The rapid evolution of wireless technologies necessitates automated design frameworks to address antenna miniaturization and performance optimization within constrained development cycles. This study demonstrates a machine learning enhanced workflow integrating Quantum-Behaved Dynamic Particle Swarm Optimization (QDPSO) with ANSYS HFSS simulations to accelerate antenna design. The QDPSO algorithm autonomously optimized loop dimensions in 11.53 seconds, achieving a resonance frequency of 1.4208 GHz a 12.7 percent reduction compared to conventional 1.60 GHz designs. Machine learning models (SVM, Random Forest, XGBoost, and Stacked ensembles) predicted resonance frequencies in 0.75 seconds using 936 simulation datasets, with stacked models showing superior training accuracy (R2=0.9825) and SVM demonstrating optimal validation performance (R2=0.7197). The complete design cycle, encompassing optimization, prediction, and ANSYS validation, required 12.42 minutes on standard desktop hardware (Intel i5-8500, 16GB RAM), contrasting sharply with the 50-hour benchmark of PSADEA-based approaches. This 240 times of acceleration eliminates traditional trial-and-error methods that often extend beyond seven expert-led days. The system enables precise specifications of performance targets with automated generation of fabrication-ready parameters, particularly benefiting compact consumer devices requiring rapid frequency tuning. By bridging AI-driven optimization with CAD validation, this framework reduces engineering workloads while ensuring production-ready designs, establishing a scalable paradigm for next-generation RF systems in 6G and IoT applications.

This study presents an integrated methodology for fault detection in wind turbine blades using 3D-printed scaled models, finite element simulations, experimental modal analysis, and machine learning techniques. A scaled model of the NREL 5MW blade was fabricated using 3D printing, and crack-type damages were introduced at critical locations. Finite Element Analysis was employed to predict the impact of these damages on the natural frequencies, with the results validated through controlled hammer impact tests. Vibration data was processed to extract both time-domain and frequency-domain features, and key discriminative variables were identified using statistical analyses (ANOVA). Machine learning classifiers, including Support Vector Machine and K-Nearest Neighbors, achieved classification accuracies exceeding 94%. The results revealed that vibration modes 3, 4, and 6 are particularly sensitive to structural anomalies for this blade. This integrated approach confirms the feasibility of combining numerical simulations with experimental validations and paves the way for structural health monitoring systems in wind energy applications.

Magnetism is widely used for the wireless localization and actuation of robots and devices for medical procedures. However, current static magnetic localization methods suffer from large required magnets and are limited to only five degrees of freedom due to a fundamental constraint of the rotational symmetry around the magnetic axis. We present the small-scale magneto-oscillatory localization (SMOL) method, which is capable of wirelessly localizing a millimeter-scale tracker with full six degrees of freedom in deep biological tissues. The SMOL device uses the temporal oscillation of a mechanically resonant cantilever with a magnetic dipole to break the rotational symmetry, and exploits the frequency-response to achieve a high signal-to-noise ratio with sub-millimeter accuracy over a large distance of up to 12 centimeters and quasi-continuous refresh rates up to 200 Hz. Integration into real-time closed-loop controlled robots and minimally-invasive surgical tools are demonstrated to reveal the vast potential of the SMOL method.

Recent years have seen significant growth of quantum technologies, and specifically quantum sensing, both in terms of the capabilities of advanced platforms and their applications. One of the leading platforms in this context is nitrogen-vacancy (NV) color centers in diamond, providing versatile, high-sensitivity, and high-resolution magnetic sensing. Nevertheless, current schemes for spin resonance magnetic sensing (as applied by NV quantum sensing) suffer from tradeoffs associated with sensitivity, dynamic range, and bandwidth. Here we address this issue, and implement machine learning tools to enhance NV magnetic sensing in terms of the sensitivity/bandwidth tradeoff in large dynamic range scenarios. We experimentally demonstrate this new approach, reaching an improvement in the relevant figure of merit by a factor of up to 5. Our results promote quantum machine learning protocols for sensing applications towards more feasible and efficient quantum technologies.

Hand gesture recognition using radar often relies on computationally expensive fast Fourier transforms. This paper proposes an alternative approach that bypasses fast Fourier transforms using resonate-and-fire neurons. These neurons directly detect the hand in the time-domain signal, eliminating the need for fast Fourier transforms to retrieve range information. Following detection, a simple Goertzel algorithm is employed to extract five key features, eliminating the need for a second fast Fourier transform. These features are then fed into a recurrent neural network, achieving an accuracy of 98.21% for classifying five gestures. The proposed approach demonstrates competitive performance with reduced complexity compared to traditional methods

Extracting information from radiofrequency (RF) signals using artificial neural networks at low energy cost is a critical need for a wide range of applications from radars to health. These RF inputs are composed of multiples frequencies. Here we show that magnetic tunnel junctions can process analogue RF inputs with multiple frequencies in parallel and perform synaptic operations. Using a backpropagation-free method called extreme learning, we classify noisy images encoded by RF signals, using experimental data from magnetic tunnel junctions functioning as both synapses and neurons. We achieve the same accuracy as an equivalent software neural network. These results are a key step for embedded radiofrequency artificial intelligence.

Developed at KAUST (King Abdullah University of Science and Technology), machine learning differentiates the gases according to the way they induce slight temperature changes in the sensor as they interact with it. Smart electronic sensors have applications from medical diagnostics to the detection of industrial gas leaks. The challenge is to accurately detect the target gas among the complex mixture of chemicals typically found in the air, said Usman Yaqoob, a postdoc in the labs of Mohammad Younis, who led the research. "Existing sensing technologies still suffer from cross-sensitivity," Yaqoob said in a statement. On the hardware side is a heated strip of silicon called a microbeam resonator.

The high demand for fabricating microresonators with desired optical properties has led to various techniques to optimize geometries, mode structures, nonlinearities and dispersion. Depending on applications, the dispersion in such resonators counters their optical nonlinearities and influences the intracavity optical dynamics. In this paper, we demonstrate the use of a machine learning (ML) algorithm as a tool to determine the geometry of microresonators from their dispersion profiles. The training dataset with ~460 samples is generated by finite element simulations and the model is experimentally verified using integrated silicon nitride microresonators. Two ML algorithms are compared along with suitable hyperparameter tuning, out of which Random Forest (RF) yields the best results. The average error on the simulated data is well below 15%.

Diamond nanoparticles (nanodiamonds) offer an attractive opportunity to achieve high spatial resolution because they can easily be close to the target within a few 10 nm simply by attaching them to its surface [8]. A physical model for such a randomly oriented nanodiamond ensemble (NDE) is available [8], but the complexity of actual experimental conditions still limits the accuracy of deducing magnetic fields. Here, we demonstrate magnetic field imaging with high accuracy of 1.8 µT combining NDE and machine learning without any physical models. We also discover the field direction dependence of the NDE signal, suggesting the potential application for vector magnetometry and improvement of the existing model. Our method further enriches the performance of NDE to achieve the accuracy to visualize mesoscopic current and magnetism in atomic-layer materials [9-13] and to expand the applicability in arbitrarily shaped materials [7], including living organisms [14, 15]. This achievement will bridge machine learning and quantum sensing for accurate measurements. The nitrogen-vacancy (NV) center in diamond [Figure 1(a)] is a point defect where a nitrogen atom replaces a carbon atom in the lattice accompanied by a neighboring vacancy. By measuring its photoluminescence intensity while irradiating the laser and microwaves, NV's electron spin resonance can be detected, which is called optically detected microwave resonance (ODMR) [16]. As the NV's spin level splits against the magnetic field in the direction of the NV symmetry axis (111) due to the Zeeman effect [17], the determination of the ODMR frequency serves as quantum sensing of the field [3]. To obtain a nanoscale spatial resolution, we must attach the NV centers close to the sample within a few 10 nm [18].