36   Artículos

 
en línea
Roman Rybka, Yury Davydov, Danila Vlasov, Alexey Serenko, Alexander Sboev and Vyacheslav Ilyin    
Developing a spiking neural network architecture that could prospectively be trained on energy-efficient neuromorphic hardware to solve various data analysis tasks requires satisfying the limitations of prospective analog or digital hardware, i.e., local... ver más
Revista: Big Data and Cognitive Computing    Formato: Electrónico

 
en línea
Vladislav Kholkin, Olga Druzhina, Valerii Vatnik, Maksim Kulagin, Timur Karimov and Denis Butusov    
For the last two decades, artificial neural networks (ANNs) of the third generation, also known as spiking neural networks (SNN), have remained a subject of interest for researchers. A significant difficulty for the practical application of SNNs is their... ver más
Revista: Big Data and Cognitive Computing    Formato: Electrónico

 
en línea
Sergey Shchanikov, Ilya Bordanov, Alexey Kucherik, Evgeny Gryaznov and Alexey Mikhaylov    
Arrays of memristive devices coupled with photosensors can be used for capturing and processing visual information, thereby realizing the concept of ?in-sensor computing?. This is a promising concept associated with the development of compact and low-pow... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
John S. Venker, Luke Vincent and Jeff Dix    
A Spiking Neural Network (SNN) is realized within a 65 nm CMOS process to demonstrate the feasibility of its constituent cells. Analog hardware neural networks have shown improved energy efficiency in edge computing for real-time-inference applications, ... ver más
Revista: Journal of Low Power Electronics and Applications    Formato: Electrónico

 
en línea
Farzad Nikfam, Raffaele Casaburi, Alberto Marchisio, Maurizio Martina and Muhammad Shafique    
Revista: Information    Formato: Electrónico

 
en línea
Arash Khajooei Nejad, Mohammad (Behdad) Jamshidi and Shahriar B. Shokouhi    
This paper introduces Tensor-Organized Memory (TOM), a novel neuromorphic architecture inspired by the human brain?s structural and functional principles. Utilizing spike-timing-dependent plasticity (STDP) and Hebbian rules, TOM exhibits cognitive behavi... ver más
Revista: Computers    Formato: Electrónico

 
en línea
Alexander Sboev, Roman Rybka, Dmitry Kunitsyn, Alexey Serenko, Vyacheslav Ilyin and Vadim Putrolaynen    
In this paper, we demonstrate that fixed-weight layers generated from random distribution or logistic functions can effectively extract significant features from input data, resulting in high accuracy on a variety of tasks, including Fisher?s Iris, Wisco... ver más
Revista: Big Data and Cognitive Computing    Formato: Electrónico

 
en línea
Krishnamurthy V. Vemuru    
Edge detectors are widely used in computer vision applications to locate sharp intensity changes and find object boundaries in an image. The Canny edge detector is the most popular edge detector, and it uses a multi-step process, including the first step... ver más
Revista: Future Internet    Formato: Electrónico

 
en línea
Huynh Cong Viet Ngu and Keon Myung Lee    
Due to energy efficiency, spiking neural networks (SNNs) have gradually been considered as an alternative to convolutional neural networks (CNNs) in various machine learning tasks. In image recognition tasks, leveraging the superior capability of CNNs, t... ver más
Revista: Applied Sciences    Formato: Electrónico

 
en línea
Leonardo Lucio Custode, Hyunho Mo, Andrea Ferigo and Giovanni Iacca    
Remaining useful life (RUL) prediction is a key enabler for predictive maintenance. In fact, the possibility of accurately and reliably predicting the RUL of a system, based on a record of its monitoring data, can allow users to schedule maintenance inte... ver más
Revista: Algorithms    Formato: Electrónico

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