scholarly journals An Interaction Between User and an Augmented Reality System using A Generalized Finite State Automata and A Universal Turing Machine

2019 ◽  
Vol 8 (12) ◽  
pp. 1084-1090
Keyword(s):  
Augmented Reality ◽  
Turing Machine ◽  
Regular Language ◽  
Formal Language ◽  
Linear Complexity ◽  
Finite State Automata ◽  
Augmented Reality System ◽  
Universal Turing Machine ◽  
Finite State ◽  
Single Response

Most interactions between users and augmented reality system (ARS) are that user assigns a marker to ARS, and the ARS responds the marker. In this context, a marker is mapped to an ARS's response, or in general, an array of markers is mapped to an array of ARS's responses. This interaction is a constant or linear complexity interaction since there is only a bijective mapping between a set of markers and a set of ARS's responses. In this research, we propose the expansion of user - ARS complexity into the polynomial. It is an interaction in which not only one marker for a single response (or an array of markers for an array of ARS's responses), but the interaction by which user provides a string of markers as a word of markers (i.e., a combination of multiple markers as a word) for a single ARS's response. The set of strings of markers to the ARS provided by users built a regular language. So that, the complexity of the user-ARS interaction became polynomial. This interaction was implemented by stating the user's language by means of a generalization of finite state automata (gFSA) and placing a universal Turing machine (UTM) between user and ARS, where the UTM as an interpreter translating or mapping the user language to ARS. To summarize our research, overall we apply the idea of a formal language into the interaction between the user and ARS, thereby changing the complexity of the interaction to polynomial even expandable to nondeterministic polynomials.

Finite-State Technology

2012 ◽  
Author(s):  
Lauri Karttunen
Keyword(s):  
Language Processing ◽  
Regular Language ◽  
Morphological Analysis ◽  
Finite State Automata ◽  
Regular Expressions ◽  
Finite State Transducers ◽  
Finite State ◽  
Shallow Parsing ◽  
Basic Concepts ◽  
State Language

The article introduces the basic concepts of finite-state language processing: regular languages and relations, finite-state automata, and regular expressions. Many basic steps in language processing, ranging from tokenization, to phonological and morphological analysis, disambiguation, spelling correction, and shallow parsing, can be performed efficiently by means of finite-state transducers. The article discusses examples of finite-state languages and relations. Finite-state networks can represent only a subset of all possible languages and relations; that is, only some languages are finite-state languages. Furthermore, this article introduces two types of complex regular expressions that have many linguistic applications, restriction and replacement. Finally, the article discusses the properties of finite-state automata. The three important properties of networks are: that they are epsilon free, deterministic, and minimal. If a network encodes a regular language and if it is epsilon free, deterministic, and minimal, the network is guaranteed to be the best encoding for that language.

scholarly journals KNUTH–BENDIX FOR GROUPS WITH INFINITELY MANY RULES

2000 ◽  
Vol 10 (05) ◽  
pp. 539-589 ◽  
Author(s):  
D. B. A. EPSTEIN ◽  
P. J. SANDERS
Keyword(s):  
Computer Program ◽  
Regular Language ◽  
The Body ◽  
Finite State Automaton ◽  
Finite State Automata ◽  
Central Feature ◽  
Rapid Reduction ◽  
Automatic Structures ◽  
Small Constant ◽  
Finite State

We introduce a new class of groups with solvable word problem, namely groups specified by a confluent set of short-lex-reducing Knuth–Bendix rules which form a regular language. This simultaneously generalizes short-lex-automatic groups and groups with a finite confluent set of short-lex-reducing rules. We describe a computer program which looks for such a set of rules in an arbitrary finitely presented group. Our main theorem is that our computer program finds the set of rules, if it exists, given enough time and space. (This is an optimistic description of our result — for the more pessimistic details, see the body of the paper.) The set of rules is embodied in a finite state automaton in two variables. A central feature of our program is an operation, which we call welding, used to combine existing rules with new rules as they are found. Welding can be defined on arbitrary finite state automata, and we investigate this operation in abstract, proving that it can be considered as a process which takes as input one regular language and outputs another regular language. In our programs we need to convert several nondeterministic finite state automata to deterministic versions accepting the same language. We show how to improve somewhat on the standard subset construction, due to special features in our case. We axiomatize these special features, in the hope that these improvements can be used in other applications. The Knuth–Bendix process normally spends most of its time in reduction, so its efficiency depends on doing reduction quickly. Standard data structures for doing this can become very large, ultimately limiting the set of presentations of groups which can be so analyzed. We are able to give a method for rapid reduction using our much smaller two variable automaton, encoding the (usually infinite) regular language of rules found so far. Time taken for reduction in a given group is a small constant times the time taken for reduction in the best schemes known (see [5]), which is not too bad since we are reducing with respect to an infinite set of rules, whereas known schemes use a finite set of rules. We hope that the method described here might lead to the computation of automatic structures in groups for which this is currently infeasible. Some proofs have been omitted from this paper in the interests of brevity. Full details are provided in [4].

scholarly journals “M-Auto” The Augmented Reality-Based (AR) Learning Media Application for the Finite-State Automata (FA) Reduction Subject of Language and Automata Theory Courses

2020 ◽  
Vol 1 (2) ◽  
pp. 45
Author(s):  
Fajar Ananda Saputra ◽  
F Ti Ayyu Sayyidul Laily ◽  
Dimas Prasetyo Buseri ◽  
Imro’aturrozaniyah Imro’aturrozaniyah ◽  
Kartika Candra Kirana
Keyword(s):  
Augmented Reality ◽  
System Development ◽  
Automata Theory ◽  
Learning Activity ◽  
Finite State Automata ◽  
Need Analysis ◽  
Library Research ◽  
Finite State ◽  
The Subject ◽  
Learning Interest

AbstractA proper learning process should contain innovative, amusing, challenging, and motivating aspects. It should be able to provide an opportunity for the students to develop their creativity and independence based on their interests and talent. Less interesting and tedious classroom learning activity indicates the factor of the students’ learning interest degradation, for example as in the language and automata theory and finite-state automata reduction subject. The current research aims to aid language and automata theory in a learning activity to be easier to acquire. With the Augmented Reality-based learning media, the researcher hopes that the students can develop their understanding and their interest in a learning activity, especially for finite-state automata subjects. The subject of the current research is the Augmented Reality-based application as the learning media for language and automata theory and finite-state automata material. The researcher employs several research methodologies such as literature review, library research, and questionnaire to support the current research. The application is designed according to system development that consists of problem identification, appropriateness study, need analysis, concept designing, content designing, script designing, graphic designing, system production, and system examination. The result of the current research is the AR-based learning media application for the finite-state automata reduction subject of language and automata theory. Keywords: Learning Media, Finite-State Automata Reduction, Augmented Reality

Computing with Neurons: A Summary

1998 ◽  
Author(s):  
Christof Koch
Keyword(s):  
Turing Machine ◽  
Neural Coding ◽  
Ionic Currents ◽  
Finite Alphabet ◽  
Universal Turing Machine ◽  
Internal States ◽  
Voltage Dependent ◽  
Finite State ◽  
Limited Precision ◽  
The Brain

We now have arrived at the end of the book. The first 16 chapters dealt with linear and nonlinear cable theory, voltage-dependent ionic currents, the biophysical origin of spike initiation and propagation, the statistical properties of spike trains and neural coding, bursting, dendritic spines, synaptic transmission and plasticity, the types of interactions that can occur among synaptic inputs in a passive or active dendritic arbor, and the diffusion and buffering of calcium and other ions. We attempted to weave these disparate threads into a single tapestry in Chaps. 17-19, demonstrating how these elements interact within a single neuron. The penultimate chapter dealt with various unconventional biophysical and biochemical mechanisms that could instantiate computations at the molecular and the network levels. It is time to summarize. What have we learned about the way brains do or do not compute? The brain has frequently been compared to a universal Turing machine (for a very lucid account of this, see Hofstadter, 1979). A Turing machine is a mathematical abstraction meant to clarify what is meant by algorithm, computation, and computable. Think of it as a machine with a finite number of internal states and an infinite tape that can read messages composed with a finite alphabet, write an output, and store intermediate results as memory. A universal Turing machine is one that can mimic any arbitrary Turing machine. We are here not interested in the renewed debate as to whether or not the brain can, in principle, be treated as such a machine (Lucas, 1964; Penrose, 1989), but whether this is a useful way to conceptualize nervous systems in this manner. Because brains have limited precision, only finite amounts of memory and do not live forever, they cannot possibly be like “real” Turing machines. It is therefore more appropriate to ask: to what extent can brains be treated as finite state machines or automata! Such a machine only has finite computational and memory resources (Hopcroft and Ullman, 1979). The answer has to be an ambiguous “it depends.”

Recognizing Lexicographically Smallest Words and Computing Successors in Regular Languages

2021 ◽  
pp. 1-22
Author(s):  
Lukas Fleischer ◽  
Jeffrey Shallit
Keyword(s):  
Regular Language ◽  
Formal Language ◽  
Equivalence Classes ◽  
Regular Languages ◽  
State Complexity ◽  
Finite State ◽  
Finite State Transducer ◽  
Enumeration Problem ◽  
Necessary And Sufficient ◽  
Input Formula

For a formal language [Formula: see text], the problem of language enumeration asks to compute the length-lexicographically smallest word in [Formula: see text] larger than a given input [Formula: see text] (henceforth called the [Formula: see text]-successor of [Formula: see text]). We investigate this problem for regular languages from a computational complexity and state complexity perspective. We first show that if [Formula: see text] is recognized by a DFA with [Formula: see text] states, then [Formula: see text] states are (in general) necessary and sufficient for an unambiguous finite-state transducer to compute [Formula: see text]-successors. As a byproduct, we obtain that if [Formula: see text] is recognized by a DFA with [Formula: see text] states, then [Formula: see text] states are sufficient for a DFA to recognize the subset [Formula: see text] of [Formula: see text] composed of its lexicographically smallest words. We give a matching lower bound that holds even if [Formula: see text] is represented as an NFA. It has been known that [Formula: see text]-successors can be computed in polynomial time, even if the regular language is given as part of the input (assuming a suitable representation of the language, such as a DFA). In this paper, we refine this result in multiple directions. We show that if the regular language is given as part of the input and encoded as a DFA, the problem is in [Formula: see text]. If the regular language [Formula: see text] is fixed, we prove that the enumeration problem of the language is reducible to deciding membership to the Myhill-Nerode equivalence classes of [Formula: see text] under [Formula: see text]-uniform [Formula: see text] reductions. In particular, this implies that fixed star-free languages can be enumerated in [Formula: see text], arbitrary fixed regular languages can be enumerated in [Formula: see text] and that there exist regular languages for which the problem is [Formula: see text]-complete.

scholarly journals A Navigation and Augmented Reality System for Visually Impaired People

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3061
Author(s):  
Alice Lo Valvo ◽  
Daniele Croce ◽  
Domenico Garlisi ◽  
Fabrizio Giuliano ◽  
Laura Giarré ◽  
...  
Keyword(s):  
Augmented Reality ◽  
Visually Impaired ◽  
Virtual Path ◽  
Physical Support ◽  
Augmented Reality System ◽  
Virtual Paths ◽  
Visually Impaired People ◽  
Impaired People ◽  
Autonomous Mobility ◽  
Indoor And Outdoor

In recent years, we have assisted with an impressive advance in augmented reality systems and computer vision algorithms, based on image processing and artificial intelligence. Thanks to these technologies, mainstream smartphones are able to estimate their own motion in 3D space with high accuracy. In this paper, we exploit such technologies to support the autonomous mobility of people with visual disabilities, identifying pre-defined virtual paths and providing context information, reducing the distance between the digital and real worlds. In particular, we present ARIANNA+, an extension of ARIANNA, a system explicitly designed for visually impaired people for indoor and outdoor localization and navigation. While ARIANNA is based on the assumption that landmarks, such as QR codes, and physical paths (composed of colored tapes, painted lines, or tactile pavings) are deployed in the environment and recognized by the camera of a common smartphone, ARIANNA+ eliminates the need for any physical support thanks to the ARKit library, which we exploit to build a completely virtual path. Moreover, ARIANNA+ adds the possibility for the users to have enhanced interactions with the surrounding environment, through convolutional neural networks (CNNs) trained to recognize objects or buildings and enabling the possibility of accessing contents associated with them. By using a common smartphone as a mediation instrument with the environment, ARIANNA+ leverages augmented reality and machine learning for enhancing physical accessibility. The proposed system allows visually impaired people to easily navigate in indoor and outdoor scenarios simply by loading a previously recorded virtual path and providing automatic guidance along the route, through haptic, speech, and sound feedback.

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