Search results for: assembly feature

Authors: Peng Hou

Abstract:

Natural products produced from marine bacteria serve as an immense reservoir for anti-infective drugs and therapeutic agents. Nowadays, heterologous expression of gene clusters of interests has been widely adopted as an effective strategy for natural product discovery. Briefly, the heterologous expression flowchart would be: biosynthetic gene cluster identification, pathway construction and expression, and product detection. However, gene cluster capture using traditional Transformation-associated recombination (TAR) protocol is low-efficient (0.5% positive colony rate). To make things worse, most of these putative new natural products are only predicted by bioinformatics analysis such as antiSMASH, and their corresponding natural products biosynthetic pathways are either not expressed or expressed at very low levels under laboratory conditions. Those setbacks have inspired us to focus on seeking new technologies to efficiently edit and refractor of biosynthetic gene clusters. Recently, two cutting-edge techniques have attracted our attention - the CRISPR-Cas9 and Gibson Assembly. By now, we have tried to pretreat Brevibacillus laterosporus strain genomic DNA with CRISPR-Cas9 nucleases that specifically generated breaks near the gene cluster of interest. This trial resulted in an increase in the efficiency of gene cluster capture (9%). Moreover, using Gibson Assembly by adding/deleting certain operon and tailoring enzymes regardless of end compatibility, the silent construct (~80kb) has been successfully refactored into an active one, yielded a series of analogs expected. With the appearances of the novel molecular tools, we are confident to believe that development of a high throughput mature pipeline for DNA assembly, transformation, product isolation and identification would no longer be a daydream for marine natural product discovery.

Keywords: biosynthesis, CRISPR-Cas9, DNA assembly, refactor, TAR cloning

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Authors: Khalaf Khatatneh, Nabeel Al-Milli, Amjad Hudaib, Monther Ali Tarawneh

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Software fault prediction identify potential faults in software modules during the development process. In this paper, we present a novel approach for software fault prediction by combining a feedforward neural network with particle swarm optimization (PSO). The PSO algorithm is employed as a feature selection technique to identify the most relevant metrics as inputs to the neural network. Which enhances the quality of feature selection and subsequently improves the performance of the neural network model. Through comprehensive experiments on software fault prediction datasets, the proposed hybrid approach achieves better results, outperforming traditional classification methods. The integration of PSO-based feature selection with the neural network enables the identification of critical metrics that provide more accurate fault prediction. Results shows the effectiveness of the proposed approach and its potential for reducing development costs and effort by detecting faults early in the software development lifecycle. Further research and validation on diverse datasets will help solidify the practical applicability of the new approach in real-world software engineering scenarios.

Keywords: feature selection, neural network, particle swarm optimization, software fault prediction

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Authors: Anthony John Walker, Glen Bright

Abstract:

The challenge faced by manufactures, when producing custom products, is that each product needs exact components. This can cause work-in-process instability due to component matching constraints imposed on assembly cells. Clearing type flow control policies have been used extensively in mediating server access between multiple arrival processes. Although the stability and performance of clearing policies has been well formulated and studied in the literature, the growth in arrival to departure sequence disorder for each arriving job, across a serving resource, is still an area for further analysis. In this paper, a closed form industrial model has been formulated that characterizes arrival-to-departure sequence disorder through stable manufacturing systems under clearing type flow control policy. Specifically addressed are the effects of sequence disorder imposed on a downstream assembly cell in terms of work-in-process instability induced through component matching constraints. Results from a simulated manufacturing system show that steady state average sequence disorder in parallel upstream processing cells can be balanced in order to decrease downstream assembly system instability. Simulation results also show that the closed form model accurately describes the growth and limiting behavior of average sequence disorder between parts arriving and departing from a manufacturing system flow controlled via clearing policy.

Keywords: assembly system constraint, custom products, discrete sequence disorder, flow control

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Authors: Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, Yann LeCun

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Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coherent video data. We focus on feature learning from unlabeled video data, using the assumption that adjacent video frames contain semantically similar information. This assumption is exploited to train a convolutional pooling auto-encoder regularized by slowness and sparsity. We establish a connection between slow feature learning to metric learning and show that the trained encoder can be used to define a more temporally and semantically coherent metric.

Keywords: machine learning, pattern clustering, pooling, classification

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Authors: A. M. Iordache, S. M. Iordache, V. Barna, M. Elisa, I. C. Vasiliu, C. R. Stefan, I. Chilibon, I. Stamatin, S. Caramizoiu, C. E. A. Grigorescu

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The general objective of the presentation is the development of an e-tongue like sensor based on modified screen printed electrode (SPE) structures with a receptor part made of porphyrins/metalloporphyrins chemically bound to graphene (the sensitive assembly) to act as antennas and “capture” the histamine molecules. Using a single, ultra-sensitive electrochemical sensor, we measured the concentration of histamine, a compound which is strongly connected to the level of freshness in foods (the caution level of histamine is 50 ppm, whereas the maximum accepted levels range from 200 ppm to 500 ppm). Our approach for the chemical immobilization of the porphyrins onto the surface of the graphenes was via substitution reaction: a solution of graphene in SOCl2 was heated to 800C for 6 hours. Upon cooling, the metallo-porphyrins were added and ultrasonicated for 4 hours. The solution was then allowed to cool to room temperature and then centrifuged in order to separate the deposit. The sensitive assembly was drop casted onto the carbon SPE and cyclic voltammetry was performed in the presence of histamine. The reaction is quasi-reversible and the sensor showed an oxidation potential for histamine at 600 mV. The results indicate a linear dependence of concentration of histamine as function of intensity. The results are reproducible; however the chemical stability of the sensitive assembly is low.

Keywords: histamine, cyclic voltammetry, metallo-porphyrin, food freshness

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Authors: Mustafa Alshawaqfeh, Bilal Wajidy, Echin Serpedin, Jan Suchodolski

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Inflammatory Bowel disease (IBD) is a disease of the colon with characteristic inflammation. Clinically IBD is detected using laboratory tests (blood and stool), radiology tests (imaging using CT, MRI), capsule endoscopy and endoscopy. There are two variants of IBD referred to as Ulcerative Colitis (UC) and Crohn’s disease. This study employs a hybrid feature selection method that combines a correlation-based variable ranking approach with exhaustive search wrapper methods in order to find potential biomarkers for UC. The proposed biomarkers presented accurate discriminatory power thereby identifying themselves to be possible ingredients to UC therapeutics.

Keywords: ulcerative colitis, biomarker detection, feature selection, inflammatory bowel disease (IBD)

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Authors: Ovidiu Vlaicu, Leontina Banica, Dan Otelea, Andrei-Jose Petrescu, Costin-Ioan Popescu

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Hepatitis C Virus (HCV) infects 170 million peoples worldwide. Major advances have been made recently in HCV standard of care with interferon-free therapy being already approved. Despite major progress in HCV therapy, the genotype associated treatment efficacy and toxicity still represent issues to address. To identify endogenous factors involved in different stages of HCV life cycle, we have developed a trans-packaging system for HCV subgenomic replicons lacking core protein gene. Huh7 cells were used to generate a packaging cell line expressing the core protein in an inducible manner. The core packaging cell line was able to trans-complemented various subgenomic replicons to secret infectious trans-complemented HCV particles (HCV-TCP). Further, we constructed subgenomic replicons with foreign epitopes suitable for immunoaffinity purification or fluorescence microscopy studies. We have shown that the insertion has not effects on the efficacy of trans-complementation yielding similar titers to the control subgenomic replicon. This system will be a valuable tool in studying pre- and post-assembly events in HCV life cycle and for the fast identification of HCV assembly inhibitors.

Keywords: assembly inhibitors, core protein, HCV, trans-complementation

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Authors: Glenda B. Minguito, Jenith L. Banluta

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One of the critical aspects of humanitarian operations is the distribution of relief goods to the affected community. The common assumption is that relief goods are prepositioned during disasters which are not applicable in developing countries like the Philippines. During disasters, the on-the-ground government agencies and responders have to procure, sort, weigh and pack the relief goods. There is a need to review the relief goods preparation as it seriously affects the delivery of necessary aid for human survival. This study also identifies the ideal location of the assembly hub to minimize the distance to the affected community. This paper reveals that location and resources are dependent on the type of disasters encountered at the local level. The Center-of-Gravity method and Multiple Activity Chart were applied in the analysis.

Keywords: humanitarian supply chain, location decision, resource allocation, local level

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Authors: Hyun-Ji Choi

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This paper aims to examine the meaning of ‘state’ as a monopoly of violence, in regard with South Korean democratic transition. Since institutional democratization in 1987, it is conventionally known that governmental authority has exercised its power through law and police force, rather than inclusive or private violence. In other words, 1987 pro-democracy movement has been a critical juncture for a step towards democratic consolidation. However, state coercion may continually be exerted despite institutional specification by law in South Korean context. Explicit case would be amendment of ‘the Law on Assembly and Demonstration’ which determines citizens’ right to take collective action mostly against government actions. This paper investigates amendment process of the law along with social reality since 1987 until 2015 to see how effectively institutionalization has progressed.

Keywords: democratic transition, historical institutionalism, state coercion, the law on Assembly and Demonstration

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Authors: Parvaneh Eskandari, Rachel O'Reilly

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In the last years, controlling the self-assembly behavior of stimuli-responsive nano-objects, including micelles, vesicles, worm-like, etc., at different conditions is considered a pertinent challenge in the polymer community. The aim of the project was to synthesize aggregation-induced emission (AIE)-active stimuli-responsive polymeric nano-objects to control the self-assemblies morphologies of the prepared nano-objects. Two types of nanoobjects, micelle and vesicles, including PDMAEMA-b-P(BzMA-TPEMA) [PDMAEMA: poly(N,Ndimethylaminoethyl methacrylate); P(BzMA-TPEMA): poly[benzyl methacrylate-co- tetraphenylethene methacrylate]] were synthesized by using reversible addition−fragmentation chain-transfer (RAFT)- mediated polymerization-induced self-assembly (PISA), which combines polymerization and self-assembly in a single step. Transmission electron microscope and dynamic light scattering (DLS) analysis were used to confirm the formed self-assemblies morphologies. The controlled self-assemblies were applied as nitrophenolic compounds (NPCs) adsorbents from wastewater, thanks to their CO2-responsive part, PDMAEMA. Moreover, the fluorescence-active part of the prepared nano-objects, P(BzMA-TPEMA), played a key role in the detection of the NPCs at the aqueous solution. The optical properties of the prepared nano-objects were studied by UV/Vis and fluorescence spectroscopies. For responsivity investigations, the hydrodynamic diameter and Zeta-potential (ζ-potential) of the sample's aqueous solution were measured by DLS. In the end, the prepared nano-objects were used for the detection and adsorption of different NPCs.

Keywords: aggregation-induced emission polymers, stimuli-responsive polymers, reversible addition−fragmentation chain-transfer polymerization, polymerization-induced self-assembly, wastewater treatment

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Authors: Junki Min, Heeyoung Lee, Wonseok Chung

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Traditional marine concrete structures are difficult to construct and may cause environmental pollution. This study presents new concrete bridge system in the marine. The main feature of the proposed bridge is that precast girders and precast coping are applied to facilitate assembly and to improve constructability. In addition, the moment of the girder is reduced by continuation the joint. In this study, a full-scale joint specimen with a span of 7.0 m was fabricated and tested to evaluate the composite behavior of the joint. A finite element model was also developed and compared against the experimental results. All members of the test specimen behaved stably up to the design load. It was found that the precast joint of the proposed bridge showed the composite behavior efficiently before the failure.

Keywords: finite element analysis, full-scale test, coping, joint performance, marine structure, precast

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Authors: S. Shaaban, T. McNamara, S. Hudson

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This paper reports on the performance of deliberately unbalanced, reliable, non-automated and assembly lines that merge, whose workstations differ in terms of their mean operation times. Simulations are carried out on 5- and 8-station lines with 1, 2 and 4 buffer capacity units, % degrees of line imbalance of 2, 5 and 12, and 24 different patterns of means imbalance. Data on two performance measures, namely throughput and average buffer level were gathered, statistically analysed and compared to a merging balanced line counterpart. It was found that the best configurations are a balanced line arrangement and a monotone decreasing order for each of the parallel merging lines, with the first generally resulting in a lower throughput and the second leading to a lower average buffer level than those of a balanced line.

Keywords: average buffer level, merging lines, simulation, throughput, unbalanced

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Authors: Shital Patil, Surendra Bhosale

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Cardiac diseases are the leading causes of mortality and morbidity in the world, from recent few decades accounting for a large number of deaths have emerged as the most life-threatening disorder globally. Machine learning and Artificial intelligence have been playing key role in predicting the heart diseases. A relevant set of feature can be very helpful in predicting the disease accurately. In this study, we proposed a comparative analysis of 4 different features selection methods and evaluated their performance with both raw (Unbalanced dataset) and sampled (Balanced) dataset. The publicly available Z-Alizadeh Sani dataset have been used for this study. Four feature selection methods: Data Analysis, minimum Redundancy maximum Relevance (mRMR), Recursive Feature Elimination (RFE), Chi-squared are used in this study. These methods are tested with 8 different classification models to get the best accuracy possible. Using balanced and unbalanced dataset, the study shows promising results in terms of various performance metrics in accurately predicting heart disease. Experimental results obtained by the proposed method with the raw data obtains maximum AUC of 100%, maximum F1 score of 94%, maximum Recall of 98%, maximum Precision of 93%. While with the balanced dataset obtained results are, maximum AUC of 100%, F1-score 95%, maximum Recall of 95%, maximum Precision of 97%.

Keywords: cardio vascular diseases, machine learning, feature selection, SMOTE

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Authors: Yavuz Unal, Kemal Polat, H. Erdinc Kocer

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In this study, a decision support system comprising three stages has been proposed to detect the disc abnormalities of the lumbar region. In the first stage named the feature extraction, T2-weighted sagittal and axial Magnetic Resonance Images (MRI) were taken from 55 people and then 27 appearance and shape features were acquired from both sagittal and transverse images. In the second stage named the feature weighting process, k-means clustering based feature weighting (KMCBFW) proposed by Gunes et al. Finally, in the third stage named the classification process, the classifier algorithms including multi-layer perceptron (MLP- neural network), support vector machine (SVM), Naïve Bayes, and decision tree have been used to classify whether the subject has lumbar disc or not. In order to test the performance of the proposed method, the classification accuracy (%), sensitivity, specificity, precision, recall, f-measure, kappa value, and computation times have been used. The best hybrid model is the combination of k-means clustering based feature weighting and decision tree in the detecting of lumbar disc disease based on both sagittal and axial MR images.

Keywords: lumbar disc abnormality, lumbar MRI, lumbar spine, hybrid models, hybrid features, k-means clustering based feature weighting

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Authors: Amir Hajian, Sepehr Damavandinejadmonfared

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In this paper the issue of dimensionality reduction is investigated in finger vein recognition systems using kernel Principal Component Analysis (KPCA). One aspect of KPCA is to find the most appropriate kernel function on finger vein recognition as there are several kernel functions which can be used within PCA-based algorithms. In this paper, however, another side of PCA-based algorithms -particularly KPCA- is investigated. The aspect of dimension of feature vector in PCA-based algorithms is of importance especially when it comes to the real-world applications and usage of such algorithms. It means that a fixed dimension of feature vector has to be set to reduce the dimension of the input and output data and extract the features from them. Then a classifier is performed to classify the data and make the final decision. We analyze KPCA (Polynomial, Gaussian, and Laplacian) in details in this paper and investigate the optimal feature extraction dimension in finger vein recognition using KPCA.

Keywords: biometrics, finger vein recognition, principal component analysis (PCA), kernel principal component analysis (KPCA)

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Authors: Nadine Sangster, Akil James, Rondell Duke, Aaron Ameerali, Terrence Lalla

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The market place of the 21st century is evolving into one of merging national markets, fragmented consumer markets, and rapidly changing product technologies. The use of new technologies has become vital to the manufacturing industry for their survival and sustainability. This work focused on the assembly type industry in a small developing country and aimed at identifying the use of advanced manufacturing technologies and their impact on this sector of the manufacturing industry. It was found that some technologies were being used and that they had improved the effectiveness of those companies but there was still quite a bit of room for improvements. Some of the recommendations included benchmarking against international standards, the adoption of a “made in TT” campaign and the effective utilisation of the technologies to improve manufacturing effectiveness and thus improve competitive advantages and strategies.

Keywords: advanced manufacturing technology, Trinidad and Tobago, manufacturing, industrial engineering

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Authors: Daleesha M. Viswanathan, Sumam Mary Idicula

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Dynamic hand gestures are an intrinsic component in sign language communication. Extracting spatial temporal features of the hand gesture trajectory plays an important role in a dynamic gesture recognition system. Finding a discrete feature descriptor for the motion trajectory based on the orientation feature is the main concern of this paper. Kalman filter algorithm and Hidden Markov Models (HMM) models are incorporated with this recognition system for hand trajectory tracking and for spatial temporal classification, respectively.

Keywords: orientation features, discrete feature vector, HMM., Indian sign language

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Authors: Yagya Raj Pandeya, Joonwhoan Lee

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The dynamic behavior of music and video makes it difficult to evaluate musical instrument playing in a video by computer system. Any television or film video clip with music information are rich sources for analyzing musical instruments using modern machine learning technologies. In this research, we integrate the audio and video information sources using convolutional neural network (CNN) and pass network learned features through recurrent neural network (RNN) to preserve the dynamic behaviors of audio and video. We use different pre-trained CNN for music and video feature extraction and then fine tune each model. The music network use 2D convolutional network and video network use 3D convolution (C3D). Finally, we concatenate each music and video feature by preserving the time varying features. The long short term memory (LSTM) network is used for long-term dynamic feature characterization and then use late fusion with generalized mean. The proposed network performs better performance to recognize the musical instrument using audio-video multimodal neural network.

Keywords: multimodal, 3D convolution, music-video feature extraction, generalized mean

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Authors: A. Nawaz

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The present study uses Halliday’s transitivity model to analyze and interpret ideological stance in PM Nawaz Sharif’s political discourse. His famous speech at the 71st UN assembly was analyzed qualitatively using clausal analysis approach to investigate the communicative functions of the linguistic choices made in the address. The study discovers that among the six process types under the transitivity model, material, relational and mental processes appear most frequently in the speech, making up almost 86% of the whole. Verbal processes rank 4th, whereas existential and behavioral are the least occurring processes covering only 2 and 1 percent respectively. The dominant use of material processes suggests that Nawaz Sharif and his government are the main actors working on several concrete projects to produce a sense of developmental progression and continuity. Using relational and mental processes the PM, along with establishing proximity with masses and especially Kashmiri, gives guarantees and promises. The linguistic analysis concludes Kashmir dispute as being the central theme of the address, since it covers more than half of the discourse. The address calls for a strong action instead of formal assurances and wishful thoughts. The study establishes that language structures can yield certain connotations and ideologies which are not overt for readers. This is in affirmation to the supposition that language form performs a communicative function and is not merely fortuitous.

Keywords: Hallidian perspective on language, implicit meanings, Nawaz Sharif, political ideologies, political speeches, transitivity, UN Assembly

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Authors: F. Jiménez, R. Jódar, M. Martín, G. Sánchez, G. Sciavicco

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Abstract—Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time, to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Its application to unsupervised classification is restricted to a limited number of experiments in the literature. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We present a feature selection wrapper model composed by a multi-objective evolutionary algorithm, the clustering method Expectation-Maximization (EM), and the classifier C4.5 for the unsupervised classification of data extracted from a psychological test named BASC-II (Behavior Assessment System for Children - II ed.) with two objectives: Maximizing the likelihood of the clustering model and maximizing the accuracy of the obtained classifier. We present a methodology to integrate feature selection for unsupervised classification, model evaluation, decision making (to choose the most satisfactory model according to a a posteriori process in a multi-objective context), and testing. We compare the performance of the classifier obtained by the multi-objective evolutionary algorithms ENORA and NSGA-II, and the best solution is then validated by the psychologists that collected the data.

Keywords: evolutionary computation, feature selection, classification, clustering

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Authors: M. Hossain, H. P. Zhu, A. B. Yu

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This paper presents a numerical investigation of regime transition of flow of ellipsoidal particles and a comparison with that of spherical particle assembly. Particle assemblies constituting spherical and ellipsoidal particle of 2.5:1 aspect ratio are examined at separate instances in similar flow conditions in a shear cell model that is numerically developed based on the discrete element method. Correlations among elastically scaled stress, kinetically scaled stress, coordination number and volume fraction are investigated, and show important similarities and differences for the spherical and ellipsoidal particle assemblies. In particular, volume fractions at points of regime transition are identified for both types of particles. It is found that compared with spherical particle assembly, ellipsoidal particle assembly has higher volume fraction for the quasistatic to intermediate regime transition and lower volume fraction for the intermediate to inertial regime transition. Finally, the relationship between coordination number and volume fraction shows strikingly distinct features for the two cases, suggesting that different from spherical particles, the effect of the shear rate on the coordination number is not significant for ellipsoidal particles. This work provides a glimpse of currently running work on one of the most attractive scopes of research in this field and has a wide prospect in understanding rheology of more complex shaped particles in light of the strong basis of simpler spherical particle rheology.

Keywords: DEM, granular rheology, non-spherical particles, regime transition

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Authors: Qiang Zhang, Xiaojian Hu

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In multi-vehicle tracking, based on feature selection, the tracking system efficiently tracks vehicles in a video with minimal error in combination with color attribution, which focuses on presenting a simple and fast, yet accurate and robust solution to the problem such as inaccurately and untimely responses of statistics-based adaptive traffic control system in the intersection scenario. In this study, a real-time tracking system is proposed for multi-vehicle tracking in the intersection scene. Considering the complexity and application feasibility of the algorithm, in the object detection step, the detection result provided by virtual loops were post-processed and then used as the input for the tracker. For the tracker, lightweight methods were designed to extract and select features and incorporate them into the adaptive color tracking (ACT) framework. And the approbatory online feature selection algorithms are integrated on the mature ACT system with good compatibility. The proposed feature selection methods and multi-vehicle tracking method are evaluated on KITTI datasets and show efficient vehicle tracking performance when compared to the other state-of-the-art approaches in the same category. And the system performs excellently on the video sequences recorded at the intersection. Furthermore, the presented vehicle tracking system is suitable for surveillance applications.

Keywords: real-time, multi-vehicle tracking, feature selection, color attribution

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Authors: Tianyang Xu

Abstract:

Small underwater targets generally have a non-centrosymmetric geometry, and the acoustic scattering field of the target has spatial inhomogeneity under active sonar detection conditions. In view of the above problems, this paper takes the hemispherical cylindrical shell as the research object, and considers the angle continuity implied in the echo characteristics, and proposes a cluster-driven research method for the non-uniform characteristics of target echo angle. First, the target echo features are extracted, and feature vectors are constructed. Secondly, the t-SNE algorithm is used to improve the internal connection of the feature vector in the low-dimensional feature space and to construct the visual feature space. Finally, the implicit angular relationship between echo features is extracted under unsupervised condition by cluster analysis. The reconstruction results of the local geometric structure of the target corresponding to different categories show that the method can effectively divide the angle interval of the local structure of the target according to the natural acoustic scattering characteristics of the target.

Keywords: underwater target;, non-uniform characteristics;, cluster-driven method;, acoustic scattering characteristics

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Authors: A. Elrefaei

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The possibility of having sheets with different thicknesses and materials in one assembly facilitates the optimal material distribution within the final product and reduces the weight of the structure. Ability of joining process to assembly these different material combinations is always a challenge to the designer. In this study, 0.6 mm thick 6061-T6 and 2 mm thick 5182-O were robot CMT welded using ER5356 and ER4043 filler metals. The thermal effect of welding resulted in a loss of hardness in the 6061 HAZ. Joints welded by ER5356 filler metal were much higher in fracture load than joints welded by ER4043 and the elongation of joints welded by ER5356 was almost double its corresponding joints welded by ER4043 filler. Owing to the big difference in formability and thickness of base metals, the fracture in forming test occurred in the softened 6061 HAZ out from the weld centerline.

Keywords: aluminum, CMT, mechanical, welding

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Authors: Sayed Mansour, Mohammad Aldoasri, Nagib Elmarzugi, Nadia A. Al-Mouallimi

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The archaeological large stone used in construction of ancient Pharaonic tombs, temples, obelisks and other sculptures, always subject to physicomechanical deterioration and destructive forces, leading to their partial or total broken. The task of reassembling this type of artifact represent a big challenge for the conservators. Recently, the researchers are turning to new technologies to improve the properties of traditional adhesive materials and techniques used in re-assembly of broken large stone. The epoxy resins are used extensively in stone conservation and re-assembly of broken stone because of their outstanding mechanical properties. The introduction of nanoparticles to polymeric adhesives at low percentages may lead to substantial improvements of their mechanical performances in structural joints and large objects. The aim of this study is to evaluate the effectiveness of clay nanoparticles in enhancing the performances of epoxy adhesives used in re-assembly of archaeological massive stone by adding proper amounts of those nanoparticles. The nanoparticles reinforced epoxy nanocomposite was prepared by direct melt mixing with a nanoparticles content of 3% (w/v), and then mould forming in the form of rectangular samples, and used as adhesive for experimental stone samples. Scanning electron microscopy (SEM) was employed to investigate the morphology of the prepared nanocomposites, and the distribution of nanoparticles inside the composites. The stability and efficiency of the prepared epoxy-nanocomposites and stone block assemblies with new formulated adhesives were tested by aging artificially the samples under different environmental conditions. The effect of incorporating clay nanoparticles on the mechanical properties of epoxy adhesives was evaluated comparatively before and after aging by measuring the tensile, compressive, and Elongation strength tests. The morphological studies revealed that the mixture process between epoxy and nanoparticles has succeeded with a relatively homogeneous morphology and good dispersion in low nano-particles loadings in epoxy matrix was obtained. The results show that the epoxy-clay nanocomposites exhibited superior tensile, compressive, and Elongation strength. Moreover, a marked improvement of the mechanical properties of stone joints increased in all states by adding nano-clay to epoxy in comparison with pure epoxy resin.

Keywords: epoxy resins, nanocomposites, clay nanoparticles, re-assembly, archaeological massive stones, mechanical properties

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Authors: Salam Khalifa, Naveed Ahmed

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We present a new method to reconstruct a temporally coherent 3D animation from single or multi-view RGB-D video data using unbiased feature point sampling. Given RGB-D video data, in form of a 3D point cloud sequence, our method first extracts feature points using both color and depth information. In the subsequent steps, these feature points are used to match two 3D point clouds in consecutive frames independent of their resolution. Our new motion vectors based dynamic alignment method then fully reconstruct a spatio-temporally coherent 3D animation. We perform extensive quantitative validation using novel error functions to analyze the results. We show that despite the limiting factors of temporal and spatial noise associated to RGB-D data, it is possible to extract temporal coherence to faithfully reconstruct a temporally coherent 3D animation from RGB-D video data.

Keywords: 3D video, 3D animation, RGB-D video, temporally coherent 3D animation

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Authors: Ahmed Kamil Hasan Al-Ali, Bouchra Senadji, Ganesh Naik

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We propose a system to real environmental noise and channel mismatch for forensic speaker verification systems. This method is based on suppressing various types of real environmental noise by using independent component analysis (ICA) algorithm. The enhanced speech signal is applied to mel frequency cepstral coefficients (MFCC) or MFCC feature warping to extract the essential characteristics of the speech signal. Channel effects are reduced using an intermediate vector (i-vector) and probabilistic linear discriminant analysis (PLDA) approach for classification. The proposed algorithm is evaluated by using an Australian forensic voice comparison database, combined with car, street and home noises from QUT-NOISE at a signal to noise ratio (SNR) ranging from -10 dB to 10 dB. Experimental results indicate that the MFCC feature warping-ICA achieves a reduction in equal error rate about (48.22%, 44.66%, and 50.07%) over using MFCC feature warping when the test speech signals are corrupted with random sessions of street, car, and home noises at -10 dB SNR.

Keywords: noisy forensic speaker verification, ICA algorithm, MFCC, MFCC feature warping

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Authors: Munaf Rashid

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For content-based human action retrieval systems, search accuracy is often inferior because of the following two reasons 1) global information pertaining to videos is totally ignored, only low level motion descriptors are considered as a significant feature to match the similarity between query and database videos, and 2) the semantic gap between the high level user concept and low level visual features. Hence, in this paper, we propose a method that will address these two issues and in doing so, this paper contributes in two ways. Firstly, we introduce a method that uses both global and local information in one framework for an action retrieval task. Secondly, to minimize the semantic gap, a user concept is involved by incorporating features weight updating (FWU) Relevance Feedback (RF) approach. We use statistical characteristics to dynamically update weights of the feature descriptors so that after every RF iteration feature space is modified accordingly. For testing and validation purpose two human action recognition datasets have been utilized, namely Weizmann and UCF. Results show that even with a number of visual challenges the proposed approach performs well.

Keywords: relevance feedback (RF), action retrieval, semantic gap, feature descriptor, codebook

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Authors: Muhammad Imran Ahmad, Ruzelita Ngadiran, Mohd Nazrin Md Isa, Nor Ashidi Mat Isa, Mohd ZaizuIlyas, Raja Abdullah Raja Ahmad, Said Amirul Anwar Ab Hamid, Muzammil Jusoh

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This paper presents two technique, local feature extraction using image spectrum and low frequency spectrum modelling using GMM to capture the underlying statistical information to improve the performance of face recognition system. Local spectrum features are extracted using overlap sub block window that are mapping on the face image. For each of this block, spatial domain is transformed to frequency domain using DFT. A low frequency coefficient is preserved by discarding high frequency coefficients by applying rectangular mask on the spectrum of the facial image. Low frequency information is non Gaussian in the feature space and by using combination of several Gaussian function that has different statistical properties, the best feature representation can be model using probability density function. The recognition process is performed using maximum likelihood value computed using pre-calculate GMM components. The method is tested using FERET data sets and is able to achieved 92% recognition rates.

Keywords: local features modelling, face recognition system, Gaussian mixture models, Feret

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Authors: Nuran Bahar, Mehmet A. Akcayol

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In the aviation industry, many faults may occur frequently during the maintenance processes and assembly operations of complex structured aircrafts because of their high dependencies of components. These faults affect the quality of aircraft parts or developed modules adversely. Technical employee requires long time and high labor force while checking the correctness of each component. In addition, the person must be trained regularly because of the ever-growing and changing technology. Generally, the cost of this training is very high. Augmented Reality (AR) technology reduces the cost of training radically and improves the effectiveness of the training. In this study, the usage of AR technology in the aviation industry has been investigated and the effectiveness of AR with heads-up display glasses has been examined. An application has been developed for comparison of production process with AR and manual one.

Keywords: aerospace, assembly quality, augmented reality, heads-up display

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