Requirements Of Real-time Systems Research Articles

Improving the Verification of Real-Time Systems Using Time Petri Nets

Formal verification methods using Time Petri Net have called the attention of researchers and practitioners in real-time systems design during the last two decades. Special attention was dedicated to methods that could be integrated to the design process since the very beginning, that is, in the requirement phase. However, real-time systems requirements are always concerned with quantitative temporal properties, and therefore, a verification technique should give some feedback on target values for these properties. This paper presents an alternative algorithm—based on reachability—to treat the real-time verification of discrete systems. The proposed method is based on an enumerative technique to generate the complete state space that has some advantages—since it has to be done only once—and disadvantages—since the process is combinatorial. However, our proposal leads to better results when compared to other available techniques, especially to complex problems, besides being able to evaluate quantitative and qualitative properties in the same process. Timed Computation Tree Logic is used as specification language, and Timed Transition Graph (TTG) is introduced to represent system functional behavior. A new algorithm is proposed to build a TTG and applied to a case study to illustrates the operation of the proposed algorithm.

A High-Precision Detection Approach for Catenary Geometry Parameters of Electrical Railway

This paper aims to develop a high-precision detection approach for catenary geometry parameters of electrical railway based on vision technology. The novel idea of the proposed detection method is that, the image coordinates of the spot formed on the contact line by a laser emitter are mapped to the world system to obtain the conductor height and stagger. In the proposed detection system, the key factors that affect the detection accuracy include two components, the localization of the spot and the correction of the detection value. To solve these problems, the genetic particle filter algorithm based on particle swarm optimization (PSO-GAPF) is presented to track and locate the spot in the image. Compared with the standard particle filter, PSO-GAPF can improve the particle deprivation and increase the accuracy of state estimation. In addition, Kalman filter (KF) is used for correcting the detected value of catenary geometry parameters. In this paper, first of all, the model of spot target is established according to the feature distribution of gray color histogram, and the tracking and locating of the spot in the image are realized by PSO-GAPF. Second, the detection formulas of catenary geometry parameters are deduced by utilizing the space coordinate transformation to compute the conductor height and stagger. At last, the correcting of the catenary geometry parameters is achieved by KF. The real-life detection results show that the proposed method has high precision, and can meet the requirements of real-time catenary geometry parameters detection system.

Method for Optimizing Pseudo Code Ranging Accuracy by Carrier Observation

In view of the low precision of the pseudo code ranging, the carrier ranging and the ambiguity of the whole week, the observation is very difficult, a method using carrier smoothing pseudo range measurement is proposed. The method of using carrier phase measurement values to improve carrier ranging pseudo code ranging precision, and at the same time, pseudo code ranging can lock the auxiliary carrier phase, improve the efficiency and accuracy of carrier phase observation, meet the requirement of real-time system. In the end of the paper, the simulation results show the improvement of the carrier smoothing pseudo range to the pseudo range observation.

Real-time auto-adaptive margin generation for MLC-tracked radiotherapy

In radiotherapy, abdominal and thoracic sites are candidates for performing motion tracking. With real-time control it is possible to adjust the multileaf collimator (MLC) position to the target position. However, positions are not perfectly matched and position errors arise from system delays and complicated response of the electromechanic MLC system. Although, it is possible to compensate parts of these errors by using predictors, residual errors remain and need to be compensated to retain target coverage. This work presents a method to statistically describe tracking errors and to automatically derive a patient-specific, per-segment margin to compensate the arising underdosage on-line, i.e. during plan delivery.The statistics of the geometric error between intended and actual machine position are derived using kernel density estimators. Subsequently a margin is calculated on-line according to a selected coverage parameter, which determines the amount of accepted underdosage. The margin is then applied onto the actual segment to accommodate the positioning errors in the enlarged segment.The proof-of-concept was tested in an on-line tracking experiment and showed the ability to recover underdosages for two test cases, increasing in the underdosed area about and , respectively. The used dose model was able to predict the loss of dose due to tracking errors and could be used to infer the necessary margins.The implementation had a running time of 23 ms which is compatible with real-time requirements of MLC tracking systems. The auto-adaptivity to machine and patient characteristics makes the technique a generic yet intuitive candidate to avoid underdosages due to MLC tracking errors.

Spherical FSO receivers for UAV communication: geometric coverage models

We present several analytical models to calculate the intersection surface of a spherical cap with a solid cone where either one of them is experiencing perturbations in its attitude. These models are developed to quantitatively analyze the mutual alignment, and thus performance, of mobile free-space optical (FSO) communication nodes used in air-to-ground and ground-to-air FSO links. Same models can be also applied to various geometrical alignment problems in the presence of instability. Examples include coverage area of imaging, radar, and optical communication satellites while orbiting unstable orbits. These models provide a closed-form direct mapping between platform dynamics and FSO link performance facilitating a priori performance prediction and easing feedback requirements in real-time tracking systems.

Map-Based Probabilistic Visual Self-Localization.

Accurate and efficient self-localization is a critical problem for autonomous systems. This paper describes an affordable solution to vehicle self-localization which uses odometry computed from two video cameras and road maps as the sole inputs. The core of the method is a probabilistic model for which an efficient approximate inference algorithm is derived. The inference algorithm is able to utilize distributed computation in order to meet the real-time requirements of autonomous systems in some instances. Because of the probabilistic nature of the model the method is capable of coping with various sources of uncertainty including noise in the visual odometry and inherent ambiguities in the map (e.g., in a Manhattan world). By exploiting freely available, community developed maps and visual odometry measurements, the proposed method is able to localize a vehicle to 4m on average after 52 seconds of driving on maps which contain more than 2,150km of drivable roads.

Learning Framework for Robust Obstacle Detection, Recognition, and Tracking

This paper introduces a general framework for detection, recognition, and tracking preceding vehicles and pedestrians based on a deep learning approach. The proposed framework combines a novel deep learning approach with the use of multiple sources of local patterns and depth information to yield robust on-road vehicle and pedestrian detection, recognition, and tracking. The proposed system is first based on robust obstacle detection to identify obstacles appearing along the road that are likely to be vehicles and pedestrians, implemented as an efficient adaptive U-V disparity algorithm. Second, the results from the obstacle detection stage are input into a novel vehicle and pedestrian recognition system based on a deep learning model that processes multiple sources of depth information and local patterns. Finally, the results from the recognition stage are used to track detected vehicles or pedestrians in the next frame by means of a proposed tracking and validation model. The proposed framework has been thoroughly evaluated by inputting several vehicle and pedestrian data sets that were collected under various driving conditions. Experimental results show that this framework provides robust vehicle and pedestrian detection, recognition, and tracking with high accuracy, and also satisfies the real-time requirements of driver assistance systems.

A real-time lane marking localization, tracking and communication system

In this paper, we present an in-vehicle computing system capable of localizing lane markings and communicating them to drivers. To the best of our knowledge, this is the first system that combines the Maximally Stable Extremal Region (MSER) technique with the Hough transform to detect and recognize lane markings (i.e., lines and pictograms). Our system begins by localizing the region of interest using the MSER technique. A three-stage refinement computing algorithm is then introduced to enhance the results of MSER and to filter out undesirable information such as trees and vehicles. To achieve the requirements of real-time systems, the Progressive Probabilistic Hough Transform (PPHT) is used in the detection stage to detect line markings. Next, the recognition of the color and the form of line markings is performed; this it is based on the results of the application of the MSER to left and right line markings. The recognition of High-Occupancy Vehicle pictograms is performed using a new algorithm, based on the results of MSER regions. In the tracking stage, Kalman filter is used to track both ends of each detected line marking. Several experiments are conducted to show the efficiency of our system.

A Novel Fault Diagnosis Framework Based on Reliability Analysis and Arithmetic Circuit

To meet the real-time diagnosis requirements of complex systems, this paper presents a novel fault diagnosis framework based on dynamic fault tree and arithmetic circuit. It pays attention to meeting two challenges: model development and real-time reasoning. Specifically, we use a dynamic fault tree to model dynamic fault modes and calculate some quantitative parameters using algebraic technique and Bayesian network (BN) in order to avoid the state space explosion problem. Furthermore, we compile a BN into an arithmetic circuit to obtain answers to probabilistic queries by evaluating and differentiating the arithmetic circuit. In addition, we incorporate sensors information into diagnosis process and propose the schemes on how to update the diagnostic importance factor and the minimal cut sequences. Finally, the example of a train-ground communication system is used to demonstrate the proposed method.

A resource-efficient multimedia encryption scheme for embedded video sensing system based on unmanned aircraft

Battery-powered unmanned aerial vehicle based video sensing system is more cost-saving and energy-saving than traditional aircraft based systems. However, high-volume real-time sensing data is more vulnerable in unmanned system than that in manned system. Meanwhile, the computation and energy resources in such system are very limited, which restricts the use of complex encryption process on video data. Therefore, how to achieve confidentiality of video data under limited resources efficiently needs to be addressed. Firstly, resources constraints with their development trends in video sensing system are studied. Secondly, an information-utility-value-oriented resource-efficient encryption optimization model under resources constraints is given. Thirdly, based on this model, a video-compression-independent speed-adjustable lightweight encryption scheme with its improved version is proposed. Fourthly, a DSP and ARM based embedded secure video sensing system is designed, and the proposed encryption scheme has been implemented in it. In addition, theoretical analyses based on information theory and experimental analyses on throughputs show that the proposed encryption schemes can meet the real-time requirements of system under the tight resources constraints.

Driver fatigue detection based on eye state.

Nowadays, more and more traffic accidents occur because of driver fatigue. In order to reduce and prevent it, in this study, a calculation method using PERCLOS (percentage of eye closure time) parameter characteristics based on machine vision was developed. It determined whether a driver's eyes were in a fatigue state according to the PERCLOS value. The overall workflow solutions included face detection and tracking, detection and location of the human eye, human eye tracking, eye state recognition, and driver fatigue testing. The key aspects of the detection system incorporated the detection and location of human eyes and driver fatigue testing. The simplified method of measuring the PERCLOS value of the driver was to calculate the ratio of the eyes being open and closed with the total number of frames for a given period. If the eyes were closed more than the set threshold in the total number of frames, the system would alert the driver. Through many experiments, it was shown that besides the simple detection algorithm, the rapid computing speed, and the high detection and recognition accuracies of the system, the system was demonstrated to be in accord with the real-time requirements of a driver fatigue detection system.

FPGA hardware implementation of the LZMA compression algorithm

Software-based LZMA( Lempel Ziv Markov-chain algorithm) nondestructive compression algorithm is very slow and consumes too much CPU( central processing unit) resources during the data compression process,as a result it can not meet the requirements of real-time systems. On the basis of the improved algorithm,a hardware accelerator for LZMA was designed with FPGA( field programmable gate array) implementation. The hardware accelerator is composed of LZ77 compressor,range encoder and send out controller.Ping-pong operation,parallel matching method with high performance,pipeline processing structure and other acceleration techniques were used to speed up LZMA compression algorithm. While at the same time,data compressed by the circuit is still compatible with standard LZMA file format. The compression rate of the circuit is speeded up to 125 Mb / s,nearly 10 times faster than that of the software based LZMA. The processing relative data of each clock is speeded up nearly 200 times. Results from the experiments on ML605 basing on a Virtex-6 FPGA development kit,show the accelerator is correct and feasible.

Research of Automobile Collision Avoidance System Based on Video Image Processing

In this paper, an automobile collision avoidance system based on video image processing is proposed for vehicle active safety. Firstly, considering the requirement of real-time system, FPGA is responsible for the core task of video image processing. In the process, this paper introduces an image segmentation algorithm to extract the Region of Interest, a fast median filtering algorithm and an improved adaptive threshold Ostu segmentation algorithm. Secondly, in the design of alarm system, we use the STM32 chip which is based on Cortex-M3 kernel as the embedded platform. Finally, in order to analyze the data obtained by image processing and make smart decisions on the embedded platform, we establish a double parameter alarm mechanism of automobile security model. It can help the drivers to take appropriate hedging measures. The system in this paper has the characteristics of low cost, real-time and high reliability. Through the debugging and experiments, the system has reached the expected effect and met the requirements of automobile collision avoidance system.

Worst Case Response Time Analysis of Sporadic Traffic in MVB

The Multifunction Vehicle Bus (MVB) protocol is well applied to train network control system where a set of process variables must be transferred between network devices. To cope with the real-time requirements of such system, the MVB protocol is designed to support both periodic and sporadic traffic. The periodic traffic is handled according to a static scheduling table, thus the schedulability can be certain after system initialization. However, for the sporadic traffic, the situation is different due to their irregular occurrence in the system. Therefore, more complex analysis must be made in order to guarantee its timing requirements. In this paper, a schedulability analysis is made by executing worst case response time analysis for sporadic traffic in MVB network.

Study on Adaptive PID Control Algorithm Based on RBF Neural Network

Aim at the limitation of traditional PID controller has certain limitation, the traditional PID control is often difficult to obtain satisfactory control performance, and the RBF neural network is difficult to meet the requirement of real-time control system. To overcome it, an adaptive PID control strategy based on (RBF) neural network is proposed in this paper. The results show that the proposed controller is practical and effective, because of the adaptability, strong robustness and satisfactory control performance. It is also revealed from simulation results that the proposed control algorithm is valid for DC motor and also provides the theoretical and experimental basis. DOI: http://dx.doi.org/10.11591/telkomnika.v13i2.6958

Embedded Robot Vision System Based on DSP

Aim at the real-time problem of industrial robot vision system, design a embedded robot vision system based on DSP microprocessor. This system can use CCD camera and the ultrasonic sensor to collect the target environment information. It also can use the processor DSP to process the images and recognize target. And then through the communication module, send results in the form of wireless to the upper computer, providing target object information for robot control layer. This system completes the software and hardware system design, image collection & processing and robot control, as well as meet the real-time requirements of machine vision system.

Flow-Based Specification of Time Design Requirements

This paper focuses on design requirements in real-time systems where information is processed to produce a response within a specified time. Nowadays, computer control applications embedded in chips have grown in significance in many aspects of human life. These systems need a high level of reliability to gain the trust of users. Ensuring correctness in the early stages of the design process is especially a major challenge in these systems. Faulty requirements lead to errors in the final product that have to be fixed later, often at a high cost. A crucial step in this process is modeling the intended system. This paper explores the potential of flow-based modeling in expressing design requirements in real-time systems that include time constraints and synchronization. The main emphasized problem is how to represent time. The objective is to assist real-time system requirement engineers, in an early state of the development, to express the timing behavior of the developed system. Several known examples are modeled and the results point to the viability of the flow-based representation in comparison with such time specifications as state-based and line-based modeling.

Research on Face Recognition Technology of Real-Time Video High Performance Similarity

A new similarity measurement standard is proposed, namely background similarity matching. Learning algorithm based on kernel function is utilized in the method for feature extraction and classification of face image. Meanwhile, a real-time video face recognition method is proposed, image binary algorithm in similarity calculation is introduced, and a video face recognition system is designed and implemented [1-2]. The system is provided with a camera to obtain face images, and face recognition is realized through image preprocessing, face detection and positioning, feature extraction, feature learning and matching. Design, image preprocessing, feature positioning and extraction, face recognition and other major technologies of face recognition systems are introduced in details. Lookup mode from top down is improved, thereby improving lookup accuracy and speed [3-4]. The experimental results showed that the method has high recognition rate. Higher recognition rate still can be obtained even for limited change images of face images and face gesture with slightly uneven illumination. Meanwhile, training speed and recognition speed of the method are very fast, thereby fully meeting real-time requirements of face recognition system [5]. The system has certain face recognition function and can well recognize front faces.

Line-of-Sight Rate Estimation Based on UKF for Strapdown Seeker

Aiming at the problem that the strapdown imaging seeker cannot measure line-of-sight (LOS) rate directly, this paper presents an effective algorithm for LOS rate estimation. To address the problem, the reference frames and angles are defined. According to the relative kinematics and attitude relationship between missile and target, the nonlinear state equations and measurement equations of LOS rate are derived, and the estimation algorithm based on unscented Kalman filter (UKF) is proposed. Considering the estimation accuracy mainly depends on body LOS (BLOS) angle accuracy and gyro accuracy, the paper is unprecedented to research how these two factors impact the LOS angle and rate accuracy by simulation. Semiphysical simulation experiment verifies the correctness and accuracy of the algorithm, and the result shows that the estimation algorithm can meet the accuracy and real-time requirements of guidance system simultaneously. Thus LOS rate estimation algorithm based on UKF provides a theoretical basis for engineering applications of strapdown imaging seeker.

Real-time big data processing framework: Challenges and solutions

Data type and amount in human society is growing in amazing speed which is caused by emerging new services as cloud computing, internet of things and location-based services, the era of big data has arrived. As data has been fundamental resource, how to manage and utilize big data better has attracted much attention. Especially, with the development of internet of things, how to processing large amount real-time data has become a great challenge in research and applications. Recently, cloud computing technology has attracted much attention with high-performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied. This paper studied the challenges of big data firstly and concludes all these challenges into six iss ues. In order to improve the performance of real-time processing of large data, this paper builds a kind of real-time big data processi ng (RTDP) architecture based on the cloud computing technology and then proposed the four layers of the architecture, and hierarc hical computing model. This paper proposed a multi-level storage model and the LMA-based application deployment method to meet the real-time and heterogeneity requirements of RTDP system. We use DSMS, CEP, batch-based MapReduce and other processing mode and FPGA, GPU, CPU, ASIC technologies differently to processing the data at the terminal of data collection. We structured the dat a and then upload to the cloud server and MapReduce the data combined with the powerful computing capabilities cloud architecture. This paper points out the general framework for future RTDP system and calculation methods, is currently the general method RTDP system design.