Related papers: Explainable and Hardware-Efficient Jamming Detecti…
Conventional anti-jamming method mostly rely on frequency hopping to hide or escape from jammer. These approaches are not efficient in terms of bandwidth usage and can also result in a high probability of jamming. Different from existing…
Anomalous audio in speech recordings is often caused by speaker voice distortion, external noise, or even electric interferences. These obstacles have become a serious problem in some fields, such as high-quality music mixing and speech…
Non-Intrusive Load Monitoring (NILM) is an advanced, and cost-effective technique for monitoring appliance-level energy consumption. However, its adaptability is hindered by the lack of transparency and explainability. To address this…
In this paper, we propose an algorithm for channel sensing, collaboration, and transmission for networks of Tactical Communication Systems that are facing intrusions from hostile Jammers. Members of the network begin by scanning the…
Mobile Edge Computing (MEC) enables low-latency applications by bringing computation closer to the user, but dynamic task arrivals and communication threats like jamming complicate reliable task offloading and resource allocation. In this…
In this work, we investigate the feasibility and effectiveness of employing deep learning algorithms for automatic recognition of the modulation type of received wireless communication signals from subsampled data. Recent work considered a…
Deterministic communications are essential for industrial automation, ensuring strict latency requirements and minimal jitter in packet transmission. Modern production lines, specializing in robotics, require higher flexibility and…
In this paper, we propose a novel joint task offloading and user scheduling (JTO-US) framework for 5G mobile edge computing (MEC) systems under security threats from jamming attacks. The goal is to minimize the delay and the ratio of…
This work proposes a novel resource allocation strategy for anti-jamming in Cognitive Radio using Active Inference ($\textit{AIn}$), and a cognitive-UAV is employed as a case study. An Active Generalized Dynamic Bayesian Network…
This paper proposes a novel framework for lung sound event detection, segmenting continuous lung sound recordings into discrete events and performing recognition on each event. Exploiting the lightweight nature of Temporal Convolution…
Deploying Small Language Models (SLMs) on edge platforms is critical for real-time, privacy-sensitive generative AI, yet constrained by memory, latency, and energy budgets. Quantization reduces model size and cost but suffers from device…
Global Navigation Satellite Systems (GNSS) are fundamental in ubiquitously providing position and time to a wide gamut of systems. Jamming remains a realistic threat in many deployment settings, civilian and tactical. Specifically, in…
Designing lightweight convolutional neural network (CNN) models is an active research area in edge AI. Compute-in-memory (CIM) provides a new computing paradigm to alleviate time and energy consumption caused by data transfer in von Neumann…
Noise and inconsistency commonly exist in real-world information networks, due to inherent error-prone nature of human or user privacy concerns. To date, tremendous efforts have been made to advance feature learning from networks, including…
Software logs generated by sophisticated network emulators in the telecommunications industry, such as VIAVI TM500, are extremely complex, often comprising tens of thousands of text lines with minimal resemblance to natural language. Only…
This work is motivated by recent developments in Deep Neural Networks, particularly the Transformer architectures underlying applications such as ChatGPT, and the need for performing inference on mobile devices. Focusing on emerging…
Diffusion Language Models (DLMs) offer a promising parallel generation paradigm but suffer from slow inference due to numerous refinement steps and the inability to use standard KV caching. We introduce CDLM (Consistency Diffusion Language…
Structural pests, such as termites, pose a serious threat to wooden buildings, resulting in significant economic losses due to their hidden and progressive damage. Traditional detection methods, such as visual inspections and chemical…
The ability of Convolutional Neural Networks (CNNs) to accurately process real-time telemetry has boosted their use in safety-critical and high-performance computing systems. As such systems require high levels of resilience to errors, CNNs…
During the past two decades, epileptic seizure detection and prediction algorithms have evolved rapidly. However, despite significant performance improvements, their hardware implementation using conventional technologies, such as…