Search results for: simultaneous sparse representation

Authors: Jianhong Xiang, Hao Xiang, Linyu Wang

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Compressed sensing (CS) has a wide range of applications in sparse signal reconstruction. Aiming at the problems of low recovery accuracy and long reconstruction time of existing reconstruction algorithms in medical imaging, this paper proposes a corrected smoothing L0 algorithm based on compressed sensing (CSL0). First, an approximate hyperbolic tangent function (AHTF) that is more similar to the L0 norm is proposed to approximate the L0 norm. Secondly, in view of the "sawtooth phenomenon" in the steepest descent method and the problem of sensitivity to the initial value selection in the modified Newton method, the use of the steepest descent method and the modified Newton method are jointly optimized to improve the reconstruction accuracy. Finally, the CSL0 algorithm is simulated on various images. The results show that the algorithm proposed in this paper improves the reconstruction accuracy of the test image by 0-0. 98dB.

Keywords: smoothed L0, compressed sensing, image processing, sparse reconstruction

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Authors: Krzysztof Malczewski

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Magnetic Resonance Imaging (MRI) reconstruction algorithm using compressed sensing is presented in this paper. It is exhibited that the offered approach improves MR images spatial resolution in circumstances when highly undersampled k-space trajectories are applied. Compressed Sensing (CS) aims at signal and images reconstructing from significantly fewer measurements than were conventionally assumed necessary. Magnetic Resonance Imaging (MRI) is a fundamental medical imaging method struggles with an inherently slow data acquisition process. The use of CS to MRI has the potential for significant scan time reductions, with visible benefits for patients and health care economics. In this study the objective is to combine super-resolution image enhancement algorithm with CS framework benefits to achieve high resolution MR output image. Both methods emphasize on maximizing image sparsity on known sparse transform domain and minimizing fidelity. The presented algorithm considers the cardiac and respiratory movements.

Keywords: super-resolution, MRI, compressed sensing, sparse-sense, image enhancement

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Authors: Amy M. Kwon

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Support vector machine (SVM) is a well-known algorithm in machine learning due to its superior performance, and it also functions well in multiple-instance (MI) problems. Our study proposes a schematic algorithm to select instances based on Hausdorff distance, which can be adapted to SVMs as input vectors under the MI setting. Based on experiments on five benchmark datasets, our strategy for adapting representation outperformed in comparison with original approach. In addition, task execution times (TETs) were reduced by more than 80% based on MissSVM. Hence, it is noteworthy to consider this representation adaptation to SVMs under MI-setting.

Keywords: support vector machine, Margin, Hausdorff distance, representation selection, multiple-instance learning, machine learning

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Authors: Minjeoung Kim

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The majority of Korean female members of parliament(MPs) had been elected from the proportional representation till the 19th assemblies but in the 20th general election women MPs of the district representation is slightly more than women MPs of the proportional representation. The chance of women candidates to win is not as low as we assume. Therefore this study aims to reveal which factors influence the election of women candidates, other factors except the political party, because the effect of political party is already well known. Gangnam Eul is selected because female candidate was elected in spite of the low percentage of vote won by her political party. According to the survey, the female candidate was elected thanks to her policies and election pledges. Therefore, women candidates can be elected when they are nominated as candidates by their party in a safe constituency but also they can be elected with their good policies and election pledges in an unsafe constituency. And also the degree of the education, the age and the profession of voters influenced the support of female candidate.

Keywords: women candidates, 20th general election, winning in the district representation, policies and election pledges

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Authors: Chi-Lun Liu

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Requirements modeling and analysis are important in successful information systems' maintenance. Unified Modeling Language (UML) class diagrams are useful standards for modeling information systems. To our best knowledge, there is a lack of a systems development methodology described by the organism metaphor. The core concept of this metaphor is adaptation. Using the knowledge representation and reasoning approach and ontologies to adopt new requirements are emergent in recent years. This paper proposes an organic methodology which is based on constructivism theory. This methodology is a knowledge representation and reasoning approach to analyze new requirements in the class diagrams maintenance. The process and rules in the proposed methodology automatically analyze inconsistencies in the class diagram. In the big data era, developing an automatic tool based on the proposed methodology to analyze large amounts of class diagram data is an important research topic in the future.

Keywords: knowledge representation, reasoning, ontology, class diagram, software engineering

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Authors: Ho Jeong Jin, Chang Won Seo, Choon Sik Cho, Bong Yong Choi, Kwang Kyun Na, Sang Rok Lee

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In this paper, a radar signal processing technique using the LFM-FSK (Linear Frequency Modulation-Frequency Shift Keying) is proposed for reducing the false alarm rate based on the compressive sensing. The LFM-FSK method combines FMCW (Frequency Modulation Continuous Wave) signal with FSK (Frequency Shift Keying). This shows an advantage which can suppress the ghost phenomenon without the complicated CFAR (Constant False Alarm Rate) algorithm. Moreover, the parametric sparse algorithm applying the compressive sensing that restores signals efficiently with respect to the incomplete data samples is also integrated, leading to reducing the burden of ADC in the receiver of radars. 24 GHz FMCW signal is applied and tested in the real environment with FSK modulated data for verifying the proposed algorithm along with the compressive sensing.

Keywords: compressive sensing, LFM-FSK radar, radar signal processing, sparse algorithm

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Authors: Somesh Misha, Smita Raghuvanshi, Suresh Gupta

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Increasing concentration of green house gases (GHG's), such as CO2 is of major concern and start showing its impact nowadays. The recent studies are focused on developing the continuous system using photoautotrophs for CO2 mitigation and simultaneous production of primary and secondary metabolites as a value addition. The advent of carbon concentrating mechanism had blurred the distinction between autotrophs and heterotrophs and now the paradigm has shifted towards the carbon capture and utilization (CCU) rather than carbon capture and sequestration (CCS). In the present work, a bioreactor was developed utilizing the chemolithotrophic bacterial species using CO2 mitigation and simultaneous value addition. The kinetic modeling was done and the biokinetic parameters are obtained for developing the bioreactor. The bioreactor was developed and studied for its operation and performance in terms of volumetric loading rate, mass loading rate, elimination capacity and removal efficiency. The characterization of effluent from the bioreactor was carried out for the products obtained using the analyzing techniques such as FTIR, GC-MS, and NMR. The developed bioreactor promised an economic, efficient and effective solution for CO2 mitigation and simultaneous value addition.

Keywords: CO2 mitigation, bio-reactor, chemolithotrophic bacterial species, FTIR, GC-MS, NMR

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Authors: Hyun-Woo Cho

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Unexpected operational events or abnormalities of industrial processes have a serious impact on the quality of final product of interest. In terms of statistical process control, fault detection and diagnosis of processes is one of the essential tasks needed to run the process safely. In this work, nonlinear representation of process measurement data is presented and evaluated using a simulation process. The effect of using different representation methods on the diagnosis performance is tested in terms of computational efficiency and data handling. The results have shown that the nonlinear representation technique produced more reliable diagnosis results and outperforms linear methods. The use of data filtering step improved computational speed and diagnosis performance for test data sets. The presented scheme is different from existing ones in that it attempts to extract the fault pattern in the reduced space, not in the original process variable space. Thus this scheme helps to reduce the sensitivity of empirical models to noise.

Keywords: fault diagnosis, nonlinear technique, process data, reduced spaces

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Authors: Fuad Noman, Sh-Hussain Salleh, Chee-Ming Ting, Hadri Hussain, Syed Rasul

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In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.

Keywords: electrocardiogram, dictionary learning, sparse coding, classification

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Authors: A. Mohamed Mydeen, Pallapa Venkataram

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The learning process needs to be so pervasive to impart the quality in acquiring the knowledge about a subject by making use of the advancement in the field of information and communication systems. However, pervasive learning paradigms designed so far are system automation types and they lack in factual pervasive realm. Providing factual pervasive realm requires subtle ways of teaching and learning with system intelligence. Augmentation of intelligence with pervasive learning necessitates the most efficient way of representing knowledge for the system in order to give the right learning material to the learner. This paper presents a method of representing knowledge for Pervasive Toolroom Maintenance System (PTMS) in which a learner acquires sublime knowledge about the various kinds of tools kept in the toolroom and also helps for effective maintenance of the toolroom. First, we explicate the generic model of knowledge representation for PTMS. Second, we expound the knowledge representation for specific cases of toolkits in PTMS. We have also presented the conceptual view of knowledge representation using ontology for both generic and specific cases. Third, we have devised the relations for pervasive knowledge in PTMS. Finally, events are identified in PTMS which are then linked with pervasive data of toolkits based on relation formulated. The experimental environment and case studies show the accuracy and efficient knowledge representation of toolkits in PTMS.

Keywords: knowledge representation, pervasive computing, agent technology, ECA rules

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Authors: Ashbeelah Shafaqat Ali

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Pakistan is a country where the transgender community has not been accepted as a third gender yet, but in recent years Pakistani drama industry has taken an initiative to include Transgender characters in the past few years. This research based on qualitative method i.e. content analysis and in-depth interviews investigates the depiction of transgender community in Pakistani television dramas. This study examined two dramas i.e.' Khuda Mera Bhi Hai' and 'Alif Allah Aur Insaan' to analyze the representation of transgender community whereas, in-depth Interviews from 15 transgender people lived in Lahore to observe their opinion regarding their representation in Pakistani television dramas. Snow-ball sampling technique was used for conducting interviews from the transgender community. The results concluded that transgender community did not get equal coverage in Pakistani television dramas but inclusion as characters were observed. This study is helpful in providing a base for observing role of Pakistani television dramas in the development of transgender identity. The major finding revealed is that the inclusion of representation of transgender community in Pakistani television dramas has indicated a successful development towards positive representation. Although, it was suggested by the interviewers that before producing a television drama, appropriate research must be conducted to depict the real life story, problems and struggles of the transgender community. Furthermore, it was analyzed that only fair and equal representation of transgender community by Pakistani drama industry can be beneficial in promoting the third gender rights in the society.

Keywords: Pakistani dramas, portrayal, stereotypes, transgender

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Authors: M. S. Jimah, A. C. Achuenu, M. Momodu

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Group communications over public infrastructure are prone to a lot of security issues. Existing network protocols like Protocol Independent Multicast Sparse Mode (PIM SM) and Protocol Independent Multicast Dense Mode (PIM DM) do not have inbuilt security features. Therefore, any user or node can easily access the group communication as long as the user can send join message to the source nodes, the source node then adds the user to the network group. In this research, a hybrid method of salting and hashing to encrypt information in the source and stub node was designed, and when stub nodes need to connect, they must have the appropriate key to join the group network. Object oriented analysis design (OOAD) was the methodology used, and the result shows that no extra controlled bandwidth overhead cost was added by encrypting and the hybrid model was more securing than the existing PIM SM, PIM DM and Zhang secure PIM SM.

Keywords: group communications, multicast, PIM SM, PIM DM, encryption

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Authors: Tianyu Chen

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China's school district education policy has encouraged parents to purchase properties in school districts with high-quality education resources. Shanghai has implemented "Simultaneous Admission to Public and Private Schools" (SAPPS) since 2018, which has covered all nine-year compulsory education by 2020. This study examines the impact of SAPPS on the housing market, specifically the premium effect of houses located in dual-school districts. Based on the Hedonic Pricing Model and the Signaling Theory, data is collected from 585 second-hand house transactions in Pudong New Area, Shanghai, and it is analyzed with the Difference-in-Differences (DID) model. The results indicate that the implementation of SAPPS has exacerbated the premium of dual school district housing and weakened the effect of the policy to a certain degree. To ensure equal access to education for all students, the government should work both on the supply and demand sides of the education resource equation.

Keywords: simultaneous admission to public and private schools, housing prices, education policy, education equity

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Authors: Archana Rao R., Deepak P., Chayashree P. D., Darshan H. S.

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Cognitive abilities in bilinguals have been widely studied over the last few decades. Bilingualism has been found to extensively facilitate the ability to store and manipulate information in Working Memory (WM). The mechanism of WM includes primary memory, attentional control, and secondary memory, each of which makes a contribution to WM. Many researches have been done in an attempt to measure WM capabilities through both verbal (phonological) and nonverbal tasks (visuospatial). Since there is a lot of speculations regarding the relationship between WM and bilingualism, further investigation is required to understand the nature of WM in bilinguals, i.e., with respect to sequential and simultaneous bilinguals. Hence the present study aimed to highlight the verbal working memory abilities in sequential and simultaneous bilinguals with respect to the processing and recall abilities of nouns and verbs. Two groups of bilinguals aged between 18-30 years were considered for the study. Group 1 consisted of 20 (10 males and 10 females) sequential bilinguals who had acquired L1 (Kannada) before the age of 3 and had exposure to L2 (English) for a period of 8-10 years. Group 2 consisted of 20 (10 males and 10 females) simultaneous bilinguals who have acquired both L1 and L2 before the age of 3. Working memory abilities were assessed using two tasks, and a set of stimuli which was presented in gradation of complexity and the stimuli was inclusive of frequent and infrequent nouns and verbs. The tasks involved the participants to judge the correctness of the sentence and simultaneously remember the last word of each sentence and the participants are instructed to recall the words at the end of each set. The results indicated no significant difference between sequential and simultaneous bilinguals in processing the nouns and verbs, and this could be attributed to the proficiency level of the participants in L1 and the alike cognitive abilities between the groups. And recall of nouns was better compared to verbs, maybe because of the complex argument structure involved in verbs. Similarly, authors found a frequency of occurrence of nouns and verbs also had an effect on WM abilities. The difference was also found across gradation due to the load imposed on the central executive function and phonological loop.

Keywords: bilinguals, nouns, verbs, working memory

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Authors: Vahid Ebrahimipour

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Word representation and context meaning of text-based documents play an essential role in knowledge modeling. Business procedures written in natural language are meant to store technical and engineering information, management decision and operation experience during the production system life cycle. Context meaning representation is highly dependent upon word sense, lexical relativity, and sematic features of the argument. This paper proposes a method for lexical semantic analysis and context meaning representation of maintenance activity in a mass production system. Our approach constructs a straightforward lexical semantic approach to analyze facilitates semantic and syntactic features of context structure of maintenance report to facilitate translation, interpretation, and conversion of human-readable interpretation into computer-readable representation and understandable with less heterogeneity and ambiguity. The methodology will enable users to obtain a representation format that maximizes shareability and accessibility for multi-purpose usage. It provides a contextualized structure to obtain a generic context model that can be utilized during the system life cycle. At first, it employs a co-occurrence-based clustering framework to recognize a group of highly frequent contextual features that correspond to a maintenance report text. Then the keywords are identified for syntactic and semantic extraction analysis. The analysis exercises causality-driven logic of keywords’ senses to divulge the structural and meaning dependency relationships between the words in a context. The output is a word contextualized representation of maintenance activity accommodating computer-based representation and inference using OWL/RDF.

Keywords: lexical semantic analysis, metadata modeling, contextual meaning extraction, ontology modeling, knowledge representation

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Authors: Yu Liang, Wei Wei

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Spatial representation of numbers and numerical magnitude are important for preschoolers’ mathematical ability. Mental number line, a typical index to measure numbers spatial representation, and numerical comparison are both related to arithmetic obviously. However, they seem to rely on different mechanisms and probably influence arithmetic through different mechanisms. In line with this idea, preschool children were trained with two tasks to investigate which one is more important for approximate arithmetic. The training of numerical processing and number line estimation were proved to be effective. They both improved the ability of approximate arithmetic. When the difficulty of approximate arithmetic was taken into account, the performance in number line training group was not significantly different among three levels. However, two harder levels achieved significance in numerical comparison training group. Thus, comparing spatial representation ability, symbolic approximation arithmetic relies more on numerical magnitude. Educational implications of the study were discussed.

Keywords: approximate arithmetic, mental number line, numerical magnitude, preschooler

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Authors: Hyeongbok Kim, Lingling Zhao, Xiaohong Su

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Deep reinforcement learning has been applied to address various problems in robotics, such as autonomous driving and unmanned aerial vehicle. However, because of the sparse reward penalty for a collision with obstacles during the navigation mission, the agent fails to learn the optimal policy or requires a long time for convergence. Therefore, using obstacles and enemy agents, in this paper, we present a curriculum-based boost learning method to effectively train compound skills during multi-agent reinforcement learning. First, to enable the agents to solve challenging tasks, we gradually increased learning difficulties by adjusting reward shaping instead of constructing different learning environments. Then, in a benchmark environment with static obstacles and moving enemy agents, the experimental results showed that the proposed curriculum learning strategy enhanced cooperative navigation and compound collision avoidance skills in uncertain environments while improving learning efficiency.

Keywords: curriculum learning, hard exploration, multi-agent reinforcement learning, robotic navigation, sparse reward

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Authors: Youji Miyake

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In aircraft assembly, a large number of preparatory holes are required for screw and rivet joints. Currently, many holes are drilled manually because it is difficult to machine the holes using conventional computerized numerical control(CNC) machines. The application of industrial robots to drill the hole has been considered as an alternative to the CNC machines. However, the rigidity of robot arms is so low that vibration is likely to occur during drilling. In this study, it is proposed constant-load feed machining as a method to perform high-precision drilling while minimizing the thrust force, which is considered to be the cause of vibration. In this method, the drill feed is realized by a constant load applied onto the tool so that the thrust force is theoretically kept below the applied load. The performance of the proposed method was experimentally examined through the deep hole drilling of plastic and simultaneous drilling of metal/plastic stack plates. It was confirmed that the deep hole drilling and simultaneous drilling could be performed without generating vibration by controlling the tool feed rate in the appropriate range.

Keywords: constant load feed machining, robotic drilling, deep hole, simultaneous drilling

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Authors: Debajyoti Mukherjee, Arathy B. S., Arpita Sahu, Saranga P. Pogula

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A functional model to mimic the functional correctness of a neural network compute accelerator IP is very crucial for design validation. Neural network workloads are based on a Brain Floating Point (BF-16) data type. The major challenge we were facing was the incompatibility of GCC compilers to the BF-16 datatype, which we addressed with a native BF-16 generator integrated into our functional model. Moreover, working with big GEMM (General Matrix Multiplication) or SpMM (Sparse Matrix Multiplication) Work Loads (Dense or Sparse) and debugging the failures related to data integrity is highly painstaking. In this paper, we are addressing the quality challenge of such a complex neural network accelerator design by proposing a functional model-based scoreboard or software model using SystemC. The proposed functional model executes the assembly code based on the ISA of the processor IP, decodes all instructions, and executes as expected to be done by the DUT. The said model would give a lot of visibility and debug capability in the DUT, bringing up micro-steps of execution.

Keywords: ISA, neural network, Brain Float-16, DUT

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Authors: Sandra Young

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The biodiversity literature is vast and heterogeneous. In today’s data age, numbers of data integration and standardisation initiatives aim to facilitate simultaneous access to all the literature across biodiversity domains for research and forecasting purposes. Ontologies are being used increasingly to organise this information, but the rationalisation intrinsic to ontologies can hit obstacles when faced with the intrinsic fluidity and inconsistency found in the domains comprising biodiversity. Essentially the problem is a conceptual one: biological taxonomies are formed on the basis of specific, physical specimens yet nomenclatural rules are used to provide labels to describe these physical objects. These labels are ambiguous representations of the physical specimen. An example of this is with the genus Melpomene, the scientific nomenclatural representation of a genus of ferns, but also for a genus of spiders. The physical specimens for each of these are vastly different, but they have been assigned the same nomenclatural reference. While there is much research into the conceptual stability of the taxonomic concept versus the nomenclature used, to the best of our knowledge as yet no research has looked empirically at the literature to see the conceptual plurality or singularity of the use of these species’ names, the linguistic representation of a physical entity. Language itself uses words as symbols to represent real world concepts, whether physical entities or otherwise, and as such lexicography has a well-founded history in the conceptual mapping of words in context for dictionary making. This makes it an ideal candidate to explore this problem. The lexicographic approach uses corpus-based analysis to look at word use in context, with a specific focus on collocated word frequencies (the frequencies of words used in specific grammatical and collocational contexts). It allows for inconsistencies and contradictions in the source data and in fact includes these in the word characterisation so that 100% of the available evidence is counted. Corpus analysis is indeed suggested as one of the ways to identify concepts for ontology building, because of its ability to look empirically at data and show patterns in language usage, which can indicate conceptual ideas which go beyond words themselves. In this sense it could potentially be used to identify if the hierarchical structures present within the empirical body of literature match those which have been identified in ontologies created to represent them. The first stages of this research have revealed a hierarchical structure that becomes apparent in the biodiversity literature when annotating scientific species’ names, common names and more general names as classes, which will be the focus of this paper. The next step in the research is focusing on a larger corpus in which specific words can be analysed and then compared with existing ontological structures looking at the same material, to evaluate the methods by means of an alternative perspective. This research aims to provide evidence as to the validity of the current methods in knowledge representation for biological entities, and also shed light on the way that scientific nomenclature is used within the literature.

Keywords: ontology, biodiversity, lexicography, knowledge representation, corpus linguistics

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Authors: M. Hijazi, A. Zeki, A. Ismail

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An enormous amount of valuable human knowledge is preserved in documents. The rapid growth in the number of machine-readable documents for public or private access requires the use of automatic text classification. Text classification can be defined as assigning or structuring documents into a defined set of classes known in advance. Arabic text classification methods have emerged as a natural result of the existence of a massive amount of varied textual information written in the Arabic language on the web. This paper presents a review on the published researches of Arabic Text Classification using classical data representation, Bag of words (BoW), and using conceptual data representation based on semantic resources such as Arabic WordNet and Wikipedia.

Keywords: Arabic text classification, Arabic WordNet, bag of words, conceptual representation, semantic relations

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Authors: Mohsen Sanayei, Jerzy Szpunar, Sami Penttilä

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Exposure of Nickel-based superalloys to high temperature and harsh environment such as Super-Critical Water (SCW) environment leads to the formation of oxide scales composed of multiple and complex phases that are difficult to differentiate with conventional analysis techniques. In this study, we used simultaneous Electron Backscatter Diffraction (EBSD) and Energy Dispersive X-ray Spectroscopy (EDS) to analyze the complex oxide scales formed on several Nickel-based Superalloys exposed to high temperature SCW. Multi-layered structures of Iron, Nickel, Chromium and Molybdenum oxides and spinels were clearly identified using the simultaneous EBSD-EDS analysis technique. Furthermore, the orientation relationship between the oxide scales and the substrate has been investigated.

Keywords: electron backscatter diffraction, energy dispersive x-ray spectroscopy, superalloy, super-critical water

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Authors: Aviv Yaniv, Ron Ben Arosh, Nadav Gasner, Michael Konviser, Arbel Yaniv

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This paper introduces two methods for the evaluation of Question Decomposition Meaning Representation (QDMR) as predicted by sequence-to-sequence model and COPYNET parser for natural language questions processing, motivated by the fact that previous evaluation metrics used for this task do not take into account some characteristics of the representation, such as partial ordering structure. To this end, several heuristics to extract such partial dependencies are formulated, followed by the hereby proposed evaluation methods denoted as Proportional Graph Matcher (PGM) and Conversion to Normal String Representation (Nor-Str), designed to better capture the accuracy level of QDMR predictions. Experiments are conducted to demonstrate the efficacy of the proposed evaluation methods and show the added value suggested by one of them- the Nor-Str, for better distinguishing between high and low-quality QDMR when predicted by models such as COPYNET. This work represents an important step forward in the development of better evaluation methods for QDMR predictions, which will be critical for improving the accuracy and reliability of natural language question-answering systems.

Keywords: NLP, question answering, question decomposition meaning representation, QDMR evaluation metrics

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Authors: Yadpiroon Onmek, Jean Triboulet, Sebastien Druon, Bruno Jouvencel

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The objective of this paper is to develop the 3D underwater reconstruction of archaeology object, which is based on the fusion between a sonar system and stereo camera system. The underwater images are obtained from a calibrated camera system. The multiples image pairs are input, and we first solve the problem of image processing by applying the well-known filter, therefore to improve the quality of underwater images. The features of interest between image pairs are selected by well-known methods: a FAST detector and FLANN descriptor. Subsequently, the RANSAC method is applied to reject outlier points. The putative inliers are matched by triangulation to produce the local sparse point clouds in 3D space, using a pinhole camera model and Euclidean distance estimation. The SFM technique is used to carry out the global sparse point clouds. Finally, the ICP method is used to fusion the sonar information with the stereo model. The final 3D models have a précised by measurement comparing with the real object.

Keywords: 3D reconstruction, archaeology, fusion, stereo system, sonar system, underwater

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Authors: Abdulrahman R. Alenezi

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This research investigates the effects of facility disruptions on-demand allocation within the context of the Reliable Facility Location Problem (RFLP). We explore two distinct scenarios: one where primary and backup facilities can fail simultaneously and another where such simultaneous failures are not possible. The RFLP model is tailored to reflect these scenarios, incorporating different approaches to transportation cost calculations. Utilizing a Lagrange relaxation method, the model achieves high efficiency, yielding an average optimality gap of 0.1% within 12.2 seconds of CPU time. Findings indicate that primary facilities are typically sited closer to demand points than backup facilities. In cases where simultaneous failures are prohibited, demand points are predominantly assigned to the nearest available facility. Conversely, in scenarios permitting simultaneous failures, demand allocation may prioritize factors beyond mere proximity, such as failure rates. This study highlights the critical influence of facility reliability on strategic location decisions, providing insights for enhancing resilience in supply chain networks.

Keywords: reliable supply chain network, facility location problem, reliable facility location model, LaGrange relaxation

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Authors: Subham Ghosh, Arnab Nandi

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Background: The proliferation of Wireless Body Area Networks (WBAN) and Internet of Things (IoT) applications demonstrates the potential for continuous monitoring of physical changes in the body. These technologies are vital for health monitoring tasks, such as identifying coughing and breathing activities, which are necessary for disease diagnosis and management. Monitoring activities such as coughing and deep breathing can provide valuable insights into a variety of medical issues. Wearable radio-based antenna sensors, which are lightweight and easy to incorporate into clothing or portable goods, provide continuous monitoring. This mobility gives it a substantial advantage over stationary environmental sensors like as cameras and radar, which are constrained to certain places. Furthermore, using compressive techniques provides benefits such as reduced data transmission speeds and memory needs. These wearable sensors offer more advanced and diverse health monitoring capabilities. Methodology: This study analyzes the feasibility of using a semi-flexible antenna operating at 2.4 GHz (ISM band) and positioned around the neck and near the mouth to identify three activities: coughing, deep breathing, and idleness. Vector network analyzer (VNA) is used to collect time-varying complex reflection coefficient data from perturbed antenna nearfield. The reflection coefficient (S11) conveys nuanced information caused by simultaneous variations in the nearfield radiation of three activities across time. The signatures are sparsely represented with gaussian windowed Gabor spectrograms. The Gabor spectrogram is used as a sparse representation approach, which reassigns the ridges of the spectrogram images to improve their resolution and focus on essential components. The antenna is biocompatible in terms of specific absorption rate (SAR). The sparsely represented Gabor spectrogram pictures are fed into a lightweight deep learning (DL) model for feature extraction and classification. Two antenna locations are investigated in order to determine the most effective localization for three different activities. Findings: Cross-validation techniques were used on data from both locations. Due to the complex form of the recorded S11, separate analyzes and assessments were performed on the magnitude, phase, and their combination. The combination of magnitude and phase fared better than the separate analyses. Various sliding window sizes, ranging from 1 to 5 seconds, were tested to find the best window for activity classification. It was discovered that a neck-mounted design was effective at detecting the three unique behaviors.

Keywords: activity recognition, antenna, deep-learning, time-frequency

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Authors: Aamir Ayub, Yasir Shehzad, Shakeel Ahmad

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This research paper aims to understand how the Pashtuns have been represented in relationship to terrorism in post-9/11 Bollywood and Lollywood movies. It focuses particularly on ‘Torbaaz’ from Bollywood and ‘Waar’ from Lollywood in order to define the nature of Pashtun characterization, the functioning of intelligence agencies, as well as the socio-political side of the represented narratives. In this research, the analytical approach developed is applied to contemplate how these films represent or fail to represent Pashtun identity, taking into consideration the cultural, historical and social dimensions. The study also aims to examine the effects of the media, particularly on the different ethnic groups’ perceptions of terrorism. In this case, it covers how the movie relates actual events in society – specifically, socio-political – to the messages in the film regarding the Pashtun people and their portrayal. Such elements may constitute the portrayal of intelligence agencies and their fight against terrorism, state-security dynamics, and the Pashtun society. In conclusion, this research paper focuses on the representation of Pashtuns in films after 9/11 and addresses the issue concerning the representation of ethnic groups in the method of the theme of terrorism. It provides ideas about the role of media in influencing the mind of the society and their attitude towards certain communities after geopolitics upheavals.

Keywords: pashtun representation, terrorism, 9/11 attacks, socio-political implications, ethnic representation in media

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Authors: Hebert Montegranario, Mauricio Londoño

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Solutions of Helmholtz equation are essential in seismic imaging methods like full wave inversion, which needs to solve many times the wave equation. Traditional methods like Finite Element Method (FEM) or Finite Differences (FD) have sparse matrices but may suffer the so called pollution effect in the numerical solutions of Helmholtz equation for large values of the wave number. On the other side, global radial basis functions have a better accuracy but produce full matrices that become unstable. In this research we combine the virtues of both approaches to find numerical solutions of Helmholtz equation, by applying a meshless method that produce sparse matrices by local radial basis functions. We solve the equation with absorbing boundary conditions of the kind Clayton-Enquist and PML (Perfect Matched Layers) and compared with results in standard literature, showing a promising performance by tackling both the pollution effect and matrix instability.

Keywords: Helmholtz equation, meshless methods, seismic imaging, wavefield inversion

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Authors: Niloofar Fathizaviyehkord

Abstract:

Just as two hands cannot make a good boxer, knowing two or more languages cannot make a skillful interpreter. Interpreting, either consecutive or simultaneous, is a cognitively demanding task requiring not only linguistic and discourse knowledge but also strategic competence. Moreover, experience level can play a very crucial role in the strategies interpreters may employ since translation and interpretation quality is a matter of experience, besides other factors, as well. With regard to the significance of strategic competence, this study investigated what strategies are mainly employed by interpreters, what strategies are employed more frequently, and whether experience level can affect the choice of strategies by interpreters or not. To collect the necessary data, the first retrospective interviews were held with 20 interpreters experienced more or less in simultaneous and consecutive interpretation to see what strategies other than those classified in the literature are employed by interpreters. Then, several classifications of strategies in literature were merged with those emerging from the retrospective interviews to come up with a comprehensive questionnaire on interpreting strategies. After seeking five experts’ opinions regarding the wording/content of the questionnaire, it was given to 60 interpreters. The statistical analysis of the questionnaire data and experience level through ANOVA showed experience level could affect the choice of strategies. This study closes with the theoretical/practical implications of the findings for interpreter training.

Keywords: experience level, consecutive and simultaneous, interpretation strategies, translation

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Authors: Sara El-Hanboushy, Hayam Lotfy, Yasmin Fayez, Engy Shokry, Mohammed Abdelkawy

Abstract:

Simple, specific, accurate and precise spectrophotometric methods are developed and validated for simultaneous determination of pseudoephedrine sulphate (PSE) and loratadine (LOR) in combined dosage form based on spectral analysis technique. Pseudoephedrine (PSE) in binary mixture could be analyzed either by using its resolved zero order absorption spectrum at its λ max 256.8 nm after subtraction of LOR spectrum or in presence of LOR spectrum by absorption correction method at 256.8 nm, dual wavelength (DWL) method at 254nm and 273nm, induced dual wavelength (IDWL) method at 256nm and 272nm and ratio difference (RD) method at 256nm and 262 nm. Loratadine (LOR) in the mixture could be analyzed directly at 280nm without any interference of PSE spectrum or at 250 nm using its recovered zero order absorption spectrum using constant multiplication(CM).In addition, simultaneous determination for PSE and LOR in their mixture could be applied by induced amplitude modulation method (IAM) coupled with amplitude multiplication (PM).

Keywords: dual wavelength (DW), induced amplitude modulation method (IAM) coupled with amplitude multiplication (PM), loratadine, pseudoephedrine sulphate, ratio difference (RD)

Procedia PDF Downloads 323