Electronic Networks Research Articles

Multiplexable all-optical nonlinear activator for optical computing

As an alternative solution to surpass electronic neural networks, optical neural networks (ONNs) offer significant advantages in terms of energy consumption and computing speed. Despite the optical hardware platform could provide an efficient approach to realizing neural network algorithms than traditional hardware, the lack of optical nonlinearity limits the development of ONNs. Here, we proposed and experimentally demonstrated an all-optical nonlinear activator based on the stimulated Brillouin scattering (SBS). Utilizing the exceptional carrier dynamics of SBS, our activator supports two types of nonlinear functions, saturable absorption and rectified linear unit (Relu) models. Moreover, the proposed activator exhibits large dynamic response bandwidth (∼11.24 GHz), low nonlinear threshold (∼2.29 mW), high stability, and wavelength division multiplexing identities. These features have potential advantages for the physical realization of optical nonlinearities. As a proof of concept, we verify the performance of the proposed activator as an ONN nonlinear mapping unit via numerical simulations. Simulation shows that our approach achieves comparable performance to the activation functions commonly used in computers. The proposed approach provides support for the realization of all-optical neural networks.

Self-Powered Pressure-Temperature Bimodal Sensing Based on the Piezo-Pyroelectric Effect for Robotic Perception.

Multifunctional sensors have played a crucial role in constructing high-integration electronic networks. Most of the current multifunctional sensors rely on multiple materials to simultaneously detect different physical stimuli. Here, we demonstrate the large piezo-pyroelectric effect in ferroelectric Pb(Mg1/3Nb2/3)O3-PbTiO3 (PMN-PT) single crystals for simultaneous pressure and temperature sensing. The outstanding piezoelectric and pyroelectric properties of PMN-PT result in rapid response speed and high sensitivity, with values of 46 ms and 28.4 nA kPa-1 for pressure sensing, and 1.98 s and 94.66 nC °C-1 for temperature detection, respectively. By leveraging the distinct differences in the response speed of piezoelectric and pyroelectric responses, the piezo-pyroelectric effect of PMN-PT can effectively detect pressure and temperature from mixed-force thermal stimuli, which enables a robotic hand for stimuli classification. With appealing multifunctionality, fast speed, high sensitivity, and compact structure, the proposed self-powered bimodal sensor therefore holds significant potential for high-performance artificial perception.

Exploring Types of Photonic Neural Networks for Imaging and Computing-A Review.

Photonic neural networks (PNNs), utilizing light-based technologies, show immense potential in artificial intelligence (AI) and computing. Compared to traditional electronic neural networks, they offer faster processing speeds, lower energy usage, and improved parallelism. Leveraging light's properties for information processing could revolutionize diverse applications, including complex calculations and advanced machine learning (ML). Furthermore, these networks could address scalability and efficiency challenges in large-scale AI systems, potentially reshaping the future of computing and AI research. In this comprehensive review, we provide current, cutting-edge insights into diverse types of PNNs crafted for both imaging and computing purposes. Additionally, we delve into the intricate challenges they encounter during implementation, while also illuminating the promising perspectives they introduce to the field.

Psychometric properties and factor structure of the traditional Chinese version of the Community Integration Questionnaire-Revised in traumatic brain injury survivors.

This study aimed to translate and validate the traditional Chinese version of the Community Integration Questionnaire-Revised (TC-CIQ-R) in patients with traumatic brain injury (TBI). We included participants aged ≥20 years and diagnosed as having TBI for ≥6 months from neurosurgical clinics. The 18-item TC-CIQ-R, Participation Measure - 3 Domains, 4 Dimensions (PM-3D4D), Extended Glasgow Outcome Scale (GOSE), and Taiwanese Quality of Life After Brain Injury (TQOLIBRI) were completed. The sample included 180 TBI survivors (54% male, mean age 47 years) of whom 87% sustained a mild TBI. Exploratory factor analysis extracted four factors - home integration, social integration, productivity, and electronic social networking - which explained 63.03% of the variation, after discarding the tenth item with a factor loading of 0.25. For criterion-related validity, the TC-CIQ-R was significantly correlated with the PM-3D4D; convergent validity was exhibited by demonstrating the associations between the TC-CIQ-R and TQOLIBRI. Known-group validity testing revealed significant differences in the subdomain and total scores of the TC-CIQ-R between participants with a mean GOSE score of ≤6 and >7 (all P < 0.001). The TC-CIQ-R exhibited acceptable Cronbach's α values (0.68-0.88). We suggest the 17-item TC-CIQ-R as a valid tool for rehabilitation professionals, useful for both clinical practice and research in assessing community integration levels following TBI.

Legal Regulation of Insurance against Cyber-Attack Risks

The Internet has recently witnessed significant growth to become an important part of our daily lives, as it has grown to become the main part of our business as well. Nowadays, anything we do on smart devices can be seen by others, either with our knowledge or consent or without, it may seem obvious, as we can share our work on social networking sites. At first glance, however, it appears to represent a serious threat, including privacy breaches and data theft, and threats become even more important when these breaches directly affect businesses, customers, and the general public. It became necessary to protect the requirements of this development from those cyber risks to minimize the negative effects that these attacks and intrusions can have on facilities and infrastructure. The world has witnessed a technical development in the use of Internet devices and electronic networks, and this use has exposed these networks and devices to the risks of cyberattacks, data breaches, obtaining personal, financial, and commercial infor

Towards constructing a DOE-based practical optical neural system for ship recognition in remote sensing images

The optical neural network system based on the 4f system (4f-ONN) is a feasible solution for on-orbit real-time target detection and recognition on remote sensing images, as it can directly modulate and encode the two-dimensional image information. However, traditional 4f systems based on spatial light modulators (SLMs) often encounter misalignment errors during assembly due to the reflective optical path used in SLMs. Additionally, implementing electronic SLM in space-based applications introduces problems such as particle number reversal, significantly reducing the reliability of the system. To address these issues, this paper proposes the adoption of diffractive optical elements (DOEs) to construct the 4f-ONN system. The DOE-based design offers a more compact structure, enhanced reliability, and reduced energy consumption, making it highly suitable for on-orbit image processing applications. Due to the expensive and time-consuming nature of the DOE manufacturing process, a design approach for a 4f system based on DOE was pursued through software simulation in this paper. The simulation phase involved the utilization of electronic neural networks to acquire the physical parameters of the DOE mask, while incorporating the array theorem and Fraunhofer diffraction theorem to accurately calculate the physical dimensions of the DOE. The effectiveness of the adopted DOE-based 4f system was initially validated through experiments on a simple pattern dataset. Subsequently, simulation experiments were conducted on three public datasets, namely Mnist, Fashion-mnist, and QuickDraw16, to confirm the efficacy of the DOE-based 4f system in classification tasks. Lastly, target recognition experiments were performed on the GF-2 dataset, and a corresponding hardware system was developed to demonstrate the potential of the DOE-based 4f system in on-orbit image processing.

Digital Emporium

Abstract: E-commerce, an integral facet of modern business, facilitates the buying and selling of goods and services over electronic networks, particularly the internet. This paradigm shift in commerce has transformed traditional business models, witnessing remarkable global growth. In India, e-commerce has flourished with the proliferation of internet usage, especially among startups embracing it as a distinctive business model. Despite its widespread adoption, e-commerce remains underexplored. This research endeavors to delineate the e-commerce landscape, analyzing trends and key variables pivotal for success. Additionally, the study examines user preferences and purchasing behaviors across various e-commerce platforms, including JD, Taobao, Pinduoduo, and Tmall, aiming to enhance marketing efficiency through understanding consumer tendencies and motivations. Through empirical analysis and feedback collection, the study elucidates the diverse preferences of consumers across different platforms, shedding light on factors influencing platform choice, such as product category, age, and gender. Ultimately, this research seeks to contribute to a deeper understanding of e-commerce dynamics and its implications for businesses and consumers alike.

Experts vs. Peers: The Role of Situational Characteristics in Opinion Seeking on Electronic Networks of Practice

With advances in information and communication technologies (ICT), organizations of various forms now deploy an increasing number of ICT-enabled persuasive systems in several domains. Traditional computer-mediated communication (CMC) theories mainly focus on the effectiveness of media in the synchronous/asynchronous spectrum for effectively matching medium with communication task. The contemporary communication environment is rich with asynchronous channels such as email, Web, and text messaging, which makes it important to go beyond synchronicity and determine the nuances among various asynchronous channels. No rigorous research has compared the effectiveness of these channels in the persuasive systems domain where organizations use technology to persuade users to modify their behavior in a direction that they mutually agree to be desirable. In this paper, we study the effectiveness of CMC and the strategy used to frame the persuasive message. We explore persuasive strategies of praising, reminding, suggesting, and rewarding for health behavior and promotion. We model user experience as a mediator between channel strategy combinations and persuasive effectiveness. Through controlled user studies, we compared sixteen combinations of communication channel and persuasive strategy with or without emoticons. We found that channel/strategy combinations affect persuasive effectiveness (mediated by user experience) in varying degrees. Our findings contribute to the body of CMC and persuasive system knowledge and have practical implications for online advertising, health promotion, and persuasive technology design.

Digital Emporium

Abstract: E-commerce has become an indispensable part of contemporary business, revolutionizing the way goods and services are bought and sold by utilizing electronic networks, particularly the internet. This shift in commerce has significantly altered traditional business frameworks, witnessing remarkable global expansion. In India, e-commerce has experienced substantial growth alongside the widespread adoption of internet usage, particularly among startups, which have adopted it as a distinctive business model. Despite its widespread acceptance, e-commerce remains a field that warrants further exploration. This research aims to provide a comprehensive overview of the e-commerce landscape by analyzing trends and crucial factors essential for success. Furthermore, the study investigates consumer preferences and purchasing behaviors across various e-commerce platforms, including JD, Taobao, Pinduoduo, and Tmall, with the goal of enhancing marketing effectiveness through a deep understanding of consumer inclinations and motivations. Through empirical analysis and gathering feedback, this study aims to illuminate the varied preferences of consumers across different platforms, shedding light on factors that influence platform selection, such as product categories, age, and gender. Ultimately, the objective of this research is to contribute to a more profound understanding of e-commerce dynamics and their implications for both businesses and consumers.

Multiplexed orbital angular momentum beams demultiplexing using hybrid optical-electronic convolutional neural network

Advancements in optical communications have increasingly focused on leveraging spatial-structured beams such as orbital angular momentum (OAM) beams for high-capacity data transmission. Conventional electronic convolutional neural networks exhibit constraints in efficiently demultiplexing OAM signals. Here, we introduce a hybrid optical-electronic convolutional neural network that is capable of completing Fourier optics convolution and realizing intensity-recognition-based demultiplexing of multiplexed OAM beams under variable simulated atmospheric turbulent conditions. The core part of our demultiplexing system includes a 4F optics system employing a Fourier optics convolution layer. This optical spatial-filtering-based convolutional neural network is utilized to realize the training and demultiplexing of the 4-bit OAM-coded signals under simulated atmospheric turbulent conditions. The current system shows a demultiplexing accuracy of 72.84% under strong turbulence scenarios with 3.2 times faster training time than all electronic convolutional neural networks.

A flexible plane-wire MoO2-anchored carbon nanofiber matrix as freestanding anodes for lithium storage with enhanced cyclability

Inspired by beds of silkworm cocoon, a flexible ″plane-wire″ MoO2-anchored carbon nanofiber matrix has been successfully prepared through electrospinning and subsequent annealing processes. It delivers an initial discharge capacity of 877 mAh g−1 at 200 mA g−1 with a high initial coulombic efficiency of 84.7 % and showcases a high reversible specific capacity of 743 mAh g−1 in the second cycle when employed as free-standing anodes for lithium batteries. It also exhibits good rate performance and superb cyclability, which delivering a specific capacity of 364 mAh g−1 at a high current density of 2 A g−1 and maintains a notable specific capacity of 674 mAh g−1 even after 2000 cycles. It is found that, the carbon nanofiber matrix serves as a robust electronic conductive network to reduce the charge transfer resistance and promote the transportation of Li+, and provides excellent support to buffer volume changes of the MoO2, resulting in enhanced rate performance and cyclability. Moreover, these electrodes are designed to be free-standing, which can improve the flexibility of the electrode and initial coulombic efficiency. It is believed that the as-prepared ″plane-wire″ MoO2-anchored carbon nanofiber matrix is a promising anode material for high-performance LIBs.

Investigating the Simultaneous Performance of AI and Blockchain on E-banking Transaction

Although electronic banking networks are new to communities, it can be argued that there are good reasons for their expansion. The banking industry is aware of the power and ability of social networks and enables banks to reach customers more than traditional channels, but currently, banking in social networks is limited to product marketing, customer interaction and support. As the channels and service delivery mechanisms in banks develop, bank managers have the right to worry about the monitoring of transactions in such networks and it becomes their main concern. Although electronic banking networks are new to communities, it can be argued that there are good reasons for their expansion. Banking is aware of the power and capability of social networks and allows banks to reach customers more than traditional channels, but currently banking on social networks is limited to product marketing, customer interaction and support. It is As the channels of banks and service delivery mechanisms evolve, bank managers have the right to control transactions in such networks, and this becomes their main concern.

Leveraging Blockchain Interoperability for Interdependent Networks

With the rapid emergence of technologies, coupling interdependent consumer electronics networks have become imperative to enable information exchange, holistic data analysis, and effective decision-making techniques. While interdependent networks can discover crucial information through intuitive understanding and efficient data-driven algorithms, it is crucial to leverage secure and effective data exchange among the underlying networks of consumer electronics. Blockchain comes into play when it requires ensuring trustless communication and data exchange amongst multiple interdependent networks. Blockchain interoperability is evolving and gaining widespread popularity in industry and research due to its ability to exchange data among multiple blockchains. This paper presents Divide-LeapChain, a next-generation efficient and effective interoperable blockchain framework for interdependent networks of heterogeneous consumer electronics devices. The Divide-LeapChain addresses the critical issues of intra- and inter-blockchain communication, including spatial and temporal uncertainties in data traversal, exchange, validation, and cross-chain swaps. It promises significant drops-off in verification steps without weakening blockchain integrity, which extends the possibilities to apply blockchain applications on heterogeneous embedded consumer Internet-of-Things (IoT) devices.

Prediction of Cancer Symptom Trajectory Using Longitudinal Electronic Health Record Data and Long Short-Term Memory Neural Network.

Ability to predict symptom severity and progression across treatment trajectories would allow clinicians to provide timely intervention and treatment planning. However, such predictions are difficult because of sparse and inconsistent assessment, and simplistic measures such as the last observed symptom severity are often used. The purpose of this study is to develop a model for predicting future cancer symptom experiences on the basis of past symptom experiences. We performed a retrospective, longitudinal analysis using records of patients with cancer (n = 208) hospitalized between 2008 and 2014. A long short-term memory (LSTM)-based recurrent neural network, a linear regression, and random forest models were trained on previous symptoms experienced and used to predict future symptom trajectories. We found that at least one of three tested models (LSTM, linear regression, and random forest) outperform predictions based solely on the previous clinical observation. LSTM models significantly outperformed linear regression and random forest models in predicting nausea (P < .1) and psychosocial status (P < .01). Linear regression outperformed all models when predicting oral health (P < .01), while random forest outperformed all models when predicting mobility (P < .01) and nutrition (P < .01). We can successfully predict patients' symptom trajectories with a prediction model, built with sparse assessment data, using routinely collected nursing documentation. The results of this project can be applied to better individualize symptom management to support cancer patients' quality of life.

Unsupervised Anomaly Detection and Diagnosis in Power Electronic Networks: Informative Leverage and Multivariate Functional Clustering Approaches

Unsupervised Anomaly Detection and Diagnosis in Power Electronic Networks: Informative Leverage and Multivariate Functional Clustering Approaches

Computational Approaches to Extreme Quantum Functional Matter

Study of quantum materials under extreme conditions require novel theoretical and computational approaches that can incorporate complexities from the presence of multiple degrees of freedom and quantum entanglement in correlated systems. Recent theoretical and computational studies of quantum functional matter focus on capturing this fascinating interplay and its outcome via employments of the state-of-the-art ab initio electronic structure methods, machine learning, and tensor network methodologies. In this article we would like to briefly outline plans and objectives of the quantum functional matter research.

Assessment of QOS indicators of a network with UDP and TCP traffic under a node peak load mode

The object of research is Markov models of network nodes with UDP (User Datagram Protocol) and TCP (Transmission Control Protocol) traffic and their differences. The task solved is the lack of Markov models of network nodes describing the behavior of TCP traffic from the point of view of packet retransmissions and packet delivery guarantees. Markov models of network nodes describing traffic behavior with guaranteed packet delivery have been further advanced. Given the comparison of the models, the differences from the classic models serving TCP traffic were shown, for each packet flow, an additional dimensionally was added to the graph of states and transitions, which takes into account the retransmission of a lost packet. The comparison graph shows similar behavior of queue length and packet loss for both types of traffic. But the nature of the curves is different. With TCP traffic, packet loss can exceed 5 percent. In addition, lost packets must be retransmitted, which increases the load on the network node. More failures and packet queue lengths at a network node during peak load typically occur with TCP traffic compared to UDP traffic. At peak load, the difference in service failures can reach 20–30 percent. The main reason is that TCP uses flow control and rate-limiting mechanisms to avoid network congestion and ensure efficient data transfer between nodes. The Markov model of TCP traffic requires an additional dimensionally on the graph of states and transitions, which affects the behavior of queues and packet failures. The investigated problem was solved due to the universality and diversity of the mathematical apparatus of Markov mass service systems. The results could be used in network modeling software products for building and reengineering the topology of electronic communications networks at enterprises and organizations

Photonic neuromorphic architecture for tens-of-task lifelong learning.

Scalable, high-capacity, and low-power computing architecture is the primary assurance for increasingly manifold and large-scale machine learning tasks. Traditional electronic artificial agents by conventional power-hungry processors have faced the issues of energy and scaling walls, hindering them from the sustainable performance improvement and iterative multi-task learning. Referring to another modality of light, photonic computing has been progressively applied in high-efficient neuromorphic systems. Here, we innovate a reconfigurable lifelong-learning optical neural network (L2ONN), for highly-integrated tens-of-task machine intelligence with elaborated algorithm-hardware co-design. Benefiting from the inherent sparsity and parallelism in massive photonic connections, L2ONN learns each single task by adaptively activating sparse photonic neuron connections in the coherent light field, while incrementally acquiring expertise on various tasks by gradually enlarging the activation. The multi-task optical features are parallelly processed by multi-spectrum representations allocated with different wavelengths. Extensive evaluations on free-space and on-chip architectures confirm that for the first time, L2ONN avoided the catastrophic forgetting issue of photonic computing, owning versatile skills on challenging tens-of-tasks (vision classification, voice recognition, medical diagnosis, etc.) with a single model. Particularly, L2ONN achieves more than an order of magnitude higher efficiency than the representative electronic artificial neural networks, and 14× larger capacity than existing optical neural networks while maintaining competitive performance on each individual task. The proposed photonic neuromorphic architecture points out a new form of lifelong learning scheme, permitting terminal/edge AI systems with light-speed efficiency and unprecedented scalability.

Blocking Dangerous Content in Electronic Communications Networks: Evidence from Netherlands, United States and Singapore

In recent years, the blocking of internet sites with harmful content or those deemed to be (illegal content) has increased. The policy of blocking dangerous sites has pros and cons regarding freedom of expression. Blocking is considered to be carried out to control citizens' expression, which can impact all areas, especially freedom of opinion, which can be seen as a threat to certain parties. This research aims to compare criminalization policies for blocking dangerous content in electronic communications networks in the Netherlands, the United States, and Singapore and provide thoughts on law enforcement models for blocking dangerous content in more democratic electronic communication networks. This is normative legal research using a law and case approach and comparing several countries. The conceptual approach is directed at the concept of a democratic state and the protection of human rights to formulate relevant policy models. The results of blocking studies in the Netherlands, The USA, and Singapore already have regulations and policy directions for blocking dangerous content, or at least rules regarding law enforcement for cybercrime. The difference is in the level of sensitivity to the specifications of existing problems. The policy model for blocking dangerous content with special regulations outside the Criminal Code must be carried out in a non-repressive manner so that it does not violate the ultimum remedium principle and becomes over-criminalized. The formulation of policy model of all crime prevention plans using the criminal law system, which pays attention to the problem of formulating criminal acts (criminalization), criminal responsibility, and criminal regulations and punishment.

Physics‐Aware Analytic‐Gradient Training of Photonic Neural Networks

AbstractPhotonic neural networks (PNNs) have emerged as promising alternatives to traditional electronic neural networks. However, the training of PNNs, especially the chip implementation of analytic gradient descent algorithms that are recognized as highly efficient in traditional practice, remains a major challenge because physical systems are not differentiable. Although training methods such as gradient‐free and numerical gradient methods are proposed, they suffer from excessive measurements and limited scalability. State‐of‐the‐art in situ training method is also cost‐challenged, requiring expensive in‐line monitors and frequent optical I/O switching. Here, a physics‐aware analytic‐gradient training (PAGT) method is proposed that calculates the analytic gradient in a divide‐and‐conquer strategy, overcoming the difficulty induced by chip non‐differentiability in the training of PNNs. Multiple training cases, especially a generative adversarial network, are implemented on‐chip, achieving a significant reduction in time consumption (from 31 h to 62 min) and a fourfold reduction in energy consumption, compared to the in situ method. The results provide low‐cost, practical, and accelerated solutions for training hybrid photonic‐digital electronic neural networks.