Search results for: graph representation of circuit networks

Authors: Mohammad Reza Taherkhani‎

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Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make a connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.

Keywords: underwater sensor networks, clustering, learning automata, energy consumption

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Authors: Jesús Antonio Puente Fernández, Luis Javier Garcia Villalba

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New trends on streaming videos such as series or films require a high demand of network resources. This fact results in a huge problem within traditional IP networks due to the rigidity of its architecture. In this way, Software Defined Networks (SDN) is a new concept of network architecture that intends to be more flexible and it simplifies the management in networks with respect to the existing ones. These aspects are possible due to the separation of control plane (controller) and data plane (switches). Taking the advantage of this separated control, it is easy to deploy a monitoring tool independent of device vendors since the existing ones are dependent on the installation of specialized and expensive hardware. In this paper, we propose a framework that optimizes the traffic monitoring in SDN networks that decreases the number of monitoring queries to improve the network traffic and also reduces the overload. The performed experiments (with and without the optimization) using a video streaming delivery between two hosts demonstrate the feasibility of our monitoring proposal.

Keywords: optimization, monitoring, software defined networking, statistics, query

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Authors: Xupin Zhang, Maria Caterina Bramati, Enrest Fokoue

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Understanding the career placement of graduates from the university is crucial for both the qualities of education and ultimate satisfaction of students. In this research, we adapt the capabilities of dynamic Bayesian networks to characterize and predict students’ job placement using data from various universities. We also provide elements of the estimation of the indicator (score) of the strength of the network. The research focuses on overall findings as well as specific student groups including international and STEM students and their insight on the career path and what changes need to be made. The derived Bayesian network has the potential to be used as a tool for simulating the career path for students and ultimately helps universities in both academic advising and career counseling.

Keywords: dynamic bayesian networks, indicator estimation, job placement, social networks

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Authors: Belbahi Ahlam

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With the phenomenal growth in the Internet, network security has become an integral part of computer and information security. In order to come up with measures that make networks more secure, it is important to learn about the vulnerabilities that could exist in a computer network and then have an understanding of the typical attacks that have been carried out in such networks. The first half of this paper will expose the readers to the classical network attacks that have exploited the typical vulnerabilities of computer networks in the past and solutions that have been adopted since then to prevent or reduce the chances of some of these attacks. The second half of the paper will expose the readers to the different network security controls including the network architecture, protocols, standards and software/ hardware tools that have been adopted in modern day computer networks.

Keywords: network security, attacks and controls, computer and information, solutions

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Authors: Rabiaa Gammoudi, Najet Rebei, Othman Hasnaoui

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Photovoltaic systems use different techniques for tracking the Maximum Power Point (MPPT) to provide the highest possible power to the load regardless of the climatic conditions variation. In this paper, the proposed method is the Open Circuit (OC) method with sudden and random variations of insolation. The simulation results of the water pumping system controlled by OC method are validated by an experimental experience in real-time using a test bench composed by a centrifugal pump powered by a PVG via a boost chopper for the adaptation between the source and the load. The output of the DC/DC converter supplies the motor pump LOWARA type, assembly by means of a DC/AC inverter. The control part is provided by a computer incorporating a card DS1104 running environment Matlab/Simulink for visualization and data acquisition. These results show clearly the effectiveness of our control with a very good performance. The results obtained show the usefulness of the developed algorithm in solving the problem of degradation of PVG performance depending on the variation of climatic factors with a very good yield.

Keywords: PVWPS (PV Water Pumping System), maximum power point tracking (MPPT), open circuit method (OC), boost converter, DC/AC inverter

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Authors: Y. Kobori, S. Tanaka, N. Tsukiji, N. Takai, H. Kobayashi

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This paper proposes two kinds of new single-inductor dual-output (SIDO) DC-DC switching converters with ripple-based hysteretic control. First SIDO converters of type 1 utilize the triangular signal generated by the CR-circuit connected across the inductor. This triangular signal is used for generating the PWM signal instead of the saw-tooth signal used in the conventional converters. Second SIDO converters of type 2 utilize the triangular signal generated by the CR-circuit connected across the voltage error amplifier. This paper describes circuit topologies, Operation principles, simulation results and experimental results of the proposed SIDO converters. In simulation results of both type of SIDO converters, static output voltage ripples are less than 5mVpp and over/under shoots of the dynamic load regulations for the output current step are less than +/- 10mV. In experimental results of single output converter of type 2, static output voltage ripples are about 20mVpp. Output ripples of SIDO type 1 converter are about 80mVpp.

Keywords: DC-DC converter, switching converter, SIDO converter, hysteretic control, ripple-based control

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Authors: Tolga Yazıcı

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As a matter of the fact that online social networks like Twitter, Facebook and MySpace have experienced an extensive growth in recent years. Social media offers individuals with a tool for communicating and interacting with one another. These social networks enable people to stay in touch with other people and express themselves. This process makes the users of online social networks active creators of content rather than being only consumers of traditional media. That’s why millions of people show strong desire to learn the methods and tools of digital content production and necessary communication skills. However, the booming interest in communication and interaction through online social networks and high level of eagerness to invent and implement the ways to participate in content production raise some privacy and security concerns. This presentation aims to open the assumed revolutionary, democratic and liberating nature of the online social media up for discussion by reviewing some recent political developments in Turkey. Firstly, the role of Internet and online social networks in mobilizing collective movements through social interactions and communications will be questioned. Secondly, some cases from Gezi and Okmeydanı Protests and also December 17-25 period will be presented in order to illustrate misinformation and manipulation in social media and violation of individual privacy through online social networks in order to damage social unity and stability contradictory to democratic nature of online social networking.

Keywords: online social media networks, democratic participation, social movements, social polarization, privacy of individuals, Turkey

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Authors: Vinay A. Sharma, Shiva Prasad H. C.

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The initial stages of any project are often observed to be in a mixed set of conditions. Setting up the project is a tough task, but taking the initial decisions is rather not complex, as some of the critical factors are yet to be introduced into the scenario. These simple initial decisions potentially shape the timeline and subsequent events that might later be plotted on it. Proceeding towards the solution for a problem is the primary objective in the initial stages. The optimization in the solutions can come later, and hence, the resources deployed towards attaining the solution are higher than what they would have been in the optimized versions. A ‘logic’ that counters the problem is essentially the core of the desired solution. Thus, if the problem is solved, the deployment of resources has led to the required logic being attained. As the project proceeds along, the individuals working on the project face fresh challenges as a team and are better accustomed to their surroundings. The developed, optimized solutions are then considered for implementation, as the individuals are now experienced, and know better of the consequences and causes of possible failure, and thus integrate the adequate tolerances wherever required. Furthermore, as the team graduates in terms of strength, acquires prodigious knowledge, and begins its efficient transfer, the individuals in charge of the project along with the managers focus more on the optimized solutions rather than the traditional ones to minimize the required resources. Hence, as time progresses, the authorities prioritize attainment of the required logic, at a lower amount of dedicated resources. For empirical analysis of the stated theory, leaders and key figures in organizations are surveyed for their ideas on appropriate logic required for tackling a problem. Key-pointers spotted in successfully implemented solutions are noted from the analysis of the responses and a metric for measuring logic is developed. A graph is plotted with the quantifiable logic on the Y-axis, and the dedicated resources for the solutions to various problems on the X-axis. The dedicated resources are plotted over time, and hence the X-axis is also a measure of time. In the initial stages of the project, the graph is rather linear, as the required logic will be attained, but the consumed resources are also high. With time, the authorities begin focusing on optimized solutions, since the logic attained through them is higher, but the resources deployed are comparatively lower. Hence, the difference between consecutive plotted ‘resources’ reduces and as a result, the slope of the graph gradually increases. On an overview, the graph takes a parabolic shape (beginning on the origin), as with each resource investment, ideally, the difference keeps on decreasing, and the logic attained through the solution keeps increasing. Even if the resource investment is higher, the managers and authorities, ideally make sure that the investment is being made on a proportionally high logic for a larger problem, that is, ideally the slope of the graph increases with the plotting of each point.

Keywords: decision-making, leadership, logic, strategic management

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Authors: Raj Kumar Rajak, Bharat Mishra

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Bio-batteries represent an entirely new long-term, reasonable, reachable and ecofriendly approach to produce sustainable energy. In the present experimental work, we have studied the effect of generation of power by bio-battery using different electrode pairs. The tests show that it is possible to generate electricity using cow dung as an electrolyte. C-Mg electrode pair shows maximum voltage and SCC (Short Circuit Current) while C-Zn electrode pair shows less OCV (Open Circuit Voltage) and SCC. We have chosen C-Zn electrodes because Mg electrodes are not economical. By the studies of different electrodes and cow dung, it is found that C-Zn electrode battery is more suitable. This result shows that the bio-batteries have the potency to full fill the need of electricity demand for lower energy equipment.

Keywords: bio-batteries, electricity, cow-dung, electrodes, non-conventional

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Authors: Jiahe Liu, HongyangYu, Feng Qian, Miao Luo

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This study aimed to address the problem of improving the confidence of key points by fusing multi-view information, thereby estimating human posture more accurately. We first obtained multi-view image information and then used the MvP algorithm to fuse this multi-view information together to obtain a set of high-confidence human key points. We used these as the input for the Spatio-Temporal Graph Convolution (ST-GCN). ST-GCN is a deep learning model used for processing spatio-temporal data, which can effectively capture spatio-temporal relationships in video sequences. By using the MvP algorithm to fuse multi-view information and inputting it into the spatio-temporal graph convolution model, this study provides an effective method to improve the accuracy of human posture estimation and provides strong support for further research and application in related fields.

Keywords: multi-view, pose estimation, ST-GCN, joint fusion

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Authors: Ehsan Yadegari

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While there are several metaheuristics and exact approaches to solving the Supply Chain Network Design (SCND) problem, there still remains an unfilled gap in using the Teaching-Learning-Based Optimization (TLBO) algorithm. The algorithm has demonstrated desirable results with problems with complicated combinational optimization. The present study introduces a Discrete Self-Study TLBO (DSS-TLBO) with priority-based solution representation that can solve a supply chain network configuration model to lower the total expenses of establishing facilities and the flow of materials. The network features four layers, namely suppliers, plants, distribution centers (DCs), and customer zones. It is designed to meet the customer’s demand through transporting the material between layers of network and providing facilities in the best economic Potential locations. To have a higher quality of the solution and increase the speed of TLBO, a distinct operator was introduced that ensures self-adaptation (self-study) in the algorithm based on the four types of local search. In addition, while TLBO is used in continuous solution representation and priority-based solution representation is discrete, a few modifications were added to the algorithm to remove the solutions that are infeasible. As shown by the results of experiments, the superiority of DSS-TLBO compared to pure TLBO, genetic algorithm (GA) and firefly Algorithm (FA) was established.

Keywords: supply chain network design, teaching–learning-based optimization, improved metaheuristics, discrete solution representation

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Authors: G. Yoga Sairam Teja, K. Harsha Vardhan, A. Vinay Kumar, K. Nithish Kumar, Ch. Shanthi Priyag

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The emergence of the Internet of Things (IoT) has facilitated better device control and monitoring in the modern world. The constant monitoring of a patient would be drastically altered by the usage of IoT in healthcare. As we've seen in the case of the COVID-19 pandemic, it's important to keep oneself untouched while continuously checking on the patient's heart rate and temperature. Additionally, patients with paralysis should be closely watched, especially if they are elderly and in need of special care. Our "IoT BASED PATIENT HEALTH MONITORING SYSTEM" project uses IoT to track patient health conditions in an effort to address these issues. In this project, the main board is an 8051 microcontroller that connects a number of sensors, including a heart rate sensor, a temperature sensor (LM-35), and a saline water measuring circuit. These sensors are connected via an ESP832 (WiFi) module, which enables the sending of recorded data directly to the cloud so that the patient's health status can be regularly monitored. An LCD is used to monitor the data in offline mode, and a buzzer will sound if any variation from the regular readings occurs. The data in the cloud may be viewed as a graph, making it simple for a user to spot any unusual conditions.

Keywords: IoT, ESP8266, 8051 microcontrollers, sensors

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Authors: Yuta Nakano, Kozo Kajiwara, Atsushi Hori, Takeshi Fujita

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In association with the wave of electric vehicles (EV), low energy consumption systems have become more and more important. One of the key technologies to realize low energy consumption is a dynamic vision sensor (DVS), or we can call it an event sensor, neuromorphic vision sensor and so on. This sensor has several features, such as high temporal resolution, which can achieve 1 Mframe/s, and a high dynamic range (120 DB). However, the point that can contribute to low energy consumption the most is its sparsity; to be more specific, this sensor only captures the pixels that have intensity change. In other words, there is no signal in the area that does not have any intensity change. That is to say, this sensor is more energy efficient than conventional sensors such as RGB cameras because we can remove redundant data. On the other side of the advantages, it is difficult to handle the data because the data format is completely different from RGB image; for example, acquired signals are asynchronous and sparse, and each signal is composed of x-y coordinate, polarity (two values: +1 or -1) and time stamp, it does not include intensity such as RGB values. Therefore, as we cannot use existing algorithms straightforwardly, we have to design a new processing algorithm to cope with DVS data. In order to solve difficulties caused by data format differences, most of the prior arts make a frame data and feed it to deep learning such as Convolutional Neural Networks (CNN) for object detection and recognition purposes. However, even though we can feed the data, it is still difficult to achieve good performance due to a lack of intensity information. Although polarity is often used as intensity instead of RGB pixel value, it is apparent that polarity information is not rich enough. Considering this context, we proposed to use the timestamp information as a data representation that is fed to deep learning. Concretely, at first, we also make frame data divided by a certain time period, then give intensity value in response to the timestamp in each frame; for example, a high value is given on a recent signal. We expected that this data representation could capture the features, especially of moving objects, because timestamp represents the movement direction and speed. By using this proposal method, we made our own dataset by DVS fixed on a parked car to develop an application for a surveillance system that can detect persons around the car. We think DVS is one of the ideal sensors for surveillance purposes because this sensor can run for a long time with low energy consumption in a NOT dynamic situation. For comparison purposes, we reproduced state of the art method as a benchmark, which makes frames the same as us and feeds polarity information to CNN. Then, we measured the object detection performances of the benchmark and ours on the same dataset. As a result, our method achieved a maximum of 7 points greater than the benchmark in the F1 score.

Keywords: event camera, dynamic vision sensor, deep learning, data representation, object recognition, low energy consumption

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Authors: Huda Alazmi

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Social studies educational research has, so far, devoted very little attention towards spatial thinking in classroom teaching. To help address such paucity, this study explores the spatial thinking instructional experiences of middle school social studies teachers in Kuwait. The goal is to learn their teaching practices and assess teacher understanding for the spatial thinking concept to enable future improvements. Using a qualitative study approach, the researcher conducted semi-structured interviews to examine the relevant experiences of 14 social studies teachers. The findings revealed three major themes: (1) concepts of space, (2) tools of representation, and (3) spatial reasoning. These themes illustrated how social studies teachers focus predominantly upon simple concepts of space, using multiple tools of representation, but avoid addressing critical spatial reasoning. The findings help explain the current situation while identifying weaker areas for further analysis and improvement.

Keywords: spatial thinking, concepts of space, spatial representation, spatial reasoning

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Authors: Abdelghafour Galadi

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In this paper, accurate power MOSFET models including quasi-saturation effect are presented. These models have no internal node voltages determined by the circuit simulator and use one JFET or one depletion mode MOSFET transistors controlled by an “effective” gate voltage taking into account the quasi-saturation effect. The proposed models achieve accurate simulation results with an average error percentage less than 9%, which is an improvement of 21 percentage points compared to the commonly used standard power MOSFET model. In addition, the models can be integrated in any available commercial circuit simulators by using their analytical equations. A description of the models will be provided along with the parameter extraction procedure.

Keywords: power MOSFET, drift layer, quasi-saturation effect, SPICE model

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Authors: Danilo López, Johana Hernández, Edwin Rivas

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The use of the Multilayer Perceptron Neural Networks (MLPNN) technique is presented to estimate the future state of use of a licensed channel by primary users (PUs); this will be useful at the spectral decision stage in cognitive radio networks (CRN) to determine approximately in which time instants of future may secondary users (SUs) opportunistically use the spectral bandwidth to send data through the primary wireless network. To validate the results, sequences of occupancy data of channel were generated by simulation. The results show that the prediction percentage is greater than 60% in some of the tests carried out.

Keywords: cognitive radio, neural network, prediction, primary user

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Authors: Tobias Milz, L. Macpherson, Varvara Vetrova

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The law is a fundamental pillar of human societies as it shapes, controls and governs how humans conduct business, behave and interact with each other. Recent advances in computer-assisted technologies such as NLP, data science and AI are creating opportunities to support the practice, research and study of this pervasive domain. It is therefore not surprising that there has been an increase in investments into supporting technologies for the legal industry (also known as “legal tech” or “law tech”) over the last decade. A sub-discipline of particular appeal is concerned with assisted legal research. Supporting law researchers and practitioners to retrieve information from the vast amount of ever-growing legal documentation is of natural interest to the legal research community. One tool that has been in use for this purpose since the early nineteenth century is legal citation indexing. Among other use cases, they provided an effective means to discover new precedent cases. Nowadays, computer-assisted network analysis tools can allow for new and more efficient ways to reveal the “hidden” information that is conveyed through citation behavior. Unfortunately, access to openly available legal data is still lacking in New Zealand and access to such networks is only commercially available via providers such as LexisNexis. Consequently, there is a need to create, analyze and provide a legal citation network with sufficient data to support legal research tasks. This paper describes the development and analysis of a legal citation Network for New Zealand containing over 300.000 decisions from 125 different courts of all areas of law and jurisdiction. Using python, the authors assembled web crawlers, scrapers and an OCR pipeline to collect and convert court decisions from openly available sources such as NZLII into uniform and machine-readable text. This facilitated the use of regular expressions to identify references to other court decisions from within the decision text. The data was then imported into a graph-based database (Neo4j) with the courts and their respective cases represented as nodes and the extracted citations as links. Furthermore, additional links between courts of connected cases were added to indicate an indirect citation between the courts. Neo4j, as a graph-based database, allows efficient querying and use of network algorithms such as PageRank to reveal the most influential/most cited courts and court decisions over time. This paper shows that the in-degree distribution of the New Zealand legal citation network resembles a power-law distribution, which indicates a possible scale-free behavior of the network. This is in line with findings of the respective citation networks of the U.S. Supreme Court, Austria and Germany. The authors of this paper provide the database as an openly available data source to support further legal research. The decision texts can be exported from the database to be used for NLP-related legal research, while the network can be used for in-depth analysis. For example, users of the database can specify the network algorithms and metrics to only include specific courts to filter the results to the area of law of interest.

Keywords: case citation network, citation analysis, network analysis, Neo4j

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Authors: Mamidi Ramakrishna Rao

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Doubly-fed induction generator (DFIG) is currently the choice for many wind turbines. These generators, when connected to the grid through a converter, is subjected to varied power system conditions like voltage variation, frequency variation, short circuit fault conditions, etc. Further, many countries like Canada, Germany, UK, Scotland, etc. have distinct grid codes relating to wind turbines. Accordingly, following the network faults, wind turbines have to supply a definite reactive current. To satisfy the requirements including reactive current capability, an optimum electrical design becomes a mandate for DFIG to function. This paper intends to optimize the equivalent circuit parameters of an electrical design for satisfactory DFIG performance. Direct search method has been used for optimization of the parameters. The variables selected include electromagnetic core dimensions (diameters and stack length), slot dimensions, radial air gap between stator and rotor and winding copper cross section area. Optimization for 2 MW DFIG has been executed separately for three objective functions - maximum reactive power capability (Case I), maximum efficiency (Case II) and minimum weight (Case III). In the optimization analysis program, voltage variations (10%), power factor- leading and lagging (0.95), speeds for corresponding to slips (-0.3 to +0.3) have been considered. The optimum designs obtained for objective functions were compared. It can be concluded that direct search method of optimization helps in determining an optimum electrical design for each objective function like efficiency or reactive power capability or weight minimization.

Keywords: direct search, DFIG, equivalent circuit parameters, optimization

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Authors: Suman

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In the continuous interaction between the past and the present that historiography is, each time when history gets re/written, a new representation emerges. This new representation is a reflection of the earlier archives and their interpretations, fragmented remembrances of the past, as well as the reactions to the present. Memory, or lack thereof, and stereotyping generally play a major role in this representation. William Dalrymple’s Nine Lives: In Search of the Sacred in Modern India (2009) is one such written account that sets out to narrate the representations of religion and culture of India and contemporary reactions to it. Dalrymple’s nine saints belong to different castes, sects, religions, and regions. By dealing with their religions and expressions of those religions, and through the lived mysticism of these nine individuals, the book engages with some important issues like class, caste and gender in the contexts provided by historical as well as present India. The paper studies the development of religion and accompanied feeling of religiosity in modern as well as historical contexts through a study of these elements in the book. Since, the language used in creation of texts and the literary texts thus produced create a new reality that questions the stereotypes of the past, and in turn often end up creating new stereotypes or stereotypical representations at times, the paper seeks to actively engage with the text in order to identify and study such stereotypes, along with their changing representations. Through a detailed examination of the book, the paper seeks to unravel whether some socio-cultural stereotypes existed earlier, and whether there is development of new stereotypes from Dalrymple’s point of view as an outsider writing on issues that are deeply rooted in the cultural milieu of the country. For this analysis, the paper takes help from the psycho-literary theories of stereotyping and representation.

Keywords: stereotyping, representation, William Dalrymple, religion

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Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

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Authors: Elaheh Vaezpour

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Data sensing in wireless sensor networks is done by query deceleration the network by the users. In many applications of the wireless sensor networks, many users send queries to the network simultaneously. If the queries are processed separately, the network’s energy consumption will increase significantly. Therefore, it is very important to aggregate the queries before sending them to the network. In this paper, we propose a multiple query optimization framework based on sensors physical and temporal correlation. In the proposed method, queries are merged and sent to network by considering correlation among the sensors in order to reduce the communication cost between the sensors and the base station.

Keywords: wireless sensor networks, multiple query optimization, data correlation, reducing energy consumption

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Authors: Tai Nguyen, Minh Bui, Huong Ninh, Tu Nguyen, Hai Tran

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GraphCut algorithm has been widely utilized to solve various types of computer vision problems. Its expensive computational cost encouraged many researchers to improve the speed of the algorithm. Recent works proposed schemes that work on parallel computing platforms such as CUDA. However, the problem of low convergence speed prevents the usage of GraphCut for real time applications. In this paper, we propose global suppression heuristic to boost the conver-gence process of the algorithm. A parallel implementation of GraphCut algorithm on CUDA designed for the image stitching problem is introduced. Our method achieves up to 3× time boost on the graph of size 80 × 480 compared to the best sequential GraphCut algorithm while achieving satisfactory stitched images, suitable for panorama applications. Our source code will be soon available for further research.

Keywords: CUDA, graph cut, image stitching, texture synthesis, maxflow/mincut algorithm

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Authors: S. Adjiri-Bailiche, S. M. Boudia, H. Daaou, S. Hadouche, A. Benzaoui

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In this work the study of changes in the wind speed depending on the altitude is calculated and described by the model of the neural networks, the use of measured data, the speed and direction of wind, temperature and the humidity at 10 m are used as input data and as data targets at 50m above sea level. Comparing predict wind speeds and extrapolated at 50 m above sea level is performed. The results show that the prediction by the method of artificial neural networks is very accurate.

Keywords: MATLAB, neural network, power low, vertical extrapolation, wind energy, wind speed

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Authors: Engin Eyceyurt, Josko Zec

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The current and future cellular mobile communication networks generate enormous amounts of data. Networks have become extremely complex with extensive space of parameters, features and counters. These networks are unmanageable with legacy methods and an enhanced design and optimization approach is necessary that is increasingly reliant on machine learning. This paper proposes that machine learning as a viable approach for uplink throughput prediction. LTE radio metric, such as Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Noise Ratio (SNR) are used to train models to estimate expected uplink throughput. The prediction accuracy with high determination coefficient of 91.2% is obtained from measurements collected with a simple smartphone application.

Keywords: drive test, LTE, machine learning, uplink throughput prediction

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Authors: Jayesh M. Sonawane, Prakash C. Ghosh

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Landfill leachate emerges as a promising feedstock for microbial fuel cells (MFCs). In the present investigation, direct air-breathing cathode-based MFCs are fabricated to investigate the potential of landfill leachate. Three MFCs that have different cathode areas are fabricated and investigated for 17 days under open circuit conditions. The maximum open circuit voltage (OCV) is observed to be as high as 1.29 V. The maximum cathode area specific power density achieved in the reactor is 1513 mW m^-2. Further studies are under progress to understand the origin of high OCV obtained from landfill leachate-based MFCs.

Keywords: microbial fuel cells, landfill leachate, air-breathing cathode, performance study

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Authors: Motahareh Fadaei

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Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.

Keywords: clustering, energy consumption‎, learning automata, underwater sensor networks

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Authors: Saeid Haghjoo, Ebrahim Reyhani, Fahimeh Kolahdouz

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The present study aimed to evaluate the understanding of the students in Tehran universities (Iran) about the numerical representation of the average rate of change based on the Structure of Observed Learning Outcomes (SOLO). In the present descriptive-survey research, the statistical population included undergraduate students (basic sciences and engineering) in the universities of Tehran. The samples were 604 students selected by random multi-stage clustering. The measurement tool was a task whose face and content validity was confirmed by math and mathematics education professors. Using Cronbach's Alpha criterion, the reliability coefficient of the task was obtained 0.95, which verified its reliability. The collected data were analyzed by descriptive statistics and inferential statistics (chi-squared and independent t-tests) under SPSS-24 software. According to the SOLO model in the prestructural, unistructural, and multistructural levels, basic science students had a higher percentage of understanding than that of engineering students, although the outcome was inverse at the relational level. However, there was no significant difference in the average understanding of both groups. The results indicated that students failed to have a proper understanding of the numerical representation of the average rate of change, in addition to missconceptions when using physics formulas in solving the problem. In addition, multiple solutions were derived along with their dominant methods during the qualitative analysis. The current research proposed to focus on the context problems with approximate calculations and numerical representation, using software and connection common relations between math and physics in the teaching process of teachers and professors.

Keywords: average rate of change, context problems, derivative, numerical representation, SOLO taxonomy

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Authors: Mohammed Panthakkal Abdul Muthalif, Shanmugasundaram Kanagaraj, Jumi Park, Hangyu Park, Youngson Choe

Abstract:

The present study reports the incorporation of calcium ions into the CuS counter electrodes (CEs) in order to modify the photovoltaic performance of quantum dot-sensitized solar cells (QDSSCs). Metal ion-doped CuS thin film was prepared by the chemical bath deposition (CBD) method on FTO substrate and used directly as counter electrodes for TiO₂/CdS/CdSe/ZnS photoanodes based QDSSCs. For the Ca-doped CuS thin films, copper nitrate and thioacetamide were used as anionic and cationic precursors. Calcium nitrate tetrahydrate was used as doping material. The surface morphology of Ca-doped CuS CEs indicates that the fragments are uniformly distributed, and the structure is densely packed with high crystallinity. The changes observed in the diffraction patterns suggest that Ca dopant can introduce increased disorder into CuS material structure. EDX analysis was employed to determine the elemental identification, and the results confirmed the presence of Cu, S, and Ca on the FTO glass substrate. The photovoltaic current density – voltage characteristics of Ca-doped CuS CEs shows the specific improvements in open circuit voltage decay (Voc) and short-circuit current density (Jsc). Electrochemical impedance spectroscopy results display that Ca-doped CuS CEs have greater electrocatalytic activity and charge transport capacity than bare CuS. All the experimental results indicate that 20% Ca-doped CuS CE based QDSSCs exhibit high power conversion efficiency (η) of 4.92%, short circuit current density of 15.47 mA cm⁻², open circuit photovoltage of 0.611 V, and fill factor (FF) of 0.521 under illumination of one sun.

Keywords: Ca-doped CuS counter electrodes, surface morphology, chemical bath deposition method, electrocatalytic activity

Procedia PDF Downloads 164

Authors: Sara Alshehri, Bayan Alenzi, Atheer Alshehri, Samia Chelloug, Zainab Almry, Hussah Albugmai

Abstract:

This paper concerns body area networks sensor that collect signals around a human body. The black hole attacks are the main security challenging problem because the data traffic can be dropped at any node. The focus of our proposed solution is to efficiently route data packets while detecting black hole nodes.

Keywords: body sensor networks, security, black hole, routing, broadcasting, OMNeT++

Procedia PDF Downloads 645

Authors: Iman Farasat, Howard M. Salis

Abstract:

Engineered genetic circuits reprogram cellular behavior to act as living computers with applications in detecting cancer, creating self-controlling artificial tissues, and dynamically regulating metabolic pathways. Phenemenological models are often used to simulate and design genetic circuit behavior towards a desired behavior. While such models assume that each circuit component’s function is modular and independent, even small changes in a circuit (e.g. a new promoter, a change in transcription factor expression level, or even a new media) can have significant effects on the circuit’s function. Here, we use statistical thermodynamics to account for the several factors that control transcriptional regulation in bacteria, and experimentally demonstrate the model’s accuracy across 825 measurements in several genetic contexts and hosts. We then employ our first principles model to design, experimentally construct, and characterize a family of signal amplifying genetic circuits (genetic OpAmps) that expand the dynamic range of cell sensors. To develop these models, we needed a new approach to measuring the in vivo binding free energies of transcription factors (TFs), a key ingredient of statistical thermodynamic models of gene regulation. We developed a new high-throughput assay to measure RNA polymerase and TF binding free energies, requiring the construction and characterization of only a few constructs and data analysis (Figure 1A). We experimentally verified the assay on 6 TetR-homolog repressors and a CRISPR/dCas9 guide RNA. We found that our binding free energy measurements quantitatively explains why changing TF expression levels alters circuit function. Altogether, by combining these measurements with our biophysical model of translation (the RBS Calculator) as well as other measurements (Figure 1B), our model can account for changes in TF binding sites, TF expression levels, circuit copy number, host genome size, and host growth rate (Figure 1C). Model predictions correctly accounted for how these 8 factors control a promoter’s transcription rate (Figure 1D). Using the model, we developed a design framework for engineering multi-promoter genetic circuits that greatly reduces the number of degrees of freedom (8 factors per promoter) to a single dimensionless unit. We propose the Ptashne (Pt) number to encapsulate the 8 co-dependent factors that control transcriptional regulation into a single number. Therefore, a single number controls a promoter’s output rather than these 8 co-dependent factors, and designing a genetic circuit with N promoters requires specification of only N Pt numbers. We demonstrate how to design genetic circuits in Pt number space by constructing and characterizing 15 2-repressor OpAmp circuits that act as signal amplifiers when within an optimal Pt region. We experimentally show that OpAmp circuits using different TFs and TF expression levels will only amplify the dynamic range of input signals when their corresponding Pt numbers are within the optimal region. Thus, the use of the Pt number greatly simplifies the genetic circuit design, particularly important as circuits employ more TFs to perform increasingly complex functions.

Keywords: transcription factor, synthetic biology, genetic circuit, biophysical model, binding energy measurement

Procedia PDF Downloads 473