arXiv subjects

Chimezie Eguzo

Publications and source records attributed to Chimezie Eguzo.

5 recordsLinked to original sources

arXiv2026

Hardware-efficient neural networks for FPGA-based radio triggering of extensive air showers

We present a hardware-efficient hybrid trigger for FPGA-based radio detection of extensive air showers. The hybrid design consists of a lightweight denoiser that cleans raw ADC traces and a compact classifier that operates on the denoised output, enabling robust near-threshold pulse detection in high-interference environments. Both neural networks are trained quantization-aware. Signals are generated from detector-folded CoREAS/CORSIKA simulations and embedded into measured noise to form a realistic benchmark. The trigger reaches an AUC of 0.992 while fitting comfortably within the resource budget of a Zynq-7000 Z-7020, with microsecond-scale latency and sub-watt power consumption. RTL validation confirms agreement between the fixed-point hardware and the quantized software model, demonstrating that neural denoising combined with classification provides reliable, low-cost radio triggering in noisy environments.

astro-ph.IM↗

arXiv2026

A dual-polarization whitened-template trigger for real-time radio detection of extensive air showers

Autonomous radio stations require a first-stage trigger that rejects measured background while preserving weak extensive-air-shower pulses under tight hardware constraints. We present a dual-polarization trigger based on a 16-sample whitened pulse template evaluated continuously on the orthogonal north-south and east-west channels of an Auger Engineering Radio Array station. The template is derived from detector-folded air-shower pulses and whitened using the covariance of measured background. Responses from both polarizations are combined within a fixed timing radius and compared with a calibrated threshold. On an independent test set, the trigger reaches an efficiency of 0.97, with a 95 percent confidence interval of 0.96 to 0.98, at a frame-equivalent candidate rate of 3.39 kHz. At the same operating point, an unwhitened dual-polarization template reaches 0.41 efficiency and an amplitude trigger reaches 0.16. Efficiency remains at least 0.94 in every populated signal-to-noise bin. Replay of 159.29 ms of continuous measured background yields a score-cluster rate of 6.64 kHz, below the 71.4 kHz capacity of the downstream module. The FPGA implementation accepts one dual-polarization sample per clock, closes timing at 200 MHz, and uses 2377 lookup tables, 5330 registers, and 32 digital signal-processing blocks. Register-transfer-level simulation agrees bit for bit with the reference implementation for 400 traces. These results show that background covariance, pulse morphology, and dual-polarization consistency can be combined in a compact real-time radio trigger.

astro-ph.IM↗

arXiv2026

Hybrid neural denoising for resource-efficient near- and sub-threshold radio triggering of extensive air showers

Autonomous radio self-triggering for extensive air showers must reject variable radio-frequency interference while preserving sensitivity to weak pulses and remaining compatible with station-level edge hardware. This work presents a hybrid neural trigger in which waveform recovery and signal classification are treated as a single deployment-constrained problem. A compact convolutional denoiser maps a noisy single-channel trace to a cleaned estimate of the air-shower pulse, which is then evaluated by a compact classifier. The method is tested with measured high-interference background traces and detector-folded air-shower pulses from the Pierre Auger Offline simulation chain, with signals concentrated in the near- and sub-threshold regime. Model selection and deployment are linked through hyperparameter optimisation, quantisation-aware training, fixed-point quantisation, hls4ml firmware export, high-level synthesis, and register-transfer-level validation. The denoiser alone turns a simple peak-envelope decision into an efficient weak-pulse trigger, showing that the cleaned waveform carries trigger-relevant information beyond a final classifier score. In the full denoiser-classifier chain, the hybrid trigger improves signal-background separation and efficiency at fixed false-positive rates: at a false-positive rate of 10^-4, it retains about 41% of held-out signal traces in the weak-signal benchmark, while the classical peak-envelope trigger retains none. The cleaned waveform preserves timing and peak-amplitude structure for station-level diagnostics, feature extraction, and selective readout. The firmware meets timing on representative FPGA targets with microsecond-scale latency and compact arithmetic-resource demand. These results establish hybrid neural denoising as a practical route toward radio-only triggering for weak and inclined air-shower signals in noisy environments.

astro-ph.IM↗

arXiv2026

Quantization Effects of Artificial Neural Networks for Embedded Edge-Computing Applications

This paper examines the use of Quantized Neural Networks (QNNs) for two resource-constrained scientific applications: automated calibration of semi- conductor quantum bits (qubits) and scientific particle detectors. We evaluate the trade-offs between Post-Training Quantization (PTQ), Quantization-Aware Train- ing (QAT), and ultra-low-bit Binary Neural Networks (BNNs) with respect to la- tency and resource usage. Our results demonstrate that PTQ and QAT are easily implementable solutions achieving a four-fold reduction in memory usage for U- shaped CNN (U-Net) architectures, whereas BNNs are better suitable for use cases with challenging latency requirements. For the training of non-differentiable custom BNNs , we propose a novel, hardware-constrained learning approach using Genetic Algorithms (GAs). We showcase a Look-Up Table (LUT)-based BNN architecture suitable for direct conversion to Very High-Speed Integrated Circuit Hardware De- scription Language (VHDL) via the HCL4BNN framework. This method achieves nanosecond-scale inference latencies at 10 ns to 15 ns without requiring specialized Digital Signal Processor (DSP) or Block RAM (BRAM) resources.

cs.NE↗

arXiv2025

On Noise-Sensitive Automatic Tuning of Gate-Defined Sensor Dots

In gate-defined quantum dot systems, the conductance change of electrostatically coupled sensor dots allows the observation of the quantum dots' charge and spin states. Therefore, the sensor dot must be optimally sensitive to changes in its electrostatic environment. A series of conductance measurements varying the two sensor-dot-forming barrier gate voltages serve to tune the dot into a corresponding operating regime. In this paper, we analyze the noise characteristics of the measured data and define a criterion to identify continuous regions with a sufficient signal-gradient-to-noise ratio. Hence, accurate noise estimation is required when identifying the optimal operating regime. Therefore, we evaluate several existing noise estimators, modify them for 1D data, optimize their parameters, and analyze their quality based on simulated data. The estimator of Chen et al. turns out to be best suited for our application concerning minimally scattering results. Furthermore, using this estimator in an algorithm for flank-of-interest classification in measured data shows the relevance and applicability of our approach.

cond-mat.mes-hall↗