4 research outputs found
Recent Advances in Deep Learning Techniques for Face Recognition
In recent years, researchers have proposed many deep learning (DL) methods
for various tasks, and particularly face recognition (FR) made an enormous leap
using these techniques. Deep FR systems benefit from the hierarchical
architecture of the DL methods to learn discriminative face representation.
Therefore, DL techniques significantly improve state-of-the-art performance on
FR systems and encourage diverse and efficient real-world applications. In this
paper, we present a comprehensive analysis of various FR systems that leverage
the different types of DL techniques, and for the study, we summarize 168
recent contributions from this area. We discuss the papers related to different
algorithms, architectures, loss functions, activation functions, datasets,
challenges, improvement ideas, current and future trends of DL-based FR
systems. We provide a detailed discussion of various DL methods to understand
the current state-of-the-art, and then we discuss various activation and loss
functions for the methods. Additionally, we summarize different datasets used
widely for FR tasks and discuss challenges related to illumination, expression,
pose variations, and occlusion. Finally, we discuss improvement ideas, current
and future trends of FR tasks.Comment: 32 pages and citation: M. T. H. Fuad et al., "Recent Advances in Deep
Learning Techniques for Face Recognition," in IEEE Access, vol. 9, pp.
99112-99142, 2021, doi: 10.1109/ACCESS.2021.309613
Modified Ring-Oscillator Physical Unclonable Function (RO-PUF) based PRBS Generation as a Device Signature in Distributed Brain Implants
In this paper, we propose and evaluate a method of generating low-cost device
signatures for distributed wireless brain implants, using a Pseudo-Random
Binary Sequence (PRBS) Generator that utilizes a modified Ring-Oscillator-based
Physical Unclonable Function (RO-PUF). The modified RO-PUF's output is used as
a seed for the PRBS generator, which creates a multi-bit output that can be
mapped to a time-slot when the implant is allowed to communicate with the
external world using duty-cycled time-division multiplexing. A 9-bit PRBS
generator is shown in hardware (with a TSMC 65 nm test chip implementation)
that demonstrates < 100 nW Power consumption in measurement (72% lower power
and 78% lower area than a traditional 9-bit RO-PUF implementation), which
supports 26 implants with the probability of time-slot collision being < 50%.
This potentially creates a pathway for low-cost device signature generation for
highly resource-constrained scenarios such as wireless, distributed neural
implants.Comment: 5 pages, 5 Figure
Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review
Brain–Computer Interfaces (BCIs) have become increasingly popular in recent years due to their potential applications in diverse fields, ranging from the medical sector (people with motor and/or communication disabilities), cognitive training, gaming, and Augmented Reality/Virtual Reality (AR/VR), among other areas. BCI which can decode and recognize neural signals involved in speech and handwriting has the potential to greatly assist individuals with severe motor impairments in their communication and interaction needs. Innovative and cutting-edge advancements in this field have the potential to develop a highly accessible and interactive communication platform for these people. The purpose of this review paper is to analyze the existing research on handwriting and speech recognition from neural signals. So that the new researchers who are interested in this field can gain thorough knowledge in this research area. The current research on neural signal-based recognition of handwriting and speech has been categorized into two main types: invasive and non-invasive studies. We have examined the latest papers on converting speech-activity-based neural signals and handwriting-activity-based neural signals into text data. The methods of extracting data from the brain have also been discussed in this review. Additionally, this review includes a brief summary of the datasets, preprocessing techniques, and methods used in these studies, which were published between 2014 and 2022. This review aims to provide a comprehensive summary of the methodologies used in the current literature on neural signal-based recognition of handwriting and speech. In essence, this article is intended to serve as a valuable resource for future researchers who wish to investigate neural signal-based machine-learning methods in their work