Narcotics should be strictly controlled as they can cause great disruption to society. Narcotics mostly flow into ports from major narcotic makers via transit points and through cargo containers. To prevent narcotic entry through smuggling, airports use animals or detect narcotics through X-rays. However, the use of animals in ports is not practical, and the method using X-rays sometimes does not detect substance narcotics with low atomic numbers. In this paper, we aimed to detect and classify narcotics using ion mobility spectrometry (IMS) data generated by inhaling air inside the container. To classify narcotic IMS data consisting of time-series data, the performance was improved using a time-series classification machine learning algorithm instead of the threshold method previously used. To this end, K-nearest neighbor, time-series forest, and random convolutional kernel algorithms were applied to the proposed algorithm considering the features of narcotic IMS data. The results demonstrate that the proposed algorithm outperforms the existing algorithm, and it reduces the classification performance processing time up to 5 s with more than 0.9 accuracy level.

With the advancement of artificial intelligence, the inference capabilities of Autonomous Underwater Vehicles (AUVs) have significantly improved, leading to growing interest in AUV applications. To ensure reliable operations, the field of underwater communications demands robust schemes that account for AUV mobility and enable the formation of underwater cellular networks. Conventional approaches using Linear Frequency Modulation (LFM) and Zadoff–Chu sequence (ZCS) sequences for frame detection and Cell ID (CID) assignment degrade substantially under severe Doppler conditions. In particular, AUVs experience pronounced Doppler shifts due to their mobility in underwater channels. In this study, we propose a methodology in which distinct Superimposed Adjusted-HFM (SA-HFM) signals are assigned to multiple buoys, allowing AUVs to jointly perform frame detection, CID assignment, and Doppler estimation in challenging underwater environments. To validate the proposed scheme, an ocean experiment was conducted in the East Sea of the Republic of Korea. The results demonstrate that the SA-HFM-based signals successfully achieved frame detection, CID assignment, and Doppler estimation at distances ranging from 500 m to approximately 2 km, even when the AUV moved at speeds of 1.02–1.54 m/s. The experimental results indicate that the proposed approach can offer robust underwater communication and facilitate the deployment of underwater cellular networks for mobile AUV operations.