Real-time Execution Research Articles

Assessment of Methods for the Real-Time Simulation of Electronic and Thermal Circuits

Time–domain simulation of electronic and thermal circuits is required by a large array of applications, such as the design and optimization of electric vehicle powertrain components. While efficient execution is always a desirable feature of simulation codes, in certain cases like System-in-the-Loop setups, real-time performance is demanded. Whether real-time code execution can be achieved or not in a particular case depends on a series of factors, which include the mathematical formulation of the equations that govern the system dynamics, the techniques used in code implementation, and the capabilities of the hardware architecture on which the simulation is run. In this work, we present an evaluation framework of numerical methods for the simulation of electronic and thermal circuits from the point of view of their ability to deliver real-time performance. The methods were compared using a set of nontrivial benchmark problems and relevant error metrics. The computational efficiency of the simulation codes was measured under different software and hardware environments, to determine the feasibility of using them in industrial applications with reduced computational power.

НЕЙРОСЕТЕВОЙ АЛГОРИТМ ПОЛНОКАДРОВОГО РАСПОЗНАВАНИЯ НАДВОДНЫХ ОБЪЕКТОВ В РЕАЛЬНОМ ВРЕМЕНИ

The article explores modern neural network architectures for the automatic detection and recognition of marine surface objects and obstacles of given classes throughout the full image area, applicable for execution in real or near real time on an optoelectronic vision system to au-tomate and improve the safety of civil marine navigation. A formal statement of the problem of automatic detection of objects on images is given. The state-of-the-art algorithms for detecting objects in images based on use of artificial convolutional neural networks were reviewed, their comparison was made and a reasonable choice was made in favor of the most efficient neuralnetwork architecture in terms of computational complexity to recognition accuracy. The subject area is studied, as well as publicly available databases of surface objects suitable for use in the training of algorithms using artificial neural networks. The article concluded that there is insuffi-cient labeled data for training neural network algorithms, as a result of which the authors inde-pendently collected research images and video sequences, prepared and labeled the collected data containing surface marine objects and other obstacles that represent a navigation hazard for ships. Based on the selected neural network architecture, a new neural network algorithm for automatic full-frame detection and recognition of surface objects was developed, and an artificial neural network was trained using the prepared database of images of typical objects. The resulting algorithm was tested by the authors on a validation data set, the quality of its work was estimated using various metrics, and the algorithm’s performance was measured. Conclusions are made about the necessity to expand the collected database of images of typical marine objects, further steps are proposed to improve the accuracy of the developed software and algorithmic complex and its implementation to be used in a marine optoelectronic machine vision system for automa-tion and improving the safety of civil navigation.

On the Use of Probabilistic Worst-Case Execution Time Estimation for Parallel Applications in High Performance Systems

Some high performance computing (HPC) applications exhibit increasing real-time requirements, which call for effective means to predict their high execution times distribution. This is a new challenge for HPC applications but a well-known problem for real-time embedded applications where solutions already exist, although they target low-performance systems running single-threaded applications. In this paper, we show how some performance validation and measurement-based practices for real-time execution time prediction can be leveraged in the context of HPC applications on high-performance platforms, thus enabling reliable means to obtain real-time guarantees for those applications. In particular, the proposed methodology uses coordinately techniques that randomly explore potential timing behavior of the application together with Extreme Value Theory (EVT) to predict rare (and high) execution times to, eventually, derive probabilistic Worst-Case Execution Time (pWCET) curves. We demonstrate the effectiveness of this approach for an acoustic wave inversion application used for geophysical exploration.

Cruise Control for a Two-Wheeled Mobile Vehicle Using Its Mixed Logical Dynamical System Model

This paper proposes a cruise control for a two-wheeled mobile vehicle on a two-dimensional plane. The cruise control is executed using model predictive control (MPC), such that input and state constraints can be addressed explicitly. For real-time execution of MPC, we model the dynamics of the vehicle as a mixed logical dynamical system rather than an underlying nonlinear model. The cruise control is then reduced to a mixed integer quadratic programming problem, for which efficient solvers using the convexities are available. Simulations and experiments demonstrate the effectiveness of the proposed cruise control through a computation time comparison with the nonlinear MPC method.

Development of a diesel engine’s digital twin for predicting propulsion system dynamics

A digital twin is the essential part of a recent and unavoidable trend in ship operation digitalisation. The digital twin is a virtual replica of real ship or a particular system that coexists with its physical counterpart and maps the dynamic behaviour in real-time. Thus, the digital twin combines physical space real-time data with a set of dynamic models representing the physical counterpart in the cyberspace. The problem of digital twin development is a trade-off between insight into the dynamic process and real-time execution constraint. This paper describes a modelling approach that combines continuous time-domain cycle-mean value engine model with the crank-angle resolved phenomenological combustion model, satisfying the real-time execution constraint. The set of conservation laws, notably energy and mass, supplemented with the phenomenological Wiebe combustion model, is treated in the integral form allowing transformation into a set of nonlinear algebraic equations. The solution of the resulting system exhibits fast speed and accuracy as compared with the traditional approach combining differential equations and Runge-Kutta solver.

A score identification parallel system based on audio-to-score alignment

This paper presents a parallel system for searching a digital score of classical music in a personal library. The application scenario of the system is for a musician who wants to search for a specific score in its own device by playing an excerpt of a few seconds of the composition. We propose a solution, based on audio-to-score alignment, which allows to identify the correct score in a database of musical pieces in real time. This is a challenging task because we focus on a real-time system targeted for handheld devices characterized by both mobility and low power consumption. Experimental results show that it is possible to achieve real-time execution in the tested scenarios using parallel computing techniques with ARM processors.

A Novel Cost-Efficient Framework for Critical Heartbeat Task Scheduling Using the Internet of Medical Things in a Fog Cloud System.

Recently, there has been a cloud-based Internet of Medical Things (IoMT) solution offering different healthcare services to wearable sensor devices for patients. These services are global, and can be invoked anywhere at any place. Especially, electrocardiogram (ECG) sensors, such as Lead I and Lead II, demands continuous cloud services for real-time execution. However, these services are paid and need a lower cost-efficient process for the users. In this paper, this study considered critical heartbeat cost-efficient task scheduling problems for healthcare applications in the fog cloud system. The objective was to offer omnipresent cloud services to the generated data with minimum cost. This study proposed a novel health care based fog cloud system (HCBFS) to collect, analyze, and determine the process of critical tasks of the heartbeat medical application for the purpose of minimizing the total cost. This study devised a health care awareness cost-efficient task scheduling (HCCETS) algorithm framework, which not only schedule all tasks with minimum cost, but also executes them on their deadlines. Performance evaluation shows that the proposed task scheduling algorithm framework outperformed the existing algorithm methods in terms of cost.

Search-space Decomposition for System-level Design Space Exploration of Embedded Systems

The development of large-scale multi- and many-core platforms and the rising complexity of embedded applications have led to a significant increase in the number of implementation possibilities for a single application. Furthermore, rising demands on safe, energy-efficient, or real-time capable application execution make the problem of determining feasible implementations that are optimal with respect to such design objectives even more of a challenge. State-of-the-art Design Space Exploration (DSE) techniques for this problem demonstrably suffer from the vast and sparse search spaces posed by modern embedded systems, emphasizing the need for novel design methodologies in this field. Based on the idea of reducing problem complexity by a suitable decomposition of the system specification—in particular, by a reduction of target architecture or task mapping options—the work at hand proposes a portfolio of dynamic decomposition mechanisms that automatically decompose any system specification based on a short pre-exploration of the complete system. We present a two-phase approach consisting of (a) a set of novel data extraction and representation techniques combined with (b) a selection of filtering operations that automatically extract a decomposed system specification based on information gathered during pre-exploration. In particular, we employ heat map data structures and threshold as well as graph-partitioning filters to reduce problem complexity. The proposed decomposition procedure can seamlessly be integrated in any DSE flow, constituting a flexible extension for existing DSE approaches. Furthermore, it improves existing static decomposition techniques and other heuristics relying on information about the problem instance, since systems with irregular architectural topology or distribution of resource types can now be decomposed based on an automatic, problem-independent pre-exploration phase. We illustrate the efficiency of the proposed decomposition portfolio applied to state-of-the-art DSEs for many-core systems as well as networked embedded systems from the automotive domain. Experimental results show significant increases in optimization quality of up to 87% within constant DSE time compared to existing approaches.

An ontology-driven framework to support the dynamic formation of an interdisciplinary healthcare team

Background and purposeTeamwork has become a modus operandi in healthcare and delivery of patient care by an interdisciplinary healthcare team (IHT) is now a prevailing modality of care. We argue that a formal and automated support framework is needed for an IHT to properly leverage information technology resources. Such a framework should allow for patient preferences and expand a representation of a clinical workflow with a formal model of dynamic formation of a team, especially with regards to team leader- and membership, and the assignment of tasks to team members. Our goal was to develop such a support framework, present its prototype software implementation and verify the implementation using a proof-of-concept use case. Specifically, we focused on clinical workflows for in-patient tertiary care and on patient preferences with regards to selecting team members and team leaders. Materials and methodsDrawing on the research on clinical teamwork we defined the conceptual foundations for the proposed framework. Then, we designed its architecture and used ontology-driven design and first-order logic with associated reasoning methods to create and operationalize architectural elements. Finally, we incorporated existing solutions for business workflow modeling and execution as a backend for implementing the proposed framework. ResultsWe developed a Team and Workflow Management Framework (TWMF) with semantic components that allow for formalizing and operationalizing team formation in in-patient tertiary care setting and support provider-related patient preferences. We also created a prototype software implementation of TWMF using the IBM Business Process Manager platform. This implementation was evaluated in several simulated patient scenarios. ConclusionsTWMF integrates existing workflow technologies and extends them with the capabilities to support dynamic formation of an IHT. Results of this research can be used to support real-time execution of clinical workflows, or to simulate their execution in order to assess the impact of various conditions (e.g., patterns of work shifts, staffing) on IHT operations.

Importance of Graphical User Interface in the design of P300 based Brain–Computer Interface systems

Objectives: Develop an effective and intuitive Graphical User Interface (GUI) for a Brain–Computer Interface (BCI) system, that achieves high classification accuracy and Information Transfer Rates (ITRs), while using a simple classification technique. Objectives also include the development of an output device, that is capable of real time execution of the selected commands. Methods:A region based T9 BCI system with familiar face presentation cues capable of eliciting strong P300 responses was developed. Electroencephalogram (EEG) signals were collected from the Oz, POz, CPz and Cz electrode locations on the scalp and subsequently filtered, averaged and used to extract two features. These feature sets were classified using the Nearest Neighbour Approach (NNA). To complement the developed BCI system, a ‘drone prototype’ capable of simulating six different movements, each over a range of eight distinct selectable distances, was also developed. This was achieved through the construction of a body with 4 movable legs, capable of tilting the main body forward, backward, up and down, as well as a pointer capable of turning left and right. Results:From ten participants, with normal or corrected to normal vision, an average accuracy of 91.3 ± 4.8% and an ITR of 2.2 ± 1.1 commands/minute (12.2 ± 6.0 bits/minute) was achieved. Conclusion:The proposed system was shown to elicit strong P300 responses. When compared to similar P300 BCI systems, which utilise a variety of more complex classifiers, competitive accuracy and ITR results were achieved, implying the superiority of the proposed GUI. Significance: This study supports the hypothesis that more research, time and care should be taken when developing GUIs for BCI systems.

A Parameterized Formal Model for the Analysis of Preemption-Threshold Scheduling in Real-Time Systems

Time-Critical systems are both complex and critical. Therefore, thorough verification processes of such systems must be performed, including behavior and timing correctness. These systems are usually designed as a set of several interacting tasks and sharing one or more processors. Several scheduling policies have been proposed to manage the dispatch of these tasks for these shared resources so as to respect their predefined deadlines. In this context, preemption threshold scheduling algorithms have been proposed to improve real-time schedulability by managing preemptiveness of tasks. The advantage of these algorithms highly depends on a proper assignment of a set of scheduling attributes, which are priority and preemption threshold. In this paper, we propose a high-level parameterized formal model for the description, analysis and comparison of preemptive, non-preemptive and threshold-based scheduling policies. This model allows to formally verify a set of timing-constraints, in particular schedulability analysis as well as to provide a set of experimental results based on real-time executions. A stochastic version of our model is also proposed allowing to define probabilistic features and to model stochastic real-time tasks, scheduling mechanisms or energy consumption.

An Empirical Study on System Level Aspects of Internet of Things (IoT)

Internet of Things (IoT) is an integration of the Sensor, Embedded, Computing, and Communication technologies. The purpose of the IoT is to provide seamless services to anything, anytime at any place. IoT technologies play a crucial role everywhere, which brings the fourth revolution of disruptive technologies after the internet and Information and Communication Technology (ICT). The Research & Development community has predicted that the impact of IoT will be more than the internet and ICT on society, which improves the well-being of society and industries. Addressing the predominant system-level design aspects like energy efficiency, robustness, scalability, interoperability, and security issues result in the use of a potential IoT system. This paper presents the current state of art of the functional pillars of IoT and its emerging applications to motivate academicians and researches to develop real-time, energy-efficient, scalable, reliable, and secure IoT applications. This paper summarizes the architecture of IoT, with the contemporary status of IoT architectures. Highlights of the IoT system-level issues to develop more advanced real-time IoT applications have been discussed. Millions of devices exchange information using different communication standards, and interoperability between them is a significant issue. This paper provides the current status of the communication standards and application layer protocols used in IoT with the detailed analysis. The computing paradigms like Cloud, Cloudlet, Fog, and Edge computing facilitate IoT with various services like data offloading, resource and device management, etc. In this paper, an exhaustive analysis of Edge Computing in IoT with different edge computing architectures and existing status are deliberated. The widespread adoption of IoT in society has resulted in privacy and security issues. This paper emphasizes on analyzing the security challenges, privacy and security threats, conventional mitigation techniques, and further scope for IoT security. The features like fewer memory footprints, scheduling, real-time task execution, fewer interrupt, and thread switching latency of Real-Time Operating Systems (RTOS) enables the development of time critical IoT applications. Also, this review offers the analysis of the RTOS’s suitable for IoT with the current status and networking stack. Finally, open research issues in IoT system development are discussed.

Real-Time Estimation of Drivers' Trust in Automated Driving Systems

Trust miscalibration issues, represented by undertrust and overtrust, hinder the interaction between drivers and self-driving vehicles. A modern challenge for automotive engineers is to avoid these trust miscalibration issues through the development of techniques for measuring drivers' trust in the automated driving system during real-time applications execution. One possible approach for measuring trust is through modeling its dynamics and subsequently applying classical state estimation methods. This paper proposes a framework for modeling the dynamics of drivers' trust in automated driving systems and also for estimating these varying trust levels. The estimation method integrates sensed behaviors (from the driver) through a Kalman lter-based approach. The sensed behaviors include eye-tracking signals, the usage time of the system, and drivers' performance on a non-driving-related task (NDRT). We conducted a study (n = 80) with a simulated SAE level 3 automated driving system, and analyzed the factors that impacted drivers' trust in the system. Data from the user study were also used for the identi cation of the trust model parameters. Results show that the proposed approach was successful in computing trust estimates over successive interactions between the driver and the automated driving system. These results encourage the use of strategies for modeling and estimating trust in automated driving systems. Such trust measurement technique paves a path for the design of trust-aware automated driving systems capable of changing their behaviors to control drivers' trust levels to mitigate both undertrust and overtrust.

Validation of a Real-Time Capable Multibody Vehicle Dynamics Formulation for Automotive Testing Frameworks Based on Simulation

The growing functionalities implemented on vehicles have increased the importance of simulation in the design process. This complexity is mainly driven by the introduction of electrified powertrains, Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS). Additionally, the automotive industry must reduce development times and cost, while keeping flexible development capabilities and fulfilling demanding regulation standards for safety-critical systems. Existing testing frameworks based on simulation implement typically analytical models to ensure real-time performance, and provide limited flexibility to perform Hardware in the Loop (HiL) setup based tests. In this work a vehicle modelling approach which guarantees high accuracy and real-time capabilities is proposed. Moreover, the proposed approach is validated firstly with real vehicle data, demonstrating that it can fairly reproduce the behaviour of the vehicle tested; and secondly, in a HiL setup to demonstrate the real-time execution capabilities of the approach.

Locomo-Net: A Low -Complex Deep Learning Framework for sEMG-Based Hand Movement Recognition for Prosthetic Control.

Background: The enhancement in the performance of the myoelectric pattern recognition techniques based on deep learning algorithm possess computationally expensive and exhibit extensive memory behavior. Therefore, in this paper we report a deep learning framework named ‘Low-Complex Movement recognition-Net’ (LoCoMo-Net) built with convolution neural network (CNN) for recognition of wrist and finger flexion movements; grasping and functional movements; and force pattern from single channel surface electromyography (sEMG) recording. The network consists of a two-stage pipeline: 1) input data compression; 2) data-driven weight sharing. Methods: The proposed framework was validated on two different datasets- our own dataset (DS1) and publicly available NinaPro dataset (DS2) for 16 movements and 50 movements respectively. Further, we have prototyped the proposed LoCoMo-Net on Virtex-7 Xilinx field-programmable gate array (FPGA) platform and validated for 15 movements from DS1 to demonstrate its feasibility for real-time execution. Results: The effectiveness of the proposed LoCoMo-Net was verified by a comparative analysis against the benchmarked models using the same datasets wherein our proposed model outperformed Twin- Support Vector Machine (SVM) and existing CNN based model by an average classification accuracy of 8.5 % and 16.0 % respectively. In addition, hardware complexity analysis is done to reveal the advantages of the two-stage pipeline where approximately 27 %, 49 %, 50 %, 23 %, and 43 % savings achieved in lookup tables (LUT’s), registers, memory, power consumption and computational time respectively. Conclusion: The clinical significance of such sEMG based accurate and low-complex movement recognition system can be favorable for the potential improvement in quality of life of an amputated persons.

Real-Time Model Predictive Control of Rigid Body Motion via Discretization Using the Cayley Map

This paper presents a fast nonlinear model predictive control method for rigid body dynamical systems such as spacecraft or aerial vehicles on the special Euclidean group SE(3). The focus of this research is on the real-time execution of the optimal control on a low-cost embedded computer. The position and orientation of a rigid body are expressed as a 6-dimensional vector via the Cayley map for SE(3). Based on the representation, the motion can be exactly discretized on the algebra se(3). As a result, coarse sampling intervals can be selected to reduce the number of prediction steps. Furthermore, the recursive discretization technique is applied to effectively reduce the decision variables of the nonlinear optimization problem by eliminating states from them. Simulation results on a Raspberry Pi single-board computer are given to prove that the present model predictive control method is feasible in real time. The effectiveness of the present controller is further verified by an experiment using a fully actuated hexarotor unmanned aerial vehicle.

A Container Based Edge Offloading Framework for Autonomous Driving

Autonomous driving is one of the most innovative applications nowadays. However, autonomous driving is still suffering from heavy calculation, high energy consumption and strict real-time execution constraints. Different from cloud computing, edge computing deploys calculation, storage and service on the edge of network. It is a better platform to serve efficiency and privacy oriented autonomous driving service offloading. To this end, we proposed a container-based edge offloading framework for autonomous driving. This framework builds an Offloading Decision Module, an Offloading Scheduler Module and an Edge Offloading Middleware on top of the lightweight virtualization. It provides the abstraction and management of the execution environment in the granularity of containers on edge. Therefore, it enables the privacy preserve and resource isolation for autonomous driving execution constraints. Its utility preferable offloading schedule strategy formalized the multi-application multi-edge nodes mapping problem into a multiple multidimensional knapsack problem (MMKP) and gave a utility oriented greedy algorithm (GA) for real-time solving. The experimental results show that the proposed framework has high feasibility and isolation meanwhile can guarantee millisecond-level autonomous driving offloading on edge.

An integrative approach for path planning and tracking of shape aware mobile robot in structured environment using vision sensor

A shape-aware path planning algorithm is necessary for real-time execution of a task by a mobile robot whereas path planning algorithms available in literature consider mobile robot as a point object. Current work proposes a shape-aware A* path planning approach with a heuristic function to accommodate the shape of mobile robot. In this paper, the detection, tracking and control of the robot has been carried out for the mobile robot while performing a task in a structured environment. For the implementation and validation, an overhead camera is used to capture the images of obstacles in the task space and published in ROS platform. The captured images are also processed using OpenCV software for detection and tracking using Kanade-Lucas-Tomasi (KLT) and Kalman filter algorithms for different test scenarios. The proposed approach accurately detects and tracks the shape aware mobile robot with percentage error ranging from 6%-10% in different cases.

Energy Efficient Light Weight Security Algorithm for Low Power IOT Devices

Internet of Things (IoT) is the state of art which connects, communicates, intelligently resolves and processes data between physical devices and smart phone or to a centralized server. Billions of users are centrally coordinated via the internet. The number of ubiquitous IoT devices will surpass the number of humans. For secured data transfer, IoT requires strenuous focus on security. Inspite of the secured IoT layered approach integrated in its architecture, yet they are susceptible to thwarting attacks. With proliferating applications and innovations, there is a stringent need to preserve user privacy and anonymize interactions using a lightweight cryptographic algorithm. Existing cryptographic algorithms have constraints on power, limited battery, real time execution, latency, code length and memory. In this research, initially comparison of the existing algorithms is made. Subsequently, Augmented Security and Optimized memory space is achieved for the data channelized via IoT by using the combination of the Light weight masked AES (Advanced Encryption Standard) andMD5 (Message Digest) hash algorithm. This chaining technique is implemented using VHDL Coding, Xilinx ISE and ModelSim 6.5 software tool. In the proposed algorithm, area, power and timing factors are reduced using optimization techniques, which drastically reduces the power consumed, and chip area. Chip area is calculated in terms of gate equivalents and power consumption is reduced through clock gating and operand isolation techniques.

IoT Based Flow Valve Control and Accounting System

The most important and necessary factor for all living individuals in the present world is water. Drinking water utilization suffers from many problems or difficulties in real-time execution. Nowadays, due to increasing population providing drinking water facilities to everywhere is a big challenge resulted in insufficiency of water. Water contamination is the main cause for scarcity of water. The main reasons for water pollution are use of pesticides, chemical fertilizers and Industrialization. Due to this, Water gets contaminated and it causes severe problems like waterborne infections to individual lives and it also hazardous to aquatic life. Due to all this there is need for water quality checking in specific duration of time or regularly. Parameters that are to be checked to assess the water quality are Temperature, pH, turbidity and Salinity. Based on the measurement obtained about the parameter the water usage will be decided.