Finite State Machine Model Research Articles

Sports Video Structure Analysis and Feature Extraction in Long Jump Video

How to help people to find their favorite sports in the massive video, in order to achieve this goal in the finite state machine (FSM) theory based on in-depth research. Combined with the actual situation of this paper, first, we determined the reasonable FSM model. Then, a fast robust global motion estimation algorithm is used to estimate the global motion of the video sequences, and the foreground is separated from the background by motion compensation. After the foreground of the video, a series of image feature extraction algorithm, which is based on the main color histogram and histogram, is used to extract the low level feature extraction. Finally, the results of the experimental operation, the performance of the algorithm, and the results of the system are presented.

Research on the Dynamic Evolution Behavior of Group Loitering Air Vehicles

Abstract In this paper we focus on integrated Reconnaissance/Strike LAV, in order to reveal the evolution regularity when group LAVs combats cooperatively. The evolution of cooperative behavior of group LAVs, which is described with finite state machine, can be regarded as a conversion process of a LAV in different task states, using the rate equation for probability analysis. Then based on the missions of integration of reconnaissance, attack, and damage effectiveness evaluation, we build the model of finite state machine based on behavior state transition. Solved with Runge-Kutta method. We can analyze how the key technology quota of LAV impact on the operational effectiveness of Group LAVs.the fractional order control approach.

A heuristic transition executability analysis method for generating EFSM-specified protocol test sequences

Automatic executable test sequence generation is a challenging task for protocol test based on the Extended Finite State Machine (EFSM) model. Transition executability analysis (TEA) can guarantee the executability of generated test sequences. However, as a reachability analysis technique, it often suffers from the state explosion problem. To remedy the situation, this paper proposes a heuristic transition executability analysis (HTEA) method for compressing the explored state space during test sequences derivation. In our approach, a transition feasibility guidance matrix is first constructed through extracting the inherent constraint relationship between each adjacent transitions in the EFSM under testing. Then, relying on the built matrix and related runtime feedback, we define an objective function to dynamically evaluate state nodes in the HTEA expanding tree. Finally, the proposed objective function is utilized to heuristically guide the state space traversal in the HTEA tree for executable test sequences generation. Consequently, the executable test generation problem can be reformulated into a multi-objective search problem based on a HTEA tree. Experimental results impressively show the potential of the novel method for avoiding the state explosion during test generation, compared with the classic TEA technique in the breath-first way.

Geometric analysis of pathways dynamics: Application to versatility of TGF-β receptors

We propose a new geometric approach to describe the qualitative dynamics of chemical reactions networks. By this method we identify metastable regimes, defined as low dimensional regions of the phase space close to which the dynamics is much slower compared to the rest of the phase space. These metastable regimes depend on the network topology and on the orders of magnitude of the kinetic parameters. Benchmarking of the method on a computational biology model repository suggests that the number of metastable regimes is sub-exponential in the number of variables and equations. The dynamics of the network can be described as a sequence of jumps from one metastable regime to another. We show that a geometrically computed connectivity graph restricts the set of possible jumps. We also provide finite state machine (Markov chain) models for such dynamic changes. Applied to signal transduction models, our approach unravels dynamical and functional capacities of signalling pathways, as well as parameters responsible for specificity of the pathway response. In particular, for a model of TGFβ signalling, we find that the ratio of TGFBR2 to TGFBR1 receptors concentrations can be used to discriminate between metastable regimes. Using expression data from the NCI60 panel of human tumor cell lines, we show that aggressive and non-aggressive tumour cell lines function in different metastable regimes and can be distinguished by measuring the relative concentrations of receptors of the two types.

FSM-F: Finite State Machine Based Framework for Denial of Service and Intrusion Detection in MANET.

Due to the continuous advancements in wireless communication in terms of quality of communication and affordability of the technology, the application area of Mobile Adhoc Networks (MANETs) significantly growing particularly in military and disaster management. Considering the sensitivity of the application areas, security in terms of detection of Denial of Service (DoS) and intrusion has become prime concern in research and development in the area. The security systems suggested in the past has state recognition problem where the system is not able to accurately identify the actual state of the network nodes due to the absence of clear definition of states of the nodes. In this context, this paper proposes a framework based on Finite State Machine (FSM) for denial of service and intrusion detection in MANETs. In particular, an Interruption Detection system for Adhoc On-demand Distance Vector (ID-AODV) protocol is presented based on finite state machine. The packet dropping and sequence number attacks are closely investigated and detection systems for both types of attacks are designed. The major functional modules of ID-AODV includes network monitoring system, finite state machine and attack detection model. Simulations are carried out in network simulator NS-2 to evaluate the performance of the proposed framework. A comparative evaluation of the performance is also performed with the state-of-the-art techniques: RIDAN and AODV. The performance evaluations attest the benefits of proposed framework in terms of providing better security for denial of service and intrusion detection attacks.

A Test Generation Method for EFSM-based Protocols Using the Transitions Feasibility Estimation

Although extensive studies have been done on the protocol conformance testing based on an extended finite state machine (EFSM) model, the automatic generation of feasible test sequences is still a challenging task. A generated test sequence may be infeasible, due to the conflicts among the transition conditions and context variables of EFSMs. This paper proposed a test sequence generation approach for EFSM-based protocols conformance test by using the transition feasibility estimation. Firstly, our method generates candidate transition paths from a directed graph G which is derived from the EFSM model under testing ignoring all the predicates; Then, we designed a fitness function to guide the test generation with a trade-off among path feasibility, coverage criterion and path length. Finally, an adaptive exploration algorithm is developed to generate executable test sequences through expanding CPs. An experiment was designed to validate the effectiveness of the proposed method with two classic EFSM models. The experimental results show that our approach is more effective by comparing it to the TEA method based on breadth-first search (BFS).

Toward automatically quantifying the impact of a change in systems

Software maintenance is becoming more challenging with the increased complexity of the software and the frequently applied changes. Performing impact analysis before the actual implementation of a change is a crucial task during system maintenance. While many tools and techniques are available to measure the impact of a change at the code level, only a few research work is done to measure the impact of a change at an earlier stage in the development process. Measuring the impact of a change at the model level speeds up the maintenance process allowing early discovery of critical components of the system before applying the actual change at the code level. In this paper, we present model-based impact analysis approach for state-based systems such as telecommunication or embedded systems. The proposed approach uses model dependencies to automatically measure the expected impact for a requested change instead of relying on the expertise of system maintainers, and it generates two impact sets representing the lower bound and the upper bound of the impact. Although it can be extended to other behavioral models, the presented approach mainly addresses extended finite-state machine (EFSM) models. An empirical study is conducted on six EFSM models to investigate the usefulness of the proposed approach. The results show that on average the size of the impact after a single modification (a change in a one EFSM transition) ranges between 14 and 38 % of the total size of the model. For a modification involving multiple transitions, the average size of the impact ranges between 30 and 64 % of the total size of the model. Additionally, we investigated the relationships (correlation) between the structure of the EFSM model, and the size of the impact sets. Upon preliminary analysis of the correlation, the concepts of model density and data density were defined, and it was found that they could be the major factors influencing the sizes of impact sets for models. As a result, these factors can be used to determine the types of models for which the proposed approach is the most appropriate.

A hybrid epidemic model for deindividuation and antinormative behavior in online social networks

With the increasing popularity of user-contributed sites, the phenomenon of “social pollution”, the presence of abusive posts has become increasingly prevalent. In this paper, we describe a novel approach to investigate negative behavior dynamics in online social networks as epidemic phenomena. We show that using hybrid automata, it is possible to explain the contagion of antinormative behavior in certain online commentaries. We present two variations of a finite-state machine model for time-varying epidemic dynamics, namely triggered state transition and iterative local regression, which differ with respect to accuracy and complexity.We validate the model with experiments over a dataset of 400,000 comments on 800 YouTube videos, classified by genre, and indicate how different epidemic patterns of behavior can be tied to specific interaction patterns among users.

Impact of baseline characteristics on outcomes of carotid artery stenting in acute ischemic stroke patients.

Carotid artery stenting is an effective treatment for ischemic stroke patients with moderate-to-severe carotid artery stenosis. However, the midterm outcome for patients undergoing this procedure varies considerably with baseline characteristics. To determine the impact of baseline characteristics on outcomes following carotid artery stenting, data from 107 eligible patients with a first episode of ischemic stroke were collected by retrospective chart review. A modified Rankin Scale (mRS) was used to divide patients into two baseline groups, mRS ≤2 and mRS >2. A three-step decision-tree statistical analysis was conducted. After weighting the decision-tree parameters, the following impact hierarchy was obtained: admission low-density lipoprotein, gouty arthritis, chronic kidney disease, ipsilateral common carotid artery resistance index, contralateral ophthalmic artery resistance index, sex, and dyslipidemia. The finite-state machine model demonstrated that, in patients with baseline mRS ≤2, 46% had an improved mRS score at follow-up, whereas 54% had a stable mRS score. In patients with baseline mRS >2, a stable mRS score was observed in 75%, improved score in 23%, and a poorer score in 2%. Admission low-density lipoprotein was the strongest predictive factor influencing poststenting outcome. In addition, our study provides further evidence that carotid artery stenting can be of benefit in first-time ischemic stroke patients with baseline mRS scores >2.

Генерация функциональных тестов для HDL-описаний на основе проверки моделей

Automated test generation is a promising direction in hardware verification research area. Functional test generation methods based on models are widespread at the moment. In this paper, a functional test generation method based on model checking is proposed and compared to existing solutions. Automated model extraction from the hardware design’s source code is used. Supported HDLs include VHDL and Verilog. Several kinds of models are used at different steps of the test generation method: guarded action decision diagram (GADD), high-level decision diagram (HLDD) and extended finite-state machine (EFSM). The high-level decision diagram model (which is extracted from the GADD model) is used as a functional model. The extended finite-state machine model is used as a coverage model. The aim of test generation is to cover all the transitions of the extended finite state machine model. Such criterion leads to the high HDL source code coverage. Specifications based on transition and state constraints of the EFSM are extracted for this purpose. Later, the functional model and the specifications are automatically translated into the input format of the nuXmv model checking tool. nuXmv performs model checking and generates counterexamples. These counterexamples are translated to functional tests that can be simulated by the HDL simulator. The proposed method has been implemented as a part of the HDL Retrascope framework. Experiments show that the method can generate shorter tests than the FATE and RETGA methods providing the same or better source code coverage.

An automated software reliability prediction system for safety critical software

Software reliability is one of the most important software quality indicators. It is concerned with the probability that the software can execute without any unintended behavior in a given environment. In previous research we developed the Reliability Prediction System (RePS) methodology to predict the reliability of safety critical software such as those used in the nuclear industry. A RePS methodology relates the software engineering measures to software reliability using various models, and it was found that RePS's using Extended Finite State Machine (EFSM) models and fault data collected through various software engineering measures possess the most satisfying prediction capability. In this research the EFSM-based RePS methodology is improved and implemented into a tool called Automated Reliability Prediction System (ARPS). The features of the ARPS tool are introduced with a simple case study. An experiment using human subjects was also conducted to evaluate the usability of the tool, and the results demonstrate that the ARPS tool can indeed help the analyst apply the EFSM-based RePS methodology with less number of errors and lower error criticality.

Active vibration control of a full scale aircraft wing using a reconfigurable controller

This work highlights the design of a Reconfigurable Active Vibration Control (AVC) System for aircraft structures using adaptive techniques. The AVC system with a multichannel capability is realized using Filtered-X Least Mean Square algorithm (FxLMS) on Xilinx Virtex-4 Field Programmable Gate Array (FPGA) platform in Very High Speed Integrated Circuits Hardware Description Language, (VHDL). The HDL design is made based on Finite State Machine (FSM) model with Floating point Intellectual Property (IP) cores for arithmetic operations. The use of FPGA facilitates to modify the system parameters even during runtime depending on the changes in user’s requirements. The locations of the control actuators are optimized based on dynamic modal strain approach using genetic algorithm (GA). The developed system has been successfully deployed for the AVC testing of the full-scale wing of an all composite two seater transport aircraft. Several closed loop configurations like single channel and multi-channel control have been tested. The experimental results from the studies presented here are very encouraging. They demonstrate the usefulness of the system’s reconfigurability for real time applications.

Modeling and Recognition of Software Applications using FSM

Finite State Machines (FSMs) are mathematical abstractions which have a rich use in computer applications. If we build a FSM for software, then it becomes much easier and simpler to understand, debug and modify. The insights developed in FSMs have had great influence on various domains. FSM is as important as any other computer science tool. This paper minimizes the gap which currently exists between software development and the formal method of theoretical computer science. So importance of FSMs has been explored over various such domains in this article. These domains include spoken web technology that enables a user to access a massive network of voice sites through speech. Information transfer protocol for Vehicular Computing is another domain, where users can get on road support services using vehicular sensors and global position system. Adding on to the domain list, we have modeled FSMs for Ankle Monitor and Sensor Dust which are applications of mobile ad-hoc networks. Ankle monitor is primarily used to track movements of an individual through wireless communication and Sensor Dusts are tiny sensors which monitor the environment in which they are deployed. We have also modeled Palm Operating System, which is a mobile OS that runs on Linux kernel. This paper presents the FSM for the booting sequence and the user interface of Palm OS. To conclude with we have taken up the domain of satellite simulation and have presented a FSM for the steps involved in Satellite launching and Image Handling within a satellite. Therefore, by modeling a range of applications using FSM we attempt to add-on to the significance of this concept and at the same time provide a single document comprising numerous FSM models. This concept can be used by software developers for easy modeling of their designs and can further use it to verify and debug the model whenever required.

FSM Model of a Simple Photovoltaic System

The paper describes a simulation model of a simple photovoltaic system intended as a tool for testing the use of finite state machines for simulations representing a long-term operation of renewable energy sources. The mathematical model of the photovoltaic system is described first. Then it is used to build a finite state machine model that calculates a power output of the photovoltaic system for changing values of a solar irradiance and a temperature. Data measured on a real photovoltaic installation are used to verify model’s accuracy through its comparison with a previously created and verified Matlab model. The finite state machine model presented in this paper was created using Ptolemy II software.

QEFSM model and Markov Algorithm for translating Quran reciting rules into Braille code

The Holy Quran is the central religious verbal text of Islam. Muslims are expected to read, understand, and apply the teachings of the Holy Quran. The Holy Quran was translated to Braille code as a normal Arabic text without having its reciting rules included. It is obvious that the users of this transliteration will not be able to recite the Quran the right way. Through this work, Quran Braille Translator (QBT) presents a specific translator to translate Quran verses and their reciting rules into the Braille code. Quran Extended Finite State Machine (QEFSM) model is proposed through this study as it is able to detect the Quran reciting rules (QRR) from the Quran text. Basis path testing was used to evaluate the inner work for the model by checking all the test cases for the model. Markov Algorithm (MA) was used for translating the detected QRR and Quran text into the matched Braille code. The data entries for QBT are Arabic letters and diacritics. The outputs of this study are seen in the double lines of Braille symbols; the first line is the proposed Quran reciting rules and the second line is for the Quran scripts.

Load Modeling For Power System Requirement and Capability Assessment

Load modeling is essential for designing and operating power systems. This paper presents an approach for load modeling on smaller power systems that could be “islanded,” an approach that preserves the detail of a full differential equation simulation of relevant loads while requiring far less computation by employing behavioral models of important loads. Mixed domain models, e.g., stochastic, finite-state machine, and differential equation models, are employed to provide accuracy in a computationally tractable framework. Where simple load models may not be adequate, particularly for generation-constrained systems (in a paper by Sotiropoulos ), and full models are computationally unfavorable, this approach provides excellent results that enable “what-if” studies and flexible re-evaluation during power system design and operational assessment. Naval vessels, particularly warships with relatively large and increasing load power requirements, offer a unique laboratory for understanding isolated power grids. This paper examines the DDG-51 power distribution system as an example.

Effective Extraction of State Invariant for Software Verification

In software design of complex systems, more time and effort are spent on verification than on constructions. Model checking for software verification techniques offer a large potential to obtain and early integration of verification in the design process. This paper describes how to easily specify and the software properties and to understand the software generating automatically invariant. In this paper deal with issue that state invariant is a property that holds in every reachable state. Not only can be used in understanding and analysis of complex software systems. In addition, it can be used for system verifications such as checking safety, consistency, and completeness. For these reasons, there are many vital researches for deriving state invariant from finite state machine models. In this research was to be considered to extract state invariant. Thus it is likely to be too complex for the user to understand. This paper let the user focus on some interested parts (called scopes) rather than a whole state space in a model. Computation Tree Logic (CTL) is used to specify scopes in which he/she is interested. Given a scope in CTL, forward reachability analysis is used to find out a set of states inside it. Obviously, a set of states calculated in this way is a subset of every reachable state. Keywords: Software verification, Invariant, Scopes, Model Checking

Iterated Boolean games

Iterated games are well-known in the game theory literature. We study iterated Boolean games. These are games in which players repeatedly choose truth values for Boolean variables they have control over. Our model of iterated Boolean games assumes that players have goals given by formulae of Linear Temporal Logic (LTL), a formalism for expressing properties of state sequences. In order to represent the strategies of players in such games, we use a finite state machine model. After introducing and formally defining iterated Boolean games, we investigate the computational complexity of their associated game-theoretic decision problems, as well as semantic conditions characterising classes of LTL properties that are preserved by equilibrium points (pure-strategy Nash equilibria) whenever they exist.

Inferring extended finite state machine models from software executions

The ability to reverse-engineer models of software behaviour is valuable for a wide range of software maintenance, validation and verification tasks. Current reverse-engineering techniques focus either on control-specific behaviour (e.g., in the form of Finite State Machines), or data-specific behaviour (e.g., as pre / post-conditions or invariants). However, typical software behaviour is usually a product of the two; models must combine both aspects to fully represent the software's operation. Extended Finite State Machines (EFSMs) provide such a model. Although attempts have been made to infer EFSMs, these have been problematic. The models inferred by these techniques can be non-deterministic, the inference algorithms can be inflexible, and only applicable to traces with specific characteristics. This paper presents a novel EFSM inference technique that addresses the problems of inflexibility and non-determinism. It also adapts an experimental technique from the field of Machine Learning to evaluate EFSM inference techniques, and applies it to three diverse software systems.

Structural models of finite-state machines for their implementation on programmable logic devices and systems on chip

Digital systems based on programmable logic devices and systems on chip often use register buffers for the transmission of signals between functional units. In this paper, structural models of finite state machines (FSMs) that allow the use of input and output buffer triggers as memory elements of the finite state machine are considered. To this end, a new classification of structural models of finite state machines is proposed in which all finite state machines are divided into six classes A, B, C, D, E, and F. In the models C and D, output buffer triggers are used as memory elements; in the models E and F, input buffer triggers are used for this purpose. A definition of the set of internal states of FSMs in each class is given and synthesis methods for the FSMs of the classes C-F are proposed. The results of experiments showed that the use of FSM models of the classes C-F reduces the number of internal memory elements by 2.22% to 25.83% on the average and sometimes even by 100%.