Fuzzified contrast enhancement for nearly invisible images
R Kumar, AK Bhandari - … on Circuits and Systems for Video …, 2021 - ieeexplore.ieee.org
Image enhancement is a basic requirement for any computer vision application for further
processing of an image. A common limitation with most of the existing methods, when …
processing of an image. A common limitation with most of the existing methods, when …
Low-power detection and classification for in-sensor predictive maintenance based on vibration monitoring
In this work, a new custom design of an anomaly detection and classification system is
proposed. It is composed of a convolutional Auto-Encoder (AE) hardware design to perform …
proposed. It is composed of a convolutional Auto-Encoder (AE) hardware design to perform …
Digital watermarking of ecg data for secure wireless commuication
S Kaur, R Singhal, O Farooq… - … conference on recent …, 2010 - ieeexplore.ieee.org
Use of wireless technology has made the bio-medical data vulnerable to attacks like
tampering, hacking etc. This paper proposes the use of digital watermarking to increase the …
tampering, hacking etc. This paper proposes the use of digital watermarking to increase the …
Low power tiny binary neural network with improved accuracy in human recognition systems
Human Activity Recognition requires very high accuracy to be effectively employed into
practical applications, ranging from elderly care to microsurgical devices. The highest …
practical applications, ranging from elderly care to microsurgical devices. The highest …
Low-power HWAccelerator for AI edge-computing in human activity recognition systems
In this paper, an energy efficient HW accelerator for AI edge-computing in Human Activity
Recognition is proposed. The system processes samples from a tri-axial accelerometer and …
Recognition is proposed. The system processes samples from a tri-axial accelerometer and …
A partially binarized hybrid neural network system for low-power and resource constrained human activity recognition
A custom Human Activity Recognition system is presented based on the resource-
constrained Hardware (HW) implementation of a new partially binarized Hybrid Neural …
constrained Hardware (HW) implementation of a new partially binarized Hybrid Neural …
Quantized ID-CNN for a low-power PDM-to-PCM conversion in TinyML KWS applications
P Vitolo, GD Licciardo, AC Amendola… - 2022 IEEE 4th …, 2022 - ieeexplore.ieee.org
This paper proposes a novel low-power HW accelerator for audio PDM-to-PCM conversion
based on artificial neural network. The system processes samples from a digital MEMS …
based on artificial neural network. The system processes samples from a digital MEMS …
A hardware architecture for svpwm digital control with variable carrier frequency and amplitude
L Di Benedetto, A Donisi, GD Licciardo… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
A novel digital controller for the space-vector pulsewidth modulation (SVPWM) algorithm
used in three-phase power inverters is shown. From an analysis of the vector representation …
used in three-phase power inverters is shown. From an analysis of the vector representation …
Low-power anomaly detection and classification system based on a partially binarized autoencoder for in-sensor computing
This work proposes a new ultra low-power fault detection system, suitable for extreme edge
or in-sensor computing. The system is composed of a hybrid HW/SW architecture: a …
or in-sensor computing. The system is composed of a hybrid HW/SW architecture: a …
A resource constrained neural network for the design of embedded human posture recognition systems
A custom HW design of a Fully Convolutional Neural Network (FCN) is presented in this
paper to implement an embeddable Human Posture Recognition (HPR) system capable of …
paper to implement an embeddable Human Posture Recognition (HPR) system capable of …