Search results for: Camera network

Authors: M. Safa, S. Samarasinghe

Abstract:

An artificial neural network (ANN) approach was used to model the energy consumption of wheat production. This study was conducted over 35,300 hectares of irrigated and dry land wheat fields in Canterbury in the 2007-2008 harvest year.1 In this study several direct and indirect factors have been used to create an artificial neural networks model to predict energy use in wheat production. The final model can predict energy consumption by using farm condition (size of wheat area and number paddocks), farmers- social properties (education), and energy inputs (N and P use, fungicide consumption, seed consumption, and irrigation frequency), it can also predict energy use in Canterbury wheat farms with error margin of ±7% (± 1600 MJ/ha).

Keywords: Artificial neural network, Canterbury, energy consumption, modelling, New Zealand, wheat.

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Authors: Mónica Pérez-Meza, Rodrigo Montúfar-Chaveznava

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When reconstructing a scenario, it is necessary to know the structure of the elements present on the scene to have an interpretation. In this work we link 3D scenes reconstruction to evolutionary algorithms through the vision stereo theory. We consider vision stereo as a method that provides the reconstruction of a scene using only a couple of images of the scene and performing some computation. Through several images of a scene, captured from different positions, vision stereo can give us an idea about the threedimensional characteristics of the world. Vision stereo usually requires of two cameras, making an analogy to the mammalian vision system. In this work we employ only a camera, which is translated along a path, capturing images every certain distance. As we can not perform all computations required for an exhaustive reconstruction, we employ an evolutionary algorithm to partially reconstruct the scene in real time. The algorithm employed is the fly algorithm, which employ “flies" to reconstruct the principal characteristics of the world following certain evolutionary rules.

Keywords: 3D Reconstruction, Computer Vision, EvolutionaryAlgorithms, Vision Stereo.

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Authors: L. Pina, M. Mika, R. Havlikova, M. Landova, L. Sveda, R. Hudec, V. Marsikova, A. Inneman

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We developed a non-contact method for the in-situ monitoring of the thermal forming of glass and Si foils to optimize the manufacture of mirrors for high-resolution space x-ray telescopes. Their construction requires precise and light-weight segmented optics with angular resolution better than 5 arcsec. We used 75x25 mm Desag D263 glass foils 0.75 mm thick and 0.6 mm thick Si foils. The glass foils were shaped by free slumping on a frame at viscosities in the range of 109.3-1012 dPa·s, the Si foils by forced slumping above 1000°C. Using a Nikon D80 digital camera, we took snapshots of a foil-s shape every 5 min during its isothermal heat treatment. The obtained results we can use for computer simulations. By comparing the measured and simulated data, we can more precisely define material properties of the foils and optimize the forming technology.

Keywords: Glass, in-situ monitoring, silicone, thermal forming, x-ray telescope

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Authors: A. Mihoc, K. Cater

Abstract:

On the bridge of a ship the officers are looking for visual aids to guide navigation in order to reconcile the outside world with the position communicated by the digital navigation system. Aids to navigation include: Lighthouses, lightships, sector lights, beacons, buoys, and others. They are designed to help navigators calculate their position, establish their course or avoid dangers. In poor visibility and dense traffic areas, it can be very difficult to identify these critical aids to guide navigation. The paper presents the usage of Augmented Reality (AR) as a means to present digital information about these aids to support navigation. To date, nautical navigation related mobile AR applications have been limited to the leisure industry. If proved viable, this prototype can facilitate the creation of other similar applications that could help commercial officers with navigation. While adopting a user centered design approach, the team has developed the prototype based on insights from initial research carried on board of several ships. The prototype, built on Nexus 9 tablet and Wikitude, features a head-up display of the navigational aids (lights) in the area, presented in AR and a bird’s eye view mode presented on a simplified map. The application employs the aids to navigation data managed by Hydrographic Offices and the tablet’s sensors: GPS, gyroscope, accelerometer, compass and camera. Sea trials on board of a Navy and a commercial ship revealed the end-users’ interest in using the application and further possibility of other data to be presented in AR. The application calculates the GPS position of the ship, the bearing and distance to the navigational aids; all within a high level of accuracy. However, during testing several issues were highlighted which need to be resolved as the prototype is developed further. The prototype stretched the capabilities of Wikitude, loading over 500 objects during tests in a major port. This overloaded the display and required over 45 seconds to load the data. Therefore, extra filters for the navigational aids are being considered in order to declutter the screen. At night, the camera is not powerful enough to distinguish all the lights in the area. Also, magnetic interference with the bridge of the ship generated a continuous compass error of the AR display that varied between 5 and 12 degrees. The deviation of the compass was consistent over the whole testing durations so the team is now looking at the possibility of allowing users to manually calibrate the compass. It is expected that for the usage of AR in professional maritime contexts, further development of existing AR tools and hardware is needed. Designers will also need to implement a user-centered design approach in order to create better interfaces and display technologies for enhanced solutions to aid navigation.

Keywords: Compass error, GPS, maritime navigation, mobile augmented reality.

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Authors: Igor Astrov, Mikhail Pikkov, Rein Paluoja

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This paper focuses on a critical component of the situational awareness (SA), the control of autonomous vertical flight for vectored thrust aerial vehicle (VTAV). With the SA strategy, we proposed a flight control procedure to address the dynamics variation and performance requirement difference of flight trajectory for an unmanned helicopter model with vectored thrust configuration. This control strategy for chosen model of VTAV has been verified by simulation of take-off and forward maneuvers using software package Simulink and demonstrated good performance for fast stabilization of motors, consequently, fast SA with economy in energy can be asserted during search-and-rescue operations.

Keywords: Neural network predictive controller, situational awareness, vectored thrust aerial vehicle.

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Authors: J. Tospornsampan, I. Kita, M. Ishii, Y. Kitamura

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In this paper a combination approach of two heuristic-based algorithms: genetic algorithm and tabu search is proposed. It has been developed to obtain the least cost based on the split-pipe design of looped water distribution network. The proposed combination algorithm has been applied to solve the three well-known water distribution networks taken from the literature. The development of the combination of these two heuristic-based algorithms for optimization is aimed at enhancing their strengths and compensating their weaknesses. Tabu search is rather systematic and deterministic that uses adaptive memory in search process, while genetic algorithm is probabilistic and stochastic optimization technique in which the solution space is explored by generating candidate solutions. Split-pipe design may not be realistic in practice but in optimization purpose, optimal solutions are always achieved with split-pipe design. The solutions obtained in this study have proved that the least cost solutions obtained from the split-pipe design are always better than those obtained from the single pipe design. The results obtained from the combination approach show its ability and effectiveness to solve combinatorial optimization problems. The solutions obtained are very satisfactory and high quality in which the solutions of two networks are found to be the lowest-cost solutions yet presented in the literature. The concept of combination approach proposed in this study is expected to contribute some useful benefits in diverse problems.

Keywords: GAs, Heuristics, Looped network, Least-cost design, Pipe network, Optimization, TS

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Authors: Kiyohiro Yamazaki

Abstract:

A purpose of this study is to examine how a firm without fundamental technology is able to gain the competitive advantage. This paper examines three case studies, Sony in the flat display TV industry, Casio in the digital camera industry and Nintendo in the home game machine industry. This paper maintain the firms without fundamental technology construct two advantages, economic advantage and organizational advantage. An economic advantage involves the firm can select either high-tech or cheap devices out of several device makers, and change the alternatives cheaply and quickly. In addition, organizational advantage means that a firm without fundamental technology is not restricted by organizational inertia and cognitive restraints, and exercises the characteristic of strength.

Keywords: Firm without fundamental technology, economic advantage, organizational advantage, Sony, Casio, Nintendo.

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Authors: P. Saravanan, T. Arunkumar

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Vehicular Adhoc Network (VANET) is a new technology which aims to ensure intelligent inter-vehicle communications, seamless internet connectivity leading to improved road safety, essential alerts, and access to comfort and entertainment. VANET operations are hindered by mobile node’s (vehicles) uncertain mobility. Routing algorithms use metrics to evaluate which path is best for packets to travel. Metrics like path length (hop count), delay, reliability, bandwidth, and load determine optimal route. The proposed scheme exploits link quality, traffic density, and intersections as routing metrics to determine next hop. This study enhances Geographical Routing Protocol (GRP) using fuzzy controllers while rules are optimized with Bee Swarm Optimization (BSO). Simulations results are compared to conventional GRP.

Keywords: Bee Swarm Optimization (BSO), Geographical Routing Protocol (GRP), Vehicular Adhoc Network (VANET).

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Authors: T. S. Chou, K. K. Yen, J. Luo

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The network traffic data provided for the design of intrusion detection always are large with ineffective information and enclose limited and ambiguous information about users- activities. We study the problems and propose a two phases approach in our intrusion detection design. In the first phase, we develop a correlation-based feature selection algorithm to remove the worthless information from the original high dimensional database. Next, we design an intrusion detection method to solve the problems of uncertainty caused by limited and ambiguous information. In the experiments, we choose six UCI databases and DARPA KDD99 intrusion detection data set as our evaluation tools. Empirical studies indicate that our feature selection algorithm is capable of reducing the size of data set. Our intrusion detection method achieves a better performance than those of participating intrusion detectors.

Keywords: Intrusion detection, feature selection, k-nearest neighbors, fuzzy clustering, Dempster-Shafer theory

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Authors: Nermine Ahmed Hendy, Hania Farag

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Emotion recognition is an important research field that finds lots of applications nowadays. This work emphasizes on recognizing different emotions from speech signal. The extracted features are related to statistics of pitch, formants, and energy contours, as well as spectral, perceptual and temporal features, jitter, and shimmer. The Artificial Neural Networks (ANN) was chosen as the classifier. Working on finding a robust and fast ANN classifier suitable for different real life application is our concern. Several experiments were carried out on different ANN to investigate the different factors that impact the classification success rate. Using a database containing 7 different emotions, it will be shown that with a proper and careful adjustment of features format, training data sorting, number of features selected and even the ANN type and architecture used, a success rate of 85% or even more can be achieved without increasing the system complicity and the computation time

Keywords: Classification, emotion recognition, features extraction, feature selection, neural network

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Authors: Khine Phyu, Aung Myint Aye

Abstract:

This paper presents results of measurements campaign carried out at a carrier frequency of 24GHz with the help of TPLINK router in indoor line-of-sight (LOS) scenarios. Firstly, the radio wave propagation strategies are analyzed in some rooms with router of point to point Ad hoc network. Then floor attenuation is defined for 3 floors in experimental region. The free space model and dual slope models are modified by considering the influence of corridor conditions on each floor. Using these models, indoor signal attenuation can be estimated in modeling of indoor radio wave propagation. These results and modified models can also be used in planning the networks of future personal communications services.

Keywords: radio wave signal analyzing, LOS radio wavepropagation, indoor radio wave propagation, free space model, tworay model and indoor attenuation.

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Authors: Shah Ahsanuzzaman Md. Tariq, Fabrizio Granelli

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IEEE 802.11e is the enhanced version of the IEEE 802.11 MAC dedicated to provide Quality of Service of wireless network. It supports QoS by the service differentiation and prioritization mechanism. Data traffic receives different priority based on QoS requirements. Fundamentally, applications are divided into four Access Categories (AC). Each AC has its own buffer queue and behaves as an independent backoff entity. Every frame with a specific priority of data traffic is assigned to one of these access categories. IEEE 802.11e EDCA (Enhanced Distributed Channel Access) is designed to enhance the IEEE 802.11 DCF (Distributed Coordination Function) mechanisms by providing a distributed access method that can support service differentiation among different classes of traffic. Performance of IEEE 802.11e MAC layer with different ACs is evaluated to understand the actual benefits deriving from the MAC enhancements.

Keywords: 802.11e, fairness, enhanced distributed channelaccess, access categories, quality of Service.

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Authors: Afaf Alharbi, Qianni Zhang

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The identification of malignant tissue in histopathological slides holds significant importance in both clinical settings and pathology research. This paper presents a methodology aimed at automatically categorizing cancerous tissue through the utilization of a multiple instance learning framework. This framework is specifically developed to acquire knowledge of the Bernoulli distribution of the bag label probability by employing neural networks. Furthermore, we put forward a neural network-based permutation-invariant aggregation operator, equivalent to attention mechanisms, which is applied to the multi-instance learning network. Through empirical evaluation on an openly available colon cancer histopathology dataset, we provide evidence that our approach surpasses various conventional deep learning methods.

Keywords: Attention Multiple Instance Learning, Multiple Instance Learning, transfer learning, histopathological slides, cancer tissue classification.

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Authors: Zhaojun Wang, Zongdi Sun, Zhiyuan Liu

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First of all, the carbon trading price and trading volume in Shanghai are transformed by Fourier transform, and the frequency response diagram is obtained. Then, the frequency response diagram is analyzed and the Blackman filter is designed. The Blackman filter is used to filter, and the carbon trading time domain and frequency response diagram are obtained. After wavelet analysis, the carbon trading data were processed; respectively, we got the average value for each 5 days, 10 days, 20 days, 30 days, and 60 days. Finally, the data are used as input of the Back Propagation Neural Network model for prediction.

Keywords: Shanghai carbon trading, carbon trading price, carbon trading volume, wavelet analysis, BP neural network model.

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Authors: Javed Ahmed, M. N. Jafri, J. Ahmad, Muhammad I. Khan

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Real-time object tracking is a problem which involves extraction of critical information from complex and uncertain imagedata. In this paper, we present a comprehensive methodology to design an artificial neural network (ANN) for a real-time object tracking application. The object, which is tracked for the purpose of demonstration, is a specific airplane. However, the proposed ANN can be trained to track any other object of interest. The ANN has been simulated and tested on the training and testing datasets, as well as on a real-time streaming video. The tracking error is analyzed with post-regression analysis tool, which finds the correlation among the calculated coordinates and the correct coordinates of the object in the image. The encouraging results from the computer simulation and analysis show that the proposed ANN architecture is a good candidate solution to a real-time object tracking problem.

Keywords: Image processing, machine vision, neural networks, real-time object tracking.

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Authors: Shishir Kumar, Chhaya Gangwal, Seema Raj

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Public health informatics (PHI) which has seen successful implementation in the developed world, become the buzzword in the developing countries in providing improved healthcare with enhanced access. In rural areas especially, where a huge gap exists between demand and supply of healthcare facilities, PHI is being seen as a major solution. There are factors such as growing network infrastructure and the technological adoption by the health fraternity which provide support to these claims. Public health informatics has opportunities in healthcare by providing opportunities to diagnose patients, provide intra-operative assistance and consultation from a remote site. It also has certain barriers in the awareness, adaptation, network infrastructure, funding and policy related areas. There are certain medico-legal aspects involving all the stakeholders which need to be standardized to enable a working system. This paper aims to analyze the potential and challenges of Public health informatics services in rural communities.

Keywords: PHI, e-health, Public health.

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Authors: C. Benjbara, A. Habbani

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Wireless networks are getting more and more used in every new technology or feature, especially those without infrastructure (Ad hoc mode) which provide a low cost alternative to the infrastructure mode wireless networks and a great flexibility for application domains such as environmental monitoring, smart cities, precision agriculture, and so on. These application domains present a common characteristic which is the need of coexistence and intercommunication between modules belonging to different types of ad hoc networks like wireless sensor networks, mesh networks, mobile ad hoc networks, vehicular ad hoc networks, etc. This vision to bring to life such heterogeneous networks will make humanity duties easier but its development path is full of challenges. One of these challenges is the communication complexity between its components due to the lack of common or compatible protocols standard. This article proposes a new patented routing protocol based on the OLSR standard in order to resolve the heterogeneous ad hoc networks communication issue. This new protocol is applied on a specific network architecture composed of MANET, VANET, and FANET.

Keywords: Ad hoc, heterogeneous, ID-Node, OLSR.

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Authors: Alnouss M. Ahmed, Al-Nuaimi A. Ibrahim

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In this paper, the energy performance of a selected UHDE Ammonia plant is optimized by conducting heat integration through waste heat recovery and the synthesis of a heat exchange network (HEN). Minimum hot and cold utility requirements were estimated through IChemE spreadsheet. Supporting simulation was carried out using HYSYS software. The results showed that there is no need for heating utility while the required cold utility was found to be around 268,714 kW. Hence a threshold pinch case was faced. Then, the hot and cold streams were matched appropriately. Also, waste heat recovered resulted with savings in HP and LP steams of approximately 51.0% and 99.6%, respectively. An economic analysis on proposed HEN showed very attractive overall payback period not exceeding 3 years. In general, a net saving approaching 35% was achieved in implementing heat optimization of current studied UHDE Ammonia process.

Keywords: Ammonia, Energy Optimization, Heat Exchange Network and Techno-Economic Analysis.

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Authors: A.V.Naresh Babu, S.Sivanagaraju

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A considerable progress has been achieved in transient stability analysis (TSA) with various FACTS controllers. But, all these controllers are associated with single transmission line. This paper is intended to discuss a new approach i.e. a multi-line FACTS controller which is interline power flow controller (IPFC) for TSA of a multi-machine power system network. A mathematical model of IPFC, termed as power injection model (PIM) presented and this model is incorporated in Newton-Raphson (NR) power flow algorithm. Then, the reduced admittance matrix of a multi-machine power system network for a three phase fault without and with IPFC is obtained which is required to draw the machine swing curves. A general approach based on L-index has also been discussed to find the best location of IPFC to reduce the proximity to instability of a power system. Numerical results are carried out on two test systems namely, 6-bus and 11-bus systems. A program in MATLAB has been written to plot the variation of generator rotor angle and speed difference curves without and with IPFC for TSA and also a simple approach has been presented to evaluate critical clearing time for test systems. The results obtained without and with IPFC are compared and discussed.

Keywords: Flexible alternating current transmission system (FACTS), first swing stability, interline power flow controller (IPFC), power injection model (PIM).

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Authors: Olumide Simeon Ogunnusi, Shukor Abd Razak, Michael Kolade Adu

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Inter-agent communication manager facilitates communication among mobile agents via message passing mechanism. Until now, all Foundation for Intelligent Physical Agents (FIPA) compliant agent systems are capable of exchanging messages following the standard format of sending and receiving messages. Previous works tend to secure messages to be exchanged among a community of collaborative agents commissioned to perform specific tasks using cryptosystems. However, the approach is characterized by computational complexity due to the encryption and decryption processes required at the two ends. The proposed approach to secure agent communication allows only agents that are created by the host agent server to communicate via the agent communication channel provided by the host agent platform. These agents are assumed to be harmless. Therefore, to secure communication of legitimate agents from intrusion by external agents, a 2-phase policy enforcement system was developed. The first phase constrains the external agent to run only on the network server while the second phase confines the activities of the external agent to its execution environment. To implement the proposed policy, a controller agent was charged with the task of screening any external agent entering the local area network and preventing it from migrating to the agent execution host where the legitimate agents are running. On arrival of the external agent at the host network server, an introspector agent was charged to monitor and restrain its activities. This approach secures legitimate agent communication from Man-in-the Middle and Replay attacks.

Keywords: Agent communication, introspective agent, isolation of agent, policy enforcement system.

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Authors: Bertalan Forstner, Dr. Hassan Charaf

Abstract:

In the world of Peer-to-Peer (P2P) networking different protocols have been developed to make the resource sharing or information retrieval more efficient. The SemPeer protocol is a new layer on Gnutella that transforms the connections of the nodes based on semantic information to make information retrieval more efficient. However, this transformation causes high clustering in the network that decreases the number of nodes reached, therefore the probability of finding a document is also decreased. In this paper we describe a mathematical model for the Gnutella and SemPeer protocols that captures clustering-related issues, followed by a proposition to modify the SemPeer protocol to achieve moderate clustering. This modification is a sort of link management for the individual nodes that allows the SemPeer protocol to be more efficient, because the probability of a successful query in the P2P network is reasonably increased. For the validation of the models, we evaluated a series of simulations that supported our results.

Keywords: Peer-to-Peer, model, performance, networkmanagement.

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Authors: Kifah Tout, Nisrine Sinno, Mohamad Mikati

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Many studies have focused on the nonlinear analysis of electroencephalography (EEG) mainly for the characterization of epileptic brain states. It is assumed that at least two states of the epileptic brain are possible: the interictal state characterized by a normal apparently random, steady-state EEG ongoing activity; and the ictal state that is characterized by paroxysmal occurrence of synchronous oscillations and is generally called in neurology, a seizure. The spatial and temporal dynamics of the epileptogenic process is still not clear completely especially the most challenging aspects of epileptology which is the anticipation of the seizure. Despite all the efforts we still don-t know how and when and why the seizure occurs. However actual studies bring strong evidence that the interictal-ictal state transition is not an abrupt phenomena. Findings also indicate that it is possible to detect a preseizure phase. Our approach is to use the neural network tool to detect interictal states and to predict from those states the upcoming seizure ( ictal state). Analysis of the EEG signal based on neural networks is used for the classification of EEG as either seizure or non-seizure. By applying prediction methods it will be possible to predict the upcoming seizure from non-seizure EEG. We will study the patients admitted to the epilepsy monitoring unit for the purpose of recording their seizures. Preictal, ictal, and post ictal EEG recordings are available on such patients for analysis The system will be induced by taking a body of samples then validate it using another. Distinct from the two first ones a third body of samples is taken to test the network for the achievement of optimum prediction. Several methods will be tried 'Backpropagation ANN' and 'RBF'.

Keywords: Artificial neural network (ANN), automatic prediction, epileptic seizures analysis, genetic algorithm.

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Authors: K. Kalaiselvan, A. Elango, N. M. Nagarajan

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Today automobile and aerospace industries realise Laser Beam Welding for a clean and non contact source of heating and fusion for joining of sheets. The welding performance is mainly based on by the laser welding parameters. Some concepts related to Artificial Neural Networks and how can be applied to model weld bead geometry and mechanical properties in terms of equipment parameters are reported in order to evaluate the accuracy and compare it with traditional modeling schemes. This review reveals the output features of Titanium and Aluminium weld bead geometry and mechanical properties such as ultimate tensile strength, yield strength, elongation and reduction of the area of the weld using Artificial Neural Network.

Keywords: Laser Beam Welding (LBW), Artificial Neural Networks (ANN), Optimization, Titanium and Aluminium sheets.

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Authors: Surender Kumar Soni

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Wireless Sensor Networks (WSNs) are wireless networks consisting of number of tiny, low cost and low power sensor nodes to monitor various physical phenomena like temperature, pressure, vibration, landslide detection, presence of any object, etc. The major limitation in these networks is the use of nonrechargeable battery having limited power supply. The main cause of energy consumption WSN is communication subsystem. This paper presents an efficient grid formation/clustering strategy known as Grid based level Clustering and Aggregation of Data (GCAD). The proposed clustering strategy is simple and scalable that uses low duty cycle approach to keep non-CH nodes into sleep mode thus reducing energy consumption. Simulation results demonstrate that our proposed GCAD protocol performs better in various performance metrics.

Keywords: Ad hoc network, Cluster, Grid base clustering, Wireless sensor network.

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Authors: Muhammad Aqil, Ichiro Kita, Akira Yano, Nishiyama Soichi

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This paper presents an alternate approach that uses artificial neural network to simulate the flood level dynamics in a river basin. The algorithm was developed in a decision support system environment in order to enable users to process the data. The decision support system is found to be useful due to its interactive nature, flexibility in approach and evolving graphical feature and can be adopted for any similar situation to predict the flood level. The main data processing includes the gauging station selection, input generation, lead-time selection/generation, and length of prediction. This program enables users to process the flood level data, to train/test the model using various inputs and to visualize results. The program code consists of a set of files, which can as well be modified to match other purposes. This program may also serve as a tool for real-time flood monitoring and process control. The running results indicate that the decision support system applied to the flood level seems to have reached encouraging results for the river basin under examination. The comparison of the model predictions with the observed data was satisfactory, where the model is able to forecast the flood level up to 5 hours in advance with reasonable prediction accuracy. Finally, this program may also serve as a tool for real-time flood monitoring and process control.

Keywords: Decision Support System, Neural Network, Flood Level

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Authors: M.R. Ebrahimi, M. Ehsan

Abstract:

This paper presents a practical scheme that can be used for allocating the transmission loss to generators and loads. In this scheme first the share of a generator or load on the current through a branch is determined using Z-bus modified matrix. Then the current components are decomposed and the branch loss allocation is obtained. A motivation of proposed scheme is to improve the results of Z-bus method and to reach more fair allocation. The proposed scheme has been implemented and tested on several networks. To achieve practical and applicable results, the proposed scheme is simulated and compared on the transmission network (400kv) of Khorasan region in Iran and the 14-bus standard IEEE network. The results show that the proposed scheme is comprehensive and fair to allocating the energy losses of a power market to its participants.

Keywords: Transmission Loss, Loss Allocation, Z-bus modifiedmatrix, current Components Decomposition and Restructured PowerSystems

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Authors: Namal A. Senanayake, A. Beg, Withana C. Prasad

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This paper analyzes the patterns of the Monte Carlo data for a large number of variables and minterms, in order to characterize the circuit path length behavior. We propose models that are determined by training process of shortest path length derived from a wide range of binary decision diagram (BDD) simulations. The creation of the model was done use of feed forward neural network (NN) modeling methodology. Experimental results for ISCAS benchmark circuits show an RMS error of 0.102 for the shortest path length complexity estimation predicted by the NN model (NNM). Use of such a model can help reduce the time complexity of very large scale integrated (VLSI) circuitries and related computer-aided design (CAD) tools that use BDDs.

Keywords: Monte Carlo data, Binary decision diagrams, Neural network modeling, Shortest path length estimation.

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Authors: Sorin Valcan, Mihail Găianu

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Labeling is a very costly and time consuming process which aims to generate datasets for training neural networks in several functionalities and projects. For driver monitoring system projects, the need of labeled images has a significant impact on the budget and distribution of effort. This paper presents the modifications done to a ground truth data generation algorithm for 2D eyes location on infrared images with drivers in order to improve the quality of the data and performance of the trained neural networks. The algorithm restrictions become tougher which makes it more accurate but also less constant. The resulting dataset becomes smaller and shall not be altered by any kind of manual labels adjustment before being used in the neural networks training process. These changes resulted in a much better performance of the trained neural networks.

Keywords: Labeling automation, infrared camera, driver monitoring, eye detection, Convolutional Neural Networks.

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Authors: Wilfred Fritz

Abstract:

Grid connected solar PV inverters need to be compliant to standard regulations regarding unwanted harmonic generation. This paper gives an introduction to harmonics, solar PV inverter voltage regulation and balancing through compensation and investigates the behaviour of harmonic generation at different power levels. Practical measurements of harmonics and power levels with a power quality data logger were made, on a test network at a university in Germany. The test setup and test results are discussed. The major finding was that between the morning and afternoon load peak windows when the PV inverters operate under low solar insolation and low power levels, more unwanted harmonics are generated. This has a huge impact on the power quality of the grid as well as capital and maintenance costs. The design of a single-tuned harmonic filter towards harmonic mitigation is presented.

Keywords: Harmonics, power quality, pulse width modulation, total harmonic distortion.

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Authors: Salim Ouchtati, Jean Sequeira, Mouldi Bedda

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In the context of the handwriting recognition, we propose an off line system for the recognition of the Arabic handwritten words of the Algerian departments. The study is based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm. The used parameters to form the input vector of the neural network are extracted on the binary images of the handwritten word by several methods. The Distribution parameters, the centered moments of the different projections of the different segments, the centered moments of the word image coding according to the directions of Freeman, and the Barr features applied binary image of the word and on its different segments. The classification is achieved by a multi layers perceptron. A detailed experiment is carried and satisfactory recognition results are reported.

Keywords: Handwritten word recognition, neural networks, image processing, pattern recognition, features extraction.

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