Atom-Atom-Path similarity and Sphere Exclusion clustering: tools for prioritizing fragment hits Background: After performing a fragment based screen the resulting hits need to be prioritized for follow-up structure elucidation and chemistry. This paper describes a new similarity metric, Atom-Atom-Path (AAP) similarity that is used in conjunction with the Directed Sphere Exclusion (DISE) clustering method to effectively organize and prioritize the fragment hits. The AAP similarity rewards common substructures and recognizes minimal structure differences. The DISE method is order-dependent and can be used to enrich fragments with properties of interest in the first clusters.Results: The merit of the software is demonstrated by its application to the MAP4K4 fragment screening hits using ligand efficiency (LE) as quality measure. The first clusters contain the hits with the highest LE. The clustering results can be easily visualized in a LE-over-clusters scatterplot with points colored by the members' similarity to the corresponding cluster seed. The scatterplot enables the extraction of preliminary SAR. Conclusions: The detailed structure differentiation of the AAP similarity metric is ideal for fragment-sized molecules. The order-dependent nature of the DISE clustering method results in clusters ordered by a property of interest to the teams. The combination of both allows for efficient prioritization of fragment hit for follow-ups. We have described the implementation and use of AAP similarity coupled with DISE clustering as a tool to organize fragment hits. The presorting of the hits by decreasing LE ensures that the most interesting clusters appear at the top, thus, drawing the attention of the review team to the most promising compounds. While we have used LE in the discussion here, in principle the dataset can be presorted and re-clustered based on other properties or efficiency indices. The detailed description of atom neighborhoods used in the AAP similarity allows for the differentiation of even small changes in molecular structure. The combination of the AAP similarity and DISE clustering has also been successfully applied to prioritize high throughput screening hits [31] . The code for this software is available in Additional file 3. Screening of low-affinity and low-molecular weight fragments has become a powerful approach to identify lead matter and to initiate medicinal chemistry programs [1] . It has been shown that binding affinity does not increase linear with the molecular size [2] . Fragments are more likely to bind with high ligand efficiencies [3, 4] and can reveal important interactions required in more mature drug-like molecules. Choosing fragment hits for structure determination and chemical follow-up involves the evaluation of many project-specific parameters. Ligand efficiency (LE) is often used as one measure for assessing the quality of initial fragment hits.. Due to the generally higher hit rates of fragment screens (1-10%) most campaigns on libraries of 1,000-5,000 compounds will produce more hits, than downstream structural biology and chemistry resources can absorb for hit-to-lead development. To increase the chance of success, careful prioritization of the initial hits by a group of experienced specialists from different areas of drug discovery is important for advancing the most promising fragment hits. Fragment hit triage in advance of a structure determination typically weighs the LE parameter. However, many other properties including affinity, selectivity, and most importantly the chemical structure of the compound need critical consideration. In addition, fragment libraries often contain related molecules providing initial SAR and confidence in scaffold types. Clustering hit sets helps bring related molecules and features together for consideration, but the cluster order is usually determined by the algorithm and is independent of other factors such as LE. This results in a functional randomization of the order of the experimental data making trends harder to identify. To direct the attention of the specialists to the most promising hits we have employed a directed clustering method using a new similarity algorithm, to group hits with respect to both structure and data.. The Directed Sphere Exclusion (DISE) algorithm [5] used for the fragment clustering is an extension of the Sphere Exclusion (SE) algorithm [6, 7] . The first step consists of ordering input molecules by a non-structural parameter such as LE, which is followed by a diverse subset selection using the SE algorithm. The selected molecules are considered as cluster seeds. In the final step, the remaining molecules are assigned to the cluster seeds. The investigator has the choice of two assigning rules: Assign the remaining molecules to (a) the first cluster seed whose similarity to the candidate molecule is within the specified threshold or (b) the most similar cluster seed.. Sorting the input molecules according to decreasing LE ensures that fragments having the highest LE are evaluated first in the cluster seed selection. Assigning the remaining molecules to the cluster seeds using rule (a) yields clusters with decreasing maximum LE while using rule (b) produces clusters that are structurally more consistent. In either case, the DISE clustering places the fragment clusters with favorable LE on top of the list, thus, directing the focus of the review team to the most promising clusters. Consequently, molecules with low LE (LE < 0.3, typically 30-50% of hits in most screens) are either moved towards the bottom of the list or included in clusters with a higher LE compound as cluster seeds, and therefore do not distract the team from clusters that are of higher interest. To our knowledge there are no other clustering methods that allow the clustering to be influenced by a target property. Frequently used clustering methods in chemical informatics [8] include hierarchical clustering, Jarvis Patrick clustering and k-means clustering. They are either order independent or aim to minimize the order dependency (k-means clustering). This means that their results depend only on the similarity matrix and cannot be influenced by external target properties.. Equally important is the choice of similarity metric. Many similarity metrics have been published in the past [9] [10] [11] [12] [13] [14] [15] . For fragment hit clustering, the similarity metric should meet the following requirements: (1) To satisfy the perception of the medicinal chemists, the similarity metric should put a high weight on common substructures. (2) The similarity metric should be sensitive to small changes in substitution. The latter is important because fragments are small in comparison to High Throughput Screening (HTS) compounds. Therefore, small changes have a strong effect on similarity in fragments.. Fingerprint based similarity methods generate fingerprints by systematically recording patterns present in compounds. Generally linear or circular patterns originating from each atom are used. The similarity of two compounds is then computed by comparing the patterns present or absent in each [9] [10] [11] [12] [13] [14] [15] [16] [17] . Their relative orientation is not stored while recording the patterns. Consequently, the relative orientation of the patterns in the compounds is not taken into consideration when computing the similarity. When comparing compound 1a using linear fingerprints to 1b a low similarity is found due to the difference of the central atom on the piperidine/piperazine ring as noted by Stahl et al. [15] . When using circular fingerprints compounds 1a and 1b are nearest neighbors. However, because circular fingerprints have a small pattern size and do not encode any information on the relative position of the patterns, the closest compound to 2a is 1b.. Graph based similarity methods such as Maximum Common Substructure (MCSS) [12, 18] and Maximum Overlapping Set (MOS) [15] similarity based methods put a high weight on the common substructure. The similarity is computed from the ratio of the number of atoms and bonds in the MCSS or MOS to the total number of atoms and bonds. Because the similarity calculation is based on the integer counts, it is not sensitive to differences in substitution patterns. Stahl et al. introduced two empirical correction terms to mitigate this problem: one to penalize different arrangements of common substructures and the second to increase the similarity value if the two molecules have a substructure in common that exceeds 70% of the smaller molecule [15] . When comparing the compounds in Figure 1 the pairs 1a/ 1b and 2a/2b constitute nearest neighbors in agreement with chemical intuition.. For clustering fragment hits we have developed a new similarity metric: the Atom-Atom-Path (AAP) similarity metric that does not need empirical terms. AAP, too, puts a high weight on the common substructures. In the case of the compounds in Figure 1 it correctly recognizes the pairs 1a/1b and 2a/2b as nearest neighbors. The calculation of the AAP similarity of two molecules A and B consists of four steps. In the first step, each atom is described by the set of linear paths originating from it. In the second step, the atom-to-atom similarity of each atom in A to each atom in B is computed. This similarity matrix is used in the third step to map each atom in the smaller molecule to a unique atom in the other. The final step consists of computing the AAP similarity from the atom-to-atom similarities of the mapped atom pairs.. Because the set of atom paths encodes the neighborhood of the given atom, the more similar the sets of paths of two atoms are, the larger the atom-to-atom similarity is. An atom-to-atom similarity of 1 indicates that the two atoms are embedded in a large substructure present in both molecules. As a result, the AAP similarity of a molecule pair increases with the size of the common substructures. The change of a single atom affects the atom-to-atom similarities of neighboring atoms. The AAP similarity is sensitive to very small changes in substitution. Hence, we found that molecules with AAP similarities as low as 0.3 still share many common features. The similarity distribution of AAP as well as circular and linear fingerprint similarities for the full pairwise comparison of 4000 compounds from the Novartis-GNF Malaria Box data [19] sets is shown in Additional file 1: Figure S3 . The distributions show how the AAP similarities are generally much lower than the fingerprint based similarities.. Similarity for use to organize chemical compounds for medicinal chemists is highly subjective [20] . During repeated use at Genentech we have found that clustering using AAP similarity consistently gives results that are in agreement with medicinal chemists' perception. The mapping of atoms ensures that each atom is considered equally and the description of the atoms ensures a fine grained weighting through their environment. This results in clusters in which ring systems have a high weight avoiding the formation of clusters with large differences in scaffolds. To provide a comparison with Tanimoto similarity using linear and circular fingerprints we have included the clustering of a random subset of the Novartis-GNF Malaria Box dataset using three similarity metrics as Additional file 2. All programs required to cluster molecules using the DISE method and the AAP similarity are available in Additional file 3. Clustering using the DISE algorithm is performed by applying two command line programs to the input data, i.e. sdfSorter.csh and sdfCluster.pl ( Figure 2 ). sdfSorter.csh sorts records in SDF files by the value of one or more fields that must be present in the records. sdfCluster.pl performs the SE clustering by internally executing the sequence of command line programs shown in Figure 2 . The programs sdfMCSSSphereExclusion.csh and sdfMCSSNNFinder.csh use the AAP similarity for finding the cluster seeds and for assigning the cluster members to the closest seed. Below we describe the algorithm to compute the AAP similarity.

O B O B I B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O B I O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O B I O O O O O O O B O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O B O O O O O O O B O O B O O B O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B


MUBII-TB-DB: a database of mutations associated with antibiotic resistance in Mycobacterium tuberculosis Background: Tuberculosis is an infectious bacterial disease caused by Mycobacterium tuberculosis. It remains a major health threat, killing over one million people every year worldwide. An early antibiotic therapy is the basis of the treatment, and the emergence and spread of multidrug and extensively drug-resistant mutant strains raise significant challenges. As these bacteria grow very slowly, drug resistance mutations are currently detected using molecular biology techniques. Resistance mutations are identified by sequencing the resistance-linked genes followed by a comparison with the literature data. The only online database is the TB Drug Resistance Mutation database (TBDReaM database); however, it requires mutation detection before use, and its interrogation is complex due to its loose syntax and grammar. Description: The MUBII-TB-DB database is a simple, highly structured text-based database that contains a set of Mycobacterium tuberculosis mutations (DNA and proteins) occurring at seven loci: rpoB, pncA, katG; mabA(fabG1)-inhA, gyrA, gyrB, and rrs. Resistance mutation data were extracted after the systematic review of MEDLINE referenced publications before March 2013. MUBII analyzes the query sequence obtained by PCR-sequencing using two parallel strategies: i) a BLAST search against a set of previously reconstructed mutated sequences and ii) the alignment of the query sequences (DNA and its protein translation) with the wild-type sequences. The post-treatment includes the extraction of the aligned sequences together with their descriptors (position and nature of mutations). The whole procedure is performed using the internet. The results are graphs (alignments) and text (description of the mutation, therapeutic significance). The system is quick and easy to use, even for technicians without bioinformatics training. Conclusion: MUBII-TB-DB is a structured database of the mutations occurring at seven loci of major therapeutic value in tuberculosis management. Moreover, the system provides interpretation of the mutations in biological and therapeutic terms and can evolve by the addition of newly described mutations. Its goal is to provide easy and comprehensive access through a client-server model over the Web to an up-to-date database of mutations that lead to the resistance of M. tuberculosis to antibiotics. Tuberculosis (TB) is an infectious disease caused by a slow-growing bacterium, Mycobacterium tuberculosis, which has been linked to humans since the beginning of the early human expansion from east Africa [1] . Tuberculosis was the main cause of deaths in Western Europe between the seventeenth century and the end of the nineteenth century [2] . It remains a major health threat, killing more than a million individuals every year worldwide. Although the WHO claims that its goal to halt and reverse the TB epidemic by 2015 has already been achieved and that the TB mortality rate has decreased by 41% since 1990, the global burden of TB remains. In 2011, there were an estimated 8.7 million new cases of TB, and 1.4 million people died from TB.. No fully effective vaccination is possible because the Bacille de Calmette et Guérin (BCG)-vaccine protection against TB varies among populations [3] and provides only limited protection during childhood [4] . The limitation of the TB expansion is thus based on the improvement and generalization of diagnostic methods [5] and early antibiotic therapy. The WHO recommends the following combined therapy employing first-line antibiotics: rifampicin (or rifabutin), isoniazid, pyrazinamide and ethambutol for two months followed by rifampicin and isoniazid for four months [6] .. However, irregular and low-dose treatment has led to the emergence and spread of multidrug-resistant (MDR) and extensively drug-resistant (XDR) Mycobacterium tuberculosis complex (MTBC) strains that present significant challenges in disease control [6] [7] [8] . The resistance increases in high MDR-TB burden countries, where the number of cases was estimated to be 60,000 worldwide in 2011. In these countries, the proportion of resistant strains varies from 5 to 19% of new TB cases and up to 50% for retreatment cases. This increase in resistance has led to the promotion of laboratory antimicrobial testing for all isolates [6] . In the case of antibiotic resistance, the therapy is conducted using second-line drugs, such as aminoglycosides or non-conventional antibiotics (such as fluoroquinolones).. The traditional phenotypic drug susceptibility test induces serious delays in the detection of resistance due to the extremely slow growth of M. tuberculosis, the answer being obtained in most cases two weeks after the isolation of the bacteria. Another drawback of the in vitro susceptibility testing is the inadequate detection of resistance to new drugs and to pyrazinamide [9] [10] [11] . The rapid diagnosis of drug resistance by molecular methods is essential to initiate effective antibiotic therapies and to prevent the transmission of drug-resistant strains [12] .. M. tuberculosis acquires drug resistance primarily through mutations in specific genes [13, 14] . The mutations associated with drug resistance occur in rpoB for rifampicin (RIF), in katG and the mabA (fabG1)-inhA operon for isoniazid (INH), in pncA for pyrazinamide (PZA), in rrs for amikacin (AMK) and in gyrA and gyrB for fluoroquinolones (FQs) [13, [15] [16] [17] .. Several commercial molecular kits are available to detect M. tuberculosis resistance using line-probe assays or real-time PCR, and these kits allow for the prediction of drug resistance in clinical specimens within one working day [9] . They can detect the mutations responsible for resistance to RIF alone (GeneXpert MTB/RIF (Cepheid), INNO-LiPA® Rif. TB (Innogenetics)), to RIF + INH (GenoType® MTBDRplus (Hain LifeScience GmbH)), or to FQs + AMK (GenoType® MTBDRsl (Hain LifeScience GmbH)), but their sensitivity varies, depending on the antibiotic [18] . These kits, unfortunately, detect only the most frequent mutations [18, 19] . An alternative approach that allows for the exhaustive detection of mutations consists of the PCR amplification and sequencing of resistance-linked genes [20, 21] and, more experimentally, of complete genome sequencing [22] . It is, however, time consuming to compare the sequences with the reference genome and the mutation identity with the literature data. Recently, Sandgren et al. [23] established a comprehensive database that gathers information on the mutations associated with TB drug resistance and the frequency of the most common mutations associated with resistance to specific drugs. The TBDReaM database is a free online resource that allows for the molecular diagnosis of resistant TB after the processing of the amplified sequences by any method. Although very helpful, the TBDReaM database has not been updated since April 2010, and its usage remains time consuming because it demands prior processing of the TB nucleotide sequences to detect mutations and cannot be easily interrogated due to its relaxed grammatical conception.. Here, we describe MUBII-TB-DB, a database of the mutations of M. tuberculosis linked to resistance to firstline and second-line antibiotics that can be used to identify the mutations from a submitted sequence. This database and its related software, MUBII, have been developed to satisfy the need of clinical microbiology labs to easily analyze M. tuberculosis sequences and to link the results to a potential therapeutic failure. MUBII combines the use of reconstructed mutated gene sequences that can be searched by BLAST, aligned against the wild-type gene sequence, and compared with the mutation database. This concept can be easily adapted to the microbiological identification of other microbial mutations. MUBII is the analysis and interpretation engine and is entirely written in Python 2.7. The external programs that are called from Python are BLAST2seq [25] , BLASTN, BLASTX [26] and the alignment tools MUSCLE [27] and MAFFT [28] . The routines transeq and showalign from EMBOSS [29] are also used. The scheme of the global organization is shown in Figure 2 .. The Web interface exclusively uses CGI and HTML. Cascading Style Sheets are used to color the alignments. These decisions were made to achieve simplicity and reliability on every browser. The Web server runs under the APACHE MPM Prefork http server on a LINUX Debian server.

O O O O O O O O O O O B O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O B B O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O


Path planning for autonomous vehicle based on heuristic searching using online images In automatic navigation of mobile systems, a path network is required to enable robot/vehicle autonomous motions. Path planning is considered as a significantly important part in creating the path network and thus to be a necessary task for any autonomous vehicle system. This paper proposes a method that constructs the shortest path for vehicle auto-navigation in outdoor environments. The method using two layers of GIS information of online map images, which support to estimate not only the shape of road network but also the directed road. This is also the advantage as compared to methods, which use only aerial/satellite images. Accomplishing the estimation according to the use of this application requires several stages as follows. First, a raw road network is detected using the road map and the satellite image. Second, the road network is refined and represented by a direct graph. Third, the road network is converted into the global coordinate, which is much more convenient for performing online auto-navigation task than the other types of coordinate. Finally, the shortest path for motion is estimated by heuristic searching method based on a hybrid algorithm that is originated from Dijkstra algorithm in a combination with greedy breadth-first search algorithm. The experimental results demonstrate robustness and effectiveness of the proposed method for path network estimation under large scenes of outdoor environments. Nowadays, there have been many research areas on intelligent systems, autonomous robot in outdoor environments, especially intelligent transportation in outdoor environments, such as in [1] [2] [3] . Autonomous vehicle navigation becomes an important in various applications of motion path planning, localization task. In automatic navigation of mobile systems, path planning is required to create path network for any robot/vehicle auto-traveling and considered to be the initial step in any autonomous vehicle systems. So far, researches on path planning have been achieved several milestone successes in industrial applications as well as in academic disciplines, including applications in mobile robot/vehicle and aerospace. There are many studies on the planning algorithms and implementations [4] . So far, there have been several proposed methods for road detecting and path planning [5] [6] [7] . Studies on planning algorithm and implementations have considered the issues of road detection and path planning. They can be categorized into two folds of global and local path planning methods, respectively. The global path planning is concerned with the high-level path, the whole path for movement from the source to the destination of travel itinerary. It deals with the navigation around the global region. Contrarily, the local path planning is related to the low-level path, which is suitable for further detail of specific paths. In essence, it is a segment of a certain global path but with more details to allow for avoiding local obstacle. An autonomous robot/vehicle has to deal with in reality such as determining appropriate turning-angle and speed.. The objective of this paper is to develop an efficient application for constructing the shortest path, which provides a real trajectory for autonomous vehicle navigation in the outdoor environments. Although path planning product can be provided by several commercial services, these services are typically with high cost and sometimes not all of its characteristic will be used or valid for the actual demand of a user, e.g., traffic conditions might not be useful for some applications or some special regional is not updated in the commercial version. In our proposal, the global path for vehicle motion is self-constructed using road map and satellite images, which are retrieved from free charge online service. In the case that roads are outdated in some regions of the map services, updating map is required and can be performed by road detection using aerial/satellite images. The proposed method consists of several parts as follows. A road network is estimated using road map and satellite images, which is retrieved from online map services, such as Google Maps, OpenStreetMap, and Bing Maps service. The road network is refined using some image processing techniques. The road network in image pixel coordinate is converted into the global coordinate system, so that it provides more convenient for online vehicle navigation. Finally, the shortest path for vehicle motion is estimated based on the shortest path planning algorithms, such as Dijkstra, greedy breadth-first search algorithm. This paper presents the method to construct an efficient shortest path planning based on the road map and satellite images for autonomous vehicle motion in outdoor environments. The results consist of path details in global coordinate, which support for control tracking for autonomous navigation. The method focuses on the estimation of the path in the global coordinates without using expensive commercial services. It consists of several stages. The road network is estimated using road map images, which is retrieved from free of charge online Google Maps services. The road network is refined and combined with the result of road detection using satellite image.For convenience in real-time online vehicle navigator, the road network in image pixel coordinate is converted into the global coordinate system. The hybrid method based on the Dijkstra algorithm in combination of greedy breadth-first search technique is applied to estimate the shortest path. By the use of road map type, which takes advantage of knowledge maps to provide high confident of the shortest path for vehicle navigation. One disadvantage of this method is that the method depends on the updating road information. This problem is compensated using satellite image. The experimental results demonstrate the effectiveness of this method from large scene of outdoor environments.

B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Nonlinear finite element analysis of four-pile caps supporting columns subjected to generic loading The paper presents the development of an adaptable strut-and-tie model that can be applied to the design or analysis of four-pile caps that support axial compression and biaxial flexure from a supported rectangular column. Due to an absence of relevant test data, the model is validated using non-linear finite element analyses (NLFEA). The results indicate that the use of the proposed model would lead to safe and economical designs. The proposed model can be easily extended to any number of piles, providing a rational procedure for the design of wide range of pile caps. In traditional design practice, pile caps are assumed to acts as beams spanning between piles. The depth of a cap is then selected to provide adequate shear capacity and the required amount of longitudinal reinforcement is calculated using engineering beam theory. Quite recently, methods for the design of pile cap have been developed that are based on the strut-and-tie approach. These include the methods in the Canadian CSA Code (1984), by Schlaich et al. (1987) , in the AASHTO LRFD code (1994) , in the Spanish concrete code EHE (1999), by Reineck (2002) , and in the American ACI318 building code (2002) . These methods assume that an internal load resisting truss, so-called strut-and-tie model, carries the forces through the pile cap in which concrete compressive struts act between the column and piles and steel ties (reinforcement) act between piles.. Results of elastic analyses, as example that one obtained by Iyer & Sam (1992) , illustrate that there is a complex state of straining in these three-dimensional pile caps and that the Strut-and-Tie Theory provides a rational basis for design. Adebar et al. (1990) , Adebar & Zhou (1996) , Bloodworth et al (2003) and Caves & Fenton (2004) have provided experimental evidence demonstrating that the use of sectional approaches based on engineering beam theory are not appropriate for the design of pile caps. As further illustrated in the research conducted by Blévot & Fremy (1967) , Clarke (1972) , Suzuki et al (1998 Suzuki et al ( , 1999 Suzuki et al ( , 2000 and Suzuki & Otsuki (2002) , many pile caps designed to fail in flexure by engineering beam theory have been reported to fail in shear. This is highly undesirable behavior as there is neither warning cracks nor pronounced deformations before these types of brittle shear failures occur.. These unexpected shear failures can be explained in two ways. Firstly, engineering beam theory was originally developed for structural elements with significant deformation capacity. As a consequence, if this theory is applied to elements with limited deformation capacity such as pile caps, the calculated effective depth will tend to overestimate the concrete contribution from shear. Secondly, engineering beam theory usually leads to more longitudinal reinforcement than would be calculated by using a strut-and-tie approach, and for the specific situation of four-pile caps, Clarke (1972) concluded that this difference can be higher than 20%.. Consequently, pile caps designed using engineering beam theory have a tendency to be over reinforced and as consequence, shear failures may occur as a result of longitudinal splitting of compression struts before yielding of the longitudinal reinforcement.. Although the strut-and-tie approach provides a more rational basis for the design of pile caps, it is only commonly applied for the design of simple pile caps such as pile caps supporting square columns subjected to axial load. This is believed to be due to the complexity and uncertainties as to the appropriate strut-and-tie model to use for more complex loading conditions. Thus, designers have chosen to rely on the use of engineering beam theory for the design of even slightly more complex pile caps, including four-pile caps that support axial compression and biaxial flexure from a single rectangular column.. To address the situation of pile caps supporting columns under general situation (axial compression and biaxial flexure), an adaptable strut-and-tie model for four-pile caps is proposed in this paper. Unfortunately there is no experimental test data on the performance of this type of four-pile caps. Thus, non-linear finite element analysis (NLFEA) has been applied to make the best possible prediction of the behavior of these pile caps. A NLFEA program was selected for use that was specifically written for predicting the behavior of a three-dimensional continuum of structural concrete subjected to a complex state of stress. This program will be validated herein by available test data. The result of the analyses of four-pile caps supporting axial compression and biaxial flexure from a single column will illustrate the appropriateness of the proposed model. This model can be further extended for the design of more complex pile caps. Due to the lack of a generic strut-and-tie model for the design of pile caps to support realistically complex loadings from columns, designers commonly use engineering beam theory or very simplified strut-and-tie models for the design of pile caps. In the latter approach, knowing the piles reactions due to the simultaneous action of biaxial flexure and axial compression, the highest reaction is multiplied by the number of piles, in a manner that an equivalent compressive axial load is found. This equivalent load is then used for the design of the pile cap, by using the available strut-and-tie models developed for pile caps supporting square columns under the basic situation of axial compression.. To encourage the use of more appropriate design procedures for pile caps, an adaptable 3-dimensional strut-and-tie model was presented in this paper. The main strength of the proposed model is that it provides a clear methodology for calculating the design forces and capacity of four-pile caps supporting columns subjected to axial compressive load and biaxial flexure. The proposed methodology is shown by analyses to result in safe and economical design solutions.. The performance of the proposed model was evaluated using non-linear analyses. The results show that the predicted capacities are greater than those calculated from the adaptable strut-and-tie model. As was presented in Fig. 5 , the lower the shear span-to-depth ratio, c/d, the higher was the failure load. However, as the same strut-and-tie model was applied for the same loading condition, the same capacity at reinforcement yielding and at failure would have been expected for all specimens. The differences in the predicted behavior can be explained by the significant influence of the concrete tensile strength in the bottom region of the pile caps, which is not considered in the present formulation and in most codes of practice.. In order to obtain ductile behavior at the ultimate state, two conditions should be considered in the use of the presented model. Firstly, based on the results shown in Table 3 keywords: pile caps, strut-and-tie models, flexure strength, shear strength, concrete design

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B I B I B I B I


Resource scheduling in a high-performance multimedia server Abstract-Supporting continuous media (CM) data£such as video and audio£imposes stringent demands on the retrieval performance of a multimedia server. In this paper, we propose and evaluate a set of data-placement and retrieval algorithms to exploit the full capacity of the disks in a multimedia server. The data-placement algorithm declusters every object over all of the disks in the server£using a time-based declustering unit£with the aim of balancing the disk load. As for runtime retrieval, the quintessence of the algorithm is to give each disk advance notification of the blocks that have to be fetched in the impending time periods, so that the disk can optimize its service schedule accordingly. Moreover, in processing a block request for a replicated object, the server will dynamically channel the retrieval operation to the most lightly loaded disk that holds a copy of the required block. We have implemented a multimedia server based on these algorithms. Performance tests reveal that the server achieves very high disk efficiency. Specifically, each disk is able to support up to 25 MPEG-1 streams. Moreover, experiments suggest that the aggregate retrieval capacity of the server scales almost linearly with the number of disks. N recent years, demand for multimedia applications that are capable of manipulating both continuous media (CM) data, such as video and audio, and non-CM data (e.g., text and images), has been growing rapidly. Some examples are interactive multimedia education [1] and news on demand [28] . Such an application requires the underlying multimedia server to organize and retrieve its data in a way that would meet the performance objectives of the application. For non-CM data, the performance objective is typically to minimize the response time, i.e., the elapsed time between the issuance of a data request and the delivery of the target data. In the case of CM data, in addition to minimizing response time, there is also a constraint on the acceptable time interval between the delivery of successive portions of the data. For example, successive frames of an MPEG-1 [13] video may have to be retrieved within 1 seccontrast, a multimedia server needs to respect the temporal relationship between the components of a CM stream. For instance, it does not make sense to retrieve the frames of an MPEG video randomly. A multimedia server, therefore, needs to take into account this requirement for ordered retrieval in placing CM data on disk. In addition, the intradisk data-placement strategy should maximize the access efficiency of individual disks, while the interdisk dataplacement policy should lead to a balanced load across all of the disks in the system. While good data-placement policies are crucial, the ultimate performance determinant of a multimedia server is its data-retrieval algorithm. This algorithm comprises a component that decides when to request for each portion of an object, and a disk scheduling component that governs the way that the disks service their requests. Note that the need for the first component is unique to multimedia servers featuring CM data support; traditional systems that store only non-CM data simply request for all of the blocks of an object at once and let the disk scheduler figure out the best way to fetch these blocks. A big challenge in designing the data-retrieval algorithm is to optimize its handling of variable bit rate streams, which could overload a disk during some periods while leaving the disk lightly utilized at other times. In order for the system to perform well under such loads, the data-retrieval algorithm needs to be able to take full advantage of lull periods to fetch data blocks that will be needed shortly.. The multimedia server project at the Institute of Systems Science was initiated in April 1994. The ultimate goal of the project is to build a distributed server that is capable of supporting both CM and non-CM data. As a first step toward this goal, we have implemented a centralized CM server. The primary contribution of this paper is a set of dataplacement and retrieval algorithms that enable our server to efficiently manage its disks, the bottleneck resources in 30 ond of each other in order to achieve jitter-free display. These stringent performance objectives raise a number of issues in the construction of multimedia servers.. The first issue in building a multimedia server is data placement, both within individual disks and across multiple disks. In traditional storage servers that support only non-CM data, the order in which different portions of an object are retrieved is usually immaterial; what matters is the time that is needed to retrieve all of the portions. In the server. We focus on how the algorithms handle independent multimedia objects; the issue of scheduling multiple dependent objects has been addressed elsewhere (e.g., [8] ). The data-placement algorithm declusters every object over all of the disks, using a time-based declustering unit, with the aim of balancing the disk load. Within each disk, objects that are accessed more frequently are allotted pages in the middle cylinders, whereas objects that are less popular reside in the outer and inner cylinders. As for runtime retrieval, the quintessence of the algorithm is to give each disk advance notification of the blocks that have to be fetched in the impending time periods, so that the disk can optimize its service schedule accordingly. Moreover, in processing a block request for a replicated object, the server will dynamically channel the retrieval operation to the most lightly loaded disk that holds a copy of the required block. Extensive evaluations confirmed that the combination of these algorithms results in a very efficient multimedia server. Our implementation manages to achieve near jitter-free retrieval for up to 25 concurrent MPEG-1 streams, each averaging 1.2 Mbits/sec, out of a single Seagate Elite 9 drive that is capable of reaching only a maximum sequential retrieval speed of around 6 Mbytes/sec. Moreover, experiments suggest that the aggregate retrieval bandwidth of the server scales almost linearly with the number of disks.. The remainder of this paper is organized as follows: In Section 2, we present a framework for this study by introducing a taxonomy of the design decisions, and by classifying several related work according to this taxonomy. Section 3 presents an overview of our multimedia server. Following that, the intradisk and interdisk dataplacement/retrieval algorithms are introduced in Sections 4 and 5, respectively. Section 6 describes the accompanying memory management algorithm. Following that, Section 7 gives the results of a series of experiments that highlights the performance of our implementation, while Section 8 uses a simulation model to show how the server might scale up. Finally, our conclusions are presented in Section 9. In this paper, we focus on the problem of implementing a multimedia server that supports continuous media (CM) data including video and audio. Our primary contribution in this work is a set of intra-and interdisk data-placement and retrieval algorithms that are designed to exploit the full capacity of the disks in a server, in order to maximize the number of retrieval streams that can be supported. The data-placement algorithm declusters every object over all of the disks, using a time-based declustering unit, with the aim of balancing the disk load distribution. Within each disk, objects that are accessed more frequently are allotted pages in the middle cylinders to minimize seek delays, whereas objects that are less popular reside in the outer and inner cylinders. As for runtime retrieval, the quintessence of the algorithm is to give each disk advance notification of the blocks that have to be fetched in the impending time periods, so that the disk can optimize its service schedule accordingly. Moreover, in processing a block request for a replicated object, the server will dynamically channel the retrieval operation to the most lightly loaded disk that holds a copy of the required block.. We have implemented a multimedia server based on the above algorithms. Performance tests on our implementation confirm that these algorithms lead to very efficient disk utilization, thus enabling the multimedia server to support a large number of concurrent users. We managed to achieve near jitter-free retrieval for up to 25 concurrent MPEG-1 streams, each averaging 1.2 Mbits/sec, out of a single Seagate Elite 9 drive that is capable of reaching only a maximum sequential retrieval speed of around 6 Mbytes/sec. Moreover, experiments suggest that the total retrieval capacity of the server scales up almost linearly with the number of disks. Therefore, the server has a capacity equal to 30 Mbits/sec times the number of data disks, and new requests are admitted on a first-comefirst-serve basis up to this capacity.. We are extending this work in several ways. Currently, we are in the process of adapting our implementation to a distributed system architecture. We are also partnering a number of companies to deploy our servers to support multimedia-on-demand applications. Finally, we hope to extend the server into a full-fledged multimedia DBMS.

O O O O O B I O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


A database of local fields Abstract. We describe our online database of finite extensions of Q p , and how it can be used to facilitate local analysis of number fields.

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The role of 3-D interactive visualization in blind surveys of HI in galaxies Upcoming H I surveys will deliver large datasets, and automated processing using the full 3-D information (two positional dimensions and one spectral dimension) to find and characterize H I objects is imperative. In this context, visualization is an essential tool for enabling qualitative and quantitative human control on an automated source finding and analysis pipeline. We discuss how Visual Analytics, the combination of automated data processing and human reasoning, creativity and intuition, supported by interactive visualization, enables flexible and fast interaction with the 3-D data, helping the astronomer to deal with the analysis of complex sources. 3-D visualization, coupled to modeling, provides additional capabilities helping the discovery and analysis of subtle structures in the 3-D domain. The requirements for a fully interactive visualization tool are: coupled 1-D/2-D/3-D visualization, quantitative and comparative capabilities, combined with supervised semi-automated analysis. Moreover, the source code must have the following characteristics for enabling collaborative work: open, modular, well documented, and well maintained. We review four state of-the-art, 3-D visualization packages assessing their capabilities and feasibility for use in the case of 3-D astronomical data. The Square Kilometre Array (SKA) and its precursors are opening up new opportunities for radio astronomy in terms of data collection and sensitivity. Two types of blind surveys are planned with SKA-pathfinders:. 1. shallow (very large sky coverage): WALLABY with ASKAP (Johnston et al., 2008; Duffy et al., 2012) , shallow and medium-deep APERTIF surveys with the WSRT (Verheijen et al., 2009 ). 2. deep (high sensitivity, small solid angle): CHI-LES with the J-VLA (Perley et al., 2011; Fernandez et al., 2013) ; LADUMA with MeerKAT (Jonas, 2009; Holwerda et al., 2012) and DIN-GO with ASKAP (Johnston et al., 2008; Duffy et al., 2012 ).. The first type of H I surveys will detect ∼ 10 3 sources weekly, of which 0.2% will consist of well resolved sources, 6.5% will have a limited number of resolution elements, and 93% will at best be marginally resolved (Duffy et al., 2012) . This predicted weekly data rate is high, and fully automated pipelines will be required for processing the data (see section 3). The first and second category of sources will contain a wealth of morphological and kinematic information. However, in cases with complex kinematics it will be difficult to extract all information in (Verheijen et al., 2008; Oosterloo et al., 2010) , which allows a full inventory of the northern radio sky complemented by a wealth of optical, near-IR data, and other radio observatories such as the Low-Frequency Array (LOFAR). Part of the APERTIF surveys will be a medium deep blind survey of a few hundred square degrees with a 3σ column density depth of 2 − 5 × 10 19 cm −2 . A full 12 hour integration will provide ∼ 2.4 TB of complex visibilities sampling a 3. • ×3. • region of the sky and the following data reduction will generate three dimensional data sets of the H I line emission, with axes right ascension (RA or α), declination (DEC or δ), and frequency (λ) or recession velocity (v) . The typical size of a data cube will be 2048 × 2048 pixels for the spatial coordinates (each pixel covers 5 arcsec) and 16384 spectral channels, which correspond to 16384 pixels in the third dimension covering a bandwidth of 300 MHz (∼ 60, 000 km/s). The disk storage needed for each data cube is about 0.25 TB, assuming a single Stokes component, I, and 32 bits per pixel format. The final product after observing the northern sky will be of the order of 5 PB of data cubes.. Examining these numbers it is clear that the storage, data reduction, visualization and analysis to obtain scientific results requires the development of new tools and algorithms which must exploit new solutions and ideas to deal with this large volume of data. The Tera-scale volume of these datasets produces, in fact, both technical issues (e.g., dimension of the data much larger than the available random access memory (RAM) on a normal workstation) and visualization challenges (i.e., the presence in each dataset of a large number of small sources with limited signal-to-noise ratio (SNR)). In this section we have defined the requirements that visualization of H I emission, in the survey era, must satisfy. We briefly summarize them here: a) astronomical world coordinates in order to combine the visualization of H I data with data obtained at other wavebands; b) 3-D capabilities (i.e., presence of interactive volume rendering for grid data of dimension < 10 7 voxels and interactive color and opacity function widgets); c) interactive linking between 1-D/2-D/3-D views; d) quantification: physical data units, labels, and statistical tools; e) linked 1-D/2-D/3-D selection tools; f) 3-D segmentation techniques; g) interactive smoothing; h) comparative visualization (multiple views, overlaid visualizations, etc.); i) tools for generating tilted-ring and N -body models; j) interoperability with VO tools. H I observations are moving into the era of big surveys. Upcoming H I surveys, such as those envisaged with APERTIF and ASKAP, will deliver big data sets leading the radio astronomer into the regime of the so-called Fourth Paradigm (i.e., data-intensive scientific discovery, Hey et al. (2009)) .. APERTIF is expected to start its observing campaign of the northern sky in 2017. The daily APERTIF data cube will have dimensions of 2048 × 2048 × 16384 ∼ 68.7 × 10 9 voxels and the expected number of H I source detections is ∼ 100 every day. WALLABY will have similar characteristics. The large volume of data creates new needs, in terms of tools and algorithms which must exploit new ideas and solutions for storage, data reduction, visualization, and analysis to obtain scientific results.. Visual analytics, 2.6, the combination of automated data processing with human reasoning, creativity and intuition, supported by interactive visualization, is one of the prime methodologies that allow putting the human in the investigation loop. In this paper, we defined the visualization prerequisites and future perspective for applying this paradigm to H I observations focusing on the introduction of 3-D visualization in the process of source finding and analysis. In fact, the current astronomy visualization software has very limited 3-D capabilities for grid data (section 2); while general purpose visualization software (section 4, 5) is not aimed at the analysis of H I data.. In this paper we showed:. i) more than 99% of the voxels in the H I datasets that APERTIF will deliver is dominated by noise and the sources are hidden in it (see Fig.1 ). The current source finder software can extract them with high reliability and completeness (Whiting, 2012; Serra et al., 2015) . The typical volume of individual sources will be 50 3 = 1.25 × 10 5 voxels (up to 512 3 ∼ 1.3 × 10 8 in the case of occasional large galaxies), reducing the storage, I/O bandwidth and computational demands for visualization to a level accessible on desktops and laptops. The predicted weekly data rate, on the other hand, is high (∼ 10 3 sources). Fortunately, only a subset of these (2-3 sources per day) will be highly resolved (more than 10 resolution elements) or show complex features such as tails and extra-planar-gas. A powerful interactive visualization tool will be needed for fast inspection and analysis of these objects. ii) the analysis of the sources, for example producing moment maps and rotation curves, will also be done in an automated way. In particularly complex cases, human interaction will be necessary to drive the automated algorithm in the data volume and provide immediate feedback on the quality of the results (see section 3.3.2). Visualization tools with supervised semi-automated analysis algorithms will be needed. In fact, it becomes necessary to produce refined data with minimal time but maintaining the same level of quality. For example, the derivation of the rotation curve of a galaxy passes through the creation of the so-called tilted-ring model which, then, is compared to the data. This process has been converted to an automatic algorithm. However, significant kinematic features different from the Keplerian rotation (e.g., tidal tails, see Fig.2 ) will be present in part of the data. The current algorithms can not automatically flag these features for the analysis. Therefore, human intervention is necessary to separate the regularly rotation disk and different kinematic features, and to feed the fitting algorithm with the selection, so that the user can judge the results quantitatively. iii) in section 3.3, we showed that 3-D visualization can enable an immediate overview of the kinematics of a galaxy, leading to improved understanding of the coherence in the data. Moreover, a high level of interactivity in all visualization aspects (rendering, smoothing, retrieving quantitative information, and comparative features) will be the key for enabling a fast inspection of the data. On the other hand, volume rendering has its limitations due to current 2-D input and output hardware. Some examples of these limitations are projection issues and the impossibility to move the cursor pixel by pixel. Adding 1-D/2-D views linked to the 3-D representation resolves these limitations.. The combination with high-dimensional visualization techniques, which can help in finding outliers and patterns in the oceans of data, is also necessary. iv) in section 4 we identified the requirements for the visualization and analysis of H I in galaxies: interactive visualization with quantitative and comparative capabilities with 3-D selection techniques and supervised semi-automated analysis. Moreover, the source code must have the following characteristics for enabling collaborative work: open, modular, well documented, and well maintained. After a study of the state ofthe-art of the open-source and actively maintained visualization packages with rendering of grid data capabilities (see section 5), we adopted 3DSlicer as a platform for developing a fully interactive desktop H I data visualization tool with quantitative and comparative features (section 5.2). These techniques can also be used for other astronomical datasets such as 3-D datasets provided by recent Integral Field Unit (IFU) observations (Sánchez et al., 2012; Karman et al., 2014; Richard et al., 2015) . In that case, collaborative work is necessary to identify the key features needed to provide quantitative visualization.. In conclusion, the success of a visualization tool depends heavily on the number of people using it over its life time. The life time of a software package depends on several factors such as usability, maintainability, and whether it has been developed with good insight in the subtle aspects of the data and its interpretation. KARMA is a perfect example of a successful package, developed in the mid 90's but still widely used by radio astronomers to date. Our aim is to achieve an analogous result exploiting the current hardware and algorithmic paradigms, focusing on the linking between 2-D and 3-D visualization, quantitative/comparative features and high-dimensional visualization.

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