Earlier Algorithms Research Articles

A Hybrid Routing Approach for Vehicular Delay Tolerant Network

This paper presents a novel approach for routing algorithm suitable for vehicular delay tolerant networks. Present approach abstracts the properties of two very popular routing algorithms Spray & Wait and Maxprop giving a new algorithm to improve the performance. Proposed algorithm is implemented in java and simulated on opportunistic network environment simulator. Performance of proposed algorithm is compared with algorithms used in delay tolerant networks on parameters like number of bundles dropped, delivery probability and overhead ratio. Results of the simulation indicate better performance of the proposed algorithm compared with earlier algorithms (e.g. MaxProp, Spray and Wait, ProPHET) on many parameters.

A MIN-CUT BASED FILTER FOR AIRBORNE LIDAR DATA

LiDAR (Light Detection and Ranging) is a routinely employed technology as a 3-D data collection technique for topographic mapping. Conventional workflows for analyzing LiDAR data require the ground to be determined prior to extracting other features of interest. Filtering the terrain points is one of the fundamental processes to acquire higher-level information from unstructured LiDAR point data. There are many ground-filtering algorithms in literature, spanning several broad categories regarding their strategies. Most of the earlier algorithms examine only the local characteristics of the points or grids, such as the slope, and elevation discontinuities. Since considering only the local properties restricts the filtering performance due to the complexity of the terrain and the features, some recent methods utilize global properties of the terrain as well. This paper presents a new ground filtering method, Min-cut Based Filtering (MBF), which takes both local and global properties of the points into account. MBF considers ground filtering as a labeling task. First, an energy function is designed on a graph, where LiDAR points are considered as the nodes on the graph that are connected to each other as well as to two auxiliary nodes representing ground and off-ground labels. The graph is constructed such that the data costs are assigned to the edges connecting the points to the auxiliary nodes, and the smoothness costs to the edges between points. Data and smoothness terms of the energy function are formulated using point elevations and approximate ground information. The data term conducts the likelihood of the points being ground or off-ground while the smoothness term enforces spatial coherence between neighboring points. The energy function is optimized by finding the minimum-cut on the graph via the alpha-expansion algorithm. The resulting graph-cut provides the labeling of the point cloud as ground and off-ground points. Evaluation of the proposed method on the ISPRS test dataset for ground filtering demonstrates that the results are comparable with most current existing methods. An overall average filtering accuracy for the 15 ISPRS test areas is 91.3%.

A MIN-CUT BASED FILTER FOR AIRBORNE LIDAR DATA

Abstract. LiDAR (Light Detection and Ranging) is a routinely employed technology as a 3-D data collection technique for topographic mapping. Conventional workflows for analyzing LiDAR data require the ground to be determined prior to extracting other features of interest. Filtering the terrain points is one of the fundamental processes to acquire higher-level information from unstructured LiDAR point data. There are many ground-filtering algorithms in literature, spanning several broad categories regarding their strategies. Most of the earlier algorithms examine only the local characteristics of the points or grids, such as the slope, and elevation discontinuities. Since considering only the local properties restricts the filtering performance due to the complexity of the terrain and the features, some recent methods utilize global properties of the terrain as well. This paper presents a new ground filtering method, Min-cut Based Filtering (MBF), which takes both local and global properties of the points into account. MBF considers ground filtering as a labeling task. First, an energy function is designed on a graph, where LiDAR points are considered as the nodes on the graph that are connected to each other as well as to two auxiliary nodes representing ground and off-ground labels. The graph is constructed such that the data costs are assigned to the edges connecting the points to the auxiliary nodes, and the smoothness costs to the edges between points. Data and smoothness terms of the energy function are formulated using point elevations and approximate ground information. The data term conducts the likelihood of the points being ground or off-ground while the smoothness term enforces spatial coherence between neighboring points. The energy function is optimized by finding the minimum-cut on the graph via the alpha-expansion algorithm. The resulting graph-cut provides the labeling of the point cloud as ground and off-ground points. Evaluation of the proposed method on the ISPRS test dataset for ground filtering demonstrates that the results are comparable with most current existing methods. An overall average filtering accuracy for the 15 ISPRS test areas is 91.3%.

An efficient algorithm for assignment of flow direction over flat surfaces in raster DEMs based on distance transform

Flat surfaces or areas with no local gradient are general types of terrain in raster Digital Elevation Models (DEMs). When a DEM is used for automated hydrological analysis, it is necessary to assign flow directions over flat surfaces. This paper presents an improved algorithm for assigning flow directions over flat surfaces that builds on earlier work and offers efficiency improvements. The improved algorithm use distance transform method to calculate the distance values of cells in flat surfaces towards lower terrain and away from higher terrain, which can replace the recursive process in the earlier algorithm by a linear process, and reduce random data access. Furthermore, the improved algorithm assigns weights with different values and signs to the two distance values to guarantee that all flat surfaces are drained, which avoids the need to label each flat surface using the flood-fill algorithm in the earlier work. Comparisons of speed indicate that the improved algorithm is more efficient in assigning flow directions over flat surfaces.

Cluster Search Algorithm for Finding Multiple Optima

The black box functions found in computer experiments often result in multimodal optimization programs. Optimization that focuses on a single best optimum may not achieve the goal of getting the best answer for the purposes of the experiment. This paper builds upon an algorithm introduced in [1] that is successful for finding multiple optima within the input space of the objective function. Here we introduce an alternative cluster search algorithm for finding these optima, making use of clustering. The cluster search algorithm has several advantages over the earlier algorithm. It gives a forward view of the optima that are present in the input space so the user has a preview of what to expect as the optimization process continues. It employs pattern search, in many instances, closer to the minimum’s location in input space, saving on simulator point computations. At termination, this algorithm does not need additional verification that a minimum is a duplicate of a previously found minimum, which also saves on simulator point computations. Finally, it finds minima that can be “hidden” by close larger minima.

An Almost Optimal Algorithm for Computing Nonnegative Rank

Here, we give an algorithm for deciding if the nonnegative rank of a matrix $M$ of dimension $m \times n$ is at most $r$ which runs in time $(nm)^{O(r^2)}$. This is the first exact algorithm that runs in time singly exponential in $r$. This algorithm (and earlier algorithms) are built on methods for finding a solution to a system of polynomial inequalities (if one exists). Notably, the best algorithms for this task run in time exponential in the number of variables but polynomial in all of the other parameters (the number of inequalities and the maximum degree). Hence, these algorithms motivate natural algebraic questions whose solution have immediate algorithmic implications: How many variables do we need to represent the decision problem, and does $M$ have nonnegative rank at most $r$? A naive formulation uses $nr + mr$ variables and yields an algorithm that is exponential in $n$ and $m$ even for constant $r$. Arora et al. [Proceedings of STOC, 2012, pp. 145--162] recently reduced the number of variables to $2r^2 2^r$, and here we exponentially reduce the number of variables to $2r^2$ and this yields our main algorithm. In fact, the algorithm that we obtain is nearly optimal (under the exponential time hypothesis) since an algorithm that runs in time $(nm)^{o(r)}$ would yield a subexponential algorithm for $3$-SAT [Proceedings of STOC, 2012, pp. 145--162]. Our main result is based on establishing a normal form for nonnegative matrix factorization---which in turn allows us to exploit algebraic dependence among a large collection of linear transformations with variable entries. Additionally, we also demonstrate that nonnegative rank cannot be certified by even a very large submatrix of $M$, and this property also follows from the intuition gained from viewing nonnegative rank through the lens of systems of polynomial inequalities.

A filtration method for order-preserving matching

The problem of order-preserving matching has gained attention lately. The text and the pattern consist of numbers. The task is to find all the substrings in the text which have the same length and relative order as the pattern. The problem has applications in analysis of time series. We present a new sublinear solution based on filtration. Any algorithm for exact string matching can be used as a filtering method. If the filtration algorithm is sublinear, the total method is sublinear on average. We show by practical experiments that the new solution is more efficient than earlier algorithms.

An Efficient Modified Haar Wavelet Collocation Method for Numerical Solution of Two-Dimensional Elliptic PDEs

In this work, we present a new method for solving elliptic partial differential equations using Haar wavelet. This work improves the earlier work (Aziz et al. in Appl Math Model 37:676–694, 2013) in terms of efficiency and contains an extension to nonlinear elliptic partial differential equations as well. In this paper the earlier algorithm (Aziz et al. in Appl Math Model 37:676–694, 2013) has been modified by starting the approximation with a fourth order mixed derivative rather than approximation of the second order derivatives with respect to x and y separately which results in a more efficient algorithm than the earlier algorithm. The use of Kronecker tensor products makes the new algorithm robust and easier to implement in a programming language. A distinguishing feature of the new method is that it can be applied to a variety of boundary conditions with a little modification of the program. The method is tested on several benchmark linear as well as nonlinear models. The numerical results show convergence, simple applicability and efficiency of the method.

Compounding problem in an interactive multiple objective optimization method

This note finds and resolves a compounding problem in the interactive multi-objective optimization method developed by Park and Shin (2012). The compounding problem means that the algorithm generates a solution unexpected by the decision maker. A concrete example of the compounding problem is demonstrated and, to remove the problem, an improved algorithm is then developed. We also show that the improved algorithm converges to the known optimal solutions faster than the earlier algorithm.

Choosing Subsamples for Sequencing Studies by Minimizing the Average Distance to the Closest Leaf.

Imputation of genotypes in a study sample can make use of sequenced or densely genotyped external reference panels consisting of individuals that are not from the study sample. It also can employ internal reference panels, incorporating a subset of individuals from the study sample itself. Internal panels offer an advantage over external panels because they can reduce imputation errors arising from genetic dissimilarity between a population of interest and a second, distinct population from which the external reference panel has been constructed. As the cost of next-generation sequencing decreases, internal reference panel selection is becoming increasingly feasible. However, it is not clear how best to select individuals to include in such panels. We introduce a new method for selecting an internal reference panel—minimizing the average distance to the closest leaf (ADCL)—and compare its performance relative to an earlier algorithm: maximizing phylogenetic diversity (PD). Employing both simulated data and sequences from the 1000 Genomes Project, we show that ADCL provides a significant improvement in imputation accuracy, especially for imputation of sites with low-frequency alleles. This improvement in imputation accuracy is robust to changes in reference panel size, marker density, and length of the imputation target region.

ISPM: Improved Snow Prediction Model to Nowcast Snow/No-Snow

Till date, many of the practioners, meteorologists, researchers, academicians, scientists across the globe proposed many methodologies and tools to nowcast snow/no-snow using satellite imagery, radar imagery, physical instruments, various algorithms, models and so on, adding to it some researchers estimated the amount of snow while some researchers detected the density of snow and few discriminated the differences between wet snow and dry snow. The main crux of the present research is to nowcast the presence of snow/no-snow more accurately by making use of historical weather datasets by adopting decision trees. In this paper, we are proposing a new algorithm Improved Snow Prediction Model (ISPM), an improvement to our earlier algorithms Snow Prediction Model (SPM), Improved Supervised Learning in Quest (ISLIQ), Supervised Learning using Gain Ratio as Attribute Selection Measure (SLGAS) and Supervised Learning using Entropy as Attribute Selection Measure (SLEAS). The ISPM algorithm out performs in terms of various performance measures like sensitivity, specificity, precision, dice, error rate and accuracy when compared with other decision tree models. The proposed method provides less computational complexity by evaluating the interval range, which significantly decreases the number of split points. Experimental results show that the ISPM algorithm scales up well to both large and small datasets with large number of attributes and class labels.

Layout-Aware Mixture Preparation of Biochemical Fluids on Application-Specific Digital Microfluidic Biochips

The recent proliferation of digital microfluidic (DMF) biochips has enabled rapid on-chip implementation of many biochemical laboratory assays or protocols. Sample preprocessing, which includes dilution and mixing of reagents, plays an important role in the preparation of assays. The automation of sample preparation on a digital microfluidic platform often mandates the execution of a mixing algorithm, which determines a sequence of droplet mix-split steps (usually represented as a mixing graph). However, the overall cost and performance of on-chip mixture preparation not only depends on the mixing graph but also on the resource allocation and scheduling strategy, for instance, the placement of boundary reservoirs or dispensers, mixer modules, storage units, and physical design of droplet-routing pathways. In this article, we first present a new mixing algorithm based on a number-partitioning technique that determines a layout-aware mixing tree corresponding to a given target ratio of a number of fluids. The mixing graph produced by the proposed method can be implemented on a chip with a fewer number of crossovers among droplet-routing paths as well as with a reduced reservoir-to-mixer transportation distance. Second, we propose a routing-aware resource-allocation scheme that can be used to improve the performance of a given mixing algorithm on a chip layout. The design methodology is evaluated on various test cases to demonstrate its effectiveness in mixture preparation with the help of two representative mixing algorithms. Simulation results show that on average, the proposed scheme can reduce the number of crossovers among droplet-routing paths by 89.7% when used in conjunction with the new mixing algorithm, and by 75.4% when an earlier algorithm [Thies et al. 2008] is used.

Computing points of bounded height in projective space over a number field

We construct an algorithm for solving the following problem: given a number field $K$, a positive integer $N$, and a positive real number $B$, determine all points in $\mathbb P^N(K)$ having relative height at most $B$. A theoretical analysis of the efficiency of the algorithm is provided, as well as sample computations showing how the algorithm performs in practice. Two variants of the method are described, and examples are given to compare their running times. In the case $N=1$ we compare our method to an earlier algorithm for enumerating elements of bounded height in number fields.

SU‐E‐T‐806: Very Fast GPU‐Based IMPT Dose Computation

Purpose:Designing particle therapy treatment plans is a dosimetrist‐in‐the‐loop optimization wherein the conflicting constraints of achieving a desired tumor dose distribution must be balanced against the need to minimize the dose to nearby OARs. IMPT introduces an additional, inner, numerical optimization step in which the dosimetrist's current set of constraints are used to determine the weighting of beam spots. Very fast dose calculations are needed to enable the dosimetrist to perform many iterations of the outer optimization in a commercially reasonable time.Methods:We have developed a GPU‐based convolution‐type dose computation algorithm that more accurately handles heterogeneities than earlier algorithms by redistributing energy from dose computed in a water volume. The depth dependence of the beam size is handled by pre‐processing Bragg curves using a weighted superposition of Gaussian bases. Additionally, scattering, the orientation of treatment ports, and the non‐parallel propagation of beams are handled by large, but sparse, energy‐redistribution matrices that implement affine transforms.Results:We tested our algorithm using a brain tumor dataset with 1 mm voxels and a single treatment port from the patient's anterior through the sinuses. The resulting dose volume is 100 × 100 × 230 mm with 66,200 beam spots on a 3 × 3 × 2 mm grid. The dose computation takes <1 msec on a GeForce GTX Titan GPU with the Gamma passing rate for 2mm/2% criterion of 99.1% compared to dose calculated by an alternative dose algorithm based on pencil beams. We will present comparisons to Monte Carlo dose calculations.Conclusion:Our high‐speed dose computation method enables the IMPT spot weights to be optimized in <1 second, resulting in a nearly instantaneous response to user changes to dose constraints. This permits the creation of higher quality plans by allowing the dosimetrist to evaluate more alternatives in a short period of time.

Energy-efficient topology control algorithm for maximizing network lifetime in wireless sensor networks with mobile sink

Uneven energy consumption is an inherent problem in wireless sensor networks characterized by multi-hop routing and many-to-one traffic pattern. Such unbalanced energy dissipation can significantly reduce network lifetime. In this paper, we study the problem of prolonging network lifetime in large-scale wireless sensor networks where a mobile sink gathers data periodically along the predefined path and each sensor node uploads its data to the mobile sink over a multi-hop communication path. By using greedy policy and dynamic programming, we propose a heuristic topology control algorithm with time complexity O(n(m+nlogn)), where n and m are the number of nodes and edges in the network, respectively, and further discuss how to refine our algorithm to satisfy practical requirements such as distributed computing and transmission timeliness. Theoretical analysis and experimental results show that our algorithm is superior to several earlier algorithms for extending network lifetime.

Computer Program in MATLAB for Optimum Allocation for Multivariate Stratified Sampling

In this paper we develop a MAT LAB computer program for the optimum allocation for multivariate stratified sampling with non-linear cost function –travel cost. The generalized MATLAB program for solving multi variate or multi objective linear programming based on with some major modifications in earlier algorithms. The proposed modified algorithm as well as MATLAB program simplifies the earlier algorithm on Multivariate linear programming problems. The problem of determining the optimum allocations are formulated as Nonlinear Programming Problems , in which each NLPP has a convex objective function and a non-linear cost constraint. The NLLP’s are then solved using Lagrange Multiplier technique and the explicit formula for variance is obtained.

Morphologically rich Urdu grammar parsing using Earley algorithm

AbstractThis work presents the development and evaluation of an extended Urdu parser. It further focuses on issues related to this parser and describes the changes made in the Earley algorithm to get accurate and relevant results from the Urdu parser. The parser makes use of a morphologically rich context free grammar extracted from a linguistically-rich Urdu treebank. This grammar with sufficient encoded information is comparable with the state-of-the-art parsing requirements for the morphologically rich Urdu language. The extended parsing model and the linguistically rich extracted-grammar both provide us better evaluation results in Urdu/Hindi parsing domain. The parser gives 87% of f-score, which outperforms the existing parsing work of Urdu/Hindi based on the tree-banking approach.

Rational approximations and quantum algorithms with postselection

We study the close connection between rational functions that approximate a given Boolean function, and quantum algorithms that compute the same function using postselection. We show that the minimal degree of the former equals (up to a factor of 2) the minimal query complexity of the latter. We give optimal (up to constant factors) quantum algorithms with postselection for the Majority function, slightly improving upon an earlier algorithm of Aaronson. Finally we show how Newman's classic theorem about low-degree rational approximation of the absolute-value function follows from these algorithms.

Ultrasound and cytological diagnostics of thyroid - its proper application in case of coexisting disturbing clinical signs and symptoms, suggestive of active proliferative lesion

Ultrasound and cytological diagnostics of thyroid - its proper application in case of coexisting disturbing clinical signs and symptoms, suggestive of active proliferative lesion

A Time-Dependent Vehicle Routing Problem with Time Windows for E-Commerce Supplier Site Pickups Using Genetic Algorithm

The VRP is classified as an NP-hard problem. Hence exact optimization methods may be difficult to solve these problems in acceptable CPU times, when the problem involves real-world data sets that are very large. To get solutions in determining routes which are realistic and very close to the actual solution, we use heuristics and metaheuristics which are of the combinatorial optimization type. A literature review of VRPTW, TDVRP, and a metaheuristic such as the genetic algorithm was conducted. In this paper, the implementation of the VRPTW and its extension, the time-dependent VRPTW (TDVRPTW) has been carried out using the model as well as metaheuristics such as the genetic algorithm (GA). The algorithms were implemented, using Matlab and HeuristicLab optimization software. A plugin was developed using Visual C# and DOT NET framework 4.5. Results were tested using Solomon’s 56 benchmark instances classified into groups such as C1, C2, R1, R2, RC1, RC2, with 100 customer nodes, 25 vehicles and each vehicle capacity of 200. The results were comparable to the earlier algorithms developed and in some cases the current algorithm yielded better results in terms of total distance travelled and the average number of vehicles used.