Finite State Research Articles

An image encryption scheme based on the four-dimensional chaotic system and the mealy finite state machine

This paper proposes a four-dimensional hyperchaotic system to overcome the defect of weak encryption effect due to the simple complexity of low-dimensional chaotic systems in chaos-based image encryption schemes, which is verified to have better chaotic properties by dynamics analysis, sensitivity analysis, and randomness test. In addition, this paper proposes an image encryption scheme by combining the proposed chaotic system with the Mealy finite state machine (MFSM) to overcome the problem that some schemes are not resistant to statistical analysis. Firstly, the chaotic sequence is applied in the Knuth-Durstenfeld shuffling method to scramble the original image efficiently; secondly, according to the different information contained in the bit-plane, the pixel is decomposed and cyclically shifted at the bit level to achieve bit-level scrambling and diffusion; thirdly, the chaotic sequence is applied to select the rules for DNA encoding, and the diffusion process is achieved by Mealy finite state machine transformation; lastly, decoding the diffused image by selecting DNA rules randomly to get cipher image. This paper gives the experimental results, demonstrating that the scheme is highly secure. It can improve the encryption scheme’s sensitivity to plaintext and resist attacks such as differential attacks and select attacks.

Model checking programs in process-oriented IEC 61131-3 Structured Text

The process-oriented programming is a paradigm based on the process concept where each process is a concurrent finite state machine inside. The paradigm is intended for PLC (programmable logic controllers) developers to write Industry 4.0-enabled software. The poST language is a promising process-oriented extension of the IEC 61131-3 Structured Text (ST) language designed to provide a conceptual consistency of the PLC source code with technological description of the process under control. This language combines the advantages of FSM-based programming with the standard syntax of the ST language. We propose transformational semantics of poST providing rules for translation of poST language statements to Promela — the input language of the SPIN model checker. Following these semantic rules, our Xtext-based translator outputs a Promela model for the poST program. Our contribution is the poST transformational semantics and the method for automatic generation of the Promela code from poST control programs. The resulting Promela program is ready to be verified with SPIN model checker against linear temporal logic requirements to the source poST program. In the paper we provide an overview of related work, as well as a brief description of the poST and Promela languages. Further, the Promela poST translation rules cover control flow statements, process creation and state management constructs, and timeout management. Then we define service processes for modeling the external environment and managing high-level LTL specifications. Then we present the main ideas of implementing the translator poST to Promela. We also illustrate our approach using the example of a system for managing electricity consumption and production, including renewable sources.

Basic Approaches for Reducing Power Consumption in Finite State Machine Circuits—A Review

Methods for reducing power consumption in circuits of finite state machines (FSMs) are discussed in this review. The review outlines the main approaches to solving this problem that have been developed over the last 40 years. The main sources of power dissipation in CMOS circuits are shown; the static and dynamic components of this phenomenon are analyzed. The power consumption saving can be achieved by using coarse-grained methods common to all digital systems. These methods are based on voltage or/and clock frequency scaling. The review shows the main structural diagrams generated by the use of these methods when optimizing the power characteristics of FSM circuits. Also, there are various known fine-grained methods taking into account the specifics of both FSMs and logic elements used. Three groups of the fine-grained methods targeting FPGA-based FSM circuits are analyzed. These groups include clock gating, state assignment, and replacing look-up table (LUT) elements by embedded memory blocks (EMBs). The clock gating involves a separate or joint use of such approaches as the (1) decomposition of FSM inputs and (2) disabling FSM inputs. The aim of the power-saving state assignment is to reduce the switching activity of a resulting FSM circuit. The replacement of LUTs by EMBs allows a reduction in the power consumption due to a decrease in the number of FSM circuit elements and their interconnections. We hope that the review will help experts to use known methods and develop new ones for reducing power consumption. We think that a good knowledge and understanding of existing methods of reducing power consumption is a prerequisite for the development of new, more effective methods to solve this very important problem. Although the methods considered are mainly aimed at FPGA-based FSMs, they can be modified, if necessary, and used for the power consumption optimization of FSM circuits implemented with other logic elements.

A Study on Quotient Structures of Bipolar Fuzzy Finite State Machines

This article introduces different congruence relations on the bipolar fuzzy set associated with the bipolar fuzzy finite state machine. Each congruence relation associates a semigroup with the bipolar fuzzy finite automata. We also discuss characterizing a bipolar fuzzy finite state machine by defining an admissible relation.

Vector Symbolic Finite State Machines in Attractor Neural Networks.

Hopfield attractor networks are robust distributed models of human memory, but they lack a general mechanism for effecting state-dependent attractor transitions in response to input. We propose construction rules such that an attractor network may implement an arbitrary finite state machine (FSM), where states and stimuli are represented by high-dimensional random vectors and all state transitions are enacted by the attractor network's dynamics. Numerical simulations show the capacity of the model, in terms of the maximum size of implementable FSM, to be linear in the size of the attractor network for dense bipolar state vectors and approximately quadratic for sparse binary state vectors. We show that the model is robust to imprecise and noisy weights, and so a prime candidate for implementation with high-density but unreliable devices. By endowing attractor networks with the ability to emulate arbitrary FSMs, we propose a plausible path by which FSMs could exist as a distributed computational primitive in biological neural networks.

Simulation Modeling Using Markovian Decision Theory on Cash Flow Analysis of Central Bank of Nigeria

This research aims to investigate the cash flow analysis of the Central Bank of Nigeria using Markovian decision theory. The specific objectives include determining the (i) cash flow solvency ratio, (ii) cash flow adequacy ratio, (iii) sufficiency ratio, (iv) cash flow profitability ratio, and (v) estimating the optimal policy of cash flow ratios performance in CBN. The identified problems are the effects of (i) insolvency, (ii) inadequacy, (iii) insufficiency, and (v) Unprofitability on Central Bank of Nigeria (CBN) cash flows performance.The methodology involves a research design tailored toward collecting, arranging, and determining cash flow data for model prediction and optimization. The Markov chain is introduced as an operator to evaluate the distribution of cash flow ratios in the long term, using initial state vectors and state transition probabilities for forecasting behavior. Data validation is performed using graphical and Pearson moment correlation coefficient methods. The pre-model analysis of CBN cash flows problem during the period of January 2012 to December 2017 identifies six finite current states. State-2012 cash flows performance was exceptional (above the zero line), reflecting 100%, while State-2013 and 2015 reflect 75%, and State-2014, 2016 & 2017 reflect 50% healthy cash flows status. The model results introduce the Markovian Cash Flow Ratios Monitoring Curve (MCFRMC), specifying the minimum values for healthy status. The research explores the present status of cash flow ratios, presenting forecasted ratios in the form of an optimum policy or solution. Pearson moment correlation coefficient validation of the prototype and model results in a coefficient of 1.0, indicating a 100% higher performance of the model. Further research reveals strategic cash inflows policy allocation to the cash flows indicators, with (i) operational activity receiving 24%, (ii) investment active e

Realtime polymorphic malicious behavior detection in blockchain-based smart contracts

Abstract This paper proposes an innovative approach to achieving real-time polymorphic behavior detection, and its direct application to blockchain-focused smart-contracts. We devise a method based on a non-deterministic finite state machine to perform approximate pattern-matching, using a look-ahead mechanism implemented through a concept similar to that of a sliding window, and using threshold-based similarity checking at every state in the automaton. We introduce and formalize our approach, discuss the challenges we faced and then test it in a real-world environment. The experimental results obtained showed a significant speed-up of our approach, as compared to the classic similarity measures used commonly in such scenarios.

Experimental assessment of a JANUS-based consensus protocol

This paper proposes a distributed, JANUS-based protocol that enables an underwater acoustic network to reach consensus on arbitrary local opinions as numeric state variables. An envisioned scenario where nodes shall agree on parameters describing the acoustic environment is used to evaluate the protocol. The scenario exemplifies the protocol’s potential in future applications where nodes use the environment description to decide on appropriate modulation and coding schemes.The evaluation is based on numerical simulations and sea experiments in a challenging acoustic environment. The numerical simulations allowed examining the performance for different parameter values regarding the timing of transmission events and state transitions in the finite state machine implementation of the protocol. The best parameter configuration was used in the sea experiments conducted in a bay in the Baltic Sea. The experiments comprised several deployments of five to six commercial modems.Results from the experiments show that the protocol can achieve a consensus up to 89% of the time in the tested environment, and up to 96% of the time if the state variables are permitted to differ by one discretisation step maximum across the network. In addition, if the network separates due to environmental conditions, connected components appear to achieve consensus more often when the links are more reliable. Finally, it is shown that when different consensus processes are active in parallel, packets from one process do not interfere with the opinions in different processes, besides the existing probability of packet loss due to packet collision.

Iterative Reconstruction of Micro Computed Tomography Scans Using Multiple Heterogeneous GPUs.

Graphics processing units (GPUs) facilitate massive parallelism and high-capacity storage, and thus are suitable for the iterative reconstruction of ultrahigh-resolution micro computed tomography (CT) scans by on-the-fly system matrix (OTFSM) calculation using ordered subsets expectation maximization (OSEM). We propose a finite state automaton (FSA) method that facilitates iterative reconstruction using a heterogeneous multi-GPU platform through parallelizing the matrix calculations derived from a ray tracing system of ordered subsets. The FSAs perform flow control for parallel threading of the heterogeneous GPUs, which minimizes the latency of launching ordered-subsets tasks, reduces the data transfer between the main system memory and local GPU memory, and solves the memory-bound of a single GPU. In the experiments, we compared the operation efficiency of OS-MLTR for three reconstruction environments. The heterogeneous multiple GPUs with job queues for high throughput calculation speed is up to five times faster than the single GPU environment, and that speed up is nine times faster than the heterogeneous multiple GPUs with the FIFO queues of the device scheduling control. Eventually, we proposed an event-triggered FSA method for iterative reconstruction using multiple heterogeneous GPUs that solves the memory-bound issue of a single GPU at ultrahigh resolutions, and the routines of the proposed method were successfully executed on each GPU simultaneously.

Implementation of EnDat Interface Master Using Configurable Logic Block in MCU

In this study, we propose an implementation method of the Encoder Data (EnDat) interface master for slave encoders using only a configurable logic block (CLB) and a serial peripheral interface (SPI) integrated into microcontroller units. By programming the CLB device to execute logic functions and finite state machines designed for the EnDat interface master operation, we realize the EnDat and SPI clocks that are required for the EnDat interface master operation. This approach is cost-efficient because additional hardware components, such as a field-programmable gate array or a complex programmable logic device, are unnecessary for the master implementation. We build a one-axis feed drive system that is powered by an AC motor and equipped with an EnDat linear encoder for measuring table speed and position. By performing various experiments for table position and speed control based on the built feed drive system, we verify the performance and practical usefulness of the implemented EnDat interface master. The maximum EnDat clock frequency without the propagation delay compensation is achieved by 2 MHz, which can cope with 16 kHz control cycle frequency. The usefulness is demonstrated by showing the table speed and position control performance that are acceptable in real applications.

Finite barrier bound state

A boundary mode localized on one side of a finite-size lattice can tunnel to the opposite side which results in unwanted couplings. Conventional wisdom tells that the tunneling probability decays exponentially with the size of the system which thus requires many lattice sites before eventually becoming negligibly small. Here we show that the tunneling probability for some boundary modes can apparently vanish at specific wavevectors. Thus, similar to bound states in the continuum, a boundary mode can be completely trapped within very few lattice sites where the bulk bandgap is not even well-defined. More intriguingly, the number of trapped states equals the number of lattice sites along the normal direction of the boundary. We provide two configurations and validate the existence of this peculiar finite barrier-bound state experimentally in a dielectric photonic crystal at microwave frequencies. Our work offers extreme flexibility in tuning the coupling between localized states and channels as well as a new mechanism that facilitates unprecedented manipulation of light.

Forward starting options pricing under a regime-switching jump-diffusion model with Wishart stochastic volatility and stochastic interest rate

Vanilla options become effective immediately after they are entered, while some exotic options will only come to effective some time after they are bought or sold. Forward starting options are one kind of such exotic options started actually at some pre-specified future date. This paper presents an extension of regime-switching jump diffusion model, in which the parameters are driven by a continuous time and stationary Markov chain on a finite state space, by introducing the Wishart process into the instantaneous variance-covariance matrix of the risky asset price and stochastic interest rate. We derive the discounted conditional joint characteristic function and the forward characteristic function of the log-asset price and its the instantaneous variance-covariance process, and thereby the price of forward starting options are well evaluated by the probabilistic approach combined with the Fourier-cosine (COS) method. We also provide efficient Monte Carlo simulation of this proposed model, and simulated solutions to forward starting options pricing within a two-state regime switching framework. Numerical results show that the COS method is accurate and efficient for pricing forward starting options. Finally, we analyse impacts of some main parameters (especially, parameters in the Wishart stochastic volatility) in this proposed model on option prices and Δ values. Also, we consider the forward implied volatility. Furthermore, the forward starting options under the regime-switching jump diffusion model with Wishart stochastic volatility and stochastic interest rate which we derived are more generalized than those recently appeared in the derivatives pricing literature, and thus have wider application.

Design and Analysis of a New Fault-Tolerant QCA-based Nano-Scale Circuit for Morphological Operations in Image Processing

Quantum-dot cellular automata (QCA) is based on traditional cellular automata models in which each cell has one of the finite distinct states at any given time. Cellular automata refer to the use of Coulombic contact rather than wires to connect cells. The discrete character of cellular automata and quantum mechanics have been exploited to develop nano-scale devices that can conduct computations with high switching frequency and low power dissipation. QCA has been utilized in many categories for fault-tolerant and reversible logic computing. On the other hand, the morphology algorithm is an area of image processing that provides a unified methodology and strong tools for image/video processing applications. This work proposes a unique fault-tolerant approach based on QCA to execute the morphological operations in image processing. Two fault-tolerant 5-input majority gates are employed in the implemented QCA architecture, and a control line is used to accomplish dilation and erosion. To assess the capabilities of the proposed fault-tolerant circuit for morphological procedures, its performance is analyzed and confirmed. The proposed design is compared to earlier results, which significantly improve fault-tolerant ability.

Real-time system call-based ransomware detection

Ransomware, particularly crypto ransomware, has emerged as the go-to malware for threat actors aiming to compromise data on Android devices as well as in general. In this paper, we present a ransomware detection technique based on behaviours observed in the system calls performed by the malware. We first describe our repeatable and extensible methodology for extracting the system call log and patterns. We then identify and present some common high-level system call behavioural patterns exhibited by crypto ransomware, and evaluate these patterns. We further describe the implementation of a streaming implementation that utilises regular expressions for modelling malware behaviours and finite state machines for detecting crypto ransomware behaviours in real time. The success of our proof of concept evaluation allows us to envision our proposed technique applied as part of a self-protection system on Android phones against malware.

Study of six coordinated cobalt(III) oxophlorin with different axial ligands: Optimization of geometry and determining of energy and electronic configuration at various spin states by employing of B3LYP, BV86P and M06-2X methods

The six coordinated CoIIIoxophlorin have been studied with imidazole, pyridine and t-butylcyanide as axial ligands using B3LYP, BV86P and MO6-2X methods. Conversion between two isomers [(L)2CoIII(PO)][Formula: see text] and [(L)2CoIII(PO)][Formula: see text] can be occurred at various spin multiplicities, namely singlet, triplet and quintet states. L is employed to show axial ligand namely, imidazole or pyridine. London forces have a basic role in the stability of the mentioned complexes due to non-specific solvent effects. The latter fact has been obtained using the PCM model. Also, it is specified that minimum geometries have not been obtained for some parallel or perpendicular six-coordinated complexes with special axial ligands at a finite spin state. The B3LYP method indicates that one and three [Formula: see text]-electron can be found on the Co atom in every optimized complex at triplet and quintet spin states, respectively. Also, another [Formula: see text]-electron is placed on the ring of oxophlorin. This fact is obtained for different isomers of [(IM2(CoIII(PO)] and [(Py)2CoIII(PO)] at triplet and quintet spin states, while these complexes are optimized using B3LYP. Besides, results obtained from B3LYP show that the most stable state for six coordinated CoIIIoxophlorin with any axial ligand is the singlet state. Based on crystal field theory and molecular orbital theory, electron configuration and hybridization of cobalt in [(IM)2CoIII(PO)] at singlet state can be written as t2g6 eg0 and sp3d2, respectively. Former electronic configuration indicates a strong field with low spin for d orbitals of cobalt, and latter hybridization is expected for a metal with a coordination number of 6 in a complex with Oh symmetry. The results obtained are completely satisfied by NBO analysis.

Accelerating Finite State Machine-Based Testing Using Reinforcement Learning

Accelerating Finite State Machine-Based Testing Using Reinforcement Learning

Intelligent Decision-Making Method for Vehicles in Emergency Conditions Based on Artificial Potential Fields and Finite State Machines

Intelligent Decision-Making Method for Vehicles in Emergency Conditions Based on Artificial Potential Fields and Finite State Machines

A Parking Detection Algorithm Based on Multitransitory Finite-State Machine Using Magnetic Wireless Sensor Network

A Parking Detection Algorithm Based on Multitransitory Finite-State Machine Using Magnetic Wireless Sensor Network

Autonomous Dynamic Hitch-Hiking Control of a Bionic Robotic Remora

Remora suckerfish and its hitch-hiking behavior bring enormous inspiration into the engineering field. In this paper, a bioinspired hitch-hiking behavior is accomplished automatically by a robotic remora, which creates possibilities for prolonging its endurance and multi-robot cooperation. First, the definition of the hitch-hiking task and the mechatronic design of robotic remora are introduced. Then, aiming at the hitch-hiking task, the LED-marker-based underwater visual localization method and planar state synchronization controller are developed. The localization method includes a complete framework from LED detection to marker pose optimization, which considers the refraction correction to improve the localization precision in water. The synchronization controller is decomposed into the lateral and longitudinal sub-controllers to overcome the challenges caused by underactuated dynamic. Besides, a finite state machine is designed to model the state and action transition during the hitch-hiking task. Extensive experimental results demonstrate the effectiveness of the proposed method. The autonomous hitch-hiking task toward a moving host is successfully implemented by a robotic fish for the first time. Such results may offer valuable insights into the future autonomous operation of underwater robots.

Cable-driven agribot prototype: Enabling precision agriculture through innovative design

The ongoing depopulation of rural areas and the increasing demand for high-quality food, along with the need to improve the quality of work in terms of stress, injuries, and social disputes, needs significant attention towards rethinking and organizing agricultural practices to meet these needs and improve the quality of life. These changes call for the implementation of new reforms, incentive systems, and the exploration of innovative systems for field operations. In this context, the research, design, and commercialization of electrified autonomous and collaborative systems capable of precision agriculture applications, known as agribots, are gaining paramount importance. This article presents an innovative multi-robot for smart farming. The agribot is composed by a lightweight cart designed to traverse crop field rows through a cable-way system installed on two heavy carts equipped with a power train system and PV rooftop panels. The agribot is conceived to achieve a high level of autonomy, reduce energy consumption, and minimize soil compaction while working on flat fields with low-height crops. The article is structured in two parts: the first part describes the agribot's ICT network, the heavy carts electric grid and the power systems, providing an energy consumption analysis related to the traversing of a crop field row.The second part focuses on the control, actions coordination, and supervision system (supervisor), implemented using Robot Operating System (ROS). The supervisor system, driven by a decision-making system based on a finite state machine, oversees the execution of path planning for the agribot, providing high modularity and flexibility to adapt to various system actions.