A distributed control system status report of the munich accelerator control

We will start this exciting journey exploring the scientific domain of digital image processing with Raspberry Pi. To begin the journey, you must be comfortable with the basics of single board computers (SBCs) and with Raspberry Pi. This chapter discusses the definition, history, and philosophy behind SBCs. It compares SBCs to regular computers. Then it moves toward the most popular and best selling SBC of all time, the Raspberry Pi. By the end of this chapter, you will have adequate knowledge to set up your own Raspberry Pi independently. This chapter aims to make you comfortable with the basic concepts of SBCs and Raspberry Pi setup.

During this time, Single Board Computers (SBCs) are widely used in monitoring and control applications. Many relatively low-cost SBCs are available. This paper explains the implementation of an SBC LattePanda for online monitoring of powers through Local Area Networks (LANs). The SBC was used as the main part of the monitoring system that mainly performed acquisition of voltage and current signals sent by some sensors. A program for the microcontroller on the SBC was written for the acquisition processes. Furthermore, a client/server application was developed using Delphi to perform online monitoring of the signals. Some experimentation was performed to mainly ensure that the microcontroller could capture the voltage and current signals with precise and constant sampling timing as well as send the data to the client/server application successfully. A PicoScope was utilized during the experimentation to gain the timing information performed by the microcontroller. For calibration of the voltage and current, a 4.5-digit digital multimeter was implemented. The final stage of the experimentation revealed that the intended signals could be sampled with a constant timing and in this case 128 samples per channel was chosen. The developed application could also show the expected parameters including the signal of powers using an additional remote computers connected to the LAN.

Traditional forensic analysis of hard disks and external media typically involves a powered down machine and “dead analysis” of these devices. Forensic acquisition of hard drives and external media has traditionally been by one of several means: standalone forensic duplicator; using a hardware write-blocker or dock attached to a laptop, computer, workstation, etc., forensic operating systems that live boot from a USB, CD/DVD or virtual machines with preinstalled operating systems. Standalone forensics acquisition and imaging devices generally cost thousands of dollars. In this paper, we propose the use of single board computers as forensic imaging devices. Single board computers can provide a low budget forensic imaging solution that can be used in a lab, remote acquisition, or even be configured as portable imaging devices. This project tests different ARM processor based single board computers and the software available at the present time. The project includes image acquisition using a write-blocker, software write-blockers and without write-blockers to test the various configurations. The final results demonstrate clearly that ARM based single board computers can be used as low cost and low energy forensic imaging devices.

High Performance Computing have several issues on architecture, resources, computational model and data. The challenge is establishing the mature architecture with scalable resources. The cluster architecture and multicore architecture implement to produce high performance on computation and process. This research works on architecture development and performance analysis. The cluster architecture build on Raspberry Pi, a single board computer, implement MPICH2. Raspberry Pi cluster build on Raspbian Wheezy operating system and test by metrics computation applications. The multicore architecture build on single computer with Core i5 and Core i7 architecture. The research use himeno98 and himeno16Large tools to analysis the processor and memory allocation. The test run on 1000x1000 matrices and benchmarked with OpenMP. The analysis focuses on CPU Time, FLOPS, and score. The result show on cluster architecture have 2576,07 sec in CPU Time, 86,96 MLPOS, and 2,69 score. The result on Core i5 architecture have 55,57 sec in CPU time, 76,30 MLOPS, and 0,92 score. The result in Core i7 architecture have 59,56 sec CPU Time, 1427,61 MLOPS, and 17,23 score. The cluster and multicore architecture results show that computing process are effected by architecture models. High performance computing architecture that has been built on this result can give learn on the development of HPC architecture models, and baseline performance. In the future it will use for determine the delivery architecture model on HPC and can be test by more variation of load.

Single board computer (SBC) is a compact built-in circuit with processing unit, memory access, and general purpose input and output units (Deep Learning Pipeline: Building A Deep Learning Model With TensorFlow [Authors: Hisham El-Amir, Mahmoud Hamdy]; Deep Learning with TensorFlow: Explore neural networks with Python [Authors: Giancarlo Zaccone, Md. RezaulKarim, Ahmed Menshawy]; Goodfellow et al., Deep learning, vol 1. MIT Press, Cambridge, 2016). The SBC is mainly compatible and portable in nature; these SBCs are the perfect option for ready-to-mount devices and for prototyping. Some of the famous SBCs in the present market include Raspberry pi, Ordroid, Jetson Nano, NVIDIA XAVIER, etc. Table 1 illustrates the comparison of important commonly used single board computers, capable to compute and import tensor flow packages. The NVIDIA SBCs are also enabled with GPU cores to make GPU coding Compute Unified Device Architecture (CUDA). The process runs in GPU framework for faster computation.

The vehicle industry manufacturer is showing an increased interest in remote diagnostics by improving customer relationship management and commercial interest. The special nature of construction machines fact that architectures and applications proposed for vehicular communication are not optimized for these types of vehicles. This paper proposes a new remote diagnostic architecture based on IEEE 802.11n for construction machines communication to the infrastructure on site. The mobile Internet is used to transmit collected data to the central monitoring application. We discuss the possible network selection algorithms, mobility and security protocols that can be implemented on the On Board Units (OBU). The proposed architecture is based on Host Identity Protocol (HIP). This protocol introduces a Host Identifier (HI) for naming the endpoints and use intensive cryptographic operation for security and mobility management. Due to their minimal computing capabilities, many single board computers used as OBU themselves suffer from applications which need intensive CPU usage. Thus, a performance analysis of HIP, implemented on single board computers, is proposed by the end of this paper.

High performance computing (HPC) devices is no longer exclusive for academic,\nR&D, or military purposes. The use of HPC device such as supercomputer now\ngrowing rapidly as some new area arise such as big data, and computer\nsimulation. It makes the use of supercomputer more inclusive. Todays\nsupercomputer has a huge computing power, but requires an enormous amount of\nenergy to operate. In contrast a single board computer (SBC) such as Raspberry\nPi has minimum computing power, but require a small amount of energy to\noperate, and as a bonus it is small and cheap. This paper covers the result of\nutilizing many Raspberry Pi 2 SBCs, a quad-core Cortex A7 900 MHz, as a cluster\nto compensate its computing power. The high performance linpack (HPL) is used\nto benchmark the computing power, and a power meter with resolution 10mV / 10mA\nis used to measure the power consumption. The experiment shows that the\nincrease of number of cores in every SBC member in a cluster is not giving\nsignificant increase in computing power. This experiment give a recommendation\nthat 4 nodes is a maximum number of nodes for SBC cluster based on the\ncharacteristic of computing performance and power consumption.\n

In this paper, a simple one degree of freedom robot, designed for both research and teaching purposes, is described. The experimental set-up consists of a DC-motor, rate generator, harmonic drive, control system and load. A system has an open architecture and allows the programmer to distribute the intelligence between the single board computer (SBC) and PC compatible computer, which are connected through an interface. The SBC collects data from different sensors. System software resides on SBC and PC compatible computer. A PD controller has been designed and successfully implemented. Different new adaptive control algorithms, which require only link position measurements, were developed and tested, both on SUN workstation and on the experimental set-up.

Trends in network media and wiring strategies: LAN life cycle cost implications

The Beam-on-Target accelerator (B-O-T) became operational during this quarter. Initial tests on this system included high-voltage conditioning of the accelerating column, ion source operation in a vacuum environment, operation of the ion source by computer-controlled light links across the high-voltage interface, and a thorough check-out of various safety interlock systems. An increase in the source strength of the INS has been studied, and some of the necessary changes to the ion source, jet target, and beam transport systems are discussed. ERDA is presently considering these impacts. Meanwhile, the INS project has been placed back into the Congressional budget. Preliminary calculations have been undertaken on the possibility of surrounding the irradiation experimental volume with a fission blanket. Further work is cited on understanding the possible geometry for tailoring spectra. The use of a subsonic target in conjunction with the blanket is being studied. New systems for measuring the intense beam density profiles and beam emittances are being evaluated. Other developments during the quarter included the 70-percent Title I review of the INS facility, continuing calculations on neutron shielding, skyshine, and other radiological protection, and further successful operation of the prototype distillation column.

Advancements are being made to a system that uses an inertial measurement unit (IMU) to locally position an electromagnetic induction (EMI) sensor. The Small Area Inertial Navigation Tracking system (SAINT) has been interfaced to an EM61-HH with the EMI data logged on a handheld computer. To improve timing accuracy of the IMU and EMI data, a single board computer (SBC) is being implemented to collect measurements from both. This SBC system will include digital I/O options to interface to other advanced EMI systems under development. Operationally, the system can be swept about over a localized EMI anomaly for thirty seconds. In post-processing, the EMI sensor data is mapped out in both position and orientation. The positions and EMI data can be used in a dipole-based inversion. The inversion finds the magnetic polarization terms along an object’s axes which can be related to an object’s size and shape. Accurate inversion of these terms is possible because of the centimeter level positioning provided by the IMU system. With these terms well determined, the system can be used to classify objects as either unexploded ordnance or as clutter. Issues of sweep pattern and sensor time response on the inversion are being explored. Results from fitting repeated data over a four inch steel sphere are presented. The data was collected using three different sweep patterns over the sphere. No significant difference in the fits was observed between the sweep patterns.

The Internet of Things (IoT) allows users to gather data from the physical environment. While sensors in public spaces are already widely used, users are reluctant to deploy sensors for shared data at their homes. The deployment of IoT nodes at the users premises presents privacy issues regarding who can access to their data once it is sent to the Cloud which the users cannot control. In this paper we present an energy-efficient and low cost solution for environmental monitoring at the users home. Our system is built completely with open source components and is easy to reproduce. We leverage the infrastructure and trust of a community network to store and control the access to the monitored data. We tested our solution during several months on different low-capacity single board computers (SBC) and it showed to be stable. Our results suggest that this solution could become a permanently running service in SBCs at the users homes.

This paper describes the laboratory facilities for a course on microprocessors in electromechanical systems. This course is designed to introduce mechanical engineers to basic concepts of microprocessor architectures, programming, and interfacing, with special focus on implementation of digital controllers for real-world electromechanical systems. Laboratory facilities at each lab station include a Pentium-based PC for code development and an 80188-based single board computer (SEC) for real-time embedded control. Since both the PC and the SEC have their own analog I/O capabilities, true distributed control is possible, with the PC acting as process monitor, plant simulator, or control supervisor, capable of downloading new controller setpoints to the SBC as process conditions change.

A flexible and modular control system for astronomical telescopes and their associated observational instruments is implemented using an eight-bit microprocessor, and the various design features of a compact single board computer are discussed. A simple message protocol and system of UART serial links is used to achieve modularity and expandability of such a control system employing a central minicomputer. The single board computer, together with a small number of additional standard boards, is used to replace purpose designed serializing and interfacing hardware.

This article describes the prototype of the read-out subsystem which will be subject to the BESIII data acquisition system. According to the purpose of the BESIII, the event rate will be about 4000 Hz and the data rate up to 50 Mbytes/sec after Level 1 trigger. The read-out subsystem consists of some read-out crates and a read-out computer whose function is to initialize the hardware, to collect the event data from the front-end electronics after Level 1 trigger, to transfer data fragments to the computer in online form through two levels of computer pre-processing and high-speed network transmission. In this model, the crate level read-out implementation is based on the commercial single board computer MVME5100 running the VxWorks operating system.The article outlines the structure of the crate level testing platform of hardware and software. It puts emphasis on the framework of the read-out test model, data process flow and test method at crate level. Especially, it enumerates the key technologies in the process of design and analyses the test results. In addition, results which summarize the performance of the single board computer from the data transferring aspects will be presented.