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Journal articles on the topic 'Embedded computer systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 August 2024

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1

Chakraborty, Pratic. "Embedded Machine Learning and Embedded Systems in the Industry." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (November 30, 2021): 1872–75. http://dx.doi.org/10.22214/ijraset.2021.39067.

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Abstract: Machine learning is the buzz word right now. With the machine learning algorithms one can make a computer differentiate between a human and a cow. Can detect objects, can predict different parameters and can process our native languages. But all these algorithms require a fair amount of processing power in order to be trained and fitted as a model. Thankfully, with the current improvement in technology, processing power of computers have significantly increased. But there is a limitation in power consumption and deployability of a server computer. This is where “tinyML” helps the industry out. Machine Learning has never been so easy to access before!

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2

Thiruvathukal, George K., and Yung-Hsiang Lu. "Efficient Computer Vision for Embedded Systems." Computer 55, no. 4 (April 2022): 15–19. http://dx.doi.org/10.1109/mc.2022.3145677.

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3

Leveson, Nancy G. "Software safety in embedded computer systems." Communications of the ACM 34, no. 2 (February 1991): 34–46. http://dx.doi.org/10.1145/102792.102799.

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4

Lee, B. H. "Embedded Internet Systems: Poised for Takeoff [Embedded Systems]." IEEE Internet Computing 2, no. 3 (May 1998): 24–29. http://dx.doi.org/10.1109/mic.1998.683796.

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5

Hickey, Patrick C., Lee Pike, Trevor Elliott, James Bielman, and John Launchbury. "Building embedded systems with embedded DSLs." ACM SIGPLAN Notices 49, no. 9 (November 26, 2014): 3–9. http://dx.doi.org/10.1145/2692915.2628146.

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6

Karsai, Gabor, Fabio Massacci, Leon Osterweil, and Ina Schieferdecker. "Evolving Embedded Systems." Computer 43, no. 5 (May 2010): 34–40. http://dx.doi.org/10.1109/mc.2010.135.

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7

Guo, Li, Dajiang Zhou, Jinjia Zhou, Shinji Kimura, and Satoshi Goto. "Lossy Compression for Embedded Computer Vision Systems." IEEE Access 6 (2018): 39385–97. http://dx.doi.org/10.1109/access.2018.2852809.

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8

Egyhazy, Csaba. "Using database machines in embedded computer systems." Information & Management 8, no. 4 (April 1985): 197–203. http://dx.doi.org/10.1016/0378-7206(85)90016-3.

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9

Jiang, Zi Li. "Research of Development of Embedded Systems." Applied Mechanics and Materials 539 (July 2014): 547–50. http://dx.doi.org/10.4028/www.scientific.net/amm.539.547.

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With embedded systems widely used and the continuous improvement of the user to the data processing and management needs, a variety of smart devices and database technology are tightly combined, this has got integrated attention. Embedded system is dedicated computer system that is application-centric, based on computer technology, software and hardware can be tailored to meet the stringent requirements of the application system, which strict with functionality, reliability, cost, size and power consumption. It is the product of the development of computer technology, now it has become an important part of the computer field. The embedded database will be in everywhere in the near future. Looking at the current international and domestic embedded database application, market has entered a stage of accelerated development based on embedded database applications.

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10

Jonsson, E., and S. Asmussen. "A Practical Dependability Measure for Embedded Computer Systems." IFAC Proceedings Volumes 26, no. 2 (July 1993): 647–52. http://dx.doi.org/10.1016/s1474-6670(17)49023-9.

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11

Yamamoto, Yota, Nobuyuki Masuda, Ryuji Hirayama, Hirotaka Nakayama, Takashi Kakue, Tomoyoshi Shimobaba, and Tomoyoshi Ito. "Special-purpose computer for electroholography in embedded systems." OSA Continuum 2, no. 4 (March 18, 2019): 1166. http://dx.doi.org/10.1364/osac.2.001166.

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12

Lent, Bogdan. "Data flow driven computer for embedded control systems." Microprocessing and Microprogramming 19, no. 5 (December 1987): 385–99. http://dx.doi.org/10.1016/0165-6074(87)90250-x.

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13

Sun, H. "Programming for Embedded Systems." Computer Communications 27, no. 1 (January 2004): 143. http://dx.doi.org/10.1016/s0140-3664(03)00217-2.

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14

Runciman, B. "The Embedded Systems Paradox." ITNOW 55, no. 2 (May 28, 2013): 51. http://dx.doi.org/10.1093/itnow/bwt024.

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15

Liu, Li Min. "A Survey for Concept of Embedded Systems." Applied Mechanics and Materials 63-64 (June 2011): 73–76. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.73.

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Embedded systems are the most popular computer application systems. Their applications have changed life of people in the world. They can be found anywhere, and almost can be used in every field. But few people know the concept of an embedded system. In this paper, the concept and definition of embedded systems are discussed and analyzed. A considered concept of embedded system should include several factors, computer core, to build in, managed object and relevant software, despite to issue a consistent definition is difficult.

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16

Watkins, D. "Editorial: Microsoft Research embedded systems." IEE Proceedings - Software 152, no. 5 (2005): 209. http://dx.doi.org/10.1049/ip-sen:20059072.

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17

Martonosi, Margaret. "Embedded systems in the wild." ACM SIGPLAN Notices 41, no. 7 (July 12, 2006): 1. http://dx.doi.org/10.1145/1159974.1134651.

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18

Arnold, Kwofie. "Analysis of Embedded Designs in Mechatronic Systems." International Journal of Advance Research and Innovation 8, no. 4 (2020): 30–34. http://dx.doi.org/10.51976/ijari.842006.

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As technology rises in almost every field, mechatronic systems are of no exception as embedded systems are incorporated as part of their design. This introduces mechatronic system to high computer intelligence manipulations and high task performance, However, the story is not always the same as engineers are unable to implement efficient embedded designs for mechatronics systems. Embedded mechatronic systems rely on many factors. Some include vibration, electrical and electromagnetic, mechanical and the intelligence of the software component. In addition, reducing the associated cost, size, and complexities for process innovation becomes highly significant. The project analysis efficient design techniques for embedded systems which includes design robustness, intelligent embedded system software, power consumption and memory optimization. Embedded systems have become ubiquitous and as a result optimization of the design and performance of programs that run on these systems have continued to remain as significant challenges to the computer systems research community

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19

Neville-Neil, George. "The Truth About Embedded Systems." Queue 1, no. 2 (April 2003): 4–5. http://dx.doi.org/10.1145/644254.644260.

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20

Antimirov, V. M. "Enhancing the performance of radiation-hardened embedded computer systems." Russian Microelectronics 35, no. 3 (May 2006): 200–204. http://dx.doi.org/10.1134/s1063739706030097.

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21

Pudner, A. "DLM — a powerful ai computer for embedded expert systems." Future Generation Computer Systems 3, no. 4 (December 1987): 299–306. http://dx.doi.org/10.1016/0167-739x(87)90034-3.

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22

Ortega, Luis D., Erick S. Loyaga, Patricio J. Cruz, Henry P. Lema, Jackeline Abad, and Esteban A. Valencia. "Low-Cost Computer-Vision-Based Embedded Systems for UAVs." Robotics 12, no. 6 (October 27, 2023): 145. http://dx.doi.org/10.3390/robotics12060145.

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Unmanned Aerial Vehicles (UAVs) are versatile, adapting hardware and software for research. They are vital for remote monitoring, especially in challenging settings such as volcano observation with limited access. In response, economical computer vision systems provide a remedy by processing data, boosting UAV autonomy, and assisting in maneuvering. Through the application of these technologies, researchers can effectively monitor remote areas, thus improving surveillance capabilities. Moreover, flight controllers employ onboard tools to gather data, further enhancing UAV navigation during surveillance tasks. For energy efficiency and comprehensive coverage, this paper introduces a budget-friendly prototype aiding UAV navigation, minimizing effects on endurance. The prototype prioritizes improved maneuvering via the integrated landing and obstacle avoidance system (LOAS). Employing open-source software and MAVLink communication, these systems underwent testing on a Pixhawk-equipped quadcopter. Programmed on a Raspberry Pi onboard computer, the prototype includes a distance sensor and basic camera to meet low computational and weight demands.Tests occurred in controlled environments, with systems performing well in 90% of cases. The Pixhawk and Raspberry Pi documented quad actions during evasive and landing maneuvers. Results prove the prototype’s efficacy in refining UAV navigation. Integrating this cost-effective, energy-efficient model holds promise for long-term mission enhancement—cutting costs, expanding terrain coverage, and boosting surveillance capabilities.

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23

Katz, Daniel S., and Jeremy Kepner. "Embedded/Real-Time Systems." International Journal of High Performance Computing Applications 15, no. 2 (May 2001): 186–90. http://dx.doi.org/10.1177/109434200101500212.

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24

Du, Danbing. "Experimental Study on Neural Feedback in Embedded System Teaching Processing Based on ERP Signal Analysis." International Journal of Emerging Technologies in Learning (iJET) 14, no. 12 (June 27, 2019): 109. http://dx.doi.org/10.3991/ijet.v14i12.10715.

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Embedded system teaching is an indispensable part in the process of computer language learning. This paper analyzes the neural feedback of embedded system learners in learning embedded systems by observing the generation and fluctua-tion of EEG signals. The ERP signals of computer learners in learning embedded system are monitored by ERP technology. The results show that the embedded system leads to the changes in EEG of computer linguists, and the changes of EEG amplitudes caused by different embedded systems. Combined with previous studies, it is shown that the embedded system processing has different effects on neural feedback. The analysis of EEG signals provides some theoretical basis and new ideas for embedded system teaching.

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25

Filman, R. E. "Embedded internet systems come home." IEEE Internet Computing 5, no. 1 (January 2001): 52–53. http://dx.doi.org/10.1109/mic.2001.895142.

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26

Glesner, Sabine, Rubino Geiß, and Boris Boesler. "Verified Code Generation for Embedded Systems." Electronic Notes in Theoretical Computer Science 65, no. 2 (April 2002): 19–36. http://dx.doi.org/10.1016/s1571-0661(04)80394-3.

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27

I nyoman, Edy saputra Edy, IGAP Raka Agung, and Yoga Divayana. "ED-255EK Embeded Education Platform Sebagai Modul Praktikum Embeded System Dengan Robot Arm Module Dan Voice Module." Majalah Ilmiah Teknologi Elektro 18, no. 1 (May 6, 2019): 91. http://dx.doi.org/10.24843/mite.2019.v18i01.p13.

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Abstract— Literally embedded systems are defined as equipment that computes a particular job. Embedded Linux System was also developed as a means of education, one of which is ED-255EK Embedded Education Platform, designed to be very practical and consume very low power. ED-255EK is also equipped with a PXA255 processor that is compatible with ARM version 5TE ISA, so it can be used to carry out various kinds of lab work or experiments on embedded systems. The working principle of this tool begins with the configuration of data communication between Fedora Linux and ED-255EK. When the communication goes well, it is downloaded by the ED-255EK system to a laptop or computer that has been installed with the Fedora 9.0 Linux operating system. To take advantage of the ED-255FPGA module, driver module installation is required first, after which the application program is installed. Laptops or computers that have installed the Fedora 9.0 Linux operating system will act as input and output programs making it easier to monitor each module that is being run. The module used is the ARM 1923 Robot Module and Voice Module ED4321.

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28

Maclean, Stuart, and Sean Smith. "Object-oriented programming for embedded systems." ACM SIGPLAN Notices 30, no. 9 (September 1995): 3–8. http://dx.doi.org/10.1145/214448.214449.

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29

Ait-Ameur, Y., G. Bel, F. Boniol, S. Pairault, and V. Wiels. "Robustness analysis of avionics embedded systems." ACM SIGPLAN Notices 38, no. 7 (July 11, 2003): 123–32. http://dx.doi.org/10.1145/780731.780750.

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30

Henzinger, Thomas A., Benjamin Horowitz, and Christoph Meyer Kirsch. "Embedded Control Systems Development with Giotto." ACM SIGPLAN Notices 36, no. 8 (August 2001): 64–72. http://dx.doi.org/10.1145/384196.384208.

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31

Sullivan, Gaynor. "Embedded real-time systems." Microprocessors and Microsystems 17, no. 8 (October 1993): 499. http://dx.doi.org/10.1016/0141-9331(93)90037-8.

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32

Isoaho, Jouni, Seppo Virtanen, and Juha Plosila. "Current Challenges in Embedded Communication Systems." International Journal of Embedded and Real-Time Communication Systems 1, no. 1 (January 2010): 1–21. http://dx.doi.org/10.4018/jertcs.2010103001.

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This article defines and analyses key challenges met in future embedded systems in networked multimedia and communication applications. Self-awareness, interoperability and embedded security are used to characterize different aspects of designing and implementing next generation embedded systems. The dynamic nature of applications and implementations as well as possible technological faults and variations need to be considered in system verification and modeling. A new design layer needs to be added to current NoC platforms in order to build procedures that take into account dynamic system reconfigurations, fault-tolerance aspects and flexible portability. Increased modularity and networked implementations create a need for trust management mechanisms between system components and technology for analyzing validity and correctness of received application and system configuration information.

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33

Chan, Hing Kai. "Agent-oriented embedded electronic measuring systems." Communications of the ACM 53, no. 3 (March 2010): 157–62. http://dx.doi.org/10.1145/1666420.1666459.

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34

Goddard, Ivan. "Division of Labor in Embedded Systems." Queue 1, no. 2 (April 2003): 32–36. http://dx.doi.org/10.1145/644254.644266.

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35

Vahid, F., and T. Givargis. "Platform tuning for embedded systems design." Computer 34, no. 3 (March 2001): 112–14. http://dx.doi.org/10.1109/mc.2001.910903.

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36

Paul, J. M. "What's in a name [embedded systems]." Computer 39, no. 3 (March 2006): 87–89. http://dx.doi.org/10.1109/mc.2006.110.

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37

Yu, Huiqun, and Yongqiang Sun. "Hybridity in embedded computing systems." Journal of Computer Science and Technology 11, no. 1 (January 1996): 90–96. http://dx.doi.org/10.1007/bf02943525.

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38

Jian Feng. "Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model." Journal of Electrical Systems 20, no. 3s (April 4, 2024): 2726–37. http://dx.doi.org/10.52783/jes.3169.

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The process of evaluating the effectiveness, speed, and general capabilities of a computer system that is embedded inside a larger device or system and created for a particular purpose is known as embedded computer performance evaluation. Specialized computing systems called embedded systems are built into a wide range of gadgets, including medical, industrial, , automotive and consumer electronics. In this manuscript, Optimized Meta-path extracted graph neural network for embedded computer performance evaluation model(MEGNN-EC-PEM) is proposed. Initially, the input data is obtained from real-time sensor measurements and system metrics for model training and testing. The input image is pre-processed using Orthogonal Master-slave Adaptive Notch Filter (OMANF) and it removes the noise from the collected data. Then, the pre- processed data are fed to embedded computer performance using Meta-path extracted graph neural network (MEGNN). In general, MEGNN does not express adapting optimization techniques to determine optimal parameters to assure precise embedded computer performance evaluation model. Hence, the Hunter–prey optimization algorithm (HPOA) is used to optimize Meta-path extracted graph neural network which accurately categorized embedded computer performance. Then, the proposed MEGNN-EC-PEM is implemented and the performance metrics like Modelling Accuracy, latency, Throughput, memory cost and Energy Consumption are analyzed. The performance of the MEGNN-EC-PEM approach attains 19.41%, 20.08% and 32.57% higher modelling accuracy, 22.41%, 23.08% and 24.57% lower latency and 23.01%, 23.08% and 24.07% higher throughput when analyzed through existing techniques like a state-based modelling approach for effective performance evaluation of embedded system architectures at transaction level (SMA-EPE-ESAT), Evaluating the performance of pre-trained convolutional neural network for audio classification on embedded schemes for anomaly detection in smart cities (PCNN-ACES-ADSC) and Animal behaviour classification via deep learning on embedded systems (ABC-VDL-ES) methods respectively.

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39

Schoeberl, Martin, Andreas Engelbredt Dalsgaard, René Rydhof Hansen, Stephan E. Korsholm, Anders P. Ravn, Juan Ricardo Rios Rivas, Tórur Biskopstø Strøm, Hans Søndergaard, Andy Wellings, and Shuai Zhao. "Safety-critical Java for embedded systems." Concurrency and Computation: Practice and Experience 29, no. 22 (December 14, 2016): e3963. http://dx.doi.org/10.1002/cpe.3963.

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40

Takala, Jarmo, Shuvra S. Bhattacharyya, and Gang Qu. "Embedded Digital Signal Processing Systems." EURASIP Journal on Embedded Systems 2007 (2007): 1. http://dx.doi.org/10.1155/2007/27517.

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41

Bouaziz, Samir, Paolo Lombardi, Roger Reynaud, and Gunasekaran S. Seetharaman. "Embedded Systems for Intelligent Vehicles." EURASIP Journal on Embedded Systems 2007 (2007): 1–4. http://dx.doi.org/10.1155/2007/29239.

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42

Girault, Alain, S. Ramesh, and Jean-Pierre Talpin. "Synchronous Paradigm in Embedded Systems." EURASIP Journal on Embedded Systems 2007 (2007): 1–2. http://dx.doi.org/10.1155/2007/32903.

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43

Chantem, Thidapat, Nan Guan, and Duo Liu. "Sustainable embedded software and systems." Sustainable Computing: Informatics and Systems 22 (June 2019): 152–54. http://dx.doi.org/10.1016/j.suscom.2019.05.003.

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44

Dewald, Kevin, and Daniel Jacoby. "Signal Processing In Embedded Systems." IEEE Latin America Transactions 11, no. 1 (February 2013): 664–67. http://dx.doi.org/10.1109/tla.2013.6502881.

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45

Metzger, Andreas. "Feature interactions in embedded control systems." Computer Networks 45, no. 5 (August 2004): 625–44. http://dx.doi.org/10.1016/j.comnet.2004.03.002.

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46

Watterson, C., and D. Heffernan. "Runtime verification and monitoring of embedded systems." IET Software 1, no. 5 (October 1, 2007): 172–79. http://dx.doi.org/10.1049/iet-sen:20060076.

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47

Michel Deves de Souza, Eduardo, Nathalia Nathalia Adriana de Oliveira, Douglas Almeida dos Santos Almeida dos Santos, and Douglas Rossi de Melo. "RVSH - Um processador RISC-V para fins didáticos." Anais do Computer on the Beach 14 (May 3, 2023): 450–52. http://dx.doi.org/10.14210/cotb.v14.p450-452.

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ABSTRACTEmbedded systems constitute the class of computers that presentthe most significant volume and are increasingly present ineveryday life. The main element of these systems is the processor,which can be found in discrete form, represented by a physicalcomponent, or cores, as used in programmable logic devices.Processors of the same architecture share the same instructionset but may differ in the organization’s implementation. RISC(Reduced Instruction Set Computer) is the class of architecturesthat favors a simple, reduced instruction set. RISC-V is an exampleof such architecture, which consists of an initiative by academiaand industry to be open and free, aiming for easy and optimizedimplementations. However, due to the recent disclosure of itsfeatures and specifications, RISC-V needs more reference materialfor digital and embedded system designs. This work proposesthe RVSH, a simple RISC-V processor for teaching and researchactivities. The implementation aims to allow the adoption of thisarchitecture in topics such as digital systems, computer architecture,microcontrollers, and embedded systems design.

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48

Son, Sung Hoon. "Design of Virtual Machine Monitor for Embedded Systems." Applied Mechanics and Materials 263-266 (December 2012): 1629–32. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.1629.

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Recently virtualization has been one of the most popular research topics in system software area. While there are many commercial virtualization products for general-purpose computer system, little efforts are made to virtualize embedded system. In this paper, we design and implement a virtual machine monitor which divides each physical hardware resource of an embedded system into logical ones and reorganizes them into many virtual machines so that several real-time operating systems run concurrently on a single embedded system. We measure various performance metrics of the virtual machine monitor developed on a real embedded system. The results of the measurement study show that our virtual machine monitor has enough potentiality of its application to real-world embedded systems.

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49

Beck, J. E., and D. P. Siewiorek. "Automatic configuration of embedded multicomputer systems." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 17, no. 2 (1998): 84–95. http://dx.doi.org/10.1109/43.681259.

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50

Syschikov, Alexey, Yuriy Sheynin, Boris Sedov, and Vera Ivanova. "Domain-Specific Programming Environment for Heterogeneous Multicore Embedded Systems." International Journal of Embedded and Real-Time Communication Systems 5, no. 4 (October 2014): 1–23. http://dx.doi.org/10.4018/ijertcs.2014100101.

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Nowadays embedded systems are used in a broad range of domains such as avionics, space, automotive, mobile, domestic appliances etc. Sophisticated software determines the quality of embedded systems and requires high-qualified experts for software development. Software becomes the main assert of embedded systems that is valuable to retain in changing computing platforms in embedded systems evolution. Computing platforms for embedded systems became multicore processors and SoC, they can change in the embedded system lifetime that could be long (dozen of years for an automobile and airplane). It requires software porting to new platforms as a regular process. Many tools and approaches allow developing of software for domain area experts, but mainly for general-purpose computing systems. In this paper the authors present the complex technology and tools that allows involving domain experts in software development for embedded systems. The proposed technology has various aspects and abilities that can be used to build verifiable and portable software for a wide range of embedded platforms.

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