Search results for: noise filters

Authors: Kaustav Mukherjee

Abstract:

In this paper we propose a Neural Style Transfer solution whereby we have created a Lightweight Separable Convolution Kernel Based GAN Architecture (SC-GAN) which will very useful for designing filter for Mobile Phone Cameras and also Edge Devices which will convert any image to its 2D ANIMATED COMIC STYLE Movies like HEMAN, SUPERMAN, JUNGLE-BOOK. This will help the 2D animation artist by relieving to create new characters from real life person's images without having to go for endless hours of manual labour drawing each and every pose of a cartoon. It can even be used to create scenes from real life images.This will reduce a huge amount of turn around time to make 2D animated movies and decrease cost in terms of manpower and time. In addition to that being extreme light-weight it can be used as camera filters capable of taking Comic Style Shots using mobile phone camera or edge device cameras like Raspberry Pi 4,NVIDIA Jetson NANO etc. Existing Methods like CartoonGAN with the model size close to 170 MB is too heavy weight for mobile phones and edge devices due to their scarcity in resources. Compared to the current state of the art our proposed method which has a total model size of 31 MB which clearly makes it ideal and ultra-efficient for designing of camera filters on low resource devices like mobile phones, tablets and edge devices running OS or RTOS. .Owing to use of high resolution input and usage of bigger convolution kernel size it produces richer resolution Comic-Style Pictures implementation with 6 times lesser number of parameters and with just 25 extra epoch trained on a dataset of less than 1000 which breaks the myth that all GAN need mammoth amount of data. Our network reduces the density of the Gan architecture by using Depthwise Separable Convolution which does the convolution operation on each of the RGB channels separately then we use a Point-Wise Convolution to bring back the network into required channel number using 1 by 1 kernel.This reduces the number of parameters substantially and makes it extreme light-weight and suitable for mobile phones and edge devices. The architecture mentioned in the present paper make use of Parameterised Batch Normalization Goodfellow etc al. (Deep Learning OPTIMIZATION FOR TRAINING DEEP MODELS page 320) which makes the network to use the advantage of Batch Norm for easier training while maintaining the non-linear feature capture by inducing the learnable parameters

Keywords: comic stylisation from camera image using GAN, creating 2D animated movie style custom stickers from images, depth-wise separable convolutional neural network for light-weight GAN architecture for EDGE devices, GAN architecture for 2D animated cartoonizing neural style, neural style transfer for edge, model distilation, perceptual loss

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Authors: Nasser Ghassembaglou, Abdullah Bolek, Oktay Yilmaz, Ertan Oznergiz, Hikmet Kocabas, Safak Yilmaz

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High quality air filters production using nanofibers, as a functional material, has frequently been investigated. As it is more environmentally friendly, melting method has been selected to produce nanofibers. Spun-bond production systems consist of extruder, spin-pump, nozzle package and attenuators. Spin-pump makes molten polymer steady, which flows through extruder. Fibers are formed by regular melts passing through nuzzle holes under high pressure. Attenuator prolongs fibers to micron size to be collected on a conveyor. Different designs of attenuator systems have been studied in this research; new analysis have been done on existed designs considering fibers effect on air flow; it was comprehended that, at fibers presence, there is an air flow which agglomerates fibers as a negative effect. So some new representations have been designed and CFD analysis have been done on them. Afterwards, one of these representations selected as the most optimum and effective design which is brought in this paper.

Keywords: attenuator, CFD, nanofiber, spun-bond

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Authors: Heeba A. Gurku

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Introduction: Cone Beam CT(CBCT) images play an integral part in proper patient positioning in cancer patients undergoing radiation therapy treatment. But these images are low in quality. The purpose of this study is to generate high-quality synthetic CT images from CBCT using generative models. Material and Methods: This study utilized two datasets from The Cancer Imaging Archive (TCIA) 1) Lung cancer dataset of 20 patients (with full view CBCT images) and 2) Pancreatic cancer dataset of 40 patients (only 27 patients having limited view images were included in the study). Cycle Generative Adversarial Networks (GAN) and its variant Attention Guided Generative Adversarial Networks (AGGAN) models were used to generate the synthetic CTs. Models were evaluated by visual evaluation and on four metrics, Structural Similarity Index Measure (SSIM), Peak Signal Noise Ratio (PSNR) Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), to compare the synthetic CT and original CT images. Results: For pancreatic dataset with limited view CBCT images, our study showed that in Cycle GAN model, MAE, RMSE, PSNR improved from 12.57to 8.49, 20.94 to 15.29 and 21.85 to 24.63, respectively but structural similarity only marginally increased from 0.78 to 0.79. Similar, results were achieved with AGGAN with no improvement over Cycle GAN. However, for lung dataset with full view CBCT images Cycle GAN was able to reduce MAE significantly from 89.44 to 15.11 and AGGAN was able to reduce it to 19.77. Similarly, RMSE was also decreased from 92.68 to 23.50 in Cycle GAN and to 29.02 in AGGAN. SSIM and PSNR also improved significantly from 0.17 to 0.59 and from 8.81 to 21.06 in Cycle GAN respectively while in AGGAN SSIM increased to 0.52 and PSNR increased to 19.31. In both datasets, GAN models were able to reduce artifacts, reduce noise, have better resolution, and better contrast enhancement. Conclusion and Recommendation: Both Cycle GAN and AGGAN were significantly able to reduce MAE, RMSE and PSNR in both datasets. However, full view lung dataset showed more improvement in SSIM and image quality than limited view pancreatic dataset.

Keywords: CT images, CBCT images, cycle GAN, AGGAN

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Authors: Daniel Bulanda, Janusz A. Starzyk, Adrian Horzyk

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The electrocardiogram (ECG) is one of the most commonly used medical tests, essential for correct diagnosis and treatment of the patient. While ECG devices generate a huge amount of data, only a small part of them carries valuable medical information. To deal with this problem, many compression algorithms and filters have been developed over the past years. However, the rapid development of new machine learning techniques poses new challenges. To address this class of problems, we created the FlexPoints algorithm that searches for characteristic points on the ECG signal and ignores all other points that do not carry relevant medical information. The conducted experiments proved that the presented algorithm can significantly reduce the number of data points which represents ECG signal without losing valuable medical information. These sparse but essential characteristic points (flex points) can be a perfect input for some modern machine learning models, which works much better using flex points as an input instead of raw data or data compressed by many popular algorithms.

Keywords: characteristic points, electrocardiogram, ECG, machine learning, signal compression

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Authors: Ju-Hong Lee, Ding-Chen Chung

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The paper deals with the optimal design of two-channel linear-phase (LP) quadrature mirror filter (QMF) banks using a metaheuristic based optimization technique. Based on the theory of two-channel QMF banks using two recursive digital all-pass filters (DAFs), the design problem is appropriately formulated to result in an objective function which is a weighted sum of the group delay error of the designed QMF bank and the magnitude response error of the designed low-pass analysis filter. Through a frequency sampling and a weighted least squares approach, the optimization problem of the objective function can be solved by utilizing a particle swarm optimization algorithm. The resulting two-channel QMF banks can possess approximately LP response without magnitude distortion. Simulation results are presented for illustration and comparison.

Keywords: quadrature mirror filter bank, digital all-pass filter, weighted least squares algorithm, particle swarm optimization

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Authors: Armin Rahimi

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In this study, we reveal co-seismic signals of two combined earthquakes, the 2006 Mw8.3 thrust and 2007 Mw8.1 normal fault earthquakes of the central Kuril Islands from GRACE observations. We compute monthly full gravitational gradient tensor in the local north-east-down frame for Kuril Islands earthquakes without spatial averaging and de-striping filters. Some of the gravitational gradient components (e.g. ΔVxx, ΔVxz) enhance high frequency components of the earth gravity field and reveal more details in spatial and temporal domain. Therefore that preseismic activity can be better illustrated. We show that the positive-negative-positive co-seismic ΔVxx due to the Kuril Islands earthquakes ranges from − 0.13 to + 0.11 milli Eötvös, and ΔVxz shows a positive-negative-positive pattern ranges from − 0.16 to + 0.13 milli Eötvös, agree well with seismic model predictions.

Keywords: GRACE observation, gravitational gradient changes, Kuril island earthquakes, PSGRN/PSCMP

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Authors: Masato Saeki

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Presented herein is a novel algorithms for calculating the damping performance of particle dampers. The particle damper is a passive vibration control technique and has many practical applications due to simple design. It consists of granular materials constrained to move between two ends in the cavity of a primary vibrating system. The damping effect results from the exchange of momentum during the impact of granular materials against the wall of the cavity. This damping has the advantage of being independent of the environment. Therefore, particle damping can be applied in extreme temperature environments, where most conventional dampers would fail. It was shown experimentally in many papers that the efficiency of the particle dampers is high in the case of resonant vibration. In order to use the particle dampers effectively, it is necessary to solve the equations of motion for each particle, considering the granularity. The discrete element method (DEM) has been found to be effective for revealing the dynamics of particle damping. In this method, individual particles are assumed as rigid body and interparticle collisions are modeled by mechanical elements as springs and dashpots. However, the computational cost is significant since the equation of motion for each particle must be solved at each time step. In order to improve the computational efficiency of the DEM, the new algorithms are needed. In this study, new algorithms are proposed for implementing the high performance DEM. On the assumption that behaviors of the granular particles in the each divided area of the damper container are the same, the contact force of the primary system with all particles can be considered to be equal to the product of the divided number of the damper area and the contact force of the primary system with granular materials per divided area. This convenience makes it possible to considerably reduce the calculation time. The validity of this calculation method was investigated and the calculated results were compared with the experimental ones. This paper also presents the results of experimental studies of the performance of particle dampers. It is shown that the particle radius affect the noise level. It is also shown that the particle size and the particle material influence the damper performance.

Keywords: particle damping, discrete element method (DEM), granular materials, numerical analysis, equivalent noise level

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Authors: Pankaj Negi

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Nowadays the demand for receiving the high quality electrical energy is being increasing as consumer wants not only reliable but also quality power. Custom power instruments are of the most well-known compensators of power quality in distributed network. This paper present a comprehensive review of compensating custom power devices mainly DSTATCOM (distribution static compensator),DVR (dynamic voltage restorer), and UPQC (unified power quality compensator) and also deals with sliding mode control and its applications to custom power devices. The sliding mode control strategy provides robustness to custom power device and enhances the dynamic response for compensating voltage sag, swell, voltage flicker, and voltage harmonics. The aim of this paper is to provide a broad perspective on the status of compensating devices in electric power distribution system and sliding mode control strategies to researchers and application engineers who are dealing with power quality and stability issues.

Keywords: active power filters(APF), custom power device(CPD), DSTATCOM, DVR, UPQC, sliding mode control (SMC), power quality

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Authors: Fruzsina Pataki-Bittó, Edit Németh

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On the one hand, new technologies and communication tools improve employee productivity and accelerate information and knowledge transfer, while on the other hand, information overload and continuous interruptions make it even harder to concentrate at work. It is a great challenge for companies to find the right balance, while there is also an ongoing demand to recruit and retain the talented employees who are able to adopt the modern work style and effectively use modern communication tools. For this reason, this research does not focus on the objective measures of office interruptions, but aims to find those disruption factors which influence the comfort and job satisfaction of employees, and the way how they feel generally at work. The focus of this research is on how employees feel about the different types of interruptions, which are those they themselves identify as hindering factors, and those they feel as stress factors. By identifying and then reducing these destructive factors, job satisfaction can reach a higher level and employee turnover can be reduced. During the research, we collected information from depth interviews and questionnaires asking about work environment, communication channels used in the workplace, individual communication preferences, factors considered as disruptions, and individual steps taken to avoid interruptions. The questionnaire was completed by 141 office workers from several types of workplaces based in Hungary. Even though 66 respondents are working at Hungarian offices of multinational companies, the research is about the characteristics of the Hungarian labor force. The most important result of the research shows that while more than one third of the respondents consider office noise as a disturbing factor, personal inquiries are welcome and considered useful, even if in such cases the work environment will not be convenient to solve tasks requiring concentration. Analyzing the sizes of the offices, in an open-space environment, the rate of those who consider office noise as a disturbing factor is surprisingly lower than in smaller office rooms. Opinions are more diverse regarding information communication technologies. In addition to the interruption factors affecting the employees' job satisfaction, the research also focuses on the role of the offices in the 21^st century.

Keywords: information overload, interruption, job satisfaction, office environment, work efficiency

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Authors: Marco Correa, Flavio Araujo, Paulo Scalize, Antonio Albuquerque

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The increasing reduction of the volumes of surface water sources which supply most municipalities, as well as the continued rise of demand for treated water, combined with the disposal of effluents from washing of decanters and filters of the water treatment plants, generates a continuous search for correct environmentally solutions to these problems. The effluents generated by the water treatment industry need to be suitably processed for return to the environment or re-use. This article shows an alternative for the dehydration of sludge from the water treatment plants (WTP) and eventual disposal of sludge drained. Using the simple design methodology, we present a case study for a drainage in tanks geotextile, full-scale, which involve five sludge drainage tanks from WTP of the Rio Verde City. Aiming to the reutilization the water drained from the sludge and enabling its reuse both at the beginning of the treatment process at the WTP and in less noble services as for watering the gardens of the local town hall. The sludge will be used to production of building materials.

Keywords: re-use, residue, sustainable, water treatment plants, sludge

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Authors: M. Prema Kumar, P. Rajesh Kumar

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The increasing interest in image fusion (combining images of two or more modalities such as infrared and visible light radiation) has led to a need for accurate and reliable image assessment methods. This paper gives a novel approach of merging the information content from several videos taken from the same scene in order to rack up a combined video that contains the finest information coming from different source videos. This process is known as video fusion which helps in providing superior quality (The term quality, connote measurement on the particular application.) image than the source images. In this technique different sensors (whose redundant information can be reduced) are used for various cameras that are imperative for capturing the required images and also help in reducing. In this paper Image fusion technique based on multi-resolution singular value decomposition (MSVD) has been used. The image fusion by MSVD is almost similar to that of wavelets. The idea behind MSVD is to replace the FIR filters in wavelet transform with singular value decomposition (SVD). It is computationally very simple and is well suited for real time applications like in remote sensing and in astronomy.

Keywords: multi sensor image fusion, MSVD, image processing, monochrome video

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Authors: Galo D. Soria, Jenny S. Casame, Eddy F. Pazmino

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In Ecuador, mining operations have significantly impacted water sources. Artisanal mining extensively relies in mercury amalgamation. Mercury is a neurotoxic substance even at low concentrations. The objective of this investigation is to exploit Hg-removal capacity of sulfur-limonene polysulfide (SLP), which is a low-cost polymer, in order to prepare granular media (sand) coated with SLP to be used in laboratory scale column-filtration systems. Preliminary results achieved 85% removal of Hg⁺⁺ from synthetic effluents using 20-cm length and 5-cm diameter columns at 119m/day average pore water velocity. During elution of the column, the SLP-coated sand indicated that Hg⁺⁺ is permanently fixed to the collector surface, in contrast, uncoated sand showed reversible retention in Hg⁺⁺ in the solid phase. Injection of 50 pore volumes decreased Hg⁺⁺ removal to 46%. Ongoing work has been focused in optimizing the synthesis of SLP and the polymer content in the porous media coating process to improve Hg⁺⁺ removal and extend the lifetime of the column-filter.

Keywords: column-filter, mercury, mining, polysulfide, water treatment

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Authors: Azza Mohsin Al-Hashami, Reginald Victor

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Treatment of wastewater involves physical, chemical, and biological processes to remove the pollutants from wastewater. This study evaluates of the effectiveness of sewage treatment plants (STP) in Samail, Oman. Samail STP has tertiary treatment using conventional activated sludge with surface aeration. The collection of wastewater is through a network with a total length of about 60 km and also by tankers for the areas outside the network. Treated wastewater from this STP is used for the irrigation of vegetation in the STP premises and as a backwash for sand filters. Some treated water is supplied to the Samail municipality, which uses it for the landscaping, road construction, and 'the Million Date Palms' project. In this study, homogenous samples were taken from eight different treatment stages along the treatment continuum for one year, at a frequency of once a month, to evaluate the physical, chemical, and biological parameters. All samples were analyzed using the standard methods for the examination of water and wastewater. The spatial variations in water quality along the continuum are discussed. Despite these variations, the treated wastewater from Samail STP was of good quality, and most of the parameters are within class A category in Oman Standards for wastewater reuse and discharge.

Keywords: wastewater, STP, treatment, processes

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Authors: Montaceur Zaghdoud, Mohamed Moussaoui, Jalel Akaichi

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Frequent subgraph mining refers usually to graph matching and it is widely used in when analyzing big data with large graphs. A lot of research works dealt with structural exact or inexact graph matching but a little attention is paid to semantic matching when graph vertices and/or edges are attributed and typed. Therefore, it seems very interesting to integrate background knowledge into the analysis and that extracted frequent subgraphs should become more pruned by applying a new semantic filter instead of using only structural similarity in graph matching process. Consequently, this paper focuses on developing a new hybrid approximate structuralsemantic graph matching to discover a set of frequent subgraphs. It uses simultaneously an approximate structural similarity function based on graph edit distance function and a possibilistic vertices similarity function based on affinity function. Both structural and semantic filters contribute together to prune extracted frequent set. Indeed, new hybrid structural-semantic frequent subgraph mining approach searches will be suitable to be applied to several application such as community detection in social networks.

Keywords: approximate graph matching, hybrid frequent subgraph mining, graph mining, possibility theory

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Authors: Dahi Ghareab Abdelsalam Ibrahim

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Calibration of step heights with high accuracy is needed for many applications in the industry. In general, step height consists of three bands: pass band, transition band (roll-off), and stop band. Abdelsalam used a convolution of the transfer functions of both Chebyshev type 2 and elliptic filters with WFF of the Fresnel transform in the frequency domain for producing a steeper roll-off with the removal of ripples in the pass band- and stop-bands. In this paper, we used a new method based on the Hamming window: band-pass filter for calibration of step heights in terms of perfect adjustment of pass-band, roll-off, and stop-band. The method is applied to calibrate a nominal step height of 40 cm. The step height is measured first by asynchronous dual-wavelength phase-shift interferometry. The measured step height is then calibrated by the simulation of the Hamming window: band-pass filter. The spectrum of the simulated band-pass filter is simulated at N = 881 and f0 = 0.24. We can conclude that the proposed method can calibrate any step height by adjusting only two factors which are N and f0.

Keywords: optical metrology, step heights, hamming window, band-pass filter

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Authors: Fadhl Alakwaa

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One of the sustainable goals of the system biology is understanding gene-gene interactions. Hence, gene regulatory networks (GRN) need to be constructed for understanding the disease ontology and to reduce the cost of drug development. To construct gene regulatory from gene expression we need to overcome many challenges such as data denoising and dimensionality. In this paper, we develop an integrated system to reduce data dimension and remove the noise. The generated network from our system was validated via available interaction databases and was compared to previous methods. The result revealed the performance of our proposed method.

Keywords: gene regulatory network, biclustering, denoising, system biology

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Authors: Ana Paula P. dos Santos, Claudia R. Andrade, Edson L. Zaparoli

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Pressure loss in ductworks is an important factor to be considered in design of engineering systems such as power-plants, refineries, HVAC systems to reduce energy costs. Ductwork can be composed by straight ducts and different types of fittings (elbows, transitions, converging and diverging tees and wyes). Duct fittings are significant sources of pressure loss in fluid distribution systems. Fitting losses can be even more significant than equipment components such as coils, filters, and dampers. At the present work, a conventional 90o round elbow under turbulent incompressible airflow is studied. Mass, momentum, and k-e turbulence model equations are solved employing the finite volume method. The SIMPLE algorithm is used for the pressure-velocity coupling. In order to validate the numerical tool, the elbow pressure loss coefficient is determined using the same conditions to compare with ASHRAE database. Furthermore, the effect of Reynolds number variation on the elbow pressure loss coefficient is investigated. These results can be useful to perform better preliminary design of air distribution ductworks in air conditioning systems.

Keywords: duct fitting, pressure loss, elbow, thermodynamics

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Authors: Arcady Ponosov., Ramazan Kadiev

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The work studies the global moment stability of solutions of systems of nonlinear differential Itô equations with delays. A modified regularization method (W-method) for the analysis of various types of stability of such systems, based on the choice of the auxiliaryequations and applications of the theory of positive invertible matrices, is proposed and justified. Development of this method for deterministic functional differential equations is due to N.V. Azbelev and his students. Sufficient conditions for the moment stability of solutions in terms of the coefficients for sufficiently general as well as specific classes of Itô equations are given.

Keywords: asymptotic stability, delay equations, operator methods, stochastic noise

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Authors: Maria Amelia V. C. Araujo, Billy J. Araujo, Brian Greenwood

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The flow over a backward-facing step is characterized by the presence of flow separation, recirculation and reattachment, for a simple geometry. This type of fluid behaviour takes place in many practical engineering applications, hence the reason for being investigated. Historically, fluid flows over a backward-facing step have been examined in many experiments using a variety of measuring techniques such as laser Doppler velocimetry (LDV), hot-wire anemometry, particle image velocimetry or hot-film sensors. However, some of these techniques cannot conveniently be used in separated flows or are too complicated and expensive. In this work, the applicability of the acoustic Doppler velocimetry (ADV) technique is investigated to such type of flows, at various Reynolds numbers corresponding to different flow regimes. The use of this measuring technique in separated flows is very difficult to find in literature. Besides, most of the situations where the Reynolds number effect is evaluated in separated flows are in numerical modelling. The ADV technique has the advantage in providing nearly non-invasive measurements, which is important in resolving turbulence. The ADV Nortek Vectrino+ was used to characterize the flow, in a recirculating laboratory flume, at various Reynolds Numbers (Reh = 3738, 5452, 7908 and 17388) based on the step height (h), in order to capture different flow regimes, and the results compared to those obtained using other measuring techniques. To compare results with other researchers, the step height, expansion ratio and the positions upstream and downstream the step were reproduced. The post-processing of the AVD records was performed using a customized numerical code, which implements several filtering techniques. Subsequently, the Vectrino noise level was evaluated by computing the power spectral density for the stream-wise horizontal velocity component. The normalized mean stream-wise velocity profiles, skin-friction coefficients and reattachment lengths were obtained for each Reh. Turbulent kinetic energy, Reynolds shear stresses and normal Reynolds stresses were determined for Reh = 7908. An uncertainty analysis was carried out, for the measured variables, using the moving block bootstrap technique. Low noise levels were obtained after implementing the post-processing techniques, showing their effectiveness. Besides, the errors obtained in the uncertainty analysis were relatively low, in general. For Reh = 7908, the normalized mean stream-wise velocity and turbulence profiles were compared directly with those acquired by other researchers using the LDV technique and a good agreement was found. The ADV technique proved to be able to characterize the flow properly over a backward-facing step, although additional caution should be taken for measurements very close to the bottom. The ADV measurements showed reliable results regarding: a) the stream-wise velocity profiles; b) the turbulent shear stress; c) the reattachment length; d) the identification of the transition from transitional to turbulent flows. Despite being a relatively inexpensive technique, acoustic Doppler velocimetry can be used with confidence in separated flows and thus very useful for numerical model validation. However, it is very important to perform adequate post-processing of the acquired data, to obtain low noise levels, thus decreasing the uncertainty.

Keywords: ADV, experimental data, multiple Reynolds number, post-processing

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Authors: Ben Soltane Cheima, Ittansa Yonas Kelbesa

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Speaker Identification (SI) is the task of establishing identity of an individual based on his/her voice characteristics. The SI task is typically achieved by two-stage signal processing: training and testing. The training process calculates speaker specific feature parameters from the speech and generates speaker models accordingly. In the testing phase, speech samples from unknown speakers are compared with the models and classified. Even though performance of speaker identification systems has improved due to recent advances in speech processing techniques, there is still need of improvement. In this paper, a Closed-Set Tex-Independent Speaker Identification System (CISI) based on a Multiple Classifier System (MCS) is proposed, using Mel Frequency Cepstrum Coefficient (MFCC) as feature extraction and suitable combination of vector quantization (VQ) and Gaussian Mixture Model (GMM) together with Expectation Maximization algorithm (EM) for speaker modeling. The use of Voice Activity Detector (VAD) with a hybrid approach based on Short Time Energy (STE) and Statistical Modeling of Background Noise in the pre-processing step of the feature extraction yields a better and more robust automatic speaker identification system. Also investigation of Linde-Buzo-Gray (LBG) clustering algorithm for initialization of GMM, for estimating the underlying parameters, in the EM step improved the convergence rate and systems performance. It also uses relative index as confidence measures in case of contradiction in identification process by GMM and VQ as well. Simulation results carried out on voxforge.org speech database using MATLAB highlight the efficacy of the proposed method compared to earlier work.

Keywords: feature extraction, speaker modeling, feature matching, Mel frequency cepstrum coefficient (MFCC), Gaussian mixture model (GMM), vector quantization (VQ), Linde-Buzo-Gray (LBG), expectation maximization (EM), pre-processing, voice activity detection (VAD), short time energy (STE), background noise statistical modeling, closed-set tex-independent speaker identification system (CISI)

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Authors: C. Fernandez-Plaza, I. Abad, E. Diaz, I. Diaz

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Births occurring before the 37th week of gestation are considered preterm births. A threat of preterm is defined as the beginning of regular uterine contractions, dilation and cervical effacement between 23 and 36 gestation weeks. To author's best knowledge, the factors that determine the beginning of the birth are not completely defined yet. In particular, the incidence of sleep habits on preterm births is weekly studied. The aim of this study is to develop a model to predict the factors affecting premature delivery on pregnancy, based on the above potential risk factors, including those derived from sleep habits and light exposure at night (introduced as 12 variables obtained by a telephone survey using two questionnaires previously used by other authors). Thus, three groups of variables were included in the study (maternal, fetal and sleep habits). The study was approved by Research Ethics Committee of the Principado of Asturias (Spain). An observational, retrospective and descriptive study was performed with 481 births between January 1, 2015 and May 10, 2016 in the University Central Hospital of Asturias (Spain). A statistical analysis using SPSS was carried out to compare qualitative and quantitative variables between preterm and term delivery. Chi-square test qualitative variable and t-test for quantitative variables were applied. Statistically significant differences (p < 0.05) between preterm vs. term births were found for primiparity, multi-parity, kind of conception, place of residence or premature rupture of membranes and interruption during nights. In addition to the statistical analysis, machine learning methods to look for a prediction model were tested. In particular, tree based models were applied as the trade-off between performance and interpretability is especially suitable for this study. C5.0, recursive partitioning, random forest and tree bag models were analysed using caret R-package. Cross validation with 10-folds and parameter tuning to optimize the methods were applied. In addition, different noise reduction methods were applied to the initial data using NoiseFiltersR package. The best performance was obtained by C5.0 method with Accuracy 0.91, Sensitivity 0.93, Specificity 0.89 and Precision 0.91. Some well known preterm birth factors were identified: Cervix Dilation, maternal BMI, Premature rupture of membranes or nuchal translucency analysis in the first trimester. The model also identifies other new factors related to sleep habits such as light through window, bedtime on working days, usage of electronic devices before sleeping from Mondays to Fridays or change of sleeping habits reflected in the number of hours, in the depth of sleep or in the lighting of the room. IF dilation < = 2.95 AND usage of electronic devices before sleeping from Mondays to Friday = YES and change of sleeping habits = YES, then preterm is one of the predicting rules obtained by C5.0. In this work a model for predicting preterm births is developed. It is based on machine learning together with noise reduction techniques. The method maximizing the performance is the one selected. This model shows the influence of variables related to sleep habits in preterm prediction.

Keywords: machine learning, noise reduction, preterm birth, sleep habit

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Authors: Al-Sayed A. Bakr, W. A. Makled

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The precipitated low magnesium calcite ooids in assembled softening unit from natural Mediterranean seawater samples were used as adsorbent media in a comparative study with granular activated carbon media in a two separated single-media filtration vessels (operating in parallel) for removal of iron from aqueous solutions. In each vessel, the maximum bed capacity, which required to be filled, was 13.2 l and the bed filled in the vessels of ooids and GAC were 8.6, and 6.6 l, respectively. The operating conditions applied to the semi-pilot filtration unit were constant pH (7.5), different temperatures (293, 303 and 313 k), different flow rates (20, 30, 40, 50 and 60 l/min), different initial Fe(II) concentrations (15–105 mg/ l) and the calculated adsorbent masses were 34.1 and 123 g/l for GAC and calcite ooids, respectively. At higher temperature (313 k) and higher flow rate (60 l/min), the maximum adsorption capacities for ferrous ions by GAC and calcite ooids filters were 3.87 and 1.29 mg/g and at lower flow rate (20 l/min), the maximum adsorption capacities were 2.21 and 3.95 mg/g, respectively. From the experimental data, Freundlich and Langmuir adsorption isotherms were used to verify the adsorption performance. Therefore, the calcite ooids could act as new highly effective materials in iron removal from aqueous solutions.

Keywords: water treatment, calcite ooids, activated carbon, Fe(II) removal, filtration

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Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

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Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder

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Authors: Mohd Ayyub Khan, Nadeem Akhtar

Abstract:

Due to recent advances in wireless communication technologies and hand-held devices, there is a huge demand for video-based applications such as video surveillance, video conferencing, remote surgery, Digital Video Broadcast (DVB), IPTV, online learning courses, YouTube, WhatsApp, Instagram, Facebook, Interactive Video Games. However, the raw videos posses very high bandwidth which makes the compression a must before its transmission over the wireless channels. The High Efficiency Video Codec (HEVC) (also called H.265) is latest state-of-the-art video coding standard developed by the Joint effort of ITU-T and ISO/IEC teams. HEVC is targeted for high resolution videos such as 4K or 8K resolutions that can fulfil the recent demands for video services. The compression ratio achieved by the HEVC is twice as compared to its predecessor H.264/AVC for same quality level. The compression efficiency is generally increased by removing more correlation between the frames/pixels using complex techniques such as extensive intra and inter prediction techniques. As more correlation is removed, the chances of interdependency among coded bits increases. Thus, bit errors may have large effect on the reconstructed video. Sometimes even single bit error can lead to catastrophic failure of the reconstructed video. In this paper, we study the performance of HEVC bitstream over additive white Gaussian noise (AWGN) channel. Moreover, HEVC over Quadrature Amplitude Modulation (QAM) combined with forward error correction (FEC) schemes are also explored over the noisy channel. The video will be encoded using HEVC, and the coded bitstream is channel coded to provide some redundancies. The channel coded bitstream is then modulated using QAM and transmitted over AWGN channel. At the receiver, the symbols are demodulated and channel decoded to obtain the video bitstream. The bitstream is then used to reconstruct the video using HEVC decoder. It is observed that as the signal to noise ratio of channel is decreased the quality of the reconstructed video decreases drastically. Using proper FEC codes, the quality of the video can be restored up to certain extent. Thus, the performance analysis of HEVC presented in this paper may assist in designing the optimized code rate of FEC such that the quality of the reconstructed video is maximized over wireless channels.

Keywords: AWGN, forward error correction, HEVC, video coding, QAM

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Authors: Said Elkassimi, Said Safi, B. Manaut

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This paper presented a study of three algorithms, the equalization algorithm to equalize the transmission channel with ZF and MMSE criteria, application of channel Bran A, and adaptive filtering algorithms LMS and RLS to estimate the parameters of the equalizer filter, i.e. move to the channel estimation and therefore reflect the temporal variations of the channel, and reduce the error in the transmitted signal. So far the performance of the algorithm equalizer with ZF and MMSE criteria both in the case without noise, a comparison of performance of the LMS and RLS algorithm.

Keywords: adaptive filtering second equalizer, LMS, RLS Bran A, Proakis (B) MMSE, ZF

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Authors: Zachary Huffman, Joana Rocha

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Wall-pressure fluctuations induced by the turbulent boundary layer (TBL) developed over aircraft are a significant source of aircraft cabin noise. Since the power spectral density (PSD) of these pressure fluctuations is directly correlated with the amount of sound radiated into the cabin, the development of accurate empirical models that predict the PSD has been an important ongoing research topic. The sound emitted can be represented from the pressure fluctuations term in the Reynoldsaveraged Navier-Stokes equations (RANS). Therefore, early TBL empirical models (including those from Lowson, Robertson, Chase, and Howe) were primarily derived by simplifying and solving the RANS for pressure fluctuation and adding appropriate scales. Most subsequent models (including Goody, Efimtsov, Laganelli, Smol’yakov, and Rackl and Weston models) were derived by making modifications to these early models or by physical principles. Overall, these models have had varying levels of accuracy, but, in general, they are most accurate under the specific Reynolds and Mach numbers they were developed for, while being less accurate under other flow conditions. Despite this, recent research into the possibility of using alternative methods for deriving the models has been rather limited. More recent studies have demonstrated that an artificial neural network model was more accurate than traditional models and could be applied more generally, but the accuracy of other machine learning techniques has not been explored. In the current study, an original model is derived using a stepwise regression algorithm in the statistical programming language R, and TBL wall-pressure fluctuations PSD data gathered at the Carleton University wind tunnel. The theoretical advantage of a stepwise regression approach is that it will automatically filter out redundant or uncorrelated input variables (through the process of feature selection), and it is computationally faster than machine learning. The main disadvantage is the potential risk of overfitting. The accuracy of the developed model is assessed by comparing it to independently sourced datasets.

Keywords: aircraft noise, machine learning, power spectral density models, regression models, turbulent boundary layer wall-pressure fluctuations

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Authors: Zuzanna Miodonska, Michal Krecichwost, Pawel Badura

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Sigmatism (lisping) is a speech disorder in which sibilant consonants are mispronounced. The diagnosis of this phenomenon is usually based on the auditory assessment. However, the progress in speech analysis techniques creates a possibility of developing computer-aided sigmatism diagnosis tools. The aim of the study is to statistically verify whether specific acoustic features of sibilant sounds may be related to pronunciation correctness. Such knowledge can be of great importance while implementing classifiers and designing novel tools for automatic sibilants pronunciation evaluation. The study covers analysis of various speech signal measures, including features proposed in the literature for the description of normative sibilants realization. Amplitudes and frequencies of three fricative formants (FF) are extracted based on local spectral maxima of the friction noise. Skewness, kurtosis, four normalized spectral moments (SM) and 13 mel-frequency cepstral coefficients (MFCC) with their 1st and 2nd derivatives (13 Delta and 13 Delta-Delta MFCC) are included in the analysis as well. The resulting feature vector contains 51 measures. The experiments are performed on the speech corpus containing words with selected sibilant sounds (/ʃ, ʒ/) pronounced by 60 preschool children with proper pronunciation or with natural pathologies. In total, 224 /ʃ/ segments and 191 /ʒ/ segments are employed in the study. The Mann-Whitney U test is employed for the analysis of stigmatism and normative pronunciation. Statistically, significant differences are obtained in most of the proposed features in children divided into these two groups at p < 0.05. All spectral moments and fricative formants appear to be distinctive between pathology and proper pronunciation. These metrics describe the friction noise characteristic for sibilants, which makes them particularly promising for the use in sibilants evaluation tools. Correspondences found between phoneme feature values and an expert evaluation of the pronunciation correctness encourage to involve speech analysis tools in diagnosis and therapy of sigmatism. Proposed feature extraction methods could be used in a computer-assisted stigmatism diagnosis or therapy systems.

Keywords: computer-aided pronunciation evaluation, sigmatism diagnosis, speech signal analysis, statistical verification

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Authors: A.R. Eskandari, M.R. Eskandari

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A 2D complete identification algorithm for dielectric and multiple objects immersed in air is presented. The employed technique consists of initially retrieving the shape and position of the scattering object using a linear sampling method and then determining the electric permittivity and conductivity of the scatterer using adjoint sensitivity analysis. This inversion algorithm results in high computational speed and efficiency, and it can be generalized for any scatterer structure. Also, this method is robust with respect to noise. The numerical results clearly show that this hybrid approach provides accurate reconstructions of various objects.

Keywords: inverse scattering, microwave imaging, two-dimensional objects, Linear Sampling Method (LSM)

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Authors: Rutej R. Mehta, Michael A. Chappell

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Introduction: Arterial spin labelling (ASL) is an increasingly popular perfusion MRI technique, in which arterial blood water is magnetically labelled in the neck before flowing into the brain, providing a non-invasive measure of cerebral blood flow (CBF). The accuracy of ASL CBF measurements, however, is hampered by dispersion effects; the distortion of the ASL labelled bolus during its transit through the vasculature. In spite of this, the current recommended implementation of ASL – the white paper (Alsop et al., MRM, 73.1 (2015): 102-116) – does not account for dispersion, which leads to the introduction of errors in CBF. Given that the transport time from the labelling region to the tissue – the arterial transit time (ATT) – depends on the region of the brain and the condition of the patient, it is likely that these errors will also vary with the ATT. In this study, various dispersion models are assessed in comparison with the white paper (WP) formula for CBF quantification, enabling the errors introduced by the WP to be quantified. Additionally, this study examines the relationship between the errors associated with the WP and the ATT – and how this is influenced by dispersion. Methods: Data were simulated using the standard model for pseudo-continuous ASL, along with various dispersion models, and then quantified using the formula in the WP. The ATT was varied from 0.5s-1.3s, and the errors associated with noise artefacts were computed in order to define the concept of significant error. The instantaneous slope of the error was also computed as an indicator of the sensitivity of the error with fluctuations in ATT. Finally, a regression analysis was performed to obtain the mean error against ATT. Results: An error of 20.9% was found to be comparable to that introduced by typical measurement noise. The WP formula was shown to introduce errors exceeding 20.9% for ATTs beyond 1.25s even when dispersion effects were ignored. Using a Gaussian dispersion model, a mean error of 16% was introduced by using the WP, and a dispersion threshold of σ=0.6 was determined, beyond which the error was found to increase considerably with ATT. The mean error ranged from 44.5% to 73.5% when other physiologically plausible dispersion models were implemented, and the instantaneous slope varied from 35 to 75 as dispersion levels were varied. Conclusion: It has been shown that the WP quantification formula holds only within an ATT window of 0.5 to 1.25s, and that this window gets narrower as dispersion occurs. Provided that the dispersion levels fall below the threshold evaluated in this study, however, the WP can measure CBF with reasonable accuracy if dispersion is correctly modelled by the Gaussian model. However, substantial errors were observed with other common models for dispersion with dispersion levels similar to those that have been observed in literature.

Keywords: arterial spin labelling, dispersion, MRI, perfusion

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Authors: Mojtaba Abolfazli, Mohammad Gahderi, Alireza Ashoorizadeh, Rahim Zeinali

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Interconnection between countries provides noticeable economic, technical and environmental benefits. Iran grid has an excellent condition for connection to neighbouring countries. There are two main options including High Voltage Direct Current (HVDC) and High Voltage Alternative Current (HVAC) for interconnection between the grids. At present, all of Iran cross border interconnections are HVAC while HVDC brings more benefits in comparison which should be considered by system planners. This paper presents a comprehensive review of technical considerations of HVDC for interconnection of Iran grid to neighbouring countries. Converter technology, converter transformers, converter valves, filters, and transmission link are studied for a good cognition to HVDC. In addition, a comparison between HVDC and HVAC for transmitting of power is discussed. Finally, a conclusion on HVDC technology and components is drawn out to provide a comprehensive knowledge for system planners.

Keywords: interconnection, HVDC, HVAC, voltage sourced converter, current sourced converter

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