The proliferation of drones, or unmanned aerial vehicles (UAVs), has raised
significant safety concerns due to their potential misuse in activities such as
espionage, smuggling, and infrastructure disruption. This paper addresses the
critical need for effective drone detection and classification systems that
operate independently of UAV cooperation. We evaluate various convolutional
neural networks (CNNs) for their ability to detect and classify drones using
spectrogram data derived from consecutive Fourier transforms of signal
components. The focus is on model robustness in low signal-to-noise ratio (SNR)
environments, which is critical for real-world applications. A comprehensive
dataset is provided to support future model development. In addition, we
demonstrate a low-cost drone detection system using a standard computer,
software-defined radio (SDR) and antenna, validated through real-world field
testing. On our development dataset, all models consistently achieved an
average balanced classification accuracy of >= 85% at SNR > -12dB. In the field
test, these models achieved an average balance accuracy of > 80%, depending on
transmitter distance and antenna direction. Our contributions include: a
publicly available dataset for model development, a comparative analysis of CNN
for drone detection under low SNR conditions, and the deployment and field
evaluation of a practical, low-cost detection system.

本研究提供了用于模型开发的公开数据集，并对低信噪比条件下的卷积神经网络进行了无人机检测的比较分析，同时部署和现场评估了一个实用、低成本的无人机检测系统。

低信噪比环境下稳健低成本的无人机检测与分类

Robust Low-Cost Drone Detection and Classification in Low SNR  Environments

The accurate segmentation of medical images is crucial for diagnosing and
treating diseases. Recent studies demonstrate that vision transformer-based
methods have significantly improved performance in medical image segmentation,
primarily due to their superior ability to establish global relationships among
features and adaptability to various inputs. However, these methods struggle
with the low signal-to-noise ratio inherent to medical images. Additionally,
the effective utilization of channel and spatial information, which are
essential for medical image segmentation, is limited by the representation
capacity of self-attention. To address these challenges, we propose a
multi-dimension transformer with attention-based filtering (MDT-AF), which
redesigns the patch embedding and self-attention mechanism for medical image
segmentation. MDT-AF incorporates an attention-based feature filtering
mechanism into the patch embedding blocks and employs a coarse-to-fine process
to mitigate the impact of low signal-to-noise ratio. To better capture complex
structures in medical images, MDT-AF extends the self-attention mechanism to
incorporate spatial and channel dimensions, enriching feature representation.
Moreover, we introduce an interaction mechanism to improve the feature
aggregation between spatial and channel dimensions. Experimental results on
three public medical image segmentation benchmarks show that MDT-AF achieves
state-of-the-art (SOTA) performance.

提出了一种面向医学图像分割的多维注意力变换器 (MDT-AF) 方法，通过重新设计补丁嵌入和自注意机制来解决医学图像分割中存在的低信噪比和特征表示容量有限的问题，并取得了当前最先进的性能。