Search results for: attractor neural networks

Authors: Jui-Chen Huang, Shou-Hsiung Cheng

Abstract:

This study examined their willingness to use telecare for people who have had experience falling in the last three months in Taiwan. This study adopted convenience sampling and a structural questionnaire to collect data. It was based on the definition and the constructs related to the Health Belief Model (HBM). HBM is comprised of seven constructs: perceived benefits (PBs), perceived disease threat (PDT), perceived barriers of taking action (PBTA), external cues to action (ECUE), internal cues to action (ICUE), attitude toward using (ATT), and behavioral intention to use (BI). This study adopted Fuzzy Neural Network (FNN) to put forward an effective method. It shows the dependence of ATT on PB, PDT, PBTA, ECUE, and ICUE. The training and testing data RMSE (root mean square error) are 0.028 and 0.166 in the FNN, respectively. The training and testing data RMSE are 0.828 and 0.578 in the regression model, respectively. On the other hand, as to the dependence of ATT on BI, as presented in the FNN, the training and testing data RMSE are 0.050 and 0.109, respectively. The training and testing data RMSE are 0.529 and 0.571 in the regression model, respectively. The results show that the FNN method is better than the regression analysis. It is an effective and viable good way.

Keywords: fall, fuzzy neural network, health belief model, telecare, willingness

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Authors: Taha Bensiradj, Samira Moussaoui

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Hybrid Sensors and Vehicular Networks (HSVN), represent a hybrid network, which uses several generations of Ad-Hoc networks. It is used especially in Intelligent Transport Systems (ITS). The HSVN allows making collaboration between the Wireless Sensors Network (WSN) deployed on the border of the road and the Vehicular Network (VANET). This collaboration is defined by messages exchanged between the two networks for the purpose to inform the drivers about the state of the road, provide road safety information and more information about traffic on the road. Moreover, this collaboration created by HSVN, also allows the use of a network and the advantage of improving another network. For example, the dissemination of information between the sensors quickly decreases its energy, and therefore, we can use vehicles that do not have energy constraint to disseminate the information between sensors. On the other hand, to solve the disconnection problem in VANET, the sensors can be used as gateways that allow sending the messages received by one vehicle to another. However, because of the short communication range of the sensor and its low capacity of storage and processing of data, it is difficult to ensure the exchange of road messages between it and the vehicle, which can be moving at high speed at the time of exchange. This represents the time where the vehicle is in communication range with the sensor. This work is the proposition of a communication protocol between the sensors and the vehicle used in HSVN. The latter has as the purpose to ensure the exchange of road messages in the available time of exchange.

Keywords: HSVN, ITS, VANET, WSN

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Authors: K. Padmavathi, K. Sri Ramakrishna

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This study describes a procedure for the detection of Left and Right Bundle Branch Block (LBBB and RBBB) ECG patterns using spectral Coherence(SC) technique and LM Neural Network. The Coherence function finds common frequencies between two signals and evaluate the similarity of the two signals. The QT variations of Bundle Blocks are observed in lead V1 of ECG. Spectral Coherence technique uses Welch method for calculating PSD. For the detection of normal and Bundle block beats, SC output values are given as the input features for the LMNN classifier. Overall accuracy of LMNN classifier is 99.5 percent. The data was collected from MIT-BIH Arrhythmia database.

Keywords: bundle block, SC, LMNN classifier, welch method, PSD, MIT-BIH, arrhythmia database

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Authors: Halimatu S. Abdullahi, Ray E. Sheriff, Fatima Mahieddine

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Agriculture is essential to the continuous existence of human life as they directly depend on it for the production of food. The exponential rise in population calls for a rapid increase in food with the application of technology to reduce the laborious work and maximize production. Technology can aid/improve agriculture in several ways through pre-planning and post-harvest by the use of computer vision technology through image processing to determine the soil nutrient composition, right amount, right time, right place application of farm input resources like fertilizers, herbicides, water, weed detection, early detection of pest and diseases etc. This is precision agriculture which is thought to be solution required to achieve our goals. There has been significant improvement in the area of image processing and data processing which has being a major challenge. A database of images is collected through remote sensing, analyzed and a model is developed to determine the right treatment plans for different crop types and different regions. Features of images from vegetations need to be extracted, classified, segmented and finally fed into the model. Different techniques have been applied to the processes from the use of neural network, support vector machine, fuzzy logic approach and recently, the most effective approach generating excellent results using the deep learning approach of convolution neural network for image classifications. Deep Convolution neural network is used to determine soil nutrients required in a plantation for maximum production. The experimental results on the developed model yielded results with an average accuracy of 99.58%.

Keywords: convolution, feature extraction, image analysis, validation, precision agriculture

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Authors: Paramjit Mahey, Monica Sharma, Jasbinder Singh

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Fiber based access networks can deliver performance that can support the increasing demands for high speed connections. One of the new technologies that have emerged in recent years is Passive Optical Networks. This paper is targeted to show the simultaneous delivery of triple play service (data, voice and video). The comparative investigation and suitability of various data rates is presented. It is demonstrated that as we increase the data rate, number of users to be accommodated decreases due to increase in bit error rate.

Keywords: BER, PON, TDMPON, GPON, CWDM, OLT, ONT

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Authors: Yun-An Chen, Hung-Jun Lin, Tai-Horng Young, Der-Zen Liu

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Decellularized brain extracellular matrix had been shown that it has the ability to influence on cell proliferation, differentiation and associated cell phenotype. However, this scaffold is thought to have poor mechanical properties and rapid degradation, it is hard for cell recellularization. In this study, we used decellularized brain extracellular matrix combined with chitosan, which is naturally occurring polysaccharide and non-cytotoxic polymer, forming a 3-D scaffold for neural stem/precursor cells (NSPCs) regeneration. HE staining and DAPI fluorescence staining confirmed decellularized process could effectively vanish the cellular components from the brain. GAGs and collagen I, collagen IV were be showed a great preservation by Alcain staining and immunofluorescence staining respectively. Decellularized brain extracellular matrix was well mixed in chitosan to form a 3-D scaffold (DB-C scaffold). The pore size was approximately 50±10 μm examined by SEM images. Alamar blue results demonstrated NSPCs had great proliferation ability in DB-C scaffold. NSPCs that were cultured in this complex scaffold differentiated into neurons and astrocytes, as reveled by NSPCs expression of microtubule-associated protein 2 (MAP2) and glial fibrillary acidic protein (GFAP). In conclusion, DB-C scaffold may provide bioinformatics cues for NSPCs generation and aid for CNS injury functional recovery applications.

Keywords: brain, decellularization, chitosan, scaffold, neural stem/precursor cells

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Authors: Thohidul Islam, Md. Hamidul Haque, Robin Kumar Biswas

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Floods are one of the deadliest natural disasters which are very complex to model; however, machine learning is opening the door for more reliable and accurate flood prediction. In this research, a multilayer perceptron neural network (MLP) is developed to model the rainfall-water level relation, in a subtropical monsoon climatic region of the Bangladesh-India border. Our experiments show promising empirical results to forecast the water level for 1 day lead time. Our best performing MLP model achieves 98.7% coefficient of determination with lower model complexity which surpasses previously reported results on similar forecasting problems.

Keywords: flood forecasting, machine learning, multilayer perceptron network, regression

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Authors: Seyed Mohsen Jameii

Abstract:

Wireless underwater sensor networks are a type of sensor networks that are located in underwater environments and linked together by acoustic waves. The application of these kinds of network includes monitoring of pollutants (chemical, biological, and nuclear), oil fields detection, prediction of the likelihood of a tsunami in coastal areas, the use of wireless sensor nodes to monitor the passing submarines, and determination of appropriate locations for anchoring ships. This paper proposes a boundary coverage algorithm for intrusion detection in underwater sensor networks. In the first phase of the proposed algorithm, optimal deployment of nodes is done in the water. In the second phase, after the employment of nodes at the proper depth, clustering is executed to reduce the exchanges of messages between the sensors. In the third phase, the algorithm of "divide and conquer" is used to save energy and increase network efficiency. The simulation results demonstrate the efficiency of the proposed algorithm.

Keywords: boundary coverage, clustering, divide and ‎conquer, underwater sensor nodes

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Authors: Dariush Jafari, S. Mostafa Nowee

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In this study a ternary system containing sodium chloride as solute, water as primary solvent and ethanol as the antisolvent was considered to investigate the application of artificial neural network (ANN) in prediction of sodium solubility in the mixture of water as the solvent and ethanol as the antisolvent. The system was previously studied using by Extended UNIQUAC model by the authors of this study. The comparison between the results of the two models shows an excellent agreement between them (R2=0.99), and also approves the capability of ANN to predict the thermodynamic behavior of ternary electrolyte systems which are difficult to model.

Keywords: thermodynamic modeling, ANN, solubility, ternary electrolyte system

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Authors: Min-Woo Kim, Ok-Kyun Na, Jun-Ho Byun, Jong-Hwan Park, Seung-Hwa Yang, Joon-Hong Park, Young-Chul Park

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This study is focused on the comparative analysis and improvement to grasp the flow characteristic of the Anti-Splash Device located under the P/V Valve and new concept design models using the CFD analysis and Artificial Neural Network. The P/V valve located upper deck to solve the pressure rising and vacuum condition of inner tank of the liquid cargo ships occurred oil outflow accident by transverse and longitudinal sloshing force. Anti-Splash Device is fitted to improve and prevent this problem in the shipbuilding industry. But the oil outflow accidents are still reported by ship owners. Thus, four types of new design model are presented by study. Then, comparative analysis is conducted with new models and existing model. Mostly the key criterion of this problem is flux in the outlet of the Anti-Splash Device. Therefore, the flow and velocity are grasped by transient analysis. And then it decided optimum model and design parameters to develop model. Later, it needs to develop an Anti-Splash Device by Flow Test to get certification and verification using experiment equipment.

Keywords: anti-splash device, P/V valve, sloshing, artificial neural network

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Authors: Rana I. Abdulghani, Amera I. Melhum

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With the abnormal growth of the usage of computers over networks and under the consideration or agreement of most of the computer security experts who said that the goal of building a secure system is never achieved effectively, all these points led to the design of the intrusion detection systems(IDS). This research adopts a comparison between two techniques for network intrusion detection, The first one used the (Particles Swarm Optimization) that fall within the field (Swarm Intelligence). In this Act, the algorithm Enhanced for the purpose of obtaining the minimum error rate by amending the cluster centers when better fitness function is found through the training stages. Results show that this modification gives more efficient exploration of the original algorithm. The second algorithm used a (Back propagation NN) algorithm. Finally a comparison between the results of two methods used were based on (NSL_KDD) data sets for the construction and evaluation of intrusion detection systems. This research is only interested in clustering the two categories (Normal and Abnormal) for the given connection records. Practices experiments result in intrude detection rate (99.183818%) for EPSO and intrude detection rate (69.446416%) for BP neural network.

Keywords: IDS, SI, BP, NSL_KDD, PSO

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Authors: B. Zerouali, M. Kara, B. Hamaidi, H. Mahdjoub, S. Rouabhia

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In this paper, a safety analysis for crude oil tanks to prevent undesirable events that may cause catastrophic accidents. The estimation of the probability of damage to industrial systems is carried out through a series of steps, and in accordance with a specific methodology. In this context, this work involves developing an assessment tool and risk analysis at the level of crude oil tanks system, based primarily on identification of various potential causes of crude oil tanks fire and explosion by the use of Fault Tree Analysis (FTA), then improved risk modelling by Bayesian Networks (BNs). Bayesian approach in the evaluation of failure and quantification of risks is a dynamic analysis approach. For this reason, have been selected as an analytical tool in this study. Research concludes that the Bayesian networks have a distinct and effective method in the safety analysis because of the flexibility of its structure; it is suitable for a wide variety of accident scenarios.

Keywords: bayesian networks, crude oil tank, fault tree, prediction, safety

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Authors: A. Baloglu, A. Çınar

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Different tools and technologies were implemented for Crisis Response and Management (CRM) which is generally using available network infrastructure for information exchange. Depending on type of disaster or crisis, network infrastructure could be affected and it could not be able to provide reliable connectivity. Thus any tool or technology that depends on the connectivity could not be able to fulfill its functionalities. As a solution, a new message exchange framework has been developed. Framework provides offline/online information exchange platform for CRM Information Systems (CRMIS) and it uses XML compression and packet prioritization algorithms and is based on open source web technologies. By introducing offline capabilities to the web technologies, framework will be able to perform message exchange on unreliable networks. The experiments done on the simulation environment provide promising results on low bandwidth networks (56kbps and 28.8 kbps) with up to 50% packet loss and the solution is to successfully transfer all the information on these low quality networks where the traditional 2 and 3 tier applications failed.

Keywords: crisis response and management, XML messaging, web services, XML compression, mining

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Authors: A. M. Khourshid, I. Sabry

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Friction Stir Welding (FSW), a solid state joining technique, is widely being used for joining Al alloys for aerospace, marine automotive and many other applications of commercial importance. FSW were carried out using a vertical milling machine on Al 5083 alloy pipe. These pipe sections are relatively small in diameter, 5mm, and relatively thin walled, 2 mm. In this study, 5083 aluminum alloy pipe were welded as similar alloy joints using (FSW) process in order to investigate mechanical and microstructural properties .rotation speed 1400 r.p.m and weld speed 10,40,70 mm/min. In order to investigate the effect of welding speeds on mechanical properties, metallographic and mechanical tests were carried out on the welded areas. Vickers hardness profile and tensile tests of the joints as a metallurgical feasibility of friction stir welding for joining Al 6061 aluminum alloy welding was performed on pipe with different thickness 2, 3 and 4 mm,five rotational speeds (485,710,910,1120 and 1400) rpm and a traverse speed (4, 8 and 10)mm/min was applied. This work focuses on two methods such as artificial neural networks using software (pythia) and response surface methodology (RSM) to predict the tensile strength, the percentage of elongation and hardness of friction stir welded 6061 aluminum alloy. An artificial neural network (ANN) model was developed for the analysis of the friction stir welding parameters of 6061 pipe. The tensile strength, the percentage of elongation and hardness of weld joints were predicted by taking the parameters Tool rotation speed, material thickness and travel speed as a function. A comparison was made between measured and predicted data. Response surface methodology (RSM) also developed and the values obtained for the response Tensile strengths, the percentage of elongation and hardness are compared with measured values. The effect of FSW process parameter on mechanical properties of 6061 aluminum alloy has been analyzed in detail.

Keywords: friction stir welding (FSW), al alloys, mechanical properties, microstructure

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Authors: Oluwaponmile D. Alao

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In this paper, two models have been developed to ascertain the best network needed for diagnosis of breast cancer. Breast cancer has been a disease that required the attention of the medical practitioner. Experience has shown that misdiagnose of the disease has been a major challenge in the medical field. Therefore, designing a system with adequate performance for will help in making diagnosis of the disease faster and accurate. In this paper, two models: backpropagation neural network and support vector machine has been developed. The performance obtained is also compared with other previously obtained algorithms to ascertain the best algorithms.

Keywords: breast cancer, data mining, neural network, support vector machine

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Authors: Brenden M. Shutt, Lubjana Beshaj, Paul L. Goethals, Ambrose Kam

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Communication security is of particular interest to military data networks. A relatively novel approach to network security is blockchain, a cryptographically secured distribution ledger with a decentralized consensus mechanism for data transaction processing. Recent advances in blockchain technology have proposed new techniques for both data validation and trust management, as well as different frameworks for managing dataflow. The purpose of this work is to test the feasibility of different blockchain architectures as applied to military command and control networks. Various architectures are tested through discrete-event simulation and the feasibility is determined based upon a blockchain design’s ability to maintain long-term stable performance at industry standards of throughput, network latency, and security. This work proposes a consortium blockchain architecture with a computationally inexpensive consensus mechanism, one that leverages a Proof-of-Identity (PoI) concept and a reputation management mechanism.

Keywords: blockchain, consensus mechanism, discrete-event simulation, fog computing

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Authors: Saurabh Shukla, G. N. Pandey

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The fires have become the most serious disasters to forest resources and the human environment. In recent years, due to climate change, human activities and other factors the frequency of forest fires has increased considerably. The monitoring and prevention of forest fires have now become a global concern for forest fire prevention organizations. Currently, the methods for forest fire prevention largely consist of patrols, observation from watch towers. Thus, software like deployment of the wireless sensor network to prevent forest fire is being developed to get a better estimate of the temperature and humidity prospects. Now days, wireless sensor networks are beginning to be deployed at an accelerated pace. It is not unrealistic to expect that in coming years the world will be covered with wireless sensor networks. This new technology has lots of unlimited potentials and can be used for numerous application areas including environmental, medical, military, transportation, entertainment, crisis management, homeland defense, and smart spaces.

Keywords: deployment, sensors, wireless sensor networks, forest fires

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Authors: Ammarah Irum, Muhammad Ali Tahir

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Document-level sentiment analysis in Urdu is a challenging Natural Language Processing (NLP) task due to the difficulty of working with lengthy texts in a language with constrained resources. Deep learning models, which are complex neural network architectures, are well-suited to text-based applications in addition to data formats like audio, image, and video. To investigate the potential of deep learning for Urdu sentiment analysis, we implemented five different deep learning models, including Bidirectional Long Short Term Memory (BiLSTM), Convolutional Neural Network (CNN), Convolutional Neural Network with Bidirectional Long Short Term Memory (CNN-BiLSTM), and Bidirectional Encoder Representation from Transformer (BERT). In this study, we developed a hybrid deep learning model called BiLSTM-Single Layer Multi Filter Convolutional Neural Network (BiLSTM-SLMFCNN) by fusing BiLSTM and CNN architecture. The proposed and baseline techniques are applied on Urdu Customer Support data set and IMDB Urdu movie review data set by using pre-trained Urdu word embedding that are suitable for sentiment analysis at the document level. Results of these techniques are evaluated and our proposed model outperforms all other deep learning techniques for Urdu sentiment analysis. BiLSTM-SLMFCNN outperformed the baseline deep learning models and achieved 83%, 79%, 83% and 94% accuracy on small, medium and large sized IMDB Urdu movie review data set and Urdu Customer Support data set respectively.

Keywords: urdu sentiment analysis, deep learning, natural language processing, opinion mining, low-resource language

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Authors: Lin Cheng, Zijiang Yang

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Program synthesis is the task to automatically generate programs based on user specification. In this paper, we present a framework that synthesizes programs from flow charts that serve as accurate and intuitive specification. In order doing so, we propose a deep neural network called GRCNN that recognizes graph structure from its image. GRCNN is trained end-to-end, which can predict edge and node information of the flow chart simultaneously. Experiments show that the accuracy rate to synthesize a program is 66.4%, and the accuracy rates to recognize edge and node are 94.1% and 67.9%, respectively. On average, it takes about 60 milliseconds to synthesize a program.

Keywords: program synthesis, flow chart, specification, graph recognition, CNN

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Authors: Ruvaitha Banu, Amaladhithyan Krishnamoorthy

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IoT networks today solve various consumer problems, from home automation systems to aiding in driving autonomous vehicles with the exploration of multiple devices. For example, in an autonomous vehicle environment, multiple sensors are available on roads to monitor weather and road conditions and interact with each other to aid the vehicle in reaching its destination safely and timely. IoT systems are predominantly dependent on the cloud environment for data storage, and computing needs that result in latency problems. With the advent of Fog networks, some of this storage and computing is pushed to the edge/fog nodes, saving the network bandwidth and reducing the latency proportionally. Managing the data stored in these fog nodes becomes crucial as it might also store sensitive information required for a certain application. Data management in fog nodes is strenuous because Fog networks are dynamic in terms of their availability and hardware capability. It becomes more challenging when the nodes in the network also live a short span, detaching and joining frequently. When an end-user or Fog Node wants to access, read, or write data stored in another Fog Node, then a new protocol becomes necessary to access/manage the data stored in the fog devices as a conventional static way of managing the data doesn’t work in Fog Networks. The proposed solution discusses a protocol that acts by defining sensitivity levels for the data being written and read. Additionally, a distinct data distribution and replication model among the Fog nodes is established to decentralize the access mechanism. In this paper, the proposed model implements stewardship towards the data stored in the Fog node using the application of Reinforcement Learning so that access to the data is determined dynamically based on the requests.

Keywords: IoT, fog networks, data stewardship, dynamic access policy

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Authors: Lee Jeong Min, Lee Mi Hee, Eo Yang Dam

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Geospatial feature extraction is a very important issue in the remote sensing research. In the meantime, the image classification based on statistical techniques, but, in recent years, data mining and machine learning techniques for automated image processing technology is being applied to remote sensing it has focused on improved results generated possibility. In this study, artificial neural network and decision tree technique is applied to classify the high-resolution satellite images, as compared to the MLC processing result is a statistical technique and an analysis of the pros and cons between each of the techniques.

Keywords: remote sensing, artificial neural network, decision tree, maximum likelihood classification

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Authors: Aurelija Burinskienė, Dalė Dzemydienė, Arūnas Miliauskas

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This research aims at developing the approach for more effective freight delivery and transportation process management. The road congestions and the identification of causes are important, as well as the context information recognition and management. The measure of many parameters during the transportation period and proper control of driver work became the problem. The number of vehicles per time unit passing at a given time and point for drivers can be evaluated in some situations. The collection of data is mainly used to establish new trips. The flow of the data is more complex in urban areas. Herein, the movement of freight is reported in detail, including the information on street level. When traffic density is extremely high in congestion cases, and the traffic speed is incredibly low, data transmission reaches the peak. Different data sets are generated, which depend on the type of freight delivery network. There are three types of networks: long-distance delivery networks, last-mile delivery networks and mode-based delivery networks; the last one includes different modes, in particular, railways and other networks. When freight delivery is switched from one type of the above-stated network to another, more data could be included for reporting purposes and vice versa. In this case, a significant amount of these data is used for control operations, and the problem requires an integrated methodological approach. The paper presents an approach for providing e-services for drivers by including the assessment of the multi-component infrastructure needed for delivery of freights following the network type. The construction of such a methodology is required to evaluate data flow conditions and overloads, and to minimize the time gaps in data reporting. The results obtained show the possibilities of the proposing methodological approach to support the management and decision-making processes with functionality of incorporating networking specifics, by helping to minimize the overloads in data reporting.

Keywords: transportation networks, freight delivery, data flow, monitoring, e-services

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Authors: Nonlapun Meenil, Pisitpong Intarapong, Thitima Wongsheree, Pranchalee Samanpiboon

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In Thailand, once pineapples are harvested, they must be classified into two classes based on their sweetness: sweet and unsweet. This paper has studied and developed the assessment of internal quality of pineapples using a low-cost compact spectroscopy sensor according to the Spectroscopy approach and Neural Network (NN). During the experiments, Batavia pineapples were utilized, generating 100 samples. The extracted pineapple juice of each sample was used to determine the Soluble Solid Content (SSC) labeling into sweet and unsweet classes. In terms of experimental equipment, the sensor cover was specifically designed to install the sensor and light source to read the reflectance at a five mm depth from pineapple flesh. By using a spectroscopy sensor, data on visible and near-infrared reflectance (Vis-NIR) were collected. The NN was used to classify the pineapple classes. Before the classification step, the preprocessing methods, which are Class balancing, Data shuffling, and Standardization were applied. The 510 nm and 900 nm reflectance values of the middle parts of pineapples were used as features of the NN. With the Sequential model and Relu activation function, 100% accuracy of the training set and 76.67% accuracy of the test set were achieved. According to the abovementioned information, using a low-cost compact spectroscopy sensor has achieved favorable results in classifying the sweetness of the two classes of pineapples.

Keywords: neural network, pineapple, soluble solid content, spectroscopy

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Authors: Bandhan Dey, Muhsina Bintoon Yiasha, Gulam Sulaman Choudhury

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Chest disease is one of the most problematic ailments in our regular life. There are many known chest diseases out there. Diagnosing them correctly plays a vital role in the process of treatment. There are many methods available explicitly developed for different chest diseases. But the most common approach for diagnosing these diseases is through X-ray. In this paper, we proposed a multi-classification deep learning model for diagnosing COVID-19, lung cancer, pneumonia, tuberculosis, and atelectasis from chest X-rays. In the present work, we used the transfer learning method for better accuracy and fast training phase. The performance of three architectures is considered: InceptionV3, VGG-16, and VGG-19. We evaluated these deep learning architectures using public digital chest x-ray datasets with six classes (i.e., COVID-19, lung cancer, pneumonia, tuberculosis, atelectasis, and normal). The experiments are conducted on six-classification, and we found that VGG16 outperforms other proposed models with an accuracy of 95%.

Keywords: deep learning, image classification, X-ray images, Tensorflow, Keras, chest diseases, convolutional neural networks, multi-classification

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Authors: Elmabruk Laias, Abdalla M. Hanashi, Mohammed Alnas

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Worldwide Interoperability for Microwave Access (WiMAX) became one of the most challenging issues, since it was responsible for distributing available resources of the network among all users this leaded to the demand of constructing and designing high efficient scheduling algorithms in order to improve the network utilization, to increase the network throughput, and to minimize the end-to-end delay. In this study, the proposed algorithm focuses on an efficient mechanism to serve non-real time traffic in congested networks by considering channel status.

Keywords: WiMAX, Quality of Services (QoS), OPNE, Diff-Serv (DS).

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Authors: Ksenia Volkova, Artur Petrosyan, Ignatii Dubyshkin, Alexei Ossadtchi

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Brain-computer interfaces are a growing research field producing many implementations that find use in different fields and are used for research and practical purposes. Despite the popularity of the implementations using non-invasive neuroimaging methods, radical improvement of the state channel bandwidth and, thus, decoding accuracy is only possible by using invasive techniques. Electrocorticography (ECoG) is a minimally invasive neuroimaging method that provides highly informative brain activity signals, effective analysis of which requires the use of machine learning methods that are able to learn representations of complex patterns. Deep learning is a family of machine learning algorithms that allow learning representations of data with multiple levels of abstraction. This study explores the potential of deep learning approaches for ECoG processing, decoding movement intentions and the perception of proprioceptive information. To obtain synchronous recording of kinematic movement characteristics and corresponding electrical brain activity, a series of experiments were carried out, during which subjects performed finger movements at their own pace. Finger movements were recorded with a three-axis accelerometer, while ECoG was synchronously registered from the electrode strips that were implanted over the contralateral sensorimotor cortex. Then, multichannel ECoG signals were used to track finger movement trajectory characterized by accelerometer signal. This process was carried out both causally and non-causally, using different position of the ECoG data segment with respect to the accelerometer data stream. The recorded data was split into training and testing sets, containing continuous non-overlapping fragments of the multichannel ECoG. A deep convolutional neural network was implemented and trained, using 1-second segments of ECoG data from the training dataset as input. To assess the decoding accuracy, correlation coefficient r between the output of the model and the accelerometer readings was computed. After optimization of hyperparameters and training, the deep learning model allowed reasonably accurate causal decoding of finger movement with correlation coefficient r = 0.8. In contrast, the classical Wiener-filter like approach was able to achieve only 0.56 in the causal decoding mode. In the noncausal case, the traditional approach reached the accuracy of r = 0.69, which may be due to the presence of additional proprioceptive information. This result demonstrates that the deep neural network was able to effectively find a representation of the complex top-down information related to the actual movement rather than proprioception. The sensitivity analysis shows physiologically plausible pictures of the extent to which individual features (channel, wavelet subband) are utilized during the decoding procedure. In conclusion, the results of this study have demonstrated that a combination of a minimally invasive neuroimaging technique such as ECoG and advanced machine learning approaches allows decoding motion with high accuracy. Such setup provides means for control of devices with a large number of degrees of freedom as well as exploratory studies of the complex neural processes underlying movement execution.

Keywords: brain-computer interface, deep learning, ECoG, movement decoding, sensorimotor cortex

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Authors: Jiaqi Dong, Qing-Hua Qin, Yi Xiao

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Some of the most fundamental challenges of elastic metamaterials (EMMs) optimization can be attributed to the high consumption of computational power resulted from finite element analysis (FEA) simulations that render the optimization process inefficient. Furthermore, due to the inherent mesh dependence of FEA, minuscule geometry features, which often emerge during the later stages of optimization, induce very fine elements, resulting in enormously high time consumption, particularly when repetitive solutions are needed for computing the objective function. In this study, a surrogate modelling algorithm is developed to reduce computational time in structural optimization of EMMs. The surrogate model is constructed based on a multilayer feedforward artificial neural network (ANN) architecture, trained with prepopulated eigenfrequency data prepopulated from FEA simulation and optimized through regime selection with genetic algorithm (GA) to improve its accuracy in predicting the location and width of the primary elastic band gap. With the optimized ANN surrogate at the core, a Nelder-Mead (NM) algorithm is established and its performance inspected in comparison to the FEA solution. The ANNNM model shows remarkable accuracy in predicting the band gap width and a reduction of time consumption by 47%.

Keywords: artificial neural network, machine learning, mechanical metamaterials, Nelder-Mead optimization

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Authors: Ourn Vimoil, Kallaya Suntornvongsagul

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Cambodia is considered as one of the most disaster prone countries in South East Asia, and most of natural disasters are related to floods. Cambodia, a developing country, faces significant impacts from floods, such as environmental, social, and economic losses. Using data accessed from focus group discussions and field surveys with villagers in Ba Baong commune, prey Veng province, Cambodia, the research would like to examine roles of social networks in raising community’s coping capacity with floods. The findings indicate that social capital play crucial roles in three stages of floods, namely preparedness, response, and recovery to overcome the crisis. People shared their information and resources, and extent their assistances to one another in order to adapt to floods. The study contribute to policy makers, national and international agencies working on this issue to pay attention on social networks as one factors to accelerate flood coping capacity at community level.

Keywords: social network, community, coping capacity, flood, Cambodia

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Authors: Alberto Alcalá-Alvarez, Pablo Padilla-Longoria

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The present paper describes a framework for constructing mathematical networks encoding relevant musical information from a music score for structural analysis. These graphs englobe statistical information about music elements such as notes, chords, rhythms, intervals, etc., and the relations among them, and so become helpful in visualizing and understanding important stylistic features of a music fragment. In order to build such networks, musical data is parsed out of a digital symbolic music file. This data undergoes different analytical procedures from Graph Theory, such as measuring the centrality of nodes, community detection, and entropy calculation. The resulting networks reflect important structural characteristics of the fragment in question: predominant elements, connectivity between them, and complexity of the information contained in it. Music pieces in different styles are analyzed, and the results are contrasted with the traditional analysis outcome in order to show the consistency and potential utility of this method for music analysis.

Keywords: computational musicology, mathematical music modelling, music analysis, style classification

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Authors: Laura O’Halloran, Zhipeng Cao, Clare M. Kelly, Hugh Garavan, Robert Whelan

Abstract:

Reaction time (RT) variability on cognitive tasks provides the index of the efficiency of executive control processes (e.g. attention and inhibitory control) and is considered to be a hallmark of clinical disorders, such as attention-deficit disorder (ADHD). Increased RT variability is associated with structural and functional brain differences in children and adults with various clinical disorders, as well as poorer task performance accuracy. Furthermore, the strength of functional connectivity across various brain networks, such as the negative relationship between the task-negative default mode network and task-positive attentional networks, has been found to reflect differences in RT variability. Although RT variability may provide an index of attentional efficiency, as well as being a useful indicator of neurological impairment, the brain substrates associated with RT variability remain relatively poorly defined, particularly in a healthy sample. Method: Firstly, we used the intra-individual coefficient of variation (ICV) as an index of RT variability from “Go” responses on the Stop Signal Task. We then examined the functional and structural neural correlates of ICV in a large sample of 14-year old healthy adolescents (n=1719). Of these, a subset had elevated symptoms of ADHD (n=80) and was compared to a matched non-symptomatic control group (n=80). The relationship between brain activity during successful and unsuccessful inhibitions and gray matter volume were compared with the ICV. A mediation analysis was conducted to examine if specific brain regions mediated the relationship between ADHD symptoms and ICV. Lastly, we looked at functional connectivity across various brain networks and quantified both positive and negative correlations during “Go” responses on the Stop Signal Task. Results: The brain data revealed that higher ICV was associated with increased structural and functional brain activation in the precentral gyrus in the whole sample and in adolescents with ADHD symptoms. Lower ICV was associated with lower activation in the anterior cingulate cortex (ACC) and medial frontal gyrus in the whole sample and in the control group. Furthermore, our results indicated that activation in the precentral gyrus (Broadman Area 4) mediated the relationship between ADHD symptoms and behavioural ICV. Conclusion: This is the first study first to investigate the functional and structural correlates of ICV collectively in a large adolescent sample. Our findings demonstrate a concurrent increase in brain structure and function within task-active prefrontal networks as a function of increased RT variability. Furthermore, structural and functional brain activation patterns in the ACC, and medial frontal gyrus plays a role-optimizing top-down control in order to maintain task performance. Our results also evidenced clear differences in brain morphometry between adolescents with symptoms of ADHD but without clinical diagnosis and typically developing controls. Our findings shed light on specific functional and structural brain regions that are implicated in ICV and yield insights into effective cognitive control in healthy individuals and in clinical groups.

Keywords: ADHD, fMRI, reaction-time variability, default mode, functional connectivity

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