Search results for: nano on-chip network

Authors: Wisam H. Muragaa, Kamaruzzaman Seman, Mohd Fadzli Marhusin

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Software Defined Networking (SDN) is a paradigm designed to facilitate the way of controlling the network dynamically and with more agility. Network traffic is a set of flows, each of which contains a set of packets. In SDN, a matching process is performed on every packet coming to the network in the SDN switch. Only the headers of the new packets will be forwarded to the SDN controller. In terminology, the flow header fields are called tuples. Basically, these tuples are 5-tuple: the source and destination IP addresses, source and destination ports, and protocol number. This flow information is used to provide an overview of the network traffic. Our module is meant to extract this 5-tuple with the packets and flows numbers and show them as a list. Therefore, this list can be used as a first step in the way of detecting the DDoS attack. Thus, this module can be considered as the beginning stage of any flow-based DDoS detection method.

Keywords: matching, OpenFlow tables, POX controller, SDN, table-miss

Procedia PDF Downloads 195

Authors: Sara ElElimy, Samir Moustafa

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Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

Procedia PDF Downloads 137

Authors: Kyoung-Jae Kim

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Collaborative Filtering (CF) is a popular technique for the personalization in the E-commerce domain to reduce information overload. In general, CF provides recommending items list based on other similar users’ preferences from the user-item matrix and predicts the focal user’s preference for particular items by using them. Many recommender systems in real-world use CF techniques because it’s excellent accuracy and robustness. However, it has some limitations including sparsity problems and complex dimensionality in a user-item matrix. In addition, traditional CF does not consider the emotional interaction between users. In this study, we propose recommender systems using social network and singular value decomposition (SVD) to alleviate some limitations. The purpose of this study is to reduce the dimensionality of data set using SVD and to improve the performance of CF by using emotional information from social network data of the focal user. In this study, we test the usability of hybrid CF, SVD and social network information model using the real-world data. The experimental results show that the proposed model outperforms conventional CF models.

Keywords: recommender systems, collaborative filtering, social network information, singular value decomposition

Procedia PDF Downloads 282

Authors: Purva Joshi, Rohit Thanki, Omar Hanif

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Unmanned aerial vehicles (UAVs) are becoming increasingly important in various industrial applications and sectors. Nowadays, a multi UAV network is used for specific types of communication (e.g., military) and monitoring purposes. Therefore, it is critical to reducing propagation delay during communication between UAVs, which is essential in a multi UAV network. This paper presents how the propagation delay between the base station (BS) and the UAVs is reduced using a searching algorithm. Furthermore, the iterative-based K-nearest neighbor (k-NN) algorithm and Travelling Salesmen Problem (TSP) algorthm were utilized to optimize the distance between BS and individual UAV to overcome the problem of propagation delay in multi UAV networks. The simulation results show that this proposed method reduced complexity, improved reliability, and reduced propagation delay in multi UAV networks.

Keywords: multi UAV network, optimal distance, propagation delay, K - nearest neighbor, traveling salesmen problem

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Authors: Biswajit Pal, Amit Mallik, Anil K. Barik

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Current years have witnessed a phenomenal growth in the research on the rare earth-doped transparent host materials, the essential components in optoelectronics that meet up the increasing demand for fabrication of high quality optical devices especially in telecommunication system. The combination of low phonon energy (because of fluoride environment) and high chemical durability with superior mechanical stability (due to oxide environment) makes the oxyfluoride glass–ceramics the promising and useful materials in optoelectronics. The present work reports on the undoped and doped (1 mol% Pr2O3) glass ceramics of composition 16.52 Al2O3•1.5AlF3• 12.65LaF3•4.33Na2O•64.85 SiO2 (mol%), prepared by melting technique initially that follows annealation at 450 ºC for 1 h. The glass samples so obtained were heat treated at constant 600 ºC with a variation in heat treatment schedule (10- 80 h). TEM techniques were employed to structurally characterize the glass samples. Pr2O3 affects the phase separation in the glass and delays the onset of crystallization in the glass ceramic. The modified crystallization mechanism is established from the analysis of advanced STEM/EDXS results. The phase separated droplets after annealing turn into 10-20 nm of LaF3 nano crystals those upon scrutiny are found to be dotted with the doped Pr3+ ions within the crystals themselves. The EDXS results also suggest that the inner LaF3 crystal core is swallowed by an Al enriched layer that follows a Si enriched surrounding shell as the outer core. This greatly increases the viscosity in the periphery of the crystals that restricts further crystal growth to account for the formation of nano sized crystals.

Keywords: advanced STEM/EDXS, crystallization mechanism, nano crystals, pr3+ ion doped glass and glass ceramic, structural characterization

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Authors: Kishore K. Pochampally

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The success of a supply chain heavily relies on the efficiency of the suppliers involved. In this paper, we propose a neural network approach to evaluate the efficiency of a supplier, which is being considered for inclusion in a supply chain, using the available linguistic (fuzzy) data of suppliers that already exist in the supply chain. The approach is carried out in three phases, as follows: In phase one, we identify criteria for evaluation of the supplier of interest. Then, in phase two, we use performance measures of already existing suppliers to construct a neural network that gives weights (importance values) of criteria identified in phase one. Finally, in phase three, we calculate the overall rating of the supplier of interest. The following are the major findings of the research conducted for this paper: (i) linguistic (fuzzy) ratings of suppliers such as 'good', 'bad', etc., can be converted (defuzzified) to numerical ratings (1 – 10 scale) using fuzzy logic so that those ratings can be used for further quantitative analysis; (ii) it is possible to construct and train a multi-level neural network in order to determine the weights of the criteria that are used to evaluate a supplier; and (iii) Borda’s rule can be used to group the weighted ratings and calculate the overall efficiency of the supplier.

Keywords: fuzzy data, neural network, supplier, supply chain

Procedia PDF Downloads 111

Authors: Guankai Lin, Wei Tong, Hong Zhu

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The electric current induced Joule heating effects have been investigated in La₀.₈Ba₀.₂MnO₃ ultrathin films deposited on LaAlO₃(001) single crystal substrate with smaller lattice constant by using the sol-gel method. By applying moderate bias currents (~ 10 mA), it is found that Joule self-heating simply gives rise to a temperature deviation between the thermostat and the test sample, but the intrinsic ρ(T) relationship measured at a low current (0.1 mA) changes little. However, it is noteworthy that the low-temperature transport behavior degrades from metallic to insulating state after applying higher bias currents ( > 31 mA) in a vacuum. Furthermore, metallic transport can be recovered by placing the degraded film in air. The results clearly suggest that the oxygen vacancy in the La₀.₈Ba₀.₂MnO₃ films is controllable in different atmospheres, particularly with the aid of the Joule self-heating. According to the SEM images, we attribute the controlled oxygen vacancy to the nano-sized labyrinth pattern of the films, where the large surface-to-volume ratio plays a curial role.

Keywords: controlling oxygen vacancy, joule self-heating, manganite, sol-gel method

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Authors: Soney C. George, Thomasukutty Jose, Sabu Thomas

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Membrane based separation is the most important energy –efficient separation processes. There are wide ranges of membrane based separation process such as Micro-filtration, ultra filtration, reverse osmosis, electro-dialysis etc. Among these pervaporation is one of the most promising techniques. The promising technique is in the sense that it needs an ease of process design, low energy consumption, environmentally clean, economically cost effective and easily separate azeotropic composition without losing any components, unlike distillation in a short period of time. In the present work, we developed a new bentonite clay reinforced cross-linked PVA nano-composite membranes by solution casting method. The membranes were used for the pervaporation separation of azeotropic composition of isopropanol and water mixtures. The azeotropic composition of water and isopropanol is difficult to separate and we can’t get a better separation by normal separation processes. But the better separation was achieved here using cross-linked PVA/Clay nano-composite membranes. The 2wt% bentonite clay reinforced 5vol% GA cross-linked nano-composite membranes showed better separation efficiency. The selectivity of the cross-linked membranes increases 65% upon filler loading. The water permeance is showed tremendous enhancement upon filler loading. The permeance value changes from 4100 to 8200, due to the incorporation hydrophilic bentonite clay to the cross-linked PVA membranes. The clay reinforced membranes shows better thermal stability upon filler loading was confirmed from TGA and DSC analysis. The dispersion of nanoclay in the polymeric matrix was clearly evident from the TEM analysis. The better dispersed membranes showed better separation performance. Thus the developed cross-linked PVA/Clay membranes can be effectively used for the separation of azeotropic composition of water and isopropanol.

Keywords: poly(vinyl alcohol), membrane, gluraldehyde, permeance

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Authors: Rashad Al Araj, Adil K. Tamimi

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Concrete, despite being one of the most produced materials in the world, still has weaknesses and drawbacks. Significant concern of the cementitious materials in structural applications is their quasi-brittle behavior, which causes the material to crack and lose its durability. One of the very recently proposed mitigations for this problem is the implementation of nanotechnology in the concrete mix by adding carbon nanotubes (CNTs) to it. CNTs can enhance the critical mechanical properties of concrete as a structural material. Thus, this paper demonstrates a state-of-the-art review of reinforcing concrete with CNTs, emphasizing on the structural performance. It also goes over the properties of CNTs alone, the present methods and costs associated with producing them, the possible special applications of concretes reinforced with CNTs, the key challenges and drawbacks that this new technology still encounters, and the most reliable practices and methodologies to produce CNT-reinforced concrete in the lab. This work has shown that the addition of CNTs to the concrete mix in percentages as low as 0.25% weight of cement could increase the flexural strength and toughness of concrete by more than 45% and 25%, respectively, and enhance other durability-related properties, given that an effective dispersion of CNTs in the cementitious mix is achieved. Since nano reinforcement for cementitious materials is a new technology, many challenges have to be tackled before it becomes practiced at the mass level.

Keywords: sustainability, carbon nano tube, microsilica, concrete

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Authors: Waleed A. Basuliman, Khalid S. AlSaleh, Mohamed Z. Ramadan

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Applying the anthropometric dimensions is considered one of the important factors when designing any human-machine system. In this study, the estimation of anthropometric dimensions has been improved by developing artificial neural network that aims to predict the anthropometric measurements of the male in Saudi Arabia. A total of 1427 Saudi males from age 6 to 60 participated in measuring twenty anthropometric dimensions. These anthropometric measurements are important for designing the majority of work and life applications in Saudi Arabia. The data were collected during 8 months from different locations in Riyadh City. Five of these dimensions were used as predictors variables (inputs) of the model, and the remaining fifteen dimensions were set to be the measured variables (outcomes). The hidden layers have been varied during the structuring stage, and the best performance was achieved with the network structure 6-25-15. The results showed that the developed Neural Network model was significantly able to predict the body dimensions for the population of Saudi Arabia. The network mean absolute percentage error (MAPE) and the root mean squared error (RMSE) were found 0.0348 and 3.225 respectively. The accuracy of the developed neural network was evaluated by compare the predicted outcomes with a multiple regression model. The ANN model performed better and resulted excellent correlation coefficients between the predicted and actual dimensions.

Keywords: artificial neural network, anthropometric measurements, backpropagation, real anthropometric database

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Authors: Mohammed Badereldien, Rezk Diab, Mohamoud Ali, Ayman Aboelkassem

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The stone surfaces of historical architectural heritage in Egypt are under threat from of various environmental factors such as temperature fluctuation, humidity, pollution, and microbes. Due to these factors, the facades of buildings are deteriorating deformation and disfiguration of external decoration and the formation of black accretion also often from the stone works. The aim of this study is to evaluate the effectiveness of CaCO₃ nano-particles as consolidation and protection material for calcareous stone monuments. Selected tests were carried out in order to estimate the superficial consolidating and protective effect of the treatment. When applied the nanoparticles dispersed in the acrylic copolymer; poly ethylmethacrylate (EMA)/methylacrylate (MA) (70/30, respectively) (EMA)/methylacrylate (MA) (70/30, respectively). The synthesis process of CaCO₃ nanoparticles/polymer nano-composite was prepared using in situ emulsion polymerization system. The consolidation and protection were characterized by TEM, while the penetration depth, re-aggregating effects of the deposited phase, and the surface morphology before and after treatment were examined by SEM (Scanning Electron Microscopy). Improvement of the stones' mechanical properties was evaluated by compressive strength tests. Changes in water-interaction properties were evaluated by water absorption capillarity measurements, and colorimetric measurements were used to evaluate the optical appearance. Together the results appear to demonstrate that CaCO₃/polymer nanocomposite is an efficient material for the consolidation of limestone architecture and monuments. As compared with samples treated with pure acrylic copolymer without Calcium carbonate nanoparticles, for example, CaCO₃ nanoparticles are completely compatible, strengthening limestone against thermal aging and improving its mechanical properties.

Keywords: calcium carbonate nanoparticles, consolidation, nanocomposites, calcareous stone, colorimetric measurements, compressive strength

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Authors: Giulia I. Pintea

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The Roma people are a nomadic ethnic group native to India, and they are one of the most prevalent minorities in Europe. In the past, Roma were enslaved and they were imprisoned in concentration camps during the Holocaust; today, Roma are subject to hate crimes and are denied access to healthcare, education, and proper housing. The aim of this project is to analyze how the public perception of the Roma people may be influenced by antiziganist and pro-Roma institutions in Europe. In order to carry out this project, we used social network analysis to build two large social networks: The antiziganist network, which is composed of institutions that oppress and racialize Roma, and the pro-Roma network, which is composed of institutions that advocate for and protect Roma rights. Measures of centrality, density, and modularity were obtained to determine which of the two social networks is exerting the greatest influence on the public’s perception of Roma in European societies. Furthermore, data on hate crimes on Roma were gathered from the Organization for Security and Cooperation in Europe (OSCE). We analyzed the trends in hate crimes on Roma for several European countries for 2009-2015 in order to see whether or not there have been changes in the public’s perception of Roma, thus helping us evaluate which of the two social networks has been more influential. Overall, the results suggest that there is a greater and faster exchange of information in the pro-Roma network. However, when taking the hate crimes into account, the impact of the pro-Roma institutions is ambiguous, due to differing patterns among European countries, suggesting that the impact of the pro-Roma network is inconsistent. Despite antiziganist institutions having a slower flow of information, the hate crime patterns also suggest that the antiziganist network has a higher impact on certain countries, which may be due to institutions outside the political sphere boosting the spread of antiziganist ideas and information to the European public.

Keywords: applied mathematics, oppression, Roma people, social network analysis

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Authors: A. Saravanan, B. R. Huang, C. J. Yeh, K. C. Leou, I. N. Lin

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A new class of ultra-nano diamond-graphite nano-hybrid (DGH) composite materials containing nano-sized diamond needles was developed at low temperature process. Such kind of diamond- graphite nano-hybrid composite nanowires exhibit high electrical conductivity and excellent electron field emission (EFE) properties. Few earlier reports mention that addition of N2 gas to the growth plasma requires high growth temperature (800°C) to trigger the dopants to generate the conductivity in the films. High growth temperature is not familiar with the Si-based device fabrications. We have used a novel process such as bias-enhanced-grown (beg) MPECVD process to grow diamond films at low substrate temperature (450°C). We observed that the beg-N/UNCD films thus obtained possess high conductivity of σ=987 S/cm, ever reported for diamond films with excellent Electron field emission (EFE) properties. TEM investigation indicated that these films contain needle-like diamond grains about 5 nm in diameter and hundreds of nanometers in length. Each of the grains was encased in graphitic layers about tens of nano-meters in thickness. These materials properties suitable for more specific applications, such as high conductivity for electron field emitters, high robustness for microplasma cathodes and high electrochemical activity for electro-chemical sensing. Subsequently, other hand, the highly conducting DGH films were coated on vertically aligned ZnO nanorods, there is no prior nucleation or seeding process needed due to the use of BEG method. Such a composite structure provides significant enhancement in the field emission characteristics of the cold cathode was observed with ultralow turn on voltage 1.78 V/μm with high EFE current density of 3.68 mA/ cm2 (at 4.06V/μm) due to decoration of DGH material on ZnO nanorods. The DGH/ZNRs based device get stable emission for longer duration of 562min than bare ZNRs (104min) without any current degradation because the diamond coating protects the ZNRs from ion bombardment when they are used as the cathode for microplasma devices. The potential application of these materials is demonstrated by the plasma illumination measurements that ignited the plasma at the minimum voltage by 290 V. The photoresponse (Iphoto/Idark) behavior of the DGH/ZNRs based photodetectors exhibits a much higher photoresponse (1202) than bare ZNRs (229). During the process the electron transport is easy from ZNRs to DGH through graphitic layers, the EFE properties of these materials comparable to other primarily used field emitters like carbon nanotubes, graphene. The DGH/ZNRs composite also providing a possibility of their use in flat panel, microplasma and vacuum microelectronic devices.

Keywords: bias-enhanced nucleation and growth, ZnO nanorods, electrical conductivity, electron field emission, photo-detectors

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Authors: Afshin Moazami, Andrea Schiffauerova

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The objective of this work is to investigate the development and the role of collaboration networks in the creation of knowledge and innovations in the US and Canada, with a special focus on Quebec. In order to create scientific networks, the data on journal articles were extracted from SCOPUS, and the networks were built based on the co-authorship of the journal papers. For innovation networks, the USPTO database was used, and the networks were built on the patent co-inventorship. Various indicators characterizing the evolution of the network structure and the positions of the researchers and inventors in the networks were calculated. The comparison between the United States, Canada, and Quebec was then carried out. The preliminary results show that the nature of scientific collaboration networks differs from the one seen in innovation networks. Scientists work in bigger teams and are mostly interconnected within one giant network component, whereas the innovation network is much more clustered and fragmented, the inventors work more repetitively with the same partners, often in smaller isolated groups. In both Canada and the US, an increasing tendency towards collaboration was observed, and it was found that networks are getting bigger and more centralized with time. Moreover, a declining share of knowledge transfers per scientist was detected, suggesting an increasing specialization of science. The US collaboration networks tend to be more centralized than the Canadian ones. Quebec shares a lot of features with the Canadian network, but some differences were observed, for example, Quebec inventors rely more on the knowledge transmission through intermediaries.

Keywords: Canada, collaboration, innovation network, scientific network, Quebec, United States

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Authors: G. Baraneedaran, Deepak Singh, Kollipara Tejesh

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The wireless sensors network has been an active research area over the y-ear passed. Due to the limited energy and communication ability of sensor nodes, it seems especially important to design a routing protocol for WSNs so that sensing data can be transmitted to the receiver effectively, an energy-balanced routing method based on forward-aware factor (FAF-EBRM) is proposed in this paper. In FAF-EBRM, the next-hop node is selected according to the awareness of link weight and forward energy density. A spontaneous reconstruction mechanism for Local topology is designed additionally. In this experiment, FAF-EBRM is compared with LEACH and EECU, experimental results show that FAF-EBRM outperforms LEACH and EECU, which balances the energy consumption, prolongs the function lifetime and guarantees high Qos of WSN.

Keywords: energy balance, forward-aware factor (FAF), forward energy density, link weight, network performance

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Authors: A. Kardi, R. Zagrouba, M. Alqahtani

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The Internet of Everything (IoE) presents today a very attractive and motivating field of research. It is basically based on Wireless Sensor Networks (WSNs) in which the routing task is the major analysis topic. In fact, it directly affects the effectiveness and the lifetime of the network. This paper, developed from recent works and based on extensive researches, proposes a taxonomy of routing protocols in WSNs. Our main contribution is that we propose a classification model based on nine classes namely application type, delivery mode, initiator of communication, network architecture, path establishment (route discovery), network topology (structure), protocol operation, next hop selection and latency-awareness and energy-efficient routing protocols. In order to provide a total classification pattern to serve as reference for network designers, each class is subdivided into possible subclasses, presented, and discussed using different parameters such as purposes and characteristics.

Keywords: routing, sensor, survey, wireless sensor networks, WSNs

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Authors: Khaoula Bensaida, Osama Eljamal

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The electrical energy generation through Microbial Fuel Cells (MFCs) using microorganisms is a renewable and sustainable approach. It creates truly an efficient technology for power production and wastewater treatment. MFC is an electrochemical device which turns wastewater into electricity. The most important part of MFC is microbes. Nano zero-valent Iron NZVI technique was successfully applied in degrading the chemical pollutants and cleaning wastewater. However, the use of NZVI for enhancing the current production is still not confirmed yet. This study aims to confirm the effect of these particles on the current generation by using MFC. A constructed microbial fuel cell, which utilizes domestic wastewater, has been considered for wastewater treatment and bio-electricity generation. The two electrodes were connected to an external resistor (200 ohms). Experiments were conducted in two steps. First, the MFC was constructed without adding NZVI particles (Control) while at a second step, nanoparticles were added with a concentration of 50mg/L. After 20 hours, the measured voltage increased to 5 and 8mV, respectively. To conclude, the use of zero-valent iron in an MFC system can increase electricity generation.

Keywords: bacterial growth, electricity generation, microbial fuel cell MFC, nano zero-valent iron NZVI.

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Authors: Andre Slonopas, Zona Kostic, Warren Thompson

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Obfuscation is one of the most useful tools to prevent network compromise. Previous research focused on the obfuscation of the network communications between external-facing edge devices. This work proposes the use of two edge devices, external and internal facing, which communicate via private IPv4 addresses in a software-defined pseudo-random IP hopping. This methodology does not require additional IP addresses and/or resources to implement. Statistical analyses demonstrate that the hopping surface must be at least 1e3 IP addresses in size with a broad standard deviation to minimize the possibility of coincidence of monitored and communication IPs. The probability of breaking the hopping algorithm requires a collection of at least 1e6 samples, which for large hopping surfaces will take years to collect. The probability of dropped packets is controlled via memory buffers and the frequency of hops and can be reduced to levels acceptable for video streaming. This methodology provides an impenetrable layer of security ideal for information and supervisory control and data acquisition systems.

Keywords: moving target defense, cybersecurity, network security, hopping randomization, software defined network, network security theory

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Authors: Muntadher Sallal, Gareth Owenson, Mo Adda, Safa Shubbar

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Bitcoin is a digital currency based on a peer-to-peer network to propagate and verify transactions. Bitcoin is gaining wider adoption than any previous crypto-currency. However, the mechanism of peers randomly choosing logical neighbors without any knowledge about underlying physical topology can cause a delay overhead in information propagation, which makes the system vulnerable to double-spend attacks. Aiming at alleviating the propagation delay problem, this paper introduces proximity-aware extensions to the current Bitcoin protocol, named Master Node Based Clustering (MNBC). The ultimate purpose of the proposed protocol, that are based on how clusters are formulated and how nodes can define their membership, is to improve the information propagation delay in the Bitcoin network. In MNBC protocol, physical internet connectivity increases, as well as the number of hops between nodes, decreases through assigning nodes to be responsible for maintaining clusters based on physical internet proximity. We show, through simulations, that the proposed protocol defines better clustering structures that optimize the performance of the transaction propagation over the Bitcoin protocol. The evaluation of partition attacks in the MNBC protocol, as well as the Bitcoin network, was done in this paper. Evaluation results prove that even though the Bitcoin network is more resistant against the partitioning attack than the MNBC protocol, more resources are needed to be spent to split the network in the MNBC protocol, especially with a higher number of nodes.

Keywords: Bitcoin network, propagation delay, clustering, scalability

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Authors: Mohammad Saleh Vahdatpour, Yanqing Zhang

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Understanding the neural mechanisms underlying motion detection in the human visual system has long been a fascinating challenge in neuroscience and artificial intelligence. This paper presents a spiking neural network model inspired by the processing of motion information in the primate visual system, particularly focusing on the Middle Temporal (MT) area. In our study, we propose a multi-layer spiking neural network model to perform motion detection tasks, leveraging the idea that synaptic delays in neuronal communication are pivotal in motion perception. Synaptic delay, determined by factors like axon length and myelin insulation, affects the temporal order of input spikes, thereby encoding motion direction and speed. Overall, our spiking neural network model demonstrates the feasibility of capturing motion detection principles observed in the primate visual system. The combination of synaptic delays, learning mechanisms, and shared weights and delays in SMD provides a promising framework for motion perception in artificial systems, with potential applications in computer vision and robotics.

Keywords: neural network, motion detection, signature detection, convolutional neural network

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Authors: Hatem I. Mokhtar, Randa A. Abd-El-Salam, Ghada M. Hadad

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An improved nano-composite of self-doped polyaniline nanofibers and silica-coated magnetite nanoparticles were prepared and evaluated for suitability to magnetic dispersive micro solid-phase extraction. The work focused on optimization of the composite capacity to extract four fluoroquinolones (FQs) antibiotics, ciprofloxacin, enrofloxacin, danofloxacin, and difloxacin from water and improvement of composite stability towards acid and atmospheric degradation. Self-doped polyaniline nanofibers were prepared by oxidative co-polymerization of aniline with anthranilic acid. Magnetite nanopariticles were prepared by alkaline co-precipitation and coated with silica by silicate hydrolysis on magnetite nanoparticles surface at pH 6.5. The composite was formed by self-assembly by mixing self-doped polyaniline nanofibers with silica-coated magnetite nanoparticles dispersions in ethanol. The composite structure was confirmed by transmission electron microscopy (TEM). Self-doped polyaniline nanofibers and magnetite chemical structures were confirmed by FT-IR while silica coating of the magnetite was confirmed by Energy Dispersion X-ray Spectroscopy (EDS). Improved stability of the composite magnetic component was evidenced by resistance to degrade in 2N HCl solution. The adsorption capacity of self-doped polyaniline nanofibers based composite was higher than previously reported corresponding composite prepared from polyaniline nanofibers instead of self-doped polyaniline nanofibers. Adsorption-pH profile for the studied FQs on the prepared composite revealed that the best pH for adsorption was in range of 6.5 to 7. Best extraction recovery values were obtained at pH 7 using phosphate buffer. The best solvent for FQs desorption was found to be 0.1N HCl in methanol:water (8:2; v/v) mixture. 20 mL of Spiked water sample with studied FQs were preconcentrated using 4.8 mg of composite and resulting extracts were analysed by HPLC-UV method. The prepared composite represented a suitable adsorbent phase for magnetic dispersive micro-solid phase application.

Keywords: fluoroquinolones, magnetic dispersive micro extraction, nano-composite, self-doped polyaniline nanofibers

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Authors: Prarthana Gowda, Tushar Sakorikar, Siva K. Reddy, Darim B. Ferry, Abha Misra

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Graphene, a two-dimensional carbon allotrope has demonstrated excellent electrical, mechanical and optical properties. A tunable band gap of grapheme demonstrated broad band absorption of light with a response time of picoseconds, however it suffers a fast recombination of the photo generated carriers. Many reports have explored to overcome this problem; in this presentation, we discuss defect induced enhanced photoresponse in a few layer graphene (FLG) due to exposure of infrared (IR) radiation. The two and four-fold enhancement in the photocurrent is achieved by addition of multiwalled carbon nano tubes (MWCNT) to an FLG surface and also creating the wrinkles in the FLG (WG) respectively. In our study, it is also inferred that the photo current generation is highly dependent on the morphological defects on the graphene. It is observed that the FLG (without defects) generates the photo current instantaneously, and after a prolonged exposure to the IR radiation decays the generation rate. Importantly, the presence of MWCNT on FLG enhances the stability and WG presented both stable as well as enhanced photo response.

Keywords: graphene, multiwalled carbon nano tubes, wrinkled graphene, photo detector, photo current

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Authors: M. Kebisek, L. Spendla, M. Kopcek, T. Skulavik

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In our paper we describe the security capabilities of data collection. Data are collected with probes located in the near and distant surroundings of the company. Considering the numerous obstacles e.g. forests, hills, urban areas, the data collection is realized in several ways. The collection of data uses connection via wireless communication, LAN network, GSM network and in certain areas data are collected by using vehicles. In order to ensure the connection to the server most of the probes have ability to communicate in several ways. Collected data are archived and subsequently used in supervisory applications. To ensure the collection of the required data, it is necessary to propose algorithms that will allow the probes to select suitable communication channel.

Keywords: communication, computer network, data collection, probe

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Authors: Ghada Moussa, Mamoud Owais

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Transit Route Network Design Problem (TrNDP) is the most important component in Transit planning, in which the overall cost of the public transportation system highly depends on it. The main purpose of this study is to develop a novel solution methodology for the TrNDP, which goes beyond pervious traditional sophisticated approaches. The novelty of the solution methodology, adopted in this paper, stands on the deterministic operators which are tackled to construct bus routes. The deterministic manner of the TrNDP solution relies on using linear and integer mathematical formulations that can be solved exactly with their standard solvers. The solution methodology has been tested through Mandl’s benchmark network problem. The test results showed that the methodology developed in this research is able to improve the given network solution in terms of number of constructed routes, direct transit service coverage, transfer directness and solution reliability. Although the set of routes resulted from the methodology would stand alone as a final efficient solution for TrNDP, it could be used as an initial solution for meta-heuristic procedures to approach global optimal. Based on the presented methodology, a more robust network optimization tool would be produced for public transportation planning purposes.

Keywords: integer programming, transit route design, transportation, urban planning

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Authors: N. Nalini, Lokesh B. Bhajantri

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In Distributed Sensor Networks, the sensor nodes are prone to failure due to energy depletion and some other reasons. In this regard, fault tolerance of network is essential in distributed sensor environment. Energy efficiency, network or topology control and fault-tolerance are the most important issues in the development of next-generation Distributed Sensor Networks (DSNs). This paper proposes a node fault detection and recovery using Genetic Algorithm (GA) in DSN when some of the sensor nodes are faulty. The main objective of this work is to provide fault tolerance mechanism which is energy efficient and responsive to network using GA, which is used to detect the faulty nodes in the network based on the energy depletion of node and link failure between nodes. The proposed fault detection model is used to detect faults at node level and network level faults (link failure and packet error). Finally, the performance parameters for the proposed scheme are evaluated.

Keywords: distributed sensor networks, genetic algorithm, fault detection and recovery, information technology

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Authors: Ali Asmari, Alex Symington, Htaik Than, Austin Caradonna, John Senft

Abstract:

This paper presents the collaborative effort between ULC Technologies and Con Edison in developing a groundbreaking robotic cable splicing machine. The focus is on the machine's design, which integrates advanced robotics and automation to enhance safety and efficiency in power network maintenance. The paper details the operational steps of the machine, including cable grounding, cutting, and removal of different insulation layers, and discusses its novel technological approach. The significant benefits over traditional methods, such as improved worker safety and reduced outage times, are highlighted based on the field data collected during the validation phase of the project. The paper also explores the future potential and scalability of this technology, emphasizing its role in transforming the landscape of power network maintenance.

Keywords: cable splicing machine, power network maintenance, electric distribution, electric transmission, medium voltage cable

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Authors: Ihab Elaff

Abstract:

Simulating the heart’s electrical stimulation is essential in modeling and evaluating the electrophysiology behavior of the heart. For achieving that, there are two structures in concern: the ventricles’ Myocardium, and the ventricles’ Conduction Network. Ventricles’ Myocardium has been modeled as anisotropic material from Diffusion Tensor Imaging (DTI) scan, and the Conduction Network has been extracted from DTI as a case-based structure based on the biological properties of the heart tissues and the working methodology of the Magnetic Resonance Imaging (MRI) scanner. Results of the produced activation were much similar to real measurements of the reference model that was presented in the literature.

Keywords: diffusion tensor, DTI, heart, conduction network, excitation propagation

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Authors: Ioannis Binas, Marios Moschakis

Abstract:

Electrical faults in transmission and distribution networks can have great impact on the electrical equipment used. Fault effects depend on the characteristics of the fault as well as the network itself. It is important to anticipate the network’s behavior during faults when planning a new equipment installation, as well as troubleshooting. Moreover, working backwards, we could be able to estimate the characteristics of the fault when checking the perceived effects. Different transformer winding connections dominantly used in the Greek power transfer and distribution networks and the effects of 1-phase to neutral, phase-to-phase, 2-phases to neutral and 3-phase faults on different locations of the network were simulated in order to present voltage sag characteristics. The study was performed on a generic network with three steps down transformers on two voltage level buses (one 150 kV/20 kV transformer and two 20 kV/0.4 kV). We found that during faults, there are significant changes both on voltage magnitudes and on phase angles. The simulations and short-circuit analysis were performed using the PSCAD simulation package. This paper presents voltage characteristics calculated for the simulated network, with different approaches on the transformer winding connections during symmetrical and asymmetrical faults on various locations.

Keywords: Phase angle shift, power quality, transformer winding connections, voltage sag propagation

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Authors: Sami Baraketi, Jean Marie Garcia, Olivier Brun

Abstract:

Wavelength Division Multiplexing (WDM) is the dominant transport technology used in numerous high capacity backbone networks, based on optical infrastructures. Given the importance of costs (CapEx and OpEx) associated to these networks, resource management is becoming increasingly important, especially how the optical circuits, called “lightpaths”, are routed throughout the network. This requires the use of efficient algorithms which provide routing strategies with the lowest cost. We focus on the lightpath routing and wavelength assignment problem, known as the RWA problem, while optimizing wavelength fragmentation over the network. Wavelength fragmentation poses a serious challenge for network operators since it leads to the misuse of the wavelength spectrum, and then to the refusal of new lightpath requests. In this paper, we first establish a new Integer Linear Program (ILP) for the problem based on a node-link formulation. This formulation is based on a multilayer approach where the original network is decomposed into several network layers, each corresponding to a wavelength. Furthermore, we propose an efficient heuristic for the problem based on a greedy algorithm followed by a post-treatment procedure. The obtained results show that the optimal solution is often reached. We also compare our results with those of other RWA heuristic methods.

Keywords: WDM, lightpath, RWA, wavelength fragmentation, optimization, linear programming, heuristic

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Authors: Ahmed Kayad Abdourazak, Abderafi Souad, Zejli Driss, Idriss Abdoulkader Ibrahim

Abstract:

In this paper Artificial Neural Network (ANN) is used to predict the solar irradiation in Djibouti for the first Time that is useful to the integration of Concentrating Solar Power (CSP) and sites selections for new or future solar plants as part of solar energy development. An ANN algorithm was developed to establish a forward/reverse correspondence between the latitude, longitude, altitude and monthly solar irradiation. For this purpose the German Aerospace Centre (DLR) data of eight Djibouti sites were used as training and testing in a standard three layers network with the back propagation algorithm of Lavenber-Marquardt. Results have shown a very good agreement for the solar irradiation prediction in Djibouti and proves that the proposed approach can be well used as an efficient tool for prediction of solar irradiation by providing so helpful information concerning sites selection, design and planning of solar plants.

Keywords: artificial neural network, solar irradiation, concentrated solar power, Lavenberg-Marquardt

Procedia PDF Downloads 352