Search

Purpose: Random noise-moving images (noises) can make glaucoma patients with no subjective symptoms aware of visual field abnormalities. To explore this concept, we developed a noise using computer graphics (CG) and investigated the difference in the subjective perception of visual field abnormalities between CG and conventional analog noises. Methods: We enrolled individuals with glaucoma (205 eyes), preperimetric glaucoma (PPG; 19 eyes), and normal eyes (35 eyes). For a CG noise, a series of still images was made by randomly selecting five monochromatic tones on 2-mm square dots, and these images were drawn at 60 frames per second (fps) to create a noise-moving image. The participants were asked to describe their perceived shadows on a paper. The results were categorized as follows based on the pattern deviation probability map of the Humphrey field analyzer (HFA): "agreement," "partial agreement," "disagreement," and "no response." The glaucoma stage was classified into four stages, from M1 to M4, based on the HFA's mean deviation. Result: The detection rates (agreement and partial agreement) were 80.5% and 65.4% for the CG and analog noises, respectively, with CG noise showing a significantly higher detection rate in all glaucoma eyes (P < 0.001). The detection rates tended to increase as the glaucoma stage progressed, and in Stage M3, these were 93.9% and 78.8% for the CG and analog noises, respectively. The PPG eyes did not exhibit subjective abnormalities for both noises. The specificity values were 97.1% and 100% for the CG and analog noises, respectively. Conclusion: The CG noise is more effective than the analog noise in evaluating the subjective perception of visual field abnormalities in patients with glaucoma. [ABSTRACT FROM AUTHOR]

Analog computing is often considered to be an outdated technology only to be found and cared for in museums but not as the basis of modern cutting edge computing. Nothing could be further from reality. Together with quantum computers, analog computers complement today's stored program digital computers, acting as co-processors for certain types of computationally intensive tasks. They are highly energy efficient and exhibit a degree of parallelism that by far surpasses classic digital computers. In contrast to quantum computers, analog computers do not require exotic technologies, no intricate cooling systems, no elaborate shielding systems, etc. In addition to this there already exists a plethora of proven programs for analog computers to solve a wide variety of problems. This paper describes the necessity of analog computing for the twenty-first century, gives a short introduction to the structure and programming of analog computers, before discussing future application areas. Analog computing will drastically alter the way we think about computing in the years to come as it will become an integral part of every high performance computer. It will be an indispensable technology for future AI systems, it will revolutionize many medical applications, it might even replace or at least complement, certain types of quantum computers, etc. [ABSTRACT FROM AUTHOR]

Prime factorization is a procedure of determining the prime factors of a given number N that requires super-polynomial time for conventional digital computers. Peter Shor developed a polynomial-time algorithm for quantum computers. Period finding is the key part of the algorithm, which is accomplished with the help of quantum superposition of states and quantum entanglement. The period finding can be also accomplished using classical wave superposition. In this study, we present experimental data obtained on a multi-port spin wave interferometer made of Y3Fe2(FeO4)3. Number 817 was factorized by a sequence of phase measurements. We also present the results of numerical modeling on the prime factorization of larger numbers 334 597 , 1 172 693 , 3 377 663 , and 9 363 239. The results of numerical modeling reveal significant shortcomings of the period-based approach. The major problems are associated with an inability to predict the period of the modular function, significant overhead over classical digital computers in some cases, and phase accuracy requirements. We argue that the same problems are inherent in classical analog and quantum computers. [ABSTRACT FROM AUTHOR]

This paper describes a design process for a universal development kit based on an analog computer concept that can model the dynamics of an arbitrarily complex dynamical system up to the fourth order. The constructed development kit contains digital blocks and associated analog-to-digital and digital-to-analog converters (ADCs and DAC), such that multiple-segmented piecewise-linear input–output characteristics can be used for the synthesis of the prescribed mathematical model. Polynomial input–output curves can be implemented easily by four-quadrant analog multipliers. The proposed kit was verified through several experimental scenarios, starting with simple sinusoidal oscillators and ending with generators of continuous-time robust chaotic attractors. The description of each individual part of the development kit is accompanied by links to technical documentation, allowing skilled readers in the construction of electronic systems to replicate the proposed functional example. For this purpose, the electrical scheme of the hybrid analog computer and all important source codes are available online. [ABSTRACT FROM AUTHOR]

Recovered in 1901, from a first-century BC shipwreck, the Antikythera Mechanism is considered to be the oldest extant complex geared device. It was constructed in ~150 BCE and was essentially an analog computer, an astronomical and calendrical device, designed to predict astronomical phenomena, such as lunar and solar eclipses, to maintain calendar accuracy and to predict the dates of Panhellenic Games. The device was operated manually by a user, setting a date in a dial. All necessary calculations were made using a set of gears (at least 39), while the results were displayed on several scientific scales. The Mechanism’s miniature size, the elaborate gear trains, the use of eccentrical gears and the employment of a pin–and–slot gear system to calculate the anomalous orbit of the Moon, demonstrate that the Greek mechanicians of the Hellenistic period had become far more skillful in designing geared devices than the surviving written sources imply. Geared devices matching the complexity of the Antikythera Mechanism would not appear again in Europe until the mechanical clocks of the thirteenth century. The aim of this paper is to present this ancient elaborate device in the most comprehensible way. [ABSTRACT FROM AUTHOR]

This paper strives to achieve a comprehensive review of chaos in analog circuits and lumped electronic networks. Readers will be guided from the beginning of the investigations of simple electronic circuits to the current trends in the research into chaos. The author tries to provide the key references related to this issue, including papers describing modern numerical algorithms capable of localizing chaotic and hyperchaotic motion in complex mathematical models, interesting full on-chip implementations of chaotic systems, possible practical applications of entropic signals, fractional-order chaotic systems and chaotic oscillators with mem-elements. [ABSTRACT FROM AUTHOR]

Wave-based analog computing is a new computing paradigm heralded as a potentially superior alternative to existing digital computers. Currently, there are optical and low-frequency acoustic analog Fourier transformers. However, the former suffers from phase retrieval issues, and the latter is too physically bulky for integration into CMOS-compatible chips. This paper presents a solution to these problems: the Ultrasonic Fourier Transform Analog Computing System (UFT-ACS), a metalens-based analog computer that utilizes ultrasonic waves to perform Fourier transform calculations. Through wave propagation simulations on MATLAB, the UFT-ACS has been shown to calculate the Fourier transform of various input functions with a high degree of accuracy. Moreover, the optimal selection of parameters through sufficient zero padding and appropriate truncation and bandlimiting to minimize errors is also discussed. [ABSTRACT FROM AUTHOR]

Aim: the aim of this study was to compare the flexural strength and elastic modulus of three-dimensionally (3D) printed, conventional heat-cured, and high-impact implant-supported overdenture materials specimens. Materials and Methods: Thirty implant-supported overdenture materials specimens (bar-shaped, 65.0 × 10.2 × 5.1 ± 0.2 mm3) with one central hole were fabricated using 3D-printed, heat-cured conventional, and high-impact denture base resins (n = 10/group). Autopolymerizing acrylic resin was used to attach titanium matrix housings to the central holes of the specimens. A three-point bending test was conducted using a universal testing machine and a model analog with a crosshead speed of 5 mm/min. The indicative flexural strength and elastic modulus were recorded. Data were statistically analyzed using analysis of variance (ANOVA) and the Tukey tests at α = 0.05. Results: One-way ANOVA revealed a significant effect of denture base material on the flexural strength (p < 0.001) but not on the elastic modulus (p = 0.451) of the evaluated materials. The flexural strength of the 3D-printed specimens (95.99 ± 9.87 MPa) was significantly higher than the conventional (77.18 ± 9.69 MPa; p < 0.001) and high-impact ones (82.74 ± 7.73 MPa; p = 0.002). Conclusions: The maximum flexural strength was observed in the 3D-printed implant-supported overdenture material specimens, which might indicate their suitability as an alternative to the conventionally fabricated ones. Flexural strength and elastic modulus of conventional and high-impact heat-cured implant-supported overdenture materials specimens were comparable. [ABSTRACT FROM AUTHOR]

Quantum advantage, benchmarking the computational power of quantum machines outperforming all classical computers in a specific task, represents a crucial milestone in developing quantum computers and has been driving different physical implementations since the concept was proposed. A boson sampling machine, an analog quantum computer that only requires multiphoton interference and single-photon detection, is considered to be a promising candidate to reach this goal. However, the probabilistic nature of photon sources and the inevitable loss in evolution network make the execution time exponentially increasing with the problem size. Here, we propose and experimentally demonstrate a timestamp boson sampling scheme that can effectively reduce the execution time for any problem size. By developing a time-of-flight storage technique with a precision up to picosecond level, we are able to detect and record the complete time information of 30 individual modes out of a large-scale 3D photonic chip. We perform the three-photon injection and one external trigger experiment to demonstrate that the timestamp protocol works properly and effectively reduce the execution time. We further verify that timestamp boson sampler is distinguished from other samplers in the case of limited datasets through the three heralded single photons injection experiment. The timestamp protocol can speed up the sampling process, which can be widely applied in multiphoton experiments at low-sampling rate. The approach associated with newly exploited resource from time information can boost all the count-rate-limited experiments, suggesting an emerging field of timestamp quantum optics. [ABSTRACT FROM AUTHOR]

In this article, a new model of Hopfield Neural Network (HNN) with two neurons considering a synaptic weight with a hyperbolic-type memristor is studied. Equilibrium points analysis shows that the system has an unstable line of equilibrium in the absence of the external stimuli (i.e. I 1 = 0) and presents no equilibrium point in the presence of the external stimuli (i.e. I 1 ≠ 0) ; hence the model admits hidden attractors. Analyses are carried out for both cases I 1 = 0 and I 1 ≠ 0 using appropriate tools (bifurcation diagrams and the Lyapunov exponents, phase portraits, etc.). For both modes of operations, the system exhibits complex homogeneous and heterogeneous bifurcations, respectively marked by a large number of coexisting attractors. The roads to chaos unfold in the same scenario of period doubling. The Hamiltonian plot for the case I 1 = 0 allows us to observe an increase in the energy of the neuronal structure when it migrates from regular oscillations to irregular ones. Moreover, the existence of infinitely many coexisting homogeneous solutions (chaotic or periodic) is revealed for case I 1 = 0 . In contrast, for I 1 ≠ 0 (i.e I 1 = 0.1) the new model presents infinitely many coexisting hidden heterogeneous attractors (periodic and chaotic). An electronic circuit design of the new hyperbolic memristor enables the analog computer of the whole system to be designed for future engineering applications. Simulation results based on this analog computer in PSpice confirm those of the numerical investigations. [ABSTRACT FROM AUTHOR]

Charles Babbage's Analytical Engine can be recollected as a fossilized image of the first digital computer. It is essentially distinguished from all prior and analog computers by the transcription of the 'mechanical notation', the separation of the mnemonic 'store' from the cybernetic 'mill', and the infinite miniaturization of its component parts. This substitution of finite space for an accelerating singularity of time creates the essential rupture of the digital, in which a singular calculation of mechanical force stands opposed to the universal totality of space. Babbage's criticism of Christian doctrine to preserve the mathematical consistency of mechanics and computing would result in the collapse of the Christian Trinity into a digital theology. This Arian subordinate difference of the Son to the Father would then be infinitely transcribed in a technical contradiction that would threaten to annul the metaphysical ground of any machine. Against digital and postdigital theologies alike, this rupture can only be repaired by a dialectical analysis of the digital into a hyperdigital grammar, which is created by Christ the Logos in a trinitarian ontology of computers. Digital computers can thus be vindicated from theological suspicion as incarnationally accelerated calculators of the sacraments, or 'sacramental engines' of the digital age. [ABSTRACT FROM AUTHOR]

A reservoir computer is a type of dynamical system arranged to do computation. Typically, a reservoir computer is constructed by connecting a large number of nonlinear nodes in a network that includes recurrent connections. In order to achieve accurate results, the reservoir usually contains hundreds to thousands of nodes. This high dimensionality makes it difficult to analyze the reservoir computer using tools from the dynamical systems theory. Additionally, the need to create and connect large numbers of nonlinear nodes makes it difficult to design and build analog reservoir computers that can be faster and consume less power than digital reservoir computers. We demonstrate here that a reservoir computer may be divided into two parts: a small set of nonlinear nodes (the reservoir) and a separate set of time-shifted reservoir output signals. The time-shifted output signals serve to increase the rank and memory of the reservoir computer, and the set of nonlinear nodes may create an embedding of the input dynamical system. We use this time-shifting technique to obtain excellent performance from an opto-electronic delay-based reservoir computer with only a small number of virtual nodes. Because only a few nonlinear nodes are required, construction of a reservoir computer becomes much easier, and delay-based reservoir computers can operate at much higher speeds. [ABSTRACT FROM AUTHOR]

We present a hybrid analog/digital computing circuit to solve a selective harmonic minimization problem. The approach leverages favorable attributes of digital and analog controllers to yield a fast and scalable optimization solver. A digital microcontroller programs the cost function and other user-defined inputs to the optimization problem. Voltages in the circuit represent switching angles. In steady state, the voltages converge to Karush–Kuhn–Tucker (KKT) points of the problem. We present a specific realization of the computing circuit that solves for eight independent switching angles for a quarter-wave symmetric pulsewidth modulation (PWM) driven two-level single-phase inverter. Seven undesired harmonics are minimized while retaining control over the modulation index. The proposed computing circuit is verified with simulations and a hardware implementation. The experimental results demonstrate that the proposed circuit can converge to the optimal solution in less than 5.0 ms, which is substantially faster than the existing methods and facilitates real-time implementation. Moreover, the steady-state power consumption of the hardware implementation is approximately 750 mW, which is also significantly lower than the published methods for comparable applications. The computing circuit is utilized to generate the PWM for a 2-kW single-phase inverter, which validates its feasibility in practical applications. [ABSTRACT FROM AUTHOR]

The objective of this work is to develop a fusion artificial intelligence (AI) model that combines patient electronic medical record (EMR) and physiological sensor data to accurately predict early risk of sepsis. The fusion AI model has two components—an on-chip AI model that continuously analyzes patient electrocardiogram (ECG) data and a cloud AI model that combines EMR and prediction scores from on-chip AI model to predict fusion sepsis onset score. The on-chip AI model is designed using analog circuits for sepsis prediction with high energy efficiency for integration with resource constrained wearable device. Combination of EMR and sensor physiological data improves prediction performance compared to EMR or physiological data alone, and the late fusion model has an accuracy of 93% in predicting sepsis 4 h before onset. The key differentiation of this work over existing sepsis prediction literature is the use of single modality patient vital (ECG) and simple demographic information, instead of comprehensive laboratory test results and multiple vital signs. Such simple configuration and high accuracy makes our solution favorable for real-time, at-home use for self-monitoring. [ABSTRACT FROM AUTHOR]

Analog computers have been a valuable resource for a time to solve linear as well as nonlinear differential equations before being superseded by digital computers. However, interest toward analog computing has not fully faded away. Though not competitive for accuracy or speed, analog computers have the important property of being true physical systems that can be made to behave according to a given equations set. This aspect makes analog computers interesting educational tools for hands-on work with real physical systems, where a number of effects, both linear and not linear, can easily be added at will (a feature not readily available in mechanical systems). This paper describes the implementation of a modular, very flexible, cheap, yet powerful analog computer, suitable for student laboratories. As examples of possible applications, a selection of linear and nonlinear oscillator models are described that can be used in the laboratory to introduce students to various nonlinear effects. [ABSTRACT FROM AUTHOR]

The synchronization of coherent states of light has long been an important subject of basic research and technology. Recently, a new concept for analog computers has emerged where this synchronization process can be exploited to solve computationally hard problems - potentially faster and more energy-efficient than what can be achieved with conventional computer technology today. The unit cell of such systems consists of two coherent centers that are coupled to one another in a controlled manner. Here, we experimentally characterize and analyze the synchronization process of two photon Bose-Einstein condensates, which are coupled to one another, either dispersively or dissipatively. We show that both types of coupling are robust against a detuning of the condensate frequencies and show similar time constants in establishing mutual coherence. Significant differences between these couplings arise in the behaviour of the condensate populations under imbalanced optical pumping. The combination of these two types of coupling extends the class of physical models that can be investigated using analog simulations. Coherently-coupled optical systems with different types of couplings between coherent centres are promising platforms for optical simulations of spin glasses and, therefore, for optical optimization. Here, an experimental implementation of dissipative and dispersive couplings is realized between two photon Bose-Einstein condensates, extending the range of physical models that can be addressed with optical simulators. [ABSTRACT FROM AUTHOR]

For universality in the approach, it is customary to appropriately rescale problems to a single or a set of dimensionless equations with dimensionless quantities involved or to rescale the experimental setup to a suitable size for the laboratory conditions. Theoretical results and/or experimental findings are supposed to be valid for both the original and the rescaled problems. Here, however, we show in an analog computer model nonlinear system how the experimental results depend on the scale factor. This is because the intrinsic noise in the experimental setup remains constant as it is not affected by the scale factor. The particular case considered here offers a genuine noise-level effect in significantly altering a period-doubling cascade to chaos besides producing an expected truncated cascade. By monitoring with increasing value a significant parameter in the dynamics of the problem when searching for its solution, the system alien to the noise (or better said with a negligible noise level) follows a period-doubling cascade from period one to period two to period four to period eight and, eventually, chaos. However, if the intrinsic noise strength significantly enters the evolution, there appears a parallel sequence of period doublings different from the one found in the previous case. [ABSTRACT FROM AUTHOR]

In this paper, the dynamic properties of the new 3-Dimensional autonomous system without linear terms are investigated. The nonlinear features of the explored model are highlighted in terms of bifurcations, time traces, phase portraits as well as space magnetisation graphs. Several striking phenomena are found including, periodic and chaotic oscillations, periodic windows, coexisting (symmetric and asymmetric) attractors known as the heterogeneous multistability, coexisting bifurcation branches, symmetry-restoring crisis scenario, offset-boosting properties while varying the system parameters. Compared to some interesting cases previously recorded on systems with no linear terms, the repertory of behaviours found in this work represents a unique contribution in comparison with such type of systems. A suitable analog computer is constructed and implemented to support the numerical analysis via PSim-based analogue simulations. [ABSTRACT FROM AUTHOR]

Remarkable theoretical and experimental achievements of quantum technology have convincingly shown that a flourishing quantum age is coming. The threat of quantum attacks posed to the classical cryptographic protocols, whose security lies on the computational complexity of difficult problems, is ever more severe. As a particular voting and typical daily activity, the anonymous veto has already been further studied in classical cryptography. In this paper, a novel quantum anonymous veto protocol with information-theoretic security is proposed. Benefited from the entanglement of GHZ states and quantum nonlocality, our protocol satisfies some desirable properties of reliability, privacy, verifiability, tracelessness, and fairness. Compared to the properties of classical protocols, tracelessness is a unique property in quantum anonymous veto protocol. Using the online analog quantum computer of IBM Corporation placed on the cloud, we make an experimental test of anonymous veto for four voters. Any harmful attempt of cheating from the semi-honest certificate authority center, malicious voters, and external eavesdroppers will be detected by honest participants based on the fantastic quantum world. This work to voting theory helps elucidate how quantum mechanics can be harnessed for secure strategic advantage. [ABSTRACT FROM AUTHOR]

The complexity of digital circuits is constantly rising. Analog computing can be an excellent alternative to overcome the complexity issue. Operational Amplifiers play a serious role in many analog circuit designs. This paper presents a new implementation approach to an analog subtractor. This design enjoys high speed and low power dissipation using Carbon Nanotube FETs (CNTFETs). We have also achieved a significant improvement in terms of the number of transistors and chip area. The proposed design considerably decreases power consumption and optimizes the chip area. The same performance can be achieved when designing a high-quality Differentiator. It is to remark that there is no need to use a huge number of resistors and capacitors. In this design, no additional voltage or current sources have been used. HSPICE and the Stanford 32 nm CNTFET model are used to simulate the proposed circuit. The comparisons with State-of-the-art prove the performance of this design. [Display omitted] [ABSTRACT FROM AUTHOR]

Analog computers can be revived as a feasible technology platform for low precision, energy efficient and fast computing. We justify this statement by measuring the performance of a modern analog computer and comparing it with that of traditional digital processors. General statements are made about the solution of ordinary and partial differential equations. Computational fluid dynamics are discussed as an example of large scale scientific computing applications. Several models are proposed which demonstrate the benefits of analog and digital-analog hybrid computing. [ABSTRACT FROM AUTHOR]

The Fourier transform is an important operation in signal processing. However, its exact computation on digital computers can be problematic. In this paper we consider the computability of the Fourier transform and the discrete-time Fourier transform (DTFT). We construct a computable bandlimited absolutely integrable signal that has a continuous Fourier transform, which is, however, not Turing computable. Further, we also construct a computable sequence such that the DTFT is not Turing computable. Turing computability models what is theoretically implementable on a digital computer. Hence, our result shows that the Fourier transform of certain signals cannot be computed on digital hardware of any kind, including CPUs, FPGAs, and DSPs. This also implies that there is no symmetry between the time and frequency domain with respect to computability. Therefore, numerical approaches which employ the frequency domain representation of a signal (like calculating the convolution by performing a multiplication in the frequency domain) can be problematic. Interestingly, an idealized analog machine can compute the Fourier transform. However, it is unclear whether and how this theoretical superiority of the analog machine can be translated into practice. Further, we show that it is not possible to find an algorithm that can always decide for a given signal whether the Fourier transform is computable or not. [ABSTRACT FROM AUTHOR]

In the production of voiced speech, glottal flow skewing refers to the tilting of the glottal flow pulses to the right, often characterized as a delay of the peak, compared to the glottal area. In the past four decades, several studies have addressed this phenomenon using modeling of voice production with analog circuits and computer simulations. However, previous studies measuring flow skewing in natural production of speech are sparse and they contain little quantitative data about the degree of skewing between flow and area. In the current study, flow skewing was measured from the natural production of 40 vowel utterances produced by 10 speakers. Glottal flow was measured from speech using glottal inverse filtering and glottal area was captured with high-speed videoendoscopy. The estimated glottal flow and area waveforms were parameterized with four robust parameters that measure pulse skewness quantitatively. Statistical tests obtained for all four parameters showed that the flow pulse was significantly more skewed to the right than the area pulse. Hence, this study corroborates the existence of flow skewing using measurements from natural speech production. In addition, the study yields quantitative data about pulse skewness in simultaneous measured glottal flow and area in natural production of speech. [ABSTRACT FROM AUTHOR]

Classical open and closed Newton–Cotes differential methods possessing the characteristics of multilayer symplectic structures have been constructed in the past. In this paper, we study the exponentially fitted open Newton–Cotes differential methods of order two, four, and six. It is shown that these integrators, just as their classical counterparts, preserve the volume in the phase space of a Hamiltonian system. They can be converted into a multilayer symplectic structure so that volume-preserving integrators of a Hamiltonian system are obtained. A numerical example has been carried out to show the effectiveness of the present differential method. [ABSTRACT FROM AUTHOR]

The radial Schrödinger equation for a spherically symmetric potential can be regarded as a one-dimensional classical harmonic oscillator with a time-dependent spring constant. For solving classical dynamics problems, symplectic integrators are well known for their excellent conservation properties. The class of gradient symplectic algorithms is particularly suited for solving harmonic-oscillator dynamics. By use of Suzuki’s rule [Proc. Jpn. Acad., Ser. B: Phys. Biol. Sci. 69, 161 (1993)] for decomposing time-ordered operators, these algorithms can be easily applied to the Schrödinger equation. We demonstrate the power of this class of gradient algorithms by solving the spectrum of highly singular radial potentials using Killingbeck’s method [J. Phys. A 18, 245 (1985)] of backward Newton-Ralphson iterations. [ABSTRACT FROM AUTHOR]

In this paper we provide a characterization of the complexity class PSPACE by using a purely continuous model defined with polynomial ordinary differential equations. [ABSTRACT FROM AUTHOR]

New symplectic integrators have been developed by combining molecular dynamics integration with the standard theory of molecular vibrations to solve the Hamiltonian equations of motion. The presented integrators analytically resolve the internal high-frequency molecular vibrations by introducing a translating and rotating internal coordinate system of a molecule and calculating normal modes of an isolated molecule only. The translation and rotation of a molecule are treated as vibrational motions with the vibrational frequency zero. All types of motion are thus described in terms of the normal coordinates. The method’s time reversibility requirement was used to determine the equations of motion for internal coordinate system of a molecule. The calculation of long-range forces is performed numerically within the generalized second-order leap-frog scheme, in the same way as in standard second-order symplectic methods. The new methods for integrating classical equations of motion using normal mode analysis allow us to use a long integration step and are applicable to any system of molecules with one equilibrium configuration. [ABSTRACT FROM AUTHOR]

A series of molecular dynamics (MD) simulations of nonlinear molecules has been performed to test the efficiency of newly introduced semianalytical second-order symplectic time-reversible MD integrators that combine MD and the standard theory of molecular vibrations. The simulation results indicate that for the same level of accuracy, the new algorithms allow significantly longer integration time steps than the standard second-order symplectic leap-frog Verlet method. Since the computation cost per integration step using new MD integrators with longer time steps is approximately the same as for the standard method, a significant speed-up in MD simulation is achieved. [ABSTRACT FROM AUTHOR]

The new symplectic molecular dynamics (MD) integrators presented in the first paper of this series were applied to perform MD simulations of water. The physical properties of a system of flexible TIP3P water molecules computed by the new integrators, such as diffusion coefficients, orientation correlation times, and infrared (IR) spectra, are in good agreement with results obtained by the standard method. The comparison between the new integrators’ and the standard method’s integration time step sizes indicates that the resulting algorithm allows a 3.0 fs long integration time step as opposed to the standard leap-frog Verlet method, a sixfold simulation speed-up. The accuracy of the method was confirmed, in particular, by computing the IR spectrum of water in which no blueshifting of the stretching normal mode frequencies is observed as occurs with the standard method. [ABSTRACT FROM AUTHOR]

Simulations that sample from the canonical ensemble can be generated by the addition of a single degree of freedom, provided that the system is ergodic, as described by Nosé with subsequent modifications by Hoover to allow sampling in real time. Nosé–Hoover dynamics is not ergodic for small or stiff systems and the addition of auxiliary thermostats is needed to overcome this deficiency. Nosé–Hoover dynamics, like its derivatives, does not have a Hamiltonian structure, precluding the use of symplectic integrators which are noted for their long term stability and structure preservation. As an alternative to Nosé–Hoover, the Hamiltonian Nosé–Poincaré method was proposed by Bond, Laird, and Leimkuhler [J. Comput. Phys. 151, 114 (1999)], but the straightforward addition of thermostatting chains does not sample from the canonical ensemble. In this paper a method is proposed whereby additional thermostats can be applied to a Hamiltonian system while retaining sampling from the canonical ensemble. This technique has been used to construct thermostatting chains for the Nosé and Nosé–Poincaré methods. © 2004 American Institute of Physics. [ABSTRACT FROM AUTHOR]

This study proposes a hierarchical proximity information processing system and a novel proximity sensor for realizing high-speed and robust grasp control of a robotic hand. The sensor requires both fast response and advanced situation judgment abilities. Therefore, a function that digitally samples individual reaction amounts of all detection elements is added to the netstructure proximity sensor (NSPS), which extracts the sum and center position of the distribution of reaction amounts of detection elements by high-speed analog computation on the sensor circuit. To integrate these two functions, we construct a circuit design method that enables the coexistence of a multichannel A/D converter circuit on the analog computing circuit of the NSPS without disturbing the current flow for sensing; the proposed sensor is called the "hierarchical proximity sensor." An analysis of its characteristics indicates that the sensor can be used for feedback control of the fingertip position/posture and to estimate the curvature of objects. Through an experiment conducted using a robotic hand equipped with the proposed sensor, we confirmed that the fingertip can approach an object in 0.18 s based on the high-speed analog computation information, while the information for improving the motion can be obtained by comparing the temporal change in the finger joints with the digital sampling information of the process. [ABSTRACT FROM AUTHOR]

• Analog techniques can accelerate machine learning by orders of magnitude. • Multiple strategies, by analog designers, bypass limitations of analog devices. • Examples are based on current work at IBM. Digital logic technology has been extraordinarily successful and has been fueled by incredible gains in integration density and performance achieved over the years following Moore's law. This has led to societal changes where more and more everyday functions are aided by smart devices following the path to artificial intelligence. However, in the field of deep machine learning, even this technology falls short, partly because device scaling gains are no longer easy to come by, but also due to intractable energy costs of computation. Deep learning, using labeled data, can be mapped onto artificial neural networks, arrays where the inputs and outputs are connected by programmable weights, and which can perform pattern recognition functions. The learning process consists of finding the optimum weights however this learning process is very slow for large problems. Exploiting the fact that weights do not need to be determined with high precision, as long as they can be updated precisely, the device community has recognized that analog computation approaches, using physical arrays of memristor (programmable resistor) type devices could offer significant speedup and power advantages compared to pure digital, or pure software approaches. On the other hand, the history of analog computation is not reassuring since the rule has been that more capable digital devices invariably supplant analog function. In this paper I will discuss the opportunities and limitations of using analog techniques to accelerate the learning process in resistive neural networks. [ABSTRACT FROM AUTHOR]

Metastructures hold the potential to bring a new twist to the field of spatial-domain optical analog computing: migrating from free-space and bulky systems into conceptually wavelength-sized elements. We introduce a metamaterial platform capable of solving integral equations using monochromatic electromagnetic fields. For an arbitrary wave as the input function to an equation associated with a prescribed integral operator, the solution of such an equation is generated as a complex-valued output electromagnetic field. Our approach is experimentally demonstrated at microwave frequencies through solving a generic integral equation and using a set of waveguides as the input and output to the designed metastructures. By exploiting subwavelength-scale light-matter interactions in a metamaterial platform, our wave-based, material-based analog computer may provide a route to achieve chip-scale, fast, and integrable computing elements. [ABSTRACT FROM AUTHOR]

Abstract In our strive to improve upon low power high speed devices for digital and mixed signal applications, Symmetric Lateral Bipolar Transistor has come out as a promising candidate and gained importance of late. Researchers in the field have shown improved performance parameters with this novel device for digital and analog applications. We investigate for the first time the impact of substrate bias for symmetric lateral bipolar transistor on Silicon Germanium on Insulator (SiGe-OI) with varying thickness of active device area (T SiGe), thickness of BOX layer (T Box) and k-values of dielectric BOX layer for analog/mixed signal applications. We optimize our design in terms of major performance parameters like gain and f T by varying parameters like T SiGe , T Box and k-values of dielectric BOX under substrate biased condition. We were able to achieve an improvement in gain and f T (in GHz) by almost 41.7% @ I C ˜ 1 μA/μm and 1.4% respectively by increasing the k-value of BOX from 3.9 to 22 with substrate bias. We studied for the first time complementary symmetric lateral bipolar (CSLB) inverter with the introduction of high-k BOX along with substrate bias for digital applications. The inverter shows a low noise margin (NM L) and a high noise margin (NM H) of 0.45 V and 0.37 V respectively when operated at 1.0 V. [ABSTRACT FROM AUTHOR]

Most of the physical processes arising in nature are modeled by differential equations, either ordinary (example: the spring/mass/damper system) or partial (example: heat diffusion). From the point of view of analog computability, the existence of an effective way to obtain solutions (either exact or approximate) of these systems is essential. A pioneering model of analog computation is the General Purpose Analog Computer (GPAC), introduced by Shannon [Journal of Mathematical Physics20 (1941), 337–354] as a model of the Differential Analyzer and improved by Pour-El [Transactions of the American Mathematical Society199 (1974), 1–28], Lipshitz and Rubel [Proceedings of the American Mathematical Society99(2) (1987)], Costa and Graça [Journal of Complexity19(5) (2003), 644–664] and others. The GPAC is capable of manipulating real-valued data streams. Its power is known to be characterized by the class of differentially algebraic functions, which includes the solutions of initial value problems for ordinary differential equations. We address one of the limitations of this model, which is its fundamental inability to reason about functions of more than one independent variable (the 'time' variable), as noted by Rubel [Advances in Applied Mathematics14(1) (1993), 39–50]. In particular, the Shannon GPAC cannot be used to specify solutions of partial differential equations. We extend the class of data types using networks with channels which carry information on a general complete metric space X; here we take X = C (R) , the class of continuous functions of one real (spatial) variable. We consider the original modules in Shannon's construction (constants, adders, multipliers, integrators) and we add a differential module which has one input and one output. For input u, it outputs the spatial derivative v (t) = ∂ x u (t). We then define an X-GPAC to be a network built with X-stream channels and the above-mentioned modules. This leads us to a framework in which the specifications of such analog systems are given by fixed points of certain operators on continuous data streams. Such a framework was considered by Tucker and Zucker [Theoretical Computer Science371 (2007), 115–146]. We study the properties of these analog systems and their associated operators, and present a characterization of the X-GPAC-generable functions which generalizes Shannon's results. [ABSTRACT FROM AUTHOR]

The paper proposes an analog hardware solution for the implementation of two-dimensional gradient-based algorithm. The algorithm employs the discrete cosine transform and performs missing samples reconstruction by using the compressive sensing principles. Although there is a number of algorithms for solving two-dimensional compressive sensing problems, for many of them a real-time application is a challenging task. Therefore, this paper observes an algorithm whose real-time application has relatively low complexity. Also, the reconstruction accuracy is comparable to the commonly used compressive sensing algorithms. The algorithm is observed within the several parts. The implementation of each part is considered in details, with provided discussion on the computational complexity of each part. [ABSTRACT FROM AUTHOR]

After World War II, Los Alamos was anxious for its own computer. While on a walk along the river during a picnic with their families, Enrico Fermi outlined to L.D.P. King a plan for an analog computer to simulate neutron transport. L.D.P. King built it with materials lying around the Omega Site in Los Alamos Canyon. They used the "FERMIAC" for a couple of years, and it was forgotten, until 1966, when Bengt Carlson found it in his office. The lab drew up some blueprints and it went out on loan to the Smithsonian. It is now on display at LANL's Bradbury Science Museum. There still are several questions as to how the FERMIAC was used. The FERMIAC is too fragile to handle, so our plan in the summer of 2017 was to build a museum-quality replica, much like the Italians did last year. However, the summer student was leaving before it could be re-constructed. Thus, finding ourselves in a similar situation as Fermi and King, we developed our own one-speed neutron transporter fashioned out of the chassis of a toy monster truck. We describe our "Monster Truck FERMIAC" on an XY problem. [ABSTRACT FROM AUTHOR]