Search results for: cloud network

Authors: Mehmet Hakan Karaata, Ali Hamdan, Omer Yusuf Adam Mohamed

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Consider a distributed system that delivers messages from a process to another. Such a system is often required to deliver each message to its destination regardless of whether or not the system components experience arbitrary forms of faults. In addition, each message received by the destination must be a message sent by a system process. In this paper, we first identify the necessary and sufficient conditions to detect some restricted form of Byzantine faults referred to as modifying Byzantine faults. An observable form of a Byzantine fault whose effect is limited to the modification of a message metadata or content, timing and omission faults, and message replay is referred to as a modifying Byzantine fault. We then present a distributed protocol to detect modifying Byzantine faults using optimal number of messages over node-disjoint paths.

Keywords: Byzantine faults, distributed systems, fault detection, network protocols, node-disjoint paths

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Authors: Andreas Hirseland, Lukas Kerschbaumer

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In Germany, during the past years there constantly are about one million long term unemployed who did not benefit from the prospering labor market while most short term unemployed did. Instead, they are continuously dependent on welfare and sometimes precarious short-term employment, experiencing work poverty. Long term unemployment thus turns into a main obstacle to regular employment, especially if accompanied by other impediments such as low level education (school/vocational), poor health (especially chronical illness), advanced age (older than fifty), immigrant status, motherhood or engagement in care for other relatives. Almost two thirds of all welfare recipients have multiple impediments which hinder a successful transition from welfare back to sustainable and sufficient employment. Hiring them is often considered as an investment too risky for employers. Therefore formal application schemes based on formal qualification certificates and vocational biographies might reduce employers’ risks but at the same time are not helpful for long-term unemployed and welfare recipients. The panel survey ‘Labor market and social security’ (PASS; ~15,000 respondents in ~10,000 households), carried out by the Institute of Employment Research (the research institute of the German Federal Labor Agency), shows that their chance to get back to work tends to fall to nil. Only 66 cases of such unlikely transitions could be observed. In a sequential explanatory mixed-method study, the very scarce ‘success stories’ of unlikely transitions from long term unemployment to work were explored by qualitative inquiry – in-depth interviews with a focus on biography accompanied by qualitative network techniques in order to get a more detailed insight of relevant actors involved in the processes which promote the transition from being a welfare recipient to work. There is strong evidence that sustainable transitions are influenced by biographical resources like habits of network use, a set of informal skills and particularly a resilient way of dealing with obstacles, combined with contextual factors rather than by job-placement procedures promoted by Job-Centers according to activation rules or by following formal paths of application. On the employer’s side small and medium-sized enterprises are often found to give job opportunities to a wider variety of applicants, often based on a slow but steadily increasing relationship leading to employment. According to these results it is possible to show and discuss some limitations of (German) activation policies targeting welfare dependency and long-term unemployment. Based on these findings, indications for more supportive small scale measures in the field of labor-market policies are suggested to help long-term unemployed with multiple impediments to overcome their situation.

Keywords: against-all-odds, economic sociology, long-term unemployment, mixed-methods

Procedia PDF Downloads 239

Authors: Anubhav Shrivastava, Lakshya Bhat, Shivarudraswamy

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Distributed Generation is the need of the hour. With current advancements in the DG technology, there are some major issues that need to be tackled in order to make this method of generation of energy more efficient and feasible. Among other problems, the control in voltage is the major issue that needs to be addressed. This paper focuses on control of voltage using reactive power control of DGs with the help of fuzzy logic. The membership functions have been defined accordingly and the control of the system is achieved. Finally, with the help of simulation results in Matlab, the control of voltage within the tolerance limit set (+/- 5%) is achieved. The voltage waveform graphs for the IEEE 14 bus system are obtained by using simple algorithm with MATLAB and then with fuzzy logic for 14 bus system. The goal of this project was to control the voltage within limits by controlling the reactive power of the DG using fuzzy logic.

Keywords: distributed generation, fuzzy logic, matlab, newton raphson, IEEE 14 bus, voltage regulation, radial network

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Authors: Abdollah Kavousi Fard

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This paper addresses the optimal scheduling of hybrid reconfigurable microgrids considering hybrid electric vehicle charging demands. A stochastic framework based on unscented transform to model the high uncertainties of renewable energy sources including wind turbine and photovoltaic panels, as well as the hybrid electric vehicles’ charging demand. In order to get to the optimal scheduling, the network reconfiguration is employed as an effective tool for changing the power supply path and avoiding possible congestions. The simulation results are analyzed and discussed in three different scenarios including coordinated, uncoordinated and smart charging demand of hybrid electric vehicles. A typical grid-connected microgrid is employed to show the satisfying performance of the proposed method.

Keywords: microgrid, renewable energy sources, reconfiguration, optimization

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Authors: Praveen S. Muthukumarana, Achala C. Aponso

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A large percentage of global crop harvest is lost due to crop diseases. Timely identification and treatment of crop diseases is difficult in many developing nations due to insufficient trained professionals in the field of agriculture. Many crop diseases can be accurately diagnosed by visual symptoms. In the past decade, deep learning has been successfully utilized in domains such as healthcare but adoption in agriculture for plant disease detection is rare. The literature shows that models trained with popular datasets such as PlantVillage does not generalize well on real world images. This paper attempts to find out how to make plant disease identification models that generalize well with real world images.

Keywords: agriculture, convolutional neural network, deep learning, plant disease classification, plant disease detection, plant disease diagnosis

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Authors: Vijay Kr. Yadav, Nilam Rathi

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Accurate prediction of glucose level for diabetes mellitus is required to avoid affecting the functioning of major organs of human body. This study describes the fundamental assumptions and two different methodologies of the Blood glucose prediction. First is based on the back-propagation algorithm of Artificial Neural Network (ANN), and second is based on the Neuro-Fuzzy technique, called Fuzzy Inference System (FIS). Errors between proposed methods further discussed through various statistical methods such as mean square error (MSE), normalised mean absolute error (NMAE). The main objective of present study is to develop mathematical model for blood glucose prediction before 12 hours advanced using data set of three patients for 60 days. The comparative studies of the accuracy with other existing models are also made with same data set.

Keywords: back-propagation, diabetes mellitus, fuzzy inference system, neuro-fuzzy

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Authors: Ayyaz Hussain, Tariq Sadad

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Breast cancer (BC) is one of the widespread ailments among females globally. The early prognosis of BC can decrease the mortality rate. Exact findings of benign tumors can avoid unnecessary biopsies and further treatments of patients under investigation. However, due to variations in images, it is a tough job to isolate cancerous cases from normal and benign ones. The machine learning technique is widely employed in the classification of BC pattern and prognosis. In this research, a deep convolution neural network (DCNN) called AlexNet architecture is employed to get more discriminative features from breast tissues. To achieve higher accuracy, K-nearest neighbor (KNN) classifiers are employed as a substitute for the softmax layer in deep learning. The proposed model is tested on a widely used breast image database called MIAS dataset for experimental purposes and achieved 99% accuracy.

Keywords: breast cancer, DCNN, KNN, mammography

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Authors: Hasan G. Elmazoghi, Aisha I. Alzayani, Lubna S. Bentaher

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Rainfall is a highly non-linear phenomena, which requires application of powerful supervised data mining techniques for its accurate prediction. In this study the Artificial Neural Network (ANN) technique is used to predict the mean monthly historical rainfall data collected from BENINA station in Benghazi for 31 years, the period of “1977-2006” and the results are compared against the observed values. The specific objective to achieve this goal was to determine the best combination of weather variables to be used as inputs for the ANN model. Several statistical parameters were calculated and an uncertainty analysis for the results is also presented. The best ANN model is then applied to the data of one year (2007) as a case study in order to evaluate the performance of the model. Simulation results reveal that application of ANN technique is promising and can provide reliable estimates of rainfall.

Keywords: neural networks, rainfall, prediction, climatic variables

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Authors: N. Meddeb, A. M. Makhlouf, M. A. Ben Ayed

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Due to the real-time requirement of message in Vehicular Ad hoc NETworks (VANET), it is necessary to authenticate vehicles to achieve security, efficiency, and conditional privacy-preserving. Privacy is of utmost relevance in VANETs. For this reason, we have proposed a new protocol called ‘Multilevel Authentication Protocol’ (MAP) that considers different vehicle categories. The proposed protocol is based on our Multilevel Authentication protocol for Vehicular networks (MAVnet). But the MAP leads to human safety, where the priority is given to the ambulance vehicles. For evaluation, we used the Java language to develop a demo application and deployed it on the Network Security Simulation (Nessi2). Compared with existing authentication protocols, MAP markedly enhance the communication overhead and decreases the delay of exchanging messages while preserving conditional privacy.

Keywords: Vehicular Ad hoc NETworks (VANET), vehicle categories, safety, databases, privacy, authentication, throughput, delay

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Authors: Ali Heydarimoghim

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In this paper, the optimal placement of charging and discharging stations is done to determine the location and capacity of the stations, reducing the cost of electric vehicle owners' losses, reducing the cost of distribution system losses, and reducing the costs associated with the stations. Also, observing the permissible limits of the bus voltage and the capacity of the stations and their distance are considered as constraints of the problem. Given the traffic situation in different areas of a city, we estimate the amount of energy required to charge and the amount of energy provided to discharge electric vehicles in each area. We then introduce the electricity distribution system of the city in question. Following are the scenarios for introducing the problem and introducing the objective and constraint functions. Finally, the simulation results for different scenarios are compared.

Keywords: charging & discharging stations, hybrid vehicles, optimization, replacement

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Authors: Gueridi Bachir, Dadache Derradji, Rouabah Farid

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This work is focused on the preparation and characterization of poly (vinyl alcohol)/silica gel/Nano-TiO₂, and the study of titanium dioxide (TiO₂) nanoparticles 1% on the properties of poly (vinyl alcohol) (PVA)/silica films. Fourier transform infrared (FT-IR), water contact angle, ultraviolet-visible spectrometry (UV-VIS)) were used to characterize the hybrid films obtained. The PVA/SiO₂/Nano-TiO₂ films were successfully synthesized. Owing to the FT-IR Analysis, the chemical bonds have clearly shown that the PVA backbone is linked to the (SiO₂-TiO₂) network. UV-VIS tests indicated that the hybrid films' UV shielding properties were drastically enhanced as a result of the addition of TiO₂. The water contact angle results revealed that TiO₂ nanoparticles used as a doping compound possess an important influence on the hydrophilicity of PVA/SiO₂ as thin films.

Keywords: sol-gel method, hybrid materials, PVA/SIO₂/TiO₂, spectroscopical characterization

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Authors: Li Qiang, Yang Ze-Ming, Liu Bao-Xu, Jiang Zheng-Wei

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With the increasing complexity of cyberspace security, the cyber-attack attribution has become an important challenge of the security protection systems. The difficult points of cyber-attack attribution were forced on the problems of huge data handling and key data missing. According to this situation, this paper presented a reasoning method of cyber-attack attribution based on threat intelligence. The method utilizes the intrusion kill chain model and Bayesian network to build attack chain and evidence chain of cyber-attack on threat intelligence platform through data calculation, analysis and reasoning. Then, we used a number of cyber-attack events which we have observed and analyzed to test the reasoning method and demo system, the result of testing indicates that the reasoning method can provide certain help in cyber-attack attribution.

Keywords: reasoning, Bayesian networks, cyber-attack attribution, Kill Chain, threat intelligence

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Authors: Mariam Bibi, Rubab Mehboob, Mehreen Sirshar

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Satisfying the customer requirements is the ultimate goal of producing or developing any product. The quality of the product is decided on the bases of the level of customer satisfaction. There are different techniques which have been reported during the survey which enhance the quality of the product through software defect prediction and by locating the missing software requirements. Some mining techniques were proposed to assess the individual performance indicators in collaborative environment to reduce errors at individual level. The basic intention is to produce a product with zero or few defects thereby producing a best product quality wise. In the analysis of survey the techniques like Genetic algorithm, artificial neural network, classification and clustering techniques and decision tree are studied. After analysis it has been discovered that these techniques contributed much to the improvement and enhancement of the quality of the product.

Keywords: data mining, defect prediction, missing requirements, software quality

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Authors: Fajar Ajie Setiawan, Ratu Azima Mayangsari, Bunga Aprilia

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The emergence of creative hubs around the world, including in Bandung, was primarily driven by the needs of collaborative-innovative spaces for creative industry activities such as the Maker Movement and the Coworking Movement. These activities pose challenges for identification and formulation of sets of indicators for modeling creative hubs in Bandung to help stakeholders in formulating strategies. This study intends to identify their characteristics. This research was conducted using a qualitative approach comparing three concepts of creative hub categorization and integrating them into a single instrument to analyze 12 selected creative hubs. Our results showed three new functions of creative hubs in Bandung: (1) cultural, (2) retail business, and (3) community network. Results also suggest that creative hubs in Bandung are commonly established for networking and community activities. Another result shows that there was a shifting pattern of creative hubs before the 2000s and after the 2000s, which also creates a hybrid group of creative hubs.

Keywords: creative industry, creative hubs, Ngariung, Bandung

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Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

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Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: classification, SOFM algorithm, neural network, neighborhood, RGB image

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Authors: Matthaios Katsanikas

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We study the structure of the invariant manifolds of complex unstable periodic orbits of a family of periodic orbits, in a 3D autonomous Hamiltonian system of galactic type, after a transition of this family from stability to complex instability (Hamiltonian Hopf bifurcation). We consider the case of a supercritical Hamiltonian Hopf bifurcation. The invariant manifolds of complex unstable periodic orbits have two kinds of structures. The first kind is represented by a disk confined structure on the 4D space of section. The second kind is represented by a complicated central tube structure that is associated with an extended network of tube structures, strips and flat structures of sheet type on the 4D space of section.

Keywords: dynamical systems, galactic dynamics, chaos, phase space

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Authors: Sabri Arik

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In this paper, we study the global asymptotic robust stability of delayed neural networks with norm-bounded uncertainties. By employing the Lyapunov stability theory and Homeomorphic mapping theorem, we derive some new types of sufficient conditions ensuring the existence, uniqueness and global asymptotic stability of the equilibrium point for the class of neural networks with discrete time delays under parameter uncertainties and with respect to continuous and slopebounded activation functions. An important aspect of our results is their low computational complexity as the reported results can be verified by checking some properties symmetric matrices associated with the uncertainty sets of network parameters. The obtained results are shown to be generalization of some of the previously published corresponding results. Some comparative numerical examples are also constructed to compare our results with some closely related existing literature results.

Keywords: neural networks, delayed systems, lyapunov functionals, stability analysis

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Authors: Raja Rajeswari K

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Orthogonal Frequency Division Multiplexing (OFDM), a multi Carrier transmission technique that has been used in implementing the majority of wireless applications like Wireless Network Protocol Standards (like IEEE 802.11a, IEEE 802.11n), in telecommunications (like LTE, LTE-Advanced) and also in Digital Audio & Video Broadcast standards. The latest research and development in the area of orthogonal frequency division multiplexing, Universal Filtered Multi Carrier (UFMC) & Filtered OFDM (F-OFDM) has attracted lots of attention for wideband wireless communications. In this paper UFMC & F-OFDM system are implemented and comparative analysis are carried out in terms of M-ary QAM modulation scheme over Dolph-chebyshev filter & rectangular window filter and to estimate Bit Error Rate (BER) over Rayleigh fading channel.

Keywords: UFMC, F-OFDM, BER, M-ary QAM

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Authors: G. Valentis, Q. Berthelot

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Nowadays the trend to develop ever more intelligent and autonomous systems often takes its inspiration in the living beings on Earth. Some simple isolated systems are able, once brought together, to form a strong and reliable system. When trying to adapt the idea to man-made systems it is not possible to include in their program everything the system may encounter during its life cycle. It is, thus, necessary to make the system able to take decisions based on other criteria such as its past experience, i.e. to make the system learn on its own. However, at some point the acquired knowledge depends also on environment. So the question is: if system environment is modified, how could the system respond to it quickly and appropriately enough? Here, starting from reinforcement learning to rate its decisions, and using adaptive learning algorithms for gain and loss reward, the system is made able to respond to changing environment and to adapt its knowledge as time passes. Application is made to a robot finding an exit in a labyrinth.

Keywords: reinforcement learning, neural network, autonomous systems, adaptive learning, changing environment

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Authors: Aminu Hamza

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This study examines the factors that play vital role in the adoption of mobile payment system among consumers in Nigeria. Technology Acceptance Model (TAM) was used with two additional variables to form the conceptual model. The study was conducted in three Universities in Kano state, Nigeria. Convenience sampling method was used with a total valid 202 respondents which involved the students of Bayero University Kano (BUK), Northwest University, and Kano University of Science and Technology (KUST) Wudil, Kano, Nigeria. Results of the regression analysis revealed that Perceived ease of use (PEOU) and Perceived usefulness (PU) have significant and positive correlation with the behavioral intention to adopt mobile payment system. The findings of this study would be useful to the policy makers Central Bank of Nigeria (CBN), mobile network operators and providers of the services.

Keywords: mobile payment system, Nigeria, technology adoption, technology acceptance model

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Authors: Sheikh Saeed Ahmad

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Electricity is vital for any state’s development that needs policy for planning the power network extension. This study is about simulation modeling for electricity in Balochistan province. Baseline data of electricity consumption was used of year 2004 and projected with the help of LEAP model up to subsequent 30 years. Three scenarios were created to run software. One scenario was baseline and other two were alternative or green scenarios i.e. solar and wind energy scenarios. Present study revealed that Balochistan has much greater potential for solar and wind energy for electricity production. By adopting these alternative energy forms, Balochistan can save energy in future nearly 23 and 48% by incorporating solar and wind power respectively. Thus, the study suggests to government planners, an aspect of integrating renewable sources in power system for ensuring sustainable development and growth.

Keywords: demand and supply, LEAP, solar energy, wind energy, households

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Authors: Mokhlisur Rahman

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The growing consensus among scientists and world leaders indicates that immediate action should be considered regarding the climate change phenomenon. However, climate change is no more a global issue but a personal one. Thus, individual participation is necessary to address such a significant issue. Studies show that individuals who perceive climate change as a personal issue are more likely to act toward it. This abstract presents augmented reality (A.R.) technology in the social media platform Facebook video advertising. The idea involves creating a video advertisement that enables users to interact with the video by navigating its features and experiencing the result uniquely and engagingly. This advertisement uses A.R. to bring changes, such as people making changes in real-life scenarios by simple clicks on the video and hearing an instant rewarding fact about their choices. The video shows three options: room, lawn, and driveway. Users select one option and engage in interaction based on while holding the camera in their personal spaces: Suppose users select the first option, room, and hold their camera toward spots such as by the windows, balcony, corners, and even walls. In that case, the A.R. offers users different plants appropriate for those unoccupied spaces in the room. Users can change the options of the plants and see which space at their house deserves a plant that makes it more natural. When a user adds a natural element to the video, the video content explains a piece of beneficiary information about how the user contributes to the world more to be livable and why it is necessary. With the help of A.R., if users select the second option, lawn, and hold their camera toward their lawn, the options are various small trees for their lawn to make it more environmentally friendly and decorative. The video plays a beneficiary explanation here too. Suppose users select the third option, driveway, and hold their camera toward their driveway. In that case, the A.R. video option offers unique recycle bin designs using A.I. measurement of spaces. The video plays audio information on anthropogenic contribution to greenhouse gas emission. IoT embeds tracking code in the video ad on Facebook, which stores the exact number of views in the cloud for data analysis. An online survey at the end collects short qualitative answers. This study helps understand the number of users involved and willing to change their behavior; It makes personalized advertising in social media. Considering the current state of climate change, the urgency for action is increasing. This ad increases the chance to make direct connections with individuals and gives a sense of personal responsibility for climate change to act

Keywords: motivations, climate, iot, personalized-advertising, action

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Authors: Xabier Barandiaran, Igone Guerra

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The economic and financial crisis that hit European countries in 2008 revealed the inability of governments to respond unilaterally to the so-called “wicked” problems that affect our societies. Closely linked to this, the increasing disaffection of citizens towards politics has resulted in growing distrust of the citizenry not only in the institutions in general but also in the political system, in particular. Precisely, these two factors provoked the action of the local government of Gipuzkoa (Basque Country) to move from old ways of “doing politics” to a new way of “thinking politics” based on a collaborative approach, in which innovative modes of public decision making are prominent. In this context, in 2015, the initiative Etorkizuna Eraikiz (Building the Future), a contemporary form of networked governance, was launched by the Provincial Government. The paper focuses on the Etorkizuna Eraikiz initiative, a sound commitment from a local government to build jointly with the citizens the future of the territory. This paper will present preliminary results obtained from three different experiences of co-creation developed within Etorkizuna Eraikiz in which the formulation of networked governance is a mandatory pre-requisite. These experiences show how the network building approach among the different agents of the territory as well as the co-creation of public policies is the cornerstone of this challenging mission. Through the analysis of the information and documentation gathered during the four years of Etorkizuna-Eraikiz, and, specifically by delving into the strategy promoted by the initiative, some emerging analytical conclusions resulting from the promotion of this collaborative culture will be presented. For example, some preliminary results have shown a significant positive relationship between shared leadership and the formulation of the public good. In the period 2016-2018, a total of 73 projects were launched and funding by the Provincial Government of Gipuzkoa within the Etorkizuna Eraikiz initiative, that indicates greater engagement of the citizenry in the process of policy-making and therefore improving, somehow, the quality of the public policies. These statements have been supported by the last survey about the perspectives of the citizens toward politics and policies. Some of the more prominent results show us that there is still a high level of distrust in Politics (78,9% of respondents) but a greater trust in institutions such the Political Government of Gipuzkoa (40,8% of respondents declared as “good” the performance of this provincial institution). Regarding the Etorkizuna Eraikiz Initiative, it is being more readily recognized by citizens over this period of time (25,4% of the respondents in June 2018 agreed to know about the initiative giving it a mark of 5,89 ) and thus build trust and a sense of ownership. Although, there is a clear requirement for further research on the linkages between collaborative governance and level of trust, the paper, based on these findings, will provide some managerial and theoretical implications for collaborative governance in the territory.

Keywords: network governance, collaborative governance, public sector innovation, citizen participation, trust

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Authors: Gansukh Erdenedelger, Doljinsuren Sukhbaatar, Trung Dung Dao, Byeong-Kyu Lee, Han Mo Jeong

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Two kinds of carboxylated thermally reduced graphenes (C-TRGs) having different lateral sizes are examined as a Pickering stabilizer in the suspension polymerization of methyl methacrylate. The size and the shape of the prepared composite particles are irregular due to agglomeration, more evidently when the larger C-TRG is used. In addition, C-TRG is distributed not only on the surface but also inside the composite particles. It indicates that the C-TRG alone is not a stable Pickering agent. However, a very small dosage of acrylic acid remedies all these issues, because acrylic acid interacts with C-TRG and synergizes the stabilizing effect. The compression molded composite of the core/shell poly(methyl methacrylate)/C-TRG particles exhibits a very low percolation threshold of electrical conductivity of 0.03 vol%. It demonstrates that the C-TRG shells of the composite particles effectively form a segregated conductive network throughout the composite.

Keywords: pickering, graphene, polymerization, PMMA

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Authors: Marcel Schulze, Behrem Aslan, Silke Lux, Alexandra Philipsen

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Objective: Patients with Attention Deficit/Hyperactivity Disorder (ADHD) often report that they are being flooded by sensory impressions. Studies investigating sensory processing show hypersensitivity for sensory inputs across the senses in children and adults with ADHD. Especially the auditory modality is affected by deficient acoustical inhibition and modulation of signals. While studying unimodal signal-processing is relevant and well-suited in a controlled laboratory environment, everyday life situations occur multimodal. A complex interplay of the senses is necessary to form a unified percept. In order to achieve this, the unimodal sensory modalities are bound together in a process called multisensory integration (MI). In the current study we investigate MI in an adult ADHD sample using the McGurk-effect – a well-known illusion where incongruent speech like phonemes lead in case of successful integration to a new perceived phoneme via late top-down attentional allocation . In ADHD neuronal dysregulation at rest e.g., aberrant within or between network functional connectivity may also account for difficulties in integrating across the senses. Therefore, the current study includes resting-state functional connectivity to investigate a possible relation of deficient network connectivity and the ability of stimulus integration. Method: Twenty-five ADHD patients (6 females, age: 30.08 (SD:9,3) years) and twenty-four healthy controls (9 females; age: 26.88 (SD: 6.3) years) were recruited. MI was examined using the McGurk effect, where - in case of successful MI - incongruent speech-like phonemes between visual and auditory modality are leading to a perception of a new phoneme. Mann-Whitney-U test was applied to assess statistical differences between groups. Echo-planar imaging-resting-state functional MRI was acquired on a 3.0 Tesla Siemens Magnetom MR scanner. A seed-to-voxel analysis was realized using the CONN toolbox. Results: Susceptibility to McGurk was significantly lowered for ADHD patients (ADHDMdn:5.83%, ControlsMdn:44.2%, U= 160.5, p=0.022, r=-0.34). When ADHD patients integrated phonemes, reaction times were significantly longer (ADHDMdn:1260ms, ControlsMdn:582ms, U=41.0, p<.000, r= -0.56). In functional connectivity medio temporal gyrus (seed) was negatively associated with primary auditory cortex, inferior frontal gyrus, precentral gyrus, and fusiform gyrus. Conclusion: MI seems to be deficient for ADHD patients for stimuli that need top-down attentional allocation. This finding is supported by stronger functional connectivity from unimodal sensory areas to polymodal, MI convergence zones for complex stimuli in ADHD patients.

Keywords: attention-deficit hyperactivity disorder, audiovisual integration, McGurk-effect, resting-state functional connectivity

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Authors: Boudemagh Naime

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Automatic radar detection requires some methods of adapting to variations in the background clutter in order to control their false alarm rate. The problem becomes more complicated in non-Gaussian environment. In fact, the conventional approach in real time applications requires a complex statistical modeling and much computational operations. To overcome these constraints, we propose another approach based on artificial neural network (ANN-CMLD-CFAR) using a Back Propagation (BP) training algorithm. The considered environment follows a log-normal distribution in the presence of multiple Rayleigh-targets. To evaluate the performances of the considered detector, several situations, such as scale parameter and the number of interferes targets, have been investigated. The simulation results show that the ANN-CMLD-CFAR processor outperforms the conventional statistical one.

Keywords: radat detection, ANN-CMLD-CFAR, log-normal clutter, statistical modelling

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Authors: James Rigor Camacho, Wansu Lim

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Over the last few years, the development of new wearable and processing technologies has accelerated in order to harness physiological data such as electroencephalograms (EEGs) for EEG-based applications. EEG has been demonstrated to be a source of emotion recognition signals with the highest classification accuracy among physiological signals. However, when emotion recognition systems are used for real-time classification, the training unit is frequently left to run offline or in the cloud rather than working locally on the edge. That strategy has hampered research, and the full potential of using an edge AI device has yet to be realized. Edge AI devices are computers with high performance that can process complex algorithms. It is capable of collecting, processing, and storing data on its own. It can also analyze and apply complicated algorithms like localization, detection, and recognition on a real-time application, making it a powerful embedded device. The NVIDIA Jetson series, specifically the Jetson Nano device, was used in the implementation. The cEEGrid, which is integrated to the open-source brain computer-interface platform (OpenBCI), is used to collect EEG signals. An EEG-based real-time emotion recognition system on Edge AI is proposed in this paper. To perform graphical spectrogram categorization of EEG signals and to predict emotional states based on input data properties, machine learning-based classifiers were used. Until the emotional state was identified, the EEG signals were analyzed using the K-Nearest Neighbor (KNN) technique, which is a supervised learning system. In EEG signal processing, after each EEG signal has been received in real-time and translated from time to frequency domain, the Fast Fourier Transform (FFT) technique is utilized to observe the frequency bands in each EEG signal. To appropriately show the variance of each EEG frequency band, power density, standard deviation, and mean are calculated and employed. The next stage is to identify the features that have been chosen to predict emotion in EEG data using the K-Nearest Neighbors (KNN) technique. Arousal and valence datasets are used to train the parameters defined by the KNN technique.Because classification and recognition of specific classes, as well as emotion prediction, are conducted both online and locally on the edge, the KNN technique increased the performance of the emotion recognition system on the NVIDIA Jetson Nano. Finally, this implementation aims to bridge the research gap on cost-effective and efficient real-time emotion recognition using a resource constrained hardware device, like the NVIDIA Jetson Nano. On the cutting edge of AI, EEG-based emotion identification can be employed in applications that can rapidly expand the research and implementation industry's use.

Keywords: edge AI device, EEG, emotion recognition system, supervised learning algorithm, sensors

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Authors: M. H. Hussain, I. Musirin, A. F. Abidin, S. R. A. Rahim

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This paper presents the implementation of Artificial Bees Colony (ABC) algorithm in solving Directional OverCurrent Relays (DOCRs) coordination problem for near-end faults occurring in fixed network topology. The coordination optimization of DOCRs is formulated as linear programming (LP) problem. The objective function is introduced to minimize the operating time of the associated relay which depends on the time multiplier setting. The proposed technique is to taken as a technique for comparison purpose in order to highlight its superiority. The proposed algorithms have been tested successfully on 8 bus test system. The simulation results demonstrated that the ABC algorithm which has been proved to have good search ability is capable in dealing with constraint optimization problems.

Keywords: artificial bees colony, directional overcurrent relay coordination problem, relay settings, time multiplier setting

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Authors: Romana P. Khokhar

Abstract:

The purpose of this study was to explore the impact of work-family conflict on self-esteem. On the basis of the literature reviewed, it was hypothesized that 1) work-family conflict has an impact on self- esteem, 2). There would be a gender difference on the variable of work family conflict. Data for this study was taken from a sample of 70 employees within the banking industry since this industry is generally associated with higher levels of work-family conflict. Statistical tests performed were regression and t-test. Self-esteem was assessed with the 10-item Rosenberg Self-Esteem Scale (RSE; Rosenberg, 1965) and Work-Family Conflict Scale (WFCS; Netemeyer, R. G., Boles, J. S., & McMurrian, R. 1996) was used to assess the level of work –family conflict. The results indicated that an increase in work-family conflict resulted in lower self-esteem due to the various pressures evidenced in a complicated network of direct and indirect influences. It was also determined that there is less effect of work-family conflict on the female workers, as opposed to the male population, leading to the conclusion that in the case of the female workers the impact on self-esteem was not significant.

Keywords: work and family conflict, self-esteem, employee

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Authors: Yuan-Jye Tseng, Jia-Shu Li

Abstract:

To design a product with the given product requirement and design objective, there can be alternative ways to propose the detailed design specifications of the product. In the design modeling stage, alternative design cases with detailed specifications can be modeled to fulfill the product requirement and design objective. Therefore, in the design evaluation stage, it is required to perform an evaluation of the alternative design cases for deciding the final design. The purpose of this research is to develop a product evaluation model for evaluating the alternative design cases by integrated evaluating the criteria of functional design, Kansei design, and design for supply chain. The criteria in the functional design group include primary function, expansion function, improved function, and new function. The criteria in the Kansei group include geometric shape, dimension, surface finish, and layout. The criteria in the design for supply chain group include material, manufacturing process, assembly, and supply chain operation. From the point of view of value and cost, the criteria in the functional design group and Kansei design group represent the design value of the product. The criteria in the design for supply chain group represent the supply chain and manufacturing cost of the product. It is required to evaluate the design value and the supply chain cost to determine the final design. For the purpose of evaluating the criteria in the three criteria groups, a fuzzy analytic network process (FANP) method is presented to evaluate a weighted index by calculating the total relational values among the three groups. A method using the technique for order preference by similarity to ideal solution (TOPSIS) is used to compare and rank the design alternative cases according to the weighted index using the total relational values of the criteria. The final decision of a design case can be determined by using the ordered ranking. For example, the design case with the top ranking can be selected as the final design case. Based on the criteria in the evaluation, the design objective can be achieved with a combined and weighted effect of the design value and manufacturing cost. An example product is demonstrated and illustrated in the presentation. It shows that the design evaluation model is useful for integrated evaluation of functional design, Kansei design, and design for supply chain to determine the best design case and achieve the design objective.

Keywords: design for supply chain, design evaluation, functional design, Kansei design, fuzzy analytic network process, technique for order preference by similarity to ideal solution

Procedia PDF Downloads 318