Finite State Machine Research Articles

Automatic detection of Android crypto ransomware using supervisor reduction

AbstractThis paper proposes a finite-state machine based approach to recognise crypto ransomware based on their behaviour. Malicious and benign Android applications are executed to capture the system calls they generate, which are then filtered and tokenised and converted to finite-state machines. The finite-state machines are simplified using supervisor reduction, which generalises the behavioural patterns and produces compact classification models. The classification models can be implemented in a lightweight monitoring system to detect malicious behaviour of running applications quickly. An extensive set of cross validation experiments is carried out to demonstrate the viability of the approach, which show that ransomware can be classified accurately with an F1 score of up to 93.8%.

Design and Implementation of Digital Integrated Circuit Based CPU

This paper presents a simplified 16-bit CPU design and its implementation using digital integrated circuits developed with Verilog hardware description language, while emphasizing simulation through the EDA tool. This design puts more emphasis on the critical roles played by the data path and the control unit, executing key instructions including arithmetic operations such as addition, subtraction, bitwise logic, and conditional jumps. This structurally modular CPU is both functioning and stable in nature, allowing support for future enhancements and enhancements as well, thereby serving as an effective model towards an understanding of the principles of CPU design. The execution of these basic instructions puts the core of the CPU under test and reaffirms its proper operation in respect of the detailed instruction set, while its extensibility design confirms further improvement possibilities for future complex instructions as well as finite-state machine optimization for the control unit. The present work not only proves the possibility of creating a fully working CPU, using readily available bare minimum resources to implement a prototype, but is also a breakthrough in future research with CPU architecture. The obtained results will shed light on CPU functionality optimization and extension, being the ground for building more powerful and effective processors in the future.

Coordination of NPCs in multi-agent systems based on behavior trees

Abstract. This paper addresses the design and coordination challenges of controlling non-player characters' (NPCs) behaviors in multi-agent systems (MAS) using behavior trees (BTs), which are preferred over finite state machines (FSMs) due to their hierarchical structure and ease of maintenance. While BTs effectively resolve the question of "What to do next?" for individual NPCs, their application in MAS, particularly with the integration of a blackboard system for central control, reveals limitations in efficiency, robustness, and heuristic capacity as system complexity increases. To explore solutions to these challenges, this study analyzes various algorithms that enhance the functionality of behavior trees within MAS. The research focuses on three primary areas: the optimization of behavior trees, the development of behavior tree search algorithms, and the improvement of communication algorithms within BTs. Methods involve a comparative analysis of existing and new algorithmic approaches to identify and address inefficiencies in NPC coordination. The findings indicate that advanced behavior tree configurations, when combined with innovative search and communication strategies, significantly improve the coordination, robustness, and operational efficiency of MAS. These enhancements allow for more dynamic and responsive NPC interactions in complex gaming environments.

Modeling, Simulation, and Kinematic Validation of Transfemoral Prosthetic Mechanism With Ankle Varus–Valgus Characteristic

Abstract Inspired by the kinesiology of human bionic joints, a transfemoral prosthetic mechanism based on a functional structure of parallel mechanism is developed for the transfemoral amputees. The walking interactive simulation is implemented based on human-prosthesis modeling to verify the kinematics of the designed prosthetic mechanism, as well as to explore compatibility between the amputees and prosthesis. Then, simulation-based prosthetic optimization is performed to pursue an optimized human-prosthesis model with economic metabolic consumption while eliminating compatibility errors including the joints' misalignment error between the affected limb and healthy limb, and the assembly error between human and prosthesis, so that the potential physical health problems can be avoided efficiently. This method is valuable for the optimal design of interactive rehabilitation robots. Finally, a developed proportional-integral-derivative-based (PID-based) finite-state machine (FSM) strategy is used, and the kinematic validation is carried out. The results show that the designed prosthesis possesses ankle varus–valgus characteristic, and it has a high human-like motion accuracy due to the FSM control can track prosthetic motion in each gait event. What's more, the prosthetic optimization can be an efficient method to enhance the biomechanical performance of human-prosthetic model so that the amputees have a more natural and symmetry gait.

Evaluation of a finite state machine algorithm to measure stepping with ankle accelerometry: Performance across a range of gait speeds, tasks, and individual walking ability

Wearable sensors, including accelerometers, are a widely accepted tool to assess gait in clinical and free-living environments. Methods to identify phases and subphases of the gait cycle are necessary for comprehensive assessment of pathological gait. The current study evaluated the accuracy of a finite state machine (FSM) algorithm to detect strides by identifying gait cycle subphases from ankle-worn accelerometry. Algorithm performance was challenged across a range of speeds (0.4-2.6 m/s), task conditions (e.g., single- and dual-task walking), and individual characteristics. Specifically, the study included a range of treadmill speeds in young adults and overground walking conditions in older adults with neurological disease. Manually counted and algorithm-derived stride detection from acceleration data were evaluated using error analysis and Bland-Altman plots for visualization. Overall, the algorithm successfully detected strides (>96 % accuracy) across gait speed ranges and tasks, for young and older adults. The accuracy of an FSM algorithm combined with ankle-worn accelerometers, provides an analytical approach with affordable and portable tools that permits comprehensive assessment of gait unbounded by setting and proves to perform well in in walking tasks characterized by variable walking. These algorithm capabilities and advancements are critical for identifying phase dependent gait impairments in clinical and free-living assessment.

Automatic activity-travel sequence generator using language, grammar, and machine theory

ABSTRACT Activity schedule results from a complex decision-making process characterized by several interrelated decisions. Different facets of an activity schedule such as activity type, timing, duration, etc. influence each other and this makes modeling activity schedules a complex task. This complexity has compelled researchers to explore different approaches for modeling activity schedules, among which two predominant approaches can be identified: the utility-maximization theory based econometric approach and the computational process modeling approach. Despite their advantages and a few successful practical applications, challenges still remain leaving avenues for exploration of new approaches. This paper contributes in this direction by reviewing the relationship between language, grammar, and machines in the context of sequence analysis for activity sequence generation. Following that, the paper presents a stochastic Finite State Machine that can generate activity sequences to match the frequency distribution of sequences from a given data set. Our results show that the proposed algorithm can not only generate activity sequences with a distribution similar to that of original data but can also efficiently generate new patterns not in the original data.

Robust humanoid robot vehicle ingress with a finite state machine integrated with deep reinforcement learning

Robust humanoid robot vehicle ingress with a finite state machine integrated with deep reinforcement learning

Unmanned Ship Collision Avoidance Action Plan Deduction Method under Man–Machine Interactive Negotiation in Collision Avoidance Scenarios

With the development of artificial intelligence technology, the future water traffic environment will present a new pattern of coexistence of manned ships and unmanned ships, because unmanned ships are different from manned ships in situation understanding, collision avoidance decision-making, and so on. Therefore, the obstacle avoidance planning between unmanned ships and manned ships becomes extremely complex, and collision avoidance behavior scheme deduction becomes a key step in solving the problems related to situation understanding and collision avoidance decision-making in collision avoidance scenarios. In this paper, the situation understanding of the pilot for different collision avoidance situations is integrated into the dynamic obstacle avoidance model, and an intelligent navigation collision avoidance system is proposed to assist in deducing the collision avoidance action plan of the unmanned ship in the man–machine coexistence scenario. The intelligent navigation collision avoidance system is divided into two parts, namely a ship situation understanding part and a ship obstacle avoidance part, wherein ship situation understanding is used for realizing the transition of the collision state of the unmanned ship in the deduction process by constructing a collision-state set and a behavior decision set by using a finite state machine (FSM). Regarding ship obstacle avoidance, ship velocity obstacle is calculated based on the reciprocal velocity obstacle method (RVO), and the collision avoidance action is selected by using the behavior decision generated by the FSM to realize the dynamic collision avoidance deduction of the unmanned ship. In this study, the validity and effectiveness of the intelligent navigation collision avoidance system proposed in this paper are verified by case studies in a variety of collision avoidance scenarios. The system successfully solves the problem of intelligent collision avoidance planning, provides reliable support for the intelligent collision avoidance of unmanned ships, provides a feasible solution for safety and efficiency in sea navigation, and provides a valuable reference for the design and development of future intelligent navigation collision avoidance systems for ships.

Increasing the speed of digital devices using the ASMD-FSMD method using non-blocking operators

The ASMD-FSMD method for designing digital devices is considered, which consists of constructing a block diagram of an algorithmic state machine with a data path (ASMD), which describes the behavior of the device, and creating project code in Verilog in the form of a finite state machine with a data processing path. (finite state machine with datapath – FSMD). One of the directions for the development of the ASMD-FSMD methodology is the use of features of the hardware description language (HDL). A hypothesis has been put forward: in the ASMD-FSMD technique, it is possible to apply several non-blocking assignment operators to the same variable in one synchronization cycle, which will lead to an increase in device performance. The hypothesis put forward was investigated in the design of synchronous multipliers that implement the classical multiplication algorithms c and d. Experimental studies have confirmed the validity of the put forward hypothesis, while the speed of the multipliers increases two to three times, and the cost of implementation in most cases decreases compared to the traditional approach.

Enhanced Abandoned Object Detection through Adaptive Dual-Background Modeling and SAO-YOLO Integration

Abandoned object detection is a critical task in the field of public safety. However, existing methods perform poorly when detecting small and occluded objects, leading to high false detection and missed detection rates. To address this issue, this paper proposes an abandoned object detection method that integrates an adaptive dual-background model with SAO-YOLO (Small Abandoned Object YOLO). The goal is to reduce false and missed detection rates for small and occluded objects, thereby improving overall detection accuracy. First, the paper introduces an adaptive dual-background model that adjusts according to scene changes, reducing noise interference in the background model. When combined with an improved PFSM (Pixel-based Finite State Machine) model, this enhances detection accuracy and robustness. Next, a network model called SAO-YOLO is designed. Key improvements within this model include the SAO-FPN (Small Abandoned Object FPN) feature extraction network, which fully extracts features of small objects, and a lightweight decoupled head, SODHead (Small Object Detection Head), which precisely extracts local features and enhances detection accuracy through multi-scale feature fusion. Finally, experimental results show that SAO-YOLO increases mAP@0.5 and mAP@0.5:0.95 by 9.0% and 5.1%, respectively, over the baseline model. It outperforms other advanced detection models. Ultimately, after a series of experiments on the ABODA, PETS2006, and AVSS2007 datasets, the proposed method achieved an average detection precious of 91.1%, surpassing other advanced methods. It significantly outperforms other advanced detection methods. This approach notably reduces false and missed detections, especially for small and occluded objects.

Computation with Sequences of Assemblies in a Model of the Brain.

Even as machine learning exceeds human-level performance on many applications, the generality, robustness, and rapidity of the brain's learning capabilities remain unmatched. How cognition arises from neural activity is the central open question in neuroscience, inextricable from the study of intelligence itself. A simple formal model of neural activity was proposed in Papadimitriou etal. (2020) and has been subsequently shown, through both mathematical proofs and simulations, to be capable of implementing certain simple cognitive operations via the creation and manipulation of assemblies of neurons. However, many intelligent behaviors rely on the ability to recognize, store, and manipulate temporal sequences of stimuli (planning, language, navigation, to list a few). Here we show that in the same model, sequential precedence can be captured naturally through synaptic weights and plasticity, and, as a result, a range of computations on sequences of assemblies can be carried out. In particular, repeated presentation of a sequence of stimuli leads to the memorization of the sequence through corresponding neural assemblies: upon future presentation of any stimulus in the sequence, the corresponding assembly and its subsequent ones will be activated, one after the other, until the end of the sequence. If the stimulus sequence is presented to two brain areas simultaneously, a scaffolded representation is created, resulting in more efficient memorization and recall, in agreement with cognitive experiments. Finally, we show that any finite state machine can be learned in a similar way, through the presentation of appropriate patterns of sequences. Through an extension of this mechanism, the model can be shown to be capable of universal computation. Taken together, these results provide a concrete hypothesis for the basis of the brain's remarkable abilities to compute and learn, with sequences playing a vital role.

On the right track: decoding self-regulated learning in young students’ log data with the digital train track task

Assessing self-regulated learning (SRL)—the interplay between monitoring and control behavior—remains challenging, particularly in young learners. The unobtrusive assessment with log data to investigate SRL offers a promising method to deepen the understanding of the SRL process of young students. Despite the significant potential of log data to enhance the measurement of SRL, recent research encounters new challenges of operationalization, transparency, generalizability, validity, and reproducibility. This study introduces an innovative instrument, the digital train track task (TTT), for assessing SRL with log data in young learners, focusing on monitoring and controlling behavior. Log data of 85 primary school students (second to fifth grades, aged 7–13 years) performing one simple and one complex TTT were analyzed. As a novel method, finite state machines (FSM) were applied to extract SRL-related actions and states from the log data. To evaluate and explore the potential of the digital TTT, monitoring, and control behavior during simple and complex tasks were compared, employing frequency-based statistical analysis and transition graphs. Additionally, the log data were multimethodically linked with think-aloud data. The results revealed differences in monitoring and control behavior during the simple and the complex tasks regarding frequency, duration, and transitions between the SRL-related states. Extracted SRL-related states from log data and corresponding think-aloud data showed significant correlations. Adding to the growing body of log data research, this study offers an innovative task to validly assess the metacognitive self-regulation processes of young learners during problem-solving. The transparent, theory-based operationalization of SRL in this study, taking into account recent demands for SRL log data research, allows better reproducibility and transfer and adds to the generalizability of findings from SRL log data research.

Semi-tensor product approach to controllability, reachability, and stabilizability of extended finite state machines

Semi-tensor product approach to controllability, reachability, and stabilizability of extended finite state machines

AI-Driven Traffic Simulation using Unity: Implementing Finite State Machines for Adaptive NPC Behaviour

This research develops an AI-powered traffic simulation using the Unity Engine, leveraging finite state machines (FSM) to enable adaptive and responsive non-player characters (NPCs). The integration of FSM with advanced pathfinding algorithms, such as A*, allows NPCs to dynamically adjust their behavior based on traffic conditions, obstacles, and environmental changes. The experimental results indicate a 25% improvement in route optimization and a 30% reduction in path conflicts compared to conventional static models, demonstrating the robustness of the proposed approach. Optimized navmesh deployment further enhances navigation fluidity, ensuring efficient agent movement in high-density scenarios without compromising system performance. The findings establish the effectiveness of the FSM-driven NPC behavior in simulating realistic traffic environments, contributing both to the advancement of AI applications in game development and urban planning. By providing an interactive platform for traffic management, this simulation offers a practical tool to study congestion patterns and test intervention strategies. In addition, it improves player engagement by fostering emergent gameplay through dynamic NPC interactions. Future work could explore the integration of real-time procedural generation or multiplayer functionality to enrich simulation depth and scalability. This study provides a comprehensive framework that bridges AI-based mechanics with simulation technology, providing significant insights for researchers and practitioners in game design, artificial intelligence, and urban planning.

The Bajau-Tainment: An AI-Powered Puzzle Game for Learning the Bajau Language Using Finite State Machine and Shuffle Techniques

The research titled "Bajau-tainment" Educational Game (Edu-game) is a research project that focuses on the development of a Puzzle Game designed to improve memory, specifically in language learning. In this game, players must arrange randomized letters to form a word in the Bajau language. This research applies the shuffle random algorithm, which aims to ensure that the arrangement of letters is always shuffled, making the gameplay dynamic, non-monotonous, and engaging. AI Artificial Intelligence (AI) technology will also be applied in this research. Using the Finite State Machine (FSM) model method, the game will feature a game agent character that will accompany children during gameplay, like how a friend accompanies students in a classroom. This virtual friend can display emotions such as happiness or sadness based on the game environment. The research aims to make this edu-game more appealing and interactive for children. The AI game agent will act as a friend who accompanies the child throughout the game.

An STP look at logical blocking of finite state machines: formulation, detection, and search

An STP look at logical blocking of finite state machines: formulation, detection, and search

Software patterns and data structures for the runtime coordination of robots, with a focus on real-time execution performance.

This paper introduces software patterns (registration, acquire-release, and cache awareness) and data structures (Petri net, finite state machine, and protocol flag array) to support the coordinated execution of software activities (also called "components" or "agents"). Moreover, it presents and tests an implementation for Petri nets that supports real-time execution in shared memory for deployment inside one individual robot and separates event firing and handling, enabling distributed deployment between multiple robots. Experimental validation of the introduced patterns and data structures is performed within the context of activities for task execution, control and perception, and decision making for an application on coordinated navigation.

Elevating Data Entropy and Efficiency through Symmetric Cryptographic technique Based on Finite State Machine

Elevating Data Entropy and Efficiency through Symmetric Cryptographic technique Based on Finite State Machine

Representation of Vending Device using Finite State Automata

Automata theory predominates in a number of applications derived using the technique of a finite state machine . In the paper, we study the representation of a vending machine (VM) that enhances the academic institution’s book delivery service using the concepts of the computation theories. The effectiveness regarding the virtual machine(VM) is viewed as a problem. Automata theory relies on the finite state machine (FSM) design to increase efficiency. The different components of a vending machine are examined at, and it is determined how they can be modelled as finite state machines. We go over the architectural considerations for vending machines, such as the selection of components, the layout of the machine, and the selection of input and output approaches. We use the theory of computation to examine the constraints and difficulties in vending machine design. The paper will design an FSM with fewer states in the end. As a result, we will learn how to increase the VM’s design effectiveness and cost.

Synthesis of Self-Checking Circuits for Train Route Traffic Control at Intermediate Stations with Control of Calculations Based on Weight-Based Sum Codes

When synthesizing systems for railway interlocking, it is recommended to use automated models to implement the logic of railway automation and remote control units. Finite-state machines (FSMs) can be implemented on any hardware component. When using relay technology, the functional safety of electrical interlocking is achieved by using uncontrolled (safety) relays with a high coefficient of asymmetry of failures in types 1 → 0 and 0 → 1. When using programmable components, the use of backup and diverse protection methods is required. This paper presents a flexible approach to synthesizing FSMs for railway automation and remote control units that offer both individual and route-based control. Unlike existing solutions, this proposal considers the pre-failure states of railway automation and remote control units during the finite-state machine synthesis stage. This enables the implementation of self-checking and self-diagnostic modules to manage automation units. By increasing the number of states for individual devices and considering the states of interconnected objects, the transition graphs can be expanded. This expansion allows for the synthesis of the transition graph of the control subsystem and other systems. The authors used a field-programmable gate array (FPGA) to implement a finite-state machine. In this case, the proposal is to encode the states of a finite-state machine using weight-based sum codes in the residue class ring based on a given modulus. The best coverage of errors occurring at the outputs of the logic converter in the structure of the FSM can be ensured by selecting the weighting coefficients and the value of the module. This paper presents an example of synthesizing an FPGA-based FSM using state encoding through modular weight-based sum codes. The operation of the synthesized device was modeled. It was found to operate according to the same algorithm as the real devices. When synthesizing self-checking and self-controlled train control devices, it is recommended to consider the solutions proposed in this paper.