Search results for: parallel clustering

Authors: Rossella Arcucci, Luisa D'Amore, Simone Celestino, Giuseppe Scotti, Giuliano Laccetti

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This work is the first dowel in a rather wide research activity in collaboration with Euro Mediterranean Center for Climate Changes, aimed at introducing scalable approaches in Ocean Circulation Models. We discuss designing and implementation of a parallel algorithm for solving the Variational Data Assimilation (DA) problem on Graphics Processing Units (GPUs). The algorithm is based on the fully scalable 3DVar DA model, previously proposed by the authors, which uses a Domain Decomposition approach (we refer to this model as the DD-DA model). We proceed with an incremental porting process consisting of 3 distinct stages: requirements and source code analysis, incremental development of CUDA kernels, testing and optimization. Experiments confirm the theoretic performance analysis based on the so-called scale up factor demonstrating that the DD-DA model can be suitably mapped on GPU architectures.

Keywords: data assimilation, GPU architectures, ocean models, parallel algorithm

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Authors: Zehai Yu, Hui Zhu, Linglong Lin, Huawei Liang, Biao Yu, Weixin Huang

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With the development of intelligent vehicle systems, a high-precision road map is increasingly needed in many aspects. The automatic lane lines extraction and modeling are the most essential steps for the generation of a precise lane-level road map. In this paper, an automatic lane-level road map generation system is proposed. To extract the road markings on the ground, the multi-region Otsu thresholding method is applied, which calculates the intensity value of laser data that maximizes the variance between background and road markings. The extracted road marking points are then projected to the raster image and clustered using a two-stage clustering algorithm. Lane lines are subsequently recognized from these clusters by the shape features of their minimum bounding rectangle. To ensure the storage efficiency of the map, the lane lines are approximated to cubic polynomial curves using a Bayesian estimation approach. The proposed lane-level road map generation system has been tested on urban and expressway conditions in Hefei, China. The experimental results on the datasets show that our method can achieve excellent extraction and clustering effect, and the fitted lines can reach a high position accuracy with an error of less than 10 cm.

Keywords: curve fitting, lane-level road map, line recognition, multi-thresholding, two-stage clustering

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Authors: A. K. M. Kamrul Islam, Abdelhamid Bouchachia, Suang Cang, Hongnian Yu

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Forecasting model has a great impact in terms of prediction and continues to do so into the future. Although many forecasting models have been studied in recent years, most researchers focus on different forecasting methods based on fuzzy time series to solve forecasting problems. The forecasted models accuracy fully depends on the two terms that are the length of the interval in the universe of discourse and the content of the forecast rules. Moreover, a hybrid forecasting method can be an effective and efficient way to improve forecasts rather than an individual forecasting model. There are different hybrids forecasting models which combined fuzzy time series with evolutionary algorithms, but the performances are not quite satisfactory. In this paper, we proposed a hybrid forecasting model which deals with the first order as well as high order fuzzy time series and particle swarm optimization to improve the forecasted accuracy. The proposed method used the historical enrollments of the University of Alabama as dataset in the forecasting process. Firstly, we considered an automatic clustering algorithm to calculate the appropriate interval for the historical enrollments. Then particle swarm optimization and fuzzy time series are combined that shows better forecasting accuracy than other existing forecasting models.

Keywords: fuzzy time series (fts), particle swarm optimization, clustering algorithm, hybrid forecasting model

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Authors: A. Abbasi Moshaii, M. Soltan Rezaee, M. Mohammadi Moghaddam

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Control of some mechanisms is hard because of their complex dynamic equations. If part of the complexity is resulting from uncertainties, an efficient way for solving that is robust control. By this way, the control procedure could be simple and fast and finally, a simple controller can be designed. One kind of these mechanisms is 3-RRR which is a parallel mechanism and has three revolute joints. This paper aims to robust control a 3-RRR planner mechanism and it presents that this could be used for other mechanisms. So, a significant problem in mechanisms control could be solved. The relevant diagrams are drawn and they show the correctness of control process.

Keywords: 3-RRR, dynamic equations, mechanisms control, structural uncertainty

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Authors: Alamgir Naushad, Ghulam Abbas, Shehzad Ali Shah, Ziaul Haq Abbas

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Unlike conventional networks, it is particularly challenging to manage resources efficiently in Wireless Sensor Networks (WSNs) due to their inherent characteristics, such as dynamic network topology and limited bandwidth and battery power. To ensure energy efficiency, this paper presents a routing protocol for WSNs, namely, Enhanced Hybrid Multipath Routing (EHMR), which employs hierarchical clustering and proposes a next hop selection mechanism between nodes according to a maximum residual energy metric together with a minimum hop count. Load-balancing of data traffic over multiple paths is achieved for a better packet delivery ratio and low latency rate. Reliability is ensured in terms of higher data rate and lower end-to-end delay. EHMR also enhances the fast-failure recovery mechanism to recover a failed path. Simulation results demonstrate that EHMR achieves a higher packet delivery ratio, reduced energy consumption per-packet delivery, lower end-to-end latency, and reduced effect of data rate on packet delivery ratio when compared with eminent WSN routing protocols.

Keywords: energy efficiency, load-balancing, hierarchical clustering, multipath routing, wireless sensor networks

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Authors: F. Jiménez, R. Jódar, M. Martín, G. Sánchez, G. Sciavicco

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Abstract—Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time, to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Its application to unsupervised classification is restricted to a limited number of experiments in the literature. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We present a feature selection wrapper model composed by a multi-objective evolutionary algorithm, the clustering method Expectation-Maximization (EM), and the classifier C4.5 for the unsupervised classification of data extracted from a psychological test named BASC-II (Behavior Assessment System for Children - II ed.) with two objectives: Maximizing the likelihood of the clustering model and maximizing the accuracy of the obtained classifier. We present a methodology to integrate feature selection for unsupervised classification, model evaluation, decision making (to choose the most satisfactory model according to a a posteriori process in a multi-objective context), and testing. We compare the performance of the classifier obtained by the multi-objective evolutionary algorithms ENORA and NSGA-II, and the best solution is then validated by the psychologists that collected the data.

Keywords: evolutionary computation, feature selection, classification, clustering

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Authors: Shen Jian, Hu Jie, Zhu Guo Niu, Peng Ying Hong

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There are two issues exited in the traditional visual prosthesis: lacking systematic method and the low level of humanization. To tackcle those obstacles, a visual prosthesis design method based on biologically inspired design is proposed. Firstly, a constrained FBS knowledge cell model is applied to construct the functional model of visual prosthesis in biological field. Then the clustering results of engineering domain are ob-tained with the use of the cross-domain knowledge cell clustering algorithm. Finally, a prototype system is designed to support the bio-logically inspired design where the conflict is digested by TRIZ and other tools, and the validity of the method is verified by the solution scheme

Keywords: knowledge-based engineering, visual prosthesis, biologically inspired design, biomedical engineering

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Authors: Wen Feiming

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As the ecological barrier of the Yangtze River, the ecological security of the Parallel Range-Valley area is very important. However, the unique geomorphic features aggravate the contradiction between man and land, resulting in the encroachment of ecological space. In recent years , relevant researches has focused on the single field of land science, ecology and landscape ecology, and it is difficult to systematically reflect the regularities of distribution and evolution trends of ecological patterns in the process of urban development. Therefore, from the perspective of "Production-Living-Ecological space", using spatial analysis methods such as Remote Sensing (RS) and Geographic Information Systems (GIS), this paper analyzes the evolution characteristics and driving factors of the ecological pattern of mountain towns in the parallel range-valley region from the aspects of land use structure, change rate, transformation relationship, and spatial correlation. It is concluded that the ecological pattern of mountain towns presents a trend from expansion and diffusion to agglomeration, and the dynamic spatial transfer is a trend from artificial transformation to the natural origin, while the driving effect analysis shows the significant characteristics of terrain attraction and construction barrier. Finally, combined with the evolution characteristics and driving mechanism, the evolution modes of "mountain area - concentrated growth", "trough area - diffusion attenuation" and "flat area - concentrated attenuation" are summarized, and the differentiated zoning and stratification ecological planning strategies are proposed here, in order to provide the theoretical basis for the sustainable development of mountain towns in parallel range-valley areas.

Keywords: parallel range-valley, ecological pattern, evolution characteristics, driving factors

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Authors: Moulana Mohammed

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Topic models are widely used in building clusters of documents for more than a decade, yet problems occurring in choosing optimal number of topics. The main problem is the lack of a stable metric of the quality of topics obtained during the construction of topic models. The authors analyzed from previous works, most of the models used in determining the number of topics are non-parametric and quality of topics determined by using perplexity and coherence measures and concluded that they are not applicable in solving this problem. In this paper, we used the parametric method, which is an extension of the traditional topic model with visual access tendency for visualization of the number of topics (clusters) to complement clustering and to choose optimal number of topics based on results of cluster validity indices. Developed hybrid topic models are demonstrated with different Twitter datasets on various topics in obtaining the optimal number of topics and in measuring the quality of clusters. The experimental results showed that the Visual Non-negative Matrix Factorization (VNMF) topic model performs well in determining the optimal number of topics with interactive visualization and in performance measure of the quality of clusters with validity indices.

Keywords: interactive visualization, visual mon-negative matrix factorization model, optimal number of topics, cluster validity indices, Twitter data clustering

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Authors: Rachid Ziteuni, Selt Omar

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In this paper, we propose a new polynomial metaheuristic elaboration (tabu search) for solving scheduling problems. This method allows us to solve the scheduling problem of n tasks on m identical parallel machines with unavailability periods. This problem is NP-complete in the strong sens and finding an optimal solution appears unlikely. Note that all data in this problem are integer and deterministic. The performance criterion to optimize in this problem which we denote Pm/N-c/summs of (wjCj) is the weighted sum of the end dates of tasks.

Keywords: scheduling, parallel identical machines, unavailability periods, metaheuristic, tabu search

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Authors: Umair Ashraf, Kjetil Uhlen, Sverre Eriksen, Nadeem Jelani

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Although advancement in technology has increased the reliability and ease of work in ship power system, but these advancements are also adding complexities. Ever increasing non linear loads, like power electronics (PE) devices effect the stability of the system. Frequent load variations and complex load dynamics are due to the frequency converters and motor drives, these problem are more prominent when system is connected with the weak grid. In the ship power system major consumers are thruster motors for the propulsion. For the control operation of these motors variable frequency drives (VFD) are used, mostly VFDs operate on nominal voltage of the system. Some of the consumers in ship operate on lower voltage than nominal, these consumers got supply through step down transformers. In this paper the vector control scheme is used for the control of both rectifier and inverter, parallel operation of the synchronous generators is also demonstrated. The simulation have been performed with induction motor as load on VFD and parallel RLC load. Fault analysis has been performed first for the system which do not have VFD and then for the system with VFD. Three phase to the ground, single phase to the ground fault were implemented and behavior of the system in both the cases was observed.

Keywords: non-linear load, power electronics, parallel operating generators, pulse width modulation, variable frequency drives, voltage source converters, weak grid

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Authors: Danladi Ali

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In this paper, we measured GSM signal strength in the Dnepropetrovsk city in order to predict path loss in study area using nonlinear autoregressive neural network prediction and we also, used neural network clustering to determine average GSM signal strength receive at the study area. The nonlinear auto-regressive neural network predicted that the GSM signal is attenuated with the mean square error (MSE) of 2.6748dB, this attenuation value is used to modify the COST 231 Hata and the Okumura-Hata models. The neural network clustering revealed that -75dB to -95dB is received more frequently. This means that the signal strength received at the study is mostly weak signal

Keywords: one-dimensional multilevel wavelets, path loss, GSM signal strength, propagation, urban environment and model

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Authors: Hasan Al Bashar Abul Ulayee, AKM Saifun Nabi, MD Mesbah-Ul-Awal

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Alert system for disaster management is common now a day and can play a vital role reducing devastation and saves lives and costs. An alert in right time can save thousands of human life, help to take shelter, manage other assets including live stocks and above all, a right time alert will help to take preparation to face and early recovery of the situation. In a country like Bangladesh where populations is more than 170 million and always facing different types of natural calamities and disasters, an early right time alert is very effective and implementation of alert system is challenging. The challenge comes from the time constraint of alerting the huge number of population. The other method of existing disaster management pre alert is traditional, sequential and non-selective so efficiency is not good enough. This paper describes a way by which alert can be provided to maximum number of people within the short time bound using parallel processing as well as random selection of selective target audience.

Keywords: alert system, Bangladesh, disaster management, parallel processing, SMS

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Authors: H. Aoudia, Y. Touati, E. H. Teguig, A. Ali Cherif

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Conceiving and developing routing protocols for wireless sensor networks requires considerations on constraints such as network lifetime and energy consumption. In this paper, we propose a hybrid hierarchical routing protocol named HHRP combining both clustering mechanism and multipath optimization taking into account residual energy and RSSI measures. HHRP consists of classifying dynamically nodes into clusters where coordinators nodes with extra privileges are able to manipulate messages, aggregate data and ensure transmission between nodes according to TDMA and CDMA schedules. The reconfiguration of the network is carried out dynamically based on a threshold value which is associated with the number of nodes belonging to the smallest cluster. To show the effectiveness of the proposed approach HHRP, a comparative study with LEACH protocol is illustrated in simulations.

Keywords: routing protocol, optimization, clustering, WSN

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Authors: Sergey Kopysov, Nikita Nedozhogin, Leonid Tonkov

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The article presents a parallel iterative solver for large sparse linear systems which can be used on a heterogeneous platform. Traditionally, the problem of solving linear systems does not scale well on multi-CPU/multi-GPUs clusters. For example, most of the attempts to implement the classical conjugate gradient method were at best counted in the same amount of time as the problem was enlarged. The paper proposes the pipelined variant of the conjugate gradient method (PCG), a formulation that is potentially better suited for hybrid CPU/GPU computing since it requires only one synchronization point per one iteration instead of two for standard CG. The standard and pipelined CG methods need the vector entries generated by the current GPU and other GPUs for matrix-vector products. So the communication between GPUs becomes a major performance bottleneck on multi GPU cluster. The article presents an approach to minimize the communications between parallel parts of algorithms. Additionally, computation and communication can be overlapped to reduce the impact of data exchange. Using the pipelined version of the CG method with one synchronization point, the possibility of asynchronous calculations and communications, load balancing between the CPU and GPU for solving the large linear systems allows for scalability. The algorithm is implemented with the combined use of technologies: MPI, OpenMP, and CUDA. We show that almost optimum speed up on 8-CPU/2GPU may be reached (relatively to a one GPU execution). The parallelized solver achieves a speedup of up to 5.49 times on 16 NVIDIA Tesla GPUs, as compared to one GPU.

Keywords: conjugate gradient, GPU, parallel programming, pipelined algorithm

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Authors: Semeh Ben Salem, Sami Naouali, Moetez Sallami

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The increasing amount of collected data has limited the performance of the current analyzing algorithms. Thus, developing new cost-effective algorithms in terms of complexity, scalability, and accuracy raised significant interests. In this paper, a modified effective k-means based algorithm is developed and experimented. The new algorithm aims to reduce the computational load without significantly affecting the quality of the clusterings. The algorithm uses the City Block distance and a new stop criterion to guarantee the convergence. Conducted experiments on a real data set show its high performance when compared with the original k-means version.

Keywords: pattern recognition, global terrorism database, Manhattan distance, k-means clustering, terrorism data analysis

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Authors: Chia-Feng Lu, Yuh-Jen Wang, Shin Teng, Yu-Te Wu, Sui-Hing Yan

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Brain functional networks based on resting-state EEG data were compared between patients with mild Alzheimer’s disease (mAD) and matched patients with amnestic subtype of mild cognitive impairment (aMCI). We integrated the time–frequency cross mutual information (TFCMI) method to estimate the EEG functional connectivity between cortical regions and the network analysis based on graph theory to further investigate the alterations of functional networks in mAD compared with aMCI group. We aimed at investigating the changes of network integrity, local clustering, information processing efficiency, and fault tolerance in mAD brain networks for different frequency bands based on several topological properties, including degree, strength, clustering coefficient, shortest path length, and efficiency. Results showed that the disruptions of network integrity and reductions of network efficiency in mAD characterized by lower degree, decreased clustering coefficient, higher shortest path length, and reduced global and local efficiencies in the delta, theta, beta2, and gamma bands were evident. The significant changes in network organization can be used in assisting discrimination of mAD from aMCI in clinical.

Keywords: EEG, functional connectivity, graph theory, TFCMI

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Authors: Ana Elvira Ojanguren Lopez, Javier Martin Arista

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The aims of this paper are to present the automatisation procedure adopted in the implementation of a parallel corpus of Old English, as well as, to assess the progress of automatisation with respect to tagging, annotation, and lemmatisation. The corpus consists of an aligned parallel text with word-for-word comparison Old English-English that provides the Old English segment with inflectional form tagging (gloss, lemma, category, and inflection) and lemma annotation (spelling, meaning, inflectional class, paradigm, word-formation and secondary sources). This parallel corpus is intended to fill a gap in the field of Old English, in which no parallel and/or lemmatised corpora are available, while the average amount of corpus annotation is low. With this background, this presentation has two main parts. The first part, which focuses on tagging and annotation, selects the layouts and fields of lexical databases that are relevant for these tasks. Most information used for the annotation of the corpus can be retrieved from the lexical and morphological database Nerthus and the database of secondary sources Freya. These are the sources of linguistic and metalinguistic information that will be used for the annotation of the lemmas of the corpus, including morphological and semantic aspects as well as the references to the secondary sources that deal with the lemmas in question. Although substantially adapted and re-interpreted, the lemmatised part of these databases draws on the standard dictionaries of Old English, including The Student's Dictionary of Anglo-Saxon, An Anglo-Saxon Dictionary, and A Concise Anglo-Saxon Dictionary. The second part of this paper deals with lemmatisation. It presents the lemmatiser Norna, which has been implemented on Filemaker software. It is based on a concordance and an index to the Dictionary of Old English Corpus, which comprises around three thousand texts and three million words. In its present state, the lemmatiser Norna can assign lemma to around 80% of textual forms on an automatic basis, by searching the index and the concordance for prefixes, stems and inflectional endings. The conclusions of this presentation insist on the limits of the automatisation of dictionary-based annotation in a parallel corpus. While the tagging and annotation are largely automatic even at the present stage, the automatisation of alignment is pending for future research. Lemmatisation and morphological tagging are expected to be fully automatic in the near future, once the database of secondary sources Freya and the lemmatiser Norna have been completed.

Keywords: corpus linguistics, historical linguistics, old English, parallel corpus

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Authors: Ahmed Machmoum, Malika El Kyal

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We consider iterative parallel multi-splitting method for differential algebraic equations. The main feature of the proposed idea is to use the asynchronous form. We prove that the multi-splitting technique can effectively accelerate the convergent performance of the iterative process. The main characteristic of an asynchronous mode is that the local algorithm not have to wait at predetermined messages to become available. We allow some processors to communicate more frequently than others, and we allow the communication delays tobe substantial and unpredictable. Note that synchronous algorithms in the computer science sense are particular cases of our formulation of asynchronous one.

Keywords: computer, multi-splitting methods, asynchronous mode, differential algebraic systems

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Authors: Fatéma Zahra Benchara, Mohamed Youssfi, Omar Bouattane, Hassan Ouajji, Mohamed Ouadi Bensalah

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In this paper, we propose a new distributed environment for High Performance Computing (HPC) based on mobile agents. It allows us to perform parallel programs execution as distributed one over a flexible grid constituted by a cooperative mobile agent team works. The distributed program to be performed is encapsulated on team leader agent which deploys its team workers as Agent Virtual Processing Unit (AVPU). Each AVPU is asked to perform its assigned tasks and provides the computational results which make the data and team works tasks management difficult for the team leader agent and that influence the performance computing. In this work we focused on the implementation of the Mobile Provider Agent (MPA) in order to manage the distribution of data and instructions and to ensure a load balancing model. It grants also some interesting mechanisms to manage the others computing challenges thanks to the mobile agents several skills.

Keywords: image processing, distributed environment, mobile agents, parallel and distributed computing

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Authors: Hui Li, Xueying Zhao, Ke Ma, Yu Cao, Fan Yang, Qingwen Xu, Wenbin Liu

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Soil can often link a person or item to a crime scene, which makes it a valuable evidence in forensic casework. Several techniques have been utilized in forensic soil discrimination in previous studies. Because soil contains a vast number of microbiomes, the analyse of soil microbiomes is expected to be a potential way to characterise soil evidence. In this study, we applied massively parallel sequencing (MPS) to soil bacterial profiling on the Ion Torrent Personal Genome Machine (PGM). Soils from different regions were collected repeatedly. V-region 3 and 4 of Bacterial 16S rRNA gene were detected by MPS. Operational taxonomic units (OTU, 97%) were used to analyse soil bacteria. Several bioinformatics methods (PCoA, NMDS, Metastats, LEfse, and Heatmap) were applied in bacterial profiles. Our results demonstrate that MPS can provide a more detailed picture of the soil microbiomes and the composition of soil bacterial components from different region was individualistic. In conclusion, the utility of soil bacterial profiling via MPS of the 16S rRNA gene has potential value in characterising soil evidences and associating them with their place of origin, which can play an important role in forensic science in the future.

Keywords: bacterial profiling, forensic, massively parallel sequencing, soil evidence

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Authors: Zhen Peng, Baifeng Wu

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Modern computing technology enters the era of parallel computing with the trend of sustainable and scalable parallelism. Single Instruction Multiple Data (SIMD) is an important way to go along with the trend. It is able to gather more and more computing ability by increasing the number of processor cores without the need of modifying the program. Meanwhile, in the field of scientific computing and engineering design, many computation intensive applications are facing the challenge of increasingly large amount of data. Data parallel computing will be an important way to further improve the performance of these applications. In this paper, we take the accurate collision detection in building information modeling as an example. We demonstrate a model for constructing a data parallel algorithm. According to the model, a complex object is decomposed into the sets of simple objects; collision detection among complex objects is converted into those among simple objects. The resulting algorithm is a typical SIMD algorithm, and its advantages in parallelism and scalability is unparalleled in respect to the traditional algorithms.

Keywords: data parallelism, collision detection, single instruction multiple data, building information modeling, continuous scalability

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Authors: Abhishek Bhardwaj, Manish Kumar Bajpai

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An image with its content and schema of colors presents an effective mode of information sharing and processing. By changing its color schema different visions and prospect are discovered by the users. This phenomenon of color transfer is being used by Social media and other channel of entertainment. Reinhard et al’s algorithm was the first one to solve this problem of color transfer. In this paper, we make this algorithm efficient by introducing domain parallelism among different processors. We also comment on the factors that affect the speedup of this problem. In the end by analyzing the experimental data we claim to propose a novel and efficient parallel Reinhard’s algorithm.

Keywords: Reinhard et al’s algorithm, color transferring, parallelism, speedup

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Authors: Aarushi Agarwal, Tara Singh, I.L Singh, Anju Lata Singh, Trayambak Tiwari

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Simple discrimination in near absence of attention has been widely observed. Dual-task studies with natural scenes studies have been claimed as being preattentive in nature that facilitated categorization simultaneously with the attentional demanding task. So in this study, multiple images at the periphery are presented, initiating parallel processing in near absence of attention. For the central demanding task rotated letters were presented in both conditions, while in periphery natural and animal images were presented. To understand the breakpoint of ability to perform in near absence of attention one, two and three peripheral images were presented simultaneously with central task and subjects had to respond when all belong to the same category. Individual participant performance did not show a significant difference in both conditions central and peripheral task when the single peripheral image was shown. In case of two images high-level parallel processing could take place with little attentional resources. The eye tracking results supports the evidence as no major saccade was made in a large number of trials. Three image presentations proved to be a breaking point of the capacities to perform outside attentional assistance as participants showed a confused eye gaze pattern which failed to make the natural and animal image discriminations. Thus, we can conclude attention and awareness being independent mechanisms having limited capacities.

Keywords: attention, dual task pardigm, parallel processing, break point, saccade

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Authors: Lixin Tian, Wei Xue

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Many jobs in society go underground, such as mine mining, tunnel construction and subways, which are vital to the development of society. Once accidents occur in these places, the interruption of traditional wired communication is not conducive to the development of rescue work. In order to realize the positioning, early warning and command functions of underground personnel and improve rescue efficiency, it is necessary to develop and design an emergency ground communication system. It is easy to be subjected to narrowband interference when performing conventional underground communication. Spreading communication can be used for this problem. However, general spread spectrum methods such as direct spread communication are inefficient, so it is proposed to use parallel combined spread spectrum (PCSS) communication to improve efficiency. The PCSS communication not only has the anti-interference ability and the good concealment of the traditional spread spectrum system, but also has a relatively high frequency band utilization rate and a strong information transmission capability. So, this technology has been widely used in practice. This paper presents a PCSS communication model-multiple detection parallel combined spread spectrum (MDPCSS) communication system. In this paper, the principle of MDPCSS communication system is described, that is, the sequence at the transmitting end is processed in blocks and cyclically shifted to facilitate multiple detection at the receiving end. The block diagrams of the transmitter and receiver of the MDPCSS communication system are introduced. At the same time, the calculation formula of the system bit error rate (BER) is introduced, and the simulation and analysis of the BER of the system are completed. By comparing with the common parallel PCSS communication, we can draw a conclusion that it is indeed possible to reduce the BER and improve the system performance. Furthermore, the influence of different pseudo-code lengths selected on the system BER is simulated and analyzed, and the conclusion is that the larger the pseudo-code length is, the smaller the system error rate is.

Keywords: cyclic shift, multiple detection, parallel combined spread spectrum, PN code

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Authors: Sohyoung Won, Heebal Kim, Dajeong Lim

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Genomic prediction is an effective way to measure the abilities of livestock for breeding based on genomic estimated breeding values, statistically predicted values from genotype data using best linear unbiased prediction (BLUP). Using haplotypes, clusters of linked single nucleotide polymorphisms (SNPs), as markers instead of individual SNPs can improve the reliability of genomic prediction since the probability of a quantitative trait loci to be in strong linkage disequilibrium (LD) with markers is higher. To efficiently use haplotypes in genomic prediction, finding optimal ways to define haplotypes is needed. In this study, 770K SNP chip data was collected from Hanwoo (Korean cattle) population consisted of 2506 cattle. Haplotypes were first defined in three different ways using 770K SNP chip data: haplotypes were defined based on 1) length of haplotypes (bp), 2) the number of SNPs, and 3) k-medoids clustering by LD. To compare the methods in parallel, haplotypes defined by all methods were set to have comparable sizes; in each method, haplotypes defined to have an average number of 5, 10, 20 or 50 SNPs were tested respectively. A modified GBLUP method using haplotype alleles as predictor variables was implemented for testing the prediction reliability of each haplotype set. Also, conventional genomic BLUP (GBLUP) method, which uses individual SNPs were tested to evaluate the performance of the haplotype sets on genomic prediction. Carcass weight was used as the phenotype for testing. As a result, using haplotypes defined by all three methods showed increased reliability compared to conventional GBLUP. There were not many differences in the reliability between different haplotype defining methods. The reliability of genomic prediction was highest when the average number of SNPs per haplotype was 20 in all three methods, implying that haplotypes including around 20 SNPs can be optimal to use as markers for genomic prediction. When the number of alleles generated by each haplotype defining methods was compared, clustering by LD generated the least number of alleles. Using haplotype alleles for genomic prediction showed better performance, suggesting improved accuracy in genomic selection. The number of predictor variables was decreased when the LD-based method was used while all three haplotype defining methods showed similar performances. This suggests that defining haplotypes based on LD can reduce computational costs and allows efficient prediction. Finding optimal ways to define haplotypes and using the haplotype alleles as markers can provide improved performance and efficiency in genomic prediction.

Keywords: best linear unbiased predictor, genomic prediction, haplotype, linkage disequilibrium

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Authors: Paulo Fernando Silva Filho, Elcio Hideiti Shiguemori

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The selection of specific landmarks for an Unmanned Aerial Vehicles’ Visual Navigation systems based on Automatic Landmark Recognition has significant influence on the precision of the system’s estimated position. At the same time, manual selection of the landmarks does not guarantee a high recognition rate, which would also result on a poor precision. This work aims to develop an automatic landmark selection that will take the image of the flight area and identify the best landmarks to be recognized by the Visual Navigation Landmark Recognition System. The criterion to select a landmark is based on features detected by ORB or AKAZE and edges information on each possible landmark. Results have shown that disposition of possible landmarks is quite different from the human perception.

Keywords: clustering, edges, feature points, landmark selection, X-means

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Authors: Mbida Mohamed, Ezzati Abdellah

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A strong challenge in the wireless sensor networks (WSN) is to save the energy and have a long life time in the network without having a high rate of loss information. However, topology control (TC) protocols are designed in a way that the network is divided and having a standard system of exchange packets between nodes. In this article, we will propose a clustering based and centralized routing table protocol of TC (CBCRT) which delegates a leader node that will encapsulate a single routing table in every cluster nodes. Hence, if a node wants to send packets to the sink, it requests the information's routing table of the current cluster from the node leader in order to root the packet.

Keywords: mobile wireless sensor networks, routing, topology of control, protocols

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Authors: Mais Haj Qasem, Maen M. Al Assaf, Ali Rodan

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Parallel hybrid storage systems consist of a hierarchy of different storage devices that vary in terms of data reading speed performance. As we ascend in the hierarchy, data reading speed becomes faster. Thus, migrating the application’ important data that will be accessed in the near future to the uppermost level will reduce the application I/O waiting time; hence, reducing its execution elapsed time. In this research, we implement trace-driven two-levels parallel hybrid storage system prototype that consists of HDDs and SSDs. The prototype uses data mining techniques to classify application’ data in order to determine its near future data accesses in parallel with the its on-demand request. The important data (i.e. the data that the application will access in the near future) are continuously migrated to the uppermost level of the hierarchy. Our simulation results show that our data migration approach integrated with data mining techniques reduces the application execution elapsed time when using variety of traces in at least to 22%.

Keywords: hybrid storage system, data mining, recurrent neural network, support vector machine

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Authors: Hafiz Gulfam Ahmad, Chuangdong Li, Zeeshan Ahmad

Abstract:

In this paper, we proposed a parallel IDS and honeypot based approach to detect and analyze the unknown and known attack taxonomy for improving the IDS performance and protecting the network from intruders. The main theme of our approach is to record and analyze the intruder activities by using both the low and high interaction honeypots. Our architecture aims to achieve the required goals by combing signature based IDS, honeypots and generate the new signatures. The paper describes the basic component, design and implementation of this approach and also demonstrates the effectiveness of this approach reducing the probability of network attacks.

Keywords: network security, intrusion detection, honeypot, snort, nmap

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