Infrared (IR) object detection poses unique challenges due to low texture, weak edges, sensor noise, and a strong dependence on global context. Modern convolutional object detectors, including YOLOv8, are primarily optimized for RGB imagery and often underperform when directly applied to large-scale infrared datasets. In this paper, we present a simple yet effective architectural enhancement to YOLOv8 by (1) replacing the standard Spatial Pyramid Pooling Fast (SPPF) module with a learnable multi-scale variant (SPPFPlus), and (2) integrating the Convolutional Block Attention Module (CBAM) into both the backbone and neck. The proposed approach introduces explicit parallel multi-scale context modeling and adaptive channel–spatial attention, addressing key representational limitations of infrared imagery. Extensive experiments on a large infrared dataset demonstrate an improvement of approximately 15% in detection performance over the YOLOv8 baseline, with notable gains in recall and small-object detection. The modifications are lightweight, modular, and can be seamlessly integrated into existing YOLOv8
Industrial Internet of Things (IIoT) networks supporting mission-critical control and automation require ultra-reliable, low-latency communications (URLLC). Non-stationary interference, including narrowband tones, impulsive bursts, and chirp-like signals, significantly degrades Quality of Service (QoS) by increasing Block Error Rate (BLER), Hybrid Automatic Repeat Request (HARQ) retransmissions, and worst-case latency. Conventional mitigation techniques based on Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT) exhibit limited adaptability and insufficient time-frequency resolution for transient interference. This paper presents a system-level integration and cross-layer evaluation of wavelet-based interference mitigation, integrating the Continuous Wavelet Transform (CWT) for multiscale detection, the Stationary Wavelet Transform (SWT) for shift-invariant suppression, and the Median Absolute Deviation (MAD) for adaptive thresholding. Evaluation is conducted using real 5G New Radio (NR) traces from a Firecell testbed, augmented with heterogeneous synthetic interference. Experimental results demonstrate a 3 dB gain in Effective Signal-to-Noise Ratio (ESNR), a 45% reduction in 99th-percentile latency, a 62% reduction in BLER, and a 39% increase in throughput relative to FFTnotch and STFT-based baselines. These results confirm the practical value of wavelet-domain cross-layer interference mitigation for 6G-oriented IIoT deployments.
Biomedical image segmentation plays a pivotal role in diagnostic radiology and computational pathology, enabling precise delineation of anatomical and pathological structures. However, despite advancements in deep learning-based segmentation, challenges persist in interpretability, computational tractability, and scalability. This paper proposes an advanced computational framework that integrates Large Language Models (LLMs) with segmentation architectures, quantum databases for accelerated query performance, and optimized image compression techniques. The proposed system leverages mathematical principles of variational optimization, tensor decomposition, and quantum search complexity to enhance segmentation efficiency, reduce latency, and improve decision support. A rigorous comparative analysis is performed using benchmark datasets, demonstrating superior segmentation accuracy, reduced query response time, and improved data storage efficiency. The integration of LLMs provides an interpretable interface for clinicians and radiologists, enhancing the usability of automated segmentation in real-world medical workflows.
Today, drone-based attacks represent serious threats to the security and safety of public infrastructures. For successfully detecting a malicious drone in a given zone, there are three phases: signal collection (sensing), features extraction and classifications. Signal collection can be performed using available sensing technologies such as radar, acoustics sensors and electro-optic technologies, among others. The classification phase is often achieved using general-purpose algorithms such as Naive Bayes and support vector machine (SVM). On the other hand, the features extraction phase is very problem-specific, and its performance depends on several factors such as the used sensory technology, environment, and the drone characteristics. Features engineering is a designing stage that aims at identifying the most distinctive information carriers which capture the drone's discriminative characteristics. In this paper, we present effective drones' features extraction techniques for the most popular sensory technologies available which are radar, RF analyzers, acoustic sensors, and electro-optic sensors. We focus on identifying the most distinctive features of drones and show how to extract them out of the collected signals.
The rapid expansion of 5G use cases and dense IoT deployments has intensified pressure on scarce spectrum, making reliable wideband sensing indispensable for dynamic spectrum access (DSA). This work targets energy detector-based cooperative wideband spectrum sensing (CWSS) over multiple sub-bands in complex AWGN channels, where noise uncertainty and very low SNR commonly affect performance. We propose a CWSS architecture that applies maximal-ratio combining (MRC) at the pre-detection stage and employs a k-out-of-N fusion rule at the decision center. MRC boosts the effective per-sub-band SNR before thresholding, while the fusion mechanism aggregates local binary decisions to improve global reliability with modest reporting overhead. Comparative evaluations indicate that the MRC-aided ED with k-out-of-N consistently achieves higher detection probability for a given false-alarm rate across challenging low-SNR conditions, outperforming non-cooperative sensing and conventional CSS baseline. The results demonstrate that combining MRC with k-out-of-N fusion mitigates noise-uncertainty effects, strengthens wideband hole detection, and provides a practical sensing frontend for policy-compliant DSA in 5G environments
