Digital Computer Research Articles

Using Interactive Whiteboards (IWB) in the English Classroom as Supporting Technology in the Teaching and Learning Process: Opportunities and Challenges

This research describes the teachers' and students' perception of using Interactive Whiteboards (IWB) and explain the opportunities and challenges of using IWB in English classrooms. This research uses library research. IWBs are large interactive displays used as input devices for computers. They can be used in educational institutions combining traditional presentation benefits with cutting-edge technology. IWB technology consists of a digital board, computer, and projector, which can be linked to a personal computer for easy use. It offers various tools including graphics, word processing, database, and multimedia tools. Using IWB as an educational tool increase new knowledge, provide innovations to students, improve the quality of students’ learning, facilitate interaction between teaching teams and students, improve teaching efficiency, and reduce paper usage. IWB has been found to significantly improve students' English skills, vocabulary proficiency, engagement, and attitude. However, implementing IWBs in schools presents challenges including large costs, insufficient technical support, technical issues, lack of adequate e-materials, limited time for lesson preparation, negative attitudes, lack of consensus among decision-makers, inadequate proficiency, and inadequate teacher training. Teachers need to master this technology so they can use it effectively in teaching. It is proposed that teachers receive training on utilizing IWBs more efficiently in various classroom settings. Teaching using proper operation of IWBs is a major factor in the teachers' ability to implement it successfully in the classroom. The trained teachers possessed a comprehension of the most efficient methods for utilizing the IWBs.

A Novel Method for Designing Electronic Logic Circuits

This scientific paper presents a novel method for designing single and multi-output electronic logic circuits. The produced circuit consists of two parts. The first part consists of (n-1) XOR gates, where "n" is the number of input signals to the logic circuits. This part is called the linear part of the circuit. The second part is designed by any method used in designing logic circuits. This part called the non-linear part of the circuit. The suggested method can be programmed by any high level programming language to be used on any digital computer especially with the circuits which have a large number of input signals.

The universality of the machine: labour process theory and the absorption of the skills and knowledge of labour into capital

This article contends that deskilling is best understood not as a distinct phenomenon, but as a component of a process Marx (1993: 694) called ‘absorption’. Absorption involves not only the extraction of capacities from labour but also their implementation in machines. The article reads Braverman’s (1998) analysis of Taylorism as a demonstration of how absorption entails a specific labour process of its own, which I call the absorption process. The nature of the absorption process is contingent on many social factors. This article focuses on a technical factor: the particular machines used to implement captured skills and knowledge, called here the infrastructure of absorption. Since technological capacities are ever-evolving under capital due to the continual revolutionizing of the means of production, infrastructures of absorption change over time and this necessitates new absorption processes. Braverman (1998: 132) pointed to a qualitative change in absorption with the digital computer, which he described in terms of a new ‘universality of the machine’. While Braverman rightly pointed out the computer as a novel infrastructure, he did not discern qualitative changes to the absorption process, seeing instead the extension of Taylorist processes of capture of knowledge and skill. I contend that a qualitative shift has become apparent since the rise of machine learning in around 2015. Machine learning enables a different absorption process of emergence which does not require the codification of captured knowledge. Much labour process theory (LPT) (and adjacent) research presumes that deskilling and automation operate in terms of a process of capture, however, I show that emergence presents qualitatively different means for both. I suggest that the infrastructure of machine learning presents the possibility of task-agnostic automation.

Is stochastic thermodynamics the key to understanding the energy costs of computation?

The relationship between the thermodynamic and computational properties of physical systems has been a major theoretical interest since at least the 19th century. It has also become of increasing practical importance over the last half-century as the energetic cost of digital devices has exploded. Importantly, real-world computers obey multiple physical constraints on how they work, which affects their thermodynamic properties. Moreover, many of these constraints apply to both naturally occurring computers, like brains or Eukaryotic cells, and digital systems. Most obviously, all such systems must finish their computation quickly, using as few degrees of freedom as possible. This means that they operate far from thermal equilibrium. Furthermore, many computers, both digital and biological, are modular, hierarchical systems with strong constraints on the connectivity among their subsystems. Yet another example is that to simplify their design, digital computers are required to be periodic processes governed by a global clock. None of these constraints were considered in 20th-century analyses of the thermodynamics of computation. The new field of stochastic thermodynamics provides formal tools for analyzing systems subject to all of these constraints. We argue here that these tools may help us understand at a far deeper level just how the fundamental thermodynamic properties of physical systems are related to the computation they perform.

Impact of increased screen-time on ocular health of professional college students during COVID-19: a cross-sectional study

Background: Digital Eye Strain (DES) or Computer Vision Syndrome (CVS) is a group of eye and vision related problems that result from prolonged use of digital devices. The usage of digital devices has gone up across the globe, since the declaration of the lockdown due to COVID-19. This study aimed to estimate the prevalence of digital eye strain (DES) due to increased screen time and to determine the factors associated with it, among undergraduate medical students during COVID-19. Methods: The study analyzed over 280 responses from undergraduate medical students having exposure to digital devices daily for a period more than 6 months during COVID 19 pandemic. A pre-validated questionnaire Computer Vision Syndrome Questionnaire (CVS-Q) which contains 16 symptoms associated with Digital Eye Strain (DES) was used to calculate the DES score. The frequency and intensity of these symptoms adds up to give a total score. A score ≥6 indicated the presence of DES. Data analysis was done using MS Excel and SPSS software. Results: Digital eye strain was found to be prevalent among 61% of the total respondents. It was seen that daily digital device usage increased to above 5 hours after commencement of pandemic (p=0·001). Headache was the most common symptom (83.92). DES has a female gender predominance (p=0.024). Conclusions: There is a high prevalence of digital eye strain among undergraduate medical students during pandemic. Awareness should be created among students about impact of DES on ocular health. Measures to reduce DES are recommended.

Logic-Compatible Embedded DRAM Architecture for Multifunctional Digital Storage and Compute-in-Memory

The compute-in-memory (CIM) which embeds computation inside memory is an attractive scheme to circumvent von Neumann bottlenecks. This study proposes a logic-compatible embedded DRAM architecture that supports data storage as well as versatile digital computations. The proposed configurable memory unit operates in three modes: (1) memory mode in which it works as a normal dynamic memory, (2) logic–arithmetic mode where it performs bit-wise Boolean logic and full adder operations on two words stored within the memory array, and (3) convolution mode in which it executes digitally XNOR-and-accumulate (XAC) operation for binarized neural networks. A 1.0-V 4096-word × 8-bit computational DRAM implemented in a 45-nanometer CMOS technology performs memory, logic and arithmetic operations at 241, 229, and 224 MHz while consuming the energy of 7.92, 8.09, and 8.19 pJ/cycle. Compared with conventional digital computing, it saves energy and latency of the arithmetic operation by at least 47% and 46%, respectively. For VDD = 1.0 V, the proposed CIM unit performs two 128-input XAC operations at 292 MHz with an energy consumption of 20.8 pJ/cycle, achieving 24.6 TOPS/W. This marks at least 11.9× better energy efficiency and 38.8× better delay, thereby achieving at least 461× better energy-delay product than traditional 8-bit wide computing hardware.

Scalable Architecture for Trapped-Ion Quantum Computing Using rf Traps and Dynamic Optical Potentials

Qubits based on ions trapped in linear radio-frequency traps form a successful platform for quantum computing, due to their high fidelity of operations, all-to-all connectivity, and degree of local control. In principle, there is no fundamental limit to the number of ion-based qubits that can be confined in a single 1D register. However, in practice, there are two main issues associated with long trapped-ion crystals, that stem from the “softening” of their modes of motion, upon scaling up: high heating rates of the ions’ motion and a dense motional spectrum; both impede the performance of high-fidelity qubit operations. Here, we propose a holistic, scalable architecture for quantum computing with large ion crystals that overcomes these issues. Our method relies on dynamically operated optical potentials that instantaneously segment the ion crystal into cells of a manageable size. We show that these cells behave as nearly independent quantum registers, allowing for parallel entangling gates on all cells. The ability to reconfigure the optical potentials guarantees connectivity across the full ion crystal and also enables efficient midcircuit measurements. We study the implementation of large-scale parallel multiqubit entangling gates that operate simultaneously on all cells and present a protocol to compensate for crosstalk errors, enabling full-scale usage of an extensively large register. We illustrate that this architecture is advantageous both for fault-tolerant digital quantum computation and for analog quantum simulations. Published by the American Physical Society 2024

Design of pattern image generation technique based on self-attentive residual conditional generative adversarial network

Image generation techniques have made remarkable progress in the digital image processing and computer vision. However, traditional generation models cannot meet the complexity and diversity requirements of patterned images. In view of this, the study aims to enhance the quality of generated pattern images, which uses improved residual block, and introduces a self-attention mechanism to compute the weight parameters of the input features to enhance the accuracy. Comparing with three image generation models, the research model shows lower Frechette initial distance, which is better than the other three methods, and the average Frechette initial distance values in the four scenes are 175.23, 176.41, 174.41, and 165.23. Generated mouths and eyes: the average values of Frechette initial distances reach 98.23 and 97.24, respectively. For emotion classification, the Frechette initial distance averages for sad, excited, and calm emotions were 82.34, 75.63, and 70.21, respectively. The model was trained up to 2500 iterations, and the loss value was reduced to 0.54, with an accuracy of 98.23%, confirming its effectiveness and high performance. The self attention residual network enhances the model's ability to capture image details, effectively improving the quality and accuracy of image generation, and providing a new technological path for radiation imaging data processing and analysis in radiation science.

Creative and accurate method for optimal hardware implementation of neurons and biological cells: Application in FPGA-based implementation of cardiac pacemaker cell

The sinoatrial (SA) node cells play a vital role as the principal pacemaker in mammalian hearts, generating regular and spontaneous action potentials to regulate the heart’s rhythm. Comprehending the intricate activity of the SA node’s operation necessitates a collection of differential formulas that tackle non-linear functions. The study presents a new technique to improve the digital representation of the SA node cell model, offering benefits such as decreased hardware needs, enhanced processing speed and accuracy, and reduced implementation expenses by transforming the original model’s differential equations into a unified trigonometric function. This transformation significantly simplifies the computational complexity by eliminating the need for multipliers, resulting in a streamlined set of mathematical expressions. The digital implementation of this novel method can be efficiently realized using the Coordinate Rotation Digital Computer (CORDIC) algorithm, which circumvents the necessity for cumbersome mathematical operations. To demonstrate the viability of this approach, the proposed model is successfully synthesized and implemented on a Field-Programmable Gate Array (FPGA). The results of the implementation demonstrate a significant rise in the operating frequency, which is approximately 6.14 times greater than that of the original model. Furthermore, there is a notable 45 percent decrease in power usage. The lowered hardware needs make significant scalability possible, thus allowing for the inclusion of approximately 12 times as many SA node cells on a sole FPGA board in comparison to the original design.

Multiexposed Image-Fusion Strategy Using Mutual Image Translation Learning with Multiscale Surround Switching Maps

The dynamic range of an image represents the difference between its darkest and brightest areas, a crucial concept in digital image processing and computer vision. Despite display technology advancements, replicating the broad dynamic range of the human visual system remains challenging, necessitating high dynamic range (HDR) synthesis, combining multiple low dynamic range images captured at contrasting exposure levels to generate a single HDR image that integrates the optimal exposure regions. Recent deep learning advancements have introduced innovative approaches to HDR generation, with the cycle-consistent generative adversarial network (CycleGAN) gaining attention due to its robustness against domain shifts and ability to preserve content style while enhancing image quality. However, traditional CycleGAN methods often rely on unpaired datasets, limiting their capacity for detail preservation. This study proposes an improved model by incorporating a switching map (SMap) as an additional channel in the CycleGAN generator using paired datasets. The SMap focuses on essential regions, guiding weighted learning to minimize the loss of detail during synthesis. Using translated images to estimate the middle exposure integrates these images into HDR synthesis, reducing unnatural transitions and halo artifacts that could occur at boundaries between various exposures. The multilayered application of the retinex algorithm captures exposure variations, achieving natural and detailed tone mapping. The proposed mutual image translation module extends CycleGAN, demonstrating superior performance in multiexposure fusion and image translation, significantly enhancing HDR image quality. The image quality evaluation indices used are CPBDM, JNBM, LPC-SI, S3, JPEG_2000, and SSEQ, and the proposed model exhibits superior performance compared to existing methods, recording average scores of 0.6196, 15.4142, 0.9642, 0.2838, 80.239, and 25.054, respectively. Therefore, based on qualitative and quantitative results, this study demonstrates the superiority of the proposed model.

Precision and speed: The AI revolution in oral squamous cell carcinoma detection

Oral Squamous Cell Carcinoma (OSCC) is a highly aggressive tumor with a poor prognosis and is most frequent histological neoplasm of head and neck cancers, and although it is localized in a region that is accessible to see and can be detected very early, this usually does not occur. The standard procedure for the diagnosis of oral cancer is based on histopathological examination, however, the main problem in this kind of procedure is tumor heterogeneity where a subjective component of the examination could directly impact patient-specific treatment intervention. AI can precisely analyze a vast dataset of various imaging modalities, such as fluorescent, hyperspectral, cytological, histological, radiological, endoscopic, clinical, and infrared thermal modalities. In this review, we discuss digital histopathological image analysis, computer vision and radiological analysis for the early detection of OSCC.

Numerical Modeling of Vortex-Based Superconducting Memory Cells: Dynamics and Geometrical Optimization.

The lack of dense random-access memory is one of the main obstacles to the development of digital superconducting computers. It has been suggested that AVRAM cells, based on the storage of a single Abrikosov vortex-the smallest quantized object in superconductors-can enable drastic miniaturization to the nanometer scale. In this work, we present the numerical modeling of such cells using time-dependent Ginzburg-Landau equations. The cell represents a fluxonic quantum dot containing a small superconducting island, an asymmetric notch for the vortex entrance, a guiding track, and a vortex trap. We determine the optimal geometrical parameters for operation at zero magnetic field and the conditions for controllable vortex manipulation by short current pulses. We report ultrafast vortex motion with velocities more than an order of magnitude faster than those expected for macroscopic superconductors. This phenomenon is attributed to strong interactions with the edges of a mesoscopic island, combined with the nonlinear reduction of flux-flow viscosity due to the nonequilibrium effects in the track. Our results show that such cells can be scaled down to sizes comparable to the London penetration depth, ∼100 nm, and can enable ultrafast switching on the picosecond scale with ultralow energy per operation, ∼10-19 J.

Applications of AI in martial arts: A survey

Artificial intelligence (AI) has become an interdisciplinary subject that integrates digital image processing, machine learning, and computer science, etc. A lot of work in martial arts benefits from AI technology. In this paper, we highlight and summarize the applications of AI in martial arts and indicate the main progress of related studies. Through the investigation of martial arts styles, tasks, data acquisition methods, and algorithm improvements, a paradigm that represents a possible technological process for an intelligent martial arts training system was summarized. These applications bring more scientific training and more effective technical analysis to martial arts. Furthermore, this paper delves into specific AI applications in areas such as action recognition, pose estimation, action evaluation, elite athlete support, sports betting, health care, animation generation, and competition field segmentation. By exploring these areas, the research prospects of AI applications in martial arts are comprehensively discussed. This paper inspires further research on martial arts by using AI, which includes helping researchers expand their research ideas and research methods, identify frontier issues, and innovate based on existing research.

Mitigating noise in digital and digital–analog quantum computation

Noisy Intermediate-Scale Quantum (NISQ) devices lack error correction, limiting scalability for quantum algorithms. In this context, digital-analog quantum computing (DAQC) offers a more resilient alternative quantum computing paradigm that outperforms digital quantum computation by combining the flexibility of single-qubit gates with the robustness of analog simulations. This work explores the impact of noise on both digital and DAQC paradigms and demonstrates DAQC’s effectiveness in error mitigation. We compare the quantum Fourier transform and quantum phase estimation algorithms under a wide range of single and two-qubit noise sources in superconducting processors. DAQC consistently surpasses digital approaches in fidelity, particularly as processor size increases. Moreover, zero-noise extrapolation further enhances DAQC by mitigating decoherence and intrinsic errors, achieving fidelities above 0.95 for 8 qubits, and reducing computation errors to the order of 10−3. These results establish DAQC as a viable alternative for quantum computing in the NISQ era.

Sharkovskii’s theorem and the limits of digital computers for the simulation of chaotic dynamical systems

Chaos is a unique paradigm in classical physics within which systems exhibit extreme sensitivity to the initial conditions. Thus, they need to be handled using probabilistic methods commonly based on ensembles. However, initial conditions generated by digital computers fall within the sparse set of discrete IEEE floating point numbers which have non-uniform distributions along the real axis. Therefore, there are many missing initial conditions whose absence might be expected to degrade the computed statistical properties of chaotic systems. The universality of this problem is enshrined in Sharkovskii’s theorem which is the simplest mathematical statement of the fact that no finite number representation of a chaotic dynamical system can account for all of its properties and shows that the precision of the representation limits the accuracy of the resulting digital behaviour.

ANALYZING COLLAGE AS A COMMUNICATION ACT IN THE DIGITAL AGE BASED ON THE DADAISM RENEWAL MOVEMENT'S SPIRIT

Collage combines the semiotic tradition of Roland Barthes, the phenomenological tradition of Edmund Husserl, the critical tradition of Karl Marx, and the point of view of Jurgen Habermas in visual communication. The collage technique creates trans-aesthetic elements and double codes, a Dadaist method of encoding signs. Building on previous research, this study explores collage art as a communication act and its process in the digital era. Digital photography technology and computer software facilitate quicker and easier creation of collage art, emphasizing the fundamental concept of "paste." This pasting-sticking process mirrors the communication process of art and design in the digital era, illustrating cyclical communication capable of transcending space and time. The research has several conclusions. (1) By enhancing visual communication strategies and efficiency based on the spirit of the Dadaism renewal movement, unique collages can be created as a personal sign of message(s). (2) Collages as communication act in the digital era represent dialogue in trans-aesthetics code. (3) On the other hand, with the spirit of Dadaism that contains a free mind, open creation, and emphasis on social-cultural issues, collages as a communication process are also possible to frame messages and create fallacy.

MicroLEDs for optical neuromorphic computing—application potential and present challenges

Abstract The slow non-radiative surface recombination velocity of gallium nitride (GaN) in combination with its highly efficient radiative recombination makes this material ideally suited for microLEDs with dimensions as small as 1 µm and even below, serving as the fundamental building block of micro-displays. However, due to their superior miniaturization potential and energy efficiency, GaN-based microLEDs have applications that extend well beyond display technology. Their capability to produce optical patterns with high resolution, which can be modulated at extremely high frequencies, makes them suitable for numerous other applications. We suggest exploiting these exciting properties for a new and potentially equally significant application: utilizing microLEDs in optical processing units for artificial intelligence workloads. In neuromorphic computing, relevant aspects of biological neural networks are emulated directly with either electronic circuits or photonic devices, avoiding the shortcomings of conventional digital computer technology for AI workloads, which generally require massively parallel information processing. GaN microLEDs are discussed here as a promising enabling technology for optical neuromorphic processing units. We see great potential to substantially decrease power consumption through massively parallel in-memory processing combined with efficient photon production and detection. A theoretical analysis of scalability and energy efficiency is provided. A macroscopic bench-top optical microLED demonstrator is presented, which experimentally proves the feasibility of our approach. Future potential and challenges associated with miniaturizing and scaling microLED-based optical processing units are discussed. Finally, we summarize the open research questions that require attention before fully functional and miniaturized optical neuromorphic processing units based on GaN microLEDs can be realized.

A digital CRISPR-dCas9-based gene remodeling biocomputer programmed by dietary compounds in mammals

CRISPR-dCas9 (dead Cas9 protein) technology, combined with chemical molecules and light-triggered genetic switches, offers customizable control over gene perturbation. However, these simple ON/OFF switches cannot precisely determine the sophisticated perturbation process. Here, we developed a resveratrol and protocatechuic acid-programmed CRISPR-mediated gene remodeling biocomputer (REPACRISPR) for conditional endogenous transcriptional regulation of genes invitro and invivo. Two REPACRISPR variants, REPACRISPRi and REPACRISPRa, were designed for the logic control of gene inhibition and activation, respectively. We successfully demonstrated the digital computations of single or multiplexed endogenous gene transcription by using REPACRISPRa. We also established mathematical models to predict the dose-responsive transcriptional levels of a target endogenous gene controlled by REPACRISPRa. Moreover, high levels of endogenous gene activation in mice mediated by the AND logic gate demonstrated computational control of CRISPR-dCas9-based epigenome remodeling in mice. This CRISPR-based biocomputer expands the synthetic biology toolbox and can potentially advance gene-based precision medicine. A record of this paper's transparent peer review process is included in the supplemental information.

HUMAN RESOURCE MANAGEMENT AND ARTIFICIAL INTELLIGENCE: TRANSFORMATIVE EFFECTS AND FUTURE PROSPECTS

Artificial intelligence (AI) is technology that enables computers and machines to simulate human learning, comprehension, problem solving, decision making, creativity and autonomy. Ancient civilizations contained tales of intelligent robots, such as Greek and Chinese cultures. AI as a modern phenomenon which took shape in the mid-20th century with the advent of digital computers. The first breakthrough, published in 1950, was Alan Turing's paper, ‘Computing Machinery and Intelligence. AI can make provide detailed solution to users and experts. They can act independently, replacing the need for human intelligence or intervention (a classic example being a self-driving car). Many sectors like finance, healthcare, to human resource management (HRM), rely on AI technologies. AI's integration in HRM has brought about great change in conventional HR practices used to measure efficiency, accuracy, strategic decision-making, etc. AI technologies help to provide solution to automation of repetitive tasks, analysis of sprawling volumes of data and insights facilitating decision-making. Through this research paper we try to understand the AI's effects on HR processes, in terms of recruitment, onboarding, performance appraisal, training and development, employee engagement, workforce planning; understanding the benefits and cons of implementing AI in HRM; ethical considerations and potential biases in AI algorithms relevant to HR practices; analysis of emergent AI trends with respect to HRM. Trends in AI in HR tend towards a most promising future, presenting several innovations. Accordingly, HR professionals will be expected to acquire competency with regards to new roles and responsibilities to avail AI in enhancing strategic planning, ethical oversight, and data-driven decision-making.

Deliberate Maladjustment by Microorganisms: A Medium for Images or Luminous Bacteria

Abstract In this paper, we seek to uncover the new significance of the phrase “deliberately maladjusted,” which is borrowed from video artists of the 1970s and their critical interventions (Vasulka, 1970), in the act of Microbial Image-Processing, using two artworks by the authors: A Medium for Images or Luminous Bacteria and ‘イ’(1926) by BioLuminescent Bacteria. In contrast to early video experimentations, contemporary Computational Image-Processing simulates and manipulates various visual media in a “well-adjusted” manner for the digital computation and display. With this paper, we reconsider the historical process of image-processing to focus on living entities and microorganisms and attempt to extend the possibility of works being “deliberately maladjusted.”