Search results for: polarimetric decomposition

Authors: Hyeongju Jeon, Yonghyun Kim, Yongil Kim

Abstract:

Synthetic Aperture Radar (SAR) is an effective tool for damage assessment induced by disasters due to its all-weather and night/day acquisition capability. In this paper, the 2017 Colombia landslide was observed using full-polarimetric ALOS/PALSAR-2 data. Polarimetric decompositions, including the Freeman-Durden decomposition and the Cloude decomposition, are utilized to analyze the scattering mechanisms changes before and after-landslide. These analyses are used to detect the damaged areas induced by the landslide. Experimental results validate the efficiency of the full polarimetric SAR data since the damaged areas can be well discriminated. Thus, we can conclude the proposed method using full polarimetric data has great potential for damage assessment of landslides.

Keywords: Synthetic Aperture Radar (SAR), polarimetric decomposition, damage assessment, landslide

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Authors: Najoua El Hajjaji El Idrissi, Necip Gokhan Kasapoglu

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Polarimetric Synthetic Aperture Radar-based imaging is a powerful technique used for earth observation and classification of surfaces. Forest evolution has been one of the vital areas of attention for the remote sensing experts. The information about forest areas can be achieved by remote sensing, whether by using active radars or optical instruments. However, due to several weather constraints, such as cloud cover, limited information can be recovered using optical data and for that reason, Polarimetric Synthetic Aperture Radar (PolSAR) is used as a powerful tool for forestry inventory. In this [14paper, we applied support vector machine (SVM) and Mahalanobis distance to the fully polarimetric AIRSAR P, L, C-bands data from the Nezer forest areas, the classification is based in the separation of different tree ages. The classification results were evaluated and the results show that the SVM performs better than the Mahalanobis distance and SVM achieves approximately 75% accuracy. This result proves that SVM classification can be used as a useful method to evaluate fully polarimetric SAR data with sufficient value of accuracy.

Keywords: classification, synthetic aperture radar, SAR polarimetry, support vector machine, mahalanobis distance

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Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo Ferraioli

Abstract:

In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring. SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, polarimetric SAR image, convolutional neural network, deep learnig, deep neural network

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Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo ferraioli

Abstract:

In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring . SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, deep learning, convolutional neural network, deep neural network, SAR polarimetry

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Authors: Ibrahim Watad, Ibrahim Abdulhalim

Abstract:

Conventional spectroscopic surface plasmon resonance (SPR) sensors are widely used, both in fundamental research and environmental monitoring as well as healthcare diagnostics. However, they still lack the low limit of detection (LOD) and there still a place for improvement. SPR conventional sensors are based on the detection of a dip in the reflectivity spectrum which is relatively wide. To improve the performance of these sensors, many techniques and methods proposed either to reduce the width of the dip or to increase the sensitivity. Together with that, profiting from the sharp jump in the phase spectrum under SPR, several works suggested the extraction of the phase of the reflected wave. However, existing phase measurement setups are in general more complicated compared to the conventional setups, require more stability and are very sensitive to external vibrations and noises. In this study, a simple polarimetric technique for phase extraction under SPR is presented, followed by a theoretical error analysis and an experimental verification. The advantages of the proposed technique upon existing techniques will be elaborated, together with conclusions regarding the best polarimetric function, and its corresponding optimal metal layer range of thicknesses to use under the conventional Kretschmann-Raether configuration.

Keywords: plasmonics, polarimetry, thin films, optical sensors

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Authors: Mehrnoosh Omati, Mahmod Reza Sahebi

Abstract:

The information on land use/land cover changing plays an essential role for environmental assessment, planning and management in regional development. Remotely sensed imagery is widely used for providing information in many change detection applications. Polarimetric Synthetic aperture radar (PolSAR) image, with the discrimination capability between different scattering mechanisms, is a powerful tool for environmental monitoring applications. This paper proposes a new boundary-based segmentation algorithm as a fundamental step for land cover change detection. In this method, first, two PolSAR images are segmented using integration of marker-controlled watershed algorithm and coupled Markov random field (MRF). Then, object-based classification is performed to determine changed/no changed image objects. Compared with pixel-based support vector machine (SVM) classifier, this novel segmentation algorithm significantly reduces the speckle effect in PolSAR images and improves the accuracy of binary classification in object-based level. The experimental results on Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) polarimetric images show a 3% and 6% improvement in overall accuracy and kappa coefficient, respectively. Also, the proposed method can correctly distinguish homogeneous image parcels.

Keywords: coupled Markov random field (MRF), environment, object-based analysis, polarimetric SAR (PolSAR) images

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Authors: Ravneil Nand

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Cooperative coevolution uses problem decomposition methods to solve a larger problem. The problem decomposition deals with breaking down the larger problem into a number of smaller sub-problems depending on their method. Different problem decomposition methods have their own strengths and limitations depending on the neural network used and application problem. In this paper we are introducing a new problem decomposition method known as Hidden-Neuron Level Decomposition (HNL). The HNL method is competing with established problem decomposition method in time series prediction. The results show that the proposed approach has improved the results in some benchmark data sets when compared to the standalone method and has competitive results when compared to methods from literature.

Keywords: cooperative coevaluation, feed forward network, problem decomposition, neuron, synapse

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Authors: Saad Al-Baddai, Karema Al-Subari, Elmar Lang, Bernd Ludwig

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Empirical mode decomposition (EMD), a new data-driven of time-series decomposition, has the advantage of supposing that a time series is non-linear or non-stationary, as is implicitly achieved in Fourier decomposition. However, the EMD suffers of mode mixing problem in some cases. The aim of this paper is to present a solution for a common type of signals causing of EMD mode mixing problem, in case a signal suffers of an intermittency. By an artificial example, the solution shows superior performance in terms of cope EMD mode mixing problem comparing with the conventional EMD and Ensemble Empirical Mode decomposition (EEMD). Furthermore, the over-sifting problem is also completely avoided; and computation load is reduced roughly six times compared with EEMD, an ensemble number of 50.

Keywords: empirical mode decomposition (EMD), mode mixing, sifting process, over-sifting

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Authors: Khalid Omari, Rene Chenier, Enrique Blondel, Ryan Ahola

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Northern Canadian coasts have vulnerable and very dynamic intertidal zones with very high tides occurring in several areas. The impact of climate change presents challenges not only for maintaining this biodiversity but also for navigation safety adaptation due to the high sediment mobility in these coastal areas. Thus, frequent mapping of shorelines and intertidal changes is of high importance. To help in quantifying the changes in these fragile ecosystems, remote sensing provides practical monitoring tools at local and regional scales. Traditional methods based on high-resolution optical sensors are often used to map intertidal areas by benefiting of the spectral response contrast of intertidal classes in visible, near and mid-infrared bands. Tidal areas are highly reflective in visible bands mainly because of the presence of fine sand deposits. However, getting a cloud-free optical data that coincide with low tides in intertidal zones in northern regions is very difficult. Alternatively, the all-weather capability and daylight-independence of the microwave remote sensing using synthetic aperture radar (SAR) can offer valuable geophysical parameters with a high frequency revisit over intertidal zones. Multi-polarization SAR parameters have been used successfully in mapping intertidal zones using incoherence target decomposition. Moreover, the crustal displacements caused by ocean tide loading may reach several centimeters that can be detected and quantified across differential interferometric synthetic aperture radar (DInSAR). Soil moisture change has a significant impact on both the coherence and the backscatter. For instance, increases in the backscatter intensity associated with low coherence is an indicator for abrupt surface changes. In this research, we present primary results obtained following our investigation of the potential of the fully polarimetric Radarsat-2 data for mapping an inter-tidal zone located on Tasiujaq on the south-west shore of Ungava Bay, Quebec. Using the repeat pass cycle of Radarsat-2, multiple seasonal fine quad (FQ14W) images are acquired over the site between 2016 and 2018. Only 8 images corresponding to low tide conditions are selected and used to build an interferometric stack of data. The observed displacements along the line of sight generated using HH and VV polarization are compared with the changes noticed using the Freeman Durden polarimetric decomposition and Touzi degree of polarization extrema. Results show the consistency of both approaches in their ability to monitor the changes in intertidal zones.

Keywords: SAR, degree of polarization, DInSAR, Freeman-Durden, polarimetry, Radarsat-2

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Authors: Lili Yang, Jirui Gong, Qinpu Luo, Min Liu, Bo Yang, Zihe Zhang

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Anthropogenic activities have increased nitrogen (N) inputs to grassland ecosystems. Knowledge of the impact of N addition on litter decomposition is critical to understand ecosystem carbon cycling and their responses to global climate change. The aim of this study was to investigate the effects of N addition and litter types on litter decomposition of a semi-arid temperate grassland during growing and non-growing seasons in Inner Mongolia, northern China, and to identify the relation between litter decomposition and C: N: P stoichiometry in the litter-soil continuum. Six levels of N addition were conducted: CK, N1 (0 g Nm−2 yr−1), N2 (2 g Nm−2 yr−1), N3 (5 g Nm−2 yr−1), N4 (10 g Nm−2 yr−1) and N5 (25 g Nm−2 yr−1). Litter decomposition rates and nutrient release differed greatly among N addition gradients and litter types. N addition promoted litter decomposition of S. grandis, but exhibited no significant influence on L. chinensis litter, indicating that the S. grandis litter decomposition was more sensitive to N addition than L. chinensis. The critical threshold for N addition to promote mixed litter decomposition was 10 -25g Nm−2 yr−1. N addition altered the balance of C: N: P stoichiometry between litter, soil and microbial biomass. During decomposition progress, the L. chinensis litter N: P was higher in N2-N4 plots compared to CK, while the S. grandis litter C: N was lower in N3 and N4 plots, indicating that litter N or P content doesn’t satisfy microbial decomposers with the increasing of N addition. As a result, S. grandis litter exhibited net N immobilization, while L. chinensis litter net P immobilization. Mixed litter C: N: P stoichiometry satisfied the demand of microbial decomposers, showed net mineralization during the decomposition process. With the increasing N deposition in the future, mixed litter would potentially promote C and nutrient cycling in grassland ecosystem by increasing litter decomposition and nutrient release.

Keywords: C: N: P stoichiometry, litter decomposition, nitrogen addition, nutrient release

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Authors: William Chung

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This paper is to study the use of multiple subproblems in Dantzig-Wolfe decomposition of linear programming (DW-LP). Traditionally, the decomposed LP consists of one LP master problem and one LP subproblem. The master problem and the subproblem is solved alternatively by exchanging the dual prices of the master problem and the proposals of the subproblem until the LP is solved. It is well known that convergence is slow with a long tail of near-optimal solutions (asymptotic convergence). Hence, the performance of DW-LP highly depends upon the number of decomposition steps. If the decomposition steps can be greatly reduced, the performance of DW-LP can be improved significantly. To reduce the number of decomposition steps, one of the methods is to increase the number of proposals from the subproblem to the master problem. To do so, we propose to add a quadratic approximation function to the LP subproblem in order to develop a set of approximate-LP subproblems (multiple subproblems). Consequently, in each decomposition step, multiple subproblems are solved for providing multiple proposals to the master problem. The number of decomposition steps can be reduced greatly. Note that each approximate-LP subproblem is nonlinear programming, and solving the LP subproblem must faster than solving the nonlinear multiple subproblems. Hence, using multiple subproblems in DW-LP is the tradeoff between the number of approximate-LP subproblems being formed and the decomposition steps. In this paper, we derive the corresponding algorithms and provide some simple computational results. Some properties of the resulting algorithms are also given.

Keywords: approximate subproblem, Dantzig-Wolfe decomposition, large-scale models, multiple subproblems

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Authors: Y. J. Song, Q. S. Xu, X. C. Wang, H. Wang, C. Q. Li

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The catalyst of copper oxide loaded on HZSM-5 was developed for nitrous oxide (N₂O) direct decomposition. The kinetic of nitrous oxide decomposition was studied for CuO/HZSM-5 catalyst prepared by incipient wetness impregnation method. The external and internal diffusion of catalytic reaction were considered in the investigation. Experiment results indicated that the external diffusion was basically eliminated when the reaction gas mixture gas hourly space velocity (GHSV) was higher than 9000h⁻¹ and the influence of the internal diffusion was negligible when the particle size of the catalyst CuO/HZSM-5 was small than 40-60 mesh. The experiment results showed that the kinetic of catalytic decomposition of N₂O was a first-order reaction and the activation energy and the pre-factor of the kinetic equation were 115.15kJ/mol and of 1.6×109, respectively.

Keywords: catalytic decomposition, CuO/HZSM-5, kinetic, nitrous oxide

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Authors: M. S. H. Chowdhury, Ishak Hashim

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In this paper, linear and non-linear stiff systems of ordinary differential equations are solved by the classical Adomian decomposition method (ADM) and the multi-stage Adomian decomposition method (MADM). The MADM is a technique adapted from the standard Adomian decomposition method (ADM) where standard ADM is converted into a hybrid numeric-analytic method called the multistage ADM (MADM). The MADM is tested for several examples. Comparisons with an explicit Runge-Kutta-type method (RK) and the classical ADM demonstrate the limitations of ADM and promising capability of the MADM for solving stiff initial value problems (IVPs).

Keywords: stiff system of ODEs, Runge-Kutta Type Method, Adomian decomposition method, Multistage ADM

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Authors: Ruomeng Xiao, Zhulin Zong, Longfa Yang

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Aiming at the problem that the target signal is difficult to detect under the strong ground clutter environment, this paper proposes a clutter suppression algorithm based on the combination of singular value decomposition and the Mallat fast wavelet algorithm. The method first carries out singular value decomposition on the radar echo data matrix, realizes the initial separation of target and clutter through the threshold processing of singular value, and then carries out wavelet decomposition on the echo data to find out the target location, and adopts the discard method to select the appropriate decomposition layer to reconstruct the target signal, which ensures the minimum loss of target information while suppressing the clutter. After the verification of the measured data, the method has a significant effect on the target extraction under low SCR, and the target reconstruction can be realized without the prior position information of the target and the method also has a certain enhancement on the output SCR compared with the traditional single wavelet processing method.

Keywords: clutter suppression, singular value decomposition, wavelet transform, Mallat algorithm, low SCR

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Authors: Samah Benmerbi, Kamal Amroun, Abdelkamel Tari

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The increasing number of Web service (WS)providers throughout the globe, have produced numerous Web services providing the same or similar functionality. Therefore, there is a need of tools developing the best answer of queries by selecting and composing services with total transparency. This paper reviews various QoS based Web service selection mechanisms and architectures which facilitate qualitatively optimal selection, in other fact Web service composition is required when a request cannot be fulfilled by a single web service. In such cases, it is preferable to integrate existing web services to satisfy user’s request. We introduce an automatic Web service composition method based on hypergraph decomposition using hypertree decomposition method. The problem of selection and the composition of the web services is transformed into a resolution in a hypertree by exploring the relations of dependency between web services to get composite web service via employing an execution order of WS satisfying global request.

Keywords: web service, web service selection, web service composition, QoS, hypergraph decomposition, BE hypergraph decomposition, hypertree resolution

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Authors: Kazunori Takai, Weng Kaiwei, Sadao Araki, Hideki Yamamoto

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HFC and PFC gases have been commonly and widely used as refrigerant of air conditioner and as etching agent of semiconductor manufacturing process, because of their higher heat of vaporization and chemical stability. On the other hand, HFCs and PFCs gases have the high global warming effect on the earth. Therefore, we have to be decomposed these gases emitted from chemical apparatus like as refrigerator. Until now, disposal of these gases were carried out by using combustion method like as Rotary kiln treatment mainly. However, this treatment needs extremely high temperature over 1000 °C. In the recent year, in order to reduce the energy consumption, a hydrolytic decomposition method using catalyst and plasma decomposition treatment have been attracted much attention as a new disposal treatment. However, the decomposition of fluorine-containing gases under the wet condition is not able to avoid the generation of hydrofluoric acid. Hydrofluoric acid is corrosive gas and it deteriorates catalysts in the decomposition process. Moreover, an additional process for the neutralization of hydrofluoric acid is also indispensable. In this study, the decomposition of C2F6 using zeolite and zeolite/CaO mixture as reactant was evaluated in the dry condition at 923 K. The effect of the chemical structure of zeolite on the decomposition reaction was confirmed by using H-Y, H-Beta, H-MOR and H-ZSM-5. The formation of CaF2 in zeolite/CaO mixtures after the decomposition reaction was confirmed by XRD measurements. The decomposition of C2F6 using zeolite as reactant showed the closely similar behaviors regardless the type of zeolite (MOR, Y, ZSM-5, Beta type). There was no difference of XRD patterns of each zeolite before and after reaction. On the other hand, the difference in the C2F6 decomposition for each zeolite/CaO mixtures was observed. These results suggested that the rate-determining process for the C2F6 decomposition on zeolite alone is the removal of fluorine from reactive site. In other words, the C2F6 decomposition for the zeolite/CaO improved compared with that for the zeolite alone by the removal of the fluorite from reactive site. HMOR/CaO showed 100% of the decomposition for 3.5 h and significantly improved from zeolite alone. On the other hand, Y type zeolite showed no improvement, that is, the almost same value of Y type zeolite alone. The descending order of C2F6 decomposition was MOR, ZSM-5, beta and Y type zeolite. This order is similar to the acid strength characterized by NH3-TPD. Hence, it is considered that the C-F bond cleavage is closely related to the acid strength.

Keywords: hexafluoroethane, zeolite, calcium oxide, decomposition

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Authors: Saeid Gharechelou, Stuart Green, Fiona Cawkwell

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Hedgerow has an important role for a wide range of ecological habitats, landscape, agriculture management, carbon sequestration, wood production. Hedgerow detection accurately using satellite imagery is a challenging problem in remote sensing techniques, because in the special approach it is very similar to line object like a road, from a spectral viewpoint, a hedge is very similar to a forest. Remote sensors with very high spatial resolution (VHR) recently enable the automatic detection of hedges by the acquisition of images with enough spectral and spatial resolution. Indeed, recently VHR remote sensing data provided the opportunity to detect the hedgerow as line feature but still remain difficulties in monitoring the characterization in landscape scale. In this research is used the TerraSAR-x Spotlight and Staring mode with 3-5 m resolution in wet and dry season in the test site of Fermoy County, Ireland to detect the hedgerow by acquisition time of 2014-2015. Both dual polarization of Spotlight data in HH/VV is using for detection of hedgerow. The varied method of SAR image technique with try and error way by integration of classification algorithm like texture analysis, support vector machine, k-means and random forest are using to detect hedgerow and its characterization. We are applying the Shannon entropy (ShE) and backscattering analysis in single and double bounce in polarimetric analysis for processing the object-oriented classification and finally extracting the hedgerow network. The result still is in progress and need to apply the other method as well to find the best method in study area. Finally, this research is under way to ahead to get the best result and here just present the preliminary work that polarimetric image of TSX potentially can detect the hedgerow.

Keywords: TerraSAR-X, hedgerow detection, high resolution SAR image, dual polarization, polarimetric analysis

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Authors: Siti Rohana Mohd Yatim, Ku Halim Ku Hamid, Kamariah Noor Ismail, Zulkifli Abdul Rashid

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The aim of this study is to investigate odour emission profiles from storage of food waste and to assess the potential health risk caused by exposure to volatile compounds. Food waste decomposition process was conducted for 14 days and kept at 20°C and 30°C in self-made bioreactor. VOCs emissions from both samples were collected at different stages of decomposition starting at day 0, day 1, day 3, day 5, day 7, day 10, day 12 and day 14. It was analyzed using TD-GC/MS. Findings showed that various VOCs were released during decomposition of food waste. Compounds produced were influenced by time, temperature and the physico-chemical characteristics of the compounds. The most abundant compound released was dimethyl disulfide. Potential health risk of exposure to this compound is represented by hazard ratio, HR, calculated at 1.6 x 1011. Since HR equal to or less than 1.0 is considered negligible risk, this indicates that the compound posed a potential risk to human health.

Keywords: volatile organic compounds, decomposition process, food waste, health risk

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Authors: Milad Vahidi, Mahmod R. Sahebi, Mehrnoosh Omati, Reza Mohammadi

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Forest management organizations need information to perform their work effectively. Remote sensing is an effective method to acquire information from the Earth. Two datasets of remote sensing images were used to classify forested regions. Firstly, all of extractable features from hyperspectral and PolSAR images were extracted. The optical features were spectral indexes related to the chemical, water contents, structural indexes, effective bands and absorption features. Also, PolSAR features were the original data, target decomposition components, and SAR discriminators features. Secondly, the particle swarm optimization (PSO) and the genetic algorithms (GA) were applied to select optimization features. Furthermore, the support vector machine (SVM) classifier was used to classify the image. The results showed that the combination of PSO and SVM had higher overall accuracy than the other cases. This combination provided overall accuracy about 90.56%. The effective features were the spectral index, the bands in shortwave infrared (SWIR) and the visible ranges and certain PolSAR features.

Keywords: hyperspectral, PolSAR, feature selection, SVM

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Authors: Reza Mohammadi, Mahmod R. Sahebi, Mehrnoosh Omati, Milad Vahidi

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Classification of high resolution polarimetric Synthetic Aperture Radar (PolSAR) images plays an important role in land cover and land use management. Recently, classification algorithms based on Bag of Visual Words (BOVW) model have attracted significant interest among scholars and researchers in and out of the field of remote sensing. In this paper, BOVW model with pixel based low-level features has been implemented to classify a subset of San Francisco bay PolSAR image, acquired by RADARSAR 2 in C-band. We have used segment-based decision-making strategy and compared the result with the result of traditional Support Vector Machine (SVM) classifier. 90.95% overall accuracy of the classification with the proposed algorithm has shown that the proposed algorithm is comparable with the state-of-the-art methods. In addition to increase in the classification accuracy, the proposed method has decreased undesirable speckle effect of SAR images.

Keywords: Bag of Visual Words (BOVW), classification, feature extraction, land cover management, Polarimetric Synthetic Aperture Radar (PolSAR)

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Authors: Hamdy M. Youssef, Eman A. Al-Lehaibi

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This paper presents an iteration method for the numerical solutions of a one-dimensional problem of generalized thermoelasticity with one relaxation time under given initial and boundary conditions. The thermoelastic material with variable properties as a power functional graded has been considered. Adomian’s decomposition techniques have been applied to the governing equations. The numerical results have been calculated by using the iterations method with a certain algorithm. The numerical results have been represented in figures, and the figures affirm that Adomian’s decomposition method is a successful method for modeling thermoelastic problems. Moreover, the empirical parameter of the functional graded, and the lattice design parameter have significant effects on the temperature increment, the strain, the stress, the displacement.

Keywords: Adomian, decomposition method, generalized thermoelasticity, algorithm

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Authors: Supriyono, Sumardiyono, Rendy J. Pramono

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Biodiesel is one of the alternative fuels promising for substituting petrodiesel as energy source which has an advantage as it is sustainable and eco-friendly. Due to the raw material that tends to decompose during storage, biodiesel also has the same characteristic that tends to decompose during storage. Biodiesel decomposition will form higher acid value as the result of oxidation to double bond on a fatty acid compound on biodiesel. Thus, free fatty acid value could be used to evaluate degradation of biodiesel due to the oxidation process. High free fatty acid on biodiesel could impact on the engine performance. Decomposition of biodiesel due to oxidation reaction could prevent by introducing a small amount of antioxidant. The origin of raw materials and the process for producing biodiesel will determine the effectiveness of antioxidant. Biodiesel made from high free fatty acid (FFA) crude palm oil (CPO) by using two steps esterification is vulnerable to oxidation process which is resulted in increasing on the FFA value. Tocopherol also known as vitamin E is one of the antioxidant that could improve the stability of biodiesel due to decomposition by the oxidation process. Tocopherol 0.5% concentration on palm oil biodiesel could reduce 13% of increasing FFA under temperature 80 °C and exposing time 180 minute.

Keywords: antioxidant, palm oil biodiesel, decomposition, oxidation, tocopherol

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Authors: Hana Rabbouch

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In recent years, there has been considerable growth of denoising techniques mainly devoted to medical imaging. This important evolution is not only due to the progress of computing techniques, but also to the emergence of multi-resolution analysis (MRA) on both mathematical and algorithmic bases. In this paper, a comparative study is conducted between the two best-known MRA-based decomposition techniques: the Empirical Mode Decomposition (EMD) and the Discrete Wavelet Transform (DWT). The comparison is carried out in a framework of multi-scale denoising, where a Non-Local Means (NLM) filter is performed scale-by-scale to a sample of benchmark medical images. The results prove the effectiveness of the multiscaled denoising, especially when the NLM filtering is coupled with the EMD.

Keywords: medical imaging, non local means, denoising, multiscaled analysis, empirical mode decomposition, wavelets

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Authors: Majid Forghani, Michael Khachay

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In genetics, the impact of neighboring amino acids on a target site is referred as the nearest-neighbor effect or simply neighbor effect. In this paper, a new method called wavelet particle decomposition representing the one-dimensional neighbor effect using wavelet packet decomposition is proposed. The main idea lies in known dependence of wavelet packet sub-bands on location and order of neighboring samples. The method decomposes the value of a signal sample into small values called particles that represent a part of the neighbor effect information. The results have shown that the information obtained from the particle decomposition can be used to create better model variables or features. As an example, the approach has been applied to improve the correlation of test and reference sequence distance with titer in the hemagglutination inhibition assay.

Keywords: antigenic variants, neighbor effect, wavelet packet, wavelet particle decomposition

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Authors: Salma Krafessi

Abstract:

The financial markets have a multifaceted, intricate environment, and enormous volumes of data are produced every day. To find investment possibilities, possible fraudulent activity, and market oddities, accurate anomaly identification in this data is essential. Conventional methods for detecting anomalies frequently fail to capture the complex organization of financial data. In order to improve the identification of abnormalities in financial time series data, this study presents Tucker Decomposition as a reliable multi-way analysis approach. We start by gathering closing prices for the S&P 500 index across a number of decades. The information is converted to a three-dimensional tensor format, which contains internal characteristics and temporal sequences in a sliding window structure. The tensor is then broken down using Tucker Decomposition into a core tensor and matching factor matrices, allowing latent patterns and relationships in the data to be captured. A possible sign of abnormalities is the reconstruction error from Tucker's Decomposition. We are able to identify large deviations that indicate unusual behavior by setting a statistical threshold. A thorough examination that contrasts the Tucker-based method with traditional anomaly detection approaches validates our methodology. The outcomes demonstrate the superiority of Tucker's Decomposition in identifying intricate and subtle abnormalities that are otherwise missed. This work opens the door for more research into multi-way data analysis approaches across a range of disciplines and emphasizes the value of tensor-based methods in financial analysis.

Keywords: tucker decomposition, financial markets, financial engineering, artificial intelligence, decomposition models

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Authors: F. J. Adeyeye, D. Eni, K. M. Okedoye

Abstract:

In this paper we present a Hybrid of Adomian decomposition method (ADM). This is the substitution of a One-step method of Taylor’s series approximation of orders I and II, into the nonlinear part of Adomian decomposition method resulting in a convergent series scheme. This scheme is applied to solve some Logistic problems represented as Abelian differential equation and the results are compared with the actual solution and Runge-kutta of order IV in order to ascertain the accuracy and efficiency of the scheme. The findings shows that the scheme is efficient enough to solve logistic problems considered in this paper.

Keywords: Adomian decomposition method, nonlinear part, one-step method, Taylor series approximation, hybrid of Adomian polynomial, logistic problem, Malthusian parameter, Verhulst Model

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Authors: R. H. Hwang, J. H. Park, K. B. Yi

Abstract:

Nitrous oxide (N2O) is now distinguished as an environmental pollutant. N2O is one of the representative greenhouse gases and N2O is produced by both natural and anthropogenic sources. So, it is very important to reduce N2O. N2O abatement processes are various processes such as HC-SCR, NH3-SCR and decomposition process. Among them, decomposition process is advantageous because it does not use a reducing agent. N2O decomposition is a reaction in which N2O is decomposed into N2 and O2. There are noble metals, transition metal ion-exchanged zeolites, pure and mixed oxides for N2O decomposition catalyst. Among the various catalysts, cobalt-based catalysts derived from hydrotalcites gathered much attention because spinel catalysts having large surface areas and high thermal stabilities. In this study, the effect of promoter and K addition on the activity was compared and analyzed. Co3O4 catalysts for N2O decomposition were prepared by co- precipitation method. Ce and Zr were added during the preparation of the catalyst as promoter with the molar ratio (Ce or Zr) / Co = 0.05. In addition, 1 wt% K2CO3 was doped to the prepared catalyst with impregnation method to investigate the effect of K on the catalyst performance. Characterizations of catalysts were carried out with SEM, BET, XRD, XPS and H2-TPR. The catalytic activity tests were carried out at a GHSV of 45,000 h-1 and a temperature range of 250 ~ 375 ℃. The Co3O4 catalysts showed a spinel crystal phase, and the addition of the promoter increased the specific surface area and reduced the particle and crystal size. It was exhibited that the doping of K improves the catalytic activity by increasing the concentration of Co2+ in the catalyst which is an active site for catalytic reaction. As a result, the K-doped catalyst showed higher activity than the promoter added. Also, it was found through experiments that Co2+ concentration and reduction temperature greatly affect the reactivity.

Keywords: Co₃O4, K-doped, N₂O decomposition, promoter

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Authors: Christian Meinecke, Bernd Scholz-Reiter

Abstract:

The integrated problem of production and distribution scheduling is relevant in many industrial applications. Thus, many heuristics to solve this integrated problem have been developed in the last decade. Most of these heuristics use a sequential working principal or a single decomposition and integration approach to separate and solve sub-problems. A heuristic using a multi-step decomposition and integration approach is presented in this paper and evaluated in a case study. The result show significant improved results compared with sequential scheduling heuristics.

Keywords: production and outbound distribution, integrated planning, heuristic, decomposition, integration

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Authors: Peni Rizki

Abstract:

This article presents decomposition on Aqua logo design 2013 as well as exploration on the meanings denoting marketing resolution. In the analysis, it is described decomposition details on Aqua logo design 2013, a semiotics implementation on marketing enterprise. 2013’s design is different in parts from its first establishment in 1973. Upon that, design elements such as pictures and colors are examined in semiotic theories of sign utilized as directives to the meaning constructed. Each part of the design is analyzed based on its significations that generate denotation and connotation as well as myth. At the end will be concluded the converses of Aqua logo design 2013 in reflection to its initiated marketing creativity; what pictures and colors do in it.

Keywords: design, aqua, semiotics, signification

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Authors: Hyun Kyu Lee, Won Zin Oh, June Hyun Kim, Jin Hee Kim, Sang June Choi, Hak Soo Kim

Abstract:

The oxidative dissolution of chromium oxide by manganese oxides including permanganate have been widely studied not only for the chemical decontamination of nuclear power plant, but also for the environmental control of the toxic chromate caused by naturally occurring manganese dioxide. However, little attention has been made for the spontaneous reductive decomposition of permanganate in the water, which is a competing reaction with the oxidation of the chromium oxide by permanganate. The objective of this study is to investigate the spontaneous reductive decomposition behavior of permanganate in the water, depending on the variation of acidity, temperature and concentration. Results of the experiments showed that the permanganate reductive decomposition product is manganese dioxide, and this reaction accompanies with the same molar amount of hydrogen ion consumption. Therefore, at the neutral condition (ex. potassium permanganate solution without acidic chemicals), the permanganate do not reduce by itself at any condition of temperature, concentration within the experimental range. From the results, we confirmed that the oxidation reaction for the permanganate reduction is the water oxidation that is accompanying the oxygen evolution. The experimental results on the reductive decomposition behavior of permanganate in the water also showed that the degree and rate of permanganate reduction increases with the temperature, acidity and concentration. The spontaneous decomposition of the permanganates obtained in the studies would become a good reference to select the operational condition, such as temperature, acidity and concentration, for the chemical decontamination of nuclear power plants.

Keywords: permanganate reduction, spontaneous decomposition, water oxidation, acidity, temperature, permanganate concentration, chemical decontamination, nuclear power plant

Procedia PDF Downloads 315