Search Results (110)

Hand gesture recognition (HGR) systems play a critical role in modern intelligent transportation frameworks by enabling reliable communication between pedestrians, traffic operators, and autonomous vehicles. This work presents a novel traffic hand gesture recognition method that combines nine grayscale radar images captured from multiple millimeter-wave radar nodes into a single RGB representation through an optimized rotation–shift fusion strategy. This transformation preserves complementary spatial information while minimizing inter-image interference, enabling deep learning models to more effectively utilize the distinctive micro-Doppler and spatial patterns embedded in radar measurements. Extensive experimental studies were conducted to verify the model’s performance, demonstrating that the proposed RGB fusion approach provides higher classification accuracy than single-sensor or unfused representations. In addition, the proposed model outperformed state-of-the-art methods in the literature with an accuracy of 92.55%. These results highlight its potential as a lightweight yet powerful solution for reliable gesture interpretation in future intelligent transportation and human–vehicle interaction systems. Full article

Background/Objectives: Surgical clipping remains a fundamental treatment modality for intracranial aneurysms, particularly in complex cases and those not amenable to endovascular approaches. However, it is associated with several technical challenges and potential complications that may compromise patient outcomes. This study aims to identify and analyze the most frequent and critical intraoperative pitfalls encountered during microsurgical clipping. We discuss experience-based strategies for avoiding these complications as well as practical solutions for managing them effectively when they occur. Methods: A retrospective review of our institutional experience with surgically treated intracranial aneurysms is reported. The study includes a comprehensive analysis of complications encountered across a defined series of cases, along with representative clinical cases. Results: Several categories of complications were identified, including aneurysm rupture, incomplete clipping and aneurysm remnant, vessel stenosis and brain ischemia, and new-onset seizures. Specific microsurgical techniques, intraoperative tools (e.g., indocyanine-green angiography, neurophysiological monitoring, micro-Doppler flowmetry evaluation), and decision-making algorithms are discussed to help mitigate these risks. For each scenario, tailored rescue strategies are outlined based on both the literature evidence and our clinical experience. Conclusions: Awareness of the potential pitfalls in aneurysm clipping and a structured approach to their prevention and management are crucial for optimizing surgical outcomes, and for preparing young vascular neurosurgeons. Through a combination of technical refinement and scenario-based preparedness, many complications can be anticipated and effectively addressed. Full article

To address the high computational cost and significant resource consumption of radar Doppler-based target recognition, which limits its application in real-time embedded systems, this paper proposes a lightweight CNN (Convolutional Neural Network) approach for radar target identification. The proposed approach builds a deep convolutional neural network using range-Doppler maps, and leverages data collected by frequency-modulated continuous wave (FMCW) radar from targets such as drones, vehicles, and pedestrians. This method enables efficient object detection and classification across a wide range of scenarios. To improve the performance of the proposed model, this study incorporates a coordinate attention mechanism within the convolutional neural network. This mechanism fine-tunes the network’s focus by dynamically adjusting the weights of different feature channels and spatial regions, allowing it to concentrate on the most informative areas. Experimental results show that the foundational architecture of the proposed deep learning model, RangDopplerNet Type-1, effectively captures micro-Doppler features from range-Doppler maps across diverse targets. This capability enables precise detection and classification, with the model achieving an impressive average recognition accuracy of 96.71%. The enhanced network architecture, RangeDopplerNet Type-2, reached an average accuracy of 98.08%, while retaining a compact footprint of only 403 KB. Compared with standard lightweight models such as MobileNetV2, the proposed architecture reduces model size by 97.04%. This demonstrates that, while improving accuracy, the proposed architecture also significantly reduces both computational and storage overhead.The deep learning model introduced in this study is specifically tailored for deployment on resource-constrained platforms, including mobile and embedded systems. It provides an efficient and practical approach for development of miniaturized low-power devices. Full article

Polarimetric radar offers strong potential for UAV detection, but time-varying polarization induced by rotor rotation leads to unstable echoes, degrading feature consistency and recognition accuracy. This paper proposes a unified framework that combines rotor phase compensation, adaptive polarization filtering, and a multi-branch polarization aware fusion network (MPAF-Net) to enhance micro-Doppler features. The compensation scheme improves harmonic visibility through rotation-angle-based phase alignment and polarization optimization, while MPAF-Net exploits complementary information across polarimetric channels for robust classification. The framework is validated on both simulated and measured UAV radar data under varying SNR conditions. Results show an average harmonic SNR gain of approximately 1.2 dB and substantial improvements in recognition accuracy: at 0 dB, the proposed method achieves 66.7% accuracy, about 10% higher than Pauli and Sinclair decompositions, and at 20 dB, it reaches 97.2%. These findings confirm the effectiveness of the proposed approach for UAV identification in challenging radar environments. Full article

Intraoperative ultrasound (IOUS) is an increasingly adopted adjunctive intraoperative visualization method in spinal tumor surgery, offering real-time imaging that improves lesion localization, exposure planning, and resection control. This paper focuses on IOUS findings in rare intradural entities (neuroenteric/respiratory cysts, chronic spinal subdural hematoma, tethered cord/scarring, intradural extramedullary hemangioblastomas, and arachnoid cysts) where evidence remains limited. Across these lesions, IOUS typically depicts cysts as anechoic or hypoechoic cavities with definable walls and occasional septations; CSSDH is also delimited by hypoechoic subdural collections bounded by echogenic membranes; hemangioblastomas, as well as circumscribed, homogeneous nodules often with cystic components; and arachnoid webs/cysts with their boundaries and subtle subarachnoid communications. Doppler and micro-Doppler can delineate feeding and draining vessels in highly vascular tumors, while shear wave elastography provides quantitative stiffness changes that support effective detethering. IOUS complements preoperative MRI, shortens exposure, helps tailor bone and dural openings, and allows immediate assessment of residual disease. Taken together, current data and our experience support IOUS as a safe, cost-effective, and versatile intraoperative tool for rare intradural spinal pathology, while underscoring the need for prospective studies to refine sonographic criteria and validate outcome benefits. Full article

The rapid increase in production of small unmanned rotorcrafts (SURs) has made real-time drone surveillance critical for airspace security. Effective SUR detection is essential for maintaining aviation safety, protecting privacy, and ensuring public security. However, conventional radar systems struggle to detect hovering SURs due to their low velocity and small radar cross-section (RCS), which make them nearly indistinguishable from stationary clutter. To address this issue, this paper proposes a hovering SUR detection method through identifying the micro-Doppler signal (MDS). By applying the multiple reassignment squeeze processing and exhaustive Hough transform, the proposed approach effectively enhances the accumulation of micro-Doppler signal generated by the rotor blades, which enables the separation of hovering SUR signals from stationary clutter. Numerical simulations and field experiments validate the effectiveness of the proposed method, demonstrating its potential for micro-Doppler signal detection using a UHF-band horizontally co-polarized radar system. Full article

This study presents a comparative performance analysis of three sensor technologies—microphone, magnetic pickup, and laser Doppler vibrometer—for capturing string vibration under varied excitation conditions: striking, plectrum plucking, and wire plucking. Two different magnetic pickups are included in the comparison. Measurements were taken at multiple excitation levels on a simplified electric guitar mounted on a stable platform with repeatable excitation mechanisms. The analysis focuses on each sensor’s capacity to resolve fine-scale waveform features during the initial attack while also taking into account its capability to measure general changes in instrument dynamics and timbre. We evaluate their ability to distinguish vibro-acoustic phenomena resulting from changes in excitation method and strength as well as measurement location. Our findings highlight the significant influence of sensor choice on observable string vibration. While the microphone captures the overall radiated sound, it lacks the required spatial selectivity and offers poor SNR performance 34 dB lower then other methods. Magnetic pickups enable precise string-specific measurements, offering a compelling balance of accuracy and cost-effectiveness. Results show that their low-pass frequency characteristic limits temporal fidelity and must be accounted for when analysing general sound timbre. Laser Doppler vibrometers provide superior micro-temporal fidelity, which can have critical implications for physical modeling, instrument design, and advanced audio signal processing, but have severe practical limitations. Critically, we demonstrate that the required optical target, even when weighing as little as 0.1% of the string’s mass, alters the string’s vibratory characteristics by influencing RMS energy and spectral content. Full article

Micro-Shell Resonator Gyroscopes have obvious SWaP (Size, Weight and Power) advantages and applicable accuracy for the autonomous navigation of Unmanned Ground Vehicles (UGVs), especially under GNSS-denied environments. When the Micro-Shell Resonator Gyroscope Rotary Inertial Navigation System (MSRG–RINS) operates in the whole-angle mode, its bias varies as an even-harmonic function of the pattern angle, which leads to difficulty in estimating and compensating the bias based on the MSRG in the process of attitude measurement. In this paper, an attitude measurement method based on virtual rotation self-calibration and rotary modulation is proposed for the MSRG–RINS to address this problem. The method utilizes the characteristics of the two operating modes of the MSRG, the force-rebalanced mode and whole-angle mode, to perform virtual rotation self-calibration, thereby eliminating the characteristic bias of the MSRG. In addition, the reciprocating rotary modulation method is used to suppress the residual bias of the MSRG. Furthermore, the magnetometer-aided initial alignment of the MSRG–RINS is carried out and the state-transformation extended Kalman filter is adopted to solve the large misalignment-angle problem under magnetometer assistance so as to enhance the rapidity and accuracy of initial attitude acquisition. Results from real-world experiments substantiated that the proposed method can effectively suppress the influence of MSRG’s bias on attitude measurement, thereby achieving high-precision autonomous navigation in GNSS-denied environments. In the 1 h, 3.7 km, long-range in-vehicle autonomous navigation experiments, the MSRG–RINS, integrated with a Laser Doppler Velocimetry (LDV), attained a heading accuracy of 0.35° (RMS), a horizontal positioning error of 4.9 m (RMS), and a distance-traveled accuracy of 0.24% D. Full article

Effective Cardiopulmonary Resuscitation (CPR) requires precise chest compression depth, but current out-of-hospital monitoring technologies face limitations. This study introduces a method using frequency-modulated continuous-wave (FMCW) radar to remotely and accurately monitor chest compressions. FMCW radar captures range, Doppler, and angular data, and we utilize micro-Doppler signatures for detailed motion analysis. By integrating Doppler shifts over time, chest displacement is estimated. We compare a regression model based on maximum Doppler frequency with deep convolutional neural networks (DCNNs) trained on spectrograms generated via short-time Fourier transform (STFT) and the Wigner–Ville distribution (WVD). The regression model achieved a root mean square error (RMSE) of 0.535 cm. The STFT-based DCNN improved accuracy with an RMSE of 0.505 cm, while the WVD-based DCNN achieved the best performance with an RMSE of 0.447 cm, representing an 11.5% improvement over the STFT-based DCNN. These findings highlight the potential of combining FMCW radar and deep learning to provide accurate, real-time chest compression depth measurement during CPR, supporting the development of advanced, non-contact monitoring systems for emergency medical response. Full article

Maritime search and rescue (SAR) demands reliable sensing and communication under sea clutter. Emerging integrated sensing and communication (ISAC) technology provides new opportunities for the development and modernization of maritime radio communication, particularly in relation to search and rescue. This study investigated the dual-function capability of a phase-coded frequency modulated continuous wave (FMCW) system for search and rescue at sea, in particular for life signs detection in the presence of sea clutter. The detection capability of the FMCW system was enhanced by applying phase-modulated codes on chirps, and radar-centric communication function is supported simultaneously. Various phase-coding schemes including Barker, Frank, Zadoff-Chu (ZC), and Costas were assessed by adopting the peak sidelobe level and integrated sidelobe level of the ambiguity function of the established signals. The interplay of sea waves was represented by a compound K-distribution model. A multiple-input multiple-output (MIMO) architecture with the ZC code was adopted to detect multiple objects with a high resolution for micro-Doppler determination by taking advantage of spatial coherence with beamforming. The effectiveness of the proposed method was validated on the 4-transmit, 4-receive (4 × 4) MIMO system with ZC coded FMCW signals. Monte Carlo simulations were carried out incorporating different combinations of targets and user configurations with a wide range of signal-to-noise ratio (SNR) settings. Extensive simulations demonstrated that the mean squared error (MSE) of range estimation remained low across the evaluated SNR setting, while communication performance was comparable to that of a baseline orthogonal frequency-division multiplexing (OFDM)-based system. The high performance demonstrated by the proposed method makes it a suitable maritime search and rescue solution, in particular for vision-restricted situations. Full article

This study investigates the use of a custom-built 10 GHz continuous wave micro-Doppler radar system to analyze external vibrations of idling vehicles under various conditions. Scenarios included different gear engagements with one occupant and parked gear with up to four occupants. Motivated by security concerns, such as the threat posed by idling vehicles with multiple occupants, the research explores how micro-Doppler signatures can indicate vehicle readiness to move. Experiments focused on a mid-size SUV, with similar trends seen in other vehicles. Radar data were compared to in situ accelerometer measurements, confirming that the radar system can detect subtle frequency changes, especially during gear shifts. The system’s sensitivity enables it to distinguish variations tied to gear state and passenger load. Extracted features like frequency and magnitude show strong potential for use in machine learning models, offering a non-invasive, remote sensing method for reliably identifying vehicle operational states and occupancy levels in security or monitoring contexts. Spectrogram and PSD analyses reveal consistent tonal vibrations around 30 Hz, tied to engine activity, with harmonics at 60 Hz and 90 Hz. Gear shifts produce impulse signatures primarily below 20 Hz, and transient data show distinct peaks at 50, 80, and 100 Hz. Key features at 23 Hz and 45 Hz effectively indicate engine and gear states. Radar and accelerometer data align well, supporting the potential for remote sensing and machine learning-based classification. Full article

The identification of vital signs is becoming increasingly important in various applications, including healthcare monitoring, security, smart homes, and locating entrapped persons after disastrous events, most of which are achieved using continuous-wave radars and ultra-wideband systems. Operating frequency and transmission power are important factors to consider when conducting earthquake search and rescue (SAR) operations in urban regions. Poor communication infrastructure can also impede SAR operations. This study proposes a method for vital sign detection using an integrated sensing and communication (ISAC) system where a unified orthogonal frequency division multiplexing (OFDM) signal was adopted, and it is capable of sensing life signs and carrying out communication simultaneously. An ISAC demonstration system based on software-defined radios (SDRs) was initiated to detect respiratory and heartbeat rates while maintaining communication capability in a typical office environment. The specially designed OFDM signals were transmitted, reflected from a human subject, received, and processed to estimate the micro-Doppler effect induced by the breathing and heartbeat of the human in the environment. According to the results, vital signs, including respiration and heartbeat rates, have been accurately detected by post-processing the reflected OFDM signals with a 1 MHz bandwidth, confirmed with conventional contact-based detection approaches. The potential of dual-function capability of OFDM signals for sensing purposes has been verified. The principle and method developed can be applied in wider ISAC systems for search and rescue purposes while maintaining communication links. Full article

This study proposes a novel method that uses radar for markerless motion classification by using effective features derived from micro-Doppler signals. The training phase uses three-dimensional marker coordinates captured by a motion-capture system to construct basis functions, which enable modeling of micro-motions of the human body. During the testing phase, motion classification is performed without markers, relying solely on radar signals. The feature vectors are generated by applying cross-correlation between the received radar signal and the basis functions, then compressed using principal component analysis, and classified using a simple nearest-neighbor algorithm. The proposed method achieves nearly 100% classification accuracy with a compact feature set and is accurate even at high signal-to-noise ratios. Experimental results demonstrate that to optimize training data and increase computational efficiency, the sampling duration and sampling interval must be set appropriately. Full article

Micro-Doppler signatures play a crucial role in capturing target features for the radar classification task, and the time–frequency distribution method is widely used to represent micro-Doppler signatures in many applications including human activities, ground moving target identification, and different types of drones distinguishing. However, most existing studies that utilize radar micro-Doppler spectrograms often require extended observation times to effectively represent the cyclostationarity and periodic modulation of radar signals to achieve promising classification results. In addition, the presence of noise in real-world environments poses challenges by generating weak micro-Doppler features and a low signal-to-noise ratio (SNR), leading to a significant decline in classification accuracy. In this paper, we present a novel one-dimensional (1D) denoising and classification cascaded framework designed for low-resolution radar targets using a micro-Doppler spectrum. This framework provides an effective signal-based solution for feature extraction and recognition from the single-frame micro-Doppler spectrum in a conventional pulsed radar system, which boasts high real-time efficiency and low computation requirements under conditions of low resolution and a short dwell time. Specifically, the proposed framework is implemented using two cascaded subnetworks: Firstly, for radar micro-Doppler spectrum denoising, we propose an improved 1D DnCNN subnetwork to enhance noisy or weak micro-Doppler signatures. Secondly, an AlexNet subnetwork is cascaded for the classification task, and the joint loss is calculated to update the denoising subnetwork and assist with optimal classification performance. We have conducted a comprehensive set of experiments using six types of targets with a ground surveillance radar system to demonstrate the denoising and classification performance of the proposed cascaded framework, which shows significant improvement over separate training of denoising and classification models. Full article

Driver status plays a critical role in ensuring driving safety. However, the current visual recognition-based methods for detecting driver actions and status are often limited to factors such as ambient light condition, occlusion, and privacy concerns. In contrast, millimeter-wave radar offers various advantages such as high accuracy, ease of integration, insensitivity to light condition, and low cost; therefore, it has been widely used for monitoring vital signals and in action recognition. Despite this, the existing studies on driver action recognition have been hindered by limited accuracy and a narrow range of detectable actions. In this study, we utilized a 77 GHz millimeter-wave frequency-modulated continuous-wave radar to construct a dataset encompassing seven types of driver head–hand cooperative actions. Furthermore, a deep learning network model based on VGG16-LSTM-CBAM using micro-Doppler spectrograms as input was developed for action classification. The experimental results demonstrated that, compared to the existing CNN-LSTM and ALEXNET-LSTM networks, the proposed network achieves a classification accuracy of 99.16%, effectively improving driver action detection. Full article