Search results for: recursive least square algorithm

Authors: Omer Cahana, Ofer Levi, Maya Herman

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Magnetic Resonance Imaging (MRI) is a lengthy medical scan that stems from a long acquisition time. Its length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach such as Compress Sensing (CS) or Parallel Imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. To achieve that, two conditions must be satisfied: i) the signal must be sparse under a known transform domain, and ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm must be applied to recover the signal. While the rapid advances in Deep Learning (DL) have had tremendous successes in various computer vision tasks, the field of MRI reconstruction is still in its early stages. In this paper, we present an end-to-end method for MRI reconstruction from k-space to image. Our method contains two parts. The first is sensitivity map estimation (SME), which is a small yet effective network that can easily be extended to a variable number of coils. The second is reconstruction, which is a top-down architecture with lateral connections developed for building high-level refinement at all scales. Our method holds the state-of-art fastMRI benchmark, which is the largest, most diverse benchmark for MRI reconstruction.

Keywords: magnetic resonance imaging, image reconstruction, pyramid network, deep learning

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Authors: Hao Mi, Ming Yang, Tian-yue Yang

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Ultrasonic infrared nondestructive testing is a kind of testing method with high speed, accuracy and localization. However, there are still some problems, such as the detection requires manual real-time field judgment, the methods of result storage and viewing are still primitive. An intelligent non-destructive detection system based on embedded linux is put forward in this paper. The hardware part of the detection system is based on the ARM (Advanced Reduced Instruction Set Computer Machine) core and an embedded linux system is built to realize image processing and defect detection of thermal images. The CLAHE algorithm and the Butterworth filter are used to process the thermal image, and then the boa server and CGI (Common Gateway Interface) technology are used to transmit the test results to the display terminal through the network for real-time monitoring and remote monitoring. The system also liberates labor and eliminates the obstacle of manual judgment. According to the experiment result, the system provides a convenient and quick solution for industrial non-destructive testing.

Keywords: remote monitoring, non-destructive testing, embedded Linux system, image processing

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Authors: Kingsley Nnaemeka Ogbu, Constantine Mbajiorgu

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Vegetation has a marked effect on runoff and has become an important component in hydrologic model. The watershed Resources Management (WRM) model, a process-based, continuous, distributed parameter simulation model developed for hydrologic and soil erosion studies at the watershed scale lack a crop growth component. As such, this model assumes a constant parameter values for vegetation and hydraulic parameters throughout the duration of hydrologic simulation. Our approach is to develop a crop growth algorithm based on the original plant growth model used in the Environmental Policy Integrated Climate Model (EPIC) model. This paper describes the development of a single crop growth model which has the capability of simulating all crops using unique parameter values for each crop. Simulated crop growth processes will reflect the vegetative seasonality of the natural watershed system. An existing model was employed for evaluating vegetative resistance by hydraulic and vegetative parameters incorporated into the WRM model. The improved WRM model will have the ability to evaluate the seasonal variation of the vegetative roughness coefficient with depth of flow and further enhance the hydrologic model’s capability for accurate hydrologic studies

Keywords: crop yield, roughness coefficient, PAR, WRM model

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Authors: Subhankar Kumar Sarkar

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Beetles of the subfamily Dynastinae under the family Scarabaeidae of the insect order Coleoptera are popularly known as ‘Rhinoceros beetles’ because of the characteristic horn borne by the males on their head. These horns are dedicated in mating battle against other males and have evolved as a result of phenotypic plasticity. Scarabaeidae is the largest of all families under Coleoptera and is composed of 11 subfamilies, of which the subfamily Dynastinae is represented by approximately 300 species. Some of these beetles have been reported to cause considerable damage to agriculture and forestry both in their larval and adult stages, while many of them are beneficial as they pollinate plants and recycle plant materials. Eastern Himalayas is regarded as one of the 35 biodiversity hotspot zones of the world and one of the four of India, which is exhibited by its rich and megadiverse tropical forests. However, our knowledge on the faunal diversity of these forests is very limited, particularly for the insect fauna. One such tropical forest of Eastern Himalayas is the ‘Buxa Tiger Reserve’ located between latitudes 26°30” to 26°55” North and Longitudes 89°20” to 89˚35” East of India and occupies an area of about 759.26 square kilometers. It is with this background an attempt has been made to explore the insect fauna of the forest. Insect sampling was carried out in each beat and range of Buxa Tiger Reserve in all the three seasons viz, Premonsoon, Monsoon, and Postmonsoon. Sample collections were done by sweep nets, hand picking technique and pit fall traps. UV light trap was used to collect the nocturnal insects. Morphological examinations of the collected samples were carried out with Stereozoom Binocular Microscopes (Zeiss SV6 and SV11) and were identified up to species level with the aid of relevant literature. Survey of the insect fauna of the forest resulted in the recognition of 76 scarab species, of which 8 belong to the subfamily dealt herein. Each of the 8 species represents a separate genus. The forest is dominated by the members of Xylotrupes gideon (Linnaeus) as is represented by highest number of individuals. The recorded taxa show about 12% endemism and are of mainly oriental in distribution. Premonsoon is the most favorable season for their occurrence and activity followed by Monsoon and Postmonsoon.

Keywords: Dynastinae, Scarabaeidae, diversity, Buxa Tiger Reserve

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Authors: Nosakhare Enoma, Alphose Zingoni

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The requirements for various toroidal shell forms are increasing due to new applications, available storage space and the consideration of appearance. Because of the complexity of some of these structural forms, the finite element method is nowadays mainly used for their analysis, even for simple static studies. This paper presents an easy-to-use analytical algorithm for pressurized multi-segmented toroidal shells of revolution. The membrane solution, which acts as a particular solution of the bending-theory equations, is developed based on membrane theory of shells, and a general approach is formulated for quantifying discontinuity effects at the shell junctions using the well-known Geckeler’s approximation. On superimposing these effects, and applying the ensuing solution to the problem of the pressurized toroid with four segments, closed-form stress results are obtained for the entire toroid. A numerical example is carried out using the developed method. The analytical results obtained show excellent agreement with those from the finite element method, indicating that the proposed method can be also used for complementing and verifying FEM results, and providing insights on other related problems.

Keywords: bending theory of shells, membrane hypothesis, pressurized toroid, segmented toroidal vessel, shell analysis

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Authors: Vesna Lukovic

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Online platforms as new business models play an important role in today’s economy and the functioning of the EU’s internal market. In the travel industry, algorithms used by online platforms for short-stay accommodation provide suggestions and price information to travelers. Those suggestions and recommendations are displayed in search results via recommendation (ranking) systems. There has been a growing consensus that the current legal framework was not sufficient to resolve problems arising from platform practices. In order to enhance the potential of the EU’s Single Market, smaller businesses should be protected, and their rights strengthened vis-à-vis large online platforms. The Regulation (EU) 2019/1150 of the European Parliament and of the Council on promoting fairness and transparency for business users of online intermediation services aims to level the playing field in that respect. This research looks at Airbnb through the lenses of this regulation. The research explores key determinants and finds that although regulation is an important step in the right direction, it is not enough. It does not entail sufficient clarity obligations that would make online platforms an intermediary service which both accommodation providers and travelers could use with ease.

Keywords: algorithm, online platforms, ranking, consumers, EU regulation

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Authors: Felix Givois, Nicolas R. Gauger, Matthias Kabel

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The efficient digital material characterization is of great interest to many fields of application. It consists of the following three steps. First, a 3D reconstruction of 2D scans must be performed. Then, the resulting gray-value image of the material sample is enhanced by image processing methods. Finally, partial differential equations (PDE) are solved on the segmented image, and by averaging the resulting solutions fields, effective properties like stiffness or conductivity can be computed. Due to the high resolution of current CT images, the latter is typically performed with matrix-free solvers. Among them, a solver that uses the explicit formula of the Green-Eshelby operator in Fourier space has been proposed by Moulinec and Suquet. Its algorithmic, most complex part is the Fast Fourier Transformation (FFT). In our talk, we will discuss the potential quantum advantage that can be obtained by replacing the FFT with the Quantum Fourier Transformation (QFT). We will especially show that the data transfer for noisy intermediate-scale quantum (NISQ) devices can be improved by using appropriate boundary conditions for the PDE, which also allows using semi-classical versions of the QFT. In the end, we will compare the results of the QFT-based algorithm for simple geometries with the results of the FFT-based homogenization method.

Keywords: most likelihood amplitude estimation (MLQAE), numerical homogenization, quantum Fourier transformation (QFT), NISQ devises

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Authors: Ni'mal Baroya, Andrei Ramani, Irma Prasetyowati

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The access to reproduction health services, including with safe and effective contraception were human rights regardless of social stratum and residence. In addition to individual factors, family and contextual factors were also believed to be the cause in the use of contraceptive methods. This study aimed to assess the determinants of long-term contraceptive methods (LTCM) by considering all the factors at either the individual level or contextual level. Thereby, this study could provide basic information for program development of prevalence enhancement of MKJP in East Java. The research, which used cross-sectional design, utilized Riskesdas 2013 data, particularly in East Java Province for further analysis about multilevel modeling of MKJP application. The sample of this study consisted of 20.601 married women who were not in pregnant that were drawn by using probability sampling following the sampling technique of Riskesdas 2013. Variables in this study were including the independent variables at the individual level that consisted of education, age, occupation, access to family planning services (KB), economic status and residence. As independent variables in district level were the Human Development Index (HDI, henceforth as IPM) in each districts of East Java Province, the ratio of field officers, the ratio of midwives, the ratio of community health centers and the ratio of doctors. As for the dependent variable was the use of Long-Term Contraceptive Method (LTCM or MKJP). The data were analyzed by using chi-square test and Pearson product moment correlation. The multivariable analysis was using multilevel logistic regression with 95% of Confidence Interval (CI) at the significance level of p < 0.05 and 80% of strength test. The results showed a low CPR LTCM was concentrated in districts in Madura Island and the north coast. The women which were 25 to 35 or more than 35 years old, at least high school education, working, and middle-class social status were more likely to use LTCM or MKJP. The IPM and low PLKB ratio had implications for poor CPR LTCM / MKJP.

Keywords: multilevel, long-term contraceptive methods, east java, contextual factor

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Authors: Chandu Rathnayake, Isuri Anuradha

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Early detection and accurate diagnosis of lung diseases play a crucial role in improving patient prognosis. However, conventional diagnostic methods heavily rely on subjective symptom assessments and medical imaging, often causing delays in diagnosis and treatment. To overcome this challenge, we propose a novel lung disease prediction system that integrates patient symptoms and X-ray images to provide a comprehensive and reliable diagnosis.In this project, develop a mobile application specifically designed for detecting lung diseases. Our application leverages both patient symptoms and X-ray images to facilitate diagnosis. By combining these two sources of information, our application delivers a more accurate and comprehensive assessment of the patient's condition, minimizing the risk of misdiagnosis. Our primary aim is to create a user-friendly and accessible tool, particularly important given the current circumstances where many patients face limitations in visiting healthcare facilities. To achieve this, we employ several state-of-the-art algorithms. Firstly, the Decision Tree algorithm is utilized for efficient symptom-based classification. It analyzes patient symptoms and creates a tree-like model to predict the presence of specific lung diseases. Secondly, we employ the Random Forest algorithm, which enhances predictive power by aggregating multiple decision trees. This ensemble technique improves the accuracy and robustness of the diagnosis. Furthermore, we incorporate a deep learning model using Convolutional Neural Network (CNN) with the RestNet50 pre-trained model. CNNs are well-suited for image analysis and feature extraction. By training CNN on a large dataset of X-ray images, it learns to identify patterns and features indicative of lung diseases. The RestNet50 architecture, known for its excellent performance in image recognition tasks, enhances the efficiency and accuracy of our deep learning model. By combining the outputs of the decision tree-based algorithms and the deep learning model, our mobile application generates a comprehensive lung disease prediction. The application provides users with an intuitive interface to input their symptoms and upload X-ray images for analysis. The prediction generated by the system offers valuable insights into the likelihood of various lung diseases, enabling individuals to take appropriate actions and seek timely medical attention. Our proposed mobile application has significant potential to address the rising prevalence of lung diseases, particularly among young individuals with smoking addictions. By providing a quick and user-friendly approach to assessing lung health, our application empowers individuals to monitor their well-being conveniently. This solution also offers immense value in the context of limited access to healthcare facilities, enabling timely detection and intervention. In conclusion, our research presents a comprehensive lung disease prediction system that combines patient symptoms and X-ray images using advanced algorithms. By developing a mobile application, we provide an accessible tool for individuals to assess their lung health conveniently. This solution has the potential to make a significant impact on the early detection and management of lung diseases, benefiting both patients and healthcare providers.

Keywords: CNN, random forest, decision tree, machine learning, deep learning

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Authors: Joseph Uzuazomaro Oju

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The ease at which manipulated media can be created, and the increasing difficulty in identifying fake media makes it a great threat. Most of the applications used for the creation of these high-quality fake videos and images are built with deep learning. Hence, the use of deep learning in creating a detection mechanism cannot be overemphasized. Any successful fake media that is being detected before it reached the populace will save people from the self-doubt of either a content is genuine or fake and will ensure the credibility of videos and images. The methodology introduced in this paper approaches the manipulated media detection challenge using a combo of VGG-19 and a deep capsule network. In the case of videos, they are converted into frames, which, in turn, are resized and cropped to the face region. These preprocessed images/videos are fed to the VGG-19 network to extract the latent features. The extracted latent features are inputted into a deep capsule network enhanced with a 3D -convolution dynamic routing agreement. The 3D –convolution dynamic routing agreement algorithm helps to reduce the linkages between capsules networks. Thereby limiting the poor learning shortcoming of multiple capsule network layers. The resultant output from the deep capsule network will indicate a media to be either genuine or fake.

Keywords: deep capsule network, dynamic routing, fake media detection, manipulated media

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Authors: M. Iruleswari, A. Jeyapaul Murugan

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Most fundamental part used in many Digital Signal Processing (DSP) application is a Finite Impulse Response (FIR) filter because of its linear phase, stability and regular structure. Designing a high-speed and hardware efficient FIR filter is a very challenging task as the complexity increases with the filter order. In most applications the higher order filters are required but the memory usage of the filter increases exponentially with the order of the filter. Using multipliers occupy a large chip area and need high computation time. Multiplier-less memory-based techniques have gained popularity over past two decades due to their high throughput processing capability and reduced dynamic power consumption. This paper describes the design and implementation of highly efficient Look-Up Table (LUT) based circuit for the implementation of FIR filter using Distributed arithmetic algorithm. It is a multiplier less FIR filter. The LUT can be subdivided into a number of LUT to reduce the memory usage of the LUT for higher order filter. Analysis on the performance of various filter orders with different address length is done using Xilinx 14.5 synthesis tool. The proposed design provides less latency, less memory usage and high throughput.

Keywords: finite impulse response, distributed arithmetic, field programmable gate array, look-up table

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Authors: Ademola A. Ladele, Peace I. Aburime

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Fake and substandard agricultural inputs pose a serious stumbling block to farm productivity and subsequently improved livelihood. There is, therefore, a need to pave ways for sustainable agriculture and self-sufficiency in food production by proffering solutions to this challenge. Maize farmers' perception of sharp practices among agro-input dealers in Ibadan/Ibarapa agricultural zone in Oyo state was therefore investigated. A multi-stage random sampling technique was used to select registered maize farmers in the Ibadan/Ibarapa agricultural zone of the Oyo State Agricultural Development Programme (OYSADEP). A structured questionnaire was used to collect information on the perception of sharp practices and the effects of sharp practices. A total of seventy-five maize farmers were interviewed. A focus group discussion was organized to identify ways of curbing sharp practices to complement the survey. Data were analyzed using descriptive statistics, Chi-square, and Pearson Product Moment Correlation (PPMC). Forms of sharp practices indicated were sales of expired fertilizers, expired pesticides, expired herbicides, underweight fertilizers, adulterated fertilizers, adulterated herbicides, packs containing broken seeds, infested seeds, lack of truth in labeling/wrong labels, manipulation of measuring scales, and false declaration of hecterages covered by tractor operators. The majority had unfavorable perception of agro-input dealers on sharp practices. A significant relationship was observed between respondents’ level of education and their perception of sharp practices. There were no significant relationships between respondents’ sex, marital status and religion, and their perception of sharp practices. A significant correlation exists between the forms of sharp practices and the perceived effect on agricultural production. It is concluded that the perceived effect of sharp practices was critical and the endemic culture of sharp practices prevailed in agro-input in Ibadan/Ibarapa agricultural zone. A standard regulatory system that will certify and monitor the quality of inputs should be put in place.

Keywords: agricultural productivity, agro-input dealers, maize farmers, sharp practices

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Authors: Isadora Bugarin, Taygoara F. de Oliveira

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Multiphase flows have currently been placed as a key solution for technological advances in energy and thermal sciences. The comprehension of droplet motion and behavior on non-isothermal flows is, however, rather limited. The present work consists of an investigation of a 2D droplet migration on natural convection inside a square enclosure with differentially heated walls. The investigation in question concerns the effects on drop motion of imposing different combinations of Prandtl and Rayleigh numbers while defining the drop on distinct initial positions. The finite differences method was used to compute the Navier-Stokes and energy equations for a laminar flow, considering the Boussinesq approximation. Also, a high order level set method was applied to simulate the two-phase flow. A previous analysis developed by the authors had shown that for fixed values of Rayleigh and Prandtl, the variation of the droplet initial position at the beginning of the simulation delivered different patterns of motion, in which for Ra≥10⁴ the droplet presents two very specific behaviors: it can travel through a helical path towards the center or define cyclic circular paths resulting in closed paths when reaching the stationary regime. Now, when varying the Prandtl number for different Rayleigh regimes, it was observed that this particular parameter also affects the migration of the droplet, altering the motion patterns as its value is increased. On higher Prandtl values, the drop performs wider paths with larger amplitudes, traveling closer to the walls and taking longer time periods to finally reach the stationary regime. It is important to highlight that drastic drop behavior changes on the stationary regime were not yet observed, but the path traveled from the begging of the simulation until the stationary regime was significantly altered, resulting in distinct turning over frequencies. The flow’s unsteady Nusselt number is also registered for each case studied, enabling a discussion on the overall effects on heat transfer variations.

Keywords: droplet migration, level set method, multiphase flow, natural convection in enclosure, Prandtl number

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Authors: T. Wanyama, B. Far

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Automatic irrigation systems usually conveniently protect landscape investment. While conventional irrigation systems are known to be inefficient, automated ones have the potential to optimize water usage. In fact, there is a new generation of irrigation systems that are smart in the sense that they monitor the weather, soil conditions, evaporation and plant water use, and automatically adjust the irrigation schedule. In this paper, we present an agent based smart irrigation system. The agents are built using a mix of commercial off the shelf software, including MATLAB, Microsoft Excel and KEPServer Ex5 OPC server, and custom written code. The Irrigation Scheduler Agent uses fuzzy logic to integrate the information that affect the irrigation schedule. In addition, the Multi-Agent system uses Open Platform Connectivity (OPC) technology to share data. OPC technology enables the Irrigation Scheduler Agent to communicate over the Internet, making the system scalable to a municipal or regional agent based water monitoring, management, and optimization system. Finally, this paper presents simulation and pilot installation test result that show the operational effectiveness of our system.

Keywords: community water usage, fuzzy logic, irrigation, multi-agent system

Procedia PDF Downloads 286

Authors: Qintao Shen, Lei Luo, Jun Ma, Jie Yu, Qingbo Wu, Yongqi Ma, Zhengji Liu

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Control Flow Integrity (CFI) is one of the most promising technique to defend Code-Reuse Attacks (CRAs). Traditional CFI Systems and recent Context-Sensitive CFI use coarse control flow graphs (CFGs) to analyze whether the control flow hijack occurs, left vast space for attackers at indirect call-sites. Coarse CFGs make it difficult to decide which target to execute at indirect control-flow transfers, and weaken the existing CFI systems actually. It is an unsolved problem to extract CFGs precisely and perfectly from binaries now. In this paper, we present an algorithm to get a more precise CFG from binaries. Parameters are analyzed at indirect call-sites and functions firstly. By comparing counts of parameters prepared before call-sites and consumed by functions, targets of indirect calls are reduced. Then the control flow would be more constrained at indirect call-sites in runtime. Combined with CCFI, we implement our policy. Experimental results on some popular programs show that our approach is efficient. Further analysis show that it can mitigate COOP and other advanced attacks.

Keywords: contex-sensitive, CFI, binary analysis, code reuse attack

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Authors: Omer Cahana, Maya Herman, Ofer Levi

Abstract:

Magnetic resonance imaging (MRI) is a long medical scan that stems from a long acquisition time. This length is mainly due to the traditional sampling theorem, which defines a lower boundary for sampling. However, it is still possible to accelerate the scan by using a different approach, such as compress sensing (CS) or parallel imaging (PI). These two complementary methods can be combined to achieve a faster scan with high-fidelity imaging. In order to achieve that, two properties have to exist: i) the signal must be sparse under a known transform domain, ii) the sampling method must be incoherent. In addition, a nonlinear reconstruction algorithm needs to be applied to recover the signal. While the rapid advance in the deep learning (DL) field, which has demonstrated tremendous successes in various computer vision task’s, the field of MRI reconstruction is still in an early stage. In this paper, we present an extension of the state-of-the-art model in MRI reconstruction -VarNet. We utilize VarNet by using dilated convolution in different scales, which extends the receptive field to capture more contextual information. Moreover, we simplified the sensitivity map estimation (SME), for it holds many unnecessary layers for this task. Those improvements have shown significant decreases in computation costs as well as higher accuracy.

Keywords: MRI, deep learning, variational network, computer vision, compress sensing

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Authors: Kanikanun Photchong, Phusit Phukamchanoad

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The objective of this research is to study the people’s level of participation in activities of the community, their satisfaction towards the community, the attachment they have to the community, factors that influence the attachment, as well as the characteristics of the relationships of military families’ of the Royal Guards community of Dusit District. The method used was non-probability sampling by quota sampling according to people’s age. The determined age group was 18 years or older. One set of a sample group was done per family. The questionnaires were conducted by 287 people. Snowball sampling was also used by interviewing people of the community, starting from the Royal Guards Community’s leader, then by 20 of the community’s well-respected persons. The data was analyzed by using descriptive statistics, such as arithmetic mean and standard deviation, as well as by inferential statistics, such as Independent - Samples T test (T-test), One-Way ANOVA (F-test), Chi-Square. Descriptive analysis according to the structure of the interview content was also used. The results of the research is that the participation of the population in the Royal Guards Community in various activities is at a medium level, with the average participation level during Mother’s and Father’s Days. The people’s general level of satisfaction towards the premises of the Royal Guards Community is at the highest level. The people were most satisfied with the transportation within the community and in contacting with people from outside the premises. The access to the community is convenient and there are various entrances. The attachment of the people to the Royal Guards Community in general and by each category is at a high level. The feeling that the community is their home rated the highest average. Factors that influence the attachment of the people of the Royal Guards Community are age, status, profession, income, length of stay in the community, membership of social groups, having neighbors they feel close and familiar with, and as well as the benefits they receive from the community. In addition, it was found that people that participate in activities have a high level of positive relationship towards the attachment of the people to the Royal Guards Community. The satisfaction of the community has a very high level of positive relationship with the attachment of the people to the Royal Guards Community. The characteristics of the attachment of military families’ is that they live in big houses that everyone has to protect and care for, starting from the leader of the family as well as all members. Therefore, they all love the community they live in. The characteristics that show the participation of activities within the community and the high level of satisfaction towards the premises of the community will enable the people to be more attached to the community. The people feel that everyone is close neighbors within the community, as if they are one big family.

Keywords: community attachment, community satisfaction, royal guards community, activities of the community

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Authors: Peter Shi

Abstract:

Algorithmic trading is a rapidly expanding domain within quantitative finance, constituting a substantial portion of trading volumes in the US financial market. The demand for rigorous and robust mathematical theories underpinning these trading algorithms is ever-growing. In this study, the author establishes a new stock market model that integrates the Efficient Market Hypothesis and the statistical arbitrage. The model, for the first time, finds probabilistic relations between the rational price and the market price in terms of the conditional expectation. The theory consequently leads to a mathematical justification of the old market adage: buy-low and sell-high. The thresholds for “low” and “high” are precisely derived using a max-min operation on Bayes’s error. This explicit connection harmonizes the Efficient Market Hypothesis and Statistical Arbitrage, demonstrating their compatibility in explaining market dynamics. The amalgamation represents a pioneering contribution to quantitative finance. The study culminates in comprehensive numerical tests using historical market data, affirming that the “buy-low” and “sell-high” algorithm derived from this theory significantly outperforms the general market over the long term in four out of six distinct market environments.

Keywords: efficient market hypothesis, behavioral finance, Bayes' decision, algorithmic trading, risk control, stock market

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Authors: Adeosun, Festus Idowu; Omoniyi, Isaac Tunde, Adeosun, Olamide Modinat

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This study assessed the association between household livelihood diversification and fisheries conservation policies among fishermen in coastal areas of Ogun State, Nigeria by adopting a multistage sampling procedure. The sample size was 90 fishermen from six randomly selected fishing communities (Abureji, Agbalegiyo, Ilamo, Imosan, Iseku and Wharf) along the coastline in Ogun State, Nigeria. Data were collected using pre-validated interview schedule and subjected to descriptive and inferential analytical techniques. Results revealed that majority of the fishermen were married (98.9%), in the age bracket of 41-60 years (71.1%) with mean age of 49 years, had household size of 6-15 persons (91.1%) with mean household size being 9 persons, from extended families (90.0%), either either no formal (43.3%) or only primary education (41.1%), were non-members of social groups (62.2%), and had no other occupations (93.3%). It was also reported that there was generally low level of household livelihood diversification across the fishing communities. Gill nets were the most commonly used fishing gears across the fishing communities (80.0%). This was followed by seine nets (63.3%), traps (56.7%) and trawl nets (53.3%) while fish aggregating devices (35.6%), cast nets (37.8%) and hook and line (24.4%) were the least used fishing gears in the study locations. Results further revealed that coastal fishery was characterised by conflicts among water users (64.4%), absence of protected fishing areas (81.1%), and experience of water pollution (36.7%). Majority (71.1%) of the fishermen across the study locations agreed with closed season policy as a coastal fisheries conservation strategy. This was followed by gear restriction (30.0%). Results of Chi-square analysis revealed that there were significant associations between level of household livelihood diversification and fishermen’s agreement with gear restriction (χ2 = 15.545, df = 5), and closed season (χ2 = 11.214, df = 4). The study concluded that coastal fisheries is in a poor state and that it could be improved through the introduction of gear restriction and closed season policies. The study recommended that youths in the coastal areas should venture into fishing, and that government and non-governmental agencies should organize sensitization programmes on fisheries conservation policies across the coastal areas in Ogun State.

Keywords: coastal, fisheries, conservation, livelihood

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Authors: Vladimir Simovic, Matija Varga, Tonco Marusic

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This research paper shows matrix technology models and examples of information systems in education (in the Republic of Croatia and in the Germany) in support of business, education (when learning and teaching) and e-learning. Here we researched and described the aims and objectives of the main process in education and technology, with main matrix classes of data. In this paper, we have example of matrix technology with detailed description of processes related to specific data classes in the processes of education and an example module that is support for the process: ‘Filling in the directory and the diary of work’ and ‘evaluation’. Also, on the lower level of the processes, we researched and described all activities which take place within the lower process in education. We researched and described the characteristics and functioning of modules: ‘Fill the directory and the diary of work’ and ‘evaluation’. For the analysis of the affinity between the aforementioned processes and/or sub-process we used our application model created in Visual Basic, which was based on the algorithm for analyzing the affinity between the observed processes and/or sub-processes.

Keywords: designing, education management, information systems, matrix technology, process affinity

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Authors: Guillaume Morin-Duponchelle, Ahmed Nait Chabane, Benoit Zerr, Pierre Schoesetters

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Industrial inspection can be achieved through robotic systems, allowing visual and chemical servoing. A popular scheme for visual servo-controlled robotic is the image-based servoing sys-tems. In this paper, an approach of visual and chemical servoing of a hexapod robot using a visual and chemical Jacobian matrix are proposed. The basic idea behind the visual Jacobian matrix is modeling the differential relationship between the camera system and the robotic control system to detect and track accurately points of interest in confined environments. This approach allows the robot to easily detect and navigates to the QR code or seeks a gas source localization using surge cast algorithm. To track the QR code target, a visual servoing based on Jacobian matrix is used. For chemical servoing, three gas sensors are embedded on the hexapod. A Jacobian matrix applied to the gas concentration measurements allows estimating the direction of the main gas source. The effectiveness of the proposed scheme is first demonstrated on simulation. Finally, a hexapod prototype is designed and built and the experimental validation of the approach is presented and discussed.

Keywords: chemical servoing, hexapod robot, Jacobian matrix, visual servoing, navigation

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Authors: Van Thinh Bui, Anh Tuan Tran, Quoc Viet Ngo, The Bao Pham

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Video retrieval is a searching problem on videos or clips based on content in which they are relatively close to an input image or video. The application of this retrieval consists of selecting video in a folder or recognizing a human in security camera. However, some recent approaches have been in challenging problem due to the diversity of video types, frame transitions and camera positions. Besides, that an appropriate measures is selected for the problem is a question. In order to overcome all obstacles, we propose a content-based video retrieval system in some main steps resulting in a good performance. From a main video, we process extracting keyframes and principal objects using Segmentation of Aggregating Superpixels (SAS) algorithm. After that, Speeded Up Robust Features (SURF) are selected from those principal objects. Then, the model “Bag-of-words” in accompanied by SVM classification are applied to obtain the retrieval result. Our system is performed on over 300 videos in diversity from music, history, movie, sports, and natural scene to TV program show. The performance is evaluated in promising comparison to the other approaches.

Keywords: video retrieval, principal objects, keyframe, segmentation of aggregating superpixels, speeded up robust features, bag-of-words, SVM

Procedia PDF Downloads 287

Authors: Elizabeth Behrens, Anne Timmermann, Alexander Yerkan, Joshua Thomas, Deborah Katz, Agne Paner, Melissa Larson, Shivi Jain, Seo-Hyun Kim, Celalettin Ustun, Ankur Varma, Parameswaran Venugopal, Jamile Shammo

Abstract:

Coronavirus Disease-2019 (COVID-19) causes persistent concern for poor outcomes in vulnerable populations. Patients with hematologic malignancies (HM) have been found to have higher COVID-19 case fatality rates compared to those without malignancy. While cytopenias are common in patients with HM, especially in those undergoing chemotherapy treatment, hemoglobin (Hgb) and platelet count have not yet been studied, to our best knowledge, as potential prognostic indicators for patients with HM and COVID-19. The goal of this study is to identify factors that may increase the risk of mortality in patients with HM and COVID-19. In this single-center, retrospective, observational study, 65 patients with HM and laboratory confirmed COVID-19 were identified between March 2020 and January 2021. Information on demographics, laboratory data the day of COVID-19 diagnosis, and prognosis was extracted from the electronic medical record (EMR), chart reviewed, and analyzed using the statistical software SAS version 9.4. Chi-square testing was used for categorical variable analyses. Risk factors associated with mortality were established by logistic regression models. Non-Hodgkin lymphoma (37%), chronic lymphocytic leukemia (20%), and plasma cell dyscrasia (15%) were the most common HM. Higher Hgb level upon COVID-19 diagnosis was related to decreased mortality, odd ratio=0.704 (95% confidence interval [CI]: 0.511-0.969; P = .0263). Platelet count the day of COVID-19 diagnosis was lower in patients who ultimately died (mean 127 ± 72K/uL, n=10) compared to patients who survived (mean 197 ±92K/uL, n=55) (P=.0258). Female sex was related to decreased mortality, odd ratio=0.143 (95% confidence interval [CI]: 0.026-0.785; P = .0353). There was no mortality difference between the patients who were on treatment for HM the day of COVID-19 diagnosis compared to those who were not (P=1.000). Lower Hgb and male sex are independent risk factors associated with increased mortality of HM patients with COVID-19. Clinicians should be especially attentive to patients with HM and COVID-19 who present with cytopenias. Larger multi-center studies are urgently needed to further investigate the impact of anemia, thrombocytopenia, and demographics on outcomes of patients with hematologic malignancies diagnosed with COVID-19.

Keywords: anemia, COVID-19, hematologic malignancy, prognosis

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Authors: Bo Wang

Abstract:

As the first high-resolution civil optical satellite, ZY-3 satellite is able to obtain high-resolution multi-view images with three linear array sensors. The images can be used to generate Digital Elevation Models (DEM) through dense matching of stereo images. However, due to the clouds, forest, water and buildings covered on the images, there are some problems in the dense matching results such as outliers and areas failed to be matched (matching holes). This paper introduced an algorithm to verify the accuracy of DEM that generated by ZY-3 satellite with Shuttle Radar Topography Mission (SRTM). Since the accuracy of SRTM (Internal accuracy: 5 m; External accuracy: 15 m) is relatively uniform in the worldwide, it may be used to improve the accuracy of ZY-3 DEM. Based on the analysis of mass DEM and SRTM data, the processing can be divided into two aspects. The registration of ZY-3 DEM and SRTM can be firstly performed using the conjugate line features and area features matched between these two datasets. Then the ZY-3 DEM can be refined by eliminating the matching outliers and filling the matching holes. The matching outliers can be eliminated based on the statistics on Local Vector Binning (LVB). The matching holes can be filled by the elevation interpolated from SRTM. Some works are also conducted for the accuracy statistics of the ZY-3 DEM.

Keywords: ZY-3 satellite imagery, DEM, SRTM, refinement

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Authors: Wajiha Javed, Arsalan Jabbar, Nelofer Mehboob, Muhammad Tafseer, Zahid Memon

Abstract:

Introduction: Researchers globally have strived to explore diverse factors that augment the continuation and uptake of family planning methods. Clients’ satisfaction is one of the core determinants facilitating continuation of family planning methods. There is a major debate yet scanty evidence to contrast public and private sectors with respect to client satisfaction. The objective of this study is to compare quality-of-care provided by public and private sectors of Pakistan through a client satisfaction lens. Methods: We used Pakistan Demographic Heath Survey 2012-13 dataset (Sindh province) on a total of 3133 Married Women of Reproductive Age (MWRA) aged 15-49 years. Source of family planning (public/private sector) was the main exposure variable. Outcome variable was client satisfaction judged by ten different dimensions of client satisfaction. Means and standard deviations were calculated for continuous variable while for categorical variable frequencies and percentages were computed. For univariate analysis, Chi-square/Fisher Exact test was used to find an association between clients’ satisfaction in public and private sectors. Ten different multivariate models were made. Variables were checked for multi-collinearity, confounding, and interaction, and then advanced logistic regression was used to explore the relationship between client satisfaction and dependent outcome after adjusting for all known confounding factors and results are presented as OR and AOR (95% CI). Results: Multivariate analyses showed that clients were less satisfied in contraceptive provision from private sector as compared to public sector (AOR 0.92,95% CI 0.63-1.68) even though the result was not statistically significant. Clients were more satisfied from private sector as compared to the public sector with respect to other determinants of quality-of-care (follow-up care (AOR 3.29, 95% CI 1.95-5.55), infection prevention (AOR 2.41, 95% CI 1.60-3.62), counseling services (AOR 2.01, 95% CI 1.27-3.18, timely treatment (AOR 3.37, 95% CI 2.20-5.15), attitude of staff (AOR 2.23, 95% CI 1.50-3.33), punctuality of staff (AOR 2.28, 95% CI 1.92-4.13), timely referring (AOR 2.34, 95% CI 1.63-3.35), staff cooperation (AOR 1.75, 95% CI 1.22-2.51) and complications handling (AOR 2.27, 95% CI 1.56-3.29).

Keywords: client satisfaction, family planning, public private partnership, quality of care

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Authors: H. K. Esfahani, B. Datta

Abstract:

Groundwater is one of the most important natural resources in many parts of the world; however it is widely polluted due to human activities. Currently, effective and reliable groundwater management and remediation strategies are obtained using characterization of groundwater pollution sources, where the measured data in monitoring locations are utilized to estimate the unknown pollutant source location and magnitude. However, accurately identifying characteristics of contaminant sources is a challenging task due to uncertainties in terms of predicting source flux injection, hydro-geological and geo-chemical parameters, and the concentration field measurement. Reactive transport of chemical species in contaminated groundwater systems, especially with multiple species, is a complex and highly non-linear geochemical process. Although sufficient concentration measurement data is essential to accurately identify sources characteristics, available data are often sparse and limited in quantity. Therefore, this inverse problem-solving method for characterizing unknown groundwater pollution sources is often considered ill-posed, complex and non- unique. Different methods have been utilized to identify pollution sources; however, the linked simulation-optimization approach is one effective method to obtain acceptable results under uncertainties in complex real life scenarios. With this approach, the numerical flow and contaminant transport simulation models are externally linked to an optimization algorithm, with the objective of minimizing the difference between measured concentration and estimated pollutant concentration at observation locations. Concentration measurement data are very important to accurately estimate pollution source properties; therefore, optimal design of the monitoring network is essential to gather adequate measured data at desired times and locations. Due to budget and physical restrictions, an efficient and effective approach for groundwater pollutant source characterization is to design an optimal monitoring network, especially when only inadequate and arbitrary concentration measurement data are initially available. In this approach, preliminary concentration observation data are utilized for preliminary source location, magnitude and duration of source activity identification, and these results are utilized for monitoring network design. Further, feedback information from the monitoring network is used as inputs for sequential monitoring network design, to improve the identification of unknown source characteristics. To design an effective monitoring network of observation wells, optimization and interpolation techniques are used. A simulation model should be utilized to accurately describe the aquifer properties in terms of hydro-geochemical parameters and boundary conditions. However, the simulation of the transport processes becomes complex when the pollutants are chemically reactive. Three dimensional transient flow and reactive contaminant transport process is considered. The proposed methodology uses HYDROGEOCHEM 5.0 (HGCH) as the simulation model for flow and transport processes with chemically multiple reactive species. Adaptive Simulated Annealing (ASA) is used as optimization algorithm in linked simulation-optimization methodology to identify the unknown source characteristics. Therefore, the aim of the present study is to develop a methodology to optimally design an effective monitoring network for pollution source characterization with reactive species in polluted aquifers. The performance of the developed methodology will be evaluated for an illustrative polluted aquifer sites, for example an abandoned mine site in Queensland, Australia.

Keywords: monitoring network design, source characterization, chemical reactive transport process, contaminated mine site

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Authors: Zhiqiang Yan, Chen Fan, Yafei Wang, Beicheng Xia

Abstract:

Urban lakes play an invaluable role in urban water systems such as flood control, water supply, and public recreation. However, over 38% of the urban lakes have suffered from severe eutrophication in China. Chlorine that could remarkably inhibit the growth of phytoplankton in eutrophic, has been widely used in the agricultural, aquaculture and industry in the recent past. However, little information has been reported regarding the effects of chlorine on the lake ecosystem, especially on the main aquatic species.To investigate the ecological response of main aquatic species and system stability to chlorine interference in shallow urban lakes, a mini system dynamic model was developed based on the competition and predation of main aquatic species and total phosphorus circulation. The main species of submerged macrophyte, phytoplankton, zooplankton, benthos, spiroggra and total phosphorus in water and sediment were used as variables in the model,while the interference of chlorine on phytoplankton was represented by an exponential attenuation equation. Furthermore, the eco-exergy expressing the development degree of ecosystem was used to quantify the complexity of the shallow urban lake. The model was validated using the data collected in the Lotus Lake in Guangzhoufrom1 October 2015 to 31 January 2016.The correlation coefficient (R), root mean square error-observations standard deviation ratio (RSR) and index of agreement (IOA) were calculated to evaluate accuracy and reliability of the model.The simulated values showed good qualitative agreement with the measured values of all components. The model results showed that chlorine had a notable inhibitory effect on Microcystis aeruginos，Rachionus plicatilis, Diaphanosoma brachyurum Liévin and Mesocyclops leuckarti (Claus).The outbreak of Spiroggra.spp. inhibited the growth of Vallisneria natans (Lour.) Hara, leading to a gradual decrease of eco-exergy and the breakdown of ecosystem internal equilibria. This study gives important insight into using chlorine to achieve eutrophication control and understand mechanism process.

Keywords: system dynamic model, urban lake, chlorine, eco-exergy

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Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

Abstract:

In the precision agriculture area, one of the main tasks is the automated detection of diseases in crops. Machine Learning algorithms have been studied in recent decades for such tasks in view of their potential for improving economic outcomes that automated disease detection may attain over crop fields. The latest generation of deep learning convolution neural networks has presented significant results in the area of image classification. In this way, this work has tested the implementation of an architecture of deep learning convolution neural network for the detection of diseases in different types of crops. A data augmentation strategy was used to meet the requirements of the algorithm implemented with a deep learning framework. Two test scenarios were deployed. The first scenario implemented a neural network under images extracted from a controlled environment while the second one took images both from the field and the controlled environment. The results evaluated the generalisation capacity of the neural networks in relation to the two types of images presented. Results yielded a general classification accuracy of 59% in scenario 1 and 96% in scenario 2.

Keywords: convolutional neural networks, deep learning, disease detection, precision agriculture

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Authors: Vahid Nademi

Abstract:

In response to widely used wearable medical devices equipped with a continuous glucose monitor (CGM) and insulin pump, the advanced control methods are still demanding to get the full benefit of these devices. Unlike costly clinical trials, implementing effective insulin-glucose control strategies can provide significant contributions to the patients suffering from chronic diseases such as diabetes. This study deals with a key role of two-layer insulin-glucose regulator based on model-predictive-control (MPC) scheme so that the patient’s predicted glucose profile is in compliance with the insulin level injected through insulin pump automatically. It is achieved by iterative optimization algorithm which is called an integrated perturbation analysis and sequential quadratic programming (IPA-SQP) solver for handling uncertainties due to unexpected variations in glucose-insulin values and body’s characteristics. The feasibility evaluation of the discussed control approach is also studied by means of numerical simulations of two case scenarios via measured data. The obtained results are presented to verify the superior and reliable performance of the proposed control scheme with no negative impact on patient safety.

Keywords: blood glucose monitoring, insulin pump, predictive control, optimization

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Authors: Mohsen S. Sajadieha, Danyar Molavia

Abstract:

In this paper, a truck scheduling problem is investigated at a two-touch cross-docking center with due dates for outbound trucks as a hard constraint. The objective is to minimize the total cost comprising penalty and delivery cost of delayed shipments. The sequence of unloading shipments is considered and is assumed that shipments are sent to shipping dock doors immediately after unloading and a First-In-First-Out (FIFO) policy is considered for loading the shipments. A mixed integer programming model is developed for the proposed model. Two meta-heuristic algorithms including genetic algorithm (GA) and variable neighborhood search (VNS) are developed to solve the problem in medium and large sized scales. The numerical results show that increase in due dates for outbound trucks has a crucial impact on the reduction of penalty costs of delayed shipments. In addition, by increase the due dates, the improvement in the objective function arises on average in comparison with the situation that the cross-dock is multi-touch and shipments are sent to shipping dock doors only after unloading the whole inbound truck.

Keywords: cross-docking, truck scheduling, fixed due date, door assignment

Procedia PDF Downloads 395