Skip to main content
main-content

Über dieses Buch

This book features papers presented at the International Conference on Advances in Information and Communication Technology (ICTA 2016), which was held in Thai Nguyen city, Vietnam, from December 1 to 13, 2016. The conference was jointly organized by Thai Nguyen University of Information and Communication Technology (ICTU), the Institute of Information Technology – Vietnam Academy of Science and Technology (IoIT), Feng Chia University, Taiwan (FCU), the Japan Advanced Institute of Science and Technology (JAIST) and the National Chung Cheng University, Taiwan (CCU) with the aim of bringing together researchers, academics, practitioners and students to not only share research results and practical applications but also to foster collaboration in information and communication technology research and education. The book includes the 66 best peer-reviewed papers, selected from the 150 submissions received.

Inhaltsverzeichnis

Frontmatter

Keynote Addresses

Frontmatter

Multimodal Based Clouds Computing Systems for Healthcare and Risk Forecasting Based on Subjective Analysis

In decision making most approaches are taking into account objective criteria, however the subjective correlation among decision makers provided as preference utility is necessary to be presented to provide confidence preference additive among decision makers reducing ambiguity and produce better utility preferences measurement for subjective criteria among decision makers. Most models in Decision support systems are assuming criteria as independent. Therefore, these models are ranking alternatives based on objective data analysis. Also, different type of data (time series, linguistic values, interval data, etc.) imposes some difficulties to do decision making using classical multi criteria decision making models.Sophisticated machine learning methods to estimate or extract emotions from the content created by users has been developed including support vector machines, Bayesian networks, maximum entropy approaches and concept level analysis of natural language text, supported by combinations of common-sense reasoning. These approaches are mainly based on language text processing with sufficient documents, which is usually inlarge is not available. We think Subjectiveness is related to the contextual form of criteria. Uncertainty of some criteria in decision making is also considered as other important aspect These draw backs in decision making are major research challenges that are attracting wide attention, like on big data analysis for risk prediction, medical diagnosis and other applications that are in practice more subjective to user situation and its knowledge related context. Subjectivity would be examined based on correlations between different contextual structures that is reflecting the framework of personal context, for example in nearest neighbor based correlation analysis fashion. Some of the attributes incompleteness also may lead to affect the approximation accuracy. Attributes with preference-ordered domain relations properties become one aspect in ordering properties in rough approximations.The Virtual Doctor System (VDS) developed by my group is a system assisting human doctor who is practicing medical diagnosis in real situation and environment. The interoperability is represented by utilizing the medical diagnosis cases of medical doctor, represented in machine executable fashion based on human patient interaction with virtual avatar resembling a real doctor. VDS is practiced as a virtual avatar interacting with the human patient based on physical views and mental view analysis. In this talk I outline our VDS system and then discuss related issues in subjective decision making in medical domain. Using fuzzy reasoning techniques in VDS, it has been shown that it is possible to provide better precision in circumstances that is related to partial known data and uncertainty on the acquisition of medical symptoms.

Hamido Fujita

Telematics and Advanced Transportation Services

Our Telematics and Advanced Transportation Alliance, a minor alliance between academia and industry was founded by Ministry of Science and Technology, Taiwan in 2012. In order to enhance the technical ability of telematics and provide advanced and high-quality transportation services, our alliance consists of scholars and experts from the areas of telematics and advanced traffic management. The core technologies of the alliance include Controller Area Network (CAN) bus, CANopen communication technology for Industry 4.0, WAVE DSRC networks, APP hardware/software integration, big data analysis for driving safety, and fleet management services. Our alliance has more than twenty industry members so far and has held more than 80 technical seminars, promotional activities, education training programs, and factory visits. In addition to providing personnel trainings, technical services and industry guidance for our members, innovation cooperation services are highlighted to strengthen industry academic cooperation. To integrate resource and promote services, two new core technologies, CANopen communication technology for Industry 4.0 and analysis service of intersections and reasons prone to accidents, are provided to our members and we also promote the open Internet of Vehicle (IoV) platform developed by our alliance. To strengthen the industry upgrade and guidance, seven core technologies and IoV platform are used to integrate our members’ products and assist them to get resources from Taiwan government. For autonomous operation and sustainable development, competitive advantages of our core technologies are used to develop new products and new technologies with our members to achieve the sustainability of our alliance.

Chyi-Ren Dow

Toward Affective Speech-to-Speech Translation

Speech-to-speech translation (S2ST) is the process by which a spoken utterance in one language is used to produce a spoken output in another language. The conventional approach to S2ST has focused on processing linguistic information only by directly translating the spoken utterance from the source language to the target language without taking into account para-linguistic and non-linguistic information such as the emotional states at play in the source language. This paper introduces activities of JAIST Acoustic Information Science Laboratory, Human Life Design Area, Japan Advanced Institute of Science and Technology that explore how to deal with para- and non-linguistic information among multiple languages, with a particular focus on speakers’ emotional states, in S2ST applications called “affective S2ST.” In our efforts to construct an effective system, we discuss (1) how to describe emotions in speech and how to model the perception/production of emotions and (2) the commonality and differences among multiple languages in the proposed model. We then use these discussions as context for (3) an examination of our “affective S2ST” system in operation.

Masato Akagi

ICTA 2016 Main Track

Frontmatter

A Computer Vision Based Machine for Walnuts Sorting Using Robot Operating System

Industrial machines are generally expensive to implement due to their requirement of being fast and robust. In this paper, we proposed a new approach to the problem, particularly, a walnut sorting machine that was built cheaply using open solution. On hardware side, we used readily available electronics and on the software side, Robot Operating System was adopted to handle low level hardware abstraction. Machine Learning, Computer Vision and data processing techniques were implemented on top of them using high level programming language. This resulted in a highly functional, easy to maintain yet inexpensive walnuts sorting machine - which was confirmed by our Austrian partner’s testing. This success, hopefully, will pave the way for more projects using similar approaches.

Truong Tran, Tu Nguyen, Mai Nguyen, Tuan Pham

A FPGA Based Two Level Optimized Local Filter Design for High Speed Image Processing Applications

This work presents an efficient Feild Programmable Gate Array (FPGA) based local filter design for portable and high speed image processing applications. It is highly optimized by using two level optimization. The first level optimization at design-level exploits tempo-spatial parallelism of filters by developing parallel/pipelined architecture. For exploiting spatial-parallelism, design computes partial results of multiple MACs in parallel and accumulates them via adder-tree for final result. Though it bears good performance aptitude but adder-tree incurs long critical path (4.713 ns) thus limits design performance. The critical path was reduced to 2.489 ns with temporal parallelism by pipelining the adder-tree. Design performance is further enhanced by deploying the second level optimization at post-implementation level where device aware floor-planning fine tunes the design. It aligns all utilized embedded resources of design on Xilinx Virtex-5 device and confines slice based logic across them. It results in packing the design within small area with reduced slice count and critical path (2.32 ns). After applying two levels of optimization, the design occupies 89 Slices, 3 DSP-Slices, 2 BRAM18 and achieves high frequency of 431.03 MHz.

Majida Kazmi, Arshad Aziz, Pervez Akhtar, Nassar Ikram

A Frequency Dependent Investigation of Complex Shear Modulus Estimation

Mechanical properties of tissues in terms of elasticity and viscosity provide us useful information which may be used in detecting tumors. Shear wave imaging (SWI) is a new method to quantify tissue elasticity by estimating the parameters of the complex shear modulus (CSM). The shear wave is generated by a vibrating needle at a certain frequency. In fact, CSM is a function of the vibrating frequency. Therefore, in this paper, a frequency dependent investigation of CSM will be carried in order to evaluate the estimation performance. The Extended Kalman Filter (EKF) is designed to estimate the CSM for both homogeneous and heterogeneous mediums. The root mean square (rms) error is used to evaluate the quality of the CSM estimation. Several tests were implemented to determine the range of vibrating frequency should be used for the good estimation.

Quang Hai Luong, Manh Cuong Nguyen, Tran Duc Tan

A Method to Enhance the Remote Sensing Images Based on the Local Approach Using KMeans Algorithm

The image enhancement methods based on fuzzy logic make image which quality higher clearly the traditional methods. However, actually, the methods still use the global approach, so having difficulty to enhance all land covers in remote sensing images. This paper presents a local approach based new algorithm of image enhancement for the remote sensing images, calculating thresholds automatically and combination multiple gray adjust operators.

Trung Nguyen Tu, Duc Dang Van, Huy Ngo Hoang, Thoa Vu Van

A Method for Clustering and Identifying HTTP Automated Software Communication

Application developer has trend to take advantage of web as a communication medium environment to reach users because HTTP protocol is mostly allowed in any network environment nowadays. Unfortunately, cyber criminal is also fully exploit HTTP protocol to launch variety of forbidden actions such as application level attacks or spreading malware. Consequently, normal and malicious HTTP automated software (auto-ware) traffic are transparently merged with each other. Clustering and identifying between HTTP communication are raised as serious challenge in order to early investigate internal threats. In this paper, access graph and key features are suggested, based on which HTTP auto-ware communication behavior are recognized. From there, a novelty method in clustering and identifying HTTP auto-ware is presented. Experiment shows promising results since not just malicious communications are detected but also grayware traffic are clustered into groups and identified as their purposes.

Manh Cong Tran, Hai Nam Nguyen, Minh Hieu Nguyen, Yasuhiro Nakamura

A New Neuro-Fuzzy Inference System for Insurance Forecasting

Insurance forecasting is a matter of vital importance to insurance companies for analyzing of annual income, premium and loss reserving, loss payment, etc. Recent years have also seen increasing discussion within the actuarial community of the need for insurance forecasting techniques that are more solidly grounded in rigorous machine learning methodologies. Taking advantages of knowledge-reuse and learning capability for dealing with uncertainties, hybridization of neural networks and fuzzy logic could enhance the accuracy of forecasting for insurance applications. In this paper, we propose a novel neuro-fuzzy inference system for insurance forecasting. It uses multiple parameter sets where each set is responsible for a small subset of records. The aim of each parameter set is to minimize Mean Square Error within records of the subset. The learning strategy and a rule reduction method are also proposed. Empirically validation on the benchmark and real insurance datasets show the advantages of the new system.

Le Hoang Son, Mai Ngoc Khuong, Tran Manh Tuan

A New Schema to Identify S-farnesyl Cysteine Prenylation Sites with Substrate Motifs

Protein prenylation is the addition of hydrophobic molecules to a protein or chemical compound. It is a post-translational modification that plays very important roles for many cellular processes such as DNA replication, signaling, trafficking, and other cellular functions in eukaryotes. Protein S-farnesyl cysteine prenylation is a specific kind of prenylation involved in the transfer of a farnesyl moiety to a cytoplasmic cysteine at or near the C-terminus of the target protein. Recent advancements in proteomic technology have stimulated an increasing interested in the identification of protein S-farnesyl cysteine prenylation sites. However, there is still a lack of methods proposed for the prediction of S-farnesyl cysteine sites. With a rapidly increasing number of experimentally verified S-farnesyl cysteine sites, it is motivated in proposed new method for identifying S-farnesyl cysteine prenylation sites.

Van-Nui Nguyen, Thi-Xuan Tran, Hai-Minh Nguyen, Hong-Tan Nguyen, Tzong-Yi Lee

A Novel Framework Based on Deep Learning and Unmanned Aerial Vehicles to Assess the Quality of Rice Fields

In the past few decades, boosting crop yield has been extensively regarded in many agricultural countries, especially Vietnam. Due to food demands and impossibility of crop-field area increasing, precision farming is essential to improve agricultural production and productivity. In this paper, we propose a novel framework based on some advanced techniques including deep learning, unmanned aerial vehicles (UAVs) to assess the quality of Vietnamese rice fields. UAVs are responsible for taking images of the rice fields at low or very low altitudes. Then, these images with high resolution will be processed by the deep neural networks on high performance computing systems. The main task of deep neural networks is to classify the images into many classes corresponding to low and high qualities of the rice fields. To conduct experimental results, the rice fields located in Tay Ninh province are chosen as a case study. The experimental results indicate that this approach is quite appropriate for agricultural Vietnamese practice since its accuracy is approximately 0.72.

Nguyen Cao Tri, Tran Van Hoai, Hieu N. Duong, Nguyen Thanh Trong, Vo Van Vinh, Vaclav Snasel

A Semi-supervised Learning Method for Hybrid Filtering

Recommender systems are the auto systems of providing appropriate information and removing unappropriate information for users. The recommender systems are built based on two main information filtering techniques: Collaborative filtering and content-based filtering. Content-based filtering performs effectively on documents representing as text but has problems to select information features on multimedia data. Collaborative filtering perform well on all types of information but had problems when sparse data, new uses and new items. In this paper, we propose a new unify model between collaborative filtering and content-based filtering by a semi-supervised learning method. The model is built based on two semi-supervised procedures: the first procedure semi-supervise ratings set between users and item’s features, the second procedure semi-supervise ratings set between items and users features. The first procedure allows us to detect new items that is high suitable capability with the users. The second procedure allows us to detect new users that is high suitable ability with the items. Two procedures performed simultaneously and complement each other for suitable predicted values to improve recommender results. The experimental results on real data sets show that the proposed methods utilize effectively the advantages and limit significant disadvantages of baseline filtering methods.

Thi Lien Do, Duy Phuong Nguyen

A Study on Fitness Representation in Genetic Programming

In this paper, we propose a variation on the fitness function in Genetic Programming based on Bias-Variance Genetic Programming (BVGP) [2], called BVGP*. In order to evaluate the effectiveness of this variation, we compare it with Genetic Programming [1] and Bias-Variance Genetic Programming (BVGP) [2]. The experimental results shown that the learned model by BVGP* is better than that of GP and BVGP in ability to generalize, model complexity and evaluation time.

Thuong Pham Thi, Xuan Hoai Nguyen, Tri Thanh Nguyen

Adaptive Robust Ability of High Order Sliding Mode Control for a 3-D Overhead Crane System

Traditionally, 3-D overhead crane systems are widely used in industry and automatic operation would reduce the risk. It is difficult to precisely position the payload in overhead crane due to the lack of actuators in this system. This paper develops an adaptive robust ability of high – order sliding mode controller (HOSMC). The finite time stability of the closed-loop system is proved without traditional Lyapunov theory. The results based on suitable second-order sliding surface and super – twisting controller. Simulation studies are performed to demonstrate the validity of the proposed control scheme.

Dao Phuong Nam, Nguyen Doan Phuoc, Nguyen Thi Viet Huong

An Evaluation of Hand Pyramid Structure for Hand Representation Based on Kernels

Hand posture recognition is an active research topic in computer vision and robotics with many applications ranging from automatic sign language recognition to human-system interaction. Recently, we have proposed a new descriptor for hand representation based on the kernel method (KDES) [1]. Our new descriptor inherits the main idea of KDES but we proposed three improvements to make it more robust. One of the improvements was that we introduced a new hand pyramid structure [14]. Intuitively, hand pyramid is more suitable to hand structure than conventional pyramid. In our previous work, we have demonstrated that the combination of improvements to KDES gives more accurate hand posture classification than using original KDES. However, it still lacks discussions and experimental evidences of the contribution of hand pyramid for hand representation. In this paper, we build specific hand dataset and conduct more experiments to show how hand pyramid contributes for hand representation. We will discuss deeply on the results and analyze the impact of this pyramid on hand posture classification.

Van-Toi Nguyen, Thi-Lan Le, Thanh-Hai Tran

An Evolutionary-Based Term Reduction Approach to Bilingual Clustering of Malay-English Corpora

The document clustering process groups the unstructured text documents into a predefined set of clusters in order to provide more information to the users. There are many studies conducted in clustering monolingual documents. With the enrichment of current technologies, the study of bilingual clustering would not be a problem. However clustering bilingual document is still facing the same problem faced by a monolingual document clustering which is the “curse of dimensionality”. Hence, this encourages the study of term reduction technique in clustering bilingual documents. The objective in this study is to study the effects of reducing terms considered in clustering bilingual corpus in parallel for English and Malay documents. In this study, a genetic algorithm (GA) is used in order to reduce the number of feature selected. A single-point crossover with a crossover rate of 0.8 is used. Not only that, this study also assesses the effects of applying different mutation rate (e.g., 0.1 and 0.01) in selecting the number of features used in clustering bilingual documents. The result shows that the implementation of GA does improve the clustering mapping compared to the initial clustering mapping. Not only that, this study also discovers that GA with a mutation rate of 0.01 produces the best parallel clustering mapping results compared to GA with a mutation rate of 0.1.

Rayner Alfred, Leow Ching Leong, Joe Henry Obit

An Exploratory Study on Students’ Performance Classification Using Hybrid of Decision Tree and Naïve Bayes Approaches

Students’ performance prediction can give a prior approximate knowledge of the students’ performance in future academic to the educators. However, it is not any easy task to perform prediction due to the poor identification of parameters and the lack of prediction techniques. In this paper, few parameters will be proposed and the most influenced parameters on students’ performance will be identified using chi squared. The hybrid of Decision Tree and Naïve Bayes algorithms, NBTree will be used to classify the performance of new students. NBTree classifier undergoes the training and testing process using 10-folds cross validation technique and obtained the classification accuracy of 85.9 %, which is better than the accuracy of Decision Tree and Naïve Bayes classifiers which are having 63.7 % and 72.6 % respectively. The classified performance result can be used by the educators to improve the teaching and learning process by developing new teaching methods and new teaching styles.

Yoong Yen Chuan, Wahidah Husain, Amirah Mohamed Shahiri

An Improved Method for Stock Market Forecasting Combining High-Order Time-Variant Fuzzy Logical Relationship Groups and Particle Swam Optimization

Fuzzy forecasting approaches are mainly based on the modeling of fuzzy logical relationships of the historical data. In this paper, an improved model for forecasting stock market indices which combines the High-order Time-Variant Fuzzy Logical Relationship Groups (HV-FLRGs) and Particle Swarm Optimization (PSO) is presented. Firstly, HV-FLRGs are more effective to capture fuzzy relations on time series data than the conventional time-invariant fuzzy logical relationship groups. Secondly, PSO is employed to optimize the length of intervals by searching the space of the universe of discourse. To verify the effectiveness of the proposed model, the historical data of Taiwan Futures Exchange (TAIFEX) are examined. The simulation result shows that the proposed model outperforms the previous forecasting models based on the high-order fuzzy time series. These results are very promising for the future work on the development of fuzzy time series and PSO algorithm in real-world forecasting applications.

Nghiem Van Tinh, Nguyen Cong Dieu

An Iterative Method to Solve Boundary Value Problems with Irregular Boundary Conditions

In this paper, we investigate a general model of boundary value problems with irregular boundary conditions, a model attracting a lot of attention from researchers all over the world [2–6]. By the two approaches of finding values of functions or derivatives on the unidentified boundary conditions, we propose two iterative schemes to define approximation solutions of the problem and compare the rate of convergence of the two iterative schemes. The performed numerical experiments show the effectiveness of the two proposed methods.

Vu Vinh Quang, Truong Ha Hai

BKCA, an E-Consultancy System for Studying

In education organizations, a number of quotidian and duplicated consulting questions for training programs, university regulations... make difficulties, inconvenient and time-consuming for consultants. This work proposes a method to suggest answers of similar questions, which may help both students and consultants in orientation. This method includes two main tasks: (i) query generation which extracts key phrases from input questions, (ii) searching which find out most similar questions with their answers and relevant scores using extracted key phrases. For the first task, a check strategy was used to build key phrase candidates and the Naïve Bayes Classifier was used to select key phrases. The two different approaches were experimented for the second one: (i) Similarity Comparison Searching, and (ii) Sorl Search Engine. The precision of the key phrase extraction is about 69 %. BKCA, an e-consultancy system for SoICT-HUST, was built and experimented with about 46 % relevant question-answer pairs for the Similarity Comparison searching approach, and about 39 % for the Sorl Search Engine one.

Thi Thu Trang Nguyen, Viet Thang Dinh, Tuan Dung Cao

Classifying Human Body Postures by a Support Vector Machine with Two Simple Features

Human behaviour analysis helps to monitor a person’s daily activities and detect home care emergencies. Classifying posture is an important step of human behaviour analysis. Many studies improve the accuracy of classifying. However, the number of features is big or extracting these features uses complicated formulas. Therefore, we proposed two features with simple computing. Two new features are formulated from the height and the square showing the human body’s silhouettes. Then, we choose a non-linear Support Vector Machine to classify postures based on proposed features. Experiments show Support Vector Machine classify effectively and better than other methods.

Nguyen Van Tao, Nong Thi Hoa, Quach Xuan Truong

Cluster Analysis, Classification and Forecasting Tool on DS30 for Better Investment Decision

An important aspect of finance is forecasting of stock returns. Relation can be established using fundamental information that is publicly available to predict future stock returns. This helps to extract knowledge from existing data and use them to make decisions by the investors. However other than only predicting future stock returns, our study can group stocks, choose stocks from the group to make a portfolio and make a better investment decision.

Arshiful Islam Shadman, Sheikh Shadab Towqir, M. Ahnaf Akif, Mehrab Imtiaz, Rashedur M. Rahman

Comparing Modified PSO Algorithms for MRS in Unknown Environment Exploration

Multi-robot systems (MRS) have shown clear advantages over single robots in the application of exploring unknown environments - a fundamental problem in robotics. Among algorithms which are able to be applied to MRS in the application, Particle Swarm Optimization (PSO) - a heuristic optimization technique inspired by social behavior of natural swarms - has received much attention and is well-known for its efficiency and simplicity to implement. However, when conventional PSO is applied, the problems of disconnection and collision within the system are inevitable. Two of various methods proposed to address these crucial issues are applying BOIDS and Artificial Potential Field (APF) to modify PSO. In this work, we simulated both modified algorithms on Matlab under various scenarios for analysis and comparison.

Anh-Quy Hoang, Minh-Trien Pham

Design Adaptive-CTC Controller for Tracking Target Used Mobile Robot-Pan Tilt-Stereo Camera System

The paper presents a dynamic model of mobile robot - pan tilt robot - stereo camera system. Stereo camera is placed on the head of pan tilt robot and carried by a mobile robot. The pan tilt robot will rotate to keep the image target on image frame and take the moving information of mobile robot out. The main contribute of this paper is designed an Adaptive Computed-Torque-Control controller for the dynamic system. The controller is used to control pan tilt robot track the moving target and mobile robot move to reach to the target when there are noise effects.

Le Van Chung, Duong Chinh Cuong

Estimation Localization in Wireless Sensor Network Based on Multi-objective Grey Wolf Optimizer

Determining the position of nodes of a network plays an important role in many wireless sensor networks (WSN) applications e.g. in tracking, detecting, monitoring, etc. In this paper, the multi-objective grey wolf optimizer (MGWO) for the estimating approaches of the located nodes in a network is proposed to solve the multi-objective optimization localization issues in WSNs. There two objective functions related to the estimation localization are the distance of nodes and the geometric topology that consider to formula multiobjective optimization localization. The simulation results show considerable improvements in terms of localization accuracy and convergence rate in comparison with those obtained from the other methods.

Trong-The Nguyen, Ho Thi Huong Thom, Thi-Kien Dao

Evaluation of Mobile Phone Wireless Charging System Using Solar and Inductive Coupling

The wireless charging system now becomes one of the emerging technologies especially in the application of communication systems and beneficial to the wireless electronic appliances. Among them are mobile phones, cameras, personal digital assistance (PDA), cooler, torchlight and drill. Those wireless devices require battery to store and provide power before the device can be used. Hence, in order to solve the problem of short life of the battery of mobile phone, this project proposes adding a solar charging system base on inductive coupling method to the mobile phone to improve the usage of mobile phone in term of standby time, talk-time, online applications and power consumption especially in the remote area. Inductive coupling is among the effective method in wireless charging system to charge electronics device and reduce the constraint of the power cord or wired system. Meanwhile, solar cell is among the energy harvesting devices that is widely employed in many electronics application. The outcome of the project describes the comparison of the power consumption between the wire charging systems with solar-based wireless charging system. From the analysis of the results, solar-powered mobile phone with inductive coupling produced 21 h 46 min standby time after charging for 13 h 15 min compared to the existing charging system (wired system) which produce 17 h 5 min standby time after charging for 2 h 30 min. In addition, proposed system has high power consumption in term of standby time, talk-time and online application. Based on the results of the project, it could suggest that the wireless solar-powered mobile phone can replace the existing charging system in term of standby time.

Norizam Sulaiman, Mohd Shawal Jadin, Amran Abdul Hadi, Muhamad Sharfi Najib, Mohd Ashraf Ahmad, Nurul Shazrien Amsyha Yusuf

Hardware Implementation of MFCC Feature Extraction for Speech Recognition on FPGA

In this paper, an FPGA-based Mel Frequency Cepstral Coefficient (MFCC) IP core for speech recognition is presented. The implementation results on FPGA show that the proposed MFCC core achieves higher resource usage efficiency compared with other designs.

Van-Lan Dao, Van-Danh Nguyen, Hai-Duong Nguyen, Van-Phuc Hoang

Improved Adaptive Fuzzy Sliding Mode Control for Second Order Nonlinear System

In this paper, an Adaptive Fuzzy Sliding Mode combined with Model Reference Adaptive System (MRAS) is proposed to a single input-single output nonlinear system. The main goal of this paper is the control of second order nonlinear system. Firstly, adaptive fuzzy sliding mode is applied to design the controller. Next, a performance of system and a chattering phenomenon are improved by adjusting parameters of the controller via MRAS. Finally, an example is presented to illustrate the proposed methods. By applying Lyapunov stability theory, the adaptive law that is derived in this study is robust and convergent quickly. The simulation results and analysis show that the proposed method is better than Adaptive Fuzzy Sliding Mode in the sense of robustness against disturbance and reduce the chattering.

Pham Van Thiem, Lai Khac Lai, Nguyen Thi Thanh Quynh

Improved LDPC Iterative Decoding Algorithm Based on the Reliable Extrinsic Information and Its Histogram

In this paper we proposed a new method to prevent propagating errors due to passing the error Extrinsic information between nodes during the iterative decoding of low density parity check codes based on reliable Extrinsic information. Moreover, we also proposed a new method to analyze the convergence of the iterative LDPC decoding by using the histogram of Extrinsic information.

Nguyen Anh Tuan, Pham Xuan Nghia

Improving Control Quality for Two-Wheeled Mobile Robot

An approach for improving quality of motion control and stabilizing for two-wheeled mobile robot which is affected by internal and external distubances is presented in this paper. An output feedback controller using backstepping combined with high gain observer (HGOs) technique is designed. The decoupling technique for coupling system of two-wheeled mobile robot is also applied. Results of simulation show the effectiveness of the designed controller.

Gia Thi Dinh, Nguyen Duy Cuong, Nguyen Dang Hao

Incorporation of Experience and Reference-Based Topic Trust with Interests in Social Network

Computational trust in the social media depends both on the interaction types and on the expertise, which exposes user’s interests by tags on items such as books, articles, images etc. However, when there is not any interaction among users, such a computation is impossible. In this paper, we first propose a novel model of computational trust among users in social network to incorporate the interaction among users and semantics of topics based on tags posted by users. Both types of direct and indirect interaction via intermediate ones are utilized for computation in the model. Then, we introduce algorithms for computing trust among users in social network.

Phuong Thanh Pham, Manh Hung Nguyen, Dinh Que Tran

Integrating Multisignature Scheme into the Group Signature Protocol

This paper proposes two new variants of group signature protocols with and without distinguished signing authorities based on the multisignature signature scheme to reduce significantly the signature length and masking signers public keys. The proposed protocols do not include a secret sharing and knowledge proving procedure. Thus, these protocols allow a flexible modification of the group structure by the group manager. Compared to the known group signature protocols, our protocols are designed based on integrating the multisignature scheme into the group signature protocol.

Hung Dao Tuan, Hieu Minh Nguyen, Cong Manh Tran, Hai Nam Nguyen, Moldovyan Nikolay Adreevich

Interestingnesslab: A Framework for Developing and Using Objective Interestingness Measures

The objective interestingness measures play an important role in data mining because they are used for mining, filtering and ranking the patterns. However, there is no research that collects the measures fully as well as there is no tool that can: automatically calculate the interestingness values of the patterns by using those measures, and is the framework for rapidly developing the applications related to objective interestingness measures. This paper describes Interestingnesslab - a tool of the objective interestingness measures is developed in the R language. The main functions of the tool are: mining a set of association rules and presenting them by the cardinalities ($$n,n_{X},n_{Y},n_{X\overline{Y}}$$), calculating the interestingness value of an association rule according to 1 of 109 collected measures; calculating the interestingness values of the whole rule set in many measures selected by the user; discovering the tendencies in a data set and recommending the top N items to the user; and studying the specific behavior of a set of interestingness measures in the context of a specific dataset and in an exploratory data analysis perspective. With Interestingnesslab, the user can easily and quickly reuse its functions to develop his/her own applications.

Lan Phuong Phan, Nghia Quoc Phan, Ky Minh Nguyen, Hung Huu Huynh, Hiep Xuan Huynh, Fabrice Guillet

Inverted Pendulum Control Using Fuzzy Reasoning Method Based on Hedge Algebras by Approach to Semantic Quantifying Adjustment of Linguistic Value

Fuzzy control using hedge - algebras is used in the control problem with the linguistic value of k finite depth. With semantics quantifying adjustment of the linguistic value that still preserve their order. This paper proposes the fuzzy reasoning method based on hedge algebras (HAs) by approach to semantic quantifying adjustment of linguistic value, the semantic quantifying adjustable parameters are determined by genetic algorithm. This method is applied to the problem of inverted pendulum control of Ross, the simulation results have confirmed that the given method is correct and effective.

Nguyen Duy Minh, Do Thi Mai, Nguyen Thi Thu Hien

k-Nearest Neighbour Using Ensemble Clustering Based on Feature Selection Approach to Learning Relational Data

Due to the growing amount of data generated and stored in relational databases, relational learning has attracted the interest of researchers in recent years. Many approaches have been developed in order to learn relational data. One of the approaches used to learn relational data is Dynamic Aggregation of Relational Attributes (DARA). The DARA algorithm is designed to summarize relational data with one-to-many relations. However, DARA suffers a major drawback when the cardinalities of attributes are very high because the size of the vector space representation depends on the number of unique values that exist for all attributes in the dataset. A feature selection process can be introduced to overcome this problem. These selected features can be further optimized to achieve a good classification result. Several clustering runs can be performed for different values of k to yield an ensemble of clustering results. This paper proposes a two-layered genetic algorithm-based feature selection in order to improve the classification performance of learning relational database using a k-NN ensemble classifier. The proposed method involves the task of omitting less relevant features but retaining the diversity of the classifiers so as to improve the performance of the k-NN ensemble. The result shows that the proposed k-NN ensemble is able to improve the performance of traditional k-NN classifiers.

Rayner Alfred, Kung Ke Shin, Mohd Shamrie Sainin, Chin Kim On, Paulraj Murugesa Pandiyan, Ag Asri Ag Ibrahim

Low Power ECC Implementation on ASIC

In this paper, the Low power Elliptic Curve Cryptography (ECC) structure over Galois field $$GF({2^m})$$ is studied and implemented on the Application Specific Integrated Circuit (ASIC) tool for wireless sensor network and Internet of Things (IoT) Applications. Clock gating technique is used for decreasing power consumption. The implementation is conducted by the 180 nm CMOS standard library shows that the proposed ECC structure has the power consumption of 10.4 $$\upmu $$W/MHz outweigh than previous designs.

Van-Lan Dao, Van-Tinh Nguyen, Van-Phuc Hoang

Malwares Classification Using Quantum Neural Network

Quantum neural networks (QNNs) have been explored as one of the best approach for improving the computational efficiency of neural networks. Because of the powerful and fantastic performance of quantum computation, some researchers have begun considering the implications of quantum computation on the field of artificial neural networks (ANNs). The purpose of this paper is to introduce an application of QNNs in malwares classification. Inherently Fuzzy Feedforward Neural Networks with sigmoidal hidden units was used to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that (QNN’s) gave a kind of fast and realistic results compared with the (ANN’s). Simulation results indicate that QNN is superior (with total accuracy of 98.245 %) than ANN (with total accuracy of 95.214 %).

Tu Tran Anh, The Dung Luong

Managing Secure Personal Mobile Health Information

Medical errors may cause serious public health problems and threaten the safety of the patients. Part of the errors is due to mistakes in the medical record or incomplete medical record which may trigger tragic consequences. In this paper, we present an application that manages securely personal health information on a mobile platform and keeps all the medical records of a patient in digital format. Patients are able to access their medical record at their convenience and the confidentiality of information is guaranteed. Patients are also able to share their personal health record with their respective doctor in a secure way. The application consists of several modules: incognito, access control, privacy control, authentication, encryption, multifactor authentication and emergency control. An anonymous database is created by removing all the identifier of a patient before the health record is stored in the database. This provides an extra layer of protection to the patient’s privacy. In particular, our proposed application introduces the multifactor authentication and emergency control modules which provides a multi-layered defense authentication and emergency case handler respectively. Thus, the proposed application allows the patient to assess their records conveniently and securely, and helps them in emergency situations. As such, the application is suitable for cases involving large number of patients and emergency situations such as in Hajj healthcare management.

Chan Wai Chen, Mohd Azam Osman, Zarul Fitri Zaaba, Abdullah Zawawi Talib

Mobile Online Activity Recognition System Based on Smartphone Sensors

In this paper, we propose an efficient and flexible framework for activity recognition based on smartphone sensors, so called Mobile Online Activity Recognition System (MOARS). This system comprises data collection, training, activity recognition, and feedback monitoring. It allows users to put their smartphones in any position and at any direction. In our proposed framework, a set of power-based and frequency-based features is extracted from sensor data. Then, Random Forest, Naïve Bayes, K-Nearest Neighbor (KNN), and Support Vector Machine (SVM) classification algorithms are deployed for recognizing a set of user activities. Our framework dynamically takes into account real-time user feedbacks to increase the accuracy of activity prediction. This framework is able to apply for intelligent mobile applications. A number of experiments are carried out to show the high accuracy of MOARS in detecting user activities when walking or driving a motorbike.

Dang-Nhac Lu, Thu-Trang Nguyen, Thi-Thu-Trang Ngo, Thi-Hau Nguyen, Ha-Nam Nguyen

Multi-criteria Path Planning for Disaster Relief: An Example Using the Flood Risk Map of Shalu District, Taiwan

Route planning is one of the most common location-based services today. Conventional route planning algorithms could only consider a single criterion, which cannot meet actual needs. Skyline path queries were therefore proposed for multi-criteria path queries. However, we discovered that most existing skyline path query algorithms are extremely slow, and while such slow speeds may be acceptable to the general public, they are unacceptable for disaster relief. We therefore proposed two means of improvement. Experiment simulations demonstrated the validity of our approach.

Sheng-Wei Qiu, Yi-Chung Chen, Tsu-Chiang Lei, Hsin-Ping Wang, Hsi-Min Chen

Multi-feature Based Similarity Among Entries on Media Portals

Similar measures play an important role in information processing and have been widely investigated in computer science. With the exploration of social media such as Youtube, Wikipedia, Facebook etc., a huge number of entries have been posted on these portals. They are often described by means of short text or sets of words. Discovering similar entries based on such texts has become challenges in constructing information searching or filtering engines and attracted several research interests. In this paper, we firstly introduce a model of entries posted on media or entertainment portals, which is based on their features composed of title, category, tags, and content. Then, we present a novel similar measure among entries that incorporates their features. The experimental results show the superiority of our incorporation similarity measure compared with the other ones.

Thi Hoi Nguyen, Dinh Que Tran, Gia Manh Dam, Manh Hung Nguyen

MyEpiPal: Mobile Application for Managing, Monitoring and Predicting Epilepsy Patient

An epileptic seizure can be defined as a disturbance of consciousness, motor function, sensation, emotion, and behavior. The seizures can affect almost all aspects of life, such as lifestyle, health, education, and employment. Without a proper tool, epilepsy patient and caregiver finds difficulty in managing and monitoring epilepsy. The caregivers’ lifestyle is also affected when the patient strongly dependent on them. By using current technology, which is a mobile phone, MyEpiPal application will be implemented to solve these problems. MyEpiPal will act as a self-management and monitoring tool that can support the epilepsy patient and the caregiver. This application also will enable them to monitor side-effects and effectiveness of antiepileptic medicine, predict the possibility of seizure attack, and improve the quality of life. A fully functional mobile application will be a supportive tool for monitoring and managing epilepsy patient.

Nur Ayuni Marzuki, Wahidah Husain, Amirah Mohamed Shahiri

New Block Ciphers for Wireless Moblile Netwoks

Wireless Mobile Networks (WMNs) play a fundamental role in the activities of each individual and organization. However, they have a number of limitations about hardware resources, processing capacity of the network devices. Hence, when integrating the cipher algorithms into the devices, the algorithms will have to be sure to reach high efficiency (fast processing speed and little resource expenses). This paper will demonstrate some results of constructing a new algorithm of cipher blocks based on the controlled element $$F_{2/2}$$. This is the controlled element which has been studied and proved to be suitable for developing the algorithms of block ciphers with highly integrated speed and efficiency on the hardware platform VLSI. Therefore, this algorithm will be sure to work effectively and suitable for transmitting secretly on WMNs. Furthermore, this paper will also propose the schedule which creates the on-the-fly key to counteract the attacks of the related key. Usage of this key schedule will still ensure high efficiency when integrating the cipher algorithms on WSNs.

Pham Manh Tuan, Bac Do Thi, Minh Nguyen Hieu, Nam Do Thanh

Numerical Method for Solving a Strongly Mixed Boundary Value Problem in an Unbounded Domain

In this paper we consider a strongly mixed elliptic boundary value problem in an unbounded domain, where there is one point of transmission of boundary conditions on an unbounded boundary. By a domain decomposition technique the problem is reduced to two problems, one with weakly mixed boundary conditions in a bounded subdomain and another with the Dirichlet boundary conditions in the remaining unbounded subdomain. For the problem in the bounded subdomain the finite difference method on a uniform grid is used. Meanwhile the problem in the unbounded subdomain is discretized on a non-uniform grid and for solving the resulting system of grid equations we use the method of infinite systems, developed by ourselves recently. Numerical experiments, where the grid with the monotonically increasing grid sizes on the unbounded subdomain is used, show the efficiency of the proposed method.

Dang Quang A, Dinh Hung Tran

Numerical Solution of a Fully Fourth Order Nonlinear Problem

In this paper we consider a fully fourth order nonlinear boundary value problem which models a statistically bending elastic beam. Differently from the approaches of the other authors, we reduce the problem to an operator equation for the right-hand side function. The existence and uniquenness of solution is established. Besides, we propose an iterative method for solving the problem. Some numerical examples demonstrate the efficiency of the method.

Dang Quang A, Ngo Thi Kim Quy

On the Performance of Decode-and-Forward Half-Duplex Relaying with Time Switching Based Energy Harvesting in the Condition of Hardware Impairment

In this paper, the outage performance and system throughput of a decode-and-forward half-duplex relay network, in which the relay node is equipped with time switching based energy harvesting capability, are derived rigorously. The analytical results provide theoretical insights into the effect of various system parameters, such as time switching factor, source transmission rate, transmitting-power-to-noise ratio to system performance. Our analysis is confirmed by Monte-Carlo simulation, and can serve as a guideline to design practical energy-harvested relaying systems.

Tan N. Nguyen, Phuong T. Tran, Huong-Giang Hoang, Hoang-Sy Nguyen, Miroslav Voznak

Opportunistic Multiple Relay Selection Schemes in both Full-Duplex and Half-Duplex Operation for Decode-and-Forward Cooperative Networks

An efficient technique used to prolong the lifetime of energy-constrained networks is energy harvesting (EH). In this paper, we investigate and develop the energy allocation methods for the relaying networks, in which opportunistic multiple relay selection schemes with both the full-duplex (FD) and half-duplex (HD) scheme for both EH and non-EH in decode-and-forward (DF) relaying mode. In addition, there are two policies proposed in this paper: (1) Max-Min with Self Interference Relay Selection (MMSI); (2) Max-Min Relay Selection (MMSR) are depicted for both EH and Non-EH relaying modes. Particularly, we derive closed-form expressions of outage probability to analyze the performance of systems. In addition, we propose the impact of self-interference on both policies to provide a practical insight. The results in numerical analysis reveal that the proposed MMSI scheme outperforms the MMSR mode in terms of outage probability.

Hoang-Sy Nguyen, Anh-Hoa Thi Bui, Nhat-Tan Nguyen, Miroslav Voznak

Pattern Discovery in the Financial Time Series Based on Local Trend

We introduce a method for discovering new patterns in financial time series. Our method focuses on two main tasks of time series mining: Start with time series representation which helps to reduce the dimension and extracts useful feature of raw time series; Next discover on symbolic time series to find out new useful patterns which helpfully to improve trading decision in financial domain. In our work, we are interested in some patterns which have high win ratio percent (i.e. greater 70 %). In the first phrase, (i) raw data will be split into some segments with same length, (ii) local trend will be used to convert each subsequence into symbolic (U, u, s, d or D). In second phrase, we use a sliding window with size w moved on symbolic time series to create a collection of transactions. Based on this collection, the SPAM algorithm is used to discover all patterns with low minSup. In the last phrase, win/loss constraint used to discover new patterns in financial time series will be presented. Our demonstrate based on Gold Spot dataset from 2012-01-01 to 2015-01-01 is experimented.

Mai Van Hoan, Dao The Huy, Luong Chi Mai

Performance Evaluation of SAR ADC with Organic Semiconductor

This paper presents impact of sampling and input signal frequencies on dynamic performance of a fully differential organic 6 bit SAR ADC. The simulation results proved that the total power consumption of the circuit increased linearly with the sampling frequency and was almost stable in the wide range of the input signal frequency. At the sampling frequency of 1 KHz and the input signal frequency of 10 Hz, the power dissipation, ENOB, SFDR, THD, and FoM are 443.1 $$\mu W$$, 4.83 bit, 37.71 dB, $$-34.01$$ dB, and 15.60 nJ/conv, respectively.

Huyen Thanh Pham, Toan Thanh Dao, Thang Vu Nguyen

Performance Evaluation of Wireless Networks Based on Testbed

Today the trend of using hardware environment for testing and assessmenting (testbed) is increasingly becoming more popular. This is obvious because testing on the real equipment is always more accurate than testing on the theoretical model or software simulation. In addition, the cost of computer hardware is decreasing quite fast, and its design is getting more compact, this contributes to facilitating the use of hardware system. Therefore, testbed system is a popular trend which many research institutes in the world use widely. In Vietnam at present, researching on the performance of wireless network is a current issue and there have been many researches about this. However, most researches are only testing and evaluating the result by using methods proved by mathematics model, or based on the software simulation. Building a testbed system for wireless network is meaningful for basic researches at Vietnam Institute of Information Technology, as well as for extending more co-operations with other outside organizations.

Ngo Hai Anh, Takumi Tamura, Pham Thanh Giang

Predicting Early Crop Production by Analysing Prior Environment Factors

Bangladesh has an agriculture dependent economy and hence prediction of agricultural production is of great importance to us. In this research we develop a model that considers and analyzes weather and climate prior to specific crop plantation and maps a correlation between these two. It allows us to provide information about the crop state, in quantity and quality with the possibility of early warnings so that timely interventions can be undertaken. The approach advocated in this paper is to help the people with food security and early warning system.

Tousif Osman, Shahreen Shahjahan Psyche, MD Rafik Kamal, Fouzia Tamanna, Farzana Haque, Rashedur M. Rahman

Prediction of Generalized Anxiety Disorder Using Particle Swarm Optimization

Diseases can be predicted by using historical patient information stored in clinical databases. Large data is required to ensure the accuracy of prediction. However, processing and extracting valuable information from huge data is a challenging and time-consuming task. Missing and incomplete data may easily cause the data to be ignored and not fully utilized in the prediction. In this paper we focus our study on generalized anxiety disorder. Prediction of generalized anxiety disorders is carried out using feature selection and classification approach. This research focuses on studying and implementing Particle Swarm Optimization algorithm and Fuzzy Rough Set in the classification of generalized anxiety disorders. Performance of classifier model is evaluated respectively based on the accuracy, sensitivity and specificity of results produced. It is found that the proposed hybrid approach in feature selection has different results in performance depending on the selection of classification technique.

Wahidah Husain, Saw Hui Yng, Nur’Aini Abdul Rashid, Neesha Jothi

Quality Improvement of Vietnamese HMM-Based Speech Synthesis System Based on Decomposition of Naturalness and Intelligibility Using Non-negative Matrix Factorization

Hidden Markov model (HMM)-based synthesized speech is intelligible but not natural especially under limited data condition. The goal of this study is to improve naturalness without violating acceptable intelligibility by decomposing the naturalness and intelligibility of synthesized speech using a novel asymmetric bilinear model involving non-negative matrix factorization (NMF). Subjective evaluations carried out on Vietnamese data confirmed that the achieved synthesis quality is higher than other methods under limited data condition. Since F0 contour is important for naturalness and intelligibility, especially in Vietnamese. Proposed method is capable of modifying over-smoothed F0 contour without destroying tonal information.

Anh-Tuan Dinh, Thanh-Son Phan, Masato Akagi

Reducing Middle Nodes Mapping Algorithm for Energy Efficiency in Network Virtualization

For the future of the Internet, Network Virtualization and Software-Defined Networking (SDN) are recognized as key technologies. They reshape computing and network architectures, provide a number of advantages including centralized management, scalability and resource optimization. Along with the Internet’s development, the networking devices such as routers and switches are notable part of the large energy consumption of ITC. The design of high performance and energy-efficient network by using virtualization and SDN becomes an importance issue. In this paper we heuristic present energy-efficient algorithms for virtual network embedding. The experimental results show the remarkable energy-saving level of their approaches while maintaining the acceptance ratio.

Tran Manh Nam, Nguyen Van Huynh, Nguyen Huu Thanh

Research on Enhancing Security in Cloud Data Storage

Nowadays, cloud storage service has been widely deployed by multiple providers that offer free and large-sized data storage. Nevertheless, along with the advantages are the risks of data loss, the service providers’ unauthorized access to data, and data loss due to the providers’ unsustainability. This paper will investigate how to use cloud storage capability in a safe and low-cost way. The authors will present solutions for secured cloud data storage based on the RAID redundancy mechanism, partially encrypted data and mathematical models established on the probability theory and the system reliability.

Minh Le Quang, Phan Huy Anh, Nguyen Anh Chuyen, Le Khanh Duong

Resource-Aware Scheduling in Heterogeneous, Multi-core Clusters for Energy Efficiency

The benefits and necessity of multi-core technology are undeniable and make it a critical trend in chip manufacture. This shift, however, also brings complexities in computer sciences, especially in job scheduling problem. Additionally, energy bills have been a major concern due to the increasing population of computing systems lately. The trade-off between performance and energy efficiency in such systems makes the scheduling optimization more challenging.This study aims to propose an energy-efficient scheduling solution that exploits the resource heterogeneity and utilization in computing clusters of multi-core processors. The numerical results show that the proposed policy helps saving significant energy in a heterogeneous cluster.

Xuan T. Tran

Secured-OFS: A Novel OpenFlow Switch Architecture with Integrated Security Functions

Although OpenFlow network protocol is a promising network approach with many advantages compared to traditional network approaches, it still suffers from network attacks. In this paper, we propose a novel architecture for an OpenFlow-based switch with associated multiple network security techniques, so-called Secured-OFS. The proposed Secured-OFS can not only function as a switch following the OpenFlow protocol but also help protect a network against many attack types. We implement the first FPGA-based prototype version of our proposed Secured-OFS using a Xilinx Virtex 5 xc5vtx240t device. In this first prototype version, we integrate two different DDoS defense techniques, Hop-Count Filtering and Port Ingress/Egress Filtering. The experimental results show that the switch not only fulfills the OpenFlow protocol but also be able to defense against DDoS attacks. The system achieves a maximum throughput at 19.729 Gbps while a 100 % DDoS attack detection rate is obtained.

Bao Ho, Quoc Nguyen, Cuong Pham-Quoc, Tran Ngoc Thinh

Semi-supervised Clustering in Fuzzy Min-Max Neural Network

The Fuzzy Min max Neural Network (FMNN) developed by Simpson is defined as a neural network that forms hyperboxes for classification and prediction. This paper proposes an improvement in learning algorithm in FMNN using semi-supervised clustering method, called SS-FMM. The proposed model combines the advantages of supervised learning and those of unsupervised learning. Labeled a part of data is the additional information that is used in this semi-supervised clustering method. For evaluation purpose, this algorithm is implemented on two datasets including Shape sets from CS and Thyorid disease from UCI. A part from that, in this paper, some related algorithms in FMNN are also setup on these datasets in order to compare the accuracy with proposed algorithm. The test results show that the novel algorithm has the better performance.

Dinh Minh Vu, Viet Hai Nguyen, Ba Dung Le

Smart Lecture Room for Smart Campus Building Automation System

In the recent years, the terms of “Internet of Things” and “Smart Building Automation System” come into practical implementations from a hot topic for researches and developments. Beyond the concept of facilities or appliances in an apartment or office can be controlled remotely, as an automated entity, such the system is now required to be intelligent. The intelligence include an adaptive operation with context awareness, energy efficiency, and comfort living experience. However, it is difficult to realize such the complicated systems due to a gap between a high levels of abstraction in expectations to real devices. The purpose of this work is to realize a Smart Lecture Room, from a classical campus lecture room, as a complete solution for both building managers and users. During the development, the design process for this particular system is constructed. The realized system is then be analyzed to have better improvements in the future.

Kien Tran Pham Thai

Solving Navier-Stokes Equation Using FPGA Cellular Neural Network Chip

This paper presents a method of using Cellular Neural Network (CNN) for solving hydraulic Navier-Stokes equations, which are a set of three partial differential equations with 3 functional variables and each function has three variables for time and space. The paper has 5 parts: part 1 is the introduction; Part 2 discusses the Navier-Stokes for flow through narrow slots; Part 3 analyzes and finds templates for designing CNN architecture; Part 4 provides some solutions for optimizing resources and speeding up computing; Part 5 sets up CNN chip for implementing and computing results; the last part is conclusion and developing trends.

Duc-Thai Vu, Le Hung Linh, Nguyen Mai Linh

Special Characters of Vietnamese Sign Language Recognition System Based on Virtual Reality Glove

In this paper, we introduce a method of recognition numbers and special characters of Vietnam sign language. We address a development of a glove-based gesture recognition system. A sensor glove is attached ten flex sensors and one accelerometer. Flex sensors are used for sensing the curvature of fingers and the accelerometer is used in detecting a movement of a hand. Depending on the hand’s postures, i.e., vertical, horizontal, and movement, sign language of numbers and special characters can be divided to group 1, 2, and 3, respectively. Firstly, the hand’s posture is recognized. Next, if the hand’s posture belongs to either group 1 or group 2, a matching algorithm is used to detect a number or one of special characters. If the posture belongs to group 3, a dynamic time warping algorithm is applied. The use of our system in recognizing Vietnamese sign language is illustrated. In addition, experimental results are provided.

Diep Nguyen Thi Bich, Trung-Nghia Phung, Thang Vu Tat, Lam Phi Tung

Stochastic Bounds for Inference in Topic Models

Topic models are popular for modeling discrete data (e.g., texts, images, videos, links), and provide an efficient way to discover hidden structures/semantics in massive data. The problem of posterior inference for individual texts is particularly important in streaming environments, but often intractable in the worst case. Some existing methods for posterior inference are approximate but do not have any guarantee on neither quality nor convergence rate. Online Maximum a Posterior Estimation algorithm (OPE) [13] has more attractive properties than existing inference approaches, including theoretical guarantees on quality and fast convergence rate. In this paper, we introduce three new algorithms to improve OPE (so called OPE1, OPE2, OPE3) by using stochastic bounds when doing inference. Our algorithms not only maintain the key advantages of OPE but often outperform OPE and existing algorithms. Our new algorithms have been employed to develop new effective methods for learning topic models from massive/streaming text collections.

Xuan Bui, Tu Vu, Khoat Than

Swarm Intelligence-Based Approach for Macroscopic Scale Odor Source Localization Using Multi-robot System

Odor source localization is a problem of great importance. Two mainstream methods among numerous proposed ones are probabilistic algorithms and bio-inspired algorithms. Compared to probabilistic algorithms, biomimetic approaches are much less intensive in term of computational cost. Thus, despite their slightly worse performance, biomimetic approaches have received much more attention. In this paper, a novel method based on a bio-inspired algorithm - Particle Swarm Optimization (PSO) - is proposed for a multi-robot system (MRS). The proposed algorithm makes use of wind information and immediate odor gradient to enhance the performance of the MRS. A mechanism based on Artificial Potential Field (APF) is utilized to ensure non-collision movement of the robots. This method is tested by simulation on Matlab. Data for the test scenarios, all in large scales, are generated using Fluent. Nearly 2000 runs are carried out and the simulation results confirm the proposed algorithm’s effectiveness.

Anh-Quy Hoang, Minh-Trien Pham

The Analyzes of Network-on-Chip Architectures Based on NOXIM Simulator

Network-on-Chip (NoC), an interesting paradigm, is one of the newest technologies for VLSI design. In this research, we approach the architecture, algorithms and the performance analyses of a NoC system. The highlight point of this research is implementing additional features to the embedded codes and evaluating special applications in a NoC system based on NOXIM simulator. The results indicated that in certain cases, we should know which is appropriate algorithm for implementing tasks. The evaluating a NoC’s performance is an important research trend, hence this study provides one new method for doing that.

Van-Nam Dinh, Mau-Viet Ho, Van-Cuong Nguyen, Tung-Son Ngo, Effiong Charles

The Asynchronous Cooperative Amplify-and-Forward Relay Network with Partial Feedback to Improve the System Performance

In this paper, a new near-optimum detection (NOD) scheme is combined with partial feedback technique for two dual-antenna relay nodes in the amplify-and-forward (AF) asynchronous cooperative relay network. The application of partial feedback in the proposed scheme not only offers to reduce a cooperative relaying process due to not using distributed close loop extended orthogonal space-time block code (DCL EO-STBC) encoder, but also improves the end-to-end signal noise ratio (SNR). Moreover, a near-optimum detection (NOD) scheme is used at the destination to remove completely the interference components induced by inter-symbol interference (ISI) among the relay nodes. The analysis and simulation results demonstrate that the performance of the proposed scheme outperforms the previous feedback scheme in both the perfect synchronization and imperfect synchronization assumption cases.

The-Nghiep Tran, Van-Bien Pham, Huu-Minh Nguyen

The Optimization Model for Calculating Distribution System Planning Integrated Photovoltaic

A two-stage model for the optimal planning of distribution systems with the presence of photovoltaic generation system (PV) is presented in this paper. The proposed model can determine the optimal sizing and time-frame of the equipment (feeders and transformer substations) in distribution systems. Therefore, the optimal displacement, sizing, technology and installation period of PV are also determined. The objective function is the life cycle costs minimizing of the planning project. The technical constraints are used to guarantee the operability of the distribution system including AC power flow, feeder and substation upgrading section, limited of nodal voltage and PV capacity. The binary variables are also employed in the model to represent the cost function of the equipment as well as the investment and upgrade decisions. The algorithm is programmed in GAMS. The feasibility and effectiveness of the proposed model are examined in a test system.

V. V. Thang

The Prediction of Succinylation Site in Protein by Analyzing Amino Acid Composition

Protein Succinylation is a kind of post-translational modification (PTM) where a succinyl group is attacked to a lysine residue of a protein molecule. Recent findings have demonstrated the important role of Succinylation in not only taking part in various biological processes but also associating with many diseases. There are many practical methods to identify succinylation sites but an expensive cost and time-wasting should be considered. The lack of research in structure and characteristic of protein will limit to understand and discover significantly. Therefore, this work aims to focus on develop a bioinformatics method for investigating Succinylation site based on the amino acid composition and physicochemical properties. Various features were investigated in this study, including 20 Binary coding, amino acid composition (AAC), amino acid pair composition (AAPC), solvent-accessible surface area (ASA), amino acid substitution matrix (Blosum62), and position-specific scoring matrix (PSSM). Evaluation by five-fold cross validation indicated that the selected features were effective in the identification of Succinylation sites. The model constructed from hybrid features, including BLOSUM62 and PSSM, yielded the best performance with sensitivity, specificity, accuracy and MCC measurements of 0.66, 0.68, 0.67 and 0.32, respectively.

Van-Minh Bui, Van-Nui Nguyen

The Signal Control for the Traffic Network via Image Analysis

The traffic jam issue is a current important problem which is caused by the rapid increase in population, economic growth and traffic participator. This article represents a signal control method at each intersection to help traffic vehicles to be able to run as fast as possible, and data link among these intersections helps to solve the problem of traffic network in the same time. Thereby, traffic stream management and combination of controlling light signal in traffic network will have a part in reducing traffic jam, saving time and costing for society. A solution for controlling adaptive traffic light signals based on occupied roadway area of vehicles in traffic is presented. The effectiveness of the method is illustrated by an example simulation.

Du Dao Huy, Mui Nguyen Duc

Toward Cyber-Security Architecture Framework for Developping Countries: An Assessment Model

This article aims to introduce the cyber security assess model (CSAM), an important component in cyber security architecture framework, especially for the developing country. This architecture framework is built up with the Enterprise Architecture approach and based on the ISO 27001 and ISO 27002. From the holistic perspective based on EGIF developed previously by UNDP group and the main TOGAF features, ITI-GAF is simplified to suit the awareness, capability and improvement readiness of the developing countries. The result of survey and applying in countries as Vietnam, Lao, Myanmar affirms the applicable value of ITI-GAF and the CSAM. The comprehensive, accurate and prompt assessment when applying ITI-CSAM enables the organization to identify the cybersecurity strengths and weaknesses, thereby determine the key parts need invested and its effects to the whole organization’s cybersecurity, then build up the action plan for short-term and long-term.

Nguyen Ai Viet, Le Quang Minh, Doan Huu Hau, Nguyen Ngoc Tuan, Nguyen Nhat Quang, Nguyen Dinh Chinh, Nguyen Van Luc, Pham Thanh Dat

Backmatter

Weitere Informationen

Premium Partner

    Bildnachweise