The history of analog computing: introduction to the special section

Lundberg, K.H.

doi:10.1109/mcs.2005.1432595

Cited by 24 publications

(6 citation statements)

References 22 publications

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“…In the early 20 th century came the first electronic ACs, though they were quite limited, being developed solely with passive elements (essentially RLC networks) [7]. Much more sophisticated, active designs arose with the development of the vacuum tube-based operational amplifier (op-amp), which allowed for electronic implementation of the most common mathematical operations, including integration and differentiation [2], [8], [10], [26]. These computers found practical application in artillery guidance systems during World War II, and later integrated the navigational computers of the Apollo space program [8], [27].…”

Section: B Analog Computersmentioning

confidence: 99%

“…Much more sophisticated, active designs arose with the development of the vacuum tube-based operational amplifier (op-amp), which allowed for electronic implementation of the most common mathematical operations, including integration and differentiation [2], [8], [10], [26]. These computers found practical application in artillery guidance systems during World War II, and later integrated the navigational computers of the Apollo space program [8], [27]. Concurrently, researchers were realizing the AC's potential for more general scientific and engineering calculations, namely in the study of differential equations and dynamical systems [10], [28].…”

Section: B Analog Computersmentioning

confidence: 99%

“…The former system is known as the controller, and the latter as the plant [20]. Either, or both, of these systems may be simulated through an AC, enjoying the perks described previously [8], [38]. Classical architectures, such as the proportional-integral-derivative (PID) controller, are readily implemented in an analog environment by simply connecting the corresponding function blocks to a weighted sum node, configured with the proper gain values (these have to be computed a priori, or at least adjusted experimentally).…”

Section: B Analog Computersmentioning

confidence: 99%

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CMOS Analog Simulators of Dynamical Systems

Araújo,

Oliveira,

Guerreiro

et al. 2024

IEEE Access

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Analog computing is based upon using physical processes to solve formal mathematical problems. In the past, it was the predominant instrument of scientific calculations. Now, as the physical limits imposed on digital devices compel research into alternate computing paradigms, a reexamination of the potentialities of analog computing is warranted. This work studies the application of analog CMOS cells toward the simulation of dynamical systems, and, more generally, solving sets of coupled time-dependent ordinary differential equations. Following a brief review of the fundamentals of systems theory and analog computing, the main set of computing elements is introduced, each comprising analog cells designed in a 130 nm process. These are subsequently applied to the realization of practical, special-purpose analog computing modules. Illustrative systems from various fields are selected for simulation. Though by no means comprehensive, these case studies highlight the capabilities of contemporary analog computing, especially in solving nonlinear problems. Circuit simulations show good agreement with solutions obtained from highorder numerical methods, at least over a limited range of system parameters. The article concludes with a brief discussion of broader analog computing applications, offering future prospects toward further exploration of its potentialities and limitations in a wide range of domains.INDEX TERMS Analog computers, differential equations, dynamical systems, integrated circuits.

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Section: B Analog Computersmentioning

confidence: 99%

Section: B Analog Computersmentioning

confidence: 99%

Section: B Analog Computersmentioning

confidence: 99%

See 1 more Smart Citation

CMOS Analog Simulators of Dynamical Systems

Araújo,

Oliveira,

Guerreiro

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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“…There are still domains where they are still in use in some specific applications, where classic automation is required due to operation costs and environmental conditions. Extensive historical views on analog computers can be found in [177], and also in a special issue of IEEE Control Systems dedicated to the history of analog computers, from which we cite here the editorial article [178] signed by Lundberg. Examples of analog computers can go from the ancient nomographs and sextants, to slide rules, and further to complex contemporary automation and control systems such as navigation systems, analog auto-pilot systems for vehicle navigation, military weapon system controllers, analog signal processors, etc. In essence, virtually all industrial process control systems in the pre-digital era used analog computers, known at the time as controllers, to automatically regulate temperature, flow, pressure, or other process variables and parameters.…”

Section: Computersmentioning

confidence: 99%

A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data

Abbass¹,

Leu²,

Kasmarik³

2016

IEEE Access

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Despite the advances made in artificial intelligence, software agents, and robotics, there is little we see today that we can truly call a fully autonomous system. We conjecture that the main inhibitor for advancing autonomy is lack of trust. Trusted autonomy is the scientific and engineering field to establish the foundations and ground work for developing trusted autonomous systems (robotics and software agents) that can be used in our daily life, and can be integrated with humans seamlessly, naturally and efficiently.In this paper, we review this literature to reveal opportunities for researchers and practitioners to work on topics that can create a leap forward in advancing the field of trusted autonomy. We focus the paper on the 'trust' component as the uniting technology between humans and machines. Our inquiry into this topic revolves around three sub-topics: (1) reviewing and positioning the trust modelling literature for the purpose of trusted autonomy; (2) reviewing a critical subset of sensor technologies that allow a machine to sense human states; and (3) distilling some critical questions for advancing the field of trusted autonomy. The inquiry is augmented with conceptual models that we propose along the way by recompiling and reshaping the literature into forms that enable trusted autonomous systems to become a reality. The paper offers a vision for a Trusted Cyborg Swarm, an extension of our previous Cognitive Cyber Symbiosis concept, whereby humans and machines meld together in a harmonious, seamless, and coordinated manner.

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“…It has been reported that the current-voltage characteristics of analog VLSI devices enable efficient computations in some complicated functions. 22,23) These reports indicate the potential of analog circuitries to offer a computational scheme of low power, high speed, compact size, and high parallelism. On the other hand, it is widely agreed that the analog computation suffers from an inaccuracy problem due to noise and variations among analog devices.…”

Section: Target Application Of This Workmentioning

confidence: 99%

An analog VLSI implementation of one-class support vector machine for multiclass classification of highly dimensional vectors

Zhang

Kaneko

Shibata

2014

Jpn. J. Appl. Phys.

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A one-class support vector machine (OC-SVM) is implemented using an on-chip-trainable analog VLSI processor. The one-class classification of highly dimensional sample vectors can be solved with this analog processor. Since the OC-SVM learning mechanism is complicated, a special solution scheme for the learning operation is proposed on the basis of analog computational circuitries and a fully parallel architecture. In this manner, the built VLSI processor achieves a high learning speed and a compact chip area at the same time. By combining multiple OC-SVM processors, multiclass recognition can be implemented with an arbitrary number of classes. The proof-of-concept chip is fabricated for the recognition of 64-dimensional vectors representing real image patterns. Three OC-SVM processors are combined for three classes of samples, where all the on-chip learning operations are accomplished within 0.6 µs. From the measurement results, all the test patterns are correctly recognized or rejected by the recognition system built.

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The history of analog computing: introduction to the special section

Cited by 24 publications

References 22 publications

CMOS Analog Simulators of Dynamical Systems

CMOS Analog Simulators of Dynamical Systems

A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data

An analog VLSI implementation of one-class support vector machine for multiclass classification of highly dimensional vectors

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