Analog Machines Research Articles

Máquinas de ensinar analógicas: as precursoras da inteligência artificial na aprendizagem

This study offers a brief history of the analogue teaching machines created almost a century ago, arguing that they can be considered as the true forerunners of artificial intelligence in education. To do so, it examines the premises of these old machines, pointing out that they closely resemble those currently associated with machine learning systems. These premises include the individualization of teaching, the reduction of the burden of laborious activities on teachers, and a better teaching experience by automating teaching. Current apprehensions about the arrival of AI systems also resemble those that plagued society when analogue machines first appeared - for example, the fear of teachers being replaced by robotic artefacts and the fear of a distancing between students and teachers rather than a closer relationship. By seeking out these connections, this study takes analyses the digital technologies used for learning, trying to help pave the way for a critical reflection on these uses based on valuing the human perspective over artificial intelligence.

Machine learning without a processor: Emergent learning in a nonlinear analog network

Standard deep learning algorithms require differentiating large nonlinear networks, a process that is slow and power-hungry. Electronic contrastive local learning networks (CLLNs) offer potentially fast, efficient, and fault-tolerant hardware for analog machine learning, but existing implementations are linear, severely limiting their capabilities. These systems differ significantly from artificial neural networks as well as the brain, so the feasibility and utility of incorporating nonlinear elements have not been explored. Here, we introduce a nonlinear CLLN-an analog electronic network made of self-adjusting nonlinear resistive elements based on transistors. We demonstrate that the system learns tasks unachievable in linear systems, including XOR (exclusive or) and nonlinear regression, without a computer. We find our decentralized system reduces modes of training error in order (mean, slope, curvature), similar to spectral bias in artificial neural networks. The circuitry is robust to damage, retrainable in seconds, and performs learned tasks in microseconds while dissipating only picojoules of energy across each transistor. This suggests enormous potential for fast, low-power computing in edge systems like sensors, robotic controllers, and medical devices, as well as manufacturability at scale for performing and studying emergent learning.

Evaluation of Quality of Life of Parents and Growth Parameters of their Infants with Cleft Lip and Cleft Palate before and after Primary Surgery: An Observational Study

Introduction: Cleft Lip (CL) and Cleft Palate (CP) are associated with several complications that have a significant negative impact on the Quality of Life (QoL) of affected children and caregivers. Compared to their healthy counterparts, children with these conditions experience feeding difficulties, cosmetic abnormalities, and, most importantly, diminished physical and cognitive growth, especially during the first year of their lives. Aim: To evaluate the growth parameters in CL and/or CP infants and the QoL of parents with a CL and/or CP child before and after primary surgery. Materials and Methods: For this observational study, data was collected longitudinally from the same set of Cleft lip and cleft palate (CL/CP)/CL&CP infants and their parents before and after the primary surgery. The study was conducted at the Department of Paediatric and Preventive Dentistry, Guru Nanak Institute of Dental Sciences and Research, West Bengal, ABMSS Kolkata Comprehensive Cleft Care Centre, West Bengal, and IMA Vaccination Centre Barasat, West Bengal, India, from December 2020 to May 2022. In this study, 66 children and 64 parents were included in each study and control group. Growth parameters {weight, length, Head Circumference (HC)} were measured using a weight analogue machine, infantometer, and HC measuring tape, and compared with those of healthy infants. The QoL of parents with CL and/or CP infants was quantified using a questionnaire form of “Revised Impact on Family Scale (RIOFS)” filled out by parents before and after six months of primary surgery and compared with the QoL of same-age healthy infants. The responses to the RIOFS questionnaire were scored according to recommended guidelines. The Statistical Package for Social Sciences (SPSS) Statistics 23.0 was used for data analysis. Results: Among the study groups, statistically significant differences in mean height, weight, and HC were observed between the groups of all infants with CL, CP, and CL with CP before and after the primary surgery (p<0.001). The QoL of parents from the study group improved post-surgery. The difference in mean RIOFS score between study Group-3 and study Group-4 after the primary surgery was statistically significant (p<0.001). Conclusion: The growth of CL and/or CP infants and the QoL of their caregivers are compromised compared to healthy infants. After primary surgery, the growth of CL and/or CP infants and the QoL of their parents partially improved.

Techniques of Discovery: Cryptography and Design

Among the core technologies forming the rich archaeology of computation, cryptography is perhaps a subject that has received little attention in architectural studies thus far. However, there are fruitful considerations to draw from a closer inspection of the vast repertoire of techniques that cryptography has developed over a period of about seven centuries. First of all, a deeper historical perspective will help frame cryptography as a technology for discovery rather than solely protecting military and diplomatic secrets. Secondly, these considerations can be of relevance to design as they offer thoughts for both conceptual reflections and practical applications. At a conceptual level, the symbolic, discrete computation accompanying the evolution of cryptographic methods – such as Alberti’s one in 1467 – marked a radical departure from the iconic semiotics of analogue machines. The non-mimetic nature of symbolic computation provided the technological means to significantly widen its range of applications and enhanced speculative thinking. Understood along these lines, cryptography found more general applications beyond concealing diplomatic secrets to provide a rigorous method for inquiry into unknown domains in order to make ‘noisy data’ intelligible. Finally, symbolic computation also provided more advanced techniques for abstraction that were also instrumental for constructing notational drawings, whose emergence coincided with the introduction of more advanced mathematical instruments in the renaissance.
 The essay will discuss the key paradigmatic moments in the history of cryptography such as the polyalphabetic techniques proposed by L. B. Alberti in 1467 and the use of binary cryptography by Francis Bacon in 1605. Despite their distance from the present, these experiments provide a useful segue into a discussion on how the notion of cypher as a conceptual instrument accompanying the introduction of Machine Learning models in architectural and urban design.

Neuromorphic Analog Machine Vision Enabled by Nanoelectronic Memristive Devices

Arrays of memristive devices coupled with photosensors can be used for capturing and processing visual information, thereby realizing the concept of “in-sensor computing”. This is a promising concept associated with the development of compact and low-power machine vision devices, which is crucial important for bionic prostheses of eyes, on-board image recognition systems for unmanned vehicles, computer vision in robotics, etc. This concept can be applied for the creation of a memristor based neuromorphic analog machine vision systems, and here, we propose a new architecture for these systems in which captured visual data are fed to a spiking artificial neural network (SNN) based on memristive devices without analog-to-digital and digital-to-analog conversions. Such an approach opens up the opportunities of creating more compact, energy-efficient visual processing units for wearable, on-board, and embedded electronics for such areas as robotics, the Internet of Things, and neuroprosthetics, as well as other practical applications in the field of artificial intelligence.

The use of memristive devices in machine vision systems

The comparison results of processing units with memristive devices versus modern hardware accelerators of artificial neural networks (ANNs) based on traditional electronic components, as presented in the review [1], demonstrate numerous advantages across all major indicators such as throughput, energy efficiency, accuracy, and others. This report analyzes the current state of memristive devices in addressing machine vision issues. Special attention is paid to the concept of [2] neuromorphic machine vision systems (MVS) based on memristive devices. This concept’s distinct feature lies in its fully analog system, commencing from information input to its output. It encompasses sensory and neural components. The sensory part is responsible for gathering visual information and transferring it to the neural segment for processing through the ANN model algorithm. A specific instance for implementing the input channel of the sensor component involves connecting a photodiode (PD) and a memristor in a single circuit. When the circuit is flipped in reverse bias and light falls on the PD, a photocurrent flows through it from the cathode to the anode. Depending on the light intensity and exposure time, this photocurrent alters the resistance of the memristor, thereby converting illumination into resistance. If visual information doesn’t require encoding with memristor resistances, they can be replaced with a load resistance of the same nominal value for all channels. Irrespective of the input channel variant, the signal encoding visual information is fed into the neural part without digitization. Memristors act as synapses as part of the neural part. They can be used to implement synapses in both traditional formal ANN architectures, where input information is multiplied by a pre-programmed weight, and synapses for spiking neural networks, where a memristor exhibits synaptic plasticity mechanisms similar to those in biological neural networks [3]. If the output from the suggested MVS input channel variants is connected to a device that operates on the “integrate and fire” principle, the device can be deemed as not only an input for a structured ANN, but also a presynaptic neuron for a spiking ANN. The neuron’s frequency of spikes will depend on the light intensity; the brighter the light, the higher the frequency of spikes and vice versa. Faster charge accumulation occurs in channels with low resistance. The complete analog machine vision system will function as a spike neural network, without incorporating any analog-to-digital or digital-to-analog converters. Compared to digital machine vision systems, this approach will significantly decrease energy consumption while producing wearable and on-board electronics with distinct tactical and technical features. This design can be tailored to the size of modern matrices of photo and video fixation devices and employed as a hardware accelerator for the ANN models currently used to process images, and it can serve as a foundation for advancing this area.

The principal–agent problem amid digital transformation

Despite the widespread use of digital technologies, the economic nature and essence of digital transformation remain understudied. The article examines the aspects of the principal–agent problem amid digital transformation, which implies replacing a person or analogue machines with digital technologies. Transformation is needed to gain additional economic advantages or improve the flexibility of the organization’s activities within the concept of Industry 4.0. The research demonstrates that the underlying economic reason for digital transformation is to solve the problem of information asymmetry and overcome the principal–agent conflict through the use of digital technologies. The concept of digital transformation and the principles of the principal–agent theory constitute the methodological basis of the study. Among the research methods were generalization, the inductive method of knowledge formation, and the assessment of the conclusions’ completeness with the involvement of theorists and practitioners in the field under consideration. The study allowed broadening the understanding of the principal–agent theory: establishing the roles of a human agent and a machine agent, as well as highlighting the essential economic aspects of digital transformation. The results obtained can be used to create digital transformation methodologies and research on the organization of labour in this field.

Optimization and Development of Pulverization Machine

Abstract: A Pulverizer is a machine whose purpose is to shred or crush total material into lower pieces by the repeated blows of number of plates. These machines have multitudinous feathers of operations in multitudinous industriousness A pulverizer or grinder is a mechanical device used for the grinding of multitudinous different types of paraphernalia. the pulverization is a machine which is used for the combine operation The pulverizer is a machine that pulverizes large-sized solid raw paraphernalia to the demanded size. the capper is composed of coarse crushing, fine crushing, wind conveying and other bias, and achieves the purpose of the capper in the form of highspeed impact. using wind energy to form cream at one time, barring the traditional netting process. mainly used in mining, erecting paraphernalia and other industriousness. Prolusion Husbandry is now one of the most important sectors it plays a part Indian economy. In order to further develop this sector technology has come one of the main components. The farmers on the field burn ultimate of these wastes after the harvesting of crops. Thus the agricultural waste burning sensations is being repeated every year. In order to use these wastes for some profitable benefits, like power shops, industries. So the necessary of analogous machine was felt to use all kinds of agricultural waste after shredding, which could be provident and practicable. The design is developed and manufactures a machine which will be used for producing tattered organic wastes for farmers without any use of electricity, these organic wastes will increase the effectiveness than any other conventional means, which are obviously dangerous for mortal health, terrain, landetc. Corridor used for manufacturing such a machine are agitator, hopper, collector tank, bevel gear couples, pulleys flat belt drive and oaring medium. Keywords- Pulverizer, vertical Impactcrusher, blades

Review on Pulverization Machine

Abstract: A Pulverizer is a machine whose purpose is to shred or crush total material into lower pieces by the repeated blows of number of plates. These machines have multitudinous feathers of operations in multitudinous industriousness A pulverizer or grinder is a mechanical device used for the grinding of multitudinous different types of paraphernalia. The pulverization is a machine which is used for the combine operation The pulverizer is a machine that pulverizes large-sized solid raw paraphernalia to the demanded size. the capper is composed of coarse crushing, fine crushing, wind conveying and other bias, and achieves the purpose of the capper in the form of high- speed impact. using wind energy to form cream at one time, barring the traditional netting process. mainly used in mining, erecting paraphernalia and other industriousness. Prolusion Husbandry is now one of the most important sectors it plays a part Indian economy. In order to further develop this sector technology has come one of the main components. The farmers on the field burn ultimate of these wastes after the harvesting of crops. Thus the agricultural waste burning sensations is being repeated every year. In order to use these wastes for some profitable benefits, like power shops, industries. So the necessary of analogous machine was felt to use all kinds of agricultural waste after shredding, which could be provident and practicable. The design is developed and manufactures a machine which will be used for producing tattered organic wastes for farmers without any use of electricity, these organic wastes will increase the effectiveness than any other conventional means, which are obviously dangerous for mortal health, terrain, landetc. corridor used for manufacturing such a machine are agitator, hopper, collector tank, bevel gear couples, pulleys flat belt drive and oaring medium

Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset

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.

Digital maturity levels of an industrial enterprise

Diverse viewpoints on the concept of digital maturity and digital transformation impede the assessment of the current state of affairs with the introduction of digital technologies and planning of this process. The research aims to develop the concept of digital maturity of an industrial enterprise as an inherent part of the digital transformation. In terms of theory and methodology, the research relies on the principal-agent theory, theory of entrepreneurship, and the Capability Maturity Model; applies the methods of expert interview, generalisation, hypothetical deductive and inductive methods of generating knowledge in the new field. The authors develop a six-level typology of digital maturity and define the essence of digital transformation. According to them, it is the replacement of humans and analogue machines with digital technologies. The researchers articulate their vision of the roles of an entrepreneur, manager, and performer in the process of digital transformation, which allows settling the controversy in the relationships between principal and agent related to the accessibility of information about the processes run by an agent on the principal’s instructions. The results of the research may be of use while designing practical tools for assessing digital maturity levels and developing mechanisms for digital transformation of industrial enterprises.

Introducing the Moniac: Its Historical Process in the Istanbul University Faculty of Economics

Monetary National Income Analog Computer (MONIAC) is a hydraulic machine that we can describe as an analog simulator modeling macroeconomic relationships. It was invented by the famous economist A. W. Phillips in 1949 and produced in limited numbers. The circulation of water in the machine shows the circulation of money in the economy. The economic behaviors of units such as households, firms, government, export, and import segments, and the economic variables by which these behaviors are affected can be observed while the water circulates the machine. The working principle of this machine reflects the assumptions of both Keynesian and Classical economics schools regarding economic functioning. The machine was brought to Istanbul University Faculty of Economics in the 1950s, by Prof. Dr. Besim Ustunel, one of the late faculty members. A sensational scientific production for the international academic community at that time, thus, was brought to Turkey in a very short period of time. The fact that the machine was introduced to Turkish higher education shortly after its invention is an important indicator of the ability of the Faculty of Economics to adapt to current scientific developments. Although MONIAC has been reoperational with a project initiated by the Faculty of Economics in 2017, it is not possible to use the machine actively in lessons today, which used to be one of its primary functions. However, based on this analog machine, the digital simulation that we carry out within the scope of the project can be used as an educational tool. MONIAC, in its restored original form, has been preserved by the Faculty of Economics as a historical value of the Faculty.

Fully Integrated Analog Machine Learning Classifier Using Custom Activation Function for Low Resolution Image Classification

This paper presents fully-integrated analog neural network classifier architecture for low resolution image classification that eliminates memory access. We design custom activation functions using single-stage common-source amplifiers, and apply a hardware-software co-design methodology to incorporate knowledge of the custom activation functions into the training phase to achieve high accuracy. Performing all computations entirely in the analog domain eliminates energy cost associated with memory access and data movement. We demonstrate our classifier on multinomial classification task of recognizing down-sampled handwritten digits from MNIST dataset. Fabricated in 65nm CMOS process, the measured energy consumption for down-sampled MNIST dataset is 173pJ/classification, which is $3\times $ better than state-of-the-art. The prototype IC achieves mean classification accuracy of 81.3% even after down-sampling the original MNIST images by 96% from $28\times 28$ pixels to $5\times 5$ pixels.

Bionic organization as a stage of production enterprise development in a digital transformation process

This article embraces a concept of bionic organization as a form of production enterprise which main attribute is an integration of digital technologies in the management processes and the production lines from the product idea to its delivery to a consumer. Digital technologies reinforce human competencies and abilities of analogous machines, playing a role of independent component of executive and production system. At the same time, digital technologies become full-fledged members of the organization, enriching it with bionic treats. An idea of full human substation in production management with digital technologies is far from being practicable, but a tendency towards this change is quite clear. The main purpose of this this article is to discover main features of bionic organization as an intermediate evolution stage on the way of full human substitution with machines and digital technologies. Additionally, the article focuses on the interconnections between bionic organization, digital transformation, digital maturity and management system. This analysis was made for a production enterprise set upon a way to deep digital transformation.

Repurposing Risograph Machines: The Allure of Small‐Scale Printmaking in the Era of Big Data

Paper documents are increasingly being replaced with digital files, infinitely replicable for seemingly no cost. Yet I've always felt the pull of paper, with a personal affinity for physical books and a background in magazine production. Through my recreational publishing practice, I learn of the “riso” or risograph, a duplicating machine increasingly adopted by Bay Area artists and technology corporations alike. Upon first glance, most risograph models resemble familiar Xerox machines, with their boxy, gray exteriors, protruding buttons, and hinged tops that cover glass beds for scanning.Through my own experiences and interviews with subject matter experts, however, I come to understand the allure of the risograph: its temperamental nature as an analog machine and the uniquely “human” quality of the prints it renders. I posit the risograph's popularity is a response to technological advances and resulting societal changes, acting as a reprieve to digital modes of aesthetic and community engagement. The rise of the risograph machine thus suggests both the appeal and the limits of scale: its origins as a low‐cost printer among artists highlight the draw of multiplicity, whereas its adoption among tech companies, ironically, suggest the limits of seemingly infinite content and growth.

인형뽑기의 유행과 디지털화 된 아날로그 기계 장치에 대한 환상

인형뽑기는 소득 불평등과 양극화 문제와 같은 자본주의의 모순이 첨예하게 드러나는 불황시기에 유행하게 된 문화 콘텐츠이다. 인형뽑기는 ‘부등가 교환거래’에 입각한 경미하게나마 사행성을 띤 게임으로, 투기적 자본주의에 ‘순치된 경제동물’로 길들여지는 놀이이자 오락이기도 하다. 2016∼17년에 유행한 인형뽑기는 한국사회의 병폐를 드러내는 데 징후적인 역할을 했다. 한편 인형뽑기는 사회적으로 해롭지 않고 안전한 놀이를 즐기고 싶은 새로운 세대의 취미이기도 하다. 그럼에도 불구하고 한국의 미디어는 인형뽑기의 중독성과 사행성만을 지적한다. 지금까지 인형뽑기에 대한 사회적 의미와 문화적 가능성에 대한 해명 은 없었다. 인형뽑기 놀이를 단속하려는 정부의 조치는 ‘금기의 위계화’를 통한 사행산업 전반에 대한 강렬한 통제 욕구로 보인다. 이는 사회적 약자에 대한 보호나 안전한 놀이를 보급하려는 의지와는 별 관계가 없다. 인형뽑기는 디지털화 된 아날로그 장치가 보여주는 환상에 기대 유행한 놀이이기도 하다. 인형뽑기와 같은 새로운 기술적 대상을 이해하기 위한 ‘디지털 리터러시’가 필요한 시점이다.

A Fully-Integrated Analog Machine Learning Classifier for Breast Cancer Classification

We propose a fully integrated common-source amplifier based analog artificial neural network (ANN). The performance of the proposed ANN with a custom non-linear activation function is demonstrated on the breast cancer classification task. A hardware-software co-design methodology is adopted to ensure good matching between the software AI model and hardware prototype. A 65 nm prototype of the proposed ANN is fabricated and characterized. The prototype ANN achieves 97% classification accuracy when operating from a 1.1 V supply with an energy consumption of 160 fJ/classification. The prototype consumes 50 μ W power and occupies 0.003 mm 2 die area.

Turing Computability of Fourier Transforms of Bandlimited and Discrete Signals

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.

Wave physics as an analog recurrent neural network.

Analog machine learning hardware platforms promise to be faster and more energy efficient than their digital counterparts. Wave physics, as found in acoustics and optics, is a natural candidate for building analog processors for time-varying signals. Here, we identify a mapping between the dynamics of wave physics and the computation in recurrent neural networks. This mapping indicates that physical wave systems can be trained to learn complex features in temporal data, using standard training techniques for neural networks. As a demonstration, we show that an inverse-designed inhomogeneous medium can perform vowel classification on raw audio signals as their waveforms scatter and propagate through it, achieving performance comparable to a standard digital implementation of a recurrent neural network. These findings pave the way for a new class of analog machine learning platforms, capable of fast and efficient processing of information in its native domain.

Virtual Modeling and Simulation of Transplanting Mechanism with Non-circular Planetary Gears

This article deploys an exploratory development of super rice High-performance transplanter transplanting mechanism with non-circular Gears by dint of rice transplanter transplanting mechanism analog machine accroding to UG software.This research is first to building a model of non-circular Gears designed by researcher before,and then assemble the whole to obtain the result of virtual manufacturing with non-circular Gears, and assemble it on the test machine of rice transplanter test machine, also analysis the Kinematic Principle of Simulation result by UG software.Finally, make the motion trail conform to the agronomy requirement according to correcting and consummating the fember unqualified.