Implementation of Recursive Algorithms for Serial Linkages

This paper discuss on how to develop a hard real-time machine control system with Microsoft Windows NT Operating System, where the respond must be within 0.5 milliseconds. The attempt to use a general-purpose operating system (Microsoft Windows NT 4.0 Workstation) is because there is abundance of Commercial-Off-the-Shelf Components (COTS), easily available tools (compilers, and 3 party tools) and need not to learn another operating system behavior. The most important factor is there is no single dominant operating system or tools vendor for real-time operating system. Since the no single dominant real-time operating system development under real-time operating system is riskier and more costly. Ideally codes must be portable between different operating system where it is apparent with Windows platforms, Windows 95/98, Windows ME, Windows 2000, Windows XP or even Windows CE. Firstly study was done on how Microsoft Windows NT operating system works internally from materials from Microsoft Software Development Network (MSDN). Secondly study was done how to take advantage of the operating system to create a real time application. Microsoft Windows NT is a general purpose operating system that does not support real time application development, but it has all the vital elements that a real time operating system needs. The vital elements are priority-driven, preemptive scheduler, preemptive interrupt handler (for critical interrupts), deferred procedure calls (DPC) for non-critical (DPC executes after ISR), synchronization and deterministic response time. With these vital elements and some modification to application codes to enable it to execute in higher priority level, would result a real time application. This does not mean changing all the codes to higher priority is the solution, as it would cause system to malfunction as other device driver or low-level interface run at high priority too and this would lock up the system. With the combination of priority level, synchronization a complete real time solution can be implemented and this paper covers the concept. This concept also explains what was done to overcome the need of special hardware (as recommended) to accomplish hard real-time application with the response of 0.5 milliseconds.

A multi-wavelength based optical density sensor unit was designed, developed, and evaluated to monitor microalgae growth in real time. The system consisted of five main components including: (1) laser diode modules as light sources; (2) photodiodes as detectors; (3) driver circuit; (4) flow cell; and (5) sensor housing temperature controller. The sensor unit was designed to be integrated into any microalgae culture system for both real time and non-real time optical density measurements and algae growth monitoring applications. It was shown that the sensor unit was capable of monitoring the dynamics and physiological changes of the microalgae culture in real-time. Algae biomass concentration was accurately estimated with optical density measurements at 650, 685 and 780 nm wavelengths used by the sensor unit. The sensor unit was able to monitor cell concentration as high as 1.05 g·L−1 (1.51 × 108 cells·mL−1) during the culture growth without any sample preparation for the measurements. Since high cell concentrations do not need to be diluted using the sensor unit, the system has the potential to be used in industrial microalgae cultivation systems for real time monitoring and control applications that can lead to improved resource use efficiency.

This paper presents an overview of a proposed protocol to provide application---transparent fault tolerant services in a Real Time Operating system. Fault tolerance is achieved by saving checkpoints of the processes belonging to a real time application. This approach proposes the extension of some real time system calls in order to save a recovery point when the user invokes them. This protocol allows a real time application designer to know the temporal specifications of every system call. Current real time applications are composed of several Real Time processes and they have to share data by using interprocess communication facilities provided by the operating system. The operating system has to take into account these interactions to ensure the consistency of checkpoints. This is done by tracking the communications performed since the last checkpoint and forcing dependent processes to perform a checkpoint at the same time.

For many applications such as command and control, telecommunications, and process control, it is critical that they meet timing constraints and ensure predictable computation. Furthermore, some of these applications are also distributed in nature and utilize distributed object management technology such as object request brokers (ORB) for interoperability. Therefore, it is necessary to integrate real time systems technology with distributed object management technology for many distributed real time applications. The article describes some of the issues that need to be investigated in order to develop real time data processing extensions to the common object request broker architecture (CORBA). In particular, issues on extensions to ORBs are given. A discussion of some real time services and applications hosted on real time ORBs is also provided. Finally, an overview of the work of the Object Management Group's Real time Special Interest Group is provided.

Leveraging virtualization technology, Mobile Edge Computing (MEC) deploys multiple services with different execution time requirements running as isolated processes. For instance, both real-time (RT) and non-RT applications may be (are) running on the same infrastructure using containerized virtualization. Nevertheless, sharing resources (e.g., CPU) with collocated workloads could impact the RT performance of RT applications. This paper presents PRINCIPIA, a dynamic CPU and CPU-shares allocation mechanism that opportunistically enables non-RT applications to run on underutilized CPUs while providing RT guarantees to RT applications. By monitoring MEC’s system metrics like processor’s CPU utilization and container’s CPU usage, PRINCIPIA dynamically allocates both CPU and CPU-shares to containers running non-RT applications aiming at opportunistically exploiting underutilized CPUs by containers running RT applications. We evaluate PRINCIPIA on a small-scale MEC server which uses containerized virtualization along with Linux RT Kernel to deploy both RT and non-RT applications. Our findings show that PRINCIPIA mitigates the impact on the RT performance of RT applications providing bounded processing latency in comparison with the default host Kernel scheduler.

The GNSS time transfer technology is the high precision time transfer method based on the navigation satellite system and has been widely applied in time and frequency. Benefiting from the development of GNSS time transfer technology and the expansion of user needs for real-time time service applications, real-time time transfer technology has become an important research direction. GNSS Precise Point Positioning (PPP) is one of the most commonly used methods of GNSS time transfer technology. With the development of the many facilities, the application of the real-time GNSS PPP has become wider and wider. In this paper, we demonstrate and evaluate the performance of the international GNSS service (IGS) products in the near real-time PPP time transfer technology. A set of the PPP solutions were computed from the observed data from the timekeeping laboratories, and three types of products provided by IGS, including Ultra-Rapid (IGU), Rapid (IGR), and Final (IGS final) orbit and clock offset products. IGS final and IGR products have extremely high accuracy and stability and are used as a reference for the time transfer with IGU product. The near real time clock offset and the comparison link performed with the IGU product can provide a time comparison service with an accuracy of 0. 5ns for near real time applications.

Bursty communication channels can be modeled as hidden Markov sources where the error source is assumed to exist in one of N states and the source alphabet consists of M symbols. In general the Hidden Markov Model (HMM) of a stationary source is given by P, the N x N state.,transition matrix. The N states are partitioned into M sets such that when the source is in a state belonging to set i, symbol i is generated. HMMs provide great flexibility to the design engineer in interpreting the symbols and the states. In general HMM techniques can be an invaluable tool for channel characterization. The problem with existing techniques is that they are computationally intensive and it is usually impossible to use them in real time applications. In many cases, the existing methods also give non unique models since they tend to use either too many or too few parameters in their model structures. Our main contribution is a highly efficient HMM algorithm that can be used in real time channel tracking applications. The method we use integrates our technique for estimating an equivalent (and in a number of cases, unique) model A with the forward-only procedure developed by Narciso Tan et.al. The resulting algorithm is used on an improve the model as you go basis to provide for real time channel tracking. Using empirical modeling techniques for the channel response, we generate observation sequences and then evaluate the performance of our channel tracking algorithm. The algorithm is being implemented on a C40 based development platform to test its real time applicability.

The performance of the Kalman Filter (KF) technique is most appropriate when both the process and observation are considered as white Gaussian noise. But in real time applications, this assumption is not always true. Two sorts of Kalman Filtering techniques i.e Augmented Kalman Filtering and Second Moment Information bases Kalman Filtering with colored system noise i.e (process noise and observation noise) are examined in this paper. Augmented Kalman Filtering (AKF) with colored measurement noise is shortly analyzed. Modified Kalman Filtering method (SMIKF) is proposed for colored process and observation noise. In order to check the performance of the system a real-time Mass-Spring Damper system having colored system noises is considered. The computational burdens of the proposed scheme is compared with AKF. The simulation results endorse the Performance of the suggested algorithm.

This paper discusses an environment for configuration, programming, and control of robot workcells. The controller is intended to support research in automation programming and motion control, and to provide a vehicle for conveniently integrating new sensors and other devices into a workcell in a useful way. The system consists of an interactive Programming System connected through a shared memory to a multiple-processor Real Time System that performs time-critical operations. The Programming System executes programs written in an enhanced version of AML and transmits high level commands, called verbs, to the Real Time System for execution. Verbs may either be simple, consisting essentially of a process specification and termination conditions, or they may be compositions of other verbs. The processes themselves are specified in terms of lower level entities called real time application subroutines, state vector variables, and data flow graphs which describe computations to be performed in the Real Time System.

Middleware implementation of various critical services required by large scale and complex real time applications on top of COTS operating system is currently an approach of growing interests. Its main goal is to enable significant reduction application system design separating the concerns of the application designer for the application functionality from the concerns for application independent system issues. The paper presents the middleware architecture named the Real-time Object-oriented Adaptive Fault Tolerance Support (ROAFTS) and a prototype implementation ROAFTS/Solaris realized on top of both a COTS operating systems, Solaris, and a COTS CORBA complaint ORB, Orbix. ROAFTS supports distributed real time applications, each structured as a network of Time-triggered Message-triggered Objects (TMOs), and the TMO is a major extension of a conventional object for use in hard real time applications. The major components of ROAFTS include a TMO support manager for supporting the execution of TMO's, a generic fault tolerance server, and a network surveillance manager (NSM) which provides the generic fault tolerance server with fast fault detection notices. The generic fault tolerance server and the NSM themselves have been structured as TMO's. A discussion on an effective use of CORBA standards for moderate precision real time applications to run on COTS operating systems is also presented.

Design of on-line embedded web server is a challenging part of many embedded and real time data acquisition and control system applications. The World Wide Web is a global system of interconnected computer networks that use the standard Internet Protocol Suite (TCP/IP) to serve billion of users worldwide and allows the user to interface many real time embedded applications like data acquisition, Industrial automations and safety measures etc,. This paper approached towards the design and development of on-line Interactive Data Acquisition and Control System (IDACS) using ARM based embedded web server. It can be a network, intelligent and digital distributed control system. Single chip IDACS method improves the processing capability of a system and overcomes the problem of poor real time and reliability. This system uses ARM9 Processor portability with Real Time Linux operating system (RTLinux RTOS) it makes the system more real time and handling various processes based on multi tasking and reliable scheduling mechanisms. Web server application is ported into an ARM processor using embedded `C' language. Web pages are written by Hyper text markup language (HTML); it is beneficial for real time IDACS, Mission critical applications, ATM networks and more.

The soft computing algorithms are being nowadays used for various multi input multi output complicated non linear control applications. This paper presented the development and implementation of back propagation of multilayer perceptron architecture developed in FPGA using VHDL. The usage of the FPGA (Field Programmable Gate Array) for neural network implementation provides flexibility in programmable systems. For the neural network based instrument prototype in real time application. The conventional specific VLSI neural chip design suffers the limitation in time and cost. With low precision artificial neural network design, FPGA have higher speed and smaller size for real time application than the VLSI design. The challenges are finding an architecture that minimizes the hardware cost, maximizing the performance, accuracy. The goal of this work is to realize the hardware implementation of neural network using FPGA. Digital system architecture is presented using Very High Speed Integrated Circuits Hardware Description Language (VHDL)and is implemented in FPGA chip. MATLAB ANN programming and tools are used for training the ANN. The trained weights are stored in different RAM, and is implemented in FPGA. The design was tested on a FPGA demo board. Keywords- Backpropagation, field programmable gate array (FPGA) hardware implementation, multilayer perceptron, pressure sensor, Xilinx FPGA.

An approach to estimating disturbances using a sliding mode for an optimal Kalman filter is presented. We show that the disturbance estimation problem can be converted into a discrete tracking problem by using the difference of time update and measurement update of the Kalman filter. Disturbances cannot be modeled or measured previously, so the robustness of the sliding mode provides a very effective tool for disturbance estimation. We develop and improve a disturbance estimation algorithm using a discrete sliding mode for a discrete Kalman filter. The suggested algorithm can be easily implemented on real time applications.

The state space form is an enormously powerful tool which opens the way to handling a wide range of time series models. Once a model has been put in state space form, the Kalman filter may be applied and this in turn leads to algorithms for prediction and smoothing. The state space form is described in the first section of this chapter, while the second section develops the Kalman filter. Prediction and smoothing are described in sections 3.5 and 3.6 respectively. The Kalman filter also opens the way to the maximum likelihood estimation of the unknown parameters in a model. This is done via the prediction error decomposition and a full account can be found in section 3.4. The present chapter can be read independently of the rest of the book, and taken as a guide to the uses of the state space models in areas outside engineering. On the other hand, those interested primarily in the practical aspects of structural time series modelling will be reassured to know that they do not have to master all the technical details of the Kalman filter set out here. The most important parts of the chapter with which to become familiar are sections 3.1 and 3.5, the earlier parts of sections 3.2, 3.4 and 3.6, and, for those interested in non-linear models, sub-section 3.7.1. The reader will also benefit by at least skimming through the remaining sections, since there is some reference back to the various algorithms in later chapters and it is useful to have some idea of what these algorithms do and how they fit into the overall picture.

The tax big data supervision system and method, including the distributed real-time management and control system deployed in the superior unit and each subordinate unit, the superior distributed real-time management and control system is used to receive the management and control rules, and issue the management and control rules to the subordinate distributed real-time management and control System: The distributed real-time management and control system writes the massive data in the data source at the same level into the distributed message queue Kafka in real time, and uses the distributed stream computing engine Flink to compare and calculate based on the management and control rules, and write the data that conforms to the management and control rules. Enter the alarm information into Kafka; the lower-level distributed real-time management and control system pushes the alarm information in the Kafka subscription at this level to Kafka in the upper-level distributed real-time management and control system; the upper-level distributed real-time management and control system pushes the alarm information in the Kafka subscription to users , at the same time push the alarm information to the distributed search engine ElasticSearch to save, and provide the historical alarm information query function.