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Journal articles on the topic 'Neurocomputing device'

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1

Rasras, Rashad J. "Neurocomputing Device for Image Processing and Classification." Journal of Computer Science, no. 2 (February 1, 2005): 89–91. http://dx.doi.org/10.3844/jcssp.2005.89.91.

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2

Calayir, Vehbi, and Larry Pileggi. "Device Requirements and Technology-Driven Architecture Optimization for Analog Neurocomputing." IEEE Journal on Emerging and Selected Topics in Circuits and Systems 5, no. 2 (2015): 162–72. http://dx.doi.org/10.1109/jetcas.2015.2426497.

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3

Al-Shidaifat, AlaaDdin, Shubhro Chakrabartty, Sandeep Kumar, Suvojit Acharjee, and Hanjung Song. "A Novel Characterization and Performance Measurement of Memristor Devices for Synaptic Emulators in Advanced Neuro-Computing." Micromachines 11, no. 1 (2020): 89. http://dx.doi.org/10.3390/mi11010089.

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The advanced neuro-computing field requires new memristor devices with great potential as synaptic emulators between pre- and postsynaptic neurons. This paper presents memristor devices with TiO2 Nanoparticles (NPs)/Ag(Silver) and Titanium Dioxide (TiO2) Nanoparticles (NPs)/Au(Gold) electrodes for synaptic emulators in an advanced neurocomputing application. A comparative study between Ag(Silver)- and Au(Gold)-based memristor devices is presented where the Ag electrode provides the improved performance, as compared to the Au electrode. Device characterization is observed by the Scanning Electr
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Felix, A. Pyatakovich* Lubov V. Khlivnenko Tatyana I. Yakunchenko Kristina F. Makkonen Olga V. Mevsha. "A COMPARATIVE ANALYSIS OF THE RESULTS OF CONVENTIONAL AND COMBINED METHODS OF TRAINING DIRECT PROPAGATION NEURAL NETWORK IN HEALTHY PERSONS TO DETECTING THE DEGREE OF ACTIVITY OF AN AUTONOMOUS NERVOUS SYSTEM." Indo American Journal of Pharmaceutical Sciences 04, no. 09 (2017): 3075–79. https://doi.org/10.5281/zenodo.910741.

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The article deals with a comparative analysis of the efficiency of an artificial neural network (ANN) trained with the help of algorithm back propagation, and of that trained by combining a back propagation algorithm and a variant of Cauchy stochastic training, in detecting the degree of activity of an autonomous nervous system. For the purposes realization of the project has been developed a biotechnical system, including technical device for input electrophysiological information in mode on-line. To evaluate the clinical effectiveness of the classification, records of interpulse intervals in
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5

Abed Salman, Mahdi, and Muhammed A. Mahdi. "Multi-Strategy Fusion for Enhancing Localization in Wireless Sensor Networks (WSNs)." Iraqi Journal For Computer Science and Mathematics 5, no. 1 (2024): 299–326. http://dx.doi.org/10.52866/ijcsm.2024.05.01.021.

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Localization in wireless sensor networks (WSNs) plays a crucial role in various applications thatrely on spatial information. This paper introduces the Multi-Strategy Fusion for Localization model, whichintegrates optimization techniques (ABO, DSA, EHO, and KNN) and neurocomputing techniques (BP, MTLSTM,BILSTM, and Autoencoder) to enhance localization accuracy in WSNs. The work is divided into three phases: datacollection, model building, and implementation. The first and the last are carried out in the field, while the secondis made in the laboratory. The three phases involve a few general st
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Chang, Shu-Hao, and Chin-Yuan Fan. "Using Patent Technology Networks to Observe Neurocomputing Technology Hotspots and Development Trends." Sustainability 12, no. 18 (2020): 7696. http://dx.doi.org/10.3390/su12187696.

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In recent years, development in the fields of big data and artificial intelligence has given rise to interest among scholars in neurocomputing-related applications. Neurocomputing has relatively widespread applications because it is a critical technology in numerous fields. However, most studies on neurocomputing have focused on improving related algorithms or application fields; they have failed to highlight the main technology hotspots and development trends from a comprehensive viewpoint. To fill the research gap, this study adopts a new viewpoint and employs technological fields as its mai
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7

Тихомиров, Виктор, Viktor Tikhomirov, Надежда Новикова, and Nadezhda Novikova. "Application of neurocomputing robust design to optimize the friction sintered powder material." Bulletin of Bryansk state technical university 2014, no. 3 (2014): 81–84. http://dx.doi.org/10.12737/23225.

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The paper presents a methodology for selecting the optimal composition of the friction sintered powder material to the safety of the friction devices using a neurocomputer robust design to reduce the search time and reduce the cost of the experiment and the analysis of the computational experiment.
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8

Lo Sciuto, Grazia, Christian Napoli, Paweł Kowol, et al. "Multilayer Plasmonic Nanostructures for Improved Sensing Activities Using a FEM and Neurocomputing-Based Approach." Sensors 22, no. 19 (2022): 7486. http://dx.doi.org/10.3390/s22197486.

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In order to obtain optimized elementary devices (photovoltaic modules, power transistors for energy efficiency, high-efficiency sensors) it is necessary to increase the energy conversion efficiency of these devices. A very effective approach to achieving this goal is to increase the absorption of incident radiation. A promising strategy to increase this absorption is to use very thin regions of active material and trap photons near these surfaces. The most effective and cost-effective method of achieving such optical entrapment is the Raman scattering from excited nanoparticles at the plasmoni
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9

Naz, Sidra, Muhammad Asif Zahoor Raja, Ammara Mehmood, and Aneela Zameer Jaafery. "Intelligent Predictive Solution Dynamics for Dahl Hysteresis Model of Piezoelectric Actuator." Micromachines 13, no. 12 (2022): 2205. http://dx.doi.org/10.3390/mi13122205.

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Piezoelectric actuated models are promising high-performance precision positioning devices used for broad applications in the field of precision machines and nano/micro manufacturing. Piezoelectric actuators involve a nonlinear complex hysteresis that may cause degradation in performance. These hysteresis effects of piezoelectric actuators are mathematically represented as a second-order system using the Dahl hysteresis model. In this paper, artificial intelligence-based neurocomputing feedforward and backpropagation networks of the Levenberg–Marquardt method (LMM-NNs) and Bayesian Regularizat
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10

Geng, Xueli, Qin Gao, Gang Wu, et al. "Stable and Tunable Quantum Conductance in Spider-Silk-like Synaptic Device for Neurocomputing." ACS Applied Materials & Interfaces, July 16, 2024. http://dx.doi.org/10.1021/acsami.4c06328.

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11

Feng, Xin, Jingjing Huang, Jing Ning, Dong Wang, Jincheng Zhang, and Yue Hao. "A Novel Nonvolatile Memory Device Based on Oxidized Ti3c2tx Mxene for Neurocomputing Application." SSRN Electronic Journal, 2022. http://dx.doi.org/10.2139/ssrn.4254714.

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12

Feng, Xin, Jingjing Huang, Jing Ning, Dong Wang, Jincheng Zhang, and Yue Hao. "A novel nonvolatile memory device based on oxidized Ti3C2Tx MXene for neurocomputing application." Carbon, January 2023. http://dx.doi.org/10.1016/j.carbon.2023.01.040.

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13

Kumar, Naveen, Malkeshkumar Patel, Thanh Tai Nguyen, et al. "2D‐SnS‐Embedded Schottky Device with Neurotransmitter‐Like Functionality Produced Using Proximity Vapor Transfer Method for Photonic Neurocomputing." Advanced Materials, November 10, 2024. http://dx.doi.org/10.1002/adma.202411420.

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AbstractNeuromorphic computing, which involves the creation of artificial synapses capable of mimicking biological brain activity, has intrigued researchers in the field of artificial intelligence (AI). To advance neuromorphic computing, a highly efficient 2D material‐based artificial synapse capable of performing logical and arithmetic operations must be developed. However, fabricating large, uniform films or high‐quality structures of 2D materials remains challenging because of their multistep and complex fabrication processes. In the present study, to produce large (Ø ≈ 3 in.), uniform, tra
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14

Mukherjee, Subhrajit, Debopriya Dutta, Michael Uzhansky, and Elad Koren. "Monolithic In2Se3–In2O3 heterojunction for multibit non-volatile memory and logic operations using optoelectronic inputs." npj 2D Materials and Applications 6, no. 1 (2022). http://dx.doi.org/10.1038/s41699-022-00309-5.

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AbstractStable ferroelectricity at room-temperature down to the monolayer limit, harnessed with strong sensitivity towards visible-to-near-infrared illumination in α-In2Se3, facilitates its potential as versatile building block for developing ultrathin multifunctional photonic integrated networks. Herein, we demonstrated a planar ferroelectric-semiconductor heterojunction (FeS-HJ) field-effect transistor (FET) fabricated out of α-In2Se3 and In2O3, where the ferroelectric-polarization state in α-In2Se3 is utilized to control the device characteristics. The robust in-plane (IP) polarization flip
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15

Bianchi, S., I. Muñoz-Martin, E. Covi, et al. "A self-adaptive hardware with resistive switching synapses for experience-based neurocomputing." Nature Communications 14, no. 1 (2023). http://dx.doi.org/10.1038/s41467-023-37097-5.

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AbstractNeurobiological systems continually interact with the surrounding environment to refine their behaviour toward the best possible reward. Achieving such learning by experience is one of the main challenges of artificial intelligence, but currently it is hindered by the lack of hardware capable of plastic adaptation. Here, we propose a bio-inspired recurrent neural network, mastered by a digital system on chip with resistive-switching synaptic arrays of memory devices, which exploits homeostatic Hebbian learning for improved efficiency. All the results are discussed experimentally and th
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16

Mahmoodi, M. R., M. Prezioso, and D. B. Strukov. "Versatile stochastic dot product circuits based on nonvolatile memories for high performance neurocomputing and neurooptimization." Nature Communications 10, no. 1 (2019). http://dx.doi.org/10.1038/s41467-019-13103-7.

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Abstract The key operation in stochastic neural networks, which have become the state-of-the-art approach for solving problems in machine learning, information theory, and statistics, is a stochastic dot-product. While there have been many demonstrations of dot-product circuits and, separately, of stochastic neurons, the efficient hardware implementation combining both functionalities is still missing. Here we report compact, fast, energy-efficient, and scalable stochastic dot-product circuits based on either passively integrated metal-oxide memristors or embedded floating-gate memories. The c
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17

Sengor, Cagatay N., Feridun Ay, and Cahit Perkgoz. "A deep learning approach for high-resolution and enhanced efficiency in photonic power dividers." Journal of Applied Physics 137, no. 12 (2025). https://doi.org/10.1063/5.0255080.

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With recent developments, nanophotonics has emerged as a field of intense interest offering improvements in the performance of photonic integrated circuits (PICs). By significantly increasing data rates, transmission speed, and bandwidth and reducing both transmission loss and energy consumption, nanophotonic technologies present significant advantages that could redefine the efficiency and capabilities of PICs. In this study, the design of photonic crystal power dividers is addressed using a two-stage deep learning strategy with Deep Convolutional Generative Adversarial Networks (DCGANs). The
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18

Prof.Shraddha, panbude. "Health Informatics - An International Journal (HIIJ)." July 16, 2020. https://doi.org/10.5281/zenodo.3947656.

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Health Informatics: An International Journal is a Quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care. The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
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