Real-time System Applications Research Articles

Performance evaluation of CMSIS-RTOS: benchmarks and comparison

The CMSIS-RTOS standard, recently published by ARM, defines a real-time operating system application programming interface (RTOS API) for lower-end microcontrollers. The benefits of such a standard include the improvement of portability among microcontroller applications, the incentive to the market of standardised middleware libraries, and the acceleration of the learning curve for RTOS-users. CMSIS-RTOS was designed for the ARM Cortex-M family of 32-bit RISC microcontrollers but its applicability encompasses the full range of lower-end microcontrollers. The target hardware platform of CMSIS-RTOS encompasses a different range than those where the POSIX standard is applicable. Compliance with the CMSIS-RTOS standard can be achieved by design, which is the case for new RTOSes, or by an adaptation layer on top of an existing RTOS. The performance evaluation conducted in this research covers both cases. The research question is: 'Does compliance with the CMSIS-RTOS standard impose a performance penalty?'. This is the first reported research to conduct a performance evaluation of CMSIS-RTOS; the results presented here indicate that compliance with the CMSIS-RTOS standard does not impose a performance penalty.

Thermal imaging systems for real-time applications in smart cities

In the modern world, cities need to keep up with the demand for mobility, efficient infrastructure and environmental sustainability. The future smart cities use intelligent information and communication technologies to raise the quality of life. This includes computer vision as one of the main technologies. It can observe and analyse human activities from a distance in a non-invasive manner. Traditional computer vision utilises RGB cameras, but problems with this sensor include its light dependency, and the privacy issues that can be raised by people being observed. In this paper, we propose the use of thermal imaging in real-time smart city applications. Thermal cameras operate independently of light and measure the radiated infrared waves representing the temperature of the scene. In order to showcase the possibilities, we present five different applications which use thermal imaging only. These include both indoor and outdoor scenarios with the purposes of people detection, counting and tracking, as well as one application for traffic safety evaluation.

An Operating System for Real-Time Applications with Java on TINI

An Operating System for Real-Time Applications with Java on TINI

Fast multisensor infrared image super-resolution scheme with multiple regression models

We propose a fast super-resolution scheme with multiple regression models.This study combines the information from VI images and IR images.This study divides training patches into multiple clusters by K-means clustering.We propose a soft-assignment based multiple regression method. High resolution (HR) infrared (IR) images play an important role in many areas. However, it is difficult to obtain images at a desired resolution level because of the limitation of hardware and image environment. Therefore, improving the spatial resolution of infrared images has become more and more urgent. Methods based on sparse coding have been successfully used in single-image super-resolution (SR) reconstruction. However, the existing sparse representation-based SR method for infrared (IR) images usually encounter three problems. First, IR images always lack detailed information, which leads to unsatisfying IR image reconstruction results with conventional method. Second, the existing dictionary learning methods in SR aim at learning a universal and over-complete dictionary to represent various image structures. A large number of different structural patterns exist in an image, whereas one dictionary is not capable of capturing all of the different structures. Finally, the optimization for dictionary learning and image reconstruction requires a highly intensive computation, which restricts the practical application in real-time systems. To overcome these problems, we propose a fast IR image SR scheme. Firstly, we integrate the information from visible (VI) images and IR images to improve the resolution of IR images because images acquired by different sensors provide complementary information for the same scene. Second, we divide the training patches into several clusters, then the multiple dictionaries are learned for each cluster in order to provide each patch with a more accurate dictionary. Finally, we propose an method of Soft-assignment based Multiple Regression (SMR). SMR reconstructs the high resolution (HR) patch by the dictionaries corresponding to its K nearest training patch clusters. The method has a low level of computational complexity and may be readily suitable for real-time processing applications. Numerous experiments validate that this scheme brings better results in terms of quantization and visual perception than many state-of-the-art methods, while at the same time maintains a relatively low level of time complexity. Since the main computation of this scheme is matrix multiplication, it will be easily implemented in FPGA system.

Geographical information system parallelization for spatial big data processing: a review

With the increasing interest in large-scale, high-resolution and real-time geographic information system (GIS) applications and spatial big data processing, traditional GIS is not efficient enough to handle the required loads due to limited computational capabilities.Various attempts have been made to adopt high performance computation techniques from different applications, such as designs of advanced architectures, strategies of data partition and direct parallelization method of spatial analysis algorithm, to address such challenges. This paper surveys the current state of parallel GIS with respect to parallel GIS architectures, parallel processing strategies, and relevant topics. We present the general evolution of the GIS architecture which includes main two parallel GIS architectures based on high performance computing cluster and Hadoop cluster. Then we summarize the current spatial data partition strategies, key methods to realize parallel GIS in the view of data decomposition and progress of the special parallel GIS algorithms. We use the parallel processing of GRASS as a case study. We also identify key problems and future potential research directions of parallel GIS.

Saliency-weighted graphs for efficient visual content description and their applications in real-time image retrieval systems

The exponential growth in the volume of digital image databases is making it increasingly difficult to retrieve relevant information from them. Efficient retrieval systems require distinctive features extracted from visually rich contents, represented semantically in a human perception-oriented manner. This paper presents an efficient framework to model image contents as an undirected attributed relational graph, exploiting color, texture, layout, and saliency information. The proposed method encodes salient features into this rich representative model without requiring any segmentation or clustering procedures, reducing the computational complexity. In addition, an efficient graph-matching procedure implemented on specialized hardware makes it more suitable for real-time retrieval applications. The proposed framework has been tested on three publicly available datasets, and the results prove its superiority in terms of both effectiveness and efficiency in comparison with other state-of-the-art schemes.

Estimation of Fluorescence Lifetimes Via Rotational Invariance Techniques

Estimation of signal parameters via rotational invariance techniques is a classical algorithm widely used in array signal processing for direction-of-arrival estimation of emitters. Inspired by this method, a new signal model and new fluorescence lifetime estimation via rotational invariance techniques (FLERIT) were developed for multiexponential fluorescence lifetime imaging (FLIM) experiments. The FLERIT only requires a few time bins of a histogram generated by a time-correlated single-photon counting FLIM system, greatly reducing the data throughput from the imager to the signal processing units. As a noniterative method, the FLERIT does not require initial conditions, prior information nor model selection that are usually required by widely used traditional fitting methods, including nonlinear least square methods or maximum-likelihood methods. Moreover, its simplicity means it is suitable for implementations in embedded systems for real-time applications. FLERIT was tested on synthesized and experimental fluorescent cell data showing the potentials to be widely applied in FLIM data analysis.

A WEAKLY HARD SCHEDULING APPROACH OF PARTITIONED SCHEDULING ON MULTIPROCESSOR SYSTEMS

Real-time systems or tasks can be classified into three categories, based on the “seriousness” of deadline misses – hard, soft and weakly hard real-time tasks. The consequences of a deadline miss of a hard real-time task can be prohibitively expensive because all the tasks must meet their deadlines whereas soft real-time tasks tolerate “some” deadline misses. Meanwhile, in a weakly hard real-time task, the distribution of its met and missed deadlines is stated and specified precisely. As real-time application systems increasingly come to be implemented upon multiprocessor environments, thus, this study applies multiprocessor scheduling approach for verification of weakly hard real-time tasks and to guaranteeing the timing requirements of the tasks. In fact, within the multiprocessor, the task allocation problem seem even harder than in uniprocessor case; thus, in order to cater that problem, the sufficient and efficient scheduling algorithm supported by accurate schedulability analysis technique is present to provide weakly hard real-time guarantees. In this paper, a weakly hard scheduling approach has been proposed and schedulability analysis of proposed approach consists of the partitioned multiprocessor scheduling techniques with solutions for the bin-packing problem, called R-BOUND-MP-NFRNS (R-BOUND-MP with next-fit-ring noscaling) combining with the exact analysis, named hyperperiod analysis and deadline models; weakly hard constraints and µ-pattern under static priority scheduling. Then, Matlab simulation tool is used in order to validate the result of analysis. From the evaluation results, it can be proven that the proposed approach outperforms the existing approaches in terms of satisfaction of the tasks deadlines.

Implementation of Image Processing on Raspberry Pi

Today image processing are used in various techniques, this paper presents the implementation of image processing operations on Raspberry Pi. The Raspberry Pi is a basic embedded system and being a low cost a singleboard computer used to reduce the complexity of systems in real time applications. This platform is mainly based on python. Raspberry pi consist of Camera slot Interface (CSI) to interface the raspberry pi camera. Here, the Dark and Low contrast images captured by using the Raspberry Pi camera module are enhanced in order to identify the particular region of image. This concept is used in the real time application of MAV, The MAVs are basically used to capture images and videos through the Raspberry pi camera module. Because of its credit card sized (small) and less weight in the design. However, the image captured by MAVs will consist of unwanted things due to atmospheric conditions; hence it is necessary to remove noise present in the MAVs images.

Shortest path and vehicle trajectory aided map-matching for low frequency GPS data

Map-matching algorithms that utilise road segment connectivity along with other data (i.e. position, speed and heading) in the process of map-matching are normally suitable for high frequency (1Hz or higher) positioning data from GPS. While applying such map-matching algorithms to low frequency data (such as data from a fleet of private cars, buses or light duty vehicles or smartphones), the performance of these algorithms reduces to in the region of 70% in terms of correct link identification, especially in urban and sub-urban road networks. This level of performance may be insufficient for some real-time Intelligent Transport System (ITS) applications and services such as estimating link travel time and speed from low frequency GPS data. Therefore, this paper develops a new weight-based shortest path and vehicle trajectory aided map-matching (stMM) algorithm that enhances the map-matching of low frequency positioning data on a road map. The well-known A∗ search algorithm is employed to derive the shortest path between two points while taking into account both link connectivity and turn restrictions at junctions. In the developed stMM algorithm, two additional weights related to the shortest path and vehicle trajectory are considered: one shortest path-based weight is related to the distance along the shortest path and the distance along the vehicle trajectory, while the other is associated with the heading difference of the vehicle trajectory.The developed stMM algorithm is tested using a series of real-world datasets of varying frequencies (i.e. 1s, 5s, 30s, 60s sampling intervals). A high-accuracy integrated navigation system (a high-grade inertial navigation system and a carrier-phase GPS receiver) is used to measure the accuracy of the developed algorithm. The results suggest that the algorithm identifies 98.9% of the links correctly for every 30s GPS data. Omitting the information from the shortest path and vehicle trajectory, the accuracy of the algorithm reduces to about 73% in terms of correct link identification. The algorithm can process on average 50 positioning fixes per second making it suitable for real-time ITS applications and services.

An Improved <i>l<sub>1</sub></i> Tracker for Transportation Vehicle Tracking

Transportation vehicle tracking systems need to equip with a tracking algorithm with not only good tracking accuracy, but also fast computation speed to meet the real time changes of vehicles.l1tracker has good tracking accuracy, but the high computational complexity limits its application in real-time systems. In order to solve this problem, this paper proposed a novel algorithm that utilize compressive sensing to reduce dimensions and improved Sparsity Adaptive Matching Pursuit (SAMP) algorithm to rebuild the coefficients of templates. The experimental results show that thel1-FSAMP algorithm not only improves the running speed, but also reduces the average tracking errors by 83% compared to thel1-OMP algorithm. The results show that the proposed algorithm is suitable for practical real-time tracking of transportation vehicles.

Stability-based minimization of load shedding in weakly interconnected systems for real-time applications

Abstract The transient stability of power systems is highly affected by the changes in the power generation and load levels. Due to the variability of the generating resources, the system stability can be provoked and the system stability limits can be reached as a result of certain sudden drop in the power generation. In these cases, the load shedding can act as an effective emergency corrective action for keeping system stability; however, over-shedding of loads results in severe economical as well as social security problems. Therefore, minimization of the load shedding required for the restoration of the system stability is one of the main objectives of this paper. Another critical issue related to successful load shedding is the fast assessment of the system stability, and the amount of the load shedding as well as the implementation of the load shedding corrective action. Therefore, this paper presents a fast method of stability assessment and load shedding requirements in the weakly interconnected power system. The method is an improved form of the extended equal area criterion (EEAC) where the required system equivalence is based on the availability of wide area monitoring (WAM) devices in modern power systems. The paper also investigates the impact of the implementation duration of the required load shedding. The results are verified through time domain simulations which confirm the accuracy of the presented method and its suitability for real-time applications.

Real Time Simulation of Security Systems using Face Recongnition, a review

In recent years face recognition has received substantial attention from both research communities and the market, but still remained very challenging in real applications. A lot of face recognition algorithms, along with their medications, have been developed during the past decades. In this paper we are going to present reviews about real time simulation of various security systems. These security systems are based on face detection, face tracking and face recognition. In this paper we will also present applications of real-time systems and some advancement that can be done in future to make security systems much faster. There are various algorithms available for face detection, tracking and recognition of human expressions This paper is the review of research work “spontaneous and different facial expressions and poses of individual person faces are detected and stored in the database of organization by giving appropriate and unique card id number and password. If a person comes for work then his or her face is detected and this detected face image is compared with images in the database of that particular organization and unique card id number and password. If the face image and id number are recognized then person is allowed to enter. If it is not recognized then person is not allowed to enter. An alert message is generated to administrator’s cell phone and mail to the administrator’s mail id. In the reverse we are changing the password by using random string generation and shut down the system”. Key-Words: Real-Time Simulation, Survey on Face recognition approaches, Application of Real-Time Security Systems.

The Wi-STARK architecture for resilient real-time wireless communications

Networking communications play an important role to secure a dependable and timely operation of distributed and real-time embedded system applications; however, an effective real-time support is not yet properly addressed in the wireless realm. This paper presents Wi-STARK , a novel architecture for resilient and real-time wireless communications within an one-hop communication domain. Low level reliable (frame) communications, node failure detection, membership management, and networking partition control are provided; since these low level services extend and build upon the exposed interface offered by networking technologies, Wi-STARK is in strict compliance with wireless communication standards, such as IEEE 802.15.4 and IEEE 802.11p. The Wi-STARK service interface is then offered as operating system primitives, helpful for building distributed control applications. The one-hop dependability and timeliness guarantees offered by Wi-STARK are a fundamental step towards an effective design of real-time wireless networks with multiple hops, including end-to-end schedulability analysis of networking operations.

Adaptive Checkpoint Interval Algorithm Considering Task Deadline and Lifetime Reliability for Real-Time System

Abstract Checkpointing mechanism is used to tolerate the impact of transient faults by rollback operation. Recently, it has also been used as a mechanism to enhance system's lifetime by identifying and tolerating permanent fault 5,19,10,12. However, equidistant checkpoint interval may cause task deadline violation in the system. Here, we propose an adaptive checkpoint interval placement algorithm (ADeLiRACI) that meets all tasks deadline. The checkpoint intervals are adjusted to minimize the impact of stresses and permanent faults on the running hosts. This novel mechanism allows greater applicability in real time systems with hard deadline such as weather prediction, financial transactions etc. We compare the estimated completion time for increasing fault-rate in the system against five existing algorithms. For all applications, ADeLiRACI is able to meet the hard deadline along with enhancing lifetime reliability of the system.

Data-driven Modeling of Transportation Systems and Traffic Data Analysis During a Major Power Outage in the Netherlands

Efficient methods and tools for road network planning and traffic management are critically important in the ever more urbanized world. The goal of our research is the development of a data-driven multiscale modeling approach for accurate simulation of road traffic in real-life transportation networks, with applications in real-time decision support systems and urban planning. This paper reviews the multiscale traffic models, describes the traffic sensor data collected from 25000 sensors along the arterial roads in the Netherlands, and discusses the applicability of sensor data to model calibration and validation on each modeling scale. We also present a road network graph model and the reconstructed Dutch road network. Finally, we show the results of traffic data analysis during the major power outage in North Holland on 27 March 2015, paying special attention to one of the most affected locations around the A9/E19 interchange near Amsterdam airport Schiphol.

A Square-Root-Free Matrix Decomposition Method for Energy-Efficient Least Square Computation on Embedded Systems

QR decomposition (QRD) is used to solve least-squares (LS) problems for a wide range of applications. However, traditional QR decomposition methods, such as Gram-Schmidt (GS), require high computational complexity and nonlinear operations to achieve high throughput, limiting their usage on resource-limited platforms. To enable efficient LS computation on embedded systems for real-time applications, this paper presents an alternative decomposition method, called QDRD, which relaxes system requirements while maintaining the same level of performance. Specifically, QDRD eliminates both the square-root operations in the normalization step and the divisions in the subsequent backward substitution. Simulation results show that the accuracy and reliability of factorization matrices can be significantly improved by QDRD, especially when executed on precision-limited platforms. Furthermore, benchmarking results on an embedded platform show that QDRD provides constantly better energy-efficiency and higher throughput than GS-QRD in solving LS problems. Up to 4 and 6.5 times improvement in energy-efficiency and throughput, respectively, can be achieved for small-size problems.

Usability evaluation of collaborative PPGIS-GeoCWMI for supporting public participation during municipal planning and management services

This paper focuses on evaluating usability of a Geospatial-enabled Collaborative Web Mapsharing Infrastructure (GeoCWMI), which is developed for real-time mapsharing system application as part of a municipality planning and management services. This study aims to evaluate the usability of two major GIS-enabled Public Participation System (PPS) as prototypes to determine: (i) the extent to which the proposed platform of real-time mapsharing will enable and increase public participation, and (ii) how any municipality and/or planner interacts with the prototype. In other words, how can decision-making and municipal planning processes be enhanced via a synchronized and collaborative public participation GIS (Co-PPGIS)? A mock case study scenario discussed the use of Co-PPGIS prototype components (GeoCWMI and Web Online Participatory GIS) followed by the discussion of public participation concepts, existing practice of participation, evaluation of existing PPGIS application, and proposed infrastructure. The mock case is tested during planning and development scenario where the users are asked to provide the real-time collaborative feedback in terms of rich multimedia and mapsharing. In addition, the users are asked to provide collaborative feedback with suggestions for future urban expansion regarding potential sites in the York Region, Canada. The datasets on GeoCWMI usability have been collected using a Web-enabled feedback/questionnaire system, Team Viewer, Google Analytics, Session Cam, and Hit Sniffer software. The study shows that there are significant differences in the GeoCWMI usability among the different groups of participants in relation to their education, Web experience, and participatory practices and are more usable for users with GIS/Web experience. The study contributes awareness for researchers on how to improve PPGIS applications, e.g., it is noticed that readability of Web contents may influence the results. It is important to test the usability of GeoCWMI so that future researchers may need to avoid the similar kind of problems while designing new PPGIS applications. The results of this work may contribute to existing public participation practices, to municipal planning, to decision-making or to geographic information science.

RSVP IconMessenger: icon-based brain-interfaced alternative and augmentative communication

One of the principal application areas for brain-computer interface (BCI) technology is augmentative and alternative communication (AAC), typically used by people with severe speech and physical disabilities (SSPI). Existing word- and phrase-based AAC solutions that employ BCIs that utilize electroencephalography (EEG) are sometimes supplemented by icons. Icon-based BCI systems that use binary signaling methods, such as P300 detection, combine hierarchical layouts with some form of scanning. The rapid serial visual presentation (RSVP) IconMessenger combines P300 signal detection with the icon-based semantic message construction system of iconCHAT. Language models are incorporated in the inference engine and some modifications that facilitate the use of RSVP were performed such as icon semantic role order selection and the tight fusion of language evidence and EEG evidence. The results of a study conducted with 10 healthy participants suggest that the system has potential as an AAC system in real-time typing applications. Ability to construct messages with reduced physical movement demands due to RSVP and increased message construction speed and accuracy due to the incorporation of an icon-based language model in the inference process are the significant findings of this study.

A Temperature Dependent, Single Particle, Lithium Ion Cell Model Including Electrolyte Diffusion

Low-order, explicit models of lithium ion cells are critical for real-time battery management system (BMS) applications. This paper presents a seventh-order, electrolyte enhanced single particle model (ESPM) with electrolyte diffusion and temperature dependent parameters (ESPM-T). The impedance transfer function coefficients are explicit in terms of the model parameters, simplifying the implementation of temperature dependence. The ESPM-T model is compared with a commercially available finite volume based model and results show accurate matching of pulse responses over a wide range of temperature (T) and C-rates (I). The voltage response to 30 s pulse charge–discharge current inputs is within 5% of the commercial code for 25 °C<T<50 °C at I≤12.5C and -10 °C<T<50°C at I≤1C for a graphite/nickel cobalt manganese (NCM) lithium ion cell.