Development of a front end ASIC for Dark Matter directional detection with MIMAC

A front end ASIC (BiCMOS-SiGe 0.35 \mum) has been developed within the framework of the MIMAC detector project, which aims at directional detection of non-baryonic Dark Matter. This search strategy requires 3D reconstruction of low energy (a few keV) tracks with a gaseous \muTPC. The development of this front end ASIC is a key point of the project, allowing the 3D track reconstruction. Each ASIC monitors 16 strips of pixels with charge preamplifiers and their time over threshold is provided in real time by current discriminators via two serializing LVDS links working at 320 MHz. The charge is summed over the 16 strips and provided via a shaper. These specifications have been chosen in order to build an auto triggered electronics. An acquisition board and the related software were developed in order to validate this methodology on a prototype chamber. The prototype detector presents an anode where 2 x 96 strips of pixels are monitored.Comment: 12 pages, 10 figure

Prediction-Based Spectrum Sensing Framework for Cognitive Radio

This paper presents a hardware-software deep learning architecture for prediction-based spectrum sensing in Cognitive Radio (CR) applications. A convolutional neural network-based predictor for spectrum occupancy was trained using the band power from I/Q samples acquired by a softwaredefined radio (SDR). Additionally, a second neural engine was trained for radio frequency (RF) frame detection based on spectrograms. We implemented a transfer-learning solution using a You-Only-LookOnce version 8 nano model with a synthetic dataset comprising thousands of wireless signals, including Wi-Fi, Bluetooth, and collision frames. Once trained, the two neural networks were transferred to a Raspberry Pi 5 – an affordable single-board computer – connected to two (one for Rx, one for Tx) ADALM-PLUTO SDR systems for benchmarking using over-the-air signals in the 2.4 GHz band. Together with our methodology and experimental results, the paper also presents an overview of current spectrum prediction proposals and RF frame detectors. Remarkably, to the best of our knowledge, this proposed framework is the first approach towards an Internet of Things-suitable implementation of prediction-based spectrum sensing for CR applications