Search results for: decision based artificial neural network.
11238 Performance Enhancement of Cellular OFDM Based Wireless LANs by Exploiting Spatial Diversity Techniques
Authors: S. Ali. Tajer, Babak H. Khalaj
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This paper represents an investigation on how exploiting multiple transmit antennas by OFDM based wireless LAN subscribers can mitigate physical layer error rate. Then by comparing the Wireless LANs that utilize spatial diversity techniques with the conventional ones it will reveal how PHY and TCP throughputs behaviors are ameliorated. In the next step it will assess the same issues based on a cellular context operation which is mainly introduced as an innovated solution that beside a multi cell operation scenario benefits spatio-temporal signaling schemes as well. Presented simulations will shed light on the improved performance of the wide range and high quality wireless LAN services provided by the proposed approach.
Keywords: Multiple Input Multiple Output (MIMO), Orthogonal Frequency Division Multiplexing (OFDM), and WirelessLocal Area Network (WLAN).
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 140311237 Analysis of Testing and Operational Software Reliability in SRGM based on NHPP
Authors: S. Thirumurugan, D. R. Prince Williams
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Software Reliability is one of the key factors in the software development process. Software Reliability is estimated using reliability models based on Non Homogenous Poisson Process. In most of the literature the Software Reliability is predicted only in testing phase. So it leads to wrong decision-making concept. In this paper, two Software Reliability concepts, testing and operational phase are studied in detail. Using S-Shaped Software Reliability Growth Model (SRGM) and Exponential SRGM, the testing and operational reliability values are obtained. Finally two reliability values are compared and optimal release time is investigated.Keywords: Error Detection Rate, Estimation of Parameters, Instantaneous Failure Rate, Mean Value Function, Non Homogenous Poisson Process (NHPP), Software Reliability.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 163411236 CAPWAP Status and Design Considerations for Seamless Roaming Support
Authors: M. Balfaqih, S. Haseeb, M. H. Mazlan, S. N. Hasnan, O. Mahmoud, A. Hashim
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Wireless LAN technologies have picked up momentum in the recent years due to their ease of deployment, cost and availability. The era of wireless LAN has also given rise to unique applications like VOIP, IPTV and unified messaging. However, these real-time applications are very sensitive to network and handoff latencies. To successfully support these applications, seamless roaming during the movement of mobile station has become crucial. Nowadays, centralized architecture models support roaming in WLANs. They have the ability to manage, control and troubleshoot large scale WLAN deployments. This model is managed by Control and Provision of Wireless Access Point protocol (CAPWAP). This paper covers the CAPWAP architectural solution along with its proposals that have emerged. Based on the literature survey conducted in this paper, we found that the proposed algorithms to reduce roaming latency in CAPWAP architecture do not support seamless roaming. Additionally, they are not sufficient during the initial period of the network. This paper also suggests important design consideration for mobility support in future centralized IEEE 802.11 networks.Keywords: 802.11, centralized Architecture, CAPWAP, Roaming.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 303811235 A New Intelligent, Dynamic and Real Time Management System of Sewerage
Authors: R. Tlili Yaakoubi, H. Nakouri, O. Blanpain, S. Lallahem
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The current tools for real time management of sewer systems are based on two software tools: the software of weather forecast and the software of hydraulic simulation. The use of the first ones is an important cause of imprecision and uncertainty, the use of the second requires temporal important steps of decision because of their need in times of calculation. This way of proceeding fact that the obtained results are generally different from those waited. The major idea of this project is to change the basic paradigm by approaching the problem by the "automatic" face rather than by that "hydrology". The objective is to make possible the realization of a large number of simulations at very short times (a few seconds) allowing to take place weather forecasts by using directly the real time meditative pluviometric data. The aim is to reach a system where the decision-making is realized from reliable data and where the correction of the error is permanent. A first model of control laws was realized and tested with different return-period rainfalls. The gains obtained in rejecting volume vary from 19 to 100 %. The development of a new algorithm was then used to optimize calculation time and thus to overcome the subsequent combinatorial problem in our first approach. Finally, this new algorithm was tested with 16- year-rainfall series. The obtained gains are 40 % of total volume rejected to the natural environment and of 65 % in the number of discharges.Keywords: Automation, optimization, paradigm, RTC.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 148811234 Identifying Blind Spots in a Stereo View for Early Decisions in SI for Fusion based DMVC
Authors: H. Ali, K. Hameed, N. Khan
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In DMVC, we have more than one options of sources available for construction of side information. The newer techniques make use of both the techniques simultaneously by constructing a bitmask that determines the source of every block or pixel of the side information. A lot of computation is done to determine each bit in the bitmask. In this paper, we have tried to define areas that can only be well predicted by temporal interpolation and not by multiview interpolation or synthesis. We predict that all such areas that are not covered by two cameras cannot be appropriately predicted by multiview synthesis and if we can identify such areas in the first place, we don-t need to go through the script of computations for all the pixels that lie in those areas. Moreover, this paper also defines a technique based on KLT to mark the above mentioned areas before any other processing is done on the side view.
Keywords: Side Information, Distributed Multiview Video Coding, Fusion, Early Decision.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 132911233 Spike Sorting Method Using Exponential Autoregressive Modeling of Action Potentials
Authors: Sajjad Farashi
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Neurons in the nervous system communicate with each other by producing electrical signals called spikes. To investigate the physiological function of nervous system it is essential to study the activity of neurons by detecting and sorting spikes in the recorded signal. In this paper a method is proposed for considering the spike sorting problem which is based on the nonlinear modeling of spikes using exponential autoregressive model. The genetic algorithm is utilized for model parameter estimation. In this regard some selected model coefficients are used as features for sorting purposes. For optimal selection of model coefficients, self-organizing feature map is used. The results show that modeling of spikes with nonlinear autoregressive model outperforms its linear counterpart. Also the extracted features based on the coefficients of exponential autoregressive model are better than wavelet based extracted features and get more compact and well-separated clusters. In the case of spikes different in small-scale structures where principal component analysis fails to get separated clouds in the feature space, the proposed method can obtain well-separated cluster which removes the necessity of applying complex classifiers.
Keywords: Exponential autoregressive model, Neural data, spike sorting, time series modeling.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 177011232 A Challenge to Acquire Serious Victims’ Locations during Acute Period of Giant Disasters
Authors: Keiko Shimazu, Yasuhiro Maida, Tetsuya Sugata, Daisuke Tamakoshi, Kenji Makabe, Haruki Suzuki
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In this paper, we report how to acquire serious victims’ locations in the Acute Stage of Large-scale Disasters, in an Emergency Information Network System designed by us. The background of our concept is based on the Great East Japan Earthquake occurred on March 11th, 2011. Through many experiences of national crises caused by earthquakes and tsunamis, we have established advanced communication systems and advanced disaster medical response systems. However, Japan was devastated by huge tsunamis swept a vast area of Tohoku causing a complete breakdown of all the infrastructures including telecommunications. Therefore, we noticed that we need interdisciplinary collaboration between science of disaster medicine, regional administrative sociology, satellite communication technology and systems engineering experts. Communication of emergency information was limited causing a serious delay in the initial rescue and medical operation. For the emergency rescue and medical operations, the most important thing is to identify the number of casualties, their locations and status and to dispatch doctors and rescue workers from multiple organizations. In the case of the Tohoku earthquake, the dispatching mechanism and/or decision support system did not exist to allocate the appropriate number of doctors and locate disaster victims. Even though the doctors and rescue workers from multiple government organizations have their own dedicated communication system, the systems are not interoperable.
Keywords: Crisis management, disaster mitigation, messing, MGRS, Satellite communication system.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 83011231 Power Flow Control with UPFC in Power Transmission System
Authors: Samina Elyas Mubeen, R. K. Nema, Gayatri Agnihotri
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In this paper the performance of unified power flow controller is investigated in controlling the flow of po wer over the transmission line. Voltage sources model is utilized to study the behaviour of the UPFC in regulating the active, reactive power and voltage profile. This model is incorporated in Newton Raphson algorithm for load flow studies. Simultaneous method is employed in which equations of UPFC and the power balance equations of network are combined in to one set of non-linear algebraic equations. It is solved according to the Newton raphson algorithm. Case studies are carried on standard 5 bus network. Simulation is done in Matlab. The result of network with and without using UPFC are compared in terms of active and reactive power flows in the line and active and reactive power flows at the bus to analyze the performance of UPFC.Keywords: Newton-Raphson algorithm, Load flow, Unified power flow controller, Voltage source model.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 429011230 Smart Power Scheduling to Reduce Peak Demand and Cost of Energy in Smart Grid
Authors: Hemant I. Joshi, Vivek J. Pandya
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This paper discusses the simulation and experimental work of small Smart Grid containing ten consumers. Smart Grid is characterized by a two-way flow of real-time information and energy. RTP (Real Time Pricing) based tariff is implemented in this work to reduce peak demand, PAR (peak to average ratio) and cost of energy consumed. In the experimental work described here, working of Smart Plug, HEC (Home Energy Controller), HAN (Home Area Network) and communication link between consumers and utility server are explained. Algorithms for Smart Plug, HEC, and utility server are presented and explained in this work. After receiving the Real Time Price for different time slots of the day, HEC interacts automatically by running an algorithm which is based on Linear Programming Problem (LPP) method to find the optimal energy consumption schedule. Algorithm made for utility server can handle more than one off-peak time period during the day. Simulation and experimental work are carried out for different cases. At the end of this work, comparison between simulation results and experimental results are presented to show the effectiveness of the minimization method adopted.
Keywords: Smart Grid, Real Time Pricing, Peak to Average Ratio, Home Area Network, Home Energy Controller, Smart Plug, Utility Server, Linear Programming Problem.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 168511229 The Effects of Multipath on OFDM Systems for Broadband Power-Line Communications a Case of Medium Voltage Channel
Authors: Justinian Anatory, N. Theethayi, R. Thottappillil, C. Mwase, N.H. Mvungi
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Power-line networks are widely used today for broadband data transmission. However, due to multipaths within the broadband power line communication (BPLC) systems owing to stochastic changes in the network load impedances, branches, etc., network or channel capacity performances are affected. This paper attempts to investigate the performance of typical medium voltage channels that uses Orthogonal Frequency Division Multiplexing (OFDM) techniques with Quadrature Amplitude Modulation (QAM) sub carriers. It has been observed that when the load impedances are different from line characteristic impedance channel performance decreases. Also as the number of branches in the link between the transmitter and receiver increases a loss of 4dB/branch is found in the signal to noise ratio (SNR). The information presented in the paper could be useful for an appropriate design of the BPLC systems.
Keywords: Communication channel model, Power-line communication, Transfer function, Multipath, Branched network, OFDM, QAM, performance evaluation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 184811228 Designing Ontology-Based Knowledge Integration for Preprocessing of Medical Data in Enhancing a Machine Learning System for Coding Assignment of a Multi-Label Medical Text
Authors: Phanu Waraporn
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This paper discusses the designing of knowledge integration of clinical information extracted from distributed medical ontologies in order to ameliorate a machine learning-based multilabel coding assignment system. The proposed approach is implemented using a decision tree technique of the machine learning on the university hospital data for patients with Coronary Heart Disease (CHD). The preliminary results obtained show a satisfactory finding that the use of medical ontologies improves the overall system performance.
Keywords: Medical Ontology, Knowledge Integration, Machine Learning, Medical Coding, Text Assignment.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 185011227 Cluster-Based Multi-Path Routing Algorithm in Wireless Sensor Networks
Authors: Si-Gwan Kim
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Small-size and low-power sensors with sensing, signal processing and wireless communication capabilities is suitable for the wireless sensor networks. Due to the limited resources and battery constraints, complex routing algorithms used for the ad-hoc networks cannot be employed in sensor networks. In this paper, we propose node-disjoint multi-path hexagon-based routing algorithms in wireless sensor networks. We suggest the details of the algorithm and compare it with other works. Simulation results show that the proposed scheme achieves better performance in terms of efficiency and message delivery ratio.Keywords: Clustering, multi-path, routing protocol, sensor network.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 247011226 A Qualitative Study into the Success and Challenges in Embedding Evidence-Based Research Methods in Operational Policing Interventions
Authors: Ahmed Kadry, Gwyn Dodd
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There has been a growing call globally for police forces to embed evidence-based policing research methods into police interventions in order to better understand and evaluate their impact. This research study highlights the success and challenges that police forces may encounter when trying to embed evidence-based research methods within their organisation. Ten in-depth qualitative interviews were conducted with police officers and staff at Greater Manchester Police (GMP) who were tasked with integrating evidence-based research methods into their operational interventions. The findings of the study indicate that with adequate resources and individual expertise, evidence-based research methods can be applied to operational work, including the testing of initiatives with strict controls in order to fully evaluate the impact of an intervention. However, the findings also indicate that this may only be possible where an operational intervention is heavily resourced with police officers and staff who have a strong understanding of evidence-based policing research methods, attained for example through their own graduate studies. In addition, the findings reveal that ample planning time was needed to trial operational interventions that would require strict parameters for what would be tested and how it would be evaluated. In contrast, interviewees underscored that operational interventions with the need for a speedy implementation were less likely to have evidence-based research methods applied. The study contributes to the wider literature on evidence-based policing by providing considerations for police forces globally wishing to apply evidence-based research methods to more of their operational work in order to understand their impact. The study also provides considerations for academics who work closely with police forces in assisting them to embed evidence-based policing. This includes how academics can provide their expertise to police decision makers wanting to underpin their work through evidence-based research methods, such as providing guidance on how to evaluate the impact of their work with varying research methods that they may otherwise be unaware of.
Keywords: evidence based policing, evidence-based practice, operational policing, organisational change
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 30711225 Multipath Routing Protocol Using Basic Reconstruction Routing (BRR) Algorithm in Wireless Sensor Network
Authors: K. Rajasekaran, Kannan Balasubramanian
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A sensory network consists of multiple detection locations called sensor nodes, each of which is tiny, featherweight and portable. A single path routing protocols in wireless sensor network can lead to holes in the network, since only the nodes present in the single path is used for the data transmission. Apart from the advantages like reduced computation, complexity and resource utilization, there are some drawbacks like throughput, increased traffic load and delay in data delivery. Therefore, multipath routing protocols are preferred for WSN. Distributing the traffic among multiple paths increases the network lifetime. We propose a scheme, for the data to be transmitted through a dominant path to save energy. In order to obtain a high delivery ratio, a basic route reconstruction protocol is utilized to reconstruct the path whenever a failure is detected. A basic reconstruction routing (BRR) algorithm is proposed, in which a node can leap over path failure by using the already existing routing information from its neighbourhood while the composed data is transmitted from the source to the sink. In order to save the energy and attain high data delivery ratio, data is transmitted along a multiple path, which is achieved by BRR algorithm whenever a failure is detected. Further, the analysis of how the proposed protocol overcomes the drawback of the existing protocols is presented. The performance of our protocol is compared to AOMDV and energy efficient node-disjoint multipath routing protocol (EENDMRP). The system is implemented using NS-2.34. The simulation results show that the proposed protocol has high delivery ratio with low energy consumption.Keywords: Multipath routing, WSN, energy efficient routing, alternate route, assured data delivery.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 172211224 Knowledge Based Model for Power Transformer Life Cycle Management Using Knowledge Engineering
Authors: S. S. Bhandari, N. Chakpitak, K. Meksamoot, T. Chandarasupsang
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Under the limitation of investment budget, a utility company is required to maximize the utilization of their existing assets during their life cycle satisfying both engineering and financial requirements. However, utility does not have knowledge about the status of each asset in the portfolio neither in terms of technical nor financial values. This paper presents a knowledge based model for the utility companies in order to make an optimal decision on power transformer with their utilization. CommonKADS methodology, a structured development for knowledge and expertise representation, is utilized for designing and developing knowledge based model. A case study of One MVA power transformer of Nepal Electricity Authority is presented. The results show that the reusable knowledge can be categorized, modeled and utilized within the utility company using the proposed methodologies. Moreover, the results depict that utility company can achieve both engineering and financial benefits from its utilization.Keywords: CommonKADS, Knowledge Engineering, LifeCycle Management, Power Transformer.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 230411223 A Hybrid Feature Selection and Deep Learning Algorithm for Cancer Disease Classification
Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh
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Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.
Keywords: Cancer classification, feature selection, deep learning, genetic algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 127111222 Bridge Health Monitoring: A Review
Authors: Mohammad Bakhshandeh
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Structural Health Monitoring (SHM) is a crucial and necessary practice that plays a vital role in ensuring the safety and integrity of critical structures, and in particular, bridges. The continuous monitoring of bridges for signs of damage or degradation through Bridge Health Monitoring (BHM) enables early detection of potential problems, allowing for prompt corrective action to be taken before significant damage occurs. Although all monitoring techniques aim to provide accurate and decisive information regarding the remaining useful life, safety, integrity, and serviceability of bridges, understanding the development and propagation of damage is vital for maintaining uninterrupted bridge operation. Over the years, extensive research has been conducted on BHM methods, and experts in the field have increasingly adopted new methodologies. In this article, we provide a comprehensive exploration of the various BHM approaches, including sensor-based, non-destructive testing (NDT), model-based, and artificial intelligence (AI)-based methods. We also discuss the challenges associated with BHM, including sensor placement and data acquisition, data analysis and interpretation, cost and complexity, and environmental effects, through an extensive review of relevant literature and research studies. Additionally, we examine potential solutions to these challenges and propose future research ideas to address critical gaps in BHM.
Keywords: Structural health monitoring, bridge health monitoring, sensor-based methods, machine-learning algorithms, model-based techniques, sensor placement, data acquisition, data analysis.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 30211221 Using Jumping Particle Swarm Optimization for Optimal Operation of Pump in Water Distribution Networks
Authors: R. Rajabpour, N. Talebbeydokhti, M. H. Ahmadi
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Carefully scheduling the operations of pumps can be resulted to significant energy savings. Schedules can be defined either implicit, in terms of other elements of the network such as tank levels, or explicit by specifying the time during which each pump is on/off. In this study, two new explicit representations based on timecontrolled triggers were analyzed, where the maximum number of pump switches was established beforehand, and the schedule may contain fewer switches than the maximum. The optimal operation of pumping stations was determined using a Jumping Particle Swarm Optimization (JPSO) algorithm to achieve the minimum energy cost. The model integrates JPSO optimizer and EPANET hydraulic network solver. The optimal pump operation schedule of VanZyl water distribution system was determined using the proposed model and compared with those from Genetic and Ant Colony algorithms. The results indicate that the proposed model utilizing the JPSO algorithm is a versatile management model for the operation of realworld water distribution system.Keywords: JPSO, operation, optimization, water distribution system.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 205111220 Optimal Document Archiving and Fast Information Retrieval
Authors: Hazem M. El-Bakry, Ahmed A. Mohammed
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In this paper, an intelligent algorithm for optimal document archiving is presented. It is kown that electronic archives are very important for information system management. Minimizing the size of the stored data in electronic archive is a main issue to reduce the physical storage area. Here, the effect of different types of Arabic fonts on electronic archives size is discussed. Simulation results show that PDF is the best file format for storage of the Arabic documents in electronic archive. Furthermore, fast information detection in a given PDF file is introduced. Such approach uses fast neural networks (FNNs) implemented in the frequency domain. The operation of these networks relies on performing cross correlation in the frequency domain rather than spatial one. It is proved mathematically and practically that the number of computation steps required for the presented FNNs is less than that needed by conventional neural networks (CNNs). Simulation results using MATLAB confirm the theoretical computations.Keywords: Information Storage and Retrieval, Electronic Archiving, Fast Information Detection, Cross Correlation, Frequency Domain.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 158611219 Analysis of Wi-Fi Access Networks Situation in the City Area
Authors: A. Statkus, S. Paulikas
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With increasing number of wireless devices like laptops, Wi-Fi Web Cams, network extenders, etc., a new kind of problems appeared, mostly related to poor Wi-Fi throughput or communication problems. In this paper an investigation on wireless networks and it-s saturation in Vilnius City and its surrounding is presented, covering the main problems of wireless saturation and network load during day. Also an investigation on wireless channel selection and noise levels were made, showing the impact of neighbor AP to signal and noise levels and how it changes during the day.Keywords: IEEE 802.11b/g/n, wireless saturation, client activity, channel selection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 164811218 A Trust Model using Fuzzy Logic in Wireless Sensor Network
Authors: Tae Kyung Kim, Hee Suk Seo
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Adapting various sensor devices to communicate within sensor networks empowers us by providing range of possibilities. The sensors in sensor networks need to know their measurable belief of trust for efficient and safe communication. In this paper, we suggested a trust model using fuzzy logic in sensor network. Trust is an aggregation of consensus given a set of past interaction among sensors. We applied our suggested model to sensor networks in order to show how trust mechanisms are involved in communicating algorithm to choose the proper path from source to destination.Keywords: Fuzzy, Sensor Networks, Trust.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 355511217 Multi-agent Data Fusion Architecture for Intelligent Web Information Retrieval
Authors: Amin Milani Fard, Mohsen Kahani, Reza Ghaemi, Hamid Tabatabaee
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In this paper we propose a multi-agent architecture for web information retrieval using fuzzy logic based result fusion mechanism. The model is designed in JADE framework and takes advantage of JXTA agent communication method to allow agent communication through firewalls and network address translators. This approach enables developers to build and deploy P2P applications through a unified medium to manage agent-based document retrieval from multiple sources.Keywords: Information retrieval systems, list fusion methods, document score, multi-agent systems.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 160011216 Belief Theory-Based Classifiers Comparison for Static Human Body Postures Recognition in Video
Authors: V. Girondel, L. Bonnaud, A. Caplier, M. Rombaut
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This paper presents various classifiers results from a system that can automatically recognize four different static human body postures in video sequences. The considered postures are standing, sitting, squatting, and lying. The three classifiers considered are a naïve one and two based on the belief theory. The belief theory-based classifiers use either a classic or restricted plausibility criterion to make a decision after data fusion. The data come from the people 2D segmentation and from their face localization. Measurements consist in distances relative to a reference posture. The efficiency and the limits of the different classifiers on the recognition system are highlighted thanks to the analysis of a great number of results. This system allows real-time processing.
Keywords: Belief theory, classifiers comparison, data fusion, human motion analysis, real-time processing, static posture recognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 151611215 Ecosystem Model for Environmental Applications
Authors: Cristina Schreiner, Romeo Ciobanu, Marius Pislaru
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This paper aims to build a system based on fuzzy models that can be implemented in the assessment of ecological systems, to determine appropriate methods of action for reducing adverse effects on environmental and implicit the population. The model proposed provides new perspective for environmental assessment, and it can be used as a practical instrument for decision –making.
Keywords: Ecosystem model, Environmental security, Fuzzy logic, Sustainability of habitable regions.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 198211214 Conventional and Hybrid Network Energy Systems Optimization for Canadian Community
Authors: Mohamed Ghorab
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Local generated and distributed system for thermal and electrical energy is sighted in the near future to reduce transmission losses instead of the centralized system. Distributed Energy Resources (DER) is designed at different sizes (small and medium) and it is incorporated in energy distribution between the hubs. The energy generated from each technology at each hub should meet the local energy demands. Economic and environmental enhancement can be achieved when there are interaction and energy exchange between the hubs. Network energy system and CO2 optimization between different six hubs presented Canadian community level are investigated in this study. Three different scenarios of technology systems are studied to meet both thermal and electrical demand loads for the six hubs. The conventional system is used as the first technology system and a reference case study. The conventional system includes boiler to provide the thermal energy, but the electrical energy is imported from the utility grid. The second technology system includes combined heat and power (CHP) system to meet the thermal demand loads and part of the electrical demand load. The third scenario has integration systems of CHP and Organic Rankine Cycle (ORC) where the thermal waste energy from the CHP system is used by ORC to generate electricity. General Algebraic Modeling System (GAMS) is used to model DER system optimization based on energy economics and CO2 emission analyses. The results are compared with the conventional energy system. The results show that scenarios 2 and 3 provide an annual total cost saving of 21.3% and 32.3 %, respectively compared to the conventional system (scenario 1). Additionally, Scenario 3 (CHP & ORC systems) provides 32.5% saving in CO2 emission compared to conventional system subsequent case 2 (CHP system) with a value of 9.3%.
Keywords: Distributed energy resources, network energy system, optimization, microgeneration system.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 94011213 Data Mining Techniques in Computer-Aided Diagnosis: Non-Invasive Cancer Detection
Authors: Florin Gorunescu
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Diagnosis can be achieved by building a model of a certain organ under surveillance and comparing it with the real time physiological measurements taken from the patient. This paper deals with the presentation of the benefits of using Data Mining techniques in the computer-aided diagnosis (CAD), focusing on the cancer detection, in order to help doctors to make optimal decisions quickly and accurately. In the field of the noninvasive diagnosis techniques, the endoscopic ultrasound elastography (EUSE) is a recent elasticity imaging technique, allowing characterizing the difference between malignant and benign tumors. Digitalizing and summarizing the main EUSE sample movies features in a vector form concern with the use of the exploratory data analysis (EDA). Neural networks are then trained on the corresponding EUSE sample movies vector input in such a way that these intelligent systems are able to offer a very precise and objective diagnosis, discriminating between benign and malignant tumors. A concrete application of these Data Mining techniques illustrates the suitability and the reliability of this methodology in CAD.Keywords: Endoscopic ultrasound elastography, exploratorydata analysis, neural networks, non-invasive cancer detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 186711212 Basic Research for Distinguishing Small Retinal Hemorrhages from Dust Artifact by using Hue, Lightness, and Saturation Color Space
Authors: Naoto Suzuki
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To distinguish small retinal hemorrhages in early diabetic retinopathy from dust artifacts, we analyzed hue, lightness, and saturation (HLS) color spaces. The fundus of 5 patients with diabetic retinopathy was photographed. For the initial experiment, we placed 4 different colored papers on the ceiling of a darkroom. Using each color, 10 fragments of house dust particles on a magnifier were photographed. The colored papers were removed, and 3 different colored light bulbs were suspended from the ceiling. Ten fragments of house dust particles on the camera-s object lens were photographed. We then constructed an experimental device that can photograph artificial eyes. Five fragments of house dust particles under the ocher fundus of the artificial eye were photographed. On analyzing HLS color space of the dust artifact, lightness and saturation were found to be highly sensitive. However, hue was not highly sensitive.Keywords: Dust artifact, HLS color space, Retinal hemorrhage, and Diabetic retinopathy
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 167311211 The Impacts of Local Decision Making on Customisation Process Speed across Distributed Boundaries: A Case Study
Authors: A. M. Qahtani, G. B. Wills, A. M. Gravell
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Communicating and managing customers’ requirements in software development projects play a vital role in the software development process. While it is difficult to do so locally, it is even more difficult to communicate these requirements over distributed boundaries and to convey them to multiple distribution customers. This paper discusses the communication of multiple distribution customers’ requirements in the context of customised software products. The main purpose is to understand the challenges of communicating and managing customisation requirements across distributed boundaries. We propose a model for Communicating Customisation Requirements of Multi-Clients in a Distributed Domain (CCRD). Thereafter, we evaluate that model by presenting the findings of a case study conducted with a company with customisation projects for 18 distributed customers. Then, we compare the outputs of the real case process and the outputs of the CCRD model using simulation methods. Our conjecture is that the CCRD model can reduce the challenge of communication requirements over distributed organisational boundaries, and the delay in decision making and in the entire customisation process time.
Keywords: Customisation Software Products, Global Software Engineering, Local Decision Making, Requirement Engineering, Simulation Model.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 189811210 An Adaptive Model for Blind Image Restoration using Bayesian Approach
Authors: S.K. Satpathy, S.K. Nayak, K. K. Nagwanshi, S. Panda, C. Ardil
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Image restoration involves elimination of noise. Filtering techniques were adopted so far to restore images since last five decades. In this paper, we consider the problem of image restoration degraded by a blur function and corrupted by random noise. A method for reducing additive noise in images by explicit analysis of local image statistics is introduced and compared to other noise reduction methods. The proposed method, which makes use of an a priori noise model, has been evaluated on various types of images. Bayesian based algorithms and technique of image processing have been described and substantiated with experimentation using MATLAB.Keywords: Image Restoration, Probability DensityFunction (PDF), Neural Networks, Bayesian Classifier.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 224711209 A Novel Approach towards Segmentation of Breast Tumors from Screening Mammograms for Efficient Decision Support System
Authors: M.Suganthi, M.Madheswaran
Abstract:
This paper presents a novel approach to finding a priori interesting regions in mammograms. In order to delineate those regions of interest (ROI-s) in mammograms, which appear to be prominent, a topographic representation called the iso-level contour map consisting of iso-level contours at multiple intensity levels and region segmentation based-thresholding have been proposed. The simulation results indicate that the computed boundary gives the detection rate of 99.5% accuracy.Keywords: Breast Cancer, Mammogram, and Segmentation.
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