If you are not required to use this edition for a course, you may want to check it out. As computers and other digital devices have become essential to business and commerce, they have also increasingly become a target for attacks. In order for a company or an individual to use a computing device with confidence, they must first be assured that the device is not compromised in any way and that all communications will be secure. In this chapter, we will review the fundamental concepts of information systems security and discuss some of the measures that can be taken to mitigate security threats. We will begin with an overview focusing on how organizations can stay secure.

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## Electrical and Electronics Engineers

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As it is getting increasingly difficult to achieve gains in the density and power efficiency of microelectronic computing devices because of lithographic techniques reaching fundamental physical limits, new approaches are required to maximize the benefits of distributed sensors, micro-robots or smart materials. Biologically-inspired devices, such as artificial neural networks, can process information with a high level of parallelism to efficiently solve difficult problems, even when implemented using conventional microelectronic technologies.

We describe a mechanical device, which operates in a manner similar to artificial neural networks, to solve efficiently two difficult benchmark problems computing the parity of a bit stream, and classifying spoken words.

The device consists in a network of masses coupled by linear springs and attached to a substrate by non-linear springs, thus forming a network of anharmonic oscillators.

As the masses can directly couple to forces applied on the device, this approach combines sensing and computing functions in a single power-efficient device with compact dimensions. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Competing interests: The authors have declared that no competing interests exist. Massively-parallel networks of simple units with elementary non-linear processing capabilities, such as artificial neural networks, have been used for years as efficient and robust computing systems. In general, a network of N elements is described by the state column vector.

The state vector evolves with time t as a stimulus u t is imposed on the network, according to the rich dynamics created by the interconnection of the elements in the network. F is a complicated transformation of the input signal, which is used to represent the computing capabilities of the network. During this training phase, the internal structure of the network is left untouched, and only the output weights are adjusted. It is actually observed in numerical experiments that reservoir computers perform well when the networks driven by the stimulus u t operate as systems with complex dynamics [ 3 ].

As such, reservoir computers are an efficient approach to exploit architectures such as recurrent neural networks, which are Turing equivalent [ 4 ], but which are difficult to train using conventional methods. In practice, reservoir computers have been shown to be accurate and resource-efficient solutions to a number of challenging problems e. A useful characteristic of reservoir computers is their fixed internal structure. The network is generally constructed randomly with a few deterministic constraints such as limits on the spectral radius of the network connection matrix [ 7 ], for instance.

Following the network construction, different weight vectors can be computed to enable the network to perform different tasks. This fixed structure is especially attractive from the point of view of hardware implementation, as it does not require interconnections with dynamically adjustable strengths between the network elements, or other forms of network adaptability. A number of hardware implementations of reservoir computers have been discussed, including analog electronics [ 8 ], self-assembled atomic switches [ 9 ], optoelectronic devices [ 10 ] and photonic devices [ 11 ].

This motivates the search for hardware elements that can be arranged in a network to form a dynamical system able to respond to an external stimulus, and to exhibit the echo state property. We are especially interested in dynamical systems which can be stimulated directly by physical forces, such as accelerations or mechanical pressure. In this communication, we verify numerically the hypothesis that non-linear anharmonic mechanical oscillators coupled by linear springs can perform non-trivial computations.

This opens up the possibility for miniature, energy-efficient computers. As the mechanical elements are sensitive to physical forces, it also blurs the boundary between sensors and computers, with great opportunities in control and signal processing, for instance. The objective of this study is to demonstrate that a network of non-linear mechanical oscillators can perform complex computations within the framework of reservoir computing.

We choose a specific form for the network, which is described in details below, and show that a single instance of such a network can efficiently solve two widely different computing problems. This establishes the usability of networks of mechanical oscillators as general-purpose computing devices, which are able to efficiently process information from complex physical stimuli.

The N inertial masses circles arranged in a chain are coupled to neighbors by linear springs, and to a substrate by linear or non-linear springs, with damping. A harmonic forcing, with amplitude possibly modulated by couplings to the input signal u t , is imposed on the masses.

When the drive amplitudes are large, the system can exhibit very complex dynamics, including extreme sensitivity to initial conditions chaos. At lower drive amplitudes, the dynamics can still be complex but are no longer chaotic. Because of damping, the system exhibits the echo state property when its dynamics have a single attractor. The existence of a single attractor for a given class of input signals u is verified numerically by obtaining a stable, high success probability when the oscillator network is trained for a particular task.

This demonstrates that a given network with fixed structure can process information in widely different and complex ways. This might be especially relevant technologically for space- and power-constraint applications, where it is beneficial to collocate a device sensing and processing functions. We consider as an example of a concrete implementation a network of thin doubly clamped silicon beams of length l operated in their out-of-plane mode. Such a network could be fabricated using conventional MEMS technologies.

The number density of oscillators would be approximately 3 where R w is the width-to-length ratio of the beams. The resonance frequency of such oscillators is [ 12 ] 4 where R t is the thickness-to-length ratio of the beam.

As non-linear effects must be present in the oscillators for non-trivial computing capabilities to emerge, their oscillation amplitudes should be sufficiently large. The minimum amplitude for the onset of non-linear effects in a double clamped silicon beam is approximated by [ 12 ] 5 The energy in one oscillator is , for m eff the effective mass of the beam, so the mechanical power required to drive one oscillator in the reservoir is 6 For comparison, reference [ 13 ] describes a state-of-the-art neuromorphic computing device implemented in the nm CMOS technology with 10 6 computing elements artificial neurons.

Its power consumption per neuron is on the order of 90 nW. The mechanical portion of the proposed reservoir computer with silicon beams could thus be smaller by one order or magnitude or more energy efficient by one or two orders of magnitude than a state-of-the-art microelectronic devices with the same number of computing element.

The density and power estimates of the oscillator network do not include the oscillator motion sensing, summation and amplification, which are expected to be implemented in efficient analog electronics, possibly using advanced packaging schemes such a heterogeneous integration.

Reservoir computers made of a network of coupled anharmonic mechanical oscillators therefore appear as interesting candidates for miniature, power-efficient devices that can be driven directly by physical signals external fields, inertial or pressure forces, etc.

Numerical simulations demonstrating the computing capabilities of a mechanical oscillator network are presented in section 1, while the robustness of this computing model with respect to possible variability in the hardware implementation are discussed in section 2.

The main result of this section is a numerical demonstration that a single instance of a network of coupled anharmonic oscillators, as described by Eq 2 , can perform well on different computing benchmarks.

All numerical results are obtained with the same oscillator network, excepted where explicitly mentioned for robustness evaluations. It should be emphasized that the particular parameters and structure of the network presented below were only selected to demonstrate the usefulness of networks of oscillators as computers; the optimization of these parameters and structure to achieve better performances on specific tasks will be the subject of future communications.

The amplitude A driving uniformly all the oscillators depends on the particular benchmark problem see below. Eq 2 is integrated numerically using a Runge-Kutta method. They are used to form the output signal 7 where the weights w i are computed from the data accumulated during the training period.

The parity function is considered as a first benchmark, as it requires both memory and non-trivial non-linear computational capabilities [ 15 ]. In order to increase the robustness of the estimation of the parity functions, the weights were computed on ten contiguous, equal-length sub-sections of the full oscillator chain during a training phase of duration T.

Each sub-section had its own set of weights and was sufficiently long 40 oscillators to produce a good estimate of the parity function for most inputs. Fig 2 shows a numerical example obtained for parity functions of order 3, 4 and 5. On the other hand, estimated parity values around 0 as observed more frequently for P 5 indicate that the equivalent shorter chains were unable to agree on a valid parity value.

The training process appears to be relatively robust, with the 10 th percentile of the proportion of correctly estimated parity values for repeated training runs which differ only in the randomly generated training data estimated at Driving term u t , P 3 t , P 4 t , P 5 t top to bottom, shifted vertically for clarity.

A convenient way to compare the capacitites of the oscillator networks to other results in the reservoir computing literature is to estimate their so-called memory capacity, as introduced in reference [ 3 ]. The resulting memory capacity is approximately 3.

This is a conventional benchmark for non-trivial classification tasks e. We use from this data set utterances of the words from 7 different female speakers. In this task, the driving signal u t is formed by concatenating time series of the utterances, padded with periods of silence duration The mean of each time series is removed, the time series is normalized by its standard deviation, and its absolute value is used for u t.

The time series are provided in TI at a sampling rate of In order to adapt the time series to the dynamics of the simulated oscillators, we stretch every second of time series data to As a result, the network mostly uses the low frequency content of the sound recordings to classify the utterances.

The training was performed on utterances chosen randomly between the ten digits. One set of weights was computed for each digit. Each sub-section had its own set of weights and produced a value that should be one if the digit corresponding to this set was spoken, and zero otherwise.

The values produced from the weights were integrated over each period where an utterance was submitted to the network. Fig 3 presents results for the words classification benchmark. The results correspond to the average performance of 25 different training runs, performed on the same network.

For all training runs, the variability in success rate is consistent with uncertainties from the finite sample size, indicating that the training procedure is repeatable. The results are relatively good, with the network correctly classifying randomly chosen utterances in 0. The color-scale indicates the probability that a digit presented to the device columns is classified to a certain value lines by the oscillators network.

The results can be compared to other experiments in the reservoir computing literature. It has been shown in ref. The results presented here with the oscillator network do not include any pre-processing, except for the rectification of the input time series to form the input signal u t , and the normalization of the time series amplitudes.

This is intended to reflect a simple system where the sound pressure directly modulates the driving force on the oscillators e. More complicated systems, for instance with modulation amplitudes for different oscillator groups that depend on the sound frequency, could in principle be significantly more efficient. Fig 4 shows variations in the success probability for the parity functions, as the global parameters A amplitude of oscillator drive and T period of input binary signal are varied.

Each network is trained and operated independently as described in section 1. It is observed that the region of global parameter space where the performance of the network is good is reasonably large, indicating that the precise matching of the network to a given signal especially with respect to T is not required.

Success probability P for the three parity functions P 3 , P 4 , P 5 , left to right as the period T of the input binary signal and the amplitude A of the oscillator drive are varied globally for the whole network. Each perturbed network is trained and operated independently as described in section 1. While the performances of the network do depend on the nominal value of the parameters set for the oscillators, these data demonstrate for the parity function that the network is quite robust when the parameters of individual oscillators are independently varied around these nominal values.

On the other hand, Fig 6 shows how the success probability for the parity functions is reduced when perturbations described by Eq 14 are introduced just after the training of the network has been completed. This situation corresponds to oscillator parameters that are drifting over time. Non-linear mechanical oscillators arranged in a network using linear couplings can be used to perform complex computations, as demonstrated in this communication by numerical simulations of a single instance of a network that performs well on two widely different benchmark tasks computation of parity functions, and classification of spoken words.

These results show the existence of a new class of computing devices based on networks of coupled non-linear mechanical oscillators. We have described one example of such device which, as mentioned above, performs robustly on two difficult computing tasks. It is remarkable that this device has a low complexity oscillators relative to modern microelectronic components.

As explained in the introduction, this leads to the possibility of creating small and energy-efficient devices that are highly relevant technologically. The device that was simulated in this work was constructed randomly from a small set of rules that are described in section 1.

These rules were only minimally tuned in order to obtain good performances on the benchmark tasks. It was observed, for instance, that smaller networks did not perform as well on the spoken words benchmark.

Account Options Sign in. My library Help Advanced Book Search. Elsevier , M05 9 - pages. The book focuses on the methodologies, technologies, processes, and approaches involved in the adoption of automatic control in power generation, distribution, and protection.

## U.S. Food and Drug Administration

In fact, calculation underlies many activities that are not normally thought of as mathematical. Walking across a room, for instance, requires many complex, albeit subconscious, calculations. Computers, too, have proved capable of solving a vast array of problems, from balancing a checkbook to even—in the form of guidance systems for robots—walking across a room. Before the true power of computing could be realized, therefore, the naive view of calculation had to be overcome. The inventors who laboured to bring the computer into the world had to learn that the thing they were inventing was not just a number cruncher, not merely a calculator.

## Learning Objectives

As it is getting increasingly difficult to achieve gains in the density and power efficiency of microelectronic computing devices because of lithographic techniques reaching fundamental physical limits, new approaches are required to maximize the benefits of distributed sensors, micro-robots or smart materials. Biologically-inspired devices, such as artificial neural networks, can process information with a high level of parallelism to efficiently solve difficult problems, even when implemented using conventional microelectronic technologies. We describe a mechanical device, which operates in a manner similar to artificial neural networks, to solve efficiently two difficult benchmark problems computing the parity of a bit stream, and classifying spoken words. The device consists in a network of masses coupled by linear springs and attached to a substrate by non-linear springs, thus forming a network of anharmonic oscillators. As the masses can directly couple to forces applied on the device, this approach combines sensing and computing functions in a single power-efficient device with compact dimensions.

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Digital technologies are everywhere, affecting the way we live, work, travel and play. Digitalisation is helping improve the safety, productivity, accessibility and sustainability of energy systems around the world. But it is also raising new security and privacy risks, while disrupting markets, businesses and workers. The report examines the impact of digital technologies on energy demand sectors, looks at how energy suppliers can use digital tools to improve operations, and explores the transformational potential of digitalisation to help create a highly interconnected energy system. This report seeks to provide greater clarity to decision makers in government and industry on what digitalisation means for energy, shining a light on its enormous potential and most pressing challenges. It also lays out no-regret recommendations to help steer the world towards a more secure, sustainable and smarter energy future. Over the coming decades, digital technologies are set to make energy systems around the world more connected, intelligent, efficient, reliable and sustainable. Stunning advances in data, analytics and connectivity are enabling a range of new digital applications such as smart appliances, shared mobility, and 3D printing.

## History of computing hardware

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## Digitalisation and Energy

Electrical engineers design, develop, test, and supervise the manufacture of electrical equipment. Electrical and electronics engineers work in industries including research and development, engineering services, manufacturing, telecommunications, and the federal government. Electrical and electronics engineers generally work indoors in offices. However, they may have to visit sites to observe a problem or a piece of complex equipment. Employers also value practical experience, such as internships or participation in cooperative engineering programs. Overall employment of electrical and electronics engineers is projected to grow 2 percent from to , slower than the average for all occupations.

## Introduction to Computer Information Systems/Print version

Charts and tables. Account Options Sign in. My library Help Advanced Book Search. Get print book. Covers: standards development projects, tetsing projects, software devlopment and deployment projects, education and training activities and communication activities.

Computers Computer is a machine for performing calculations automatically. An expert at calculation or at operating calculating machines. A machine that can be instructed to carry out sequences of arithmetic or logical operations automatically via computer programming. Memory and Processing.

Computers are being used in increasing numbers in the pharmaceutical industry. As microprocessors become more powerful, reliable, and less expensive we can expect the proliferation of this technology, with increasing use by even very small pharmaceutical establishments. Computer systems are used in a wide variety of ways in a pharmaceutical establishment, such as, maintenance of quarantine systems for drug components, control of significant steps in manufacturing the dosage form, control of laboratory functions, management of warehousing and distribution activities. Computer systems may control one or more of these phases, either singly or as part of a highly automated integrated complex.

The history of computing hardware covers the developments from early simple devices to aid calculation to modern day computers. Before the 20th century, most calculations were done by humans. Early mechanical tools to help humans with digital calculations, like the abacus , were called "calculating machines", called by proprietary names, or referred to as calculators.

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