sequential finite state machines

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Decomposition of Finite State Machines for Area, Delay Minimization

Decomposition of Finite State Machines for Area, Delay Minimization

Finite State Machines (FSMs) are important components of digital systems. Therefore, techniques for area efficient and fast implementation of FSMs are of great interest. The implementation of an FSM is strongly determined by the way codes are assigned to the states of an FSM. The state assign- ment problem can be stated as that of assigning codes to the states of a finite state machine while optimizing a given crite- rion. The state assignment problem has received considerable attention from researchers, because it is an important step in the process of sequential circuit synthesis. Some of the re- ported state assignment tools are NOVA [5] for area mini- mization of PLA implementations; JEDI [6] for multilevel implementations. Classical approaches for reduction in the
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A Tool for Converting Finite State Machines to SystemC

A Tool for Converting Finite State Machines to SystemC

The behavior of computer systems can be described and analyzed by means of transition systems. Understanding and gaining more insight by inspecting these systems can be of great advantage when constructing complicated systems. The most commonly used transition systems are based on explicit state enumeration known as finite state machines (FSM). FSMs are a basic component of hardware designs, they represent the transformation between inputs and outputs for sequential designs. FSMs can be represented graphically, which would help the designer to visualize and design in a more efficient way. The designer requires a fast direct way to convert the visualized design to hardware description languages (HDL) code directly in order to simulate and implement it. CAD tools support built-in visualization software or interfaces to third party software. In the work presenting in the report, we use Active HDL State Editor [2] to draw and visualize the FSM. Active HDL [14] outputs a textual representation of the FSM in ASF (Active-HDL State machines Format) [2]. The proposed tool takes as input an ASF file and generates SystemC [4] code which can be used for simulation and analysis in SystemC environment.
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12. Finite-State Machines

12. Finite-State Machines

Before we can "implement" such a diagram, we must be clearer on what items correspond to changes of input, output, and state. The combinational logical elements, such as AND- gates and OR-gates, as discussed earlier are abstractions of physical devices. In those devices, the logical values of 0 and 1 are interpretations of physical states. The output of a device is a function of its inputs, with some qualification. No device can change state instantaneously. When the input values are first presented, the device's output might be in a different state from that indicated by the function. There is some delay time or switching time associated with the device that must elapse before the output stabilizes to the value prescribed by the function. Thus, each device has an inherent sequential behavior, even if we choose to think of it as a combinational device.
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-structures Applied to Finite State Machines

-structures Applied to Finite State Machines

I N 1965, Zadeh [15] introduced the notion of fuzzy subset of a set. Since then, the theory of fuzzy sets has become a vigorous area of research in different disciplines includ- ing medical and life sciences, management sciences, social sciences, engineering, statistics, graph theory, artificial in- telligence, pattern recognition, robotics, computer networks, decision making and automata theory. The mathematical formulation of a fuzzy automaton was first proposed by Wee [14] in 1967. Santos [13] proposed fuzzy auomata as a model of pattern recognition. Malik et al. [6] introduced the notions of submachine of a fuzzy finite state machine, retrievable, separated and connected fuzzy finite state machines and discussed their basic properties. They also initiated a decom- position theorem for fuzzy finite state machiness in terms of primary submachines. On the other hand, Kumbhojkar and Chaudhari [4] provided several ways of constructing products of fuzzy finite state machines and their mutual relationships, through isomorphism and coverings. Li and Pedrycz [5] indicated that fuzzy finite state automata can be viewed as a mathematical model of computation in fuzzy systems.
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RoutineSense: A Mobile Sensing Framework for the Reconstruction of User Routines

RoutineSense: A Mobile Sensing Framework for the Reconstruction of User Routines

The time of the activity is represented by the timestamps at which the activity started and ended. Staypoints are de- fined as the locations at which the user stayed for a minimum amount of time to perform an activity. They are generated based on a finite state machine using geofencing and GPS data to determine the state of the user. Due to the im- precision in GPS readings and the continuous nature of the spatial domain, staypoints need to be clustered in order to detect recurrent visits of the same place. The clustering will allow in a second time to avoid having to character- ize a new staypoint if it belongs to an already characterized cluster. This problem is handled by using the ESOINN algo- rithm [18] to cluster staypoints. ESOINN possesses several suitable features to this purpose such as being incremental, unsupervised and does not require to know apriori the num- ber of clusters. The output of the algorithm represents the unique places visited by the user. However, some modifica- tions were required to better fit the needs of routineSense. These modifications include the removal of the forgetting mechanism of ESOINN in order to retains all the data, modi- fication of the density function in order to simplify the com- putation, therefore making it suitable for mobile comput- ing, and generation of consistent cluster labels to reuse from one increment to another. Once the staypoints are acquired and clustered, we enrich them semantically by attributing a venue to each cluster. The attribution is done by querying location-based services such as Foursquare or Google places to get a list of the k-closest venues. The venues are then ordered based on the frequency of past visits of the user. While this order requires a bootstrap phase involving user feedback, it will eventually converge to a user-specific order-
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CS 61C: Great Ideas in Computer Architecture Finite State Machines. Machine Interpreta4on

CS 61C: Great Ideas in Computer Architecture Finite State Machines. Machine Interpreta4on

•  Insertion of register allows higher clock frequency.. "[r]

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A Control System Retrofit for a Plastic Bag Making Machine

A Control System Retrofit for a Plastic Bag Making Machine

When the system is energized the system will be in state S0, due to the default transition shown (a line with a down arrow into S0). At state S0, all outputs are off. When the start_in signal is received, the system will move to state S1, and also start the motor. The system will move to state S2 when it receives the brake pulse signal from the proximity. At state S2, the outputs; brake signal (brk) and the high dc (hdc) are turned on. The transition out of state S2 has a connective junction which determines which of the two transitions is taken. The stateflow model always evaluates the transition on the labeled transition first. Therefore, in this case, it will first check whether the stop command is given (start_in is low) before taking the alternate transition to state S3. If the start signal is still on, it moves to state S3, where the hdc signal is turned off. When the clutch signal is received (brake pulse low), it moves to state S4, where brk output is turned off and clutch signal (cl) is turned on. At the same time the hdc signal is turned on. After expiry of the time delay, it moves to state S5, where the hdc signal is turned off. When the next brake pulse OR the system receives a signal from the photo circuit, it moves to state S1, and the cycle repeats. The photo circuit consists of the EXOR gate with two inputs to select clear or reverse pattern from the photo sensor, and a photo-cam proximity sensor to time when the brake is to be applied. Figs 7 and 8 showed the results of the simulation without and with the photo cam pulses. It can be seen that the model follow the desired control.
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On Detectability and Recoverability of Input State Asynchronous Sequential Machines with Transient Faults in Non-fundamental Mode

On Detectability and Recoverability of Input State Asynchronous Sequential Machines with Transient Faults in Non-fundamental Mode

Abstract—Corrective control has been successfully applied to compensating for faulty behaviors of asynchronous sequential machines. In this paper, a corrective control scheme is studied for dealing with transient faults that may happen in non- fundamental mode operations. Since a fault can occur to a machine in transient transitions, the procedure of fault diagnosis and fault tolerant control should be more complicated compared with the case of fundamental mode. We show that certain properties, called detectability and recoverability, are requisites for the existence of a fault tolerant corrective con- troller that makes the closed-loop system immune against any fault occurrence in non-fundamental mode. A simple example is provided to illustrate the proposed notions and the controller existence.
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Finite State Processing of Tone Systems

Finite State Processing of Tone Systems

sion treats phonological features as I / O tapes for Finite State Transducers in a parallel sequential incrementation PSI architecture; phonological.. phonological rules or metrical syst[r]

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Volume 30: Graph and Model Transformation 2010

Volume 30: Graph and Model Transformation 2010

Model transformation processes are nondeterministic in three respects. First, the rule applica- tions in graph model components are nondeterministic as some rules may be applicable at several matches. Second, although the operations of the basic types are functional, the evaluations of the action terms of these types may not lead to unique values as the terms can contain free variables with a variety of instantiations. Third, there may be a choice of many actions that can process a current model, and the only regulating requirement for actions is that of sequential composition, which is that one action is executed after the other. Sometimes such nondeterminism is desired, convenient, or unavoidable. But in other cases one would like to avoid nondeterminism, or cut it down at least. This can be achieved by choosing rules and actions in such a way that only one or a few of them can be applied and performed. But the rules and actions may become quite complicated. Another possibility is extra regulation which can be provided by control conditions. Definition 4 (control conditions) Let A be a set of actions. Then C is a class of control condi- tions if SEM(c) ⊆ MTP(A) for every c ∈ C .
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Architectural state-machines

Architectural state-machines

The installations described have been placed in the real world and inevitably have to deal with incomplete and conflicting data. In the responsive fiber optic field sensing the lighting environment has a structure and is attached to concepts of behavior through the state- machine model. Like the natural system that inspired its design, there is a redundancy in the field's hundreds of networked sensors that bring a greater certainty and robustness in responding to environmental changes. It appears at different scales, from responding to one light level in a single direction at one point in time to the sum of the whole field in its entire history of operation.
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TESTING WEB BASED APPLICATIONS USING FINITE STATE MACHINES EMPLOYING GENETIC ALGORITHM

TESTING WEB BASED APPLICATIONS USING FINITE STATE MACHINES EMPLOYING GENETIC ALGORITHM

Finite state machines (FSMs) provide a convenient way to model software behavior. Several methods has been proposed for deriving tests from FSMs. Theoretically, Web applications can be completely modeled with FSMs as the web pages have the data, if data is there then this have the states and if states are there, they easily represented using the FSMs. However, there may be large possible inputs to text fields, a large number of options on some Web pages, and choices as to the order in which information can be entered. Factors such as these mean that a finite state machine can become prohibitively large, even for a single page. Thus, an FSM- based testing method can only be used if techniques are found to generate FSMs that are descriptive enough to yield effective tests yet small enough to be practically useful [2].
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Notes on counting with finite machines

Notes on counting with finite machines

Second, we study the problems of counting for classes of pushdown automata (PDA). For deterministic PDA, we show in Section 4 that matching upper and lower bounds of O(log n) and Ω(log n) for all n follow from earlier results due to Pighizzini [19] and Chistikov and Majumdar [7]. Subclasses of PDA, however, require separate consideration. For deterministic counter automata, i. e., when the stack alphabet contains only one symbol apart from the bottom-of-stack, we obtain an upper bound of O( √ n) and show a matching lower bound Ω( √ n) by reducing to counting with deterministic finite automata (DFA). For alternating counter automata, we prove an upper bound of O(log n) (here we use a complexity measure that is more refined than just the number of states, so this result is not subsumed by the fact, due to Kupferman et al., that alternating finite automata count to n with dlog ne states).
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Encryptions of Data Streams using Pauli Spin ½ Matrices and Finite State Machine

Encryptions of Data Streams using Pauli Spin ½ Matrices and Finite State Machine

Cryptography is the science of transmission and reception of secret messages. Recently electronic communication has become an essential part of every aspect of human life. Message encryption has become very essential to avoid the threat against possible attacks by hackers during transmission process of the message. Finite state machines (FSM), also known as finite state automation (FSA), at their simplest, are models of the behaviors of a system or a complex object, with a limited number of defined conditions or modes, where mode transitions change with circumstance. In the present paper, new cryptographic scheme is proposed using finite state machine and Pauli spins ½ matrices.
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Input Output Switched Asynchronous Sequential Machines with Transient Faults

Input Output Switched Asynchronous Sequential Machines with Transient Faults

Abstract—Robust model matching control of input/output switched asynchronous sequential machines is addressed in this paper. The control objective is to determine the existence con- dition and design algorithm for a corrective controller that can match the stable-state behavior of the closed-loop system to that of a reference model, while invalidating any transient faults that cause unauthorized state transitions. Switching operations and correction procedures are incorporated using output feedback so that the controlled switched machine can show the desired input/output behavior and fault tolerance. A matrix expression is presented to address reachability of switched asynchronous sequential machines with output equivalence with respect to a model. The proposed reachability condition for the existence of a controller and its design procedure are outlined in a simple example.
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Medical Device Interoperability With Provable Safety Properties

Medical Device Interoperability With Provable Safety Properties

We introduce Clinical Application Modeling Language (CAML), based on Extended Finite State Machines, and use model checking to test safety properties from the hazard analysis against the[r]

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Neural Machine Translation Decoding with Terminology Constraints

Neural Machine Translation Decoding with Terminology Constraints

2017; Hokamp and Liu, 2017; Crego et al., 2016). Anderson et al. address the task of image cap- tioning with constrained beam search where con- straints are given by image tags and constraint permutations are encoded in a finite-state accep- tor (FSA). Hokamp and Liu propose grid beam search to enforce target-side constraints for do- main adaptation via terminology. However, since there is no correspondence between constraints and the source words they cover, correct constraint placement is not guaranteed and the corresponding source words may be translated more than once. Crego et al. replace entities with special tags that remain unchanged during translation and are re- placed in a post-processing step using attention weights. Given good alignments, this method can translate entities correctly but it requires training data with entity tags and excludes the entities from model scoring.
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An algorithm to generate compliance monitors from regulations

An algorithm to generate compliance monitors from regulations

Developing software systems in heavily regulated industries requires methods to ensure systems comply with regulations and law. An algorithm to generate finite state machines (FSM) from stakeholder rights and obligations for compliance monitoring is proposed. Rights and obligations define what people are permitted or required to do; these rights and obligations affect software requirements and design. The FSM allows stakeholders, software developers and compliance officers to trace events through the invocation of rights and obligations as pre- and post- conditions. Compliance is monitored by instrumenting runtime systems to report these events and detect violations. Requirements and software engineers specify the rights and obligations, and our algorithm performs three supporting tasks: 1) identify ambiguities, 2) balance rights with obligations, and 3) generate finite state machines. Preliminary validation of the algorithm includes FSMs generated from U.S. healthcare regulations and tool support to parse these specifications and generate the FSMs.
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Fault Tolerance for Input Faults in a Class of Asynchronous Sequential Machines

Fault Tolerance for Input Faults in a Class of Asynchronous Sequential Machines

In this paper, we propose a fault-tolerant corrective con- trol scheme to tolerate input faults that cause unauthorized changes of input values. We suppose that not only the exter- nal input to the controller but also the control input generated by the controller is influenced by the input fault. Unless counteracted immediately, further change of the input would violate the desired behavior. Based on the corrective control scheme for model matching, we present necessary and suf- ficient conditions for the existence of a corrective controller that invalidates any input fault occurring to the asynchronous machine. The closed-loop system will be driven to follow a reference model as if no input fault occurs. Note that all the prior works on fault-tolerant corrective control [7]–[12] focus on state transition faults and do not consider detection and tolerance methodologies for input faults.
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The Influence of Context on the Learning of Metrical Stress Systems Using Finite State Machines

The Influence of Context on the Learning of Metrical Stress Systems Using Finite State Machines

systems, this means that a rule parsing syllables into stressed feet should only be able to refer to any one of a syllable’s two immediately adjacent neighbors. The observation by Heinz is that any such combination of three syllables should only occur once in the phonological grammar, if Kenstowicz was correct in defining locality based on a window of three syllables. Taken together, these observations offer a starting point for creating an artificial learner of metrical stress structure in terms of finite-state automata, which is precisely the effort that the present article undertakes. The key variable that is investigated is the size of the context over which the learner will generalize: How much context should the learner take into account before making a generalization about a grammatical pattern? Phonologically speaking, this question seeks an empirical definition of locality; in automata-theoretic terms, the question asks how much overlap there needs to be between two different substrings before a learner can induce that a token in both substrings corresponds to a common state in an acceptor.
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