Human visual neurons rely on event-driven, energy-efficient spikes for communication, while
silicon image sensors do not. The energy-budget mismatch between biological systems and …

Student achievement prediction is one of the most important research directions in
educational data mining. Student achievement directly reflects students' course mastery and …

Integrate-and-fire (IAF) and leaky integrate-and-fire (LIF) models are the popular models for
spiking neurons and spiking neuron networks (SNN). They lack the dynamic properties of …

To enhance the efficiency of machine vision system, physical hardware capable of sensing
and encoding is essential. However, sensing and encoding color information has been …

Very large scale integration (VLSI)‐based neuromorphic systems have been evolving
quickly in recent years. These systems have been used in complex cognitive tasks such as …

Recurrent neural networks (RNN) have been extensively used to address the problem of
Bayesian inference of a hidden Markov model (HMM). However, such artificial neural …

Effectively interfacing synthetic systems with a tangible world using a growing number and
variety of sensors under the constraints of precision, resilience, and adaptability is …

In contemporary devices, the number and diversity of sensors is increasing, thus, requiring
both efficient and robust interfacing to the sensors. Implementing the interfacing systems in …

Spiking neural networks (SNNs) have shown high efficiency in information processing. Time-
to-first-spike (TTFS) coding, which encodes the information to the times of first spikes, has …

Efficient interfacing with an expanding variety of sensors is necessary for sensor systems to
act as the interface between an artificial system and the real world. A sensor system's …