Information Processing Paradigm Research Articles

Artificial Amacrine Retinal Circuits.

Event-based imaging represents a new paradigm in visual information processing that addresses the speed and energy efficiency shortcomings inherently present in the current complementary metal oxide semiconductor-based machine vision. Realizing such imaging systems has previously been sought using very large-scale integration technologies that have complex circuitries consisting of many photodiodes, differential amplifiers, capacitors, and resistors. Here, we demonstrate that event-driven sensing can be achieved using a simple one-resistor, one-capacitor (1R1C) circuit, where the capacitor is modified with colloidal quantum dots (CQDs) to have a photoresponse. This sensory circuit emulates the motion-tracking function of the biological retina, in which the amacrine cells in the bipolar-to-ganglion synaptic pathway produce a transient spiking signal only in response to changes in light intensity but remain inactive under constant illumination. When extended to a 2D imaging array, the individual sensors work independently and output signals only when a change in the light intensity is detected; hence, the concept of the frame in image processing is thereby removed. In this work, we present the fabrication and characterization of a CQD photocapacitor-based 1R1C circuit that has a spectral response at 1550 nm in the short-wave infrared (SWIR). We report on the key performance parameters including peak responsivity, noise, and optical noise equivalent power and discuss the operating mechanism that is responsible for spiking responses in these artificial retinal circuits. The present work sets the foundation for expanding the bioinspired vision sensor capability toward midwave infrared (MWIR) and long-wave infrared (LWIR) spectral regions that are invisible to human eyes and mainstream semiconductor technologies.

Bistable organic electrochemical transistors: enthalpy vs. entropy

Organic electrochemical transistors (OECTs) underpin a range of emerging technologies, from bioelectronics to neuromorphic computing, owing to their unique coupling of electronic and ionic charge carriers. In this context, various OECT systems exhibit significant hysteresis in their transfer curve, which is frequently leveraged to achieve non-volatility. Meanwhile, a general understanding of its physical origin is missing. Here, we introduce a thermodynamic framework that readily explains the emergence of bistable OECT operation via the interplay of enthalpy and entropy. We validate this model through temperature-resolved characterizations, material manipulation, and thermal imaging. Further, we reveal deviations from Boltzmann statistics for the subthreshold swing and reinterpret existing literature. Capitalizing on these findings, we finally demonstrate a single-OECT Schmitt trigger, thus compacting a multi-component circuit into a single device. These insights provide a fundamental advance for OECT physics and its application in non-conventional computing, where symmetry-breaking phenomena are pivotal to unlock new paradigms of information processing.

Nanofluidic Ionic Memristors.

Living organisms use ions and small molecules as information carriers to communicate with the external environment at ultralow power consumption. Inspired by biological systems, artificial ion-based devices have emerged in recent years to try to realize efficient information-processing paradigms. Nanofluidic ionic memristors, memory resistors based on confined fluidic systems whose internal ionic conductance states depend on the historical voltage, have attracted broad attention and are used as neuromorphic devices for computing. Despite their high exposure, nanofluidic ionic memristors are still in the initial stage. Therefore, systematic guidance for developing and reasonably designing ionic memristors is necessary. This review systematically summarizes the history, mechanisms, and potential applications of nanofluidic ionic memristors. The essential challenges in the field and the outlook for the future potential applications of nanofluidic ionic memristors are also discussed.

Navigating the Digital Horizon: The Impact and Future of Communication Technologies in Society

The article delves deeper into the dynamic and rapidly evolving realm of digital communication technologies, highlighting their transformative impact across various dimensions of modern society. It scrutinizes the latest advancements in digital communication tools and platforms, assessing how these innovations are not only altering our fundamental methods of interaction but also redefining the paradigms of information exchange and processing. The study provides an in-depth analysis of five key domainssocial media dynamics, e-commerce evolution, mobile communication progression, online educational methodologies, and telemedicine advancements. Each of these domains is thoroughly examined through three distinct lenses, offering a nuanced perspective on their current state, challenges faced, and the potential trajectories they may follow. The article aims to furnish a holistic view of the digital communication landscape, contemplating its broader implications on social behavior, economic patterns, educational practices, healthcare delivery, and governance mechanisms. This comprehensive exploration is intended to offer readers a multifaceted understanding of how digital communication technologies are not just tools of convenience, but powerful forces shaping the future contours of our global society.

Quantum entanglement: Principles and research progress in quantum information processing

Quantum entanglement is a peculiar phenomenon in quantum information science, characterized by nonclassical correlations between quantum states of subsystems in a quantum system. Since the proposal of the Einstein-Podolsky-Rosen (EPR) paradox by Einstein, Podolsky, and Rosen, quantum entanglement has sparked intense debates on local realism. Bells inequality experiment established the nonlocality of quantum mechanics. Currently, high-dimensional quantum entanglement of both deterministic and random states can be realized in systems such as photons and cold atoms. Technologies such as quantum teleportation, quantum teleportation, quantum computing, and others rely on quantum entanglement to achieve effects beyond classical limitations. Current research focuses on the implementation of macroscopic quantum entanglement and its significance in fundamental problems of quantum mechanics. Quantum entanglement opens up a new paradigm for information processing with broad application prospects. It is necessary to conduct in-depth research on the nature of quantum entanglement and its advantages in information processing. This paper reviews the theoretical foundations of quantum entanglement, methods of generation and detection, and research progress in its applications in the field of quantum information. It discusses the important applications of quantum entanglement in quantum communication, computing, and sensing and provides an outlook on the future development prospects of quantum entanglement technologies.

An evolutionary crow search algorithm equipped with interactive memory mechanism to optimize artificial neural network for disease diagnosis

Artificial neural network (ANN) is an information processing paradigm that loosely models the thinking patterns of the human brain with specifications such as real-time learning, self-adaption, and self-organization. The learning process of ANNs is complex and tackles shortcomings such as a slow convergence rate, learning time-consuming, and local minimum trapping, especially when using gradient-based optimization techniques. Although many metaheuristics have been proposed to arm ANN and solve these weaknesses, ANN learning still needs solution quality. Therefore, this study proposes an evolutionary crow search algorithm (ECSA) to optimize the hyperparameters of ANNs for diagnosing chronic diseases. ECSA introduces an evolutionary search strategy, self-adaptive adaptive flight length, and an interactive memory mechanism to alleviate the canonical crow search algorithm's shortcomings. The evolutionary search strategy and self-adaptive flight length provide a meaningful search strategy in which crows effectively explore and exploit problem spaces by maintaining population diversity. During the search process, the interactive memory mechanism records the best solution obtained during optimization. The performance of ECSA was evaluated and compared with well-known metaheuristic algorithms in terms of local and global search abilities, local optima avoidance, and convergence speed towards the promising area. Then, the results were statistically analyzed. Ultimately, the effectiveness of an adaptation of ECSA named ECSA-MLP for optimizing hyperparameters of the multilayer perceptron network for diagnosing diseases including coronavirus, breast cancer, diabetes, cardiovascular, cervical cancer, Parkinson's, mammography, and acute inflammation was compared with state-of-the-art competitor algorithms. The experimental results indicated the superiority of ECSA over competitor algorithms in optimizing the network.

In Tribute: Reflections on the Impact of Professor Michael Turvey on Motor Development

In our tribute to Professor Michael Turvey, we have two parallel goals: 1) to highlight the scientific scope of Turvey’s impact on motor development; and, 2) to expose readers to papers that they may not have read but that might cast new light on age-old questions they confront in their current research on motor development. The paper is divided into two equal time periods. In Part 1, from 1975 to 1999, we trace the emergence and growth of Dynamic Systems/Ecological Realism (perception-action) paradigms. We explain how the existing paradigms in motor development research, the descriptive and information processing paradigms were, in part, replaced by new paradigms whose existence owes much to Michael Turvey and his colleagues. We suggest that this time period was one where Turvey had the most conceptual influence on the field. In Part 2, from 2000 to 2024, we describe how factors, including the emergence of two new paradigms in motor development research may have reduced Turvey’s direct influence. But we also note that there is still much research undertaken that builds off the bases of Dynamic Systems and Perception-Action Coupling approaches including research by Turvey and his students/colleagues. We end with the suggestion that the present generation of motor development researchers may have something to gain by re-/reading research from these perspectives regardless of whether it is directly from Professor Turvey’s pen or from those whom he influenced (or influenced him).

Reconfigurable Hexapartite Cluster States by Four‐Wave Mixing Processes with a Spatially Structured Pump

AbstractThe universal quantum computation provides a new paradigm for information processing. One feasible approach is measurement‐based one‐way quantum computation utilizing cluster states. Generally, the generation of cluster states with different structures for implementing on‐demand quantum computation needs different experimental setup, which limits its scalability. Here, the reconfigurable hexapartite cluster states created by postprocessing the quadrature information of hexapartite entangled state are demonstrated. Without altering the experimental layout, nine quantum correlated states with different structures, especially three cluster states, are implemented. In particular, such method can effectively reduce the excess noise introduced by creating cluster states under limited squeezing resources. This approach provides an avenue for realizing large‐scale reconfigurable cluster states without changing the experimental architecture.

SALES PREDICTION OF ANUGERAH JAYA PANCING 99 SHOP WITH BACKPROPAGATION NEURAL NETWORKS

Artificial Neural Network (Artificial Neural Network) is an information processing paradigm inspired by the workings of biological nervous systems, such as the performance of the brain, which processes information. Toko Anugerah Jaya Pancing 99 faces challenges in predicting product sales that fluctuate according to customer demand, and external factors can also affect sales of their products. To determine store sales predictions using the backpropagation method. From the research results with data training experiments it was found that the iterations process stopped when the epoch reached 6, time 0.00.01, performance 0.317, Gradient 0.359, and Validation Checks 6. Keywords: Prediction, Backpropagation, Neural Networks, Sales

Neuromorphic Computing via Fission-based Broadband Frequency Generation.

The performance limitations of traditional computer architectures have led to the rise of brain-inspired hardware, with optical solutions gaining popularity due to the energy efficiency, high speed, and scalability of linear operations. However, the use of optics to emulate the synaptic activity of neurons has remained a challenge since the integration of nonlinear nodes is power-hungry and, thus, hard to scale. Neuromorphic wave computing offers a new paradigm for energy-efficient information processing, building upon transient and passively nonlinear interactions between optical modes in a waveguide. Here, an implementation of this concept is presented using broadband frequency conversion by coherent higher-order soliton fission in a single-mode fiber. It is shown that phase encoding on femtosecond pulses at the input, alongside frequency selection and weighting at the system output, makes transient spectro-temporal system states interpretable and allows for the energy-efficient emulation of various digital neural networks. The experiments in a compact, fully fiber-integrated setup substantiate an anticipated enhancement in computational performance with increasing system nonlinearity. The findings suggest that broadband frequency generation, accessible on-chip and in-fiber with off-the-shelf components, may challenge the traditional approach to node-based brain-inspired hardware design, ultimately leading to energy-efficient, scalable, and dependable computing with minimal optical hardware requirements.

Advances and New Research Opportunities in Quantum Computing Technology by Integrating it with Other ICCT Underlying Technologies

Purpose: To explore the vast potential and possibilities that arise from synergizing quantum computing with other foundational technologies in the field of Information, Communication, and Computing Technologies (ICCT). By integrating quantum computing with other ICCT technologies, such as artificial intelligence, data analytics, cryptography, and communication networks, researchers aim to unlock unprecedented computational power and efficiency, thereby revolutionizing various industries and scientific domains. This research seeks to unravel novel applications, enhance the robustness and scalability of quantum computing systems, and pave the way for transformative advancements that will shape the future of information processing and communication paradigms. Ultimately, this interdisciplinary exploration holds the key to unleashing the full capabilities of quantum computing and opens doors to groundbreaking innovations that were once considered beyond reach. Methodology: Exploratory research method is used to analyse and interpret various related information collected using secondary sources using Google search engine and Google Scholar search engine as well as using quasi-secondary sources including AI engine supported GPT and Bard. ABCD analysis framework is used to study the advantages, benefits, constraints, and disadvantages of integration of Quantum computing technology with other ICCT Underlying Technologies. Finally, the results are interpreted and concluded by developing 12 postulates. Findings: The results demonstrate the potential of integrating quantum computing with other ICCT underlying technologies, offering transformative improvements in computational power, security, and efficiency across various industries and applications. As quantum computing continues to advance, its integration with other ICCT technologies will lead to new opportunities for innovation and the development of more sophisticated and powerful information and communication systems. Originality/Value: The paper evaluates advances and new research opportunities in the area of quantum computing technology. A new idea of integration of quantum computing technology with other ICCT underlying technologies is proposed and the advantages, benefits, constraints, and disadvantages of integration of Quantum computing technology with other ICCT Underlying Technologies are analysed using the ABCD analysis framework. The results are interpreted in the form of 12 new postulates. Type of Paper: Exploratory research

Why the Experiential View Is Vital To Marketing Communications Research Now

Advertising and, more generally, marketing communications, along with consumer behaviors, have changed in important ways. Whereas, traditionally, brands and companies have forced information on consumers, they now are creating numerous forms of communications to trigger diverse gratifications for consumers who are most often exposed incidentally to them. This essay, therefore, urges researchers and practitioners to open up the theoretical framework of marketing communication research beyond the traditional information-processing paradigm by incorporating the experiential view. The authors develop three major arguments in favor of adding the experiential view to provide an enhanced framework for examining the effects of contemporary marketing communications. They conclude with a set of implications for both researchers and practitioners.

Optical detection of valley-polarized electron diffusion in diamond

Using the state of valley-polarization of electrons in solids is a promising new paradigm for information storage and processing. The central challenge in utilizing valley-polarization for this purpose is to develop methods for manipulating and reading out the final valley state. Here, we demonstrate optical detection of valley-polarized electrons in diamond. It is achieved by capturing images of electroluminescence from nitrogen-vacancy centers at the surface of a diamond sample that are excited by electrons drifting and diffusing through the sample. Monte Carlo simulations are performed to interpret the resulting experimental diffusion patterns. Our results give insight into the drift-diffusion of valley-polarized electrons in diamond and yield a way of analyzing the valley-polarization of ensembles of electrons.

On Information Granulation via Data Filtering for Granular Computing-Based Pattern Recognition: A Graph Embedding Case Study

Granular Computing is a powerful information processing paradigm, particularly useful for the synthesis of pattern recognition systems in structured domains (e.g., graphs or sequences). According to this paradigm, granules of information play the pivotal role of describing the underlying (possibly complex) process, starting from the available data. Under a pattern recognition viewpoint, granules of information can be exploited for the synthesis of semantically sound embedding spaces, where common supervised or unsupervised problems can be solved via standard machine learning algorithms. In this companion paper, we follow our previous paper (Martino et al. in Algorithms 15(5):148, 2022) in the context of comparing different strategies for the automatic synthesis of information granules in the context of graph classification. These strategies mainly differ on the specific topology adopted for subgraphs considered as candidate information granules and the possibility of using or neglecting the ground-truth class labels in the granulation process and, conversely, to our previous work, we employ a filtering-based approach for the synthesis of information granules instead of a clustering-based one. Computational results on 6 open-access data sets corroborate the robustness of our filtering-based approach with respect to data stratification, if compared to a clustering-based granulation stage.

Research on pattern recognition of different music types in the context of AI with the help of multimedia information processing

Music is a form of art in which the sounds are timed and organized. Music is a kind of entertainment that mixes sounds in a way that people like, find fascinating, or to which they desire to dance. Most music is created via one or more people's vocal or instrumental efforts. By the dictionary, music is defined as having at least one of the following three elements: rhythm, melody, and harmony. Music is utilized in therapy because of its apparent benefits on behavior. Various physiological circumstances have different metabolite expression patterns that can be studied using pattern recognition in multimedia information processing. Music therapy includes various activities, including singing, playing instruments, dancing, and listening to music. Music-making with artificial intelligence (AI) uses neural networks, which are massive collections of computer bits that aim to stimulate brain activity. The neural network (NN) may be bombarded with music to see if it picks up on patterns the way the human brain does when repeatedly exposed to new stimuli. It will get the hang of them eventually. Experts believed that AI would be unable to generate music unless it first mimics a human-created data collection. By providing a conceptual paradigm for multimedia information processing. The end effect will be entirely different depending on how many hours of music are placed into it. For AI to learn from patterns or features in data on its own, it needs big data (BD), fast, repeated processing, and complex algorithms. The use of technology makes the process of creating analytical models much faster. A new AI-BD tool is an opportunity, not a danger for people currently working as artists. People are beginning to ask what constitutes acceptable work as AI grows more prominent in the music and art industries to gain efficiency of 97.8%. Future music will be heavily impacted by listeners' bodies and emotions all the time. For example, wearable technology may detect a person's mood and play the music that matches it. It is the next step in personalization. The AI-BD methodology improves the efficiency, accuracy, etc., compared to other existing models by gaining 97.8%, performance analysis 97.2%, reliability ratio 95.6%, and survivability analysis 98.2%.

Basic of Artificial Neural Network

An information processing paradigm called an Artificial Neural Network (ANN) is based on how biological nervous systems, like the brain, process information. The information processing system's novel structure is the paradigm's most important feature. It is made up of many highly interconnected processing elements (neurons) that work together to solve particular problems. Similar to humans, ANNs learn from examples. Through a learning process, an ANN is set up for a specific use, like pattern recognition or data classification. Changing the neuronal synaptic connections is necessary for learning in biological systems. This is also true for ANNs. This paper provides an overview of the Artificial Neural Network, its operation, and its training. Additionally, it describes the advantages and applications of ANN.

The paradigm of information processing in the intelligent information system to support decision-making in the field of environmental safety

The paradigm of information processing in the intelligent information system to support decision-making in the field of environmental safety

Room-temperature coherent optical manipulation of hole spins in solution-grown perovskite quantum dots

Manipulation of solid-state spin coherence is an important paradigm for quantum information processing. Current systems either operate at very low temperatures or are difficult to scale up. Developing low-cost, scalable materials whose spins can be coherently manipulated at room temperature is thus highly attractive for a sustainable future of quantum information science. Here we report ambient-condition all-optical initialization, manipulation and readout of hole spins in an ensemble of solution-grown CsPbBr3 perovskite quantum dots with a single hole in each dot. The hole spins are initialized by sub-picosecond electron scavenging following circularly polarized femtosecond-pulse excitation. A transverse magnetic field induces spin precession, and a second off-resonance femtosecond-pulse coherently rotates hole spins via strong light-matter interaction. These operations accomplish near-complete quantum-state control, with a coherent rotation angle close to the π radian, of hole spins at room temperature.

Runoff Prediction of Bharathapuzha River Basin using Artificial Neural Network and SWAT Model

An attempt was made to model the non-linear system of rainfall-runoff process from Bharathapuzha River basin using an information processing paradigm, Artificial Neural Network (ANN). The results were compared with the outputs of the semi-distributed, physically-based SWAT (Soil and Water Assessment Tool) model. The ANN modelling was done using back propagation learning algorithm, tan sigmoid transfer function, and model input strategy having rainfall and other climatic variables as input by assigning number of layers as 5, 10, 15, 20, 25, 30, and 40. Different models were evaluated with respect to coefficient of correlation (r), coefficient of determination (R2 ), and root mean square error (RMSE). Among the ANN models, ANN-BP-A-5 (six input variables, 5 hidden layers) performed best, followed by ANN-BP-A40 (six input variables, 40 hidden layers). Comparison of ANN predicted runoff of the best models (ANN-BP-A-5 and ANNBP-A40) with the SWAT predicted runoff revealed that the simulated runoff using SWAT was more correlated to observed runoff than ANN predicted runoff. The ANN models underestimated the flow during the rainy season, and gave an overestimation during the summer season. However, the R2 values of 0.666 and 0.649 obtained for ANN-BP-A-5 and ANN-BP-A40, respectively, indicated that the performances of ANN models were satisfactory and ANN model can also be used for runoff prediction in data scarce areas.

Jaringan Saraf Tiruan Pengenalan Pola Karakter Kabataku Menggunakan Metode Bidirectional Associative Memory (BAM) Kontinu

Artificial neural network is an information processing paradigm with a working system using biological concepts in processing information so that it has capabilities similar to the human brain, Jst is able to solve problems using uncertainty science such as Kabataku pattern recognition. Kabataku is a character operator used in Mathematics with the concepts of X, :, + and -. Introduction This character operator uses the concept of artificial intelligence, which uses the theory and process of Artificial Neural Networks. The purpose of this study is to determine the effectiveness of the Continuous BAM method in recognizing Kabataku character patterns in arithmetic operations in mathematics. The Bidirectional Associative Memory (BAM) method has the ability in associative memory or content addressable memory can be called by using the part stored in the memory itself. BAM in an artificial neural network has 2 layers, namely the input layer and the output layer that are interconnected between the two, also called bidirectional, with the work process if the matrix weight of the signal sent from the input layer X to the output layer Y is W, then the matrix weight of the signal sent from the output layer Y to the input layer X is WT . Continuous BAM method will change the input to output more finely with a value that lies in the range [0,1]. The activation function used is the sigmoid function. The final result of pattern recognition is x1= [-8 -12], x2 =[ 8 0], x3 [12 8], x4= [16 12] Not all patterns match the target.