A modular clutter rejection technique for FLIR imagery using region-based principal component analysis

A modular clutter-rejection technique that uses region-based principal component analysis (PCA) is proposed. A major problem in FLIR ATR is the poorly centered targets generated by the preprocessing stage. Our modular clutter-rejection system usesstatic as well as dynamic region of interest (ROI) extraction to overcome the problem of poorly centered targets. In static ROI extraction, the center of the representative ROI coincides with the center of the potential target image. In dynamic ROI extraction, a representative ROI is moved in several directions with respect to the center of the potential target image to extract a number of ROIs. Each module in the proposed system applies region-based PCA to generate the feature vectors, which are subsequently used to make a decision about the identity of the potential target. Region-based PCA uses topological features of the targets to reject false alarms. In this technique, a potential target is divided into several regions and a PCA is performed on each region to extract regional feature vectors. We propose using regional feature vectors of arbitrary shapes and dimensions that are optimized for the topology of a target in a particular region. These regional feature vectors are then used by a two-class classifier based on the learning vector quantization to decide whether a potential target is a false alarm or a real target. We also present experimental results using real-life data to evaluate and compare the performance of the clutter-rejection systems with static and dynamic ROI extraction.     

Binding-blocking automata

We propose an automaton model called as binding-blocking automaton (BBA). It is a finite state automaton together with the ability to block some symbols and postpone them for reading by the head at a later time. The idea of blocking some symbols from being read by the head and unblocking when the system requires to read is motivated by peptide computing where some parts of peptide sequences are blocked by attaching an antibody with higher affinity and unblocked at a later point by the removal of the appropriate antibody. We study the variants of such systems, analyse the power of each variants and show various hierarchy results involving them. We also define normal-forms of binding-blocking automata for one of its variants and also show that the acceptance power of this variant of BBA is strictly less than that of multi-head finite automata.     

LDPChecker一个实时和混成系统模型检验工具

混成系统是一类复杂系统,线性混成系统作为其重要子类,在形式方法中,人们通常使用线性混成自动机来对它建模．虽然线性混成自动机的模型检验问题总的来说还是不可判定的,但对于其中的正环闭合自动机．其对于线性时段性质的满足性能够通过线性规划方法加以检验．为了实现自动检验正环闭合自动机对线性时段性质的满足性,设计并实现了工具LDPChecker．工具LDPChecker能够识别正环闭合自动机并对其进行相应的检验,其主要特色在于它能够对实时和混成系统检验包含可达性在内的许多实时性质,并且能够自动给出诊断信息．     

Self-assembling modular robotic structures

Cellular automata in the two-dimensional (2-D) world have been used to simulate self-replicating structures based on natural biological systems, evolution, and emergence. In self-replication, large structures consisting of multiple cells are programmed to make copies of themselves using the rule-based systems upon which the cellular automata are governed. Although cellular automata environments are computer-based tools for representing complex systems within a virtual world, the field of modular robotics partly came about as an attempt to create self-relocatable cuboids that could function as cellular automata in the three-dimensional (3-D) real world to create physical self-replicating systems. The application of cellular automata to 3-D real-world applications has resulted in lattice-type modular robotic systems. Lattice systems are cubical in nature, where each cube is a self-mobile unit that can move orthogonally to a vacant position by sliding along existing portions of structure. These lattice-type modular robotics systems have been used to study self-reconfigurability in the real world with autonomous elements.     

Membrane automata with priorities

In this paper the one-way P automata with priorities are introduced. Such automata are P systems where the membranes are only allowed to consume objects from parent membranes, under the given conditions. The result of computation of these systems is the set of multiset sequences consumed by skin membrane into the system. The rules associated in some order with each membrane cannot modify any objects, they can only move them through membrane. We show that P automata with priorities and two membranes can accept every recursively enumerated language.     

细胞自动机语言Cellang的扩展与应用

并行执行与并行描述是并行计算的两个方面,后者是并行软件技术的一个日益重要的问题,目前主流的并行语言都是针对特定的需求设计的。细胞自动机对许多问题具有自然而贴切的描述并行性,作为动力学系统仿真工具与并行离散计算模型近来受到广泛关注。通过对某些细胞自动机语言进行扩展,可以使其成为并行数值计算的良好工具,方便地描述与求解一大类并行数值计算问题。本文介绍我们对细胞自动机语言Cellang的扩展及在并行数值计算上的应用。     

Diagnosis of timed automata: Theory and application to the DAMADICS actuator benchmark problem

This paper concerns the problem of fault diagnosis in discrete-event systems which are represented by timed automata. The diagnostic algorithm for timed automata detects and identifies faults in the system based on the investigation whether the measured input and output sequences are consistent with the timed automaton. This diagnostic approach can be applied spontaneously to the discrete-event system since no a priori information about the initial state of the system is required. It is shown in the paper how the timed automaton which represents the DAMADICS actuator can be obtained and how the diagnostic algorithm based on the timed automaton is applied to detect and identify actuator faults. A representative diagnostic result is presented and discussed to illustrate the effectiveness of the method.     

A learning automata based scheduling solution to the dynamic point coverage problem in wireless sensor networks

The dynamic point coverage problem in wireless sensor networks is to detect some moving target points in the area of the network using as few sensor nodes as possible. One way to deal with this problem is to schedule sensor nodes in such a way that a node is activated only at the times a target point is in its sensing region. In this paper we propose SALA, a scheduling algorithm based on learning automata, to deal with the problem of dynamic point coverage. In SALA each node in the network is equipped with a set of learning automata. The learning automata residing in each node try to learn the maximum sleep duration for the node in such a way that the detection rate of target points by the node does not degrade dramatically. This is done using the information obtained about the movement patterns of target points while passing throughout the sensing region of the nodes. We consider two types of target points; events and moving objects. Events are assumed to occur periodically or based on a Poisson distribution and moving objects are assumed to have a static movement path which is repeated periodically with a randomly selected velocity. In order to show the performance of SALA, some experiments have been conducted. The experimental results show that SALA outperforms the existing methods such as LEACH, GAF, PEAS and PW in terms of energy consumption.     

Constant-space quantum interactive proofs against multiple provers

We present upper and lower bounds of the computational complexity of the two-way communication model of multiple-prover quantum interactive proof systems whose verifiers are limited to measure-many two-way quantum finite automata. We prove that (i) the languages recognized by those multiple-prover systems running in expected polynomial time are exactly the ones in NEXP, the nondeterministic exponential-time complexity class, (ii) if we further require verifiers to be one-way quantum finite automata, then their associated proof systems recognize context-free languages but not beyond languages in NE, the nondeterministic linear exponential-time complexity class, and moreover, (iii) when no time bound is imposed, the proof systems become as powerful as Turing machines. The first two results answer affirmatively an open question, posed by Nishimura and Yamakami [J. Comput. System Sci. 75 (2009) 255–269], of whether multiple-prover quantum interactive proof systems are more powerful than single-prover ones. Our proofs are simple and intuitive, although they heavily rely on an earlier result on multiple-prover classical interactive proof systems of Feige and Shamir [J. Comput. System Sci. 44 (1992) 259–271].     

桥式起重机的有限自动机模型和实现

本文介绍了自动机理论在桥式起重机电气逻辑控制系统中的应用，使得系统的描述、分析和PLC控制软件设计大为方便。     

A comparison of collapsed Bayesian methods for probabilistic finite automata

This paper describes several collapsed Bayesian methods, which work by first marginalizing out transition probabilities, for inferring several kinds of probabilistic finite automata. The methods include collapsed Gibbs sampling (CGS) and collapsed variational Bayes, as well as two new methods. Their targets range over general probabilistic finite automata, hidden Markov models, probabilistic deterministic finite automata, and variable-length grams. We implement and compare these algorithms over the data sets from the Probabilistic Automata Learning Competition (PAutomaC), which are generated by various types of automata. We report that the CGS-based algorithm designed to target general probabilistic finite automata performed the best for any types of data.     

Symbolic model checking for probabilistic timed automata

Probabilistic timed automata are timed automata extended with discrete probability distributions, and can be used to model timed randomised protocols or fault-tolerant systems. We present symbolic model-checking algorithms for probabilistic timed automata to verify both qualitative temporal logic properties, corresponding to satisfaction with probability 0 or 1, and quantitative properties, corresponding to satisfaction with arbitrary probability. The algorithms operate on zones, which represent sets of valuations of the probabilistic timed automaton’s clocks. Our method considers only those system behaviours which guarantee the divergence of time with probability 1. The paper presents a symbolic framework for the verification of probabilistic timed automata against the probabilistic, timed temporal logic PTCTL. We also report on a prototype implementation of the algorithms using Difference Bound Matrices, and present the results of its application to the CSMA/CD and FireWire root contention protocol case studies.     

DNA splicing: computing by observing

Motivated by several techniques for observing molecular processes in real-time we introduce a computing device that stresses the role of the observer in biological computations and that is based on the observed behavior of a splicing system. The basic idea is to introduce a marked DNA strand into a test tube with other DNA strands and restriction enzymes. Under the action of these enzymes the DNA starts to splice. An external observer monitors and registers the evolution of the marked DNA strand. The input marked DNA strand is then accepted if its observed evolution follows a certain expected pattern. We prove that using simple observers (finite automata), applied on finite splicing systems (finite set of rules and finite set of axioms), the class of recursively enumerable languages can be recognized.     

Maximal Lifetime Scheduling for Sensor Surveillance Systems with K Sensors to One Target

This paper addresses the maximal lifetime scheduling for sensor surveillance systems with K sensors to 1 target. Given a set of sensors and targets in an Euclidean plane, a sensor can watch only one target at a time and a target should be watched by k, k geq 1, sensors at any time. Our task is to schedule sensors to watch targets and pass data to the base station, such that the lifetime of the surveillance system is maximized, where the lifetime is the duration up to the time when there exists one target that cannot be watched by k sensors or data cannot be forwarded to the base station due to the depletion of energy of the sensor nodes. We propose an optimal solution to find the target watching schedule for sensors that achieves the maximal lifetime. Our solution consists of three steps: 1) computing the maximal lifetime of the surveillance system and a workload matrix by using linear programming techniques, 2) decomposing the workload matrix into a sequence of schedule matrices that can achieve the maximal lifetime, and 3) determining the sensor surveillance trees based on the above obtained schedule matrices, which specify the active sensors and the routes to pass sensed data to the base station. This is the first time in the literature that this scheduling problem of sensor surveillance systems has been formulated and the optimal solution has been found. We illustrate our optimal method by a numeric example and experiments in the end.     

A Parallel Computational Model for Heterogeneous Clusters

This paper addresses the maximal lifetime scheduling for sensor surveillance systems with K sensors to 1 target. Given a set of sensors and targets in an Euclidean plane, a sensor can watch only one target at a time and a target should be watched by k, kges1, sensors at any time. Our task is to schedule sensors to watch targets and pass data to the base station, such that the lifetime of the surveillance system is maximized, where the lifetime is the duration up to the time when there exists one target that cannot be watched by k sensors or data cannot be forwarded to the base station due to the depletion of energy of the sensor nodes. We propose an optimal solution to find the target watching schedule for sensors that achieves the maximal lifetime. Our solution consists of three steps: 1) computing the maximal lifetime of the surveillance system and a workload matrix by using linear programming techniques, 2) decomposing the workload matrix into a sequence of schedule matrices that can achieve the maximal lifetime, and 3) determining the sensor surveillance trees based on the above obtained schedule matrices, which specify the active sensors and the routes to pass sensed data to the base station. This is the first time in the literature that this scheduling problem of sensor surveillance systems has been formulated and the optimal solution has been found. We illustrate our optimal method by a numeric example and experiments in the end     

Notes on spiking neural P systems and finite automata

Spiking neural P systems (in short, SN P systems) are membrane computing models inspired by the pulse coding of information in biological neurons. SN P systems with standard rules have neurons that emit at most one spike (the pulse) each step, and have either an input or output neuron connected to the environment. A variant known as SN P modules generalize SN P systems by using extended rules (more than one spike can be emitted each step) and a set of input and output neurons. In this work we continue relating SN P modules and finite automata. In particular, we amend and improve previous constructions for the simulatons of deterministic finite automata and state transducers. Our improvements reduce the number of neurons from three down to one, so our results are optimal. We also simulate finite automata with output, and we use these simulations to generate automatic sequences.     

Control of Nondeterministic Discrete Event Systems for Simulation Equivalence

This paper studies supervisory control of discrete event systems subject to specifications modeled as nondeterministic automata. The control is exercised so that the controlled system is simulation equivalent to the (nondeterministic) specification. Properties expressed in the universal fragment of the branching-time logic can equivalently be expressed as simulation equivalence specifications. This makes the simulation equivalence a natural choice for behavioral equivalence in many applications and it has found wide applicability in abstraction-based approaches to verification. While simulation equivalence is more general than language equivalence, we show that existence as well as synthesis of both the target and range control problems remain polynomially solvable. Our development shows that the simulation relation is a preorder over automata, with the union and the synchronization of the automata serving as an infimal upperbound and a supremal lowerbound, respectively. For the special case when the plant is deterministic, the notion of state-controllable-similar is introduced as a necessary and sufficient condition for the existence of similarity enforcing supervisor. We also present conditions for the existence of a similarity enforcing supervisor that is deterministic.     

Fuzzy automata system with application to target recognition based on image processing

In order to get better image processing and target recognition, this paper presents a fuzzy automata system to target recognition. The system first performs image processing, and then accomplishes the target recognition. The system consists of four parts: image preprocessing, feature extraction, target matching and experiment. Compared with existing approaches, this paper uses both global features and local features of the target image, and carries out target recognition by using a fuzzy automata system. Simulation results show that the correct recognition rate based on the fuzzy automata system for target recognition is higher at 94.59%, an improvement on an average of 29.24%, compared to other existing approaches. Finally, some directions for future research are described.     

P Automata with membrane channels

We investigated a variant of purely communicating P systems that are able to accept multisets or even strings given as sequences of terminal symbols taken from the environment. We showed that such P automata with membrane channels equipped with only one membrane and specific activating and prohibiting rules can already recognize any recursively enumerable language of multisets and strings, respectively. Moreover, using only activating rules of a very special kind, we obtained a characterization of regular languages. This work was presented, in part, at the 8th International Symposium on Artificial Life and Robotics, Oita, Japan, January 24#x2013;26, 2003.     

Membrane systems with carriers

A membrane system is a model of computation which is inspired by some basic features of biological membranes. In this paper we consider another biologically inspired notion, viz., the notion of a carrier (or vehicle), as, e.g., used in gene cloning. We investigate the power of membrane systems where the rules for the evolving of objects are replaced by the rules that carry objects (by vehicles) through membranes. It turns out that these systems (even with a small number of membranes, a small number of carriers, and a small number of passengers taken by carriers) are computationally universal.