Search results for: global navigation satellite network

Authors: Neelam Kadyan

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Global environmental change is happening at an alarming rate in the present world. Floods, Tsunamis’, Avalanches, Change in Weather patterns, Rise in sea temperature, Landslides, are only few evidences of this change. To regulate such alarming growth of global change in environment certain regulatory system or mechanism is required. Nuisance,negligence,absolute liability,strict liability and trespass are some of the effective common law principles which are helpful in environmental problems. What we need today is sufficient law and adequate machinery to enforce the legal standards. Without law environmental standards cannot be enforced and once again there is need to adopt the common law approach in solving the problem of environmental change as through this approach the affected person can get compensation and as the same time it puts check on wrongdoer.

Keywords: global environmental problems, nuisance, negligence, trespass, strict liability, absolute liability

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Authors: Sun Zhe, Ruggero Micheletto

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As discussed extensively in many studies, noise in neural networks have an important role in the functioning and time evolution of the system. The mechanism by which noise induce stochastic resonance enhancing and influencing certain operations is not clarified nor is the mechanism of information storage and coding. With the present research we want to study the role of noise, especially focusing on the frequency peaks in a three variable Hindmarsh−Rose Small−World network. We investigated the behaviour of the network to external noises. We demonstrate that a variation of signal to noise ratio of about 10 dB induces an increase in membrane potential signal of about 15%, averaged over the whole network. We also considered the integral of the whole membrane potential as a paradigm of internal noise, the one generated by the brain network. We showed that this internal noise is attenuated with the size of the network or with the number of random connections. By means of Fourier analysis we found that it has distinct peaks of frequencies, moreover, we showed that increasing the size of the network introducing more neurons, reduced the maximum frequencies generated by the network, whereas the increase in the number of random connections (determined by the small-world probability p) led to a trend toward higher frequencies. This study may give clues on how networks utilize noise to alter the collective behaviour of the system in their operations.

Keywords: neural networks, stochastic processes, small-world networks, discrete Fourier analysis

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Authors: Yasmina Houda Bendahma, Tewfik Bouchaour, Meriem Merad, Ulrich Maschke

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The purpose of this work is to study retention of dye dissolved in distilled water, by an hydrophilic acrylic polymer network. The polymer network considered is Poly (2-hydroxyethyl methacrylate) (PHEMA): it is prepared by photo-polymerization under UV irradiation in the presence of a monomer (HEMA), initiator and an agent cross-linker. PHEMA polymer network obtained can be used in the retention of dye molecules present in the wastewater. The results obtained are interesting in the study of the kinetics of swelling and de-swelling of cross linked polymer networks PHEMA in colored aqueous solutions. The dyes used for retention by the PHEMA networks are eosin Y and Malachite Green, dissolved in distilled water. Theoretical conformational study by a simplified molecular model of system cross linked PHEMA / dye (eosin Y and Malachite Green), is used to simulate the retention phenomenon (or Docking) dye molecules in cavities in nano-domains included in the PHEMA polymer network.

Keywords: dye retention, molecular modeling, photochemically crosslinked polymer network, swelling deswelling, PHEMA, HEMA

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Authors: Abubakar Ibrahim, Muhammad Garba, Adelusi Oluwaseyi Abiodun

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Website development and administration has already become a very critical issue in many organisations due to the fact that most of the organisations have embraced the use of the internet to deliver their services and products seamlessly but even with huge advantages of being present on the internet, and website are very difficult and expensive to develop and maintain. In recent years, a number of open-source web Contents Management System (CMS) have been developed to allow organisations to internally develop and maintain their websites without the need to hire professional web developers to provide such services for them. This study aimed at performing a comparative analysis of the two most widely used open source CMS Joomla and wordpress, based on the following criteria: intuitiveness, responsiveness richness in features, meeting expectation, fill secured, ease of navigation, structure, and performance. Two identical applications were developed using the said CMS. In this study, a purposive sampling technique was adopted to administer the questionnaires, and a total of 50 respondents were selected to surf sites and fill out a questionnaire based on their experience on the two sites. Gt-matrix was used to carry out further analysis of the applications. The result shows that Joomla is the best for developing an e-commerce site due to the fact that it is best in terms of performance, better structure, meeting user expectations, rich features, and functionality. Even though Wordpress is intuitive and easy for navigation. One can still argue that Joomla is superior.

Keywords: open source, content management system, Joomla, WordPress

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Authors: Rehab Abdulmajed, Amr Hamada, Ahmed Elsaid, Hisashi Hayakawa, Ayman Mahrous

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Solar emissions have a high impact on the Earth’s magnetic field, and the prediction of solar events is of high interest. Various techniques have been used in the prediction of solar wind using mathematical models, MHD models, and neural network (NN) models. This study investigates the coronal hole (CH) derived high-speed streams (HSSs) and their correlation to the CH area and create a neural network model to predict the HSSs. Two different algorithms were used to compare different models to find a model that best simulates the HSSs. A dataset of CH synoptic maps through Carrington rotations 1601 to 2185 along with Omni-data set solar wind speed averaged over the Carrington rotations is used, which covers Solar cycles (sc) 21, 22, 23, and most of 24.

Keywords: artificial neural network, coronal hole area, feed-forward neural network models, solar high speed streams

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Authors: Jaeyoung Lee

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Autonomous driving systems require high reliability to provide people with a safe and comfortable driving experience. However, despite the development of a number of vehicle sensors, it is difficult to always provide high perceived performance in driving environments that vary from time to season. The image segmentation method using deep learning, which has recently evolved rapidly, provides high recognition performance in various road environments stably. However, since the system controls a vehicle in real time, a highly complex deep learning network cannot be used due to time and memory constraints. Moreover, efficient networks are optimized for GPU environments, which degrade performance in embedded processor environments equipped simple hardware accelerators. In this paper, a semantic segmentation network, matrix multiplication accelerator network (MMANet), optimized for matrix multiplication accelerator (MMA) on Texas instrument digital signal processors (TI DSP) is proposed to improve the recognition performance of autonomous driving system. The proposed method is designed to maximize the number of layers that can be performed in a limited time to provide reliable driving environment information in real time. First, the number of channels in the activation map is fixed to fit the structure of MMA. By increasing the number of parallel branches, the lack of information caused by fixing the number of channels is resolved. Second, an efficient convolution is selected depending on the size of the activation. Since MMA is a fixed, it may be more efficient for normal convolution than depthwise separable convolution depending on memory access overhead. Thus, a convolution type is decided according to output stride to increase network depth. In addition, memory access time is minimized by processing operations only in L3 cache. Lastly, reliable contexts are extracted using the extended atrous spatial pyramid pooling (ASPP). The suggested method gets stable features from an extended path by increasing the kernel size and accessing consecutive data. In addition, it consists of two ASPPs to obtain high quality contexts using the restored shape without global average pooling paths since the layer uses MMA as a simple adder. To verify the proposed method, an experiment is conducted using perfsim, a timing simulator, and the Cityscapes validation sets. The proposed network can process an image with 640 x 480 resolution for 6.67 ms, so six cameras can be used to identify the surroundings of the vehicle as 20 frame per second (FPS). In addition, it achieves 73.1% mean intersection over union (mIoU) which is the highest recognition rate among embedded networks on the Cityscapes validation set.

Keywords: edge network, embedded network, MMA, matrix multiplication accelerator, semantic segmentation network

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Authors: Jakyoung Kim, Sungwook Jang

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The purpose of this study is to examine the research trends of life-long education for people with disabilities using a keyword network analysis. For this purpose, 151 papers were selected from 594 papers retrieved using keywords such as 'people with disabilities' and 'life-long education' in the Korean Education and Research Information Service. The Keyword network analysis was constructed by extracting and coding the keyword used in the title of the selected papers. The frequency of the extracted keywords, the centrality of degree, and betweenness was analyzed by the keyword network. The results of the keyword network analysis are as follows. First, the main keywords that appeared frequently in the study of life-long education for people with disabilities were 'people with disabilities', 'life-long education', 'developmental disabilities', 'current situations', 'development'. The research trends of life-long education for people with disabilities are focused on the current status of the life-long education and the program development. Second, the keyword network analysis and visualization showed that the keywords with high frequency of occurrences also generally have high degree centrality and betweenness centrality. In terms of the keyword network diagram, it was confirmed that research trends of life-long education for people with disabilities are centered on six prominent keywords. Based on these results, it was discussed that life-long education for people with disabilities in the future needs to expand the subjects and the supporting areas of the life-long education, and the research needs to be further expanded into more detailed and specific areas. 

Keywords: life-long education, people with disabilities, research trends, keyword network analysis

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Authors: Remy Jonkam Oben

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Using a time series approach, this study investigates how natural resources impact financial development from a global perspective over the 1980-2019 period. Some important determinants of financial development (economic growth, trade openness, population growth, and investment) have been added to the model as control variables. Unit root tests have revealed that all the variables are integrated into order one. Johansen's cointegration test has shown that the variables are in a long-run equilibrium relationship. The vector error correction model (VECM) has estimated the coefficient of the error correction term (ECT), which suggests that the short-run values of natural resources, economic growth, trade openness, population growth, and investment contribute to financial development converging to its long-run equilibrium level by a 23.63% annual speed of adjustment. The estimated coefficients suggest that global natural resource rent has a statistically-significant negative impact on global financial development in the long-run (thereby validating the financial resource curse) but not in the short-run. Causality test results imply that neither global natural resource rent nor global financial development Granger-causes each other.

Keywords: financial development, natural resources, resource curse hypothesis, time series analysis, Granger causality, global perspective

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Authors: Chaghoub Soraya, Zhang Xiaoyan

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This research presents the first constant approximation algorithm to the p-median network design problem with multiple cable types. This problem was addressed with a single cable type and there is a bifactor approximation algorithm for the problem. To the best of our knowledge, the algorithm proposed in this paper is the first constant approximation algorithm for the p-median network design with multiple cable types. The addressed problem is a combination of two well studied problems which are p-median problem and network design problem. The introduced algorithm is a random sampling approximation algorithm of constant factor which is conceived by using some random sampling techniques form the literature. It is based on a redistribution Lemma from the literature and a steiner tree problem as a subproblem. This algorithm is simple, and it relies on the notions of random sampling and probability. The proposed approach gives an approximation solution with one constant ratio without violating any of the constraints, in contrast to the one proposed in the literature. This paper provides a (21 + 2)-approximation algorithm for the p-median network design problem with multiple cable types using random sampling techniques.

Keywords: approximation algorithms, buy-at-bulk, combinatorial optimization, network design, p-median

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Authors: Candice Lemaitre

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During the 1990s, an alliance of international intergovernmental and non-governmental organizations proposed a set of regulatory norms designed to reduce corruption. Many governments in developing countries, such as Vietnam, enacted these global anti-corruption norms into their domestic law. This article draws on empirical research to understand why these anti-corruption norms have failed to reduce corruption in Vietnam and many other developing countries. Rather than investigating state compliance with global anti-corruption provisions, a topic that has already attracted considerable attention, this article aims to explore the comparatively under-researched area of business compliance. Based on data collected from semi-structured interviews with business managers in Vietnam and archival research, this article examines how businesses in Vietnam interpret and comply with global anti-corruption norms. It investigates why different types of companies in Vietnam engage with and respond to these norms in different ways. This article suggests that global anti-corruption norms have not been effective in reducing corruption in Vietnam because there is fragmentation in the way companies in Vietnam interpret and respond to these norms. This fragmentation results from differences in the epistemic (or interpretive) communities that companies draw upon to interpret global anti-corruption norms. This article uses discourse analysis to understand how the communities interpret global anti-corruption norms. This investigation aims to generate some predictive insights into how companies are likely to respond to anti-corruption regimes based on global anti-corruption norms.

Keywords: anti-corruption, business law, legal transfer, Vietnam

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Authors: S. B. Kumbalavati, J. D. Mallapur, K. Y. Bendigeri

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A mobile Ad-hoc network (MANET) is a multihop wireless network where nodes communicate each other without any pre-deployed infrastructure. There is no central administrating unit. Hence, MANET is generally prone to many of the attacks. These attacks may alter, release or deny data. These attacks are nothing but intrusions. Intrusion is a set of actions that attempts to compromise integrity, confidentiality and availability of resources. A major issue in the design and operation of ad-hoc network is sharing the common spectrum or common channel bandwidth among all the nodes. We are performing intrusion detection using game theory approach. Game theory is a mathematical tool for analysing problems of competition and negotiation among the players in any field like marketing, e-commerce and networking. In this paper mathematical model is developed using game theory approach and intruders are detected and removed. Bandwidth utilization is estimated and comparison is made between bandwidth utilization with intrusion detection technique and without intrusion detection technique. Percentage of intruders and efficiency of the network is analysed.

Keywords: ad-hoc network, IDS, game theory, sensor networks

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Authors: Gizem Kodak, Alper Unal, Birsen Koldemir, Tayfun Acarer

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Turkish Straits System which consists of Istanbul Strait (Bosphorus), Canakkale Strait (Dardanelles) and the Marmara Sea has a strategical location on international maritime as it is a unique waterway between the Mediterranean Sea, Black Sea and the Aegean Sea. Thus, this area has great importance since it is the only waterway between Black Sea countries and the rest of the World. Turkish Straits System has dangerous environmental factors hosts more vessel every day through developing World trade and this situation results in expanding accident risks day by day. Today, a lot of precautions have been taken to ensure safe navigation and to prevent maritime accidents, and international standards are followed to avoid maritime accidents. Despite this, the environmental factors that affect this area, trigger the maritime accidents and threaten the vessels with new accidents risks in different months with different hazards. This descriptive study consists of temporal and spatial analyses of environmental factors causing maritime accidents. This study also aims at contributing to safety navigation including monthly and regionally characteristics of variables. In this context, two different data sets are created consisting of environmental factors and accidents. This descriptive study on the accidents between 2001 and 2017 the mentioned region also studies the months and places of the accidents with environmental factor variables. Environmental factor variables are categorized as dynamic and static factors. Dynamic factors are appointed as meteorological and oceanographical while static factors are appointed as geological factors that threaten safety navigation with geometrical restricts. The variables that form dynamic factors are approached meteorological as wind direction, wind speed, wave altitude and visibility. The circulations and properties of the water mass on the system are studied as oceanographical properties. At the end of the study, the efficient meteorological and oceanographical parameters on the region are presented monthly and regionally. By this way, we acquired the monthly, seasonal and regional distributions of the accidents. Upon the analyses that are done; The Turkish Straits System that connects the Black Sea countries with the other countries and which is one of the most important parts of the world trade; is analyzed on temporal and spatial dimensions on the reasons of the accidents and have been presented as environmental factor dynamics causing maritime accidents.

Keywords: descriptive study, environmental factors, maritime accidents, statistics

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Authors: Elmabruk Laias, Abdalla M. Hanashi, Mohammed Alnas

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Worldwide Interoperability for Microwave Access (WiMAX) became one of the most challenging issues, since it was responsible for distributing available resources of the network among all users this leaded to the demand of constructing and designing high efficient scheduling algorithms in order to improve the network utilization, to increase the network throughput, and to minimize the end-to-end delay. In this study, the proposed algorithm focuses on an efficient mechanism to serve non-real time traffic in congested networks by considering channel status.

Keywords: WiMAX, Quality of Services (QoS), OPNE, Diff-Serv (DS).

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Authors: Bhavini Pandya

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This paper considers Hη: T2 → T2 the Perturbed Cerbelli-Giona map. That is a family of 2-dimensional nonlinear area-preserving transformations on the torus T2=[0,1]×[0,1]= ℝ2/ ℤ2. A single parameter η varies between 0 and 1, taking the transformation from a hyperbolic toral automorphism to the “Cerbelli-Giona” map, a system known to exhibit multifractal properties. Here we study the multifractal properties of the family of maps. We apply a box-counting method by defining a grid of boxes Bi(δ), where i is the index and δ is the size of the boxes, to quantify the distribution of stable and unstable manifolds of the map. When the parameter is in the range 0.51< η <0.58 and 0.68< η <1 the map is ergodic; i.e., the unstable and stable manifolds eventually cover the whole torus, although not in a uniform distribution. For accurate numerical results we require correspondingly accurate construction of the stable and unstable manifolds. Here we use the piecewise linearity of the map to achieve this, by computing the endpoints of line segments which define the global stable and unstable manifolds. This allows the generalized fractal dimension Dq, and spectrum of dimensions f(α), to be computed with accuracy. Finally, the intersection of the unstable and stable manifold of the map will be investigated, and compared with the distribution of periodic points of the system.

Keywords: feed forward, gradient descent, neural network, integral equation

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Authors: Hussam Elias, Ninovic Perez, Holger Hirsch

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The completion of the EMC regulations worldwide is growing steadily as the usage of electronics in our daily lives is increasing more than ever. In this paper, we introduce a novel method to perform the final phase of Electromagnetic compatibility (EMC) measurement and to reduce the required test time according to the norm EN 55032 by using a developed tool and the conventional neural network(CNN). The neural network was trained using real EMC measurements, which were performed in the Semi Anechoic Chamber (SAC) by CETECOM GmbH in Essen, Germany. To implement our proposed method, we wrote software to perform the radiated electromagnetic interference (EMI) measurements and use the CNN to predict and determine the position of the turntable that meets the maximum radiation value.

Keywords: conventional neural network, electromagnetic compatibility measurement, mean absolute error, position error

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Authors: Vishnu Pratap Singh Kirar

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The main aim of the communication system is to achieve maximum performance. In cognitive radio, any user or transceiver have the ability to sense best suitable channel, while the channel is not in use. It means an unlicensed user can share the spectrum of licensed user without any interference. Though the spectrum sensing consumes a large amount of energy and it can reduce by applying various artificial intelligent methods for determining proper spectrum holes. It also increases the efficiency of Cognitive Radio Network (CRN). In this survey paper, we discuss the use of different learning models and implementation of Artificial Neural Network (ANN) to increase the learning and decision-making capacity of CRN without affecting bandwidth, cost and signal rate.

Keywords: artificial neural network, cognitive radio, cognitive radio networks, back propagation, spectrum sensing

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Authors: Vinayaga Jagadessh Raja, R. Ganesan, S. Ramesh Kumar

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An ever more significant fraction of the overall power dissipation of a network-on-chip (NoC) based system on- chip (SoC) is due to the interconnection scheme. In information, as equipment shrinks, the power contributes of NoC links starts to compete with that of NoC routers. In this paper, we propose the use of clock gating in the data encoding techniques as a viable way to reduce both power dissipation and time consumption of NoC links. The projected scramble scheme exploits the wormhole switching techniques. That is, flits are scramble by the network interface (NI) before they are injected in the network and are decoded by the target NI. This makes the scheme transparent to the underlying network since the encoder and decoder logic is integrated in the NI and no modification of the routers structural design is required. We review the projected scramble scheme on a set of representative data streams (both synthetic and extracted from real applications) showing that it is possible to reduce the power contribution of both the self-switching activity and the coupling switching activity in inter-routers links.

Keywords: Xilinx 12.1, power consumption, Encoder, NOC

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Authors: Tariq Mahmood, Faiza Lehrasab, Faraz Akrim, Muhammad Sajid nadeem, Muhammad Mushtaq, Unza waqar, Ayesha Sheraz, Shaista Andleeb

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Brown bear (Ursus arctos), a member of the family Ursidae, is distributed in a wide range of habitats in North America, Europe and Asia. Suspectedly, the global distribution range of brown bears is decreasing at the moment due to various factors. The carnivore species is categorized as ‘Least Concern’ globally by the IUCN Red List of Threatened Species. However, there are some fragmented, small populations that are on the verge of extinction, as is in Pakistan, where the species is listed as ‘Critically Endangered’, with a declining population trend. Importantly, the global historical distribution range of brown bears is undocumented. Therefore, in the current study, we reconstructed and estimated the historical distribution range of brown bears using QGIS software and also analyzed the network of protected areas in the past and current ranges of the species. Results showed that brown bear was more widely distributed in historic times, encompassing 52.6 million km² area as compared to their current distribution of 38.8 million km², resulting in a total range contraction of up to approximately 28 %. In the past, a total of N = 62,234 protected Areas, covering approximately 3.89 million km² were present in the distribution range of the species, while now a total of N= 33,313 Protected Areas, covering approximately 2.75 million km² area, are present in the current distribution range of the brown bear. The brown bear distribution range in the protected areas has also contracted by 1.15 million km² and the total percentage reduction of PAs is 29%.

Keywords: brown bear, historic distribution, range contraction, protected areas

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Authors: Helen J. Spray, Karina J. Linnell

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Local-global bias in adulthood is strongly dependent on environmental factors and a global bias is not the universal characteristic of adult perception it was once thought to be: whilst Western adults typically demonstrate a global bias, Namibian adults living in traditional villages possess a strong local bias. Furthermore, environmental effects on local-global bias have been shown to be highly gender-specific; whereas urbanisation promoted a global bias in urbanised Namibian women but not men, education promoted a global bias in urbanised Namibian men but not women. Adult populations, however, provide only a snapshot of the gene-environment interactions which shape perceptual bias. Yet, to date, there has been little work on the development of local-global bias across environmental settings. In the current study, local-global bias was assessed using a similarity-matching task with Navon figures in children aged between 4 and 15 years from across three populations: traditional Namibians, urban Namibians, and urban British. For the two Namibian groups, measures of urbanisation and education were obtained. Data were subjected to both between-group and within-group analyses. Between-group analyses compared developmental trajectories across population and gender. These analyses revealed a global bias from even as early as 4 in the British sample, and showed that the developmental onset of a global bias is not fixed. Urbanised Namibian children ultimately developed a global bias that was indistinguishable from British children; however, a global bias did not emerge until much later in development. For all populations, the greatest developmental effects were observed directly following the onset of formal education. No overall gender effects were observed; however, there was a significant gender by age interaction which was difficult to reconcile with existing biological-level accounts of gender differences in the development of local-global bias. Within-group analyses compared the effects of urbanisation and education on local-global bias for traditional and urban Namibian boys and girls separately. For both traditional and urban boys, education mediated all effects of age and urbanisation; however, this was not the case for girls. Traditional Namibian girls retained a local bias regardless of age, education, or urbanisation, and in urbanised girls, the development of a global bias was not attributable to any one factor specifically. These results are broadly consistent with aforementioned findings that education promoted a global bias in urbanised Namibian men but not women. The development of local-global bias does not follow a fixed trajectory but is subject to environmental control. Understanding how variability in the development of local-global bias might arise, particularly in the context of gender, may have far-reaching implications. For example, a number of educationally important cognitive functions (e.g., spatial ability) are known to show consistent gender differences in childhood and local-global bias may mediate some of these effects. With education becoming an increasingly prevalent force across much of the developing world it will be important to understand the processes that underpin its effects and their implications.

Keywords: cross-cultural, development, education, gender, local-global bias, perception, urbanisation, urbanization

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Authors: M. Hussain, Aqsa Farooq

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Let G = (V,E) be a connected graph and distance between any two vertices a and b in G is a−b geodesic and is denoted by d(a, b). A set of vertices W resolves a graph G if each vertex is uniquely determined by its vector of distances to the vertices in W. A metric dimension of G is the minimum cardinality of a resolving set of G. In this paper line graph of honeycomb network has been derived and then we calculated the metric dimension on line graph of honeycomb network.

Keywords: Resolving set, Metric dimension, Honeycomb network, Line graph

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Authors: Suraj Nair, Aravindkumar Vijayalingam, Alexander Perzylo, Alois Knoll

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In the last decade robotics research and development activities have grown rapidly, especially in the domain of service robotics. Integrating service robots into human occupied spaces such as homes, offices, hospitals, etc. has become increasingly worked upon. The primary motive is to ease daily lives of humans by taking over some of the household/office chores. However, several challenges remain in systematically integrating such systems in human shared work-spaces. In addition to sensing and indoor-navigation challenges, programmability of such systems is a major hurdle due to the fact that the potential user cannot be expected to have knowledge in robotics or similar mechatronic systems. In this paper, we propose a cognitive system for service robotics which allows non-expert users to easily model system behaviour in an underspecified manner through abstract tasks and objects associated with them. The system uses domain knowledge expressed in the form of an ontology along with logical reasoning mechanisms to infer all the missing pieces of information required for executing the tasks. Furthermore, the system is also capable of recovering from failed tasks arising due to on-line disturbances by using the knowledge base and inferring alternate methods to execute the same tasks. The system is demonstrated through a coffee fetching scenario in an office environment using a mobile robot equipped with sensors and software capabilities for autonomous navigation and human-interaction through natural language.

Keywords: cognitive robotics, reasoning, service robotics, task based systems

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Authors: Maria Florencia Ferrer, Saulo Fabiano Amancio-Vieira

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The use of the blockchain in the public sector is present today and no longer the future of disruptive institutional and technological models. There are still some cultural barriers and resistance to the proper use of its potential. This research aims to present the strengths and weaknesses of using a public-permitted and distributed network in the context of the public sector. Therefore, bibliographical/documentary research was conducted to raise the main aspects of the studied platform, focused on the use of the main demands of the public sector. The platform analyzed was LACChain, which is a global alliance composed of different actors in the blockchain environment, led by the Innovation Laboratory of the Inter-American Development Bank Group (IDB Lab) for the development of the blockchain ecosystem in Latin America and the Caribbean. LACChain provides blockchain infrastructure, which is a distributed ratio technology (DLT). The platform focuses on two main pillars: community and infrastructure. It is organized as a consortium for the management and administration of an infrastructure classified as public, following the ISO typologies (ISO / TC 307). It is, therefore, a network open to any participant who agrees with the established rules, which are limited to being identified and complying with the regulations. As benefits can be listed: public network (open to all), decentralized, low transaction cost, greater publicity of transactions, reduction of corruption in contracts / public acts, in addition to improving transparency for the population in general. It is also noteworthy that the platform is not based on cryptocurrency and is not anonymous; that is, it is possible to be regulated. It is concluded that the use of record platforms, such as LACChain, can contribute to greater security on the part of the public agent in the migration process of their informational applications.

Keywords: blockchain, LACChain, public sector, technological disruptions

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Authors: Olamide Shittu, Olayinka Akanle

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Value Chain Analysis is a common method of examining the stages involved in the production of a product, mostly agricultural produce, from the input to the consumption stage including the actors involved in each stage. However, the Functional Institutional Analysis is the most common method in literature employed to analyze the value chain of products. Apart from studying the relatively neglected phenomenon of recycled polymer products in Lagos Metropolis, this paper adopted the use of social network analysis to attempt a grounded theory of the nature of social network that exists among the value chain actors of the subject matter. The study adopted a grounded theory approach by conducting in-depth interviews, administering questionnaires and conducting observations among the identified value chain actors of recycled polymer products in Lagos Metropolis, Nigeria. The thematic analysis of the collected data gave the researchers the needed background to formulate a truly representative network of the social relationships among the value chain actors of recycled polymer products in Lagos Metropolis. The paper introduced concepts such as Transient and Perennial Social Ties to explain the observed social relations among the actors. Some actors have more social capital than others as a result of the structural holes that exist in their triad network. Households and resource recoverers are at disadvantaged position in the network as they have high constraints in their relationships with other actors. The study attempted to provide a new perspective in the study of the environmental value chain by analyzing the network of actors to bring about policy action points and improve recycling in Nigeria. Government and social entrepreneurs can exploit the structural holes that exist in the network for the socio-economic and sustainable development of the state.

Keywords: recycled polymer products, social network analysis, social ties, value chain analysis

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Authors: Paras Adlakha

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Today the major threat to our existing and future satellites is space debris; the collision of bodies like defunct satellites with any other objects in space, including the new age ASAT (anti-satellite) weaponry system, are the main causes of the increasing amount of space debris every year. After analyzing the current situation of space debris, low earth orbit is found to be having a large density of debris as compared to any other orbit range; that's why it is selected as the target orbit for space debris removal mission. In this paper, the complete data of 24000 debris is studied based on size, altitude, inclination, mass, number of existing satellites threaten by each debris from which the rocket bodies are the type of wreckage found to be most suited for removal. The optimal method of active debris removal using a robotic arm for capturing the body to attach a de-orbit kit is used to move the debris from its orbit without making the actual contact of servicer with the debris to reduce the further the threat of collision with defunct material. The major factors which are brought into consideration while designing the concept of debris removal are tumbling, removal of debris under a low-cost mission and decreasing the factor of collisions during the mission.

Keywords: de-orbit, debris, servicer, satellite, space junk

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Authors: Jianhong Xiang, Rui Sun, Linyu Wang

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In recent years, due to the continuous improvement of deep learning theory, Convolutional Neural Network (CNN) has played a great superior performance in the research of Hyperspectral Image (HSI) classification. Since HSI has rich spatial-spectral information, only utilizing a single dimensional or single size convolutional kernel will limit the detailed feature information received by CNN, which limits the classification accuracy of HSI. In this paper, we design a multi-scale CNN with MLP based on residual and attention modules (INRAM-3DCNN) for the HSI classification task. We propose to use multiple 3D convolutional kernels to extract the packet feature information and fully learn the spatial-spectral features of HSI while designing residual 3D convolutional branches to avoid the decline of classification accuracy due to network degradation. Secondly, we also design the 2D Inception module with a joint channel attention mechanism to quickly extract key spatial feature information at different scales of HSI and reduce the complexity of the 3D model. Due to the high parallel processing capability and nonlinear global action of the Multilayer Perceptron (MLP), we use it in combination with the previous CNN structure for the final classification process. The experimental results on two HSI datasets show that the proposed INRAM-3DCNN method has superior classification performance and can perform the classification task excellently.

Keywords: INRAM-3DCNN, residual, channel attention, hyperspectral image classification

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Authors: Aayushi Pandey, Manoj Kumar Pandey, Ashutosh Tiwari, Kireet Kumar

Abstract:

Glaciers play an important role in climate change and are sensitive phenomena of global climate change scenario. Glaciers in Himalayas are unique as they are predominantly valley type and are located in tropical, high altitude regions. These glaciers are often covered with debris which greatly affects ablation rate of glaciers and work as a sensitive indicator of glacier health. The aim of this study is to map high altitude Glacier surface with a focus on glacial lake and debris estimation using different techniques in Nagling glacier of dhauliganga basin in Himalayan region. Different Image Classification techniques i.e. thresholding on different band ratios and supervised classification using maximum likelihood classifier (MLC) have been used on high resolution sentinel 2A level 1c satellite imagery of 14 October 2017.Here Near Infrared (NIR)/Shortwave Infrared (SWIR) ratio image was used to extract the glaciated classes (Snow, Ice, Ice Mixed Debris) from other non-glaciated terrain classes. SWIR/BLUE Ratio Image was used to map valley rock and Debris while Green/NIR ratio image was found most suitable for mapping Glacial Lake. Accuracy assessment was performed using high resolution (3 meters) Planetscope Imagery using 60 stratified random points. The overall accuracy of MLC was 85 % while the accuracy of Band Ratios was 96.66 %. According to Band Ratio technique total areal extent of glaciated classes (Snow, Ice ,IMD) in Nagling glacier was 10.70 km2 nearly 38.07% of study area comprising of 30.87 % Snow covered area, 3.93% Ice and 3.27 % IMD covered area. Non-glaciated classes (vegetation, glacial lake, debris and valley rock) covered 61.93 % of the total area out of which valley rock is dominant with 33.83% coverage followed by debris covering 27.7 % of the area in nagling glacier. Glacial lake and Debris were accurately mapped using Band ratio technique Hence, Band Ratio approach appears to be useful for the mapping of debris covered glacier in Himalayan Region.

Keywords: band ratio, Dhauliganga basin, glacier mapping, Himalayan region, maximum likelihood classifier (MLC), Sentinel-2 satellite image

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Authors: Muthana Saleh Alalaki, Michael Opoku Agyeman

Abstract:

The growth in the number of Intellectual Properties (IPs) or the number of cores on the same chip becomes a critical issue in System-on-Chip (SoC) due to the intra-communication problem between the chip elements. As a result, Network-on-Chip (NoC) has emerged as a system architecture to overcome intra-communication issues. This paper presents a study of recent contributions on simulation tools for NoC. Furthermore, an overview of NoC is covered as well as a comparison between some NoC simulators to help facilitate research in on-chip communication.

Keywords: WiNoC, simulation tool, network-on-chip, SoC

Procedia PDF Downloads 498

Authors: Jiajun Wang, Xiaoge Li

Abstract:

The purpose of the aspect-level sentiment analysis task is to identify the sentiment polarity of aspects in a sentence. Currently, most methods mainly focus on using neural networks and attention mechanisms to model the relationship between aspects and context, but they ignore the dependence of words in different ranges in the sentence, resulting in deviation when assigning relationship weight to other words other than aspect words. To solve these problems, we propose a new aspect-level sentiment analysis model that combines a multi-channel convolutional network and graph convolutional network (GCN). Firstly, the context and the degree of association between words are characterized by Long Short-Term Memory (LSTM) and self-attention mechanism. Besides, a multi-channel convolutional network is used to extract the features of words in different ranges. Finally, a convolutional graph network is used to associate the node information of the dependency tree structure. We conduct experiments on four benchmark datasets. The experimental results are compared with those of other models, which shows that our model is better and more effective.

Keywords: aspect-level sentiment analysis, attention, multi-channel convolution network, graph convolution network, dependency tree

Procedia PDF Downloads 219

Authors: M. M. Muhammed, O. Khuzaima

Abstract:

This research examined and outlined some of the challenges facing Islam and Muslims in the 21^st century, considering global Islamic representation and knowledge acquisition as key objectives. It was observed that the Western media misrepresentation of Islam and the Western ethos embodied by the acquisition of western civilisation are major challenges faced by Islam and Muslims today. The problem of sectarianism, decline in the socio-economic power of Muslim communities and the archaic nature of the Islamic creed were recorded as major actors to the evolving global Islamic issues. It was therefore concluded that Islam is not the reason for these challenges, rather the action of some Muslims and non-Muslims were the contributing factors to the pandemics faced by Islam and Muslims. Some relevant recommendations were made to the Islamic world that could serve as effectual solutions to these lingering problems.

Keywords: Islam, challenges, representation, knowledge, century, global, twenty-first

Procedia PDF Downloads 233

Authors: Faezeh Aghazadeh, Mohammad Javad Sharifi

Abstract:

The application of neural networks to model a full-scale industrial acetylene hydrogenation in olefin plant has been studied. The operating variables studied are the, input-temperature of the reactor, output-temperature of the reactor, hydrogen ratio of the reactor, [C₂H₂]input, and [C₂H₆]input. The studied operating variables were used as the input to the constructed neural network to predict the [C₂H₆]output at any time as the output or the target. The constructed neural network was found to be highly precise in predicting the quantity of [C₂H₆]output for the new input data, which are kept unaware of the trained neural network showing its applicability to determine the [C₂H₆]output for any operating conditions. The enhancement of [C₂H₆]output as compared with [C₂H₆]input was a consequence of low selective acetylene hydrogenation to ethylene.

Keywords: acetylene hydrogenation, Pd-Ag/Al₂O₃, artificial neural network, modeling, optimal design

Procedia PDF Downloads 276