Recent Papers

Analog-to-digital converters (ADCs) are essential in contemporary electronic systems, facilitating the transition from analogy signals to digital processing. Conventional ADC designs frequently encounter difficulties with accuracy, energy economy, and flexibility in diverse situations. This study discusses the design and optimization of an adaptive ADC combined with machine learning techniques to improve accuracy, power consumption, and speed. The proposed adaptive ADC utilizes machine learning methods to dynamically modify essential parameters, including sampling rate, quantization levels, and resolution, according on input signal attributes. This versatility guarantees excellent performance under diverse signal situations, rendering it especially appropriates for applications in Internet of Things (IoT) devices, biomedical monitoring, and high-speed communication systems. The design methodology comprises two primary elements: the hardware architecture of the ADC and the execution of machine learning models. A low-power sequential approximation register (SAR) ADC architecture is selected for its intrinsic energy economy. The offline-trained machine learning algorithm utilizes extensive datasets of signal patterns to forecast optimal ADC configurations in real time. Reinforcement learning (RL) and decision trees are examined for model optimization. The learning model functions concurrently with the ADC, perpetually enhancing its predictions via input from performance indicators, such as signal-to-noise ratio (SNR) and effective number of bits (ENOB). Simulation findings indicate that the proposed adaptive ADC exhibits substantial enhancements compared to traditional fixed-parameter ADCs. A 20% decrease in power usage and a 15% improvement in ENOB are seen, along by increased adaptation to swiftly varying signal conditions. Moreover, hardware implementation specifics and real-time performance assessments demonstrate that the system functions effectively throughout a broad spectrum of frequencies and input signal amplitudes. This study emphasizes the possibility of integrating conventional ADC architectures with sophisticated machine learning techniques to develop more intelligent and efficient converters. The suggested method enhances ADC performance and creates opportunities for advanced data collection systems in next-generation electronics. Future study may concentrate on enhancing the machine learning model and investigating hardware-efficient learning methodologies for on-chip integration.

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