Search results for: vehicular ad-hoc networks
2047 Functional Connectivity Signatures of Polygenic Depression Risk in Youth
Authors: Louise Moles, Steve Riley, Sarah D. Lichenstein, Marzieh Babaeianjelodar, Robert Kohler, Annie Cheng, Corey Horien Abigail Greene, Wenjing Luo, Jonathan Ahern, Bohan Xu, Yize Zhao, Chun Chieh Fan, R. Todd Constable, Sarah W. Yip
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Background: Risks for depression are myriad and include both genetic and brain-based factors. However, relationships between these systems are poorly understood, limiting understanding of disease etiology, particularly at the developmental level. Methods: We use a data-driven machine learning approach connectome-based predictive modeling (CPM) to identify functional connectivity signatures associated with polygenic risk scores for depression (DEP-PRS) among youth from the Adolescent Brain and Cognitive Development (ABCD) study across diverse brain states, i.e., during resting state, during affective working memory, during response inhibition, during reward processing. Results: Using 10-fold cross-validation with 100 iterations and permutation testing, CPM identified connectivity signatures of DEP-PRS across all examined brain states (rho’s=0.20-0.27, p’s<.001). Across brain states, DEP-PRS was positively predicted by increased connectivity between frontoparietal and salience networks, increased motor-sensory network connectivity, decreased salience to subcortical connectivity, and decreased subcortical to motor-sensory connectivity. Subsampling analyses demonstrated that model accuracies were robust across random subsamples of N’s=1,000, N’s=500, and N’s=250 but became unstable at N’s=100. Conclusions: These data, for the first time, identify neural networks of polygenic depression risk in a large sample of youth before the onset of significant clinical impairment. Identified networks may be considered potential treatment targets or vulnerability markers for depression risk.Keywords: genetics, functional connectivity, pre-adolescents, depression
Procedia PDF Downloads 492046 A Survey on Traditional Mac Layer Protocols in Cognitive Wireless Mesh Networks
Authors: Anusha M., V. Srikanth
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Maximizing spectrum usage and numerous applications of the wireless communication networks have forced to a high interest of available spectrum. Cognitive Radio control its receiver and transmitter features exactly so that they can utilize the vacant approved spectrum without impacting the functionality of the principal licensed users. The Use of various channels assists to address interferences thereby improves the whole network efficiency. The MAC protocol in cognitive radio network explains the spectrum usage by interacting with multiple channels among the users. In this paper we studied about the architecture of cognitive wireless mesh network and traditional TDMA dependent MAC method to allocate channels dynamically. The majority of the MAC protocols suggested in the research are operated on Common-Control-Channel (CCC) to handle the services between Cognitive Radio secondary users. In this paper, an extensive study of Multi-Channel Multi-Radios or frequency range channel allotment and continually synchronized TDMA scheduling are shown in summarized way.Keywords: TDMA, MAC, multi-channel, multi-radio, WMN’S, cognitive radios
Procedia PDF Downloads 5472045 Artificial Neural Networks Face to Sudden Load Change for Shunt Active Power Filter
Authors: Dehini Rachid, Ferdi Brahim
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The shunt active power filter (SAPF) is not destined only to improve the power factor, but also to compensate the unwanted harmonic currents produced by nonlinear loads. This paper presents a SAPF with identification and control method based on artificial neural network (ANN). To identify harmonics, many techniques are used, among them the conventional p-q theory and the relatively recent one the artificial neural network method. It is difficult to get satisfied identification and control characteristics by using a normal (ANN) due to the nonlinearity of the system (SAPF + fast nonlinear load variations). This work is an attempt to undertake a systematic study of the problem to equip the (SAPF) with the harmonics identification and DC link voltage control method based on (ANN). The latter has been applied to the (SAPF) with fast nonlinear load variations. The results of computer simulations and experiments are given, which can confirm the feasibility of the proposed active power filter.Keywords: artificial neural networks (ANN), p-q theory, harmonics, total harmonic distortion
Procedia PDF Downloads 3762044 Optimizing Super Resolution Generative Adversarial Networks for Resource-Efficient Single-Image Super-Resolution via Knowledge Distillation and Weight Pruning
Authors: Hussain Sajid, Jung-Hun Shin, Kum-Won Cho
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Image super-resolution is the most common computer vision problem with many important applications. Generative adversarial networks (GANs) have promoted remarkable advances in single-image super-resolution (SR) by recovering photo-realistic images. However, high memory requirements of GAN-based SR (mainly generators) lead to performance degradation and increased energy consumption, making it difficult to implement it onto resource-constricted devices. To relieve such a problem, In this paper, we introduce an optimized and highly efficient architecture for SR-GAN (generator) model by utilizing model compression techniques such as Knowledge Distillation and pruning, which work together to reduce the storage requirement of the model also increase in their performance. Our method begins with distilling the knowledge from a large pre-trained model to a lightweight model using different loss functions. Then, iterative weight pruning is applied to the distilled model to remove less significant weights based on their magnitude, resulting in a sparser network. Knowledge Distillation reduces the model size by 40%; pruning then reduces it further by 18%. To accelerate the learning process, we employ the Horovod framework for distributed training on a cluster of 2 nodes, each with 8 GPUs, resulting in improved training performance and faster convergence. Experimental results on various benchmarks demonstrate that the proposed compressed model significantly outperforms state-of-the-art methods in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and image quality for x4 super-resolution tasks.Keywords: single-image super-resolution, generative adversarial networks, knowledge distillation, pruning
Procedia PDF Downloads 802043 Machine Learning Techniques in Bank Credit Analysis
Authors: Fernanda M. Assef, Maria Teresinha A. Steiner
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The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.Keywords: artificial neural networks (ANNs), classifier algorithms, credit risk assessment, logistic regression, machine Learning, support vector machines
Procedia PDF Downloads 952042 Neural Networks Underlying the Generation of Neural Sequences in the HVC
Authors: Zeina Bou Diab, Arij Daou
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The neural mechanisms of sequential behaviors are intensively studied, with songbirds a focus for learned vocal production. We are studying the premotor nucleus HVC at a nexus of multiple pathways contributing to song learning and production. The HVC consists of multiple classes of neuronal populations, each has its own cellular, electrophysiological and functional properties. During singing, a large subset of motor cortex analog-projecting HVCRA neurons emit a single 6-10 ms burst of spikes at the same time during each rendition of song, a large subset of basal ganglia-projecting HVCX neurons fire 1 to 4 bursts that are similarly time locked to vocalizations, while HVCINT neurons fire tonically at average high frequency throughout song with prominent modulations whose timing in relation to song remains unresolved. This opens the opportunity to define models relating explicit HVC circuitry to how these neurons work cooperatively to control learning and singing. We developed conductance-based Hodgkin-Huxley models for the three classes of HVC neurons (based on the ion channels previously identified from in vitro recordings) and connected them in several physiologically realistic networks (based on the known synaptic connectivity and specific glutaminergic and gabaergic pharmacology) via different architecture patterning scenarios with the aim to replicate the in vivo firing patterning behaviors. We are able, through these networks, to reproduce the in vivo behavior of each class of HVC neurons, as shown by the experimental recordings. The different network architectures developed highlight different mechanisms that might be contributing to the propagation of sequential neural activity (continuous or punctate) in the HVC and to the distinctive firing patterns that each class exhibits during singing. Examples of such possible mechanisms include: 1) post-inhibitory rebound in HVCX and their population patterns during singing, 2) different subclasses of HVCINT interacting via inhibitory-inhibitory loops, 3) mono-synaptic HVCX to HVCRA excitatory connectivity, and 4) structured many-to-one inhibitory synapses from interneurons to projection neurons, and others. Replication is only a preliminary step that must be followed by model prediction and testing.Keywords: computational modeling, neural networks, temporal neural sequences, ionic currents, songbird
Procedia PDF Downloads 622041 A Framework for Strategy Development in Small Companies: A Case Study of a Telecommunication Firm
Authors: Maryam Goodarzi, Mahdieh Sheikhi, Mehdi Goodarzi
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This study intends to offer an appropriate strategy development framework for a telecommunication firm (as a case study) which works on Information and Communication Technology (ICT) projects, development of telecommunication networks, and maintenance of local networks, according to its dominant condition. In this approach, first, the objectives were set and the mission was defined. Then, the capability was assessed by SWOT matrix. Using SPACE matrix, the strategy of the company was determined. The strategic direction is set and an appropriate and superior strategy was developed and offered employing QSPM matrix. The theoretical framework or conceptual model of the present study first involves 4 stages of framework development and then from stage 3 (assessing capability) onward, a strategic management model by Fred R. David. In this respect, the tools and methods offered in the framework are appropriate for all kinds of organizations, particularly small firms, and help strategists identify, evaluate, and select strategies.Keywords: strategy formulation, firm mission, strategic direction, space diagram, quantitative strategic planning matrix, SWOT matrix
Procedia PDF Downloads 3592040 Over the Air Programming Method for Learning Wireless Sensor Networks
Authors: K. Sangeeth, P. Rekha, P. Preeja, P. Divya, R. Arya, R. Maneesha
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Wireless sensor networks (WSN) are small or tiny devices that consists of different sensors to sense physical parameters like air pressure, temperature, vibrations, movement etc., process these data and sends it to the central data center to take decisions. The WSN domain, has wide range of applications such as monitoring and detecting natural hazards like landslides, forest fire, avalanche, flood monitoring and also in healthcare applications. With such different applications, it is being taught in undergraduate/post graduate level in many universities under department of computer science. But the cost and infrastructure required to purchase WSN nodes for having the students getting hands on expertise on these devices is expensive. This paper gives overview about the remote triggered lab that consists of more than 100 WSN nodes that helps the students to remotely login from anywhere in the world using the World Wide Web, configure the nodes and learn the WSN concepts in intuitive way. It proposes new way called over the air programming (OTAP) and its internals that program the 100 nodes simultaneously and view the results without the nodes being physical connected to the computer system, thereby allowing for sparse deployment.Keywords: WSN, over the air programming, virtual lab, AT45DB
Procedia PDF Downloads 3682039 Social Networks And Social Complexity: The Southern Italian Drive For Trade Exchange During The Late Bronze Age
Authors: Sara Fioretti
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During the Middle Bronze Age, southern Italy underwent a reorganisation of social structures where local cultures, such as the sub-Apennine and Nuragic, flourished and participated in maritime trade. This paper explores the socio-economic relationships, in both cross-cultural and potentially inter-regional settings, present within the archaeological repertoire of the southern Italian Late Bronze Age (LBA 1600 -1050 BCE). The emergence of economic relations within the connectivity of the regional settlements is explored through ceramic contexts found in the case studies Punta di Zambrone, Broglio di Trebisacce, and Nuraghe Antigori. This paper discusses the findings of a statistical and theoretical approach from an ongoing study in relation to the Mediterranean’s characterisation as a period dominated by Mycenaean influence. This study engages with a theoretical bricolage of Social Networks Entanglement, and Assertive Objects Theory to address the selective and assertive dynamics evident in the cross-cultural trade exchanges as well as consider inter-regional dynamics. Through this intersection of theory and statistical analysis, the case studies establish a small percentage of pottery as imported, whilst assertive productions have a relatively higher quantity. Overall, the majority still adheres to regional Italian traditions. Therefore, we can dissect the rhizomatic relationships cultivated by the Italian coasts and Mycenaeans and their roles within their networks through the intersection of theoretical and statistical analysis. This research offers a new perspective on the complex nature of the Late Bronze Age relational structures.Keywords: late bronze age, mediterranean archaeology, exchanges and trade, frequency distribution of ceramic assemblages, social network theory, rhizomatic exchanges
Procedia PDF Downloads 442038 Stochastic Multicast Routing Protocol for Flying Ad-Hoc Networks
Authors: Hyunsun Lee, Yi Zhu
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Wireless ad-hoc network is a decentralized type of temporary machine-to-machine connection that is spontaneous or impromptu so that it does not rely on any fixed infrastructure and centralized administration. As unmanned aerial vehicles (UAVs), also called drones, have recently become more accessible and widely utilized in military and civilian domains such as surveillance, search and detection missions, traffic monitoring, remote filming, product delivery, to name a few. The communication between these UAVs become possible and materialized through Flying Ad-hoc Networks (FANETs). However, due to the high mobility of UAVs that may cause different types of transmission interference, it is vital to design robust routing protocols for FANETs. In this talk, the multicast routing method based on a modified stochastic branching process is proposed. The stochastic branching process is often used to describe an early stage of an infectious disease outbreak, and the reproductive number in the process is used to classify the outbreak into a major or minor outbreak. The reproductive number to regulate the local transmission rate is adapted and modified for flying ad-hoc network communication. The performance of the proposed routing method is compared with other well-known methods such as flooding method and gossip method based on three measures; average reachability, average node usage and average branching factor. The proposed routing method achieves average reachability very closer to flooding method, average node usage closer to gossip method, and outstanding average branching factor among methods. It can be concluded that the proposed multicast routing scheme is more efficient than well-known routing schemes such as flooding and gossip while it maintains high performance.Keywords: Flying Ad-hoc Networks, Multicast Routing, Stochastic Branching Process, Unmanned Aerial Vehicles
Procedia PDF Downloads 1132037 Electrical Machine Winding Temperature Estimation Using Stateful Long Short-Term Memory Networks (LSTM) and Truncated Backpropagation Through Time (TBPTT)
Authors: Yujiang Wu
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As electrical machine (e-machine) power density re-querulents become more stringent in vehicle electrification, mounting a temperature sensor for e-machine stator windings becomes increasingly difficult. This can lead to higher manufacturing costs, complicated harnesses, and reduced reliability. In this paper, we propose a deep-learning method for predicting electric machine winding temperature, which can either replace the sensor entirely or serve as a backup to the existing sensor. We compare the performance of our method, the stateful long short-term memory networks (LSTM) with truncated backpropagation through time (TBTT), with that of linear regression, as well as stateless LSTM with/without residual connection. Our results demonstrate the strength of combining stateful LSTM and TBTT in tackling nonlinear time series prediction problems with long sequence lengths. Additionally, in industrial applications, high-temperature region prediction accuracy is more important because winding temperature sensing is typically used for derating machine power when the temperature is high. To evaluate the performance of our algorithm, we developed a temperature-stratified MSE. We propose a simple but effective data preprocessing trick to improve the high-temperature region prediction accuracy. Our experimental results demonstrate the effectiveness of our proposed method in accurately predicting winding temperature, particularly in high-temperature regions, while also reducing manufacturing costs and improving reliability.Keywords: deep learning, electrical machine, functional safety, long short-term memory networks (LSTM), thermal management, time series prediction
Procedia PDF Downloads 862036 How to Modernise the ECN
Authors: Dorota Galeza
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This paper argues that networks, such as the ECN and the American network, are affected by certain small events which are inherent to path dependence and preclude the full evolution towards efficiency. It is advocated that the American network is superior to the ECN in many respects due to its greater flexibility and longer history. This stems in particular from the creation of the American network, which was based on a small number of cases. Such structure encourages further changes and modifications which are not necessarily radical. The ECN, by contrast, was established by legislative action, which explains its rigid structure and resistance to change. It might be the case that the ECN is subject not so much to path dependence but to past dependence. It might have to be replaced, as happened to its predecessor. This paper is an attempt to transpose the superiority of the American network on to the ECN. It looks at concepts such as judicial cooperation, harmonization of procedure, peer review and regulatory impact assessments (RIAs), and dispute resolution procedures. The aim is to adopt these concepts into the EU setting without recourse to legal transplantation. The major difficulty is that many of these concepts have been tested only in the US and it is difficult to tell whether they could be modified to meet EU standards. Concepts such as judicial cooperation might be difficult due to different language traditions in EU member states. It is hoped that greater flexibility, as in the American network, would boost legitimacy and transparency.Keywords: ECN, networks, regulation, competition
Procedia PDF Downloads 4192035 Spatially Referenced Checklist Model Dedicated to Professional Actors for a Good Evaluation and Management of Networks
Authors: Abdessalam Hijab, Hafida Boulekbache, Eric Henry
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The objective of this article is to explain the use of geographic information system (GIS) and information and communication technologies (ICTs) in the real-time processing and analysis of data on the status of an urban sanitation network by integrating professional actors in sanitation for sustainable management in urban areas. Indeed, it is a smart geo-collaboration based on the complementarity of ICTs and GIS. This multi-actor reflection was built with the objective of contributing to the development of complementary solutions to the existing technologies to better protect the urban environment, with the help of a checklist with the spatial reference "E-Géo-LD" dedicated to the "professional/professional" actors in sanitation, for intelligent monitoring of liquid sanitation networks in urban areas. In addition, this research provides a good understanding and assimilation of liquid sanitation schemes in the "Lamkansa" sampling area of the city of Casablanca, and spatially evaluates these schemes. Downstream, it represents a guide to assess the environmental impacts of the liquid sanitation scheme.Keywords: ICT, GIS, spatial checklist, liquid sanitation, environment
Procedia PDF Downloads 2212034 Integrated Social Support through Social Networks to Enhance the Quality of Life of Metastatic Breast Cancer Patients
Authors: B. Thanasansomboon, S. Choemprayong, N. Parinyanitikul, U. Tanlamai
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Being diagnosed with metastatic breast cancer, the patients as well as their caretakers are affected physically and mentally. Although the medical systems in Thailand have been attempting to improve the quality and effectiveness of the treatment of the disease in terms of physical illness, the success of the treatment also depends on the quality of mental health. Metastatic breast cancer patients have found that social support is a key factor that helps them through this difficult time. It is recognized that social support in different dimensions, including emotional support, social network support, informational support, instrumental support and appraisal support, are contributing factors that positively affect the quality of life of patients in general, and it is undeniable that social support in various forms is important in promoting the quality of life of metastatic breast patients. However, previous studies have not been dedicated to investigating their quality of life concerning affective, cognitive, and behavioral outcomes. Therefore, this study aims to develop integrated social support through social networks to improve the quality of life of metastatic breast cancer patients in Thailand.Keywords: social support, metastatic breath cancer, quality of life, social network
Procedia PDF Downloads 1402033 Low Overhead Dynamic Channel Selection with Cluster-Based Spatial-Temporal Station Reporting in Wireless Networks
Authors: Zeyad Abdelmageid, Xianbin Wang
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Choosing the operational channel for a WLAN access point (AP) in WLAN networks has been a static channel assignment process initiated by the user during the deployment process of the AP, which fails to cope with the dynamic conditions of the assigned channel at the station side afterward. However, the dramatically growing number of Wi-Fi APs and stations operating in the unlicensed band has led to dynamic, distributed, and often severe interference. This highlights the urgent need for the AP to dynamically select the best overall channel of operation for the basic service set (BSS) by considering the distributed and changing channel conditions at all stations. Consequently, dynamic channel selection algorithms which consider feedback from the station side have been developed. Despite the significant performance improvement, existing channel selection algorithms suffer from very high feedback overhead. Feedback latency from the STAs, due to the high overhead, can cause the eventually selected channel to no longer be optimal for operation due to the dynamic sharing nature of the unlicensed band. This has inspired us to develop our own dynamic channel selection algorithm with reduced overhead through the proposed low-overhead, cluster-based station reporting mechanism. The main idea behind the cluster-based station reporting is the observation that STAs which are very close to each other tend to have very similar channel conditions. Instead of requesting each STA to report on every candidate channel while causing high overhead, the AP divides STAs into clusters then assigns each STA in each cluster one channel to report feedback on. With the proper design of the cluster based reporting, the AP does not lose any information about the channel conditions at the station side while reducing feedback overhead. The simulation results show equal performance and, at times, better performance with a fraction of the overhead. We believe that this algorithm has great potential in designing future dynamic channel selection algorithms with low overhead.Keywords: channel assignment, Wi-Fi networks, clustering, DBSCAN, overhead
Procedia PDF Downloads 1082032 Evaluation of NH3-Slip from Diesel Vehicles Equipped with Selective Catalytic Reduction Systems by Neural Networks Approach
Authors: Mona Lisa M. Oliveira, Nara A. Policarpo, Ana Luiza B. P. Barros, Carla A. Silva
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Selective catalytic reduction systems for nitrogen oxides reduction by ammonia has been the chosen technology by most of diesel vehicle (i.e. bus and truck) manufacturers in Brazil, as also in Europe. Furthermore, at some conditions, over-stoichiometric ammonia availability is also needed that increases the NH3 slips even more. Ammonia (NH3) by this vehicle exhaust aftertreatment system provides a maximum efficiency of NOx removal if a significant amount of NH3 is stored on its catalyst surface. In the other words, the practice shows that slightly less than 100% of the NOx conversion is usually targeted, so that the aqueous urea solution hydrolyzes to NH3 via other species formation, under relatively low temperatures. This paper presents a model based on neural networks integrated with a road vehicle simulator that allows to estimate NH3-slip emission factors for different driving conditions and patterns. The proposed model generates high NH3slips which are not also limited in Brazil, but more efforts needed to be made to elucidate the contribution of vehicle-emitted NH3 to the urban atmosphere.Keywords: ammonia slip, neural-network, vehicles emissions, SCR-NOx
Procedia PDF Downloads 2022031 Thermal Barrier Coated Diesel Engine With Neural Networks Mathematical Modelling
Authors: Hanbey Hazar, Hakan Gul
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In this study; piston, exhaust, and suction valves of a diesel engine were coated in 300 mm thickness with Tungsten Carbide (WC) by using the HVOF coating method. Mathematical modeling of a coated and uncoated (standardized) engine was performed by using ANN (Artificial Neural Networks). The purpose was to decrease the number of repetitions of tests and reduce the test cost through mathematical modeling of engines by using ANN. The results obtained from the tests were entered in ANN and therefore engines' values at all speeds were estimated. Results obtained from the tests were compared with those obtained from ANN and they were observed to be compatible. It was also observed that, with thermal barrier coating, hydrocarbon (HC), carbon monoxide (CO), and smoke density values of the diesel engine decreased; but nitrogen oxides (NOx) increased. Furthermore, it was determined that results obtained through mathematical modeling by means of ANN reduced the number of test repetitions. Therefore, it was understood that time, fuel and labor could be saved in this way.Keywords: Artificial Neural Network, Diesel Engine, Mathematical Modelling, Thermal Barrier Coating
Procedia PDF Downloads 5202030 Motion Detection Method for Clutter Rejection in the Bio-Radar Signal Processing
Authors: Carolina Gouveia, José Vieira, Pedro Pinho
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The cardiopulmonary signal monitoring, without the usage of contact electrodes or any type of in-body sensors, has several applications such as sleeping monitoring and continuous monitoring of vital signals in bedridden patients. This system has also applications in the vehicular environment to monitor the driver, in order to avoid any possible accident in case of cardiac failure. Thus, the bio-radar system proposed in this paper, can measure vital signals accurately by using the Doppler effect principle that relates the received signal properties with the distance change between the radar antennas and the person’s chest-wall. Once the bio-radar aim is to monitor subjects in real-time and during long periods of time, it is impossible to guarantee the patient immobilization, hence their random motion will interfere in the acquired signals. In this paper, a mathematical model of the bio-radar is presented, as well as its simulation in MATLAB. The used algorithm for breath rate extraction is explained and a method for DC offsets removal based in a motion detection system is proposed. Furthermore, experimental tests were conducted with a view to prove that the unavoidable random motion can be used to estimate the DC offsets accurately and thus remove them successfully.Keywords: bio-signals, DC component, Doppler effect, ellipse fitting, radar, SDR
Procedia PDF Downloads 1312029 Privacy-Preserving Location Sharing System with Client/Server Architecture in Mobile Online Social Network
Authors: Xi Xiao, Chunhui Chen, Xinyu Liu, Guangwu Hu, Yong Jiang
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Location sharing is a fundamental service in mobile Online Social Networks (mOSNs), which raises significant privacy concerns in recent years. Now, most location-based service applications adopt client/server architecture. In this paper, a location sharing system, named CSLocShare, is presented to provide flexible privacy-preserving location sharing with client/server architecture in mOSNs. CSLocShare enables location sharing between both trusted social friends and untrusted strangers without the third-party server. In CSLocShare, Location-Storing Social Network Server (LSSNS) provides location-based services but do not know the users’ real locations. The thorough analysis indicates that the users’ location privacy is protected. Meanwhile, the storage and the communication cost are saved. CSLocShare is more suitable and effective in reality.Keywords: mobile online social networks, client/server architecture, location sharing, privacy-preserving
Procedia PDF Downloads 3152028 Improved Predictive Models for the IRMA Network Using Nonlinear Optimisation
Authors: Vishwesh Kulkarni, Nikhil Bellarykar
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Cellular complexity stems from the interactions among thousands of different molecular species. Thanks to the emerging fields of systems and synthetic biology, scientists are beginning to unravel these regulatory, signaling, and metabolic interactions and to understand their coordinated action. Reverse engineering of biological networks has has several benefits but a poor quality of data combined with the difficulty in reproducing it limits the applicability of these methods. A few years back, many of the commonly used predictive algorithms were tested on a network constructed in the yeast Saccharomyces cerevisiae (S. cerevisiae) to resolve this issue. The network was a synthetic network of five genes regulating each other for the so-called in vivo reverse-engineering and modeling assessment (IRMA). The network was constructed in S. cereviase since it is a simple and well characterized organism. The synthetic network included a variety of regulatory interactions, thus capturing the behaviour of larger eukaryotic gene networks on a smaller scale. We derive a new set of algorithms by solving a nonlinear optimization problem and show how these algorithms outperform other algorithms on these datasets.Keywords: synthetic gene network, network identification, optimization, nonlinear modeling
Procedia PDF Downloads 1462027 Repositioning Nigerian University Libraries for Effective Information Provision and Delivery in This Age of Globalization
Authors: S. O. Uwaifo
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The paper examines the pivotal role of the library in university education through the provision of a wide range of information materials (print and non- print) required for the teaching, learning and research activities of the university. However certain impediments to the effectiveness of Nigerian university libraries, such as financial constraints, high foreign exchange, global disparities in accessing the internet, lack of local area networks, erratic electric power supply, absence of ICT literacy, poor maintenance culture, etc., were identified. Also, the necessity of repositioning Nigerian university libraries for effective information provision and delivery was stressed by pointing out their dividends, such as users’ access to Directory of Open Access Journals (DOAJ), Online Public Access Catalogue (OPAC), Institutional Repositories, Electronic Document Delivery, Social Media Networks, etc. It therefore becomes necessary for the libraries to be repositioned by way of being adequately automated or digitized for effective service delivery, in this age of globalization. Based on the identified barriers by this paper, some recommendations were proffered.Keywords: repositioning, Nigerian university libraries, effective information provision and delivery, globalization
Procedia PDF Downloads 3182026 Design of Speed Bump Recognition System Integrated with Adjustable Shock Absorber Control
Authors: Ming-Yen Chang, Sheng-Hung Ke
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This research focuses on the development of a speed bump identification system for real-time control of adjustable shock absorbers in vehicular suspension systems. The study initially involved the collection of images of various speed bumps, and rubber speed bump profiles found on roadways. These images were utilized for training and recognition purposes through the deep learning object detection algorithm YOLOv5. Subsequently, the trained speed bump identification program was integrated with an in-vehicle camera system for live image capture during driving. These images were instantly transmitted to a computer for processing. Using the principles of monocular vision ranging, the distance between the vehicle and an approaching speed bump was determined. The appropriate control distance was established through both practical vehicle measurements and theoretical calculations. Collaboratively, with the electronically adjustable shock absorbers equipped in the vehicle, a shock absorber control system was devised to dynamically adapt the damping force just prior to encountering a speed bump. This system effectively mitigates passenger discomfort and enhances ride quality.Keywords: adjustable shock absorbers, image recognition, monocular vision ranging, ride
Procedia PDF Downloads 602025 An Agent-Based Modelling Simulation Approach to Calculate Processing Delay of GEO Satellite Payload
Authors: V. Vicente E. Mujica, Gustavo Gonzalez
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The global coverage of broadband multimedia and internet-based services in terrestrial-satellite networks demand particular interests for satellite providers in order to enhance services with low latencies and high signal quality to diverse users. In particular, the delay of on-board processing is an inherent source of latency in a satellite communication that sometimes is discarded for the end-to-end delay of the satellite link. The frame work for this paper includes modelling of an on-orbit satellite payload using an agent model that can reproduce the properties of processing delays. In essence, a comparison of different spatial interpolation methods is carried out to evaluate physical data obtained by an GEO satellite in order to define a discretization function for determining that delay. Furthermore, the performance of the proposed agent and the development of a delay discretization function are together validated by simulating an hybrid satellite and terrestrial network. Simulation results show high accuracy according to the characteristics of initial data points of processing delay for Ku bands.Keywords: terrestrial-satellite networks, latency, on-orbit satellite payload, simulation
Procedia PDF Downloads 2622024 Regeneration Study on the Athens City Center: Transformation of the Historical Triangle to “Low Pollution and Restricted Vehicle Traffic Zone”
Authors: Chondrogianni Dimitra, Yorgos J. Stephanedes
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The impact of the economic crisis, coupled with the aging of the city's old core, is reflected in central Athens. Public and private users, residents, employees, visitors desire the quality upgrading of abandoned buildings and public spaces through environmental upgrading and sustainable mobility, and promotion of the international metropolitan character of the city. In the study, a strategy for reshaping the character and function of the historic Athenian triangle is proposed, aiming at its economic, environmental, and social sustainable development through feasible, meaningful, and non-landscaping solutions of low cost and high positive impact. Sustainable mobility is the main principle in re-planning the study area and transforming it into a “Low Pollution and Limited Vehicle Traffic Zone” is the main strategy. Τhe proposed measures include the development of pedestrian mobility networks by expanding the pedestrian roads and limited-traffic routes, of bicycle networks based on the approved Metropolitan Bicycle Route of Athens, of public transportation networks with new lines of electric mini-buses, and of new regulations for vehicle mobility in the historic triangle. In addition, complementary actions are proposed regarding the provision of Wi-Fi on fixed track media, development of applications that facilitate combined travel and provide real-time data, integration of micromobility (roller skates, Segway, Hoverboard), and its enhancement as a flexible means of personal mobility, and development of car-sharing, ride-sharing and dynamic carpooling initiatives.Keywords: regeneration plans, sustainable mobility, environmental upgrading, athens historical triangle
Procedia PDF Downloads 1522023 Neural Networks Based Prediction of Long Term Rainfall: Nine Pilot Study Zones over the Mediterranean Basin
Authors: Racha El Kadiri, Mohamed Sultan, Henrique Momm, Zachary Blair, Rachel Schultz, Tamer Al-Bayoumi
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The Mediterranean Basin is a very diverse region of nationalities and climate zones, with a strong dependence on agricultural activities. Predicting long term (with a lead of 1 to 12 months) rainfall, and future droughts could contribute in a sustainable management of water resources and economical activities. In this study, an integrated approach was adopted to construct predictive tools with lead times of 0 to 12 months to forecast rainfall amounts over nine subzones of the Mediterranean Basin region. The following steps were conducted: (1) acquire, assess and intercorrelate temporal remote sensing-based rainfall products (e.g. The CPC Merged Analysis of Precipitation [CMAP]) throughout the investigation period (1979 to 2016), (2) acquire and assess monthly values for all of the climatic indices influencing the regional and global climatic patterns (e.g., Northern Atlantic Oscillation [NOI], Southern Oscillation Index [SOI], and Tropical North Atlantic Index [TNA]); (3) delineate homogenous climatic regions and select nine pilot study zones, (4) apply data mining methods (e.g. neural networks, principal component analyses) to extract relationships between the observed rainfall and the controlling factors (i.e. climatic indices with multiple lead-time periods) and (5) use the constructed predictive tools to forecast monthly rainfall and dry and wet periods. Preliminary results indicate that rainfall and dry/wet periods were successfully predicted with lead zones of 0 to 12 months using the adopted methodology, and that the approach is more accurately applicable in the southern Mediterranean region.Keywords: rainfall, neural networks, climatic indices, Mediterranean
Procedia PDF Downloads 3002022 Fault-Detection and Self-Stabilization Protocol for Wireless Sensor Networks
Authors: Ather Saeed, Arif Khan, Jeffrey Gosper
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Sensor devices are prone to errors and sudden node failures, which are difficult to detect in a timely manner when deployed in real-time, hazardous, large-scale harsh environments and in medical emergencies. Therefore, the loss of data can be life-threatening when the sensed phenomenon is not disseminated due to sudden node failure, battery depletion or temporary malfunctioning. We introduce a set of partial differential equations for localizing faults, similar to Green’s and Maxwell’s equations used in Electrostatics and Electromagnetism. We introduce a node organization and clustering scheme for self-stabilizing sensor networks. Green’s theorem is applied to regions where the curve is closed and continuously differentiable to ensure network connectivity. Experimental results show that the proposed GTFD (Green’s Theorem fault-detection and Self-stabilization) protocol not only detects faulty nodes but also accurately generates network stability graphs where urgent intervention is required for dynamically self-stabilizing the network.Keywords: Green’s Theorem, self-stabilization, fault-localization, RSSI, WSN, clustering
Procedia PDF Downloads 652021 Classification of Echo Signals Based on Deep Learning
Authors: Aisulu Tileukulova, Zhexebay Dauren
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Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target.Keywords: radar, neural network, convolutional neural network, echo signals
Procedia PDF Downloads 3402020 Belonging in South Africa: Networks among African Immigrants and South African Natives
Authors: Efe Mary Isike
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The variety of relationships between migrants and host communities is an enduring theme of migration studies. On one extreme, there are numerous examples of hostility towards ‘strangers’ who are either ejected from society or denied access to jobs, housing, education, healthcare and other aspects of normal life. More moderate treatments of those identified as different include expectations of assimilation in which host communities expect socially marginalized groups to conform to norms that they define. Both exclusion and assimilation attempt to manage the problem of difference by removing it. South Africa experienced great influx of African immigrants who worked in mines and farms under harsh and exploitative conditions before and after the institutionalization of apartheid. Although these labour migrants contributed a great deal to the economic development of South Africa, they were not given citizenship status. The formal democratization in 1994 came with dreams and expectations of a more inclusive South Africa, where black South Africans hoped to maximize their potential in a more free, fair and equal society. In the same vein, it also opened spaces for an influx of especially African immigrants into the country which set the stage for a new form of contest for belonging between South African citizens and African migrant settlers. One major manifestation of this contest was the violent xenophobic attacks against African immigrants which predate that of May 2008 and has continued with lower intensity across the country since then. While it is doubtless possible to find abundant evidence of antagonism in the relations between South Africans and African immigrants, the purpose of this study is to investigate the everyday realities of migrants in ordinary places who interact with a variety of people through their livelihood activities, marriages and social relationships, moving around towns and cities, in their residential areas, in faith-based organizations and other elements of everyday life. Rather than assuming all relations are hostile, this study intends to look at the breadth of everyday relationships within a specific context. Based on the foregoing, the main task of this study is to holistically examine and explain the nature of interactions between African migrants and South African citizens by analysing the social network ties that connect them in the specific case of Umhlathuze municipality. It will also investigate the variety of networks that exists between African migrants and South Africans and examine the nature of the linkages in the various networks identified between these two groups in Umhlathuze Municipality. Apart from a review of relevant literature, policies and other official documents, this paper will employ a purposive sample survey and in-depth interview of African immigrants and South Africans within their networks in selected suburbs in KwaZulu-Natal.Keywords: migration, networks, development, host communities
Procedia PDF Downloads 2692019 Traffic Safety and Risk Assessment Model by Analysis of Questionnaire Survey: A Case Study of S. G. Highway, Ahmedabad, India
Authors: Abhijitsinh Gohil, Kaushal Wadhvaniya, Kuldipsinh Jadeja
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Road Safety is a multi-sectoral and multi-dimensional issue. An effective model can assess the risk associated with highway safety. A questionnaire survey is very essential to identify the events or activities which are causing unsafe condition for traffic on an urban highway. A questionnaire of standard questions including vehicular, human and infrastructure characteristics can be made. Responses from the age wise group of road users can be taken on field. Each question or an event holds a specific risk weightage, which contributes in creating an inappropriate and unsafe flow of traffic. The probability of occurrence of an event can be calculated from the data collected from the road users. Finally, the risk score can be calculated by considering the risk factor and the probability of occurrence of individual event and addition of all risk score for the individual event will give the total risk score of a particular road. Standards for risk score can be made and total risk score can be compared with the standards. Thus road can be categorized based on risk associated and traffic safety on it. With this model, one can assess the need for traffic safety improvement on a given road, and qualitative data can be analysed.Keywords: probability of occurrence, questionnaire, risk factor, risk score
Procedia PDF Downloads 3312018 Optimizing Operation of Photovoltaic System Using Neural Network and Fuzzy Logic
Authors: N. Drir, L. Barazane, M. Loudini
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It is well known that photovoltaic (PV) cells are an attractive source of energy. Abundant and ubiquitous, this source is one of the important renewable energy sources that have been increasing worldwide year by year. However, in the V-P characteristic curve of GPV, there is a maximum point called the maximum power point (MPP) which depends closely on the variation of atmospheric conditions and the rotation of the earth. In fact, such characteristics outputs are nonlinear and change with variations of temperature and irradiation, so we need a controller named maximum power point tracker MPPT to extract the maximum power at the terminals of photovoltaic generator. In this context, the authors propose here to study the modeling of a photovoltaic system and to find an appropriate method for optimizing the operation of the PV generator using two intelligent controllers respectively to track this point. The first one is based on artificial neural networks and the second on fuzzy logic. After the conception and the integration of each controller in the global process, the performances are examined and compared through a series of simulation. These two controller have prove by their results good tracking of the MPPT compare with the other method which are proposed up to now.Keywords: maximum power point tracking, neural networks, photovoltaic, P&O
Procedia PDF Downloads 328