Breadth-first Tree Research Articles

Deep Learning Aided Low Complex Breadth-First Tree Search for MIMO Detection

Deep Learning Aided Low Complex Breadth-First Tree Search for MIMO Detection

Controllability of Markovian jump Boolean control networks: A graphical approach

This paper investigates the controllability of Markovian jump Boolean control networks (MJBCNs) by using graphical approach. To solve this problem, a deterministic directed graph is constructed to describe the stochastic evolutionary process of the considered MJBCN under the framework of semi-tensor product of matrices. Then, combining with breadth-first search, the special subgraph with breadth-first tree as the principal part is obtained. Based on this, the necessary and sufficient conditions to verify the controllability of MJBCNs are deduced, and the problem of determining the minimal controllable time is solved completely. Subsequently, two examples are given to illustrate the validity and correctness of the method.

Giant descendant trees, matchings, and independent sets in age-biased attachment graphs

AbstractWe study two models of an age-biased graph process: the$\delta$-version of the preferential attachment graph model (PAM) and the uniform attachment graph model (UAM), withmattachments for each of the incoming vertices. We show that almost surely the scaled size of a breadth-first (descendant) tree rooted at a fixed vertex converges, for$m=1$, to a limit whose distribution is a mixture of two beta distributions and a single beta distribution respectively, and that for$m>1$the limit is 1. We also analyze the likely performance of two greedy (online) algorithms, for a large matching set and a large independent set, and determine – for each model and each greedy algorithm – both a limiting fraction of vertices involved and an almost sure convergence rate.

Path advisor

The shortest path in both the two dimensional (2D) plane and the three dimensional (3D) terrain is extensively used both in industry and academia. Although there are some map visualization tools for viewing the shortest path in 2D and 3D views, we find two limitations: (1) they are not applicable for map applications with obstacles (such as the wall in a building), and (2) they look unrealistic and strange when a road network approach is blindly adopted. Motivated by this, we developed a web-based multi-functional campus map tool called Path Advisor , which allows users to visualize the shortest path in the 2D view, the bird's eye view and the virtual reality view (VR view). Path Advisor uses Dijkstra's shortest path algorithm and breadth-first tree in the 2D view, and the weighted shortest surface path algorithm in the bird's eye view and the VR view. We shot a video for demonstrating Path Advisor at https://youtu.be/ZgdjyXXHwqg.

Set Invariance and Optimal Set Stabilization of Boolean Control Networks: A Graphical Approach

This article investigates set invariance and optimal set stabilization of Boolean control networks (BCNs) from a novel graph-theoretical perspective. First, we establish the relationship between the control invariant sets and the strongly connected components of the state transition graph (STG), and propose a new algorithm to compute the largest control invariant subset. Second, both set stabilizability and time-optimal set stabilization via state feedback are characterized by a breadth-first tree of the STG. Though the prior two problems have been attempted by existing algebraic methods, the primary advantage of our alternative graphical approach is the enhanced computational efficiency. Third, we consider for the first time, the optimal set stabilization problem with general cost criteria, not limited to time optimality. A state feedback controller is constructed for general optimal set stabilization using a shortest-path tree of the STG. Finally, it is straightforward to extend the proposed approach to handle various constraints as well as other types of logical networks by slightly modifying the STG. Two biological networks are used to test the performance of our approach. Results show that in accordance with time complexity analysis, our algorithms can dramatically reduce the running time for the first two problems compared with existing work, and effectively solve the general optimal set stabilization problem.

A BFS-Tree of ranking references for unsupervised manifold learning

Contextual information, defined in terms of the proximity of feature vectors in a feature space, has been successfully used in the construction of search services. These search systems aim to exploit such information to effectively improve ranking results, by taking into account the manifold distribution of features usually encoded. In this paper, a novel unsupervised manifold learning is proposed through a similarity representation based on ranking references. A breadth-first tree is used to represent similarity information given by ranking references and is exploited to discovery underlying similarity relationships. As a result, a more effective similarity measure is computed, which leads to more relevant objects in the returned ranked lists of search sessions. Several experiments conducted on eight public datasets, commonly used for image retrieval benchmarking, demonstrated that the proposed method achieves very high effectiveness results, which are comparable or superior to the ones produced by state-of-the-art approaches.

Novel MIMO Detection With Improved Complexity for Near-ML Detection in MIMO-OFDM Systems

In this paper, a novel efficient algorithm for MIMO detection in MIMO-OFDM systems has been proposed. This paper also shows that the complexity can be greatly reduced by applying the threshold in the breadth-first tree search algorithm step to eliminating paths. That is, this algorithm derives thresholds based on the LR-aided nonlinear algorithm and performs a simple tree search detection algorithm. This algorithm requires a lower computational complexity than the conventional QRD-M detection algorithm and achieves the same bit error performance. After the novel detection algorithm is proposed, this paper applies this algorithm to the MIMO-OFDM system for performance comparison in terms of error performance and complexity. In many cases of MIMO-OFDM receivers, the proposed method will be an excellent option for implementation.

ICI Cancellation Based on MIMO Decoding for FTN Non-Orthogonal FDM Systems

In this paper, we investigate multiple-input multiple-output (MIMO) decoding in faster-than-Nyquist non-orthogonal frequency-division multiplexing (FTN-NOFDM) for inter-carrier interference (ICI) cancellation. The MIMO decoding is based on depth-first tree search or breadth-first tree search. The sphere decoder (SD) is a commonly used decoding algorithm based on the depth-first tree search. Maximum-likelihood detection with QR decomposition and M-algorithm (QRM-MLD) is a commonly used decoding algorithm based on the breadth-first tree search. By adding the initial radius to both SD and QRM-MLD, the computational complexity of these can be further decreased without the loss of the ICI cancellation performance. In the experiment, SD, QRM-MLD, and their corresponding simplified versions are used for the ICI cancellation in a 28-Gb/s FTN-NOFDM system with transmission over a 10-km standard single-mode fiber. The 3-dB equivalent bandwidth of this system is only approximately 5.5 GHz. Under serious high-frequency distortion caused by limited bandwidth, the bit error rate performance of the FTN-NOFDM can still reach the forward error correction limit due to the bandwidth compression and effective ICI cancellation. FTN-NOFDM with 20% and 30% bandwidth savings has approximately 1.3- and 1.1-dB improvements in receiver sensitivity compared to FTN-NOFDM with 10% bandwidth savings, respectively. To the best of our knowledge, this paper is the first experimental demonstration of the depth-first tree search and breadth-first tree search for FTN-NOFDM in a bandwidth-limited optical transmission system. The experimental results show that FTN-NOFDM is a promising scheme for bandwidth-limited systems.

Scheduling multiple servers to facilitate just-in-time part-supply in automobile assembly lines

PurposeWith regard to product variety and cost competition, just-in-time (JIT) part-supply has become a critical issue in automobile assembly lines (AALs). This paper aims to investigate a multiple server scheduling problem (MSSP) encountered in the JIT part-supply process of AALs. Parts are stored in boxes and allotted from the JIT-supermarket to consumptive stations with a multiple server system. The schedule is to dispatch and sequence material boxes on each server for minimizing line-side inventory levels.Design/methodology/approachA mixed integer linear programming (MILP) model is established to formulate the proposed MSSP to pave the way for CPLEX procedure. Considering the high complexity of MSSP, a hybrid ant colony optimization (HACO) approach is developed by integrating basic ant colony optimization (ACO) with local optimizers that comprise of a fast local search and a tailored breadth-first tree search method.FindingsBoth CPLEX and HACO approach are capable of solving small-scale instances to optimality within reasonable computation time. The proposed HACO has been well enhanced with the embedded fast local search and tailored breadth-first tree search, and it performs robustly in a statistically significant manner when applied to real-world scale instances.Originality/valueNo stock-outs constraints and weighted line-side inventory level are considered in this paper, and the MSSP is solved satisfactorily to facilitate an efficient JIT part-supply of the AAL. In terms of the algorithm design, a tree search-based local optimizer is embedded into ACO to combine the mechanisms of ACO and problem-specific optimization.

The K-Best Sphere Decoding for Soft Detection of Generalized Spatial Modulation

Due to the interchannel interference and the varying active antennas in generalized spatial modulation (GSM), it is challenging to detect the transmitted data effectively while imposing low computational burden to the receiver. In this paper, we propose a novel algorithm based on the K-best sphere decoding for the soft detection of the GSM signal, which performs the breadth-first tree search in two stages sequentially. By exploiting the null space of the GSM channel, the inactive antenna searching is carried out antennawise in the first stage, in which a recursive algorithm for updating the QL decomposition (a decomposition of a matrix into a product of an orthogonal matrix and a lower triangular matrix) is proposed to calculate the branch metrics and an effective examination scheme is developed to prune those illegal or repetitive child nodes. Based on the available QL decomposition structure that has been updated recursively during the first stage, the second stage of the proposed detector is then readily concatenated to search for the modulated symbols carried on the active antennas in a one-by-one manner. Moreover, owing to the soft-input soft-output nature, the proposed detector can be applied in a turbo decoder for a coded GSM system. Both complexity analysis and simulation results corroborate the superiority of the proposed soft detector for the GSM system.

Lattice-Reduction-Aided Breadth-First Tree Searching Algorithm for MIMO Detection

We propose a lattice-reduction (LR)-aided breadth-first tree searching algorithm for MIMO detection achieving near-optimal performance with very low complexity. At each level of the tree in the search, only the paths whose accumulated metrics satisfy a particular restriction condition will be kept as the candidates. Furthermore, the number of child nodes expanded on each parent node, and the maximum number of candidates preserved at each level, are also restricted, respectively. All these measures ensure the proposed algorithm reaching a preset near-optimal performance and achieving very low average and maximum computational complexity. Simulation results verify the proposed algorithm’s higher efficiency in terms of the performance/complexity tradeoff than the existing LR-aided K-best detectors and LR-aided fixed-complexity sphere decoders.

Reduced-Complexity MIMO Detection via a Slicing Breadth-First Tree Search

A bottleneck in multiple-input multiple-output communications systems is the complexity of detection at the receiver. The complexity of optimum maximum-likelihood detection is often prohibitive, especially for large numbers of antennas and large alphabets. A suboptimal tree-search-based detector known as the $K$ -best detector is an effective scheme that provides a flexible performance-complexity tradeoff. In this paper, we identify scalar list detection as a key building block of the $K$ -best detector, and we propose an efficient low-complexity implementation of the scalar list detector for $M$ -ary QAM using a slicing operation. Embedding the slicing list detector into the $K$ -best framework leads to our proposed slicing $K$ -best detector. Simulation results show that the proposed detector offers comparable performance to the conventional $K$ -best detector, but with significantly reduced complexity when $K$ is less than the QAM alphabet size $M$ . Since the slicing list detection is performed at each visited node in the detection tree, the complexity reduction is especially significant when the number of antennas and the alphabet size are large, making the proposed detector a competitive option for high spectral-efficiency wireless systems.

Scheduling parallel-machine batch operations to maximize on-time delivery performance

In this paper we study the problem of minimizing total weighted tardiness, a proxy for maximizing on-time delivery performance, on parallel nonidentical batch processing machines. We first formulate the (primal) problem as a nonlinear integer programming model. We then show that the primal problem can be solved exactly by solving a corresponding dual problem with a nonlinear relaxation. Since both the primal and the dual problems are NP-hard, we use genetic algorithms, based on random keys and multiple choice encodings, to heuristically solve them. We find that the genetic algorithms consistently outperform a standard mathematical programming package in terms of solution quality and computation time. We also compare the smaller problem instances to a breadth-first tree search algorithm that gives evidence of the quality of the solutions.

New Self-Stabilizing Algorithms for Minimal Weakly Connected Dominating Sets

In this paper, we propose two new self-stabilizing algorithms, MWCDS-C and MWCDS-D, for minimal weakly connected dominating sets in an arbitrary connected graph. Algorithm MWCDS-C stabilizes in O(n4) steps using an unfair central daemon and space requirement at each node is O(log n) bits at each node for an arbitrary connected graph with n nodes; it uses a designated node while other nodes are identical and anonymous. Algorithm MWCDS-D stabilizes using an unfair distributed daemon with identical time and space complexities, but it assumes unique node IDs. In the literature, the best reported stabilization time for a minimal weakly connected dominating set algorithm is O(nmA) under a distributed daemon [1], where m is the number of edges and A is the number of moves to construct a breadth-first tree.

A Linear Time Self-stabilizing Algorithm for Minimal Weakly Connected Dominating Sets

In this paper, we propose a new self-stabilizing algorithm for minimal weakly connected dominating sets (called algorithm $$\mathtt{MWCDS}$$MWCDS). For an arbitrary connected graph with $$n$$n nodes, algorithm $$\mathtt{MWCDS}$$MWCDS terminates in $$O(n)$$O(n) steps using a synchronous daemon. The space requirement at each node is $$O(\log n)$$O(logn) bits. In the literature, the best reported stabilization time for a minimal weakly connected dominating set algorithm is $$O(nmA)$$O(nmA) under a distributed daemon, where $$m$$m is the number of edges and $$A$$A is the number of moves to construct a breadth-first tree.

Distributed Vertex Cover Algorithms for Wireless Sensor Networks

Vertex covering has important applications for wireless sensor networks such as monitoring link failures, facility location, clustering, and data aggregation. In this study, we designed three algorithms for constructing vertex cover in wireless sensor networks. The first algorithm, which is an adaption of the Parnas & Ron's algorithm, is a greedy approach that finds a vertex cover by using the degrees of the nodes. The second algorithm finds a vertex cover from graph matching where Hoepman's weighted matching algorithm is used. The third algorithm firstly forms a breadth-first search tree and then constructs a vertex cover by selecting nodes with predefined levels from breadth-first tree. We show the operation of the designed algorithms, analyze them, and provide the simulation results in the TOSSIM environment. Finally we have implemented, compared and assessed all these approaches. The transmitted message count of the first algorithm is smallest among other algorithms where the third algorithm has turned out to be presenting the best results in vertex cover approximation ratio.

다중로봇 협업감시 시스템에서 트리 탐색 기법을 활용한 다중표적 위치 좌표 추정

This paper proposes the location estimation techniques of distributed targets with the multi-sensor data perceived through IR sensors of the military robots. In order to match up targets with measured azimuths, we apply the maximum likelihood (ML), depth-first, and breadth-first tree search algorithms, in which we use the measured azimuths and the number of pixels on IR screen for pruning branches and selecting candidates. After matching up targets with azimuths, we estimate the coordinate of each target by obtaining the intersection point of the azimuths with the least square error (LSE) algorithm. The experimental results show the probability of missing target, mean of the number of calculating nodes, and mean error of the estimated coordinates of the proposed algorithms.

A self-stabilizing algorithm for constructing weakly connected minimal dominating sets

This paper presents a new distributed self-stabilizing algorithm for the weakly connected minimal dominating set problem. It assumes a self-stabilizing algorithm to compute a breadth-first tree. Using an unfair distributed scheduler the algorithm stabilizes in at most O ( n m A ) moves, where A is the number of moves to construct a breadth-first tree. All previously known algorithms required an exponential number of moves.

Relaxed $K$-Best MIMO Signal Detector Design and VLSI Implementation

Signal detector is a key element in a multiple-input multiple-output (MIMO) wireless communication receiver. It has been well demonstrated that nonlinear tree search MIMO detectors can achieve near-optimum detection performance, nevertheless their efficient high-speed VLSI implementations are not trivial. For example, the hardware design of hard- or soft- output detectors for a 4 times 4 MIMO system with 64 quadrature amplitude modulation (QAM) still remains missing in the open literature. As an attempt to tackle this challenge, this paper presents an implementation-oriented breadth-first tree search MIMO detector design solution. The key is to appropriately modify the conventional breadth-first tree search detection algorithm in order to largely improve the suitability for efficient hardware implementation, while maintaining good detection performance. To demonstrate the effectiveness of the proposed design solution, using 0.13-mum CMOS standard cell and memory libraries, we designed a soft-output signal detector for 4 times 4 MIMO with 64-QAM. With the silicon area of about 31 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , the detector can achieve above 100 Mb/s and realize the performance very close to that of the sphere decoding algorithm

Accuracy and Scaling Phenomena in Internet Mapping

It was recently argued that sampling a network by traversing it with paths from a small number of sources, as with traceroutes on the Internet, creates a fundamental bias in observed topological features like the degree distribution. We examine this bias analytically and experimentally. For Erdo s-Re nyi random graphs with mean degree c, we show analytically that such sampling gives an observed degree distribution P(k) approximately k(-1) for k less, similarc, despite the underlying distribution being Poissonian. For graphs whose degree distributions have power-law tails P(k) approximately k(-alpha), sampling can significantly underestimate alpha when the graph has a large excess (i.e., many more edges than vertices). We find that in order to accurately estimate alpha, one must use a number of sources which grows linearly in the mean degree of the underlying graph. Finally, we comment on the accuracy of the published values of alpha for the Internet.