Finite State Machine Approach Research Articles

Finite state machine and ultrasonic ranging-based approach for automatic grasping by aerial manipulator

Finite state machine and ultrasonic ranging-based approach for automatic grasping by aerial manipulator

Security System For Motorcycle Using Raspberry Pi and Fingerprint With Mobile-Based Finite State Machine (FSM) Method

Data from the BPS (Central Statistical Agency) show that private car theft in Indonesia is on the rise year after year, especially for two-wheeled vehicles or motorcycles. According to the BPS Crime Statistics Report, there were around 35,000 motor vehicle theft events in Indonesia in 2017 and 38,000 instances in 2018. People's concerns regarding the security of these two-wheeled vehicles are growing as a result. This study presents a fingerprint- and Raspberry Pi-based motorcycle security system. Bikes may be monitored, and their ignitions can be controlled remotely using a mobile-based Finite State Machine (FSM) technique. The Neo 6 GPS module will send longitude and latitude in real time to the Android smartphone application, which generates a map or location point so that it will send notifications if there is movement on the motorcycle. The voltage sensor connected to the Raspberry Pi circuit can detect the motor voltage. A system that uses the FSM (Finite State Machine) approach to identify changes in location data from designated parking lots and deliver messages to a push bullet is the study's output. Controlling the motorcycle ignition, which may remotely switch off the motorcycle when one is taken, is another way to provide early notice.

Disengaged response behavior when the response button is blocked: Evaluation of a micro-intervention.

In large-scale assessments, disengaged participants might rapidly guess on items or skip items, which can affect the score interpretation's validity. This study analyzes data from a linear computer-based assessment to evaluate a micro-intervention that blocked the possibility to respond for 2 s. The blocked response was implemented to prevent participants from accidental navigation and as a naive attempt to prevent rapid guesses and rapid omissions. The response process was analyzed by interpreting log event sequences within a finite-state machine approach. Responses were assigned to different response classes based on the event sequence. Additionally, post hoc methods for detecting rapid responses based on response time thresholds were applied to validate the classification. Rapid guesses and rapid omissions could be distinguished from accidental clicks by the log events following the micro-intervention. Results showed that the blocked response interfered with rapid responses but hardly led to behavioral changes. However, the blocked response could improve the post hoc detection of rapid responding by identifying responses that narrowly exceed time-bound thresholds. In an assessment context, it is desirable to prevent participants from accidentally skipping items, which in itself may lead to an increasing popularity of initially blocking responses. If, however, data from those assessments is analyzed for rapid responses, additional log data information should be considered.

The Complexity of Analytical Tasks: A Driving Force for Sensor Array Design

Introduction Analytical tasks, the detection goals and scoping of quantitative measurements, are a foundational consideration for the design of chemical sensor arrays. Often sensor arrays are expected to support a variety of possible tasks which are only vaguely defined by both the end user and array designer. Nonetheless, the expedient choices made by or forced upon array designers and fabricators can significantly impact the analytical tasks that could be supported by an array. Analytical tasks may be defined as boundaries in an abstract space defining chemically and practically relevant quantities that are to be measured; chemical sensor and instrument responses may be defined in related spaces specific to their physics. Intelligently finding a mapping between these two abstract spaces defines our problem and informs the impact of analytical tasks upon sensor array design while avoiding the “black box” problems of popular machine learning approaches. This presentation details recent work at NRL that probes the meaning and impact of analytical tasks on chemical sensor array design through general, device agnostic figures of merit developed via techniques originating in statistical mechanics, information theory, information retrieval, and theoretical computer science. Approach Multiple theoretical approaches are explored to determine and measure the impact of analytical task on chemical sensor array design. To begin, we use Ising model-like descriptions of binary sensor responses first described in a biological context by Murugan et. al. [1-3] and subsequently developed by us for chemical sensing with environmental interferents. Analytical tasks are then scoped as molecular and concentration identification against an unknown chemical background. On this framework, we explore the capabilities of a variety of approaches towards assessing the impact of analytical task on sensor array design.The first set of approaches originate in information theory and we consider the mutual information and Kullback-Leibler divergences between different sensor array configurations considered over probability distributions defining the likelihood of that sensor array needing to perform different analytical tasks. Platform-agnostic figures of merit are derived and used to show the impact of analytical tasks on sensor array design.Next, Van Riijsbergen and Melucci [4,5] have developed and elaborated upon an approach to representing and searching information in Hilbert spaces using operator theoretic methods reminiscent to those used in quantum mechanical calculations. By transferring our probabilistic sensor array models to a Hilbert space, we are able to perform similar searches against sensor arrays and by operator methods to ask logical questions of the sensor array using operators that encode analytical tasks and specific exigencies like comparisons to the effectiveness of the array for other analytical tasks.Finally, Almagor et. al. [6-9] have recently developed a finite state machine approach for formally describing the complexity of sensing tasks within formal languages. We attempt to map our binary chemical sensor array model to this methodology and to describe the formal complexity of this toy model for chemical sensing. Analytical tasks are input as restrictions on available formal words in the finite state language. Conclusions Using a simple statistical mechanical model for a chemical sensor array with background interferents, we have been able to probe multiple approaches to quantifying the impact of analytical tasks on the design and relevance of a sensor array for a set of chemical detection problems. Importantly, these approaches avoid drawbacks associated with more empirical, machine learning-based approaches, enabling a more clear understanding of the chemical landscape and how chemical sensor array design interacts with it. Our investigation has explored ways to construct figures of merit for specific families of analytical tasks using methodologies as diverse as information theory, Hilbert space search methods, and complexity techniques for automata-defined sensing.

The Complexity of Analytical Tasks: A Driving Force for Sensor Array Design

Introduction Analytical tasks, the detection goals and scoping of quantitative measurements, are a foundational consideration for the design of chemical sensor arrays. Often sensor arrays are expected to support a variety of possible tasks which are only vaguely defined by both the end user and array designer. Nonetheless, the expedient choices made by or forced upon array designers and fabricators can significantly impact the analytical tasks that could be supported by an array. Analytical tasks may be defined as boundaries in an abstract space defining chemically and practically relevant quantities that are to be measured; chemical sensor and instrument responses may be defined in related spaces specific to their physics. Intelligently finding a mapping between these two abstract spaces defines our problem and informs the impact of analytical tasks upon sensor array design while avoiding the “black box” problems of popular machine learning approaches. This presentation details recent work at NRL that probes the meaning and impact of analytical tasks on chemical sensor array design through general, device agnostic figures of merit developed via techniques originating in statistical mechanics, information theory, information retrieval, and theoretical computer science. Approaches Multiple theoretical approaches are explored to determine and measure the impact of analytical task on chemical sensor array design. To begin, we use Ising model-like descriptions of binary sensor responses first described in a biological context by Murugan et. al. [1-3] and subsequently developed by us for chemical sensing with environmental interferents. Analytical tasks are then scoped as molecular and concentration identification against an unknown chemical background. On this framework, we explore the capabilities of a variety of approaches towards assessing the impact of analytical task on sensor array design.The first set of approaches originate in information theory and we consider the mutual information and Kullback-Leibler divergences between different sensor array configurations considered over probability distributions defining the likelihood of that sensor array needing to perform different analytical tasks. Platform-agnostic figures of merit are derived and used to show the impact of analytical tasks on sensor array design.Next, Van Riijsbergen and Melucci [4,5] have developed and elaborated upon an approach to representing and searching information in Hilbert spaces using operator theoretic methods reminiscent to those used in quantum mechanical calculations. By transferring our probabilistic sensor array models to a Hilbert space, we are able to perform similar searches against sensor arrays and by operator methods to ask logical questions of the sensor array using operators that encode analytical tasks and specific exigencies like comparisons to the effectiveness of the array for other analytical tasks.Finally, Almagor et. al. [6-9] have recently developed a finite state machine approach for formally describing the complexity of sensing tasks within formal languages. We attempt to map our binary chemical sensor array model to this methodology and to describe the formal complexity of this toy model for chemical sensing. Analytical tasks are input as restrictions on available formal words in the finite state language. Conclusions Using a simple statistical mechanical model for a chemical sensor array with background interferents, we have been able to probe multiple approaches to quantifying the impact of analytical tasks on the design and relevance of a sensor array for a set of chemical detection problems. Importantly, these approaches avoid drawbacks associated with more empirical, machine learning-based approaches, enabling a more clear understanding of the chemical landscape and how chemical sensor array design interacts with it. Our investigation has explored ways to construct figures of merit for specific families of analytical tasks using methodologies as diverse as information theory, Hilbert space search methods, and complexity techniques for automata-defined sensing.

New SOGI-FLL Grid Frequency Monitoring with a Finite State Machine Approach for Better Response in the Face of Voltage Sag and Swell Faults

The SOGI-FLL (Second-Order Generalized Integrator–Frequency-Locked Loop) is a well-known and simple adaptive filter that allows for estimating the parameters of grid voltage with a small computational burden. However, the SOGI-FLL has been shown to be especially sensitive to voltage sags and voltage swells, which deeply distort the estimated parameters, especially the frequency. This problem can be alleviated by simply using a saturation block at the Frequency-Locked Loop (FLL) output to limit the impact of distortion on the estimated frequency. Improving upon this straightforward approach, in this paper we propose the use of a finite state machine (FSM) for the definition of the different states of the SOGI-FLL frequency response during a voltage sag or swell fault. The FSM approach allows for applying different gains during the fault, enhancing the SOGI-FLL transient response. The performance of the FSM-based SOGI-FLL is evaluated by using simulation results, which show a better and faster response to these kinds of faults.

On attaining user-friendly hand gesture interfaces to control existing GUIs

BackgroundHand gesture interfaces are dedicated programs that principally perform hand tracking and hand gesture prediction to provide alternative controls and interaction methods. They take advantage of one of the most natural ways of interaction and communication, proposing novel input and showing great potential in the field of the human-computer interaction. Developing a flexible and rich hand gesture interface is known to be a time-consuming and arduous task. Previously published studies have demonstrated the significance of the finite-state-machine (FSM) approach when mapping detected gestures to GUI actions. MethodsIn our hand gesture interface, we broadened the FSM approach by utilizing gesture-specific attributes, such as distance between hands, distance from the camera, and time of occurrences, to enable users to perform unique GUI actions. These attributes are obtained from hand gestures detected by the RealSense SDK employed in our hand gesture interface. By means of these gesture-specific attributes, users can activate static gestures and perform them as dynamic gestures. We also provided supplementary features to enhance the efficiency, convenience, and user-friendliness of our hand gesture interface. Moreover, we developed a complementary application for recording hand gestures by capturing hand keypoints in depth and color images to facilitate the generation of hand gesture datasets. ResultsWe conducted a small-scale user survey with fifteen subjects to test and evaluate our hand gesture interface. Anonymous feedback obtained from the users indicates that our hand gesture interface is adequately facile and self-explanatory to use. In addition, we received constructive feedback about minor flaws regarding the responsiveness of the interface. ConclusionsWe proposed a hand gesture interface along with key concepts to attain user-friendliness and effectiveness in the control of existing GUIs.

Finite State Machine Modelling to Facilitate the Resilience of Infrastructures: Reflections

The ability of an infrastructure to be resistant against hazards or to accommodate and recover from hazard-induced destructions and disturbances is characterized as resilience. Usually, infrastructures are engineered socio-technical systems or systems-of-systems. Jackson and Ferris consider the use of finite state machine (FSM) modelling as a suitable means to depict and investigate the resilience of such engineered systems. This paper discusses the capabilities and limitations of the FSM model proposed by Jackson and Ferris and if it should be used for the representation and evaluation of the resilience of an infrastructure. The discussion is conducted on a more general level. However, special attention is paid to monitoring because, on the one hand, monitoring is one of the cornerstones of resilience and, on the other hand, Scott and Ferris define a state that is emphasized by an increased level of situational awareness as a result of happened and perceived events. Consequently, the question has to be answered of how the models are able to reflect the need for routine monitoring of the resilience of infrastructures in order to initiate, if necessary, adjustment procedures as an appropriate response to changes in internal and external conditions. The results of this theoretical study are a fundamental step towards the practical application of the FSM approach for the design of resilient infrastructures.

Utilization of finite state machine approach for microgrid modeling

The paper describes the finite state machines (FSM) approach in a microgrid’s distributed energy sources modeling. It defines FSM models for a small hydro power plant, a cogeneration unit and a photovoltaic power plant. A comparison of simulation results obtained by FSM models and purely dynamic simulations models, from an execution time point of view, is presented as well.

Flexible and Scalable Energy Management of Islanded Renewable Energy Sources Storage Systems

Power management in islanded off-grid hybrid power-to-power (P2P) systems is challenging due to multiple decisions that need to be taken at each time instance within a perpetually changing environment. In this work, an energy management strategy (EMS) is proposed as a supervisory control framework that relies on a finite state machine (FSM) approach. The FSM offers a systematic way to develop a flexible decision-making algorithm that is combined with propositional-based logic in order to describe the transitions between the different operating states of the integrated system’s components. Easy extension of the EMS to larger and more complex systems without compromising the operational objectives is the major contribution of the developed algorithm. The proposed approach is implemented in an off-grid demonstration site located in Ginostra, Italy. At this site, the only source of power is a photovoltaic (PV) installation array that supports the specified residential loads. An operation analysis is conducted accompanied by an assessment of the site’s environmental footprint. The results indicate that power is supplied without interruption to the load, and CO2 emissions are decreased by 95.6 % in comparison with the former energy solution applied (diesel generators – DSG).

How to conceptualize, represent, and analyze log\xa0data from technology-based assessments? A generic framework and an application to questionnaire items

Log data from educational assessments attract more and more attention and large-scale assessment programs have started providing log data as scientific use files. Such data generated as a by-product of computer-assisted data collection has been known as paradata in survey research. In this paper, we integrate log data from educational assessments into a taxonomy of paradata. To provide a generic framework for the analysis of log data, finite state machines are suggested. Beyond its computational value, the specific benefit of using finite state machines is achieved by separating platform-specific log events from the definition of indicators by states. Specifically, states represent filtered log data given a theoretical process model, and therefore, encode the information of log files selectively. The approach is empirically illustrated using log data of the context questionnaires of the Programme for International Student Assessment (PISA). We extracted item-level response time components from questionnaire items that were administered as item batteries with multiple questions on one screen and related them to the item responses. Finally, the taxonomy and the finite state machine approach are discussed with respect to the definition of complete log data, the verification of log data and the reproducibility of log data analyses.

A Finite State Machine Approach to Algorithmic Lateral Inhibition for Real-Time Motion Detection †.

Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best-characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. The neurally-inspired lateral inhibition method, and its application to motion detection tasks, have been successfully implemented in recent years. In this paper, control knowledge of the algorithmic lateral inhibition (ALI) method is described and applied by means of finite state machines, in which the state space is constituted from the set of distinguishable cases of accumulated charge in a local memory. The article describes an ALI implementation for a motion detection task. For the implementation, we have chosen to use one of the members of the 16-nm Kintex UltraScale+ family of Xilinx FPGAs. FPGAs provide the necessary accuracy, resolution, and precision to run neural algorithms alongside current sensor technologies. The results offered in this paper demonstrate that this implementation provides accurate object tracking performance on several datasets, obtaining a high F-score value (0.86) for the most complex sequence used. Moreover, it outperforms implementations of a complete ALI algorithm and a simplified version of the ALI algorithm—named “accumulative computation”—which was run about ten years ago, now reaching real-time processing times that were simply not achievable at that time for ALI.

HDL-Based Modeling Approach for Digital Simulation of Adiabatic Quantum Flux Parametron Logic

Adiabatic quantum-flux-parametron (AQFP) circuits are currently verified by analog-based simulation, which would be an obstacle for large-scale circuits design. In this paper, we present a logic simulation model for AQFP logic. We made a functional model based on a finite-state machine approach using a hardware description language, which enables the simulation of large-scale AQFP circuits using commercially available logic simulation tools. We have developed a library for logic simulation and implemented an 8-bit carry look-ahead adder, which is composed of over 1000 Josephson junctions. We also include timing information in our logic simulation models for timing analysis. Since the library is based on a parameterized approach, it can be easily modified for different fabrication technologies and low-level circuit parameters.

Building a computing system in bacteria

Synthetic Biology Finite state machines are logic circuits with a predetermined sequence of actions that are triggered depending on the starting conditions. They are used for a variety of devices and biological systems, from vending machines to neural circuits. Roquet et al. have taken a finite state machine approach to control the expression of integrases, or enzymes that insert or excise phage DNA into or out of bacterial chromosomes. The integrases altered the DNA sequence of a plasmid to record all five possible combinations of two inputs. Such circuits can be used to record the states that the cell experienced over time and can be deployed in state-dependent gene expression programs. Science , this issue p. [10.1126/science.aad8559][1] [1]: /lookup/doi/10.1126/science.aad8559

Synthetic recombinase-based state machines in living cells.

State machines underlie the sophisticated functionality behind human-made and natural computing systems that perform order-dependent information processing. We developed a recombinase-based framework for building state machines in living cells by leveraging chemically controlled DNA excision and inversion operations to encode states in DNA sequences. This strategy enables convenient readout of states (by sequencing and/or polymerase chain reaction) as well as complex regulation of gene expression. We validated our framework by engineering state machines in Escherichia coli that used one, two, or three chemical inputs to control up to 16 DNA states. These state machines were capable of recording the temporal order of all inputs and performing multi-input, multi-output control of gene expression. We also developed a computational tool for the automated design of gene regulation programs using recombinase-based state machines. Our scalable framework should enable new strategies for recording and studying how combinational and temporal events regulate complex cell functions and for programming sophisticated cell behaviors.

BiLink: A high performance NoC router architecture using bi-directional link with double data rate

This paper presents a novel high performance Network-on-Chip (NoC) router architecture design using a bi-directional link with double data rate (BiLink). Ideally, it can provide as high as 2 times speed-up compared with the conventional NoC router. BiLink utilizes an extra link stage between routers and transmits two flits in one link per cycle using phase pipelining if both routers require to use the current link. To further increase the effective bandwidth, the direction of each link can be configured in every clock cycle to cater for different traffic loads from each side. Therefore, the data rate can be as high as 4 times compared with conventional NoC routers under uneven traffic. Centralized mode control scheme is implemented using a finite state machine (FSM) approach. Cycle-accurate simulations are carried out on both synthetic traffic patterns as well as real application benchmarks. Simulation results show that BiLink can provide as high as 90% and 250% speedup compared with conventional NoC routers for even and uneven traffic, respectively. 2X and 3X gains in throughput are obtained under even and uneven traffic, respectively, when compared with the conventional NoC router for the virtual channel flow control. The BiLink router architecture is synthesized using TSMC 65nm process technology and it is shown that an area overhead of 28% over state-of-the-art bi-directional NoC is introduced while the critical path is about 9% higher than that of the conventional routers. Despite the overhead in critical path and power consumption, a 47.45% improvement of Energy-Delay-Product (EDP) is achieved by BiLink under high injection rate traffic.

FSM Based DFS Link for Network on Chip

As low power consumption is the main design issue involved in a network on chip (NoC), researchers are concentrating more on both algorithms and architectural approaches. The conventional Dynamic Frequency Scaling (DFS) and history based Frequency Scaling (HDFS) algorithms are utilized to process the energy constrained data traffic. However, these conventional algorithms achieve higher energy efficiencies, and they result in performance degradation due to the auxiliary latency between clock domains. In this paper, we present a variable power optimization interface for NoC using a Finite State Machine (FSM) approach to attain better performance improvement. The parameters are estimated using 45 nm TSMCCMOS technology. In comparison with DFS system, the evaluation results show that FSM-DFS link achieves 81.55% dynamic power savings on the links in the on-chip network, and 37.5% leakage power savings of the link. Also, this proposed work is evaluated for various performance parameters and compared with conventional work. The simulation results are superior to conventional work.

A New Feedback DE‐ELM with Time Delay‐Based EFSM Approach for Fault Prediction of Non‐Linear Processes

Fault prediction is a significant issue for ensuring industrial process safety and reliability. In practical processes, due to complexity and non‐linearity, this leads to many difficulties for process fault prediction. Aiming to improve the fault prediction accuracy in procedure‐oriented systems, a new feedback differential evolution‐optimized extreme learning machine (FDE‐ELM) with a time delay‐based extended finite state machine (TD‐EFSM) approach is proposed. The proposed method is exemplified in the complicated Tennessee Eastman (TE) benchmark process. The results show that the new joint time‐delay EFSM‐based FDE‐ELM shows superiority not only in modelling stability but also in detection sensitivity.

Boost Full Bridge Bidirectional DC/DC Converter for Supervised Aeronautical Applications

The More Electrical Aircraft concept requires electronic devices able to efficiently and safely convert electrical power between different voltage levels. The entire realization of a bidirectional DC/DC converter, from design to validation phase, is here discussed in detail. First, a boost full bridge electrical structure is selected, adopting a Parallel Input Parallel Output (PIPO) interleaving technique and an optimal turns ratio selection for the transformers in order to reduce both weight and size of the equipment. Next, modulation schemes in both step-down and step-up modes are discussed. Successively ad hoc PI regulators for both operative modes are presented. A key idea of the paper is that the converter behavior must be related not only to the control strategy but also to a global supervision logic able to safely conduct the converter operations and to react from external stimuli. Thus, a finite state machine (FSM) approach is employed. An innovative strategy called buffer mode is presented, defined as an intelligent combination of buck and boost modes. Extensive simulations and experimental results are shown, in order to confirm the effectiveness of the proposed approach.

Improving channel capacity by nonlinearity compensation and noise suppression in high-speed multi-span optical transmission systems

In this paper, the channel capacity of 40Gb/s multi-span nonlinear optical transmission systems with nonlinearity compensation and self-adapting Wiener filtering is studied by use of Finite State Machine (FSM) approach. The comparison of channel capacity of the system with differential phase shift keying (DPSK) and on–off keying (OOK) modulation is also investigated. The channel capacity increases monotonically with the input power for transmission systems with simultaneous compensation of dispersion and nonlinearity, which performs as well as linear system. DPSK shows a much better performance and the channel capacity has an improvement of at least 48 percent over that OOK modulation. A further reduction of the amplified spontaneous emission (ASE) noise accumulation is obtained by Wiener filtering, which improves the channel capacity considerably when the input peak power is less than 3mW.