Search results for: spectral residual
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 1432

Search results for: spectral residual

922 Removal of Metals from Heavy Oil

Authors: Ali Noorian

Abstract:

Crude oil contains various compounds of hydrocarbons but low concentrations of inorganic compounds or metals. Vanadium and Nickel are the most common metals in crude oil. These metals usually exist in solution in the oil and residual fuel oil in the refining process is condensed. Deleterious effects of metals in petroleum have been known for some time. These metals do not only contaminate the product but also cause intoxication and loss of catalyst and corrosion to equipment. In this study, removal of heavy metals and petroleum residues were investigated. These methods include physical, chemical and biological treatment processes. For example, processes such as solvent extraction and hydro-catalytic and catalytic methods are effective and practical methods, but typically often have high costs and cause environmental pollution. Furthermore, biological methods that do not cause environmental pollution have been discussed in recent years, but these methods have not yet been industrialized.

Keywords: removal, metal, heavy oil, nickel, vanadium

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921 Improved Performance Scheme for Joint Transmission in Downlink Coordinated Multi-Point Transmission

Authors: Young-Su Ryu, Su-Hyun Jung, Myoung-Jin Kim, Hyoung-Kyu Song

Abstract:

In this paper, improved performance scheme for joint transmission is proposed in downlink (DL) coordinated multi-point(CoMP) in case of constraint transmission power. This scheme is that serving transmission point (TP) request a joint transmission to inter-TP and selects one pre-coding technique according to channel state information(CSI) from user equipment(UE). The simulation results show that the bit error rate(BER) and throughput performances of the proposed scheme provide high spectral efficiency and reliable data at the cell edge.

Keywords: CoMP, joint transmission, minimum mean square error, zero-forcing, zero-forcing dirty paper coding

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920 Impact of Reclamation on the Water Exchange in Bohai Bay

Authors: Luyao Liu, Dekui Yuan, Xu Li

Abstract:

As one of the most important bays of China, the water exchange capacity of Bohai Bay can influence the economic development and urbanization of surrounding cities. However, the rapid reclamation has influenced the weak water exchange capacity of this semi-enclosed bay in recent years. This paper sets two hydrodynamic models of Bohai Bay with two shorelines before and after reclamation. The mean value and distribution of Turn-over Time, the distribution of residual current, and the feature of the tracer path are compared. After comparison, it is found that Bohai Bay keeps these characteristics; the spending time of water exchange in the northern is longer than southern, and inshore is longer than offshore. However, the mean water exchange time becomes longer after reclamation. In addition, the material spreading is blocked because of the inwardly extending shorelines, and the direction changed from along the shoreline to towards the center after reclamation.

Keywords: Bohai Bay, water exchange, reclamation, turn-over time

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919 An Approach To Flatten The Gain Of Fiber Raman Amplifiers With Multi-Pumping

Authors: Surinder Singh, Adish Bindal

Abstract:

The effects of the pumping wavelength and their power on the gain flattening of a fiber Raman amplifier (FRA) are investigated. The multi-wavelength pumping scheme is utilized to achieve gain flatness in FRA. It is proposed that gain flatness becomes better with increase in number of pumping wavelengths applied. We have achieved flat gain with 0.27 dB fluctuation in a spectral range of 1475-1600 nm for a Raman fiber length of 10 km by using six pumps with wavelengths with in the 1385-1495 nm interval. The effect of multi-wavelength pumping scheme on gain saturation in FRA is also studied. It is proposed that gain saturation condition gets improved by using this scheme and this scheme is more useful for higher spans of Raman fiber length.

Keywords: FRA, WDM, pumping, flat gain

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918 Fault Detection and Isolation in Attitude Control Subsystem of Spacecraft Formation Flying Using Extended Kalman Filters

Authors: S. Ghasemi, K. Khorasani

Abstract:

In this paper, the problem of fault detection and isolation in the attitude control subsystem of spacecraft formation flying is considered. In order to design the fault detection method, an extended Kalman filter is utilized which is a nonlinear stochastic state estimation method. Three fault detection architectures, namely, centralized, decentralized, and semi-decentralized are designed based on the extended Kalman filters. Moreover, the residual generation and threshold selection techniques are proposed for these architectures.

Keywords: component, formation flight of satellites, extended Kalman filter, fault detection and isolation, actuator fault

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917 Magnetohydrodynamic Flow over an Exponentially Stretching Sheet

Authors: Raj Nandkeolyar, Precious Sibanda

Abstract:

The flow of a viscous, incompressible, and electrically conducting fluid under the influence of aligned magnetic field acting along the direction of fluid flow over an exponentially stretching sheet is investigated numerically. The nonlinear partial differential equations governing the flow model is transformed to a set of nonlinear ordinary differential equations using suitable similarity transformation and the solution is obtained using a local linearization method followed by the Chebyshev spectral collocation method. The effects of various parameters affecting the flow and heat transfer as well as the induced magnetic field are discussed using suitable graphs and tables.

Keywords: aligned magnetic field, exponentially stretching sheet, induced magnetic field, magnetohydrodynamic flow

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916 Synthesis, Spectral, Thermal, Optical and Dielectric Studies of Some Organic Arylidene Derivatives

Authors: S. Sathiyamoorthi, P. Srinivasan, K. Suganya Devi

Abstract:

Arylidene derivatives are the subclass of chalcone derivatives. Chalcone derivatives are studied widely for the past decade because of its nonlinearity. To seek new organic group of crystals which suit for fabrication of optical devices, three-member organic arylidene crystals were synthesized by using Claisen–Schmidt condensation reaction. Good quality crystals were grown by slow evaporation method. Functional groups were identified by FT-IR and FT-Raman spectrum. Optical transparency and optical band gap were determined by UV-Vis-IR studies. Thermal stability and melting point were calculated using TGA and DSC. Variation of dielectric loss and dielectric constant with frequency were calculated by dielectric measurement.

Keywords: DSC and TGA studies, nonlinear optic studies, Fourier Transform Infrared Spectroscopy, UV-vis-NIR spectra

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915 Encryption Image via Mutual Singular Value Decomposition

Authors: Adil Al-Rammahi

Abstract:

Image or document encryption is needed through e- government data base. Really in this paper we introduce two matrices images, one is the public, and the second is the secret (original). The analyses of each matrix is achieved using the transformation of singular values decomposition. So each matrix is transformed or analyzed to three matrices say row orthogonal basis, column orthogonal basis, and spectral diagonal basis. Product of the two row basis is calculated. Similarly the product of the two column basis is achieved. Finally we transform or save the files of public, row product and column product. In decryption stage, the original image is deduced by mutual method of the three public files.

Keywords: image cryptography, singular values decomposition

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914 Trans-Sphenoidal Approach to Pituitary Tumors: Analysis of 568 Cases Over a Decade

Authors: Sandeep Mohindra

Abstract:

Since 2013, the authors have operated on 568 cases of pituitary tumors through the trans-sphenoidal route, using the binostril approach. The distribution included 486 cases of non-functioning pituitary tumors, 24 cases of Growth hormone(GH) secreting tumors(acromegaly), and 28 cases of adrenocorticotrophic(ACTH) secreting tumors(Cushing's Disease). The authors utilized neuro-navigation for 18 cases, and all belonged to the functional tumor category. Complications included ICA injury in 2 cases, fatal meningitis in 5 cases, while CSF leak required repair in 28 cases. Satisfactory excision was noted in 512 cases, while recurrence/residual required repeat surgery in 32 cases. Authors conclude that trans sphenoidal route remains the best and optimal way of managing sellar tumors, especially pituitary adenomas.

Keywords: pituitary, adenoma, trans-sphenoidal, endonasal, neuronavigation

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913 Cooperative CDD Scheme Based On Hierarchical Modulation in OFDM System

Authors: Seung-Jun Yu, Yeong-Seop Ahn, Young-Min Ko, Hyoung-Kyu Song

Abstract:

In order to achieve high data rate and increase the spectral efficiency, multiple input multiple output (MIMO) system has been proposed. However, multiple antennas are limited by size and cost. Therefore, recently developed cooperative diversity scheme, which profits the transmit diversity only with the existing hardware by constituting a virtual antenna array, can be a solution. However, most of the introduced cooperative techniques have a common fault of decreased transmission rate because the destination should receive the decodable compositions of symbols from the source and the relay. In this paper, we propose a cooperative cyclic delay diversity (CDD) scheme that uses hierarchical modulation. This scheme is free from the rate loss and allows seamless cooperative communication.

Keywords: MIMO, cooperative communication, CDD, hierarchical modulation

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912 Theta-Phase Gamma-Amplitude Coupling as a Neurophysiological Marker in Neuroleptic-Naive Schizophrenia

Authors: Jun Won Kim

Abstract:

Objective: Theta-phase gamma-amplitude coupling (TGC) was used as a novel evidence-based tool to reflect the dysfunctional cortico-thalamic interaction in patients with schizophrenia. However, to our best knowledge, no studies have reported the diagnostic utility of the TGC in the resting-state electroencephalographic (EEG) of neuroleptic-naive patients with schizophrenia compared to healthy controls. Thus, the purpose of this EEG study was to understand the underlying mechanisms in patients with schizophrenia by comparing the TGC at rest between two groups and to evaluate the diagnostic utility of TGC. Method: The subjects included 90 patients with schizophrenia and 90 healthy controls. All patients were diagnosed with schizophrenia according to the criteria of Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) by two independent psychiatrists using semi-structured clinical interviews. Because patients were either drug-naïve (first episode) or had not been taking psychoactive drugs for one month before the study, we could exclude the influence of medications. Five frequency bands were defined for spectral analyses: delta (1–4 Hz), theta (4–8 Hz), slow alpha (8–10 Hz), fast alpha (10–13.5 Hz), beta (13.5–30 Hz), and gamma (30-80 Hz). The spectral power of the EEG data was calculated with fast Fourier Transformation using the 'spectrogram.m' function of the signal processing toolbox in Matlab. An analysis of covariance (ANCOVA) was performed to compare the TGC results between the groups, which were adjusted using a Bonferroni correction (P < 0.05/19 = 0.0026). Receiver operator characteristic (ROC) analysis was conducted to examine the discriminating ability of the TGC data for schizophrenia diagnosis. Results: The patients with schizophrenia showed a significant increase in the resting-state TGC at all electrodes. The delta, theta, slow alpha, fast alpha, and beta powers showed low accuracies of 62.2%, 58.4%, 56.9%, 60.9%, and 59.0%, respectively, in discriminating the patients with schizophrenia from the healthy controls. The ROC analysis performed on the TGC data generated the most accurate result among the EEG measures, displaying an overall classification accuracy of 92.5%. Conclusion: As TGC includes phase, which contains information about neuronal interactions from the EEG recording, TGC is expected to be useful for understanding the mechanisms the dysfunctional cortico-thalamic interaction in patients with schizophrenia. The resting-state TGC value was increased in the patients with schizophrenia compared to that in the healthy controls and had a higher discriminating ability than the other parameters. These findings may be related to the compensatory hyper-arousal patterns of the dysfunctional default-mode network (DMN) in schizophrenia. Further research exploring the association between TGC and medical or psychiatric conditions that may confound EEG signals will help clarify the potential utility of TGC.

Keywords: quantitative electroencephalography (QEEG), theta-phase gamma-amplitude coupling (TGC), schizophrenia, diagnostic utility

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911 Preparation and Structural Analysis of Nano-Ciprofloxacin by Fourier Transform X-Ray Diffraction, Infra-Red Spectroscopy, and Semi Electron Microscope (SEM)

Authors: Shahriar Ghammamy, Mehrnoosh Saboony

Abstract:

Purpose: To evaluate the spectral specification (IR-XRD and SEM) of nano-ciprofloxacin that prepared by up-down method (satellite mill). Methods: the ciprofloxacin was minimized to nano-scale with satellite mill and its characterization evaluated by Infrared spectroscopy, XRD diffraction and semi electron microscope (SEM). Expectation enhances the antibacterial property of nano-ciprofloxacin in comparison to ciprofloxacin. IR spectrum of nano-ciprofloxacin compared with spectrum of ciprofloxacin, and both of them were almost agreement with a difference: the peaks in spectrum of nano-ciprofloxacin were sharper than peaks in spectrum of ciprofloxacin. X-Ray powder diffraction analysis of nano-ciprofloxacin shows the diameter of particles equal to 90.9nm. (on the basis of Scherer Equation). SEM image shows the global shape for nano-ciprofloxacin.

Keywords: antibiotic, ciprofloxacin, nano, IR, XRD, SEM

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910 Preparation and Structural Analysis of Nano Ciprofloxacin by Fourier Transform Infra-Red Spectroscopy, X-Ray Diffraction and Semi Electron Microscope (SEM)

Authors: Shahriar Ghammamy, Mehrnoosh Saboony

Abstract:

Purpose: to evaluate the spectral specification(IR-XRD and SEM) of nano ciprofloxacin that prepared by up-down method (satellite mill). Methods: the ciprofloxacin was minimized to nano-scale with satellite mill and it,s characterization evaluated by Infrared spectroscopy, XRD diffraction and semi electron microscope (SEM). Expectation: to enhance the antibacterial property of nano ciprofloxacin in comparison to ciprofloxacin.IR spectrum of nano ciprofloxacin compared with spectrum of ciprofloxacin, and both of them were almost agreement with a difference: the peaks in spectrum of nano ciprofloxacin was sharper than peaks in spectrum of ciprofloxacin. X-Ray powder diffraction analysis of nano ciprofloxacin showes the diameter of particles equal to 90.9 nm (on the basis of scherrer equation). SEM image showes the global shape for nano ciprofloxacin.

Keywords: antibiotic, ciprofloxacin, nano, IR, XRD, SEM

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909 3D Model Completion Based on Similarity Search with Slim-Tree

Authors: Alexis Aldo Mendoza Villarroel, Ademir Clemente Villena Zevallos, Cristian Jose Lopez Del Alamo

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With the advancement of technology it is now possible to scan entire objects and obtain their digital representation by using point clouds or polygon meshes. However, some objects may be broken or have missing parts; thus, several methods focused on this problem have been proposed based on Geometric Deep Learning, such as GCNN, ACNN, PointNet, among others. In this article an approach from a different paradigm is proposed, using metric data structures to index global descriptors in the spectral domain and allow the recovery of a set of similar models in polynomial time; to later use the Iterative Close Point algorithm and recover the parts of the incomplete model using the geometry and topology of the model with less Hausdorff distance.

Keywords: 3D reconstruction method, point cloud completion, shape completion, similarity search

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908 Application of Waterflooding Technique in Petroleum Reservoir

Authors: Khwaja Naweed Seddiqi

Abstract:

Hydrocarbon resources are important for the redevelopment and sustainable progress of Afghanistan’s infrastructure. This paper aim is to increase the oil recovery of Hitervian reservoir of Angut oil field in north part of Afghanistan by an easy and available method, which is Buckley-Leveret frontal displacement theory. In this paper oil displacement by water that takes placed by injecting water into the under laying petroleum reservoir which called waterflooding technique is investigated. The theory is investigated in a laboratory experiment first then applied in Angut oil field which is now under the operation of a private petroleum company. Based on this study oil recovery of Angut oil field, residual oil saturation, Buckle-Leveret saturation and FBL is determined.

Keywords: waterflooding technique, two phase fluid flow, Buckley-Leveret, petroleum engineering

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907 Co-pyrolysis of Sludge and Kaolin/Zeolite to Stabilize Heavy Metals

Authors: Qian Li, Zhaoping Zhong

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Sewage sludge, a typical solid waste, has inevitably been produced in enormous quantities in China. Still worse, the amount of sewage sludge produced has been increasing due to rapid economic development and urbanization. Compared to the conventional method to treat sewage sludge, pyrolysis has been considered an economic and ecological technology because it can significantly reduce the sludge volume, completely kill pathogens, and produce valuable solid, gas, and liquid products. However, the large-scale utilization of sludge biochar has been limited due to the considerable risk posed by heavy metals in the sludge. Heavy metals enriched in pyrolytic biochar could be divided into exchangeable, reducible, oxidizable, and residual forms. The residual form of heavy metals is the most stable and cannot be used by organisms. Kaolin and zeolite are environmentally friendly inorganic minerals with a high surface area and heat resistance characteristics. So, they exhibit the enormous potential to immobilize heavy metals. In order to reduce the risk of leaching heavy metals in the pyrolysis biochar, this study pyrolyzed sewage sludge mixed with kaolin/zeolite in a small rotary kiln. The influences of additives and pyrolysis temperature on the leaching concentration and morphological transformation of heavy metals in pyrolysis biochar were investigated. The potential mechanism of stabilizing heavy metals in the co-pyrolysis of sludge blended with kaolin/zeolite was explained by scanning electron microscopy, X-ray diffraction, and specific surface area and porosity analysis. The European Community Bureau of Reference sequential extraction procedure has been applied to analyze the forms of heavy metals in sludge and pyrolysis biochar. All the concentrations of heavy metals were examined by flame atomic absorption spectrophotometry. Compared with the proportions of heavy metals associated with the F4 fraction in pyrolytic carbon prepared without additional agents, those in carbon obtained by co-pyrolysis of sludge and kaolin/zeolite increased. Increasing the additive dosage could improve the proportions of the stable fraction of various heavy metals in biochar. Kaolin exhibited a better effect on stabilizing heavy metals than zeolite. Aluminosilicate additives with excellent adsorption performance could capture more released heavy metals during sludge pyrolysis. Then heavy metal ions would react with the oxygen ions of additives to form silicate and aluminate, causing the conversion of heavy metals from unstable fractions (sulfate, chloride, etc.) to stable fractions (silicate, aluminate, etc.). This study reveals that the efficiency of stabilizing heavy metals depends on the formation of stable mineral compounds containing heavy metals in pyrolysis biochar.

Keywords: co-pyrolysis, heavy metals, immobilization mechanism, sewage sludge

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906 Omni-Relay (OR) Scheme-Aided LTE-A Communication Systems

Authors: Hassan Mahasneh, Abu Sesay

Abstract:

We propose the use of relay terminals at the cell edge of an LTE-based cellar system. Each relay terminal is equipped with an omni-directional antenna. We refer to this scheme as the Omni-Relay (OR) scheme. The OR scheme coordinates the inter-cell interference (ICI) stemming from adjacent cells and increases the desired signal level at cell-edge regions. To validate the performance of the OR scheme, we derive the average signal-to-interference plus noise ratio (SINR) and the average capacity and compare it with the conventional universal frequency reuse factor (UFRF). The results show that the proposed OR scheme provides higher average SINR and average capacity compared to the UFRF due to the assistance of the distributed relay nodes.

Keywords: the UFRF scheme, the OR scheme, ICI, relay terminals, SINR, spectral efficiency

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905 Generation of Roof Design Spectra Directly from Uniform Hazard Spectra

Authors: Amin Asgarian, Ghyslaine McClure

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Proper seismic evaluation of Non-Structural Components (NSCs) mandates an accurate estimation of floor seismic demands (i.e. acceleration and displacement demands). Most of the current international codes incorporate empirical equations to calculate equivalent static seismic force for which NSCs and their anchorage system must be designed. These equations, in general, are functions of component mass and peak seismic acceleration to which NSCs are subjected to during the earthquake. However, recent studies have shown that these recommendations are suffered from several shortcomings such as neglecting the higher mode effect, tuning effect, NSCs damping effect, etc. which cause underestimation of the component seismic acceleration demand. This work is aimed to circumvent the aforementioned shortcomings of code provisions as well as improving them by proposing a simplified, practical, and yet accurate approach to generate acceleration Floor Design Spectra (FDS) directly from corresponding Uniform Hazard Spectra (UHS) (i.e. design spectra for structural components). A database of 27 Reinforced Concrete (RC) buildings in which Ambient Vibration Measurements (AVM) have been conducted. The database comprises 12 low-rise, 10 medium-rise, and 5 high-rise buildings all located in Montréal, Canada and designated as post-disaster buildings or emergency shelters. The buildings are subjected to a set of 20 compatible seismic records and Floor Response Spectra (FRS) in terms of pseudo acceleration are derived using the proposed approach for every floor of the building in both horizontal directions considering 4 different damping ratios of NSCs (i.e. 2, 5, 10, and 20% viscous damping). Several effective parameters on NSCs response are evaluated statistically. These parameters comprise NSCs damping ratios, tuning of NSCs natural period with one of the natural periods of supporting structure, higher modes of supporting structures, and location of NSCs. The entire spectral region is divided into three distinct segments namely short-period, fundamental period, and long period region. The derived roof floor response spectra for NSCs with 5% damping are compared with the 5% damping UHS and procedure are proposed to generate roof FDS for NSCs with 5% damping directly from 5% damped UHS in each spectral region. The generated FDS is a powerful, practical, and accurate tool for seismic design and assessment of acceleration-sensitive NSCs particularly in existing post-critical buildings which have to remain functional even after the earthquake and cannot tolerate any damage to NSCs.

Keywords: earthquake engineering, operational and functional components (OFCs), operational modal analysis (OMA), seismic assessment and design

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904 Multi-Labeled Aromatic Medicinal Plant Image Classification Using Deep Learning

Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

Abstract:

Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

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903 Sol-Gel Synthesis and Optical Characterisation of TiO2 Thin Films for Photovoltaic Application

Authors: Arabi Nour El Houda, Iratni Aicha, Talaighil Razika, Bruno Capoen, Mohamed Bouazaoui

Abstract:

TiO2 thin films have been prepared by the sol-gel dip-coating technique in order to elaborate antireflective thin films for monocrystalline silicon (mono-Si). The titanium isopropoxyde was chosen as a precursor with hydrochloric acid as a catalyser for preparing a stable solution. The optical properties have been tailored with varying the solution concentration, the withdrawn speed, and the heat-treatment. We showed that using a TiO2 single layer with 64.5 nm in thickness, heat-treated at 450°C or 300°C reduces the mono-Si reflection at a level lower than 3% over the broadband spectral do mains [669-834] nm and [786-1006] nm respectively. Those latter performances are similar to the ones obtained with double layers of low and high refractive index glasses respectively.

Keywords: thin film, dip-coating, mono-crystalline silicon, titanium oxide

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902 Probabilistic Simulation of Triaxial Undrained Cyclic Behavior of Soils

Authors: Arezoo Sadrinezhad, Kallol Sett, S. I. Hariharan

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In this paper, a probabilistic framework based on Fokker-Planck-Kolmogorov (FPK) approach has been applied to simulate triaxial cyclic constitutive behavior of uncertain soils. The framework builds upon previous work of the writers, and it has been extended for cyclic probabilistic simulation of triaxial undrained behavior of soils. von Mises elastic-perfectly plastic material model is considered. It is shown that by using probabilistic framework, some of the most important aspects of soil behavior under cyclic loading can be captured even with a simple elastic-perfectly plastic constitutive model.

Keywords: elasto-plasticity, uncertainty, soils, fokker-planck equation, fourier spectral method, finite difference method

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901 Quantification of Site Nonlinearity Based on HHT Analysis of Seismic Recordings

Authors: Ruichong Zhang

Abstract:

This study proposes a recording-based approach to characterize and quantify earthquake-induced site nonlinearity, exemplified as soil nonlinearity and/or liquefaction. Alternative to Fourier spectral analysis (FSA), the paper introduces time-frequency analysis of earthquake ground motion recordings with the aid of so-called Hilbert-Huang transform (HHT), and offers justification for the HHT in addressing the nonlinear features shown in the recordings. With the use of the 2001 Nisqually earthquake recordings, this study shows that the proposed approach is effective in characterizing site nonlinearity and quantifying the influences in seismic ground responses.

Keywords: site nonlinearity, site amplification, site damping, Hilbert-Huang Transform (HHT), liquefaction, 2001 Nisqually Earthquake

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900 Performance Analysis of IDMA Scheme Using Quasi-Cyclic Low Density Parity Check Codes

Authors: Anurag Saxena, Alkesh Agrawal, Dinesh Kumar

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The next generation mobile communication systems i.e. fourth generation (4G) was developed to accommodate the quality of service and required data rate. This project focuses on multiple access technique proposed in 4G communication systems. It is attempted to demonstrate the IDMA (Interleave Division Multiple Access) technology. The basic principle of IDMA is that interleaver is different for each user whereas CDMA employs different signatures. IDMA inherits many advantages of CDMA such as robust against fading, easy cell planning; dynamic channel sharing and IDMA increase the spectral efficiency and reduce the receiver complexity. In this, performance of IDMA is analyzed using QC-LDPC coding scheme further it is compared with LDPC coding and at last BER is calculated and plotted in MATLAB.

Keywords: 4G, QC-LDPC, CDMA, IDMA

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899 Review: Wavelet New Tool for Path Loss Prediction

Authors: Danladi Ali, Abdullahi Mukaila

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In this work, GSM signal strength (power) was monitored in an indoor environment. Samples of the GSM signal strength was measured on mobile equipment (ME). One-dimensional multilevel wavelet is used to predict the fading phenomenon of the GSM signal measured and neural network clustering to determine the average power received in the study area. The wavelet prediction revealed that the GSM signal is attenuated due to the fast fading phenomenon which fades about 7 times faster than the radio wavelength while the neural network clustering determined that -75dBm appeared more frequently followed by -85dBm. The work revealed that significant part of the signal measured is dominated by weak signal and the signal followed more of Rayleigh than Gaussian distribution. This confirmed the wavelet prediction.

Keywords: decomposition, clustering, propagation, model, wavelet, signal strength and spectral efficiency

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898 Classification of Cochannel Signals Using Cyclostationary Signal Processing and Deep Learning

Authors: Bryan Crompton, Daniel Giger, Tanay Mehta, Apurva Mody

Abstract:

The task of classifying radio frequency (RF) signals has seen recent success in employing deep neural network models. In this work, we present a combined signal processing and machine learning approach to signal classification for cochannel anomalous signals. The power spectral density and cyclostationary signal processing features of a captured signal are computed and fed into a neural net to produce a classification decision. Our combined signal preprocessing and machine learning approach allows for simpler neural networks with fast training times and small computational resource requirements for inference with longer preprocessing time.

Keywords: signal processing, machine learning, cyclostationary signal processing, signal classification

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897 Molecularly Imprinted Polymer and Computational Study of (E)-2-Cyano-3-(Dimethylamino)-N-(2,4-Dioxo-1,2,3,4-Tetrahydropyrimidin-5-Yl)Acrylam-Ide and Its Applications in Industrial Applications

Authors: Asmaa M. Fahim

Abstract:

In this investigation, the (E)-2-cyano-3-(dimethylamino)-N-(2,4-dioxo-1,2,3,4-tetrahydropyrimidin-5-yl)acrylam-ide (4) which used TAM as a template which interacts with Methacrylic Acid (MAA) monomer, in the presence of CH₃CN as progen. The TAM-MMA complex interactions are dependent on stable hydrogen bonding interaction between the carboxylic acid group of TAM(Template) and the hydroxyl group of MMA(methyl methacrylate) with minimal interference of porogen CH₃CN. The physical computational studies were used to optimize their structures and frequency calculations. The binding energies between TAM with different monomers showed the most stable molar ratio of 1:4, which was confirmed through experimental analysis. The optimized polymers were investigated in industrial applications.

Keywords: molecular imprinted polymer, computational studies, SEM, spectral analysis, industrial applications

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896 Facies Sedimentology and Astronomic Calibration of the Reinech Member (Lutetian)

Authors: Jihede Haj Messaoud, Hamdi Omar, Hela Fakhfakh Ben Jemia, Chokri Yaich

Abstract:

The Upper Lutetian alternating marl–limestone succession of Reineche Member was deposited over a warm shallow carbonate platform that permits Nummulites proliferation. High-resolution studies of 30 meters thick Nummulites-bearing Reineche Member, cropping out in Central Tunisia (Jebel Siouf), have been undertaken, regarding pronounced cyclical sedimentary sequences, in order to investigate the periodicity of cycles and their related orbital-scale oceanic and climatic changes. The palaeoenvironmental and palaeoclimatic data are preserved in several proxies obtainable through high-resolution sampling and laboratories measurement and analysis as magnetic susceptibility (MS) and carbonates contents in conjunction with a wireline logging tools. The time series analysis of proxies permits to establish cyclicity orders present in the studied intervals which could be linked to the orbital cycles. MS records provide high-resolution proxies for relative sea level change in Late Lutetian strata. The spectral analysis of MS fluctuations confirmed the orbital forcing by the presence of the complete suite of orbital frequencies in the precession of 23 ka, the obliquity of 41 ka, and notably the two modes of eccentricity of 100 and 405 ka. Regarding the two periodic sedimentary cycles detected by wavelet analysis of proxy fluctuations which coincide with the long-term 405 ka eccentricity cycle, the Reineche Member spanned 0,8 Myr. Wireline logging tools as gamma ray and sonic were used as a proxies to decipher cyclicity and trends in sedimentation and contribute to identifying and correlate units. There are used to constraint the highest frequency cyclicity modulated by a long term wavelength cycling apparently controlled by clay content. Interpreted as a result of variations in carbonate productivity, it has been suggested that the marl-limestone couplets, represent the sedimentary response to the orbital forcing. The calculation of cycle durations through Reineche Member, is used as a geochronometer and permit the astronomical calibration of the geologic time scale. Furthermore, MS coupled with carbonate contents, and fossil occurrences provide strong evidence for combined detrital inputs and marine surface carbonate productivity cycles. These two synchronous processes were driven by the precession index and ‘fingerprinted’ in the basic marl–limestone couplets, modulated by orbital eccentricity.

Keywords: magnetic susceptibility, cyclostratigraphy, orbital forcing, spectral analysis, Lutetian

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895 A Visualization Classification Method for Identifying the Decayed Citrus Fruit Infected by Fungi Based on Hyperspectral Imaging

Authors: Jiangbo Li, Wenqian Huang

Abstract:

Early detection of fungal infection in citrus fruit is one of the major problems in the postharvest commercialization process. The automatic and nondestructive detection of infected fruits is still a challenge for the citrus industry. At present, the visual inspection of rotten citrus fruits is commonly performed by workers through the ultraviolet induction fluorescence technology or manual sorting in citrus packinghouses to remove fruit subject with fungal infection. However, the former entails a number of problems because exposing people to this kind of lighting is potentially hazardous to human health, and the latter is very inefficient. Orange is used as a research object. This study would focus on this problem and proposed an effective method based on Vis-NIR hyperspectral imaging in the wavelength range of 400-1000 nm with a spectroscopic resolution of 2.8 nm. In this work, three normalization approaches are applied prior to analysis to reduce the effect of sample curvature on spectral profiles, and it is found that mean normalization was the most effective pretreatment for decreasing spectral variability due to curvature. Then, principal component analysis (PCA) was applied to a dataset composing of average spectra from decayed and normal tissue to reduce the dimensionality of data and observe the ability of Vis-NIR hyper-spectra to discriminate data from two classes. In this case, it was observed that normal and decayed spectra were separable along the resultant first principal component (PC1) axis. Subsequently, five wavelengths (band) centered at 577, 702, 751, 808, and 923 nm were selected as the characteristic wavelengths by analyzing the loadings of PC1. A multispectral combination image was generated based on five selected characteristic wavelength images. Based on the obtained multispectral combination image, the intensity slicing pseudocolor image processing method is used to generate a 2-D visual classification image that would enhance the contrast between normal and decayed tissue. Finally, an image segmentation algorithm for detection of decayed fruit was developed based on the pseudocolor image coupled with a simple thresholding method. For the investigated 238 independent set samples including infected fruits infected by Penicillium digitatum and normal fruits, the total success rate is 100% and 97.5%, respectively, and, the proposed algorithm also used to identify the orange infected by penicillium italicum with a 100% identification accuracy, indicating that the proposed multispectral algorithm here is an effective method and it is potential to be applied in citrus industry.

Keywords: citrus fruit, early rotten, fungal infection, hyperspectral imaging

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894 Performance of Hybrid Image Fusion: Implementation of Dual-Tree Complex Wavelet Transform Technique

Authors: Manoj Gupta, Nirmendra Singh Bhadauria

Abstract:

Most of the applications in image processing require high spatial and high spectral resolution in a single image. For example satellite image system, the traffic monitoring system, and long range sensor fusion system all use image processing. However, most of the available equipment is not capable of providing this type of data. The sensor in the surveillance system can only cover the view of a small area for a particular focus, yet the demanding application of this system requires a view with a high coverage of the field. Image fusion provides the possibility of combining different sources of information. In this paper, we have decomposed the image using DTCWT and then fused using average and hybrid of (maxima and average) pixel level techniques and then compared quality of both the images using PSNR.

Keywords: image fusion, DWT, DT-CWT, PSNR, average image fusion, hybrid image fusion

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893 Breast Cancer Metastasis Detection and Localization through Transfer-Learning Convolutional Neural Network Classification Based on Convolutional Denoising Autoencoder Stack

Authors: Varun Agarwal

Abstract:

Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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