deformation between discontinuous campaigns. Temporal averaging is carried out on a stack of images in a single campaign in order to improve the signal-to-noise ratio of discontinuous data and minimise the loss of coherence. The temporal-averaged images are then processed by a particular interferometry procedure integrated with advanced interferometric SAR algorithms such as robust coherence estimation, non-local filtering, and selection of partially-coherent pixels. Experiments are conducted using both synthetic and real-world GBSAR data. Displacement time series at the level of a few sub-millimetres are achieved in several applications (e.g. a coastal cliff, a sand dune, a bridge, and a residential area), indicating the feasibility of the developed GBSAR data processing package for deformation monitoring of a wide range of scientific and practical applications.

Keywords: ground-based synthetic aperture radar, interferometry, small baseline subset algorithm, deformation monitoring

Procedia PDF Downloads 155
1337 Carbon-Based Electrodes for Parabens Detection

Authors: Aniela Pop, Ianina Birsan, Corina Orha, Rodica Pode, Florica Manea

Abstract:

Carbon nanofiber-epoxy composite electrode has been investigated through voltammetric and amperometric techniques in order to detect parabens from aqueous solutions. The occurrence into environment as emerging pollutants of these preservative compounds has been extensively studied in the last decades, and consequently, a rapid and reliable method for their quantitative quantification is required. In this study, methylparaben (MP) and propylparaben (PP) were chosen as representatives for paraben class. The individual electrochemical detection of each paraben has been successfully performed. Their electrochemical oxidation occurred at the same potential value. Their simultaneous quantification should be assessed electrochemically only as general index of paraben class as a cumulative signal corresponding to both MP and PP from solution. The influence of pH on the electrochemical signal was studied. pH ranged between 1.3 and 9.0 allowed shifting the detection potential value to smaller value, which is very desired for the electroanalysis. Also, the signal is better-defined and higher sensitivity is achieved. Differential-pulsed voltammetry and square-wave voltammetry were exploited under the optimum pH conditions to improve the electroanalytical performance for the paraben detection. Also, the operation conditions were selected, i.e., the step potential, modulation amplitude and the frequency. Chronomaprometry application as the easiest electrochemical detection method led to worse sensitivity, probably due to a possible fouling effect of the electrode surface. The best electroanalytical performance was achieved by pulsed voltammetric technique but the selection of the electrochemical technique is related to the concrete practical application. A good reproducibility of the voltammetric-based method using carbon nanofiber-epoxy composite electrode was determined and no interference effect was found for the cation and anion species that are common in the water matrix. Besides these characteristics, the long life-time of the electrode give to carbon nanofiber-epoxy composite electrode a great potential for practical applications.

Keywords: carbon nanofiber-epoxy composite electrode, electroanalysis, methylparaben, propylparaben

Procedia PDF Downloads 217
1336 Particle Filter Implementation of a Non-Linear Dynamic Fall Model

Authors: T. Kobayashi, K. Shiba, T. Kaburagi, Y. Kurihara

Abstract:

For the elderly living alone, falls can be a serious problem encountered in daily life. Some elderly people are unable to stand up without the assistance of a caregiver. They may become unconscious after a fall, which can lead to serious aftereffects such as hypothermia, dehydration, and sometimes even death. We treat the subject as an inverted pendulum and model its angle from the equilibrium position and its angular velocity. As the model is non-linear, we implement the filtering method with a particle filter which can estimate true states of the non-linear model. In order to evaluate the accuracy of the particle filter estimation results, we calculate the root mean square error (RMSE) between the estimated angle/angular velocity and the true values generated by the simulation. The experimental results give the highest accuracy RMSE of 0.0141 rad and 0.1311 rad/s for the angle and angular velocity, respectively.

Keywords: fall, microwave Doppler sensor, non-linear dynamics model, particle filter

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1335 Dose Saving and Image Quality Evaluation for Computed Tomography Head Scanning with Eye Protection

Authors: Yuan-Hao Lee, Chia-Wei Lee, Ming-Fang Lin, Tzu-Huei Wu, Chih-Hsiang Ko, Wing P. Chan

Abstract:

Computed tomography (CT) scan of the head is a good method for investigating cranial lesions. However, radiation-induced oxidative stress can be accumulated in the eyes and promote carcinogenesis and cataract. In this regard, we aimed to protect the eyes with barium sulfate shield(s) during CT scans and investigate the resultant image quality and radiation dose to the eye. Patients who underwent health examinations were selectively enrolled in this study in compliance with the protocol approved by the Ethics Committee of the Joint Institutional Review Board at Taipei Medical University. Participants’ brains were scanned with a water-based marker simultaneously by a multislice CT scanner (SOMATON Definition Flash) under a fixed tube current-time setting or automatic tube current modulation (TCM). The lens dose was measured by Gafchromic films, whose dose response curve was previously fitted using thermoluminescent dosimeters, with or without barium sulfate or bismuth-antimony shield laid above. For the assessment of image quality CT images at slice planes that exhibit the interested regions on the zygomatic, orbital and nasal bones of the head phantom as well as the water-based marker were used for calculating the signal-to-noise and contrast-to-noise ratios. The application of barium sulfate and bismuth-antimony shields decreased 24% and 47% of the lens dose on average, respectively. Under topogram-based TCM, the dose saving power of bismuth-antimony shield was mitigated whereas that of barium sulfate shield was enhanced. On the other hand, the signal-to-noise and contrast-to-noise ratios of DSCT images were decreased separately by barium sulfate and bismuth-antimony shield, resulting in an overall reduction of the CNR. In contrast, the integration of topogram-based TCM elevated signal difference between the ROIs on the zygomatic bones and eyeballs while preferentially decreasing the signal-to-noise ratios upon the use of barium sulfate shield. The results of this study indicate that the balance between eye exposure and image quality can be optimized by combining eye shields with topogram-based TCM on the multislice scanner. Eye shielding could change the photon attenuation characteristics of tissues that are close to the shield. The application of both shields on eye protection hence is not recommended for seeking intraorbital lesions.

Keywords: computed tomography, barium sulfate shield, dose saving, image quality

Procedia PDF Downloads 259
1334 End-to-End Pyramid Based Method for Magnetic Resonance Imaging Reconstruction

Authors: Omer Cahana, Ofer Levi, Maya Herman

Abstract:

Magnetic Resonance Imaging (MRI) is a lengthy medical scan that stems from a long acquisition time. Its length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach such as Compress Sensing (CS) or Parallel Imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. To achieve that, two conditions must be satisfied: i) the signal must be sparse under a known transform domain, and ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm must be applied to recover the signal. While the rapid advances in Deep Learning (DL) have had tremendous successes in various computer vision tasks, the field of MRI reconstruction is still in its early stages. In this paper, we present an end-to-end method for MRI reconstruction from k-space to image. Our method contains two parts. The first is sensitivity map estimation (SME), which is a small yet effective network that can easily be extended to a variable number of coils. The second is reconstruction, which is a top-down architecture with lateral connections developed for building high-level refinement at all scales. Our method holds the state-of-art fastMRI benchmark, which is the largest, most diverse benchmark for MRI reconstruction.

Keywords: magnetic resonance imaging, image reconstruction, pyramid network, deep learning

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1333 Emphasis on Difference: Ethnic and National Cultural Heritage Identities and Issues in East Asia Focusing on Korea Cases

Authors: Hyuk-Jin Lee

Abstract:

Even though 23 years have passed in the 21st century, nation-state and nationality-centered cultural identities are still the sentiments and ideologies that dominate the world. Nevertheless, as seen in many cases in Europe, a new perspective is needed to recognize mutual exchanges and influences and to view them as natural cultural exchanges between countries. The situation in East Asia is completely different from Europe. This is presumed to be from the long tradition of having an ethnocentric state concept for at least hundreds of years, quite different from Europe, where the concept of a nation-state was established relatively recently. In other words, unlike Europe, where active exchanges took place, the problem stems from the unique characteristics of East Asia, which has a strong tradition of finding its identity in 'difference'. Thus, it would not be hard to find cultural studies or news of the three East Asian countries emphasizing differences among one another. This applies to all cultural areas, including traditional architecture. For example, in the Korean traditional architecture field, buildings with effects from neighboring countries tend to be ignored, even if they are traditional Korean architecture. In addition to this, in the case of Korea, there seems to be one more cultural harmful aftereffect caused by the 36 years of Japanese colonial rule in the early 20th century; the obsessive filtering concept of 'it must be different from Japan'. In other words, the implicit ideological coercion that the definition of 'Korean cultural heritage' should not be influenced by exchanges with Japan may be found throughout Korean studies. The architectural and cultural aspects of the vast period of time, from the Three Kingdoms era to the beginning of Joseon, which was a period in which cultural influence exchanges with neighboring countries were relatively strong compared to the late Joseon Dynasty, also reflect the 'distorted filtering' caused by finding a repulsive identity against the Japanese colonial period. It is important to look the cultural heritage and traditions as they are inductively, not deductively. If not, we may often ignore or limit our own precious cultural heritage. Conversely, If Baekje, the ancient Korean Kingdom, helped Japan in construction and craftsmen played a big role in building the ancient temple, it would be a healthier perspective to view it as a cultural exchange rather than proudly seeing it as a cultural owner's perspective because this point of view is a proper reconstruction of our ancient and medieval Asian culture (strictly speaking, the color common to East Asia at the time). In particular, this study will examine this topic by giving specific examples from each field of Korean cultural studies. In the search for cultural identity, it would be more helpful for healthy relations between countries and collaborative research in the sensitive part of the interpretation of historical facts as well as cultural circles to minimize excessive meanings on originality and difference.

Keywords: cultural heritage identity, cultural ideology, East Asia, Korea

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1332 Adaptive Online Object Tracking via Positive and Negative Models Matching

Authors: Shaomei Li, Yawen Wang, Chao Gao

Abstract:

To improve tracking drift which often occurs in adaptive tracking, an algorithm based on the fusion of tracking and detection is proposed in this paper. Firstly, object tracking is posed as a binary classification problem and is modeled by partial least squares (PLS) analysis. Secondly, tracking object frame by frame via particle filtering. Thirdly, validating the tracking reliability based on both positive and negative models matching. Finally, relocating the object based on SIFT features matching and voting when drift occurs. Object appearance model is updated at the same time. The algorithm cannot only sense tracking drift but also relocate the object whenever needed. Experimental results demonstrate that this algorithm outperforms state-of-the-art algorithms on many challenging sequences.

Keywords: object tracking, tracking drift, partial least squares analysis, positive and negative models matching

Procedia PDF Downloads 521
1331 The Study on How Social Cues in a Scene Modulate Basic Object Recognition Proces

Authors: Shih-Yu Lo

Abstract:

Stereotypes exist in almost every society, affecting how people interact with each other. However, to our knowledge, the influence of stereotypes was rarely explored in the context of basic perceptual processes. This study aims to explore how the gender stereotype affects object recognition. Participants were presented with a series of scene pictures, followed by a target display with a man or a woman, holding a weapon or a non-weapon object. The task was to identify whether the object in the target display was a weapon or not. Although the gender of the object holder could not predict whether he or she held a weapon, and was irrelevant to the task goal, the participant nevertheless tended to identify the object as a weapon when the object holder was a man than a woman. The analysis based on the signal detection theory showed that the stereotype effect on object recognition mainly resulted from the participant’s bias to make a 'weapon' response when a man was in the scene instead of a woman in the scene. In addition, there was a trend that the participant’s sensitivity to differentiate a weapon from a non-threating object was higher when a woman was in the scene than a man was in the scene. The results of this study suggest that the irrelevant social cues implied in the visual scene can be very powerful that they can modulate the basic object recognition process.

Keywords: gender stereotype, object recognition, signal detection theory, weapon

Procedia PDF Downloads 202
1330 F-VarNet: Fast Variational Network for MRI Reconstruction

Authors: Omer Cahana, Maya Herman, Ofer Levi

Abstract:

Magnetic resonance imaging (MRI) is a long medical scan that stems from a long acquisition time. This length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach, such as compress sensing (CS) or parallel imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. In order to achieve that, two properties have to exist: i) the signal must be sparse under a known transform domain, ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm needs to be applied to recover the signal. While the rapid advance in the deep learning (DL) field, which has demonstrated tremendous successes in various computer vision task’s, the field of MRI reconstruction is still in an early stage. In this paper, we present an extension of the state-of-the-art model in MRI reconstruction -VarNet. We utilize VarNet by using dilated convolution in different scales, which extends the receptive field to capture more contextual information. Moreover, we simplified the sensitivity map estimation (SME), for it holds many unnecessary layers for this task. Those improvements have shown significant decreases in computation costs as well as higher accuracy.

Keywords: MRI, deep learning, variational network, computer vision, compress sensing

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1329 Relation of Optimal Pilot Offsets in the Shifted Constellation-Based Method for the Detection of Pilot Contamination Attacks

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

Abstract:

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

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1328 Simulation for Squat Exercise of an Active Controlled Vibration Isolation and Stabilization System for Astronaut’s Exercise Platform

Authors: Ziraguen O. Williams, Shield B. Lin, Fouad N. Matari, Leslie J. Quiocho

Abstract:

In a task to assist NASA in analyzing the dynamic forces caused by operational countermeasures of an astronaut’s exercise platform impacting the spacecraft, feedback delay, and signal noise were added to a simulation model of an active-controlled vibration isolation system to regulate the movement of the exercise platform. Previous simulation work was conducted primarily via MATLAB/Simulink. Two additional simulation tools used in this study were Trick and MBDyn, NASA co-developed software simulation environments. Simulation results obtained from these three tools were very similar. All simulation results support the hypothesis that an active-controlled vibration isolation system outperforms a passive-controlled system even with the addition of feedback delay and signal noise to the active-controlled system. In this paper, squat exercise was used in creating excited force to the simulation model. The exciter force from a squat exercise was calculated from the motion capture of an exerciser. The simulation results demonstrate much greater transmitted force reduction in the active-controlled system than the passive-controlled system.

Keywords: control, counterweight, isolation, vibration

Procedia PDF Downloads 98
1327 Blind Channel Estimation for Frequency Hopping System Using Subspace Based Method

Authors: M. M. Qasaymeh, M. A. Khodeir

Abstract:

Subspace channel estimation methods have been studied widely. It depends on subspace decomposition of the covariance matrix to separate signal subspace from noise subspace. The decomposition normally is done by either Eigenvalue Decomposition (EVD) or Singular Value Decomposition (SVD) of the Auto-Correlation matrix (ACM). However, the subspace decomposition process is computationally expensive. In this paper, the multipath channel estimation problem for a Slow Frequency Hopping (SFH) system using noise space based method is considered. An efficient method to estimate multipath the time delays basically is proposed, by applying MUltiple Signal Classification (MUSIC) algorithm which used the null space extracted by the Rank Revealing LU factorization (RRLU). The RRLU provides accurate information about the rank and the numerical null space which make it a valuable tool in numerical linear algebra. The proposed novel method decreases the computational complexity approximately to the half compared with RRQR methods keeping the same performance. Computer simulations are also included to demonstrate the effectiveness of the proposed scheme.

Keywords: frequency hopping, channel model, time delay estimation, RRLU, RRQR, MUSIC, LS-ESPRIT

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1326 Performance Enrichment of Deep Feed Forward Neural Network and Deep Belief Neural Networks for Fault Detection of Automobile Gearbox Using Vibration Signal

Authors: T. Praveenkumar, Kulpreet Singh, Divy Bhanpuriya, M. Saimurugan

Abstract:

This study analysed the classification accuracy for gearbox faults using Machine Learning Techniques. Gearboxes are widely used for mechanical power transmission in rotating machines. Its rotating components such as bearings, gears, and shafts tend to wear due to prolonged usage, causing fluctuating vibrations. Increasing the dependability of mechanical components like a gearbox is hampered by their sealed design, which makes visual inspection difficult. One way of detecting impending failure is to detect a change in the vibration signature. The current study proposes various machine learning algorithms, with aid of these vibration signals for obtaining the fault classification accuracy of an automotive 4-Speed synchromesh gearbox. Experimental data in the form of vibration signals were acquired from a 4-Speed synchromesh gearbox using Data Acquisition System (DAQs). Statistical features were extracted from the acquired vibration signal under various operating conditions. Then the extracted features were given as input to the algorithms for fault classification. Supervised Machine Learning algorithms such as Support Vector Machines (SVM) and unsupervised algorithms such as Deep Feed Forward Neural Network (DFFNN), Deep Belief Networks (DBN) algorithms are used for fault classification. The fusion of DBN & DFFNN classifiers were architected to further enhance the classification accuracy and to reduce the computational complexity. The fault classification accuracy for each algorithm was thoroughly studied, tabulated, and graphically analysed for fused and individual algorithms. In conclusion, the fusion of DBN and DFFNN algorithm yielded the better classification accuracy and was selected for fault detection due to its faster computational processing and greater efficiency.

Keywords: deep belief networks, DBN, deep feed forward neural network, DFFNN, fault diagnosis, fusion of algorithm, vibration signal

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1325 Channel Estimation Using Deep Learning for Reconfigurable Intelligent Surfaces-Assisted Millimeter Wave Systems

Authors: Ting Gao, Mingyue He

Abstract:

Reconfigurable intelligent surfaces (RISs) are expected to be an important part of next-generation wireless communication networks due to their potential to reduce the hardware cost and energy consumption of millimeter Wave (mmWave) massive multiple-input multiple-output (MIMO) technology. However, owing to the lack of signal processing abilities of the RIS, the perfect channel state information (CSI) in RIS-assisted communication systems is difficult to acquire. In this paper, the uplink channel estimation for mmWave systems with a hybrid active/passive RIS architecture is studied. Specifically, a deep learning-based estimation scheme is proposed to estimate the channel between the RIS and the user. In particular, the sparse structure of the mmWave channel is exploited to formulate the channel estimation as a sparse reconstruction problem. To this end, the proposed approach is derived to obtain the distribution of non-zero entries in a sparse channel. After that, the channel is reconstructed by utilizing the least-squares (LS) algorithm and compressed sensing (CS) theory. The simulation results demonstrate that the proposed channel estimation scheme is superior to existing solutions even in low signal-to-noise ratio (SNR) environments.

Keywords: channel estimation, reconfigurable intelligent surface, wireless communication, deep learning

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1324 Performance Analysis of Shunt Active Power Filter for Various Reference Current Generation Techniques

Authors: Vishal V. Choudhari, Gaurao A. Dongre, S. P. Diwan

Abstract:

A number of reference current generation have been developed for analysis of shunt active power filter to mitigate the load compensation. Depending upon the type of load the technique has to be chosen. In this paper, six reference current generation techniques viz. instantaneous reactive power theory(IRP), Synchronous reference frame theory(SRF), Perfect harmonic cancellation(PHC), Unity power factor method(UPF), Self-tuning filter method(STF), Predictive filtering method(PFM) are compared for different operating conditions. The harmonics are introduced because of non-linear loads in the system. These harmonics are eliminated using above techniques. The results and performance of system simulated on MATLAB/Simulink platform. The system is experimentally implemented using DS1104 card of dSPACE system.

Keywords: SAPF, power quality, THD, IRP, SRF, dSPACE module DS1104

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1323 Artificial Neural Networks Controller for Active Power Filter Connected to a Photovoltaic Array

Authors: Rachid Dehini, Brahim Berbaoui

Abstract:

The main objectives of shunt active power filter (SAPF) is to preserve the power system from unwanted harmonic currents produced by nonlinear loads, as well as to compensate the reactive power. The aim of this paper is to present a (PAPF) supplied by the Photovoltaic cells ,in such a way that the (PAPF) feeds the linear and nonlinear loads by harmonics currents and the excess of the energy is injected into the power system. In order to improve the performances of conventional (PAPF) This paper also proposes artificial neural networks (ANN) for harmonics identification and DC link voltage control. The simulation study results of the new (SAPF) identification technique are found quite satisfactory by assuring good filtering characteristics and high system stability.

Keywords: SAPF, harmonics current, photovoltaic cells, MPPT, artificial neural networks (ANN)

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1322 Modelling the Indonesian Goverment Securities Yield Curve Using Nelson-Siegel-Svensson and Support Vector Regression

Authors: Jamilatuzzahro, Rezzy Eko Caraka

Abstract:

The yield curve is the plot of the yield to maturity of zero-coupon bonds against maturity. In practice, the yield curve is not observed but must be extracted from observed bond prices for a set of (usually) incomplete maturities. There exist many methodologies and theory to analyze of yield curve. We use two methods (the Nelson-Siegel Method, the Svensson Method, and the SVR method) in order to construct and compare our zero-coupon yield curves. The objectives of this research were: (i) to study the adequacy of NSS model and SVR to Indonesian government bonds data, (ii) to choose the best optimization or estimation method for NSS model and SVR. To obtain that objective, this research was done by the following steps: data preparation, cleaning or filtering data, modeling, and model evaluation.

Keywords: support vector regression, Nelson-Siegel-Svensson, yield curve, Indonesian government

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1321 Switched Uses of a Bidirectional Microphone as a Microphone and Sensors with High Gain and Wide Frequency Range

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

Abstract:

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

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1320 Effect of Changing Iron Content and Excitation Frequency on Magnetic Particle Imaging Signal: A Comparative Study of Synomag® Nanoparticles

Authors: Kalthoum Riahi, Max T. Rietberg, Javier Perez y Perez, Corné Dijkstra, Bennie ten Haken, Lejla Alic

Abstract:

Magnetic nanoparticles (MNPs) are widely used to facilitate magnetic particle imaging (MPI) which has the potential to become the leading diagnostic instrument for biomedical imaging. This comparative study assesses the effects of changing iron content and excitation frequency on point-spread function (PSF) representing the effect of magnetization reversal. PSF is quantified by features of interest for MPI: i.e., drive field amplitude and full-width-at-half-maximum (FWHM). A superparamagnetic quantifier (SPaQ) is used to assess differential magnetic susceptibility of two commercially available MNPs: Synomag®-D50 and Synomag®-D70. For both MNPs, the signal output depends on increase in drive field frequency and amount of iron-oxide, which might be hampering the sensitivity of MPI systems that perform on higher frequencies. Nevertheless, there is a clear potential of Synomag®-D for a stable MPI resolution, especially in case of 70 nm version, that is independent of either drive field frequency or amount of iron-oxide.

Keywords: magnetic nanoparticles, MNPs, differential magnetic susceptibility, DMS, magnetic particle imaging, MPI, magnetic relaxation, Synomag®-D

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1319 Analog Input Output Buffer Information Specification Modelling Techniques for Single Ended Inter-Integrated Circuit and Differential Low Voltage Differential Signaling I/O Interfaces

Authors: Monika Rawat, Rahul Kumar

Abstract:

Input output Buffer Information Specification (IBIS) models are used for describing the analog behavior of the Input Output (I/O) buffers of a digital device. They are widely used to perform signal integrity analysis. Advantages of using IBIS models include simple structure, IP protection and fast simulation time with reasonable accuracy. As design complexity of driver and receiver increases, capturing exact behavior from transistor level model into IBIS model becomes an essential task to achieve better accuracy. In this paper, an improvement in existing methodology of generating IBIS model for complex I/O interfaces such as Inter-Integrated Circuit (I2C) and Low Voltage Differential Signaling (LVDS) is proposed. Furthermore, the accuracy and computational performance of standard method and proposed approach with respect to SPICE are presented. The investigations will be useful to further improve the accuracy of IBIS models and to enhance their wider acceptance.

Keywords: IBIS, signal integrity, open-drain buffer, low voltage differential signaling, behavior modelling, transient simulation

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1318 Distribution of Synechococcus and Prochlorococcus in Southeastern Coast of Peninsular Malaysia

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

Abstract:

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

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1317 Permeodynamic Particulate Matter Filtration for Improved Air Quality

Authors: Hamad M. Alnagran, Mohammed S. Imbabi

Abstract:

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

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1316 Early Diagnosis of Alzheimer's Disease Using a Combination of Images Processing and Brain Signals

Authors: E. Irankhah, M. Zarif, E. Mazrooei Rad, K. Ghandehari

Abstract:

Alzheimer's prevalence is on the rise, and the disease comes with problems like cessation of treatment, high cost of treatment, and the lack of early detection methods. The pathology of this disease causes the formation of protein deposits in the brain of patients called plaque amyloid. Generally, the diagnosis of this disease is done by performing tests such as a cerebrospinal fluid, CT scan, MRI, and spinal cord fluid testing, or mental testing tests and eye tracing tests. In this paper, we tried to use the Medial Temporal Atrophy (MTA) method and the Leave One Out (LOO) cycle to extract the statistical properties of the three Fz, Pz, and Cz channels of ERP signals for early diagnosis of this disease. In the process of CT scan images, the accuracy of the results is 81% for the healthy person and 88% for the severe patient. After the process of ERP signaling, the accuracy of the results for a healthy person in the delta band in the Cz channel is 81% and in the alpha band the Pz channel is 90%. In the results obtained from the signal processing, the results of the severe patient in the delta band of the Cz channel were 89% and in the alpha band Pz channel 92%.

Keywords: Alzheimer's disease, image and signal processing, LOO cycle, medial temporal atrophy

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1315 Noninvasive Brain-Machine Interface to Control Both Mecha TE Robotic Hands Using Emotiv EEG Neuroheadset

Authors: Adrienne Kline, Jaydip Desai

Abstract:

Electroencephalogram (EEG) is a noninvasive technique that registers signals originating from the firing of neurons in the brain. The Emotiv EEG Neuroheadset is a consumer product comprised of 14 EEG channels and was used to record the reactions of the neurons within the brain to two forms of stimuli in 10 participants. These stimuli consisted of auditory and visual formats that provided directions of ‘right’ or ‘left.’ Participants were instructed to raise their right or left arm in accordance with the instruction given. A scenario in OpenViBE was generated to both stimulate the participants while recording their data. In OpenViBE, the Graz Motor BCI Stimulator algorithm was configured to govern the duration and number of visual stimuli. Utilizing EEGLAB under the cross platform MATLAB®, the electrodes most stimulated during the study were defined. Data outputs from EEGLAB were analyzed using IBM SPSS Statistics® Version 20. This aided in determining the electrodes to use in the development of a brain-machine interface (BMI) using real-time EEG signals from the Emotiv EEG Neuroheadset. Signal processing and feature extraction were accomplished via the Simulink® signal processing toolbox. An Arduino™ Duemilanove microcontroller was used to link the Emotiv EEG Neuroheadset and the right and left Mecha TE™ Hands.

Keywords: brain-machine interface, EEGLAB, emotiv EEG neuroheadset, OpenViBE, simulink

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1314 Active Islanding Detection Method Using Intelligent Controller

Authors: Kuang-Hsiung Tan, Chih-Chan Hu, Chien-Wu Lan, Shih-Sung Lin, Te-Jen Chang

Abstract:

An active islanding detection method using disturbance signal injection with intelligent controller is proposed in this study. First, a DC\AC power inverter is emulated in the distributed generator (DG) system to implement the tracking control of active power, reactive power outputs and the islanding detection. The proposed active islanding detection method is based on injecting a disturbance signal into the power inverter system through the d-axis current which leads to a frequency deviation at the terminal of the RLC load when the utility power is disconnected. Moreover, in order to improve the transient and steady-state responses of the active power and reactive power outputs of the power inverter, and to further improve the performance of the islanding detection method, two probabilistic fuzzy neural networks (PFNN) are adopted to replace the traditional proportional-integral (PI) controllers for the tracking control and the islanding detection. Furthermore, the network structure and the online learning algorithm of the PFNN are introduced in detail. Finally, the feasibility and effectiveness of the tracking control and the proposed active islanding detection method are verified with experimental results.

Keywords: distributed generators, probabilistic fuzzy neural network, islanding detection, non-detection zone

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1313 A Simple Adaptive Atomic Decomposition Voice Activity Detector Implemented by Matching Pursuit

Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

Abstract:

A simple adaptive voice activity detector (VAD) is implemented using Gabor and gammatone atomic decomposition of speech for high Gaussian noise environments. Matching pursuit is used for atomic decomposition, and is shown to achieve optimal speech detection capability at high data compression rates for low signal to noise ratios. The most active dictionary elements found by matching pursuit are used for the signal reconstruction so that the algorithm adapts to the individual speakers dominant time-frequency characteristics. Speech has a high peak to average ratio enabling matching pursuit greedy heuristic of highest inner products to isolate high energy speech components in high noise environments. Gabor and gammatone atoms are both investigated with identical logarithmically spaced center frequencies, and similar bandwidths. The algorithm performs equally well for both Gabor and gammatone atoms with no significant statistical differences. The algorithm achieves 70% accuracy at a 0 dB SNR, 90% accuracy at a 5 dB SNR and 98% accuracy at a 20dB SNR using 30dB SNR as a reference for voice activity.

Keywords: atomic decomposition, gabor, gammatone, matching pursuit, voice activity detection

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1312 A Monopole Intravascular Antenna with Three Parasitic Elements Optimized for Higher Tesla MRI Systems

Authors: Mohammad Mohammadzadeh, Alireza Ghasempour

Abstract:

In this paper, a new design of monopole antenna has been proposed that increases the contrast of intravascular magnetic resonance images through increasing the homogeneity of the intrinsic signal-to-noise ratio (ISNR) distribution around the antenna. The antenna is made of a coaxial cable with three parasitic elements. Lengths and positions of the elements are optimized by the improved genetic algorithm (IGA) for 1.5, 3, 4.7, and 7Tesla MRI systems based on a defined cost function. Simulations were also conducted to verify the performance of the designed antenna. Our simulation results show that each time IGA is executed different values for the parasitic elements are obtained so that the cost functions of those antennas are high. According to the obtained results, IGA can also find the best values for the parasitic elements (regarding cost function) in the next executions. Additionally, two dimensional and one-dimensional maps of ISNR were drawn for the proposed antenna and compared to the previously published monopole antenna with one parasitic element at the frequency of 64MHz inside a saline phantom. Results verified that in spite of ISNR decreasing, there is a considerable improvement in the homogeneity of ISNR distribution of the proposed antenna so that their multiplication increases.

Keywords: intravascular MR antenna, monopole antenna, parasitic elements, signal-to-noise ratio (SNR), genetic algorithm

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1311 Regularized Euler Equations for Incompressible Two-Phase Flow Simulations

Authors: Teng Li, Kamran Mohseni

Abstract:

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

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