This study presents dedicated hardware for iris localization that can be used as a coprocessor in the development of real-time and low-cost embedded iris biometric systems. Though the hardware architecture is described for ...

In many integrated radio frequency (RF) transceivers, the phase-locked loop (PLL) serves as a frequency synthesizer. This work goes to test various different phase/frequency detector blocks with a standard charge pump and ...

This study gives an in-depth analysis of the architectures utilized in the analog-to-digital conversion process. The paper encompasses the design, performance, and suitability of the binary-weighted (charge distribution), ...

This study proposes a novel 6-transistor bootstrapped switch with minimized sampling and holding error obtained through sampling window error analysis for SAR ADC design. The proposed switch design strategically mitigates ...

This research paper introduces three techniques to reduce kickback noise in the Strong Arm Latch Comparator (SAL). The first technique focuses on utilizing high clock power and generating two clocks with different duty ...

This paper proposes an improved deep learning-based maximum power point tracking (MPPT) in solar photovoltaic cells considering various time series-based environmental inputs. Generally, artificial neural network-based ...

In the lower VLSI process technologies, to design the low-power VLSI circuits selection of suitable logic style becomes important to minimize the chip’s power to meet the power density need with minimum sacrifice in the ...

Recently, there has been a sharp rise in demand for hardware implementations because of the improved accuracy of Convolutional Neural Networks (CNN) on a wide range of classification and recognition applications. To achieve ...

The k-means clustering algorithm is a method that is frequently utilized for the purpose of grouping data points considering their similarity. Within the scope of this research, we investigate the viability of using a ...

Convolutional Neural Network (CNN) models have demonstrated significant benefits in the realm of computer vision and applications related to image processing. Optimizing hyperparameters in CNN models is crucial to ensuring ...