This paper proposes a convolutional neural network (CNN)-based signal detection scheme using image encoding techniques for burst signals in wireless networks. The conventional signal detection approach based on energy measurement performs poorly when detecting burst signals owing to the short signal length and relatively long sensing duration. To detect the presence of a burst signal, the proposed scheme encodes the received time-series signal into an image that is further fed to a CNN model. For image encoding techniques, recurrence plot algorithms are adopted in the proposed scheme with a CNN. In particular, the proposed scheme achieves the correct detection probability of 99% even in the presence of a short burst signal at SNR= -10dB.

Even with a plenty amount of normal samples, anomaly detection has been considered as a challenging machine learning task due to its one-class nature, i. e., the lack of anomalous samples in training time. It is only recently that a few-shot regime of anomaly detection became feasible in this regard, e. g., with a help from large vision-language pre-trained models such as CLIP, despite its wide applicability. In this paper, we explore the potential of large text-to-image generative models in performing few-shot industrial anomaly detection. Specifically, recent text-to-image models have shown unprecedented ability to generalize from few images to extract their common and unique concepts, and even encode them into a textual token to “personalize” the model: so-called textual inversion. Here, we question whether this personalization is specific enough to discriminate the given images from their potential anomalies, which are often, e. g., open-ended, local, and hard-to-detect. We observe that standard textual inversion exhibits a weaker understanding in localized details within objects, which is not enough for detecting industrial anomalies accurately. Thus, we explore the utilization of model personalization to address anomaly detection and propose Anomaly Detection via Personalization (ADP). ADP enables extracting fine-grained local details shared in the images with simple-yet an effective regularization scheme from the zero-shot transferability of CLIP. We also propose a self-tuning scheme to further optimize the performance of our detection pipeline, leveraging synthetic data generated from the personalized generative model. Our experiments show that the proposed inversion scheme could achieve state-of-the-art results on two industrial anomaly benchmarks, MVTec-AD and VisA, in the regime of few normal samples.

This paper proposes a new test method of detecting the presence of impulsive noise based on a complementary cumulative density function (CCDF). Impulsive noise severely degrades performance of communication systems and the conventional Kolmogorov–Smirnov (K–S) test may not perform well, because the test does not consider the characteristics of impulsive noise. In order to detect the presence of impulsive noise reliably, the CCDF of measurement samples is analyzed and compared with the CCDF of additive white Gaussian noise to find the difference between those CCDFs. Due to the nature of heavy-tails in impulsive noise, only the maximum difference may not be sufficient for the accurate detection of impulsive noise. Therefore, the proposed method applies the test hypothesis using the weighted sum of all the differences between those CCDFs. Simulation results justify that the proposed test is more robust and provides lower miss detection probability than the K–S test in the presence of impulsive noise.

In electronic warfare, radar emitter classification plays a crucial role in identifying threats in complex radar signal environments. Traditionally, this has been achieved using heuristic-based methods and handcrafted features. However, these methods struggle to adapt to the complexities of modern combat environments and varying radar signal characteristics. To address these challenges, this paper introduces a novel batch streaming pipeline for radar emitter classification. Our pipeline consists of two key components: radar deinterleaving and radar pattern recognition. We leveraged the DBSCAN algorithm and an RNN encoder, which are relatively light and simple models, considering the limited hardware resource environment of a military weapon system. Although we chose to utilize lightweight machine learning and deep learning models, we designed our pipeline to perform optimally through hyperparameter optimization of each component. We demonstrate the effectiveness of our proposed model and pipeline through experimental validation and analysis. Overall, this paper provides background knowledge on each model, introduces the proposed pipeline, and presents experimental results.

This paper proposes two non-uniform quantization schemes and also introduces an implementation system based on Software Defined Radio (SDR) consisting of GNU Radio and Universal Software defined Radio Peripheral (USRP) to sense a spectrum. In cognitive radio (CR) systems, detection and error probability are representative indicators of system performance. These performance indicators are influenced by various variables, among which the energy measurements quantization affected before reporting to the fusion center. In order to minimize performance loss due to the quantization errors, it is important to understand the impact of the number of quantization levels and the quantization intervals on the performance of the overall system. This paper reports a detection and error probability performance of a CR system applied non-uniform quantization schemes consisting of SDR platform.

Federated learning trains a shared global model across user devices without centralizing their private data. In each round, the server broadcasts the current model, and clients train local copies on their local datasets. They then return model updates for aggregation. In realistic wireless settings, unreliable links and limited bandwidth, together with non-independent and identical distributed (non-IID) client data, make aggregation challenging and can degrade global performance. This paper studies federated learning over wireless channels with non-IID data and proposes FedCAC, a performance-efficient aggregation method that groups clients using channel-state features to improve robustness during global updates. We compare the performance of FedCAC with that of conventional baselines under realistic wireless conditions. The simulation results show that our method improves the validation accuracy by 22% compared to standard federated learning methods.