Search results for: space detection to first responders

Authors: Refilwe Moeletsi

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Dimension stone quarrying around Brits and Belfast areas started in the early 1930s and has been growing rapidly since then. Environmental impacts associated with these quarries have not been documented, and hence this study aims at detecting any change in the environment that might have been caused by these activities. Landsat images that were used to assess land use/land cover changes in Brits quarries from 1998 - 2015. A supervised classification using maximum likelihood classifier was applied to classify each image into different land use/land cover types. Classification accuracy was assessed using Google Earth™ as a source of reference data. Post-classification change detection method was used to determine changes. The results revealed significant increase in granite quarries and corresponding decrease in vegetation cover within the study region.

Keywords: remote sensing, GIS, change detection, granite quarries

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Authors: Solaine Hachem, Frédéric Bourquin, Dominique Siegert

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The brutal collapse of bridges is mainly due to scour. Indeed, the soil erosion in the riverbed around a pier modifies the embedding conditions of the structure, reduces its overall stiffness and threatens its stability. Hence, finding an efficient technique that allows early scour detection becomes mandatory. Vibration analysis is an indirect method for scour detection that relies on real-time monitoring of the bridge. It tends to indicate the presence of a scour based on its consequences on the stability of the structure and its dynamic response. Most of the research in this field has focused on the dynamic behavior of a single pile and has examined the depth of the scour. In this paper, a bridge is fully modeled with all piles and spans and the scour is represented by a reduction in the foundation's stiffnesses. This work aims to identify the vibration modes sensitive to the rigidity’s loss in the foundations so that their variations can be considered as a scour indicator: the decrease in soil-structure interaction rigidity leads to a decrease in the natural frequencies’ values. By using the first-order perturbation method, the expression of sensitivity, which depends only on the selected vibration modes, is established to determine the deficiency of foundations stiffnesses. The solutions are obtained by using the singular value decomposition method for the regularization of the inverse problem. The propagation of uncertainties is also calculated to verify the efficiency of the inverse problem method. Numerical simulations describing different scenarios of scour are investigated on a simplified model of a real composite steel-concrete bridge located in France. The results of the modal analysis show that the modes corresponding to in-plane and out-of-plane piers vibrations are sensitive to the loss of foundation stiffness. While the deck bending modes are not affected by this damage.

Keywords: bridge’s piers, inverse problems, modal sensitivity, scour detection, vibration analysis

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Authors: Malik Muhammad Arslan, Muneeb Ullah, Dai Shihan, Daniyal Haider, Xiaodong Yang

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Chest X-rays are instrumental in the detection and monitoring of a wide array of diseases, including viral infections such as COVID-19, tuberculosis, pneumonia, lung cancer, and various cardiac and pulmonary conditions. To enhance the accuracy of diagnosis, artificial intelligence (AI) algorithms, particularly deep learning models like Convolutional Neural Networks (CNNs), are employed. However, these deep learning models demand a substantial and varied dataset to attain optimal precision. Generative Adversarial Networks (GANs) can be employed to create new data, thereby supplementing the existing dataset and enhancing the accuracy of deep learning models. Nevertheless, GANs have their limitations, such as issues related to stability, convergence, and the ability to distinguish between authentic and fabricated data. In order to overcome these challenges and advance the detection and classification of CXR normal and abnormal images, this study introduces a distinctive technique known as DCWGAN (Diverse Conditional Wasserstein GAN) for generating synthetic chest X-ray (CXR) images. The study evaluates the effectiveness of this Idiosyncratic DCWGAN technique using the ResNet50 model and compares its results with those obtained using the traditional GAN approach. The findings reveal that the ResNet50 model trained on the DCWGAN-generated dataset outperformed the model trained on the classic GAN-generated dataset. Specifically, the ResNet50 model utilizing DCWGAN synthetic images achieved impressive performance metrics with an accuracy of 0.961, precision of 0.955, recall of 0.970, and F1-Measure of 0.963. These results indicate the promising potential for the early detection of diseases in CXR images using this Inimitable approach.

Keywords: CNN, classification, deep learning, GAN, Resnet50

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Authors: Marwa Ragab, Eman El-Kimary

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Chiral separation and analysis of omeprazole and naproxen enantiomers in tablets were achieved using high-performance liquid chromatographic method with diode array detection (HPLC-DAD). Kromasil Cellucoat chiral column was used as a stationary phase for separation and the eluting solvent consisted of hexane, isopropanol and trifluoroacetic acid in a ratio of: 90, 9.9 and 0.1, respectively. The chromatographic system was suitable for the enantiomeric separation and analysis of active isomers of the drugs. Resolution values of 2.17 and 3.84 were obtained after optimization of the chromatographic conditions for omeprazole and naproxen isomers, respectively. The determination of S-isomers of each drug in their dosage form was fully validated.

Keywords: chiral analysis, esomeprazole, S-Naproxen, HPLC-DAD

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Authors: S. Pramanik

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Since the early ages, the Hindu temples have been interpreted through various Vedic philosophies. These temples are visited by pilgrims which demonstrate the rituals and religious belief of communities, reflecting a variety of actions and behaviors. Darsana— a direct seeing, is a part of the pilgrimage activity. During the process of Darsana, a devotee is prepared for entry in the temple to realize the cognizing Truth culminating in visualizing the idol of God, placed at the Garbhagriha (sanctum sanctorum). For this, the pilgrim must pass through a sequential arrangement of spaces. During the process of progress, the pilgrims visualize the spaces differently from various points of views. The viewpoints create a variety of spatial patterns in the minds of pilgrims coherent to the Hindu philosophies. The space organization and its order are perceived by various techniques of spatial analysis. A temple, as examples of Kalinga stylistic variations, has been chosen for the study. This paper intends to demonstrate some visual patterns generated during the process of Darsana (visibility) and its accessibility by Point Isovist Studies and Visibility Graph Analysis from the entrance (Simha Dwara) to The Sanctum sanctorum (Garbhagriha).

Keywords: Hindu temple architecture, point isovist, space syntax analysis, visibility graph analysis

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Authors: Ankur Dixit, Hiroaki Wagatsuma

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The crack detection on a concrete bridge is an important and constant task in civil engineering. Chronically, humans are checking the bridge for inspection of cracks to maintain the quality and reliability of bridge. But this process is very long and costly. To overcome such limitations, we have used a drone with a digital camera, which took some images of bridge deck and these images are processed by morphological component analysis (MCA). MCA technique is a very strong application of sparse coding and it explores the possibility of separation of images. In this paper, MCA has been used to decompose the image into coarse and fine components with the effectiveness of two dictionaries namely anisotropic diffusion and wavelet transform. An anisotropic diffusion is an adaptive smoothing process used to adjust diffusion coefficient by finding gray level and gradient as features. These cracks in image are enhanced by subtracting the diffused coarse image into the original image and the results are treated by Sobel edge detector and binary filtering to exhibit the cracks in a fine way. Our results demonstrated that proposed MCA framework using anisotropic diffusion followed by Sobel operator and binary filtering may contribute to an automation of crack detection even in open field sever conditions such as bridge decks.

Keywords: anisotropic diffusion, coarse component, fine component, MCA, Sobel edge detector and wavelet transform

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Authors: Haiyang Su, Kun Qian

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We developed a novel plasmonic matrix assisted laser desorption/ionization mass spectrometry (MALDI MS) approach to detect small nutrients and toxin in complex biological emulsion samples. We used silver nanoshells (SiO₂@Ag) with optimized structures as matrices and achieved direct analysis of ~6 nL of human breast milk without any enrichment or separation. We performed identification and quantitation of small nutrients and toxins with limit-of-detection down to 0.4 pmol (for melamine) and reaction time shortened to minutes, superior to the conventional biochemical methods currently in use. Our approach contributed to the near-future application of MALDI MS in a broad field and personalized design of plasmonic materials for real case bio-analysis.

Keywords: plasmonic materials, laser desorption/ionization, mass spectrometry, small nutrients, toxins

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Authors: Lina Wu, Jia Liu, Ye Li

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The goal of this project is to investigate constant properties (called the Liouville-type Problem) for a p-stable map as a local or global minimum of a p-energy functional where the domain is a Euclidean space and the target space is a closed half-ellipsoid. The First and Second Variation Formulas for a p-energy functional has been applied in the Calculus Variation Method as computation techniques. Stokes’ Theorem, Cauchy-Schwarz Inequality, Hardy-Sobolev type Inequalities, and the Bochner Formula as estimation techniques have been used to estimate the lower bound and the upper bound of the derived p-Harmonic Stability Inequality. One challenging point in this project is to construct a family of variation maps such that the images of variation maps must be guaranteed in a closed half-ellipsoid. The other challenging point is to find a contradiction between the lower bound and the upper bound in an analysis of p-Harmonic Stability Inequality when a p-energy minimizing map is not constant. Therefore, the possibility of a non-constant p-energy minimizing map has been ruled out and the constant property for a p-energy minimizing map has been obtained. Our research finding is to explore the constant property for a p-stable map from a Euclidean space into a closed half-ellipsoid in a certain range of p. The certain range of p is determined by the dimension values of a Euclidean space (the domain) and an ellipsoid (the target space). The certain range of p is also bounded by the curvature values on an ellipsoid (that is, the ratio of the longest axis to the shortest axis). Regarding Liouville-type results for a p-stable map, our research finding on an ellipsoid is a generalization of mathematicians’ results on a sphere. Our result is also an extension of mathematicians’ Liouville-type results from a special ellipsoid with only one parameter to any ellipsoid with (n+1) parameters in the general setting.

Keywords: Bochner formula, Calculus Stokes' Theorem, Cauchy-Schwarz Inequality, first and second variation formulas, Liouville-type problem, p-harmonic map

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Authors: Naghmeh Moradpoor Sheykhkanloo

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Structured Query Language Injection (SQLI) attack is a code injection technique in which malicious SQL statements are inserted into a given SQL database by simply using a web browser. Losing data, disclosing confidential information or even changing the value of data are the severe damages that SQLI attack can cause on a given database. SQLI attack has also been rated as the number-one attack among top ten web application threats on Open Web Application Security Project (OWASP). OWASP is an open community dedicated to enabling organisations to consider, develop, obtain, function, and preserve applications that can be trusted. In this paper, we propose an effective pattern recognition neural network model for detection and classification of SQLI attacks. The proposed model is built from three main elements of: a Uniform Resource Locator (URL) generator in order to generate thousands of malicious and benign URLs, a URL classifier in order to: 1) classify each generated URL to either a benign URL or a malicious URL and 2) classify the malicious URLs into different SQLI attack categories, and an NN model in order to: 1) detect either a given URL is a malicious URL or a benign URL and 2) identify the type of SQLI attack for each malicious URL. The model is first trained and then evaluated by employing thousands of benign and malicious URLs. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed approach.

Keywords: neural networks, pattern recognition, SQL injection attacks, SQL injection attack classification, SQL injection attack detection

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Authors: N. K. Caecar Pratiwi, Yunendah Nur Fuadah, Rita Magdalena, R. D. Atmaja, Sofia Saidah, Ocky Tiaramukti

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Colon cancer or colorectal cancer is a type of cancer that attacks the last part of the human digestive system. Lymphoma and carcinoma are types of cancer that attack human’s colon. Colon cancer causes deaths about half a million people every year. In Indonesia, colon cancer is the third largest cancer case for women and second in men. Unhealthy lifestyles such as minimum consumption of fiber, rarely exercising and lack of awareness for early detection are factors that cause high cases of colon cancer. The aim of this project is to produce a system that can detect and classify images into type of colon cancer lymphoma, carcinoma, or normal. The designed system used 198 data colon cancer tissue pathology, consist of 66 images for Lymphoma cancer, 66 images for carcinoma cancer and 66 for normal / healthy colon condition. This system will classify colon cancer starting from image preprocessing, feature extraction using Principal Component Analysis (PCA) and classification using K-Nearest Neighbor (K-NN) method. Several stages in preprocessing are resize, convert RGB image to grayscale, edge detection and last, histogram equalization. Tests will be done by trying some K-NN input parameter setting. The result of this project is an image processing system that can detect and classify the type of colon cancer with high accuracy and low computation time.

Keywords: carcinoma, colorectal cancer, k-nearest neighbor, lymphoma, principle component analysis

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Authors: Mouhammad Alkasassbeh, Mohammad Almseidin

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Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.

Keywords: IDS, DDoS, MLP, KDD

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Authors: David Jurado, Carlos Ávila

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Detection of early signs of breast cancer development is crucial to quickly diagnose the disease and to define adequate treatment to increase the survival probability of the patient. Computer Aided Detection systems (CADs), along with modern data techniques such as Machine Learning (ML) and Neural Networks (NN), have shown an overall improvement in digital mammography cancer diagnosis, reducing the false positive and false negative rates becoming important tools for the diagnostic evaluations performed by specialized radiologists. However, ML and NN-based algorithms rely on datasets that might bring issues to the segmentation tasks. In the present work, an automatic segmentation and detection algorithm is described. This algorithm uses image processing techniques along with the Hough transform to automatically identify microcalcifications that are highly correlated with breast cancer development in the early stages. Along with image processing, automatic segmentation of high-contrast objects is done using edge extraction and circle Hough transform. This provides the geometrical features needed for an automatic mask design which extracts statistical features of the regions of interest. The results shown in this study prove the potential of this tool for further diagnostics and classification of mammographic images due to the low sensitivity to noisy images and low contrast mammographies.

Keywords: breast cancer, segmentation, X-ray imaging, hough transform, image analysis

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Authors: Mohamad Mohsen Homayouni, Niloofar Taghipour, Ahmad Reza Memar, Niloofar Khalaji, Hamed Kiani, Seyyed Javad Seyyed Tabaei

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Background and Objective: Cryptosporidium is a coccidian protozoan parasite; its oocysts in surface water are a global health problem. Due to the low number of parasites in the water resources and the lack of laboratory culture, rapid and sensitive method for detection of the organism in the water resources is necessarily required. We applied modified acid-fast staining and PCR for the detection of the Cryptosporidium spp. and analysed the genotypes in 55 samples collected from surface water. Methods: Over a period of nine months, 55 surface water samples were collected from the five rivers in Tehran, Iran. The samples were filtered by using cellulose acetate membrane filters. By acid fast method, initial identification of Cryptosporidium oocyst were carried out on surface water samples. Then, nested PCR assay was designed for the specific amplification and analysed the genotypes. Results: Modified Ziehl-Neelsen method revealed 5–20 Cryptosporidium oocysts detected per 10 Liter. Five out of the 55 (9.09%) surface water samples were found positive for Cryptosporidium spp. by Ziehl-Neelsen test and seven (12.7%) were found positive by nested PCR. The staining results were consistent with PCR. Seven Cryptosporidium PCR products were successfully sequenced and five gp60 subtypes were detected. Our finding of gp60 gene revealed that all of the positive isolates were Cryptosporidium parvum and belonged to subtype families IIa and IId. Conclusion: Our investigations were showed that collection of water samples were contaminated by Cryptosporidium, with potential hazards for the significant health problem. This study provides the first report on detection and genotyping of Cryptosporidium species from surface water samples in Iran, and its result confirmed the low clinical incidence of this parasite on the community.

Keywords: Cryptosporidium spp., membrane filtration, subtype, surface water, Iran

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Authors: Darshna Sharma, Suban K. Sahoo

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The detection of intracellular fluoride in human cancer cell HeLa was achieved by chemosensors derived from vitamin B6 cofactors using fluorescence imaging technique. These sensors were first synthesized by condensation of pyridoxal/pyridoxal phosphate with 2-amino(thio)phenol. The anion recognition ability was explored by experimental (UV-VIS, fluorescence and 1H NMR) and theoretical DFT [(B3LYP/6-31G(d,p)] methods in DMSO and mixed DMSO-H2O system. All the developed sensors showed both naked-eye detectable color change and remarkable fluorescence enhancement in the presence of F- and AcO-. The anion recognition was occurred through the formation of hydrogen bonded complexes between these anions and sensor, followed by the partial deprotonation of sensor. The detection limit of these sensors were down to micro(nano) molar level of F- and AcO-.

Keywords: chemosensors, fluoride, acetate, turn-on, live cells imaging, DFT

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Authors: Abdulwahab Alalyani, Mahbub Rashid

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Urban Open Space (UOS) a central role to promotes community health, including daily activities, but these resources may not available, accessible enough, and or equitably be distributed. This paper measures and compares spatial equity of the availability and accessibility UOS among low, middle, and high-income neighborhoods in Riyadh city. The measurement mothdulgy for the UOSavailability was by calculating the total of UOS with respect to the population total (m2/inhabitant) and the accessibility indicted by using walking distance of a 0.25 mi (0.4 km) buffering streets network.All UOS were mapped and measured using geographical information systems. To evaluate the significant differences in UOS availability and accessibility across low, medium, and high-income Riyadh neighborhoods, we used a One-way ANOVA analysis of covariance to test the differences.The findings are as follows; finding, UOSavailability was lower than global standers. Riyadh has only 1.13 m2 per capita of UOS, and the coverage accessible area by walking distance to UOS was lower than 50%. The final finding, spatial equity of the availability and accessibility, were significantly different among Riyadh neighborhoods based on socioeconomic status. The future development of UOS should be focused on increasing Urban park availability and should be given priority to those low-income and unhealthy communities.

Keywords: distribution urban open space, urban open space accessibility, spatial equity, riyadh city

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Authors: Sabrina Howard

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'Window Seat' uses public transportation as an entry point for understanding the relationship between public space, politics, and social identity construction. This project argues that by bringing people of different races, classes, and genders in 'contact' with one another, public transit operates as a site of exposure, as people consciously and unconsciously perform social identity within these spaces. These performances offer a form of freedom that we associate with being in urban spaces while simultaneously rendering certain racialized, gendered, and classed bodies vulnerable to violence. Furthermore, due to its exposing function, public transit operates as a site through which we, as urbanites and scholars, can read social injustice and reflect on the work that is necessary to become a truly democratic society. The major questions guiding this research are: How does using public transit as the entry point provide unique insights into the relationship between social identity, politics, and public space? What ideas do Americans hold about public space and how might these ideas reflect a liberal yearning for a more democratic society? To address these research questions, 'Window Seat' critically examines ethnographic data collected on public buses and trains in Los Angeles, California, and online news media. It analyzes these sources through literature in socio-cultural psychology, sociology, and political science. It investigates the 'everyday urban hero' narrative or popular news stories that feature an individual or group of people acting against discriminatory or 'Anti-American' behavior on public buses and trains. 'Window Seat' studies these narratives to assert that by circulating stories of civility in news media, United Statsians construct and maintain ideas of the 'liberal city,' which is characterized by ideals of freedom and democracy. Furthermore, for those involved, these moments create an opportunity to perform the role of the Good Samaritan, an identity that is wrapped up in liberal beliefs in diversity and inclusion. This research expands conversations in urban studies by making a case for the political significance of urban public space. It demonstrates how these sites serve as spaces through which liberal beliefs are circulated and upheld through identity performance.

Keywords: social identity, public space, public transportation, liberalism

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Authors: Chonlakorn Wiboonsiriruk, Ekachai Phaisangittisagul, Chadchai Srisurangkul, Itsuo Kumazawa

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Public transportation is a crucial aspect of passenger transportation, with buses playing a vital role in the transportation service. Passenger counting is an essential tool for organizing and managing transportation services. However, manual counting is a tedious and time-consuming task, which is why computer vision algorithms are being utilized to make the process more efficient. In this study, different object detection algorithms combined with passenger tracking are investigated to compare passenger counting performance. The system employs the EfficientDet algorithm, which has demonstrated superior performance in terms of speed and accuracy. Our results show that the proposed system can accurately count passengers in varying conditions with an accuracy of 94%.

Keywords: computer vision, object detection, passenger counting, public transportation

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Authors: Lixin Tian, Wei Xue

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Many jobs in society go underground, such as mine mining, tunnel construction and subways, which are vital to the development of society. Once accidents occur in these places, the interruption of traditional wired communication is not conducive to the development of rescue work. In order to realize the positioning, early warning and command functions of underground personnel and improve rescue efficiency, it is necessary to develop and design an emergency ground communication system. It is easy to be subjected to narrowband interference when performing conventional underground communication. Spreading communication can be used for this problem. However, general spread spectrum methods such as direct spread communication are inefficient, so it is proposed to use parallel combined spread spectrum (PCSS) communication to improve efficiency. The PCSS communication not only has the anti-interference ability and the good concealment of the traditional spread spectrum system, but also has a relatively high frequency band utilization rate and a strong information transmission capability. So, this technology has been widely used in practice. This paper presents a PCSS communication model-multiple detection parallel combined spread spectrum (MDPCSS) communication system. In this paper, the principle of MDPCSS communication system is described, that is, the sequence at the transmitting end is processed in blocks and cyclically shifted to facilitate multiple detection at the receiving end. The block diagrams of the transmitter and receiver of the MDPCSS communication system are introduced. At the same time, the calculation formula of the system bit error rate (BER) is introduced, and the simulation and analysis of the BER of the system are completed. By comparing with the common parallel PCSS communication, we can draw a conclusion that it is indeed possible to reduce the BER and improve the system performance. Furthermore, the influence of different pseudo-code lengths selected on the system BER is simulated and analyzed, and the conclusion is that the larger the pseudo-code length is, the smaller the system error rate is.

Keywords: cyclic shift, multiple detection, parallel combined spread spectrum, PN code

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Authors: A. Benyahia, M. Zergoug, M. Amir, M. Fodil

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The main objective of this work is to enhance the Pulsed Eddy Current (PEC) response from the aluminum structure using signal processing. Cracks and metal loss in different structures cause changes in PEC response measurements. In this paper, time-frequency analysis is used to represent PEC response, which generates a large quantity of data and reduce the noise due to measurement. Power Spectral Density (PSD) after Wavelet Decomposition (PSD-WD) is proposed for defect detection. The experimental results demonstrate that the cracks in the surface can be extracted satisfactorily by the proposed methods. The validity of the proposed method is discussed.

Keywords: DT, pulsed eddy current, continuous wavelet transform, Mexican hat wavelet mother, defect detection, power spectral density.

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Authors: Amjad Abdullah, Amjad Yahya, Bushra Aljohani, Amani Alghamdi

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Change point detection is an important part of data analysis. The presence of a change point refers to a significant change in the behavior of a time series. In this article, we examine the detection of multiple change points of parameters of the exponential Lomax distribution, which is broad and flexible compared with other distributions while fitting data. We used the Schwarz information criterion and binary segmentation to detect multiple change points in publicly available data on the average temperature in the ozone layer. The change points were successfully located.

Keywords: binary segmentation, change point, exponentialLomax distribution, information criterion

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Authors: Christa L. Arnold, Margaret C. Stewart

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This research shares the results of an exploratory study examining Statement Analysis (SA) to detect deception in online truthful and untruthful social media posts. Applying a Law Enforcement methodology SA, used in criminal interview statements, this research analyzes what is stated to assist in evaluating written deceptive information. Preliminary findings reveal qualitative and quantitative nuances for SA in online deception detection and uncover insights regarding digital deceptive behavior. Thus far, findings reveal truthful statements tend to differ from untruthful statements in both content and quality.

Keywords: deception detection, online deception, social media content, statement analysis

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Authors: Zheni S. Stefanova, Kandethody M. Ramachandran

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With the increasing dependency on our computer devices, we face the necessity of adequate, efficient and effective mechanisms, for protecting our network. There are two main problems that Intrusion Detection Systems (IDS) attempt to solve. 1) To detect the attack, by analyzing the incoming traffic and inspect the network (intrusion detection). 2) To produce a prompt response when the attack occurs (intrusion prevention). It is critical creating an Intrusion detection model that will detect a breach in the system on time and also challenging making it provide an automatic and with an acceptable delay response at every single stage of the monitoring process. We cannot afford to adopt security measures with a high exploiting computational power, and we are not able to accept a mechanism that will react with a delay. In this paper, we will propose an intrusion response mechanism that is based on artificial intelligence, and more precisely, reinforcement learning techniques (RLT). The RLT will help us to create a decision agent, who will control the process of interacting with the undetermined environment. The goal is to find an optimal policy, which will represent the intrusion response, therefore, to solve the Reinforcement learning problem, using a Q-learning approach. Our agent will produce an optimal immediate response, in the process of evaluating the network traffic.This Q-learning approach will establish the balance between exploration and exploitation and provide a unique, self-learning and strategic artificial intelligence response mechanism for IDS.

Keywords: cyber security, intrusion prevention, optimal policy, Q-learning

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Authors: Yuanda Ma, Zhiyong Zhang, Zailin Yang, Guanxixi Jiang

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Anisotropy is very common in underground media, such as rock, sand, and soil. Hence, the dynamic response of anisotropy medium under elastic waves is significantly different from the isotropic one. Moreover, underground heterogeneities and structures, such as pipelines, cylinders, or tunnels, are usually made by composite materials, leading to the anisotropy of these heterogeneities and structures. Both the anisotropy of the underground medium and the heterogeneities have an effect on the surface motion of the ground. Aiming at providing theoretical references for earthquake engineering and seismology, the surface motion of anisotropic half-space with a cylindrical anisotropic inclusion embedded under the SH wave is investigated in this work. Considering the anisotropy of the underground medium, the governing equation with three elastic parameters of SH wave propagation is introduced. Then, based on the complex function method and multipolar coordinates system, the governing equation in the complex plane is obtained. With the help of a pair of transformation, the governing equation is transformed into a standard form. By means of the same methods, the governing equation of SH wave propagation in the cylindrical inclusion with another three elastic parameters is normalized as well. Subsequently, the scattering wave in the half-space and the standing wave in the inclusion is deduced. Different incident wave angle and anisotropy are considered to obtain the reflected wave. Then the unknown coefficients in scattering wave and standing wave are solved by utilizing the continuous condition at the boundary of the inclusion. Through truncating finite terms of the scattering wave and standing wave, the equation of boundary conditions can be calculated by programs. After verifying the convergence and the precision of the calculation, the validity of the calculation is verified by degrading the model of the problem as well. Some parameters which influence the surface displacement of the half-space is considered: dimensionless wave number, dimensionless depth of the inclusion, anisotropic parameters, wave number ratio, shear modulus ratio. Finally, surface displacement amplitude of the half space with different parameters is calculated and discussed.

Keywords: anisotropy, complex function method, sh wave, surface displacement amplitude

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Authors: Camila Freire Barrios, Gonzalo Rivera Talavera

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The importance of public spaces has been shown for many years, and in different disciplines, with one example being their ability for developing a sustainable social environment, especially in mega cities like Lima. The aim of this study was to explore the process of space appropriation that occurs in the Peripheral Library of the district El Agustino in Lima, Peru. Space appropriation is a process by which people develop a link with a place within a specific sociocultural context. This process has been related to positive outcomes, such as: participation and in the development of compassionate behaviors with these places. To achieve the purpose of the research, a qualitative design was selected because this will allowed exploring in deep the process in an specific context. The study interviewed six adults, all of whom were deliberately chosen to have the longest residence time in the district and also utilized the library the most. In a complementary manner, two children and one adolescent were interviewed. Likewise, two observations were made on a weekday and weekend, and public documentation information was collected. As a result, five categories linked to this process were identified. It was found that the process of space appropriation begins with the needs of the people who arrive at the library, which provides benefits to these people by fulfilling them. Next in the process, through the construction of meanings, the library is then valued as a pleasant, productive, safe and regulated place; as a result, people become identified with the library. The identification generated is subsequently reflected in the level of participation that the person has in the library, which may go in a continuum from no participating at all to a more direct involvement in the library activities, as well as voluntary and altruistic work. Finally, this process leads to the library becoming part of the neighborhood. This study allows having a better understanding of how sociospatial processes work in a Latinamerican context and in cities like Lima, where the third of the country’s population lives. Also, Lima has grown in the past 50 years in a excessively way and with lack of planification. Therefore, these results brings new research questions and highlights the importance of learning how to design public spaces in order to promote these processes to develop.

Keywords: bond with the place, place identity, public spaces, space appropriation

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Authors: Nouf Alharbi

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The electrospray deposition technique became an important method that enables fragile, nonvolatile molecules to be deposited in situ in high vacuum environments. Furthermore, it is considered one of the ways to close the gap between basic surface science and molecular engineering, which represents a gradual change in the range of scientist research. Also, this paper talked about one of the most important techniques that have been developed and aimed for helping to further develop and characterize the electrospray by providing data collected using an image charge detection instrument. Image charge detection mass spectrometry (CDMS) is used to measure speed and charge distributions of the molecular ions. As well as, some data has been included using SIMION simulation to simulate the energies and masses of the molecular ions through the system in order to refine the mass-selection process.

Keywords: charge, deposition, electrospray, image, ions, molecules, SIMION

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Authors: Bo Wang

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The geometric processing of multi-source remote sensing data using control data of different scale and different accuracy is an important research direction of multi-platform system for earth observation. In the existing block bundle adjustment methods, as the controlling information in the adjustment system, the approach using single observation scale and precision is unable to screen out the control information and to give reasonable and effective corresponding weights, which reduces the convergence and adjustment reliability of the results. Referring to the relevant theory and technology of quotient space, in this project, several subjects are researched. Multi-layer quotient space of multi-geometric features is constructed to describe and filter control data. Normalized granularity merging mechanism of multi-layer control information is studied and based on the normalized scale factor, the strategy to optimize the weight selection of control data which is less relevant to the adjustment system can be realized. At the same time, geometric positioning experiment is conducted using multi-source remote sensing data, aerial images, and multiclass control data to verify the theoretical research results. This research is expected to break through the cliché of the single scale and single accuracy control data in the adjustment process and expand the theory and technology of photogrammetry. Thus the problem to process multi-source remote sensing data will be solved both theoretically and practically.

Keywords: multi-source image geometric process, high precision geometric positioning, quotient space of multi-geometric features, optimized weight selection

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Authors: Vinod Ponnagandla, Brenton McLaury, Siamack Shirazi

Abstract:

Sand production can lead to deposition of particles or erosion. Low production rates resulting in deposition can partially clog systems and cause under deposit corrosion. Commercially available nonintrusive acoustic sand detectors are attractive as they claim to detect sand production. Acoustic sand detectors are used during oil and gas production; however, operators often do not know the threshold detection limits of these devices. It is imperative to know the detection limits to appropriately plan for cleaning of separation equipment or examine risk of erosion. These monitors are based on detecting the acoustic signature of sand as the particles impact the pipe walls. The objective of this work is to determine threshold detection limits for acoustic sand monitors that are commercially available. The minimum threshold sand concentration that can be detected in a pipe are determined as a function of flowing gas and liquid velocities. A large scale flow loop with a 4-inch test section is utilized. Commercially available sand monitors (ClampOn and Roxar) are evaluated for different flow regimes, sand sizes and pipe orientation (vertical and horizontal). The manufacturers’ recommend that the monitors be placed on a bend to maximize the number of particle impacts, so results are shown for monitors placed at 45 and 90 degree positions in a bend. Acoustic sand monitors that clamp to the outside of pipe are passive and listen for solid particle impact noise. The threshold sand rate is calculated by eliminating the background noise created by the flow of gas and liquid in the pipe for various flow regimes that are generated in horizontal and vertical test sections. The average sand sizes examined are 150 and 300 microns. For stratified and bubbly flows the threshold sand rates are much higher than other flow regimes such as slug and annular flow regimes that are investigated. However, the background noise generated by slug flow regime is very high and cause a high uncertainty in detection limits. The threshold sand rates for annular flow and dry gas conditions are the lowest because of high gas velocities. The effects of monitor placement around elbows that are in vertical and horizontal pipes are also examined for 150 micron. The results show that the threshold sand rates that are detected in vertical orientation are generally lower for all various flow regimes that are investigated.

Keywords: acoustic monitor, sand, multiphase flow, threshold

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Authors: Abdul Mannan Akhtar

Abstract:

In this paper a novel concept of a self learning smart personalized computer assistant (ACPCA) is established which is a context aware system. Based on user habits, moods, and other routines/situational reactions the system will manage various services and suggestions at appropriate times including what schedule to follow, what to watch, what software to be used, what should be deleted etc. This system will utilize a hybrid fuzzyNeural model to predict what the user will do next and support his actions. This will be done by establishing fuzzy sets of user activities, choices, preferences etc. and utilizing their combinations to predict his moods and immediate preferences. Various application of context aware systems exist separately e.g. on certain websites for music or multimedia suggestions but a personalized autonomous system that could adapt to user’s personality does not exist at present. Due to the novelty and massiveness of this concept, this paper will primarily focus on the problem establishment, product features and its functionality; however a small mini case is also implemented on MATLAB to demonstrate some of the aspects of ACPCA. The mini case involves prediction of user moods, activity, routine and food preference using a hybrid fuzzy-Neural soft computing technique.

Keywords: context aware systems, APCPCA, soft computing techniques, artificial intelligence, fuzzy logic, neural network, mood detection, face detection, activity detection

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Authors: Marco Picco, Mahmood Alam

Abstract:

Improving the energy performance of existing buildings can be challenging, particularly when facades cannot be modified, and the only available option is internal insulation. In such cases, the choice of the most suitable material becomes increasingly complex, as in addition to thermal transmittance and capital cost, the designer needs to account for the impact of the intervention on the internal spaces, and in particular the loss of usable space due to the additional layers of materials installed. This paper explores this issue by analysing a case study of an average office building needing to go through a refurbishment in order to reach the limits imposed by current regulations to achieve energy efficiency in buildings. The building is simulated through dynamic performance simulation under three different climate conditions in order to evaluate its energy needs. The use of Vacuum Insulated Panels as an option for energy refurbishment is compared to traditional insulation materials (XPS, Mineral Wool). For each scenario, energy consumptions are calculated and, in combination with their expected capital costs, used to perform a financial feasibility analysis. A holistic approach is proposed, taking into account the impact of the intervention on internal space by quantifying the value of the lost usable space and used in the financial feasibility analysis. The proposed approach highlights how taking into account different drivers will lead to the choice of different insulation materials, showing how accounting for the economic value of space can make VIPs an attractive solution for energy retrofitting under various climate conditions.

Keywords: vacuum insulated panels, building performance simulation, payback period, building energy retrofit

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Authors: Fan Yu

Abstract:

An atrium is a place for natural climate exchanging of indoor and outdoor space of buildings, which plays an active role in the overall energy conservation, climate control and environmental purification of buildings. Its greatest contribution is serving as a natural light collector and distributor to solve the problem of natural lighting in large and deep spaces. However, in real situations, the atrium space often results in energy consumption due to improper design in considering its big size and large amount use of glass. Based on the purpose of energy conservation of buildings, this paper emphasizes the significance of natural lighting of atriums. Through literature research, case analysis and other methods, four factors, namely: the light transmittance through the top of the atrium, the geometric proportion of the atrium space, the size and position of windows and the material of the surface of walls in the atrium, were studied, and the influence of different architectural compositions on the natural light distribution of the atrium is discussed. Relying on the analysis of relevant cases, it is proposed that when designing the natural lighting of the atrium, the height and width of the atrium should be paid attention to, the atrium walls are required being rough surfaces and the atrium top-level windows need to be minimized in order to introduce more natural light into the buildings and achieve the purpose of energy conservation.

Keywords: energy conservation, atrium, natural lighting, architectural design

Procedia PDF Downloads 172