Search Results (367)

Wireless sensing inside rotating industrial machines is challenging due to signal attenuation, multipath propagation, and continuous sensor motion. Smart particles equipped with wireless communication and inertial sensors provide a promising approach for monitoring such systems. However, the reliability of wireless signal transmission under rotational dynamics remains insufficiently understood, and systematic approaches for sensor selection are lacking. This paper presents a link-budget-normalised experimental characterisation of three commercial smart particles, namely MetaMotionS (BLE), WitMotion BLE, and WitMotion Wi-Fi, in a bare 300 mm diameter by 310 mm deep metallic drum fitted with six triangular lifters, at rest and at 16, 18, and 20 RPM, corresponding to 20.7–25.9% of the critical speed and Froude numbers of 0.043–0.067. Because raw received power conflates transmit power with channel behaviour, the comparison is expressed as excess path loss above free space together with second-order fading statistics. Two particles of the same protocol class differ by 23.9 dB, of which at most 4 dB is attributable to the transmission of power across the documented range of both radios, establishing that device implementation rather than protocol class governs the ranking. Two particles logged simultaneously through a single receiver observe one channel realisation, and the correlation between their signal fluctuations is not significantly different from zero at any speed (r = +0.064, −0.098, −0.083), indicating device-specific rather than environmental fading. Under rotation, the Wi-Fi particle holds an RSSI standard deviation of 1.96 dB against 6.27 and 6.58 dB for the two BLE particles (Welch ANOVA, p < 0.001; Games–Howell post hoc, all pairwise comparisons p < 0.001; |Cliff’s δ| > 0.96). A bounded, dimensionless multi-criteria selection procedure over link margin, signal variability, cross-speed consistency, packet delivery, and energy per delivered packet is introduced; the ranking is invariant under weighted-sum and TOPSIS aggregation but inverts once endurance carries a weight above 0.35, which quantifies the trade-off between link quality and battery life. Coupling between the wireless and motion streams is examined by folding both onto rotation phase. A rotation-locked component in RSSI is detected in one of nine sensor–speed combinations, with a maximum modulation amplitude of 0.92 dB. The RSSI logging cadence of approximately 1 Hz resolves the drum fundamental but lies below the Nyquist requirement for the dominant motion band at 0.91–1.04 Hz, so joint wireless–motion studies of rotating machinery require RSSI logging at 5 Hz or above on a clock shared with the inertial unit. Full article

Security operations can detect that an intrusion is under way, yet they cannot say how long an ongoing attack still needs to reach a critical objective such as data exfiltration. Prior work on multi-stage attacks identifies the active stage or predicts the next step, but does not estimate the residual time to compromise from real traffic using survival models. This paper addresses that gap through a temporal framework built on empirically measured stage durations, with three contributions. First, the MITRE ATT&CK taxonomy is given a temporal layer, in which each stage carries a duration distribution estimated empirically from the observed episodes of that stage. Second, a probability-weighted multi-path formulation combines these durations with stage-transition probabilities to estimate the time remaining before the objective. Third, the framework is validated on a real multi-stage campaign rather than on synthetic traffic, and three survival models are compared under a matched protocol as a benchmark of how learnable the durations are. Random Survival Forest, DeepSurv, and DeepHit are compared on DAPT 2020, a public advanced-persistent-threat dataset of 82,577 real network flows collected across five days. Random Survival Forest reaches a stable concordance index of 0.92, with a standard deviation of 0.006 across twenty repeated stratified splits on leakage-free features, and it retains a concordance of 0.79 when benign traffic is excluded entirely. When the three models are placed on a single concordance scale and trained on an identical subsample of 40,000 flows, DeepSurv reaches 0.955 and DeepHit 0.879, so the neural models are competitive at that scale. DeepSurv nevertheless fails to converge on the full flow set, returning no survival estimates in any of five seeds, whereas the forest fits successfully at every training size examined. A stage-transition graph recovered from the data, built from 25 observed transitions across ten multi-stage sessions, shows branching progression, and the residual time, reported at the entry to each stage, falls along the campaign, from about 139 min at reconnaissance to about 31 min at lateral movement, conditional on reaching the objective. All stage-level estimates rest on 74 episodes from a single campaign, of which 43 carry a positive duration, so cross-environment generalisation remains to be confirmed. The framework gives a security operations centre a data-driven estimate of the active attack effort that remains before compromise, supporting informed containment decisions. Full article

To address the degradation of low-probability-of-intercept (LPI) radar waveform recognition caused by noise dispersion and the masking of modulation-dependent structures in low-SNR Choi–Williams distribution (CWD) images, this paper proposes RepNRS-LPI-Net, an integrated framework for robust recognition and lightweight deployment. CWD converts each received waveform into a two-dimensional time–frequency image that characterizes temporal evolution, frequency variation, and localized energy distribution. The proposed RepDW block integrates 3 × 3, 1 × 3, and 3 × 1 depthwise branches with an identity branch during training to capture joint time–frequency, temporal-direction, and frequency-direction responses while preserving informative features. These branches are then algebraically fused for efficient deployment. In addition, NRS-ECA combines channel recalibration with channel-dependent soft shrinkage to attenuate weakly supported noise-like activations without assuming that all weak responses are noise. Focal modulation and label smoothing are conservatively adopted as auxiliary training strategies to address difficulty imbalance and confidence regularization. Experimental results show that RepNRS-LPI-Net achieves 79.553350% overall accuracy and 49.137529% low-SNR accuracy, while the deployment form reduces the parameter count to 37,142 and the learned-layer computation to 15,722,496 MACs. These results indicate that RepNRS-LPI-Net improves measured recognition performance while substantially reducing deployment complexity under the modeled multipath, Rayleigh-fading, Doppler, and AWGN conditions. Full article

Global Positioning System (GPS) positioning accuracy is strongly affected by inaccurate noise modeling and non-Gaussian pseudorange measurement errors, including heavy-tailed disturbances and abnormal outliers caused by multipath propagation and signal degradation. Conventional extended Kalman filters (EKFs) generally assume Gaussian measurement noise with fixed covariance matrices, which limits their robustness under degraded measurement conditions. This study proposes a Student-t robust quantum-behaved particle swarm optimization-based extended Kalman filter (ST-QPSO-EKF) for adaptive GPS state estimation. The proposed framework combines quantum-behaved particle swarm optimization (QPSO) with a Student-t-based robust measurement update, where the process-noise scaling factor, measurement-noise scaling factor, and Student-t degrees-of-freedom parameter are jointly optimized. The optimized parameters are obtained through an offline calibration stage and subsequently applied in the recursive GPS filtering process. A nonlinear GPS navigation simulation was conducted using Gaussian, Student-t heavy-tailed, and outlier-contaminated pseudorange measurement scenarios. The proposed method was compared with conventional EKF, QPSO-EKF, and Student-t EKF using 20 independent Monte Carlo realizations. The results demonstrate that QPSO-EKF provides improved accuracy under nominal Gaussian conditions, whereas ST-QPSO-EKF achieves superior performance under non-Gaussian measurement environments. Under Student-t heavy-tailed noise, ST-QPSO-EKF reduced the position RMSE to 3.814 m, while under outlier-contaminated noise it achieved a position RMSE of 3.952 m, outperforming the other compared methods. In addition, the proposed method maintained comparable online computational cost because the QPSO optimization was performed offline. The results indicate that jointly optimizing covariance parameters and Student-t robustness provides an effective strategy for improving GPS positioning reliability under complex pseudorange measurement conditions. Full article

Multipath routing in wireless sensor networks (WSNs) improves reliability by providing alternative forwarding paths when a route fails. However, mobile sinks make path maintenance difficult because sink movement can invalidate previously constructed source-to-sink routes. Existing protocols typically depend on either global path reconstruction, which increases control overhead, or footprint-chaining, which accumulates detours through previous sink positions and may weaken path independence. To address this problem, this paper proposes QL-LGMPRP, a reliability-aware local-grid-based multipath routing protocol that combines a sink-centered local grid, two-path delivery, link-quality-aware forwarding, and lightweight tabular Q-learning for waypoint adaptation. Mobility-related route changes are confined to the sink-centered grid, whereas a compact tabular Q-learning policy adjusts the primary-path direction using grid, link-quality, and energy-related state variables. The sink constructs a local grid around its current position, with cells sized to keep in-grid forwarding locally bounded. When an event occurs, the source computes an entry point on the grid perimeter and constructs two greedy paths: a primary path through a Q-learning-selected waypoint near the grid boundary and a backup path toward the current sink position. The Q-learning agent uses a compact tabular state representation that includes the boundary-cell index, residual-energy level, sink-grid position, and local link-quality information, and learns waypoint offsets using a reward that combines delivery success, transmission energy, and delay. This design confines routing adaptation to the sink-centered grid while allowing the waypoint policy to respond to heterogeneous link conditions. Simulation results under different sink speeds and interference conditions show that QL-LGMPRP maintains high delivery reliability while reducing detour-related forwarding costs relative to footprint-chaining and showing lower weak-link exposure than the geometric-forwarding comparison schemes. Full article

Reliable short-packet transmission is essential for MIMO-based wireless sensor systems, where low-power terminals upload short data blocks to multi-antenna gateways under stringent latency and complexity constraints. Although multi-antenna combining improves the equivalent received signal-to-noise ratio, the post-combining recovery of sparse-vector-coded packets is still limited by the high inter-column correlation of short binary spreading matrices and redundant searches over invalid indices in conventional orthogonal matching pursuit. To address these issues, this paper proposes a low-complexity sparse vector coding scheme for reliable short-packet transmission in MIMO-based wireless sensor systems. Specifically, at the transmitter, a low-coherence binary spreading matrix (LCB-SM) construction algorithm is designed based on Hadamard initialization and column-wise correlation optimization, which improves the distinguishability of sparse support positions while preserving multiplication-free encoding. At the receiver, a frozen-index-pruned orthogonal (FIP-OMP) matching pursuit algorithm is developed to exploit the predefined sparse mapping rule, thereby excluding invalid indices during atom selection and reducing noise-induced false support detection. Simulation results show that, for $N=24$, $L_s=16$, and $K=2$, the LCB-SM achieves a signal-to-noise (SNR) ratio gain of approximately $1.2$ dB over the optimized partial hadamard matrix (OPHM) at a BLER of ${10}^{-2}$; for $K=3$, it reduces the high-SNR BLER by more than two orders of magnitude compared with OPHM. Meanwhile, the FIP-OMP achieves an approximately $0.5$–$0.8$ dB gain over conventional orthogonal matching pursuit at a BLER of ${10}^{-2}$ with an average decoding time close to that of orthogonal matching pursuit (OMP) and much lower than that of multipath matching pursuit, Dynamic OMP and compressive sampling matching pursuit. A software defined radio-based wireless experiment further validates the reconstruction capability of LC-SVC for continuous sensing data, achieving an RMSE of $0.0612$. These results demonstrate that LC-SVC improves the reliability of the considered short-packet recovery task with lightweight transmitter-side encoding and low gateway-side decoding overhead. Full article

Marine cross-medium acoustic–radio collaborative networks must route traffic across heterogeneous underwater acoustic and radio links while coping with energy imbalance, congestion, and mobility-induced link instability. This paper proposes Q-Learning AODV, an AODV extension that integrates distributed Q-value updating and multipath route maintenance into existing RREQ, RREP, and HELLO procedures. The routing reward combines normalized residual energy, queue availability, inter-node distance/link stability, relative velocity, and, for air–sea links, elevation-angle information. The protocol maintains multiple node-disjoint candidate paths and forwards data through the currently highest-valued path. NS-3 simulations are reported for underwater-to-underwater, underwater-to-air, and air-to-underwater communication scenarios. Relative to conventional AODV, Q-Learning AODV increases packet delivery ratio from 55.8% to 88.3%, from 68.3% to 76.1%, and from 86.9% to 91.9%, corresponding to relative improvements of 58.2%, 11.4%, and 5.8%, respectively. The results indicate improved delivery reliability and communication-subsystem energy balancing at the cost of additional state exchange, Q-table storage, and route-selection computation. Full article

Taking a typical high-power microwave power supply system as the research object, this paper quantitatively simulates and compares electromagnetic disturbance characteristics under multiple operating conditions, systematically investigates the influence mechanism of the system on EMI, and verifies the proposed simulation model via prototype experiments. Firstly, the typical equipment composition and three operating modes of the system are elaborated. Standardized high-frequency equivalent circuits of thyristors, capacitors, and inductors are established, and parasitic parameters are extracted to construct a system-level high-frequency coupling model. Different from traditional static parasitic extraction and separated field-circuit simulation methods, the proposed global collaborative optimization co-simulation method with voltage-dependent thyristor parasitic model significantly improves EMI prediction accuracy under full-cycle multi-mode operation. Secondly, based on the dynamic device characteristics under resonant charging, energy recovery and energy supplement modes, the generation mechanisms of EMI are clarified with quantitative data. During modeling, the electrical characteristics of thyristor body diodes and inter-electrode capacitances are fully incorporated with reference to actual component parameters. The EMC co-simulation based on CST field-circuit coupling is adopted to collaboratively optimize all parameters, which reduces the approximation error introduced by local modeling and greatly improves simulation accuracy. Combined with simulation and prototype experimental verification, this paper reveals the multi-path EMI coupling mechanism of pulsed power systems. The proposed parasitic parameter-based SPICE modeling and field-circuit co-simulation method can provide quantitative analysis tools and theoretical support for the EMC suppression design of high-power microwave power supplies. Full article

In this work, we study the joint performance of the quality and reliability in terms of quality–reliability (JQR) performance of an intelligent reflecting surface (IRS)-assisted wireless communication system under severe multipath fading and shadowing. The wireless channel model is given by the Inverse Power Lomax (IPL) fading model, representing a heavy-tailed fading channel, which can describe the hyper-Rayleigh fading and is verified using two different experimentally obtained measurement scenarios, namely, the LTE-case for high-frequency, long-range cellular communications and the device-to-device (D2D) case for lower-frequency short-range communications. For the considered channel model and communication scheme, analytical expressions for the outage probability (a metric related to the reliability) and the average bit error rate for both coherent and non-coherent modulation schemes (metrics associated with the quality of the communication system) are provided. By combining the aforementioned expressions, a unified JQR curve, together with its asymptotic forms in the high signal-to-noise ratio regime and asymptotically large number of IRS elements, is derived. It is proved analytically that the use of IRS with infinite elements can remove fading, while for a finite number of IRS elements, a closed-form signal-to-noise ratio (SNR) penalty factor is presented. The numerical analysis demonstrates that coherent modulations outperform non-coherent ones, higher-order quadrature amplitude modulation (QAM) systems are highly sensitive to the multipath fading, and the LTE-case exhibits better performance compared to the D2D-case for equal settings. Moreover, the joint quality–reliability approach highlights the existence of regions where quality is more preferable than reliability, allowing the allocation of resources based on these regions. All expressions have been verified using Monte Carlo simulations with excellent agreement. Full article

The knowledge of the Line of Sight (LOS) or Not Line of Sight (NLOS) propagation condition is useful information in wireless communication system. Such knowledge can be inferred by the analysis of the signal, by using specific signal structures (e.g., preambles) or by the application of machine learning (ML) algorithms. In recent times, deep learning (DL) has been applied with success to the classification of LOS/NLOS conditions but with a significant computational time, which can be a practical issue in computing constrained devices. On the other hand, ML relies on the identification of key discriminating features, which can enhance the classification performance. This paper explores the application of entropy metrics to this classification problem. Beyond Shannon entropy, researchers have developed various entropy metrics in recent years in various domains (e.g., healthcare), but they have been scarcely applied to UWB LOS/NLOS classification to the best of the author’s knowledge. This paper addresses this gap by applying entropy metrics in combination with ML classifiers to the public eWINE dataset, characterised by seven different propagation environments where UWB signals were transmitted and recorded in LOS and NLOS conditions. The results presented in this paper show that entropy metrics can significantly enhance the LOS/NLOS classification accuracy and can produce an overall competitive performance. In addition, this paper presents a novel instance selection approach based on the use of entropy metrics, which is demonstrated to significantly outperform even the direct application of some DL algorithms on the basis of the results presented in the literature on the same eWine data set. To summarise the novelty aspects of this study, for the first time in the literature, this study presents an extensive analysis of the discriminative advantage (discrimination index) of entropy measures introduced in the research literature in other domains (e.g., mechanical problems, analysis of physiological signals) in UWB multipath environments for UWB LOS/NLOS classification. In addition, this study presents for the first time the application of an ensemble instance selection algorithm based on entropy measures to the problem of UWB LOS/NLOS classification to handle “noise” or “boundary” samples in the data set, thereby improving model generalisation. Full article

Interferometric synthetic aperture radar (InSAR) enables large-scale, all-weather, day-and-night monitoring of surface deformation, but phase unwrapping remains challenging in mining areas with large-gradient deformation. Most conventional phase unwrapping methods rely on the Itoh condition. In mining interferograms, dense fringes, low coherence and deformation-related noise can violate the Itoh condition, causing unwrapping errors to propagate into fragmented phase fields and unreliable deformation estimates. To address this problem, we propose a lightweight dilated multi-path phase unwrapping network, LDM-PUNet, for joint interferogram denoising and phase unwrapping in low-coherence mining environments. LDM-PUNet introduces multi-path parallel residual blocks with dilated depthwise separable convolutions to capture multi-scale fringe structures while reducing model complexity, and combines attention-based feature refinement with a phase-aware compound loss that integrates robust phase regression, wrapped-phase consistency and gradient consistency. To alleviate the shortage of labelled interferograms for mining deformation, we further develop a multi-effect deformation interferometric phase simulation strategy, M-DIPS, which generates training samples with controllable deformation, terrain, scattering, atmospheric and noise-related effects. Simulation tests were conducted on synthetic datasets with different deformation gradients and noise levels. LDM-PUNet improved RMSE accuracy by approximately 32.2–84.0% compared with the reference methods, while requiring only 0.02 s to process a single sample, demonstrating superior accuracy and efficiency. Real-data experiments in the Datong mining district and the 1071 working face of the Liangbei Coal Mine further demonstrate that, in long-term InSAR deformation monitoring, LDM-PUNet improves phase continuity and deformation inversion accuracy under dense fringes and decorrelation, producing highly consistent vertical displacement estimates. The proposed strategy and methods introduce deep learning into the time-series InSAR processing chain, providing an efficient and robust solution for rapid deformation monitoring in mining areas with large-gradient deformation. Full article

Termite infestation poses a critical threat to ancient timber structures because hidden in-wood activity can cause progressive structural decay before visible surface symptoms appear. Through-wood termite detection remains challenging because termite-induced electromagnetic responses are weak, small-scale, and vulnerable to timber attenuation and multipath propagation. To address this problem, this study presents one of the first investigations to formulate through-wood termite detection as a multi-frequency wireless sensing and image–wireless fusion problem for non-destructive heritage timber inspection. We propose a Reliability-Aware Image–Wireless Fusion Network (RA-IWFNet), in which the image branch captures high-resolution surface-level visual cues while the dual-band wireless branch integrates complementary mmWave radar micro-motion responses and Wi-Fi Channel State Information (CSI) channel variations. A learnable temperature-scaled fusion gate estimates input-dependent image and wireless contributions and constructs a normalized fused representation for four-class recognition, including Termite, Lyctidae, Human, and None. Here, reliability is operationally defined as learned input-adaptive relative modality contribution rather than explicit uncertainty or signal-quality estimation. RA-IWFNet is evaluated under two complementary protocols: a field-motivated protocol with joint visual and wireless degradation and a synchronized verification protocol using physically co-acquired multimodal samples. Across repeated training runs, RA-IWFNet achieves $81.91\pm 1.33\%$ accuracy and $81.96\pm 1.27\%$ Macro-F1 under field-mixed visual degradation and moderate wireless degradation. On the synchronized verification subset, gated fusion achieves $91.53\pm 2.44\%$ accuracy and $91.62\pm 2.44\%$ Macro-F1, yielding higher mean performance than single-modality and non-adaptive fusion baselines. Feature-space, error-correction, and gate-temperature analyses further support the effectiveness of adaptive modality integration. These results provide controlled laboratory feasibility evidence and suggest that multi-frequency wireless sensing combined with adaptive image–wireless fusion offers a promising non-invasive pathway toward practical through-wood termite inspection in heritage timber structures. Full article

Conventional end-to-end multipath transmission selects complete paths at the source or ingress, limiting the ability of intermediate nodes to adapt forwarding decisions to locally observed network conditions. Hop-by-hop multipath transmission provides greater flexibility by allowing each node to select a next-hop option among multiple next hops, but it must jointly address forwarding-loop prevention, path efficiency, and adaptive traffic scheduling. This paper proposes the Adaptive Hop-by-Hop Multipath Transmission Framework (AHM-TF), which integrates multipath structure construction with hop-by-hop traffic scheduling. Its Weighted-Priority Loop Pruning (WPLP) procedure constructs destination-oriented, loop-safe next-hop sets while controlling structural detours. Over the resulting forwarding structure, Exponentially Weighted Hop-by-Hop Scheduling (EWHS) combines structural path cost with locally observed congestion pressure to update node-local traffic-splitting probabilities. For a fixed self-loop-free directed topology with positive structural link costs, WPLP prevents persistent forwarding loops and preserves destination reachability under the previous-hop exclusion rule. Experiments on five network topologies show that WPLP achieves higher path availability than several existing structure-construction methods while maintaining low mean relative path stretch. Its path availability is broadly comparable to LFID, whereas its mean relative path stretch is lower on all evaluated topologies for $k\ge 3$. Transmission experiments on the GEANT topology show that AHM-TF performs comparably to the strongest multipath baselines under low load and provides clearer benefits as contention increases. Under the medium load, AHM-TF improves aggregate goodput by 14.87%, reduces the block loss ratio by 12.43 percentage points, and reduces mean one-way block delay by 2.37% relative to ECN-WRR. Under the high load, it retains the best results on these three metrics. These improvements are accompanied by higher block delay variation and block reordering, indicating reduced temporal and ordering stability in block delivery. Full article

To address subjective manual judgment in cherry fruit thinning and the difficulty of reliably acquiring ripeness and spatial structure information in dense fruit-cluster scenes, a unified visual phenotypic analysis framework for fruit-thinning decision support is proposed. The framework comprises three components: ripeness detection, cluster spatial structure modeling, and two-level fruit-thinning priority analysis, corresponding to ripeness recognition, quantitative characterization of cluster structural phenotypes, and thinning-order determination, respectively. First, a CS-Transformer and a multi-path feature enhancement module (MSFE) were designed, and the ripeness detection model DEIM-CMFNet was developed based on them. This model significantly improved ripeness recognition and instance separation under occlusion and fruit adhesion conditions, achieving AP(50), AP(50–95), and AR(50–95) of 92.1%, 80.9%, and 90.8%, respectively, on a greenhouse Meizao cherry dataset. Second, a cluster spatial structure modeling method was proposed. By combining dynamic EPS adaptive clustering with an intra-cluster reclustering mechanism, robust cluster partitioning and spatial structure representation were achieved in complex fruit-cluster scenes, thereby improving the ability of the density index D to characterize cluster crowding. In addition, by combining fruit spacing modeling with a comprehensive morphological index, local crowding and overall structural compactness of fruit clusters were jointly quantified. Finally, a two-level fruit-thinning priority strategy was constructed at both the cluster and fruit levels. Experimental results showed that, under limited fruit removal, this strategy reduced the fruit contact ratio by 44.8%, increased the minimum normalized fruit spacing by approximately 21.2%, and decreased cluster density by approximately 24.6%. This framework provides an interpretable visual phenotypic analysis approach for fruit-thinning strategy formulation in the middle and late stages of cherry production. Full article

To overcome the limitations imposed by the natural ionosphere on high-frequency (HF) communication systems, the generation of artificial electron clouds through space-based release of ionizable materials offers a controllable means of constructing localized plasma environments. However, a systematic mapping between release parameters and radio wave propagation characteristics is still lacking, which restricts engineering applications. This paper establishes a full-chain simulation framework that links release parameters, cloud morphology, and propagation characteristics by combining a two-fluid hydrodynamic model with ray tracing. Results show that as release altitude rises from 150 km to 210 km, the cloud undergoes pronounced stretching along the magnetic field (the cloud scale grows from 17.28 km to over 80 km), resulting in a broader signal shadow zone (expands from 116 km to 170 km) and a marked increase in multipath delay spread. Increasing mass from 4 kg to 12 kg raises peak electron density from 7.416 × 10¹² to 2.154 × 10¹³ m⁻³ and widens the shadow zone from 144 km to 174 km. Raising the ionization rate from 20% to 80% broadens the enhancement zone but reduces the shadow zone. Higher altitudes favor broad shielding, larger masses enhance scattering strength and duration, while higher ionization rates accelerate cloud evolution for rapid response and lower rates provide stable coverage. These findings can provide theoretical guidance for the precise design of artificial electron clouds tailored to different application requirements. Full article