In this paper three parallel recognition algorithms for threshold, matrogenic and box-threshold graphs, respectively, are given. These classes of graphs are inclusionwise comparable and depend only on their degree seq...
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In this paper three parallel recognition algorithms for threshold, matrogenic and box-threshold graphs, respectively, are given. These classes of graphs are inclusionwise comparable and depend only on their degree sequences. The algorithms run in O(log n) parallel time on a PRAM-EREW model of computation and require O(n/log n) processors when the degree sequence, ordered in decreasing fashion, is given as input.
Harmful algal blooms (HABs) are challenging to recognize because of their striped and uneven biomass distributions. To address this issue, a refined deep -learning algorithm termed HAB-Ne was developed for the recogni...
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Harmful algal blooms (HABs) are challenging to recognize because of their striped and uneven biomass distributions. To address this issue, a refined deep -learning algorithm termed HAB-Ne was developed for the recognition of HABs in GF-1 Wide Field of View (WFV) images using Noctiluca scintillans algal bloom as an example. First, a pretrained image super -resolution model was integrated to improve the spatial resolution of the GF-1 WFV images and minimize the impact of mixed pixels caused by the strip distribution. Side -window convolution was also explored to enhance the edge features of HABs and minimize the effects of uneven biomass distribution. In addition, a convolutional encoder -decoder network was constructed for threshold -free HAB recognition to address the dependence on thresholds in existing methods. HAB-Net effectively recognized HABs from GF-1 WFV images, achieving an average precision of 90.1% and an F1 -score of 0.86. HAB-Net showed more fine-grained recognition results than those of existing methods, with over 4% improvement in the F1 -Score, especially in the marginal areas of HAB distribution. The algorithm demonstrated its effectiveness in recognizing HABs in different marine environments, such as the Yellow Sea, East China Sea, and northern Vietnam. Additionally, the algorithm was proven suitable for detecting the macroalga Sargassum. This study demonstrates the potential of deep-learning-based fine-grained recognition of HABs, which can be extended to the recognition of other fine-scale and strip-distributed objects, such as oil spills and Ulva prolifera.
Due to the lack of information of subsurface lunar regolith stratification which varies along depth, the drilling device may encounter lunar soil and lunar rock randomly in the drilling process. To meet the load safet...
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Due to the lack of information of subsurface lunar regolith stratification which varies along depth, the drilling device may encounter lunar soil and lunar rock randomly in the drilling process. To meet the load safety requirements of unmanned sampling mission under limited orbital resources, the control strategy of autonomous drilling should adapt to the indeterminable lunar environments. Based on the analysis of two types of typical drilling media （i.e., lunar soil and lunar rock）, this paper proposes a multi-state control strategy for autonomous lunar drilling. To represent the working circumstances in the lunar subsurface and reduce the complexity of the control algo- rithm, lunar drilling process was categorized into three drilling states： the interface detection, initi- ation of drilling parameters for recognition and drilling medium recognition. Support vector machine （SVM） and continuous wavelet transform were employed for the online recognition of drilling media and interface, respectively. Finite state machine was utilized to control the transition among different drilling states. To verify the effectiveness of the multi-state control strategy, drilling experiments were implemented with multi-layered drilling media constructed by lunar soil simulant and lunar rock simulant. The results reveal that the multi-state control method is capable of detecting drilling state variation and adjusting drilling parameters timely under vibration interferences. The multi-state control method provides a feasible reference for the control of extraterrestrial autonomous drilling.
This paper presents an efficient algorithm which is able to accurately recognize non-deterministic signals generated by synthetic non-chaotic and chaotic stochastic processes (SPs), as well as by natural phenomena (th...
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This paper presents an efficient algorithm which is able to accurately recognize non-deterministic signals generated by synthetic non-chaotic and chaotic stochastic processes (SPs), as well as by natural phenomena (that are inherently stochastic) such as speech, image, and electroencephalographic signals. This recognition algorithm exploits a Karhunen-Loeve transform (KLT)-based model able to characterize signals in terms of non-deterministic trajectories and consists of the concatenation of (i) a training stage, which iteratively extracts suitable parameter collections by means of the KLT and (ii) a recognition procedure based on ad hoc metric that measures the trajectory-proximities, in order to associate the unknown signal to the SP which this signal can be considered a realization of. The proposed methodology is able to recognize SPs without estimating their probability density function (pdf), thus requiring a low computational complexity to be implemented. Exhaustive experimentation on specific case-studies was performed and some experimental results were compared to other existing techniques such as hidden Markov model (HMM), vector quantization (VQ), and dynamic time warping (DTW). recognition performance is similar to current best practices for non-chaotic signals and higher for chaotic ones. A better noise rejection was also achieved, and a reduction of two orders of magnitude in training-times compared with HMM was obtained, thus making the proposed methodology one of the current best practices in this field. Finally, the experimental results obtained by three different applications of the recognizer (an automatic speech recognition system. an automatic facial recognition system, and an automatic diagnosis system of the ictal and interictal epilepsy) clearly show excellent classification performance, and it is worth noting as complex filters are not needed unlike other current best practices. (C) 2008 Published by Elsevier B.V.
In a graph G, an odd hole is an induced odd cycle of length at least 5. A clique of G is a set of pairwise adjacent vertices. In this paper we consider the class C-k of graphs whose cliques have a size bounded by a co...
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In a graph G, an odd hole is an induced odd cycle of length at least 5. A clique of G is a set of pairwise adjacent vertices. In this paper we consider the class C-k of graphs whose cliques have a size bounded by a constant k. Given a graph G in C-k, we show how to recognize in polynomial time whether G contains an odd hole.
Asteroidal triple free (AT-free) graphs have been introduced as a generalization of interval graphs, since interval graphs are exactly the chordal AT-free graphs. While for interval graphs it is obvious that there is ...
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Asteroidal triple free (AT-free) graphs have been introduced as a generalization of interval graphs, since interval graphs are exactly the chordal AT-free graphs. While for interval graphs it is obvious that there is always a linear ordering of the vertices, such that for each triple of independent vertices the middle one intercepts any path between the remaining vertices of the triple, it is not clear that such an ordering exists for AT-free graphs in general. In this paper we study graphs that are defined by enforcing such an ordering. In particular, we introduce two subfamilies of AT-free graphs, namely, path orderable graphs and strong asteroid free graphs. Path orderable graphs are defined by a linear ordering of the vertices that is a natural generalization of the ordering that characterizes cocomparability graphs. On the other hand, motivation for the definition of strong asteroid free graphs comes from the fundamental work of Gallai on comparability graphs. We show that cocomparability graphs subset of path orderable graphs subset of strong asteroid free graphs subset of AT-free graphs. In addition, we settle the recognition question for the two new classes by proving that recognizing path orderable graphs is NP-complete, whereas the recognition problem for strong asteroid free graphs can be solved in polynomial time.
A biclique cutset is a cutset that induces the disjoint union of two cliques. A hole is an induced cycle with at least five vertices. A graph is biclique separable if it has no holes and each induced subgraph that is ...
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A biclique cutset is a cutset that induces the disjoint union of two cliques. A hole is an induced cycle with at least five vertices. A graph is biclique separable if it has no holes and each induced subgraph that is not a clique contains a clique cutset or a biclique cutset. The class of biclique separable graphs contains several well-studied graph classes, including triangulated graphs. We prove that for the class of biclique separable graphs, the recognition problem, the vertex coloring problem, and the clique problem can be solved efficiently. Our algorithms also yield a proof that biclique separable graphs are perfect. Our coloring algorithm is actually more general and can be applied to graphs that can be decomposed via a special type of biclique cutset. Our algorithms are based on structural results on biclique separable graphs developed in this paper. (c) 2005 Wiley Periodicals, Inc.
An algorithm for computing the k-convex closure of a subgraph relative to a given equivalence relation R among edges of a graph is given. For general graph and arbitrary relation R the time complexity is O(qn(2) + mn)...
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An algorithm for computing the k-convex closure of a subgraph relative to a given equivalence relation R among edges of a graph is given. For general graph and arbitrary relation R the time complexity is O(qn(2) + mn), where n is the number of vertices, m is the number of edges and q is the number of equivalence classes of R. A special case is an O(mn) algorithm for the usual k-convexity. We also show that Cartesian graph bundles over triangle free bases can be recognized in O(mn) time and that all representations of such graphs as Cartesian graph bundles can be found in O(mn(2)) time.
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