State Machine Research Articles

On complexity of colloid cellular automata

The colloid cellular automata do not imitate the physical structure of colloids but are governed by logical functions derived from them. We analyze the space-time complexity of Boolean circuits derived from the electrical responses of colloids-specifically ZnO (zinc oxide, an inorganic compound also known as calamine or zinc white, which naturally occurs as the mineral zincite), proteinoids (microspheres and crystals of thermal abiotic proteins), and their combinations in response to electrical stimulation. To extract Boolean circuits from colloids, we send all possible configurations of two-, four-, and eight-bit binary strings, encoded as electrical potential values, to the colloids, record their responses, and infer the Boolean functions they implement. We map the discovered functions onto the cell-state transition rules of cellular automata-arrays of binary state machines that update their states synchronously according to the same rule-creating the colloid cellular automata. We then analyze the phenomenology of the space-time configurations of the automata and evaluate their complexity using measures such as compressibility, Shannon entropy, Simpson diversity, and expressivity. A hierarchy of phenomenological and measurable space-time complexity is constructed.

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

Thermal comfort driven space heating control via hierarchical state machine strategy interacting with multiphysics simulation

Improving energy efficiency of space heating systems is as important as adopting emerging clean energy technologies for decarbonising heating. While exploring strategies for reducing energy consumption, the space occupants’ expectations to thermal comfort should not be sacrificed. Conventional space heating control systems are often developed under the hypothesis of uniform temperature distribution in a bounded space and the heating system controller is primarily operated based on the information from the temperature sensor at a fixed position. That is, the measured temperature does not represent the temperature surrounding the occupant’s location. This paper explores a new control method; the heating system control is led by the occupant’s thermal experience, expressed by the temperature surrounding the occupant. A new hierarchical state machine control strategy interacting with a Multiphysics simulation model to dynamically estimate the temperature at the point of the occupant is developed. The accuracy of the Multiphysics simulation model is verified based on the corresponding experimental test room data. A co-simulation platform is built to allow COMSOL and SIMULINK to communicate in real-time for control strategy realisation. The experimental results show that the maximum and average mean square error between the measured and simulated temperature under different operating modes is less than 0.21℃ and 0.06℃, respectively. The simulation results indicate that energy consumption could be potentially reduced around 20% with an improved thermal comfort of the occupant by 30%, compared with the conventional fixed sensor control method.

A Brain-Inspired Decision-Making method for upper limb exoskeleton based on Multi-Brain-Region structure and multimodal information fusion

For the challenges faced by upper limb exoskeleton in meeting human motion intention, this article proposes a novel brain-inspired decision-making model based on multi-brain-region information transmission mechanism and multimodal information fusion method to provide accurate prediction of motion trajectory. Firstly, a multimodal information perception and fusion method is proposed. Based on surface electromyographic (sEMG) signals and angle signals collected by sensors. Then a random forest algorithm is applied to screen the key features of sEMG signal for trajectory prediction. A multimodal information hierarchical fusion method based on dual-period mechanism is designed to solve the problem of fusion defect of information with different frequency. Finally, a multi-brain-region decision-making model based on hybrid hierarchical liquid state machine (HHLSM) is established to predict motion trajectory for the exoskeleton to meet human motion intention. Experiments show that the established model has a high computational efficiency and improves the effectiveness of exoskeleton assistance. The brain-inspired decision-making model based on HHLSM established in this paper is of great significance for further research on brain-inspired control and improving the human-robot interaction effect of upper limb exoskeleton.

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.

Multimodal Driver Condition Monitoring System Operating in the Far-Infrared Spectrum

Monitoring the psychophysical conditions of drivers is crucial for ensuring road safety. However, achieving real-time monitoring within a vehicle presents significant challenges due to factors such as varying lighting conditions, vehicle vibrations, limited computational resources, data privacy concerns, and the inherent variability in driver behavior. Analyzing driver states using visible spectrum imaging is particularly challenging under low-light conditions, such as at night. Additionally, relying on a single behavioral indicator often fails to provide a comprehensive assessment of the driver’s condition. To address these challenges, we propose a system that operates exclusively in the far-infrared spectrum, enabling the detection of critical features such as yawning, head drooping, and head pose estimation regardless of the lighting scenario. It integrates a channel fusion module to assess the driver’s state more accurately and is underpinned by our custom-developed and annotated datasets, along with a modified deep neural network designed for facial feature detection in the thermal spectrum. Furthermore, we introduce two fusion modules for synthesizing detection events into a coherent assessment of the driver’s state: one based on a simple state machine and another that combines a modality encoder with a large language model. This latter approach allows for the generation of responses to queries beyond the system’s explicit training. Experimental evaluations demonstrate the system’s high accuracy in detecting and responding to signs of driver fatigue and distraction.

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

Hard‐state Protocol Independent Multicast—Source‐Specific Multicast (HPIM‐SSM)

AbstractSource‐specific multicast is a key technology for multicast services such as IPTV broadcasting, which relies on IGMPv3/MLDv2 for source‐group membership signalling and multicast routing protocols such as PIM‐SSM for building and maintaining receiver‐initiated source‐based distribution trees across the network. The authors propose the Hard‐state Protocol Independent Multicast—Source‐Specific Multicast (HPIM‐SSM), a novel multicast routing protocol that keeps the design principles of PIM‐SSM but overcomes its limitations, such as slow convergence and the possibility of creating suboptimal trees. The state machines of HPIM‐SSM were designed to react promptly to all network events susceptible to reconfiguring the multicast trees, avoiding the need for soft‐state maintenance through the periodic transmission of control messages. Moreover, the authors eliminated the need for designated routers, which led to suboptimal trees, and introduced a control‐driven assert protocol that operates per source, allowing for considerable memory savings. Finally, the protocol enables the coexistence of multiple unicast routing protocols. HPIM‐SSM was implemented in Python, and its correctness was extensively validated through model‐checking techniques. Furthermore, a comparison between HPIM‐SSM and PIM‐SSM was conducted, encompassing both theoretical analysis and experimental evaluation of convergence times. The results demonstrate clearly that HPIM‐SSM outperforms PIM‐SSM, exhibiting significantly faster convergence times and completely avoiding suboptimal trees.

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.

A hierarchical energy management strategy for DC microgrid hybrid energy storage systems based on fractional-order sliding mode controller

A hierarchical energy management strategy (EMS) for a fuel cell (FC)-supercapacitor (SC)‑lithium battery hybrid energy storage system (HESS), based on a fractional-order sliding mode controller (FOSMC), is proposed to address the nonlinear behavior of low-voltage direct current (DC) microgrid HESS. To fully exploit the energy of the SC, the system management layer divides the EMS into maximum power operation mode and state machine control algorithm operation mode. For the first time in equipment control layer, a FOSMC, more suitable for handling nonlinear characteristics, is employed, and its stability is evaluated using the Lyapunov method. Finally, a 48 V HESS experimental platform is constructed for validation, demonstrating that the proposed EMS enhances the stability, response speed, and robustness of the HESS. Compared to conventional sliding mode controller (SMC) and integral sliding mode controller (ISMC), the adoption of a FOSMC reduces the steady-state error of the bus voltage by at least 9.1 % and increases the response speed by at least 11.1 %, while exhibiting good robustness to external disturbances and uncertainties in controller parameters. This fills the current research gap in parameter robustness validation.

Development of a robotic-assisted handling and manipulation system for the high-scale bioproduction of 3D-bioprinted organ-on-a-chip devices

Organs-on-a-chip (OoCs) have proven to mimic the basic physiological behavior of organs and the influence of therapeutics on them in greater detail than conventional models, resulting in enormous projected market growth rates. However, the breakthrough to profitable commercialization of that technology has not yet been achieved, partly because the production process chain is characterized by a high proportion of manual laboratory work. The present work addresses this point. Utilizing affordable components, a demonstrator was developed that can be integrated into an existing 3D-bioprinting system and enables the automated production of perfusion-ready OoC devices starting from pre-fabricated injection-molded microfluidic chips. To this end, a corresponding process chain was first defined, and an expandable, configurable algorithm was developed and validated in the form of a finite state machine (FSM). This algorithm controls a modified 4-axis robot arm that covers the steps upstream and downstream of the printing process in the manufacturing process and achieves success rates of up to 100 %. A virtual interface between the robot and printer enables mutual communication and full integration of the algorithm into the process chain. Steps that pose a challenge for the automation of the process chain and appropriate countermeasures and optimizations were identified. This lays the foundation for scaling and standardizing the automated production of OoCs.

Exploring Scalability of BFT Blockchain Protocols through Network Simulations

Novel Byzantine fault-tolerant (BFT) state machine replication protocols improve scalability for their practical use in distributed ledger technology, where hundreds of replicas must reach consensus. Evaluating the performance of BFT protocol implementations requires careful evaluation. We propose a new methodology using scalable network simulations to predict BFT protocol performance. Our simulation architecture allows for the integration of existing BFT implementations without modification or re-implementation, offering a cost-effective alternative to large-scale cloud experiments. We validate our method by comparing simulation results with real-world cloud deployments, showing that simulations can accurately predict performance at larger scales when network limitations dominate. In our study, we applied this methodology to assess the performance of several “blockchain-generation” BFT protocols, including HotStuff, Kauri, Narwhal & Tusk, and Bullshark, under realistic network conditions (with constrained 25 Mbit/s bandwidth) and induced faults. Kauri emerges as the top performer, achieving 6742 operations per second (op/s) with 128 replicas, outperforming BullShark (2318 op/s) and Tusk (1952 op/s). HotStuff, using secp256k1 and BLS signatures, reaches 494 op/s and 707 op/s, respectively, demonstrating the efficiency of BLS-signature aggregation for saving bandwidth. This study demonstrates that state-of-the-art asynchronous BFT protocols can achieve competitive throughput in large-scale, real-world scenarios.

Improved Fast-Response Consensus Algorithm Based on HotStuff.

Recent Byzantine Fault-Tolerant (BFT) State Machine Replication (SMR) protocols increasingly focus on scalability and security to meet the growing demand for Distributed Ledger Technology (DLT) applications across various domains. Current BFT consensus algorithms typically require a single leader node to receive and validate votes from the majority process and broadcast the results, a design challenging to scale in large systems. We propose a fast-response consensus algorithm based on improvements to HotStuff, aimed at enhancing transaction ordering speed and overall performance of distributed systems, even in the presence of faulty copies. The algorithm introduces an optimistic response assumption, employs a message aggregation tree to collect and validate votes, and uses a dynamically adjusted threshold mechanism to reduce communication delay and improve message delivery reliability. Additionally, a dynamic channel mechanism and an asynchronous leader multi-round mechanism are introduced to address multiple points of failure in the message aggregation tree structure, minimizing dependence on a single leader. This adaptation can be flexibly applied to real-world system conditions to improve performance and responsiveness. We conduct experimental evaluations to verify the algorithm's effectiveness and superiority. Compared to the traditional HotStuff algorithm, the improved algorithm demonstrates higher efficiency and faster response times in handling faulty copies and transaction ordering.

A Proven Translation from a UML State Machine Subset to Timed Automata

Although Unified Modeling Language (UML) state machines constitute a convenient modeling formalism that is widely used in many applications, the lack of formal semantics impedes carrying out automatic processing, such as formal verification. In this article, we aim to achieve a proven translation from a subset of UML state machines to timed automata. A generic abstract syntax is defined for state machines that allows us to specify state machines as a tree-like structure, explicitly illustrating the hierarchical relationships within the model. Based on this syntax, a formal asynchronous semantics for state machines and systems of state machines is established. Additionally, the semantics of timed automata is specified. Then, a translation relation from the considered set of state machines to timed automata is defined and a strong equivalence relation — namely, a timed bisimulation between the source and target models — is formally proven. The proof is carried out inductively while considering continuous (time) and discrete transitions separately. This proof allows us to demonstrate a strong similitude between these models.

The Key Ideas Behind Boki's Shared Logs

The shared log approach has emerged as an attractive state management option for distributed systems. A shared log not only serves as persistent, strongly consistent, and faulttolerant storage, its ability to provide a total order enables fine-grained state machine replication. Boki is a recent shared log system that includes an intuitive LogBook abstraction and novel shared log design choices. Despite Boki being designed as storage for serverless functions, its design principals are applicable to other distributed systems that disaggregate storage from compute.

High energy density picoliter-scale zinc-air microbatteries for colloidal robotics.

The recent interest in microscopic autonomous systems, including microrobots, colloidal state machines, and smart dust, has created a need for microscale energy storage and harvesting. However, macroscopic materials for energy storage have noted incompatibilities with microfabrication techniques, creating substantial challenges to realizing microscale energy systems. Here, we photolithographically patterned a microscale zinc/platinum/SU-8 system to generate the highest energy density microbattery at the picoliter (10-12 liter) scale. The device scavenges ambient or solution-dissolved oxygen for a zinc oxidation reaction, achieving an energy density ranging from 760 to 1070 watt-hours per liter at scales below 100 micrometers lateral and 2 micrometers thickness in size. The parallel nature of photolithography processes allows 10,000 devices per wafer to be released into solution as colloids with energy stored on board. Within a volume of only 2 picoliters each, these primary microbatteries can deliver open circuit voltages of 1.05 ± 0.12 volts, with total energies ranging from 5.5 ± 0.3 to 7.7 ± 1.0 microjoules and a maximum power near 2.7 nanowatts. We demonstrated that such systems can reliably power a micrometer-sized memristor circuit, providing access to nonvolatile memory. We also cycled power to drive the reversible bending of microscale bimorph actuators at 0.05 hertz for mechanical functions of colloidal robots. Additional capabilities, such as powering two distinct nanosensor types and a clock circuit, were also demonstrated. The high energy density, low volume, and simple configuration promise the mass fabrication and adoption of such picoliter zinc-air batteries for micrometer-scale, colloidal robotics with autonomous functions.

Chloride Adsorption on Au (111) Electrodes

There are still numerous open questions with regards to adsorption phenomena and partial charge transfer at electrode interfaces. The dynamic nature electrode structure and adsorbate ordering makes an analysis solely based on thermodynamic considerations limited. Here we present recently collected data with such an analysis, using different concentrations of chloride anions in both acidic and neutral electrolytes, for well-defined Au (111) electrodes. Cyclic voltammograms and charge density data is obtained. Then the electrocapillary equation: "-dγ = σMdE + ΓCl-RTdln(cKCl)" is used to get the relative interfacial tension plot and later the electrosorption valency. In the future we will improve on this by more accurately placing the PZC of charge density measurements, using laser induced temperature jump transients and combining our interpretation with state-of-the-art molecular-dynamics using ground state machine learned potentials. Figure 1

Calibration of Aseptic Loosening Simulation for Coatings Osteoinductive Effect.

The risk of aseptic loosening in cementless hip stems can be reduced by improving osseointegration with osteoinductive coatings favoring long-term implant stability. Osseointegration is usually evaluated in vivo studies, which, however, do not reproduce the mechanically driven adaptation process. This study aims to develop an in silico model to predict implant osseointegration and the effect of induced micromotion on long-term stability, including a calibration of the material osteoinductivity with conventional in vivo studies. A Finite Element model of the tibia implanted with pins was generated, exploiting bone-to-implant contact measures of cylindrical titanium alloys implanted in rabbits' tibiae. The evolution of the contact status between bone and implant was modeled using a finite state machine, which updated the contact state at each iteration based on relative micromotion, shear and tensile stresses, and bone-to-implant distance. The model was calibrated with in vivo data by identifying the maximum bridgeable gap. Afterward, a push-out test was simulated to predict the axial load that caused the macroscopic mobilization of the pin. The bone-implant bridgeable gap ranged between 50μm and 80μm. Predicted push-out strength ranged from 19 N to 21 N (5.4MPa-3.4MPa) depending on final bone-to-implant contact. Push-out strength agrees with experimental measurements from a previous animal study (4 ± 1MPa), carried out using the same implant material, coated, or uncoated. This method can partially replace in vivo studies and predict the long-term stability of cementless hip stems.

Implementing Custom State Machine for 3rd Person Battleground in Unity

Implementing Custom State Machine for 3rd Person Battleground in Unity

Implementation of Rapid Nucleic Acid Amplification Based on the Super Large Thermoelectric Cooler Rapid Temperature Rise and Fall Heating Module.

In the rapid development of molecular biology, nucleic acid amplification detection technology has received more and more attention. The traditional polymerase chain reaction (PCR) instrument has poor refrigeration performance during its transition from a high temperature to a low temperature in the temperature cycle, resulting in a longer PCR amplification cycle. Peltier element equipped with both heating and cooling functions was used, while the robust adaptive fuzzy proportional integral derivative (PID) algorithm was also utilized as the fundamental temperature control mechanism. The heating and cooling functions were switched through the state machine mode, and the PCR temperature control module was designed to achieve rapid temperature change. Cycle temperature test results showed that the fuzzy PID control algorithm was used to accurately control the temperature and achieve rapid temperature rise and fall (average rising speed = 11 °C/s, average falling speed = 8 °C/s) while preventing temperature overcharging, maintaining temperature stability, and achieving ultra-fast PCR amplification processes (45 temperature cycle time < 19 min). The quantitative results show that different amounts of fluorescence signals can be observed according to the different concentrations of added viral particles, and an analytical detection limit (LoD) as low as 10 copies per μL can be achieved with no false positive in the negative control. The results show that the TEC amplification of nucleic acid has a high detection rate, sensitivity, and stability. This study intended to solve the problem where the existing thermal cycle temperature control technology finds it difficult to meet various new development requirements, such as the rapid, efficient, and miniaturization of PCR.