Search Results (1,395)

The seamless handover of mobile devices is critical for maximizing the potential of smart city applications, which demand uninterrupted connectivity, ultra-low latency, and performance in diverse environments. Fifth-generation (5G) and beyond-5G networks offer advancements in massive connectivity and ultra-low latency by leveraging advanced technologies like millimeter wave, massive machine-type communication, non-orthogonal multiple access, and beam forming. However, challenges persist in ensuring smooth handovers in dense deployments, especially in higher frequency bands and with increased user mobility. This paper presents an adaptive handover management scheme that utilizes reinforcement learning to optimize handover decisions in dynamic environments. The system selects the best target cell from the available neighbor cell list by predicting key performance indicators, such as reference signal received power and the signal–interference–noise ratio, while considering the fixed time-to-trigger and hysteresis margin values. It dynamically adjusts handover thresholds by incorporating an offset based on real-time network conditions and user mobility patterns. This adaptive approach minimizes handover failures and the ping-pong effect. Compared to the baseline LIM2 model, the proposed system demonstrates a 15% improvement in handover success rate, a 3% improvement in user throughput, and an approximately 6 sec reduction in the latency at 200 km/h speed in high-mobility scenarios. Full article

The efficient acquisition and processing of large-scale terrain data has always been a focal point in the field of photogrammetry. Particularly in complex mountainous regions characterized by clouds, terrain, and airspace environments, the window for data collection is extremely limited. This paper investigates the use of airborne millimeter-wave InSAR systems for efficient terrain mapping under such challenging conditions. The system’s potential for technical application is significant due to its minimal influence from cloud cover and its ability to acquire data in all-weather and all-day conditions. Focusing on the key factors in airborne InSAR data acquisition, this study explores advanced route planning and ground control measurement techniques. Leveraging radar observation geometry and global SRTM DEM data, we simulate layover and shadow effects to formulate an optimal flight path design. Additionally, the study examines methods to reduce synchronous ground control points in mountainous areas, thereby enhancing the rapid acquisition of terrain data. The results demonstrate that this approach not only significantly reduces field work and aviation costs but also ensures the accuracy of the mountain surface data generated by airborne millimeter-wave InSAR, offering substantial practical application value by reducing field work and aviation costs while maintaining data accuracy. Full article

This paper presents a W-band power amplifier monolithic microwave integrated circuit (MMIC) that is designed for high-precision millimeter-wave systems and fabricated using a 0.1 µm GaAs pHEMT process. The amplifier’s stability was evaluated using the network determinant function, ensuring robust performance under both linear and nonlinear conditions. Simultaneous matching for gain and output power was achieved with minimal passive elements. The developed power amplifier MMIC exhibits a linear gain exceeding 20 dB and an input return loss greater than 6 dB across the 88–98 GHz range. It delivers an output power of 23.8–24.1 dBm with a power gain of 17.3–17.9 dB in the 88–97 GHz range and achieves a maximum power-added efficiency (PAE) of 24% at 94 GHz. Full article

In this paper, a new U-shaped ring spoof surface plasmon polariton (SSPP) structure is proposed as part of a bandpass filter (BPF) combined with short-circuited stubs (SCSs). The U-shaped ring unit offers miniaturization and multiple adjustable parameters. Furthermore, the lower and upper cutoff frequencies of the passband for the BPF can be adjusted by modifying the structural parameters of the SSPP units and SCSs, respectively. To validate the design, a prototype filter was first created with a frequency range of 2 to 3.7 GHz for the passband and an extended stopband that reached up to 15 GHz. On the basis of the designed BPF, a tri-band filter was realized by introducing multiple transmission zeros by loading multiple open-circuited stubs (OCSs) onto the transmission portion of SSPPs. The center frequencies of the three passbands were 1.20 GHz, 2.03 GHz, and 2.96 GHz, respectively. At the same time, the upper stopband rejection reached up to 12 GHz with an attenuation of −30 dB, about 10 times the center frequency of the first passband. The experimental results demonstrate a strong correlation between the measured and simulated outcomes, thereby validating the proposed structure and design methodology. Notably, the filter measures only 0.38λg × 0.13λg, highlighting its compact size as a significant advantage. Full article

Contact-free concealed object detection using passive millimeter-wave imaging (PMMWI) sensors is a challenging task due to a low signal-to-noise ratio (SNR) and nonuniform illumination affecting the captured image’s quality. The nonuniform illumination also generates a higher false positive rate due to the limited ability to differentiate small hidden objects from the background of images. Several concealed object detection models have demonstrated outstanding performance but failed to combat the above-mentioned challenges concurrently. This paper proposes a novel three-stage cascaded framework named BWFER-YOLOv8, which implements a new alpha-reshuffled bootstrap random sampling method in the first stage, followed by image reconstruction using an adaptive Wiener filter in the second stage. The third stage uses a novel FER-YOLOv8 architecture with a custom-designed feature extraction and regularization (FER) module and multiple regularized convolution (Conv_Reg) modules for better generalization capability. The comprehensive quantitative and qualitative analysis reveals that the proposed framework outperforms the state-of-the-art tiny YOLOv3 and YOLOv8 models by achieving 98.1% precision and recall in detecting concealed weapons. The proposed framework significantly reduces the false positive rate, by up to 1.8%, in the detection of hidden small guns. Full article

The detection of Foreign Object Debris (FOD) is crucial for maintaining safety in critical areas like airport runways. This paper presents a comprehensive review of FOD detection technologies, covering traditional, radar-based, and artificial intelligence (AI)-driven methods. Manual visual inspection and optical sensors, while widely used, are limited in scalability and reliability under adverse conditions. Radar technologies, such as millimeter-wave radar and synthetic aperture radar, offer robust performance, with advancements in algorithms and sensor fusion significantly enhancing their effectiveness. AI approaches, employing supervised and unsupervised learning, demonstrate potential for automating detection and improving precision, although challenges such as limited datasets and high computational demands persist. This review consolidates the recent progress across these domains, highlighting the need for integrated systems that combine radar and AI to improve adaptability, scalability, and small-FOD detection. By addressing these limitations, the study provides insights into future research directions and the development of innovative FOD detection solutions, contributing to safer and more efficient operational environments. Full article

The advent of millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, coupled with reconfigurable intelligent surfaces (RISs), presents a significant opportunity for advancing wireless communication technologies. This integration enhances data transmission rates and broadens coverage areas, but challenges in channel estimation (CE) remain due to the limitations of the signal processing capabilities of RIS. To address this, we propose an adaptive channel estimation framework comprising two algorithms: log-sum normalized least mean squares (Log-Sum NLMS) and hybrid normalized least mean squares-normalized least mean fourth (Hybrid NLMS-NLMF). These algorithms leverage the sparse nature of mmWave channels to improve estimation accuracy. The Log-Sum NLMS algorithm incorporates a log-sum penalty in its cost function for faster convergence, while the Hybrid NLMS-NLMF employs a mixed error function for better performance across varying signal-to-noise ratio (SNR) conditions. Our analysis also reveals that both algorithms have lower computational complexity compared to existing methods. Extensive simulations validate our findings, with results illustrating the performance of the proposed algorithms under different parameters, demonstrating significant improvements in channel estimation accuracy and convergence speed over established methods, including NLMS, sparse exponential forgetting window least mean square (SEFWLMS), and sparse hybrid adaptive filtering algorithms (SHAFA). Full article

Millimeter-wave (MMW) radar is capable of providing high temporal–spatial measurements of the ocean surface. Some topics, such as the characterization of the radar echo, have attracted widespread attention from researchers. However, most existing research studies focus on the backscatter of the ocean surface at low microwave bands, while the sea surface backscattering mechanism in the 77 GHz frequency band remains not well interpreted. To address this issue, in this paper, the investigation of the scattering mechanism is carried out for the 77 GHz frequency band ocean surface at small incidence angles. The backscattering coefficient is first simulated by applying the quasi-specular scattering model and the corrected scattering model of geometric optics (GO4), using two different ocean wave spectrum models (the Hwang spectrum and the Kudryavtsev spectrum). Then, the dependence of the sea surface normalized radar cross section (NRCS) on incidence angles, azimuth angles, and sea states are investigated. Finally, by comparison between model simulations and the radar-measured data, the 77 GHz frequency band scattering characterization of sea surfaces at the near-nadir incidence is verified. In addition, experimental results from the wave tank are shown, and the difference in the scattering mechanism is further discussed between water surfaces and oceans. The obtained results seem promising for a better understanding of the ocean surface backscattering mechanism in the MMW frequency band. It provides a new method for fostering the usage of radar technologies for real-time ocean observations. Full article

This paper explores the potential of phase change materials (PCM) for dynamically tuning the frequency response of a dumbbell u-slot defected ground structure (DGS)-based band stop filter. The DGSs are designed using co-planar waveguide (CPW) line structure on top of a barium strontium titanate (Ba_0.6Sr_0.4TiO₃) (BST) thin film. BST film is used as the high-dielectric material for the planar DGS. Lower insertion loss of less than −2 dB below the lower cutoff frequency, and enhanced band-rejection with notch depth of −39.64 dB at 27.75 GHz is achieved by cascading two-unit cells, compared to −12.26 dB rejection with a single-unit cell using BST thin film only. Further tunability is achieved by using a germanium telluride (GeTe) PCM layer. The electrical properties of PCM can be reversibly altered by transitioning between amorphous and crystalline phases. We demonstrate that incorporating a PCM layer into a DGS device allows for significant tuning of the resonance frequency: a shift in resonance frequency from 30.75 GHz to 33 GHz with a frequency shift of 2.25 GHz is achieved, i.e., 7.32% tuning is shown with a single DGS cell. Furthermore, by cascading two DGS cells with PCM, an even wider tuning range is achievable: a shift in resonance frequency from 27 GHz to 30.25 GHz with a frequency shift of 3.25 GHz is achieved, i.e., 12.04% tuning is shown by cascading two DGS cells. The results are validated through simulations and measurements, showcasing excellent agreement. Full article

Wheat is one of the most extensively grown crops in the world; however, its productivity is reduced due to salinity. This study focused on millimeter wave (MMW) irradiation to clarify the salt-stress tolerance mechanism in wheat. In the present study, wheat-root growth, which was suppressed to 77.6% of the control level under salt stress, was recovered to the control level by MMW irradiation. To reveal the salt-stress tolerance mechanism of MMW irradiation on wheat, a proteomic analysis was conducted. Proteins related to cell cycle, proliferation, and transport in biological processes, as well as proteins related to the nucleus, cytoskeleton, and cytoplasm within cellular components, were inversely correlated with the number of proteins. The results of the proteomic analysis were verified by immunoblot and other analyses. Among the proteins related to the scavenging reactive-oxygen species, superoxide dismutase and glutathione reductase accumulated under salt stress and further increased in the MMW-irradiated wheat. Among pathogen-related proteins, pathogenesis-related protein 1 and the Bowman–Birk proteinase inhibitor decreased under salt stress and recovered to the control level in the MMW-irradiated wheat. The present results indicate that MMW irradiation of wheat seeds improves plant-growth recovery from salt stress through regulating the reactive oxygen species-scavenging system and the pathogen-related proteins. These genes may contribute to the development of salt-stress-tolerant wheat through marker-assisted breeding and genome editing. Full article

This study presents a novel approach to remote speech recognition using a millimeter-wave micro-Doppler radar system operating at 94 GHz. By detecting micro-Doppler speech-related vibrations, the system enables non-contact and privacy-preserving speech recognition. Initial experiments used a piezoelectric crystal to simulate vocal cord vibrations, followed by tests with actual human speech. Advanced signal processing techniques, including short-time Fourier transform (STFT), were used to generate spectrograms and reconstruct speech signals. The system demonstrated high accuracy, with cross-correlation analysis quantitatively confirming a strong correlation between radar-reconstructed and original audio signals. These results validate the effectiveness of detecting and characterizing speech-related vibrations without direct audio recording. The findings have significant implications for applications in noisy industrial environments, enabling robust voice interaction capabilities, as well as in healthcare diagnostics and assistive technologies, where contactless and privacy-preserving solutions are essential. Future research will explore diverse real-world scenarios and the integration of advanced signal processing and machine learning techniques to further enhance accuracy and robustness. Full article

The wave optics processes in a Fabry–Pérot cavity with a length of about tens of millimeters are considered. Such cavities are used, among other applications, in optomechanical accelerometers for precise measurement of displacement of moving elements. A Fabry–Pérot cavity formed by a spherical and flat mirror is considered. The influence of parameters characterizing the alignment of the Fabry–Pérot cavity mirrors and the laser beam on the appearance of the higher order modes is investigated using numerical modeling. It is shown that the angle of inclination of the flat mirror of the cavity greatly affects the occurrence of higher order modes in addition to the fundamental mode. The levels of displacement of the axis of a spherical mirror in the vertical direction which do not cause the emergence of higher order modes is shown. The influence of the degree of displacement of the laser beam axis in the vertical direction relative to the symmetry axis of the Fabry–Pérot cavity is also investigated. Full article

Millimeter-wave automotive radar has become an essential tool for autonomous driving, providing reliable sensing capabilities under various environmental conditions. To reduce hardware size and cost, sparse arrays are widely employed in automotive radar systems. Additionally, because the targets detected by automotive radar typically exhibit sparsity, compressed sensing-based algorithms have been utilized for sparse array reconstruction, achieving superior performance. However, traditional compressed sensing algorithms generally assume that targets are located on a finite set of grid points and perform sparse reconstruction based on predefined grids. When targets are off-grid, significant off-grid errors can occur. To address this issue, we propose an automotive radar sparse reconstruction algorithm based on accelerated Atomic Norm Minimization (ANM). By using the Iterative Vandermonde Decomposition and Shrinkage Threshold (IVDST) algorithm, we can achieve fast ANM, which effectively mitigates off-grid errors while reducing reconstruction complexity. Furthermore, we adopt a Generalized Likelihood Ratio Test (GLRT) detector to eliminate noise and clutter in the automotive radar operating environment. Simulation results show that our proposed algorithm significantly improves reconstruction accuracy compared to the iterative soft threshold (IST) algorithm while maintaining the same computational complexity. The effectiveness of the proposed algorithm in practical applications is further validated through real-world data experiments, demonstrating its superior capability in clutter elimination. Full article

This paper presents a novel approach to fabricate substrate integrated waveguides (SIWs) on glass substrates with tin (Sn) through glass vias (TGVs) tailored for millimeter-wave applications. The fabrication process employs a custom-designed vacuum suctioning system to rapidly fill precise TGV holes in the glass substrate, which are formed by wafer-level glass reflow micromachining techniques with molten tin in a minute. This method offers a very fast and cost-effective alternative for complete via filling without voids compared to the conventional metallization techniques such as electroplating or sputtering. An SIW with a 3-dB cutoff frequency of 17.2 GHz was fabricated using the proposed process. The fabricated SIW shows an average insertion loss of 1.65 ± 0.54 dB across the 20–35 GHz range. These results highlight the potential of glass substrates with tin TGVs for fabricating millimeter-wave devices. Full article

Frequency-modulated continuous-wave (FMCW) millimeter-wave (mmWave) radar systems are increasingly utilized in environmental sensing due to their high range resolution and robust sensing ability in severe weather environments. However, mutual interference among radar systems significantly degrades the target detection capability. Recent advancements in interference mitigation utilizing deep learning (DL) approaches have demonstrated promising results. DL-based approaches typically have high computational costs, which makes them unsuitable for real-time applications with strict latency requirements and limited computing resources. In this paper, we propose an efficient solution for real-time radar interference mitigation. A lightweight transformer, which is smaller and faster than the baseline transformer, is designed to reduce interference. The integration of linear attention mechanisms with depthwise separable convolutions significantly reduces the network’s computational complexity while maintaining a comparable performance. In addition, a two-stage knowledge distillation (KD) process is deployed to compress the network and enhance its efficiency. The staged distillation approach alleviates the training difficulties associated with substantial differences between the teacher and student networks. Both simulated and real-world experiments demonstrate that the proposed method outperforms the state-of-the-art methods while achieving high processing speeds. Full article