相关论文: A Highly Linear and Flexible FPGA-Based Time-to-Di…

Large language models (LLMs) are both storage-intensive and computation-intensive, posing significant challenges when deployed on resource-constrained hardware. As linear layers in LLMs are mainly resource consuming parts, this paper…

The importance of simulating pinning arrays in superconducting samples for the increase of critical currents has been increasing in the last few years. Since the Time Dependent Ginzburg Landau (TDGL) can be more accurate than alternative…

In this paper, we propose a novel framework for non-stationary time-series analysis that replaces conventional correlation-based statistics with direct estimation of statistical dependence in the normalized joint density of input and target…

Convolutional Neural Networks (CNNs) are fundamental to deep learning, driving applications across various domains. However, their growing complexity has significantly increased computational demands, necessitating efficient hardware…

This paper presents a novel approach for the identification of linear time-periodic (LTP) systems in continuous time. This method is based on harmonic modeling and consists in converting any LTP system into an equivalent LTI system with…

In this paper, we present a basic model for time-interleaved photonic analog-to-digital converter (TIPADC) and analyze its linear, nonlinear, and noise performance. The basic operation mechanism of TIPADC is illustrated and the linear…

This paper presents a discrete-time analog frontend for an analog-to-digital (ADC) based equalizers. The frontend uses a discrete-time linear equalizer (DTLE) and ultralow-power 4-bit time-interleaved charge-steering flash ADC. The DTLE…

In the context of the upgrade of the Large Hadron Collider at CERN for high-luminosity operation, the particle detectors have to cope with much higher data rates and therefore need to upgrade their data acquisition systems. This upgrade is…

Digital predistortion (DPD) is crucial for linearizing radio frequency (RF) power amplifiers (PAs), improving signal integrity and efficiency in wireless systems. Neural network (NN)-based DPD methods surpass traditional polynomial models…

This paper proposes a high-precision time measurement method based on digital frequency-domain phase-fitting (DFPF) by using the digitized nuclear pulses. The averaging effect inherent in the frequency-domain cross-correlation and…

The aim of this study is to design and evaluate a simple free running Analog-Digital Converter (ADC) based on the Field Programmable Gate Array (FPGA) device to accomplish the energy and position readout of the silicon photomultiplier…

Many real world graphs contain time domain information. Temporal Graph Neural Networks capture temporal information as well as structural and contextual information in the generated dynamic node embeddings. Researchers have shown that these…

This paper describes a new all-digital technique for calibration of the mismatches in time-interleaved analog-to-digital converters (TIADCs) to reduce the circuit area. The proposed technique gives the first order approximation of the gain…

3D depth sensors using single-photon avalanche diodes (SPADs) are becoming increasingly common in applications such as autonomous navigation and object detection. Recent designs implement on-chip histogramming time-to-digital converters…

In this paper, we consider the issue of throughput and packet drop rate (PDR) optimization as two performance metrics for delay sensitive applications in network coded time division duplex (TDD) satellite systems with large round trip times…

We present a method for the evaluation, at the first level of trigger, of logical conditions with high time resolution, using the digitized times of fast signals delivered in the detectors of high rate experiments. We describe a…

The scope of data-driven fault diagnosis models is greatly extended through deep learning (DL). However, the classical convolution and recurrent structure have their defects in computational efficiency and feature representation, while the…

Time Series Classification (TSC) is an important problem with numerous applications in science and technology. Dissimilarity-based approaches, such as Dynamic Time Warping (DTW), are classical methods for distinguishing time series when…

Despite the intensive recent research on wireless single-channel full-duplex communications, relatively little is known about the transceiver chain nonidealities of full-duplex devices. In this paper, the effect of nonlinear distortion…

While there is a large body of research on efficient processing of deep neural networks (DNNs), ultra-low-latency realization of these models for applications with stringent, sub-microsecond latency requirements continues to be an…