Algorithm For Real-time Systems Research Articles

Average Max Round Robin Algorithm: A Case Study

Round Robin Algorithm is one wellknown algorithm in real time system. Several variants of round robin algorithms are in the fields. Average max round robin algorithm is a breakthrough to optimize context switching or also called quantum. Context switching is one big problem in round robin algorithm. To optimize high context switching is the key solution. This will make this algorithm efficient. There should be a way to optimize this context switching. Then the average max round robin algorithm is one solution to this problem. The average max algorithm is defined by finding the average of burst time then add the maximum burst time to the average burst time. Then calculate again the average of the two. The calculation will be iterated in the next round robin cycle. Here, in this journal, three case studies are discussed. Each with different burst times to understand this average max round robin algorithm more clearly. In the first case study we get turn around time 34 ms, and average waiting time 20.6 ms. In the second case study, we get average turn around time 21.8 ms, and the average waiting time 13 ms. And in the last case study, the third one, we get turn around time 12.2 ms, and the average waiting time 6.6 ms. There is no calculation for the second iteration for all case studies. Since the left burst time is only in one process. Optimizing the context switching, minimizing average turnaround time, and average waiting time is the key solution to round robin algorithm.

Average Max Round Robin Algorithm: A Case Study

Round Robin Algorithm is one wellknown algorithm in real time system. Several variants of round robin algorithms are in the fields. Average max round robin algorithm is a breakthrough to optimize context switching or also called quantum. Context switching is one big problem in round robin algorithm. To optimize high context switching is the key solution. This will make this algorithm efficient. There should be a way to optimize this context switching. Then the average max round robin algorithm is one solution to this problem. The average max algorithm is defined by finding the average of burst time then add the maximum burst time to the average burst time. Then calculate again the average of the two. The calculation will be iterated in the next round robin cycle. Here, in this journal, three case studies are discussed. Each with different burst times to understand this average max round robin algorithm more clearly. In the first case study we get turn around time 34 ms, and average waiting time 20.6 ms. In the second case study, we get average turn around time 21.8 ms, and the average waiting time 13 ms. And in the last case study, the third one, we get turn around time 12.2 ms, and the average waiting time 6.6 ms. There is no calculation for the second iteration for all case studies. Since the left burst time is only in one process. Optimizing the context switching, minimizing average turnaround time, and average waiting time is the key solution to round robin algorithm.

A novel hardware implemented programmable controller adapted to cooperate with AI tuning algorithms in real time systems

A novel, transistor level PID (proportional–integrating–derivative) controller is proposed that may be easily coupled with various AI algorithms responsible for adjusting its main factors. In our work we focus on a discrete-time digital approach, as it facilities realization a flexible and programmable structure that can be quickly re-configured depending on varying environment conditions. One of the novelties of the proposed solution is its parallel and asynchronous structure that requires only simple 2-phases clock. Each of the P, I and D blocks is realized as a separate channel with its own multi-bit multiplier, a summing circuit and a short delay line (in case of I and D blocks). The circuit features small chip area. For the input signals, and the coefficients of the controller being encoded as 8-bit signals the number of transistors does not exceed 13,000, which translates into the chip area not exceeding 0.1 mm2 in the CMOS 130 nm technology. Data rate of the controller can achieve 200–330 MSamples/s, with power dissipation not exceeding 1 mW. The proposed solution is suitable for miniaturized microsystems, for example high performance MEMS devices, in which small sizes and high data rate become very important features.

Route Planning for Autonomous Mobile Robots Using a Reinforcement Learning Algorithm

This research suggests a new robotic system technique that works specifically in settings such as hospitals or emergency situations when prompt action and preserving human life are crucial. Our framework largely focuses on the precise and prompt delivery of medical supplies or medication inside a defined area while avoiding robot collisions or other obstacles. The suggested route planning algorithm (RPA) based on reinforcement learning makes medical services effective by gathering and sending data between robots and human healthcare professionals. In contrast, humans are kept out of the patients’ field. Three key modules make up the RPA: (i) the Robot Finding Module (RFM), (ii) Robot Charging Module (RCM), and (iii) Route Selection Module (RSM). Using such autonomous systems as RPA in places where there is a need for human gathering is essential, particularly in the medical field, which could reduce the risk of spreading viruses, which could save thousands of lives. The simulation results using the proposed framework show the flexible and efficient movement of the robots compared to conventional methods under various environments. The RSM is contrasted with the leading cutting-edge topology routing options. The RSM’s primary benefit is the much-reduced calculations and updating of routing tables. In contrast to earlier algorithms, the RSM produces a lower AQD. The RSM is hence an appropriate algorithm for real-time systems.

Development of Energy- Efficient Load Balancing Scheduling Algorithm in Real-time Systems

Real- time systems are considered as one of the most important topics that have attracted the attention of researchers in various scientific and technical fields due to their widespread in many fields of communication, informatics, operating systems and automatic control systems. The basic idea in real- time systems is the execution of a set of tasks that are assigned to CPUs, within a time constraint associated with each of them, and this time constraint is called a deadline. Task scheduling in real- time systems is the main factor in determining the success or failure of the system. Therefore, many studies have imposed suggestions and hypotheses for improving scheduling, according to the underlined platform, whether it is single- processor, multi- processor, or multi- core processor. Researchers have recently focused on studying and providing solutions to scheduling problems, taking into account energy consumption since energy consumption today plays an important role in determining the efficiency and reliability of the system. Energy consumption can be saved either statically by turning the processor into sleep mode or dynamically by changing the frequency of the processor cores and thus reducing power consumption. However, a slight increase in frequency leads to executing more tasks in less time and thus can lead to an increase in the efficiency of the real- time system by reducing tasks which may miss its deadline at the time of execution. In this research, a new method has been proposed to control the processor frequency in an optimal manner that makes a balanced trade- off between power consumption and execution of tasks in real- time systems.

Fault Tolerance in the Joint EDF-RMS Algorithm: A Comparative Simulation Study

Failure is a systemic error that affects overall system performance and may eventually crash across the entire configuration. In Real-Time Systems (RTS), deadline is the key to successful completion of the program. If tasks effectively meet the deadline, it means the system is working in pristine order. However, missing the deadline means a systemic fault due to which the system can crash (hard RTS) or degrade inclusive performance (soft RTS). To fine-tune the RTS, tolerance is the critical issue and must be handled with extreme care. This article explains the context of fault tolerance with improvised Joint EDF-RMS algorithm in RTS. The backup method has been derived to prevent the system from being recursively migrating the same task. If any task migrates three times, this migrated task will get shifted to the backup queue. This backup queue assigns the task to a backup processor and is destined for final execution. For performance evaluation purposes, a relative graph between fault and failure rates, failure and total processor utilization along with other averages have been evaluated. Furthermore, these archived results are compared with fault-tolerant Earliest Deadline First (EDF) and Rate Monotonic Scheduling (RMS) algorithms independently in relatively similar conditions. These comparisons show better performance against overloading conditions.

Tasks Scheduling Through Hybrid Genetic Algorithm in Real-Time System on Heterogeneous Environment

Tasks Scheduling Through Hybrid Genetic Algorithm in Real-Time System on Heterogeneous Environment

High-level synthesis system based on hybrid reconfigurable microarchitecture

Subject of Research. The paper presents research of state-of-the-art methods of real-time hardware / software systems development based on FPGAs and SoCs. High-level synthesis system based on the hybrid reconfigurable NISC/TTA microarchitecture is proposed. Method. The work is based on analysis and synthesis of computer architectures and their design methods within Model-Driven Engineering, High-Level Synthesis and Hardware/Software Codesign methodologies. Main Results. Analysis of academic and commercial tools for development of hardware/software systems based on FPGAs and SoCs is performed. The key factors are determined limiting the wide introduction of high-level synthesis tools in industry. The ideology and architecture of the high-level synthesis system are developed on the basis of the original reconfigurable NISC/TTA microarchitecture. The prototype of the considered EDA system is developed. The synthesis tool and the synthesis process control interface are among the most valuable outcomes and give the possibility to explore the results of synthesis decisions made by the tool, and control the process either manually or automatically. The visualization interface of the target computational process is implemented that allows for effective representation of its multi-level organization. The end-to-end testing system is developed enabling verification of compliance between a synthesized system and its functional model. Practical Relevance. The tools implemented as part of the CAD prototype have made the synthesis process transparent and manageable for the designer and demonstrated the possibility of finding compromise design solutions in semi-automatic mode. We managed to control flexibly heuristics in the synthesizer operation, not only reducing design iterations, but also making the process convergent in principle, which is not provided by alternative technologies in many cases. NISC/TTA microarchitecture played an important role in this process. Solution of a number of test problems has shown that this design platform can be recommended for implementation of control algorithms in real-time systems and for the application in system dynamics modeling.

Using low-power platforms for Evolutionary Multi-Objective Optimization algorithms

Nowadays, the application of Evolutionary Multi-Objective Optimization (EMO) algorithms in real-time systems receives considerable interest. In this context, the energy efficiency of computational systems is of paramount relevance. Recently, the use of embedded systems based on heterogeneous (CPU + GPU) platforms is consistently increasing. For example, NVIDIA Jetson cards are low-power computers designed for development of embedded applications. They incorporate Tegra processors which feature a CUDA-capable GPU. This way, Jetson cards can be considered as a prototype of low-power computer of High-Performance Computing. In this work, our interest is focused on the NSGA-II algorithm, a well-known representative of EMO algorithms. The strength of NSGA-II lies in its Non-Dominated Sorting (NDS) procedure of a population of individuals. Our purpose on the low-power computers is twofold: to define and evaluate the parallel NSGA-II versions with major focus on NDS procedure on the Jetson platforms and to determinate the size of NSGA-II problems which can be solved. The results show that the parallel version which achieves the best performance depends on the objectives functions and the frequencies of the clocks of the cores and memory of the GPU. The analysis of the results shows the capability of the Jetson as a low-consumption platform which allows to accelerate the execution of instances of the state-of-the-art EMO algorithm--NSGA-II.

Noise-Free Speech Channel Modeling for Technical Control Systems

The paper focuses on the application of digital adaptive filtering to ensure the noise-free speech channels of control systems. Noise is an extraneous signal, which enters the channel device input together with the speech. The article conducts a comparative analysis of the three adaptation algorithms (LMS, NLMS, RLS) to be useful for noise suppression. At the same time, an adaptive suppression mechanism with two microphones on the speech channel is used. The related publications compared the algorithms by one or two criteria. This paper offers three comparison criteria: computational complexity, quality of noise suppression, and rate of convergence to the steady-state condition. The number of vector operations in algorithm procedures estimates temporary computational complexity. To compare algorithms by the other two criteria, their implementation was simulated in MATLAB. As the noise, were used the white and pink noise, a sine wave, and a model of the non-stationary signal as well. The noise suppression coefficient and the number of iterations before transition to the steady-state condition have been obtained. The algorithm RLS showed the best quality of suppression while the NLMS algorithm revealed the highest rate of convergence. Experiments have shown that the white noise is suppressed worse, but faster than the sine one. The paper explores influence of some factors on the process of adaptation. It is shown that increase in filter dimension leads to improving quality of adaptation and its speed-down. From simulation results it follows that the noise suppression coefficient has the highest values when at the filter input there are approximately equal signal and noise powers. The results allow us to make commendation to use the NLMS algorithm for real-time systems, and the RLS one for technical systems aimed at recordings of speech signals. The above algorithms can be successfully implemented on modern technology platforms, comprising high-performance digital signal processors and associated peripherals.

Real-time small target detection method Using multiple filters and IPP Libraries in Infrared Images

In this paper, we propose a fast small target detection method using multiple filters, and describe system implementation using IPP libraries. To detect small targets in Infra-Red images, it is mandatory that you should apply a filter to eliminate a background and identify the target information. Moreover, by using a suitable algorithm for the environments and characteristics of the target, the filter must remove the background information while maintaining the target information as possible. For this reason, in the proposed method we have detected small targets by applying multi area(spatial) filters in a low luminous environment. In order to apply the multi spatial filters, the computation time can be increased exponentially in case of the sequential operation. To build this algorithm in real-time systems, we have applied IPP library to secure a software optimization and reduce the computation time. As a result of applying real environments, we have confirmed a detection rate more than 90%, also the computation time of the proposed algorithm have been improved about 90% than a typical sequential computation time.

Artifact Reduction For Visually Challenged

Compression of image or video now happens to be an essential feature to fit a large amount of binary data into an available narrow bandwidth digital communication channel. Inveterate compression algorithms formulated by means of Discrete Cosine Transform and Wavelet Transform suffers a lot with the presence of undesirable frequency components which in turn pull down the visual quality of the image or video. Those frequency components create prominent visible changes, identified as artifacts. The visual quality of such images needs improvement by the elimination of these artifacts not only for consumer electronics but also for the analysis and decision making algorithms in real time systems. Artifacts can be modeled with the characteristics of neighboring pixels. The most noticeable are blocking, ringing and mosquito artifacts. The proposed adaptive filter applicable for JPEG compression algorithms detects the blocking artifacts with the new metric voted as Total Blocking Error. Subsequently the adaptive control superimposed for the corrective parameter of the localized filter and for the correlation factor to create the new adaptive quantization matrix for correcting the rounded off Discrete Cosine Transform coefficients at the transmitter side reduces the blocking artifacts. The boundary pixels of each n*n block yields the measure of blocking annoyances with this new metric. Experimentation with SAR, Medical and Conventional images proved that the value of the metric gets increased with the increase in the compression ratio and gets reduces with the proposed adaptive algorithm. Results bestow the assessment slot in for the adaptiveness suggested is strong worthy in improving the visual quality both quantitatively and qualititatively.

Applying of the NVIDIA CUDA to the video processing in the task of the roundwood volume estimation

The paper is devoted to the parallel computing. The algorithm for roundwood volume estimation had insufficient performance so it was decided to port its bottleneck part on the GPU. The analysis of various GPGPU techniques was observed and the NVIDIA CUDA technology was chosen for implementation. The results of the research have shown the high potential of the GPU implementation in the improvement performance of the computation. The speedup of the algorithm for the roundwood volume estimation is more than 300% after porting on GPU with implementation of the CUDA technology. This helps to apply the machine vision algorithm in real-time system.

Hardware and Software Solution Signal Compression Algorithms Based on the Chebyshev Polynomial

In this paper, signal compression algorithm of Chebyshev polynomial is implemented. Upper case and lover case paralleled algorithm of compression signals processing suggested. However, audio and speech signal compressing accuracy in ADSPBF-561 dual core processor are analyzed. Index Terms—Discrete cosine transform, Chebyshev polynomial, signal processors, parallel programming, library automation programming. I. INTRODUCTION Today spectral methods of digital signal processing includes several transformations. Among them, Fourier transform, cosine and many similar transformations. These transformations differ each other with basis functions and the calculation processes. Using these transformations, first we calculate spectral values and based on them spectral values signal can be restored. After that using spectral value we can research internal properties of the signal. In these values are reflected analysis of signal, filtration process, signal compression processes. Processors and digital signal processors provides the process of calculation in digital signal processing. Digital signal processing is mainly implemented in real time systems. In the real time systems signals are received, processed and transmitted simultaneously. During signal processing in real-time systems, a signal incoming from the analog-to-digital converter using spectral methods obtains spectral values of signal (1). Using these spectral values we can organize of the signal processing. The one of the main direction of digital signal processing is the signal compression. Today, there are many methods of digital signal compression based on different mathematical methods. By using these digital compression algorithms in real time systems, we can solve kind of problems like saving memory space, time delay, signal processing time, performance of digital signal processors etc. Consequently, working on digital signal compressing and developing algorithms of digital signal compressing of the real time systems becomes important. coefficients compresses signal. These algorithms refers Newton, Lagrange, method of least squares and others. Using classic algorithms to digital signal compressing gives a good effect but it can't give normal results when you use in real time systems for digital signal processing. The main reason is increasing amount of signal values leads to increase number of solvable equation. Consequently, the solving the equations requires time, this leads to a delay which is not desirable for real time systems. Therefore, today many real time systems use digital signal processors. General structure of the process of direct and inverse transformation in a specialized signal processor is given in the illustration below (Fig. 1).

Efficient implementation of background generation method based on pixel occurrences

Abstract The paper presents the implementation of modified background generation algorithm based on analysis of the frequency of occurrences of pixels. The proposed solution allows the use of the algorithm in real-time systems, which was quite difficult in the original method. The results show that the modified algorithm gives practically the same background as the original method but in significantly less time and can be applied in many tasks related to the detection and analysis of moving objects.

Genetic Algorithm Based Adaptive Scheduling Algorithm for Real Time Operating Systems

In this paper novel technique for CPU scheduling in real time operating systems by using genetic algorithm (GA) is proposed. Proposed adaptive algorithm is a combination of existing dynamic priority driven algorithm i.e. Earliest Deadline First (EDF) and new genetic algorithm (GA) based scheduling algorithm. First we have developed GA based scheduling algorithm and tested it during both under loaded and overloaded condition. Initially, in underloaded condition EDF is used for scheduling and in overloaded condition system will change to a GA based scheduling algorithm .Thus our Adaptive algorithm uses the strong features of both algorithms and overcome their drawbacks. We have simulated, proposed adaptive algorithm along with both EDF and GA based algorithms for real time systems. %Success Rate and %Effective CPU Utilization are used as performance measuring criteria for all these 3 algorithms. The evaluation of results and comparison of our proposed adaptive CPU scheduling algorithm with EDF algorithm shows that the proposed adaptive algorithm is optimal and efficient during underloaded as well as overloaded situations compared to EDF.

Swarm-like Methodologies for Executing Tasks with Deadlines

Very few studies have been carried out to test multi-robot task allocation swarm algorithms in real time systems, where each task must be executed before a deadline. This paper presents a comparative study of several swarm-like algorithms and auction based methods for this kind of scenarios. Moreover, a new paradigm called pseudo-probabilistic swarm-like, is proposed, which merges characteristics of deterministic and probabilistic classical swarm approaches. Despite that this new paradigm can not be classified as swarming, it is closely related with swarm methods. Pseudo-probabilistic swarm-like algorithms can reduce the interference between robots and are particularly suitable for real time environments. This work presents two pseudo-probabilistic swarm-like algorithms: distance pseudo-probabilistic and robot pseudo-probabilistic. The experimental results show that the pseudo-probabilistic swarm-like methods significantly improve the number of finished tasks before a deadline, compared to classical swarm algorithms. Furthermore, a very simple but effective learning algorithm has been implemented to fit the parameters of these new methods. To verify the results a foraging task has been used under different configurations.

An Algorithm to Reduce the Time Complexity of Earliest Deadline First Scheduling Algorithm in Real-Time System

To this paper we have study to Reduce the time Complexity of Earliest Deadline First (EDF), a global scheduling scheme for Earliest Deadline First in Real Time System tasks on a Multiprocessors system. Several admission control algorithms for earliest deadline first are presented, both for hard and soft real-time tasks. The average performance of these admission control algorithms is compared with the performance of known partitioning schemes. We have applied some modification to the global earliest deadline first algorithms to decrease the number of task migration and also to add predictability to its behavior. The Aim of this work is to provide a sensitivity analysis for task deadline context of multiprocessor system by using a new approach of EFDF (Earliest Feasible Deadline First) algorithm. In order to decrease the number of migrations we prevent a job from moving one processor to another processor if it is among the m higher priority jobs. Therefore, a job will continue its execution on the same processor if possible (processor affinity). The result of these comparisons outlines some situations where one scheme is preferable over the other. Partitioning schemes are better suited for hard real-time systems, while a global scheme is preferable for soft real-time systems.

Layered Hashing Algorithm for Real-time Systems

We present a hashing mechanism designated for real-time systems. As such, it is faster than standard hashing techniques, yet remains low-cost and uses a relatively small amount of resources. We study a mathematical model of the proposed mechanism, and compare the theoretical results to statistics obtained from simulations. The main contribution of this work is a procedure to determine the appropriate dimensions of the Hash-Table as a function of the required load, and a long-run analysis of the distribution of elements in the table.

A New Scheduling Algorithm for Real Time System

A New Scheduling Algorithm for Real Time System