Inspired by the extensive signal processing capabilities of the human nervous system, neuromorphic artificial sensory systems have emerged as a pivotal technology in advancing brain-like computing for applications in ...
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Inspired by the extensive signal processing capabilities of the human nervous system, neuromorphic artificial sensory systems have emerged as a pivotal technology in advancing brain-like computing for applications in humanoid robotics, prosthetics, and wearable technologies. These systems mimic the functionalities of the central and peripheral nervous systems through the integration of sensory synaptic devices and neural network algorithms, enabling external stimuli to be converted into actionable electrical signals. This review delves into the intricate relationship between synaptic device technologies and neural network processing algorithms, highlighting their mutual influence on artificial intelligence capabilities. This study explores the latest advancements in artificial synaptic properties triggered by various stimuli, including optical, auditory, mechanical, and chemical inputs, and their subsequent processing through artificialneuralnetworks for applications in image recognition and multimodal pattern recognition. The discussion extends to the emulation of biological perception via artificial synapses and concludes with future perspectives and challenges in neuromorphic system development, emphasizing the need for a deeper understanding of neural network processing to innovate and refine these complex systems.
Deep learning, a profound advancement in artificial intelligence, has demonstrated remarkable achievements, particularly in imageprocessing. The rapid evolution of deep learning in architecture, training methods, and...
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Deep learning, a profound advancement in artificial intelligence, has demonstrated remarkable achievements, particularly in imageprocessing. The rapid evolution of deep learning in architecture, training methods, and specifications has driven the expansion of imageprocessing techniques. However, the increasing complexity of model structures challenges the effectiveness of the back propagation algorithm, and issues like the accumulation of unlabeled training data and class imbalances hinder deep learning performance. To address these challenges, there's a growing need for innovative deep models and cutting-edge computing paradigms to enable more sophisticated image content analysis. In this study, we conduct a comprehensive examination of four deep learning models utilizing Convolutional neuralnetworks (CNNs), clarifying their theoretical foundations within the imageprocessing context, opening the door for further research. CNNs are notably essential for imageprocessing due to their ability to handle complex images effectively.
The electrocardiogram signal of the heart is used to monitor the health status and function of the human heart and to a doctor in diagnosing the type of disease. For this purpose, first, the scalogram of the different...
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In the exploration of robot vision systems based on artificialneuralnetworks, the research mainly focuses on their applications in 3D information recognition and processing. By simulating the processing of the human...
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The utilization of pattern recognition is on the rise extensively in information systems. The convergence of progress in imageprocessing and the accessibility of open-source libraries enables the implementation of in...
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ISBN:
(纸本)9783031734199;9783031734205
The utilization of pattern recognition is on the rise extensively in information systems. The convergence of progress in imageprocessing and the accessibility of open-source libraries enables the implementation of innovative solutions for diverse practical problems. One notable challenge pertains to automatically processing responses in mass large-scale exams. This paper introduces a developed system tailored for recognizing such exam results, showcasing its capacity to deliver dependable, effective, and impartial assessments. This system can be configured on almost any type of form. Its use also allows you to abandon the expensive and difficult to use OMR scanners. To increase productivity of system we propose to use the multicore/multithreading property of modern processors to parallelize processes within a single workstation. As a result of experiments, itwas found that the transition to multi-threaded recognition can increase productivity up to 3.5 times in comparison with single-threaded. To reduce the physical size of exam cards, it is proposed to fill in the answers with handwritten symbols instead of filling in the circles. Multilayer and convolutional neuralnetworks were used as a recognition module. A comparative evaluation of the dependence of recognition results on the architecture of neuralnetworks and the feature extraction algorithm was carried out.
Deep neuralnetworks (DNNs), particularly convolutional neuralnetworks (CNNs), have garnered significant attention in recent years for addressing a wide range of challenges in imageprocessing and computer vision. Ne...
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ISBN:
(纸本)9783031755422;9783031755439
Deep neuralnetworks (DNNs), particularly convolutional neuralnetworks (CNNs), have garnered significant attention in recent years for addressing a wide range of challenges in imageprocessing and computer vision. neural architecture search (NAS) has emerged as a crucial field aiming to automate the design and configuration of CNN models. In this paper, we propose a novel strategy to speed up the performance estimation of neural architectures by gradually increasing the size of the training set used for evaluation as the search progresses. We evaluate this approach using the CGP-NASV2 model, a multi-objective NAS method, on the CIFAR-100 dataset. Experimental results demonstrate a notable acceleration in the search process, achieving a speedup of 4.6 times compared to the baseline. Despite using limited data in the early stages, our proposed method effectively guides the search towards competitive architectures. This study highlights the efficacy of leveraging lower-fidelity estimates in NAS and paves the way for further research into accelerating the design of efficient CNN architectures.
The development of conversational artificial intelligence (AI) is examined in this research paper, with a focus on how speech and image recognition technologies can be combined to transform and interact with systems. ...
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Fruit detection refers to a method within imageprocessing and computer vision that focuses on automatically recognizing and distinguishing different types of fruits using advanced algorithms and techniques. Fruits ar...
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This work investigates the potential of neuralnetworks in the processing of artificial intelligence, especially in smart systems, and explores their applications regarding advancing automation, user experience, and r...
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neural Architecture Search (NAS) aims to automate the design process of Deep neuralnetworks (DNN) without requiring profound domain knowledge. The Deep Genetic Algorithm (DeepGA) was proposed to find the architecture...
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