2026 July-1 - 228 articles
Clouds frequently degrade optical satellite imagery, limiting the reliability of remote sensing models. However, in the literature, cloud-free analyses are often performed by excluding cloudy images from machine learning datasets and methods. This re...
Vehicle-to-Vehicle (V2V) cooperative object detection enhances environmental perception capabilities in complex traffic scenarios by sharing sensory information among vehicles, but limited transmission bandwidth and wireless channel noise can signifi...
This study examines the development of urban air mobility, which requires the creation of vertiports capable of ensuring the safe operation of electric vertical takeoff and landing (eVTOL) systems. Key operational constraints include unstable power s...
Short-term airport traffic forecasting faces a structural gap: temporal ensemble models ignore route-network dependencies that shape hub operations, while deep graph neural networks require synchronised multi-airport operational data streams unavaila...
Network Intrusion Detection (NID) is essential for cybersecurity because network traffic and security threats are growing rapidly. However, current NID methods suffer from a critical limitation: they face inherent difficulties in jointly modeling glo...
With the rapid expansion of the unmanned retail ecosystem, real-time foreign object detection (FOD) in smart vending cabinets has become a critical technology for ensuring equipment safety and protecting user rights. However, existing models often fa...
This paper presents a model-based parameter estimation approach for high-voltage circuit breaker operating mechanisms using the dynamic model and stroke curves. First, an equivalent dynamic model of the mechanism is established, and its fidelity is v...
High-frequency link inverters (HFLI) inherently suffer from voltage ringing, and the issue becomes more severe under non-unity power factor loads. Although active clamping circuits can effectively suppress the voltage ringing, they cause severe disto...
Running neural networks on battery-powered Internet of Things (IoT) sensor nodes is difficult because flash memory, SRAM, latency, and energy per inference are limited at the same time. Existing TinyML co-design methods usually improve model size or...
Wastewater contamination with synthetic organic dyes is a significant environmental challenge. Zinc oxide (ZnO) has attracted considerable attention as a non-toxic, multifunctional material for electronics, optics, piezoelectric devices, and photocat...
To address the issues of low detection accuracy and high miss rates in long-range small traffic sign detection, which are caused by insufficient feature information and susceptibility to background interference, this paper proposes an improved real-t...
This paper addresses the high-precision position tracking control problem for the rotary direct-drive valve (RDDV) subject to complex nonlinear dynamics and unknown external disturbances. To achieve superior transient and steady-state performance, a...
This work presents the design of a deployable reconfigurable monopole antenna based on a triangular cylindrical origami structure (TCO). TCO structures are three-dimensional geometries able to modify their structure if subjected to specific solicitat...
Simultaneous transmit-and-receive (STAR) antennas are important components of in-band full-duplex wireless front ends. However, transmit-to-receive leakage through the antenna limits the achievable antenna-domain isolation. This paper presents a dual...
Accurately predicting human lane-changing (LC) decisions is critical for enhancing the safety and efficiency of autonomous driving. Most existing machine learning-based LC decision models rely on immediate neighboring vehicle interaction features, wh...
Digital agriculture employs a wide range of sensing, actuation, and analytics technologies to optimize productivity, sustainability, and decision-making in farming operations. However, rural and remote regions face persistent barriers, including limi...
Social media has emerged as a crucial environment for examining public sentiment during disasters, providing immediate insights into collective emotions and urgent expectations. This research examines the emotional reactions expressed on Turkish post...
Rolling element bearing degradation monitoring is critical for predictive maintenance in rotating machinery systems. Existing methods predominantly address fault classification and remaining useful life (RUL) estimation as separate tasks, thereby fai...
This study focuses on the thermal runaway (TR) risk of lithium-ion batteries (LIBs) and explores data-driven multi-sensor early warning and risk-oriented prognostics based on the intrinsic thermal behavior of batteries. A framework is proposed to sup...
Conventional I/O Buffer Information Specification (IBIS) models often suffer from reduced fidelity in high-speed signaling because their static table-lookup mechanism cannot accurately reproduce complex transient I/O-buffer dynamics. To address this...
Effective fault detection is essential for maximizing energy production and ensuring the safe operation of wind turbines. However, supervised AI models for Supervisory Control and Data Acquisition (SCADA)-based condition monitoring are often limited...
Large language models (LLMs) are widely deployed in high-stakes decision-making tasks, raising growing security concerns. Jailbreak attacks, a major threat to LLMs, have evolved from superficial semantic evasion to exploiting inherent model propertie...
Federated learning enables privacy-preserving training of spiking neural networks on distributed neuromorphic and event-based data, but it also exposes the global model to stealthy backdoor attacks injected by malicious clients. Compared with convent...
This work presents an integrated methodology for modeling, parameter tuning, and experimental emulation of Dual Active Bridge (DAB) converters aimed at detailed simulation, control design, and real-time emulation. This is needed to bridge the gap bet...
In multi-coil, multi-load magnetically coupled resonant wireless power transfer (MCR–WPT) systems, the non-negligible cross-coupling among multiple resonators, including the transmitter (Tx), receiver 1 (Rx1), and receiver 2 (Rx2), introduces c...
Deep learning perception in autonomous vehicles (AVs) has created a critical attack surface in which adversarial patches and sensor-spoofing perturbations cascade from perception errors into unsafe driving decisions. Existing defenses face three limi...
The exponential growth of Indicators of Compromise (IoCs) has overwhelmed manual triage processes in Security Operations Centers (SOCs), necessitating automated solutions for large-scale log analysis. This study proposes a hybrid machine learning fra...
Division (DIV), reciprocal (REC), square root (SR), and reciprocal square root (RSR) are fundamental operations in digital signal processing (DSP), communication, and matrix decomposition applications. However, implementing these functions using dedi...
Oscillatory neural networks (ONNs) provide a neuromorphic computing framework that exploits the phase dynamics of coupled oscillators for parallel and energy-efficient pattern recognition. In this study, we design a single-layer, fully connected ONN...
A Large Language Model-based search for controller synthesis can yield UAV controllers with strong trajectory-tracking performance. However, low tracking error does not necessarily demonstrate closed-loop stability. This study presents a Lyapunov sta...
Many universities have developed machine learning models for student dropout prediction, and these models are commonly evaluated using accuracy-oriented metrics such as the F1 score. However, the model with the highest F1 score does not necessarily p...
Existing single-ended protection schemes for flexible DC transmission lines are negatively affected by traveling wave (TW) refraction and reflection interference and nonlinear overfitting under low-resistance faults. To address this, in this study, l...
DC microgrids are important platforms for renewable energy integration, energy storage interaction, and bidirectional power exchange. In these systems, multi-phase interleaved parallel DC-DC converters are widely used as key energy-router interfaces,...
This work presents the first design of an active frequency quadrupler based on GaN HEMTs. It is based on two cascaded frequency doubler stages that allow up-conversion from the S band to the X band, obtaining conversion gain in narrowband without the...
Accurate network simulation is essential for evaluating wireless communication systems; however, the fidelity of simulation results strongly depends on the underlying modeling assumptions, particularly at the physical (PHY) layer. In this paper, we p...
Background: This paper presents a structured narrative review of recent advances in AI-driven image generation across four complementary perspectives: generative models and architectures, quality assessment and performance metrics, application domain...
Accurate and robust weed detection is a critical prerequisite for precision agriculture and site-specific weed management. However, real-world agricultural environments pose significant challenges to existing object detectors due to severe illuminati...
Early smoke detection for forest fire prevention requires accurate and temporally stable decisions under dynamic clutter, tiny long-range targets, atmospheric degradation, and partial sensor unreliability. This paper presents FS-YOLOv3, a reliability...
Integrating energy storage and hydrogen energy into wind–solar–hydrogen storage microgrids can effectively mitigate the intermittency, volatility, and unpredictability of renewable energy. Aiming at tackling excessively idealized traditio...
Accurate estimation of insulation resistance, denoted as Riso, in high-voltage direct current energy storage systems plays a pivotal role in leakage protection and thermal runaway suppression. Conventional physical measurement techniques are inherent...
Deep learning models have achieved remarkable performance in radar jamming recognition, yet they remain highly vulnerable to adversarial attacks—small, carefully crafted perturbations that cause misclassification—posing a critical threat...
Radio Frequency Identification (RFID) is a fundamental enabling technology for the Internet of Things (IoT), providing automatic identification and data retrieval capabilities for various applications. With the increasing prevalence of ubiquitous and...
Cross-session variability in neural signal statistics often leads to distribution shifts that substantially degrade the generalization performance of motor decoding models. To address this challenge, this study proposes a CNN-enhanced Transformer wit...
3D reconstruction of construction scenes is an important enabling technology for digital and intelligent construction project management. Recurring foreground occluders and dynamic disturbances in tower-crane imagery can destabilize image registratio...
The co-existence of event-driven critical traffic and time-driven periodic traffic inevitably exacerbates cross-layer network congestion in resource-constrained edge environments. Although hybrid protocol architectures integrating the Constrained App...
Graph Neural Networks (GNNs) have achieved strong performance on graph-structured data via neighborhood message passing. Recent studies on GNNs suggest that not all feature dimensions benefit equally from message passing, motivating preference-guided...
This paper presents an 11-bit linear-tracking digital low-dropout (DLDO) regulator with variable step size control and transient enhancement (TE) techniques to achieve low output voltage overshoot/undershoot and high current efficiency. To address ov...
The growing adoption of context-aware reasoning in Large Language Model (LLM)-based applications, particularly through retrieval-augmented and prompt-enriched architectures, has substantially broadened the utility of LLMs in specialized, real-world s...
With the increasing number of stroke patients, there is a growing demand for lower-limb rehabilitation exoskeletons. While current mechanisms are preferred for their light weight and dexterous design in limited environments, the alignment of the stru...
Real-time industrial anomaly detection is increasingly shifting from cloud-based diagnosis to edge intelligence deployed close to machines. However, practical industrial scenarios are constrained by scarce fault samples, unknown anomaly types, cross-...
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