Search Results (975)

Wideband spectrum sensing in Cognitive Radio Networks (CRNs) is challenging due to sparse primary user (PU) activity and noise clustering, which obscure signals and generate false alarms. Hence, a novel “Graph Discrete Wavelet Bayesian Kernel Boosted Decision Self-Attention Clustering Neural Network (GDWB-KBSC-NN)” is proposed. When sparse PU activity is masked by irregular interference bursts, traditional sensing algorithms misclassify weak transmissions as noise, leading to low detection reliability. To resolve this, the first hidden layer employs Discrete Wavelet Sparse Bayesian Kernel Analysis (DW-SBK), integrating Discrete Wavelet Packet Transform (DWPT), Sparse Bayesian Learning (SBL), and Kernel PCA. This restores the true sparse pattern of the spectrum, separates interference from actual PU signals, and enhances detection of weak channels. Additionally, PU signals are fragmented due to cross-scale activity drift, where dynamic bandwidth switching and variable burst durations disrupt temporal continuity. Therefore, the second layer incorporates Gradient Boosted Multi-Head Fuzzy Clustering (GB-MHFC), where Gradient Boosted Decision Trees (GBDT) model nonlinear spectral–temporal patterns, Multi-Head Self-Attention (MHSA) captures long- and short-range temporal dependencies, and Fuzzy C-Means Clustering (FCM) groups feature representations into stable PU activity modes, thereby reducing misclassifications and enhancing robustness under highly dynamic CRN conditions. The proposed method demonstrates superior performance with a maximum detection probability of 0.98, classification accuracy of 98%, lowest sensing error of 5.412%, and the fastest sensing time of 3.65 s. Full article

In response to the significant challenges posed by the rapid progress of multi-spectral detection technologies to traditional stealth techniques, this paper presents a flexible transparent metasurface structure that is compatible with radar and infrared stealth. It consists of multi-layer functional patterned indium tin oxide (ITO) films and a flexible polydimethylsiloxane (PDMS) substrate. The metasurface uses a high-duty-cycle multi-scale circular ring to achieve a microwave absorption bandwidth of 30 GHz and low infrared emissivity of 0.33 in an optimized ultra-thin 2.65 mm thickness system. The simulation results show that the metasurface achieves absorption exceeding 90% in the frequency range of 10.8–40.8 GHz, which covers common radar bands like X, Ku, K, and Ka. Furthermore, the structure exhibits polarization insensitivity and sustains stable absorption in a wide range of 60 degrees of transverse magnetic (TM) fields. Meanwhile, it decreases the radar cross-section (RCS) by more than 10 dB over a wide angular range even when bent. This study presents a feasible metasurface with ultra-thin, flexible, transparent, and multi-spectral compatibility for the next generation of stealth systems. Full article

Background: Research on vibrations induced by running has gained significant attention due to its implications for athletes’ performance, injury prevention, and overall well-being. Distance running exposes the body to repetitive impulsive forces, causing significant vibrations to travel through physiological systems and biomechanical structures. These vibrations increase fatigue and the risk of injury. Although it has gained importance, research on induced vibration during running and wearable equipment for monitoring is scarce. This study aims to evaluate the performance of a measurement system for monitoring the acceleration levels of induced vibrations during long-distance running, exploring the capability of non-invasive wearable devices to characterise vibration transmissibility and exposure. Moreover, a preliminary quantitative assessment of induced vibration levels for an indoor testing scenario is given. Methods: Metrological characterisation of Xsens Motion Trackers Awinda (MTw), off-the-shelf inertial magnetic motion trackers, was performed by measuring the sensors’ frequency bandwidth in a controlled environment, providing logarithmic sweep sine excitations at different levels (2 g, 5 g, 7 g, where g is meant to be the gravitational acceleration). A testing protocol for indoor testing was derived from the literature, allowing characterisation of the sensors’ behaviour in terms of vibration transmissibility and exposure detection in the intended application. Time domain and frequency domain analyses were conducted by following the ISO 2631 standard guideline for vibration exposure assessment, and measurement uncertainty was defined, either for the dynamic correction of the sensors’ frequency behaviour or for the computed time and frequency domain metrics. In this framework, a treadmill-based test was conducted. The aim was to evaluate the Xsens sensors’ performance in measuring vibration dose exposure and transmissibility. Three MTws were placed on the subject’s right tibia, back, and forehead using elastic bands. A 25-year-old female amateur runner completed a series of tests consisting of walking for 1 min at 3.5 km/h (instrumentation setup), followed by running at two speeds (8 km/h and 11 km/h) for 2–4 min per trial, with 5 min rest periods between tests. Conclusions: The tested measurement system showed promising results due to its capability to assess vibration exposure during sports activities, but dynamic correction was found to be mandatory for accurate vibration level assessment. The main outcome of this study is a method for characterising the accelerometers embedded in the proposed devices, along with an analysis strategy for future testing campaigns. Thanks to the portability of IMUs (inertial measurement units), this approach enables the evaluation of induced vibrations during in-field running measurements. Full article

Most adaptive signal control systems rely on traffic data detected by fixed-point detectors, which suffers from characteristics of inaccuracy and latency. This study proposes a hierarchical coordinated signal control framework for arterials with asymmetric traffic, integrating traditional detector data and partial CV data. The arterial traffic operation is firstly considered, based on the traditional Webster’s model. An efficiency optimization model is then developed for the high-volume main direction traffic flow of the mainline. At last, a bandwidth maximization model is presented for the minor direction traffic. The experimental results based on VISSIM simulation scenarios demonstrate that the proposed approach performs better than the Synchro and MULTIBAND models, especially when the penetration rate of CVs is greater than 30%. In addition, as the penetration rate increases, the impact on mainline traffic is significant while the effect on arterial traffic is slight. Full article

The Galileo Search and Rescue (SAR) service is the contribution from the European constellation to the international Cospas–Sarsat system. This system uses a variety of space and ground infrastructure to detect and localize distress signals from beacons on the 406 MHz frequency. Satellites in different orbits detect the signals coming from the Earth and transmit them back to Earth stations that route them to the appropriate government authorities. On top of the standard detection and relay service, the Galileo constellation is the first to offer a Return Link Service (RLS) that acknowledges the processing of the distress signal with a Return Link Message (RLM) back to the originating beacon. This RLM is transmitted in the SAR field of the E1 signal I/NAV message, which allocates 20 bits every 2 s page. Therefore, transmitting a short RLM (80 bits) takes four consecutive pages or eight seconds. Moreover, each RLM is transmitted in parallel from two Galileo satellites. The RLS has been active since 2020, avoiding the spotlight of the GNSS community. This paper presents an analysis of the SAR Return Link Messages extracted from more than 3 months of signal-in-space data to investigate the current bandwidth use, monitor the type of SAR usage, and detect anomalies in the service. To extract and parse the Return Link Messages, we have developed and published an open-source Python library called GalileoSARlib on GitHub, which is also detailed in the paper. Full article

To address coverage gaps in high-latitude space weather monitoring caused by constraints in energy, bandwidth, and labeled samples, this study presents a systematic solution deployed in Hailar, China. We constructed a Cloud–Edge–Terminal system featuring wind–solar hybrid energy and RK3588-based edge computing, achieving six months of stable ionospheric–geomagnetic observation under −40 °C. Furthermore, we propose a Dual-Adversarial Recurrent Autoencoder (DA-RAE) for anomaly detection. Utilizing a single-source domain strategy, the model learns physical manifolds from quiet-day data, enabling zero-shot anomaly perception in the unsupervised target domain. Field tests in March 2025 demonstrated superior generalized anomaly detection capabilities, successfully identifying both transient space weather events and environmental equipment faults (baseline drifts). This work validates the value of edge intelligence for autonomous operations in extreme environments, providing a reproducible paradigm for global ground-based networks. Full article

The integrated III-V self-injection locked (SIL) laser exhibits excellent linewidth compression, noise reduction, and frequency stability. However, the laser’s low efficiency and fluctuating output power severely limit its applications in optical coherent transmission, light detection and ranging (LiDAR), spectroscopy, and so on. Based on the rate equations for a semiconductor laser coupled to counter-propagating fields in a micro-ring resonator (MRR), we systematically investigate the laser power and linewidth compression under self-locking conditions. We improve the slope efficiency by adjusting the injection phase, diode–MRR coupling efficiency, the normalized mode-coupling rate between clockwise (CW) and counter-clockwise (CCW) modes, and the MRR Q-factor. The results show that the enhanced diode–MRR coupling efficiency effectively increases the laser slope efficiency and improves the stability of the injection phase and feedback intensity. The injection phase significantly influences the range of the self-injection locked state. The normalized mode-coupling rate effectively affects the locking bandwidth and maintains stable power transfer. The MRR intrinsic Q-factor has a positive correlation with improving the laser slope efficiency and compressing the linewidth. Full article

Anurans are among the most threatened vertebrates worldwide, yet their acoustic ecology in fragmented habitats remains understudied. This research investigated acoustic overlaps and resource partitioning among amphibian species inhabiting Maceira Pond in Caparaó National Park, Brazil using bioacoustic methods. Six hours of recordings were analysed to determine key acoustic parameters and identify the resident species. A principal component analysis was used to assess acoustic parameters, whilst a cluster analysis examined acoustic similarities. Twelve species from four families were detected, of which eight were identified and five remained unidentified. Four species showed over 90% acoustic overlap, while two had less than 50%, with one at about 17%. Central frequency, peak frequency, duration, bandwidth, and pace significantly contributed to call differentiation. The R-value confirmed clustering patterns, indicating likely low acoustic interference due to few sympatric species. This study provides the first acoustic niche assessment for this community and highlights the need for further research on spatial and temporal partitioning in these threatened amphibian assemblages. Full article

To address the challenges of ambient light interference and slow overload recovery in transimpedance amplifiers (TIAs) for automotive Light Detection and Ranging (LiDAR) systems, this paper proposes a high-performance TIA with integrated ambient light cancellation and fast recovery capabilities. The core design includes an adaptive ambient light cancellation (ALC) loop that eliminates background currents up to 3 mA without relying on AC coupling capacitors, achieving a low-frequency cutoff frequency of 321 kHz to ensure the signal-to-noise ratio (SNR) of weak target signals. A multi-stage clamping and current transfer mechanism is employed to realize rapid overload recovery: under 100 mA heavy overload conditions, the recovery time is controlled around 8.7 ns, and the pulse broadening is limited to 2.7 ns, avoiding measurement blind zones. Implemented in a 0.18-μm SiGe BiCMOS process, the proposed TIA occupies a compact area of 0.15 mm², with a transimpedance gain of 80 dBΩ (10 kΩ) and a −3 dB bandwidth of 421 MHz. The input-referred noise current spectral density is 4.7 $\mathrm{pA}/\sqrt{\mathrm{Hz}}$, and the integrated equivalent input noise current from 1 Hz to 250 MHz is 73.6 nA_rms. Operating over a temperature range of −40 ℃ to 125 ℃, the TIA meets the rigorous requirements of automotive-grade applications. Performance comparisons with commercial products and state-of-the-art designs demonstrate its competitive ambient light rejection and fast recovery capabilities, validating its potential for use in direct time-of-flight (dToF) LiDAR systems for autonomous driving. Full article

In fields such as noise control, medical ultrasound, and acoustic communication, the flexible regulation of reflected sound waves has significant application value. In this work, a dual-band acoustic metasurface was designed using a split hollow cuboid with an open-hole plate (OPSHC) structure, which simultaneously achieves the direction control of reflected sound waves in both frequency bands. An OPSHC is a series structural unit, and the two center frequencies are mainly controlled by the diameters of the two openings in the structure and the position of the open-hole plate. Through finite element simulation, the influence of the center frequency of the metasurface and the position of the open-hole plate on the bandwidth of the anomalous reflection was studied. The results show that when the low-frequency center frequency is fixed, the low-frequency bandwidth of the metasurface increases with the increase in the high-frequency center frequency. When the position of the plate is moved, the low-frequency bandwidth increases and the high-frequency bandwidth decreases. This type of metasurface provides a new technical approach for broadband acoustic metasurface applications in noise control and underwater detection systems. Full article

Recent developments in unmanned aerial vehicle (UAV) activity highlight the need for advanced electromagnetic spectrum monitoring systems that can detect drones operating near sensitive or restricted areas. Such systems can identify emissions from drones even under frequency-hopping conditions, providing an early warning system and enabling a timely response to protect critical infrastructure and ensure secure operations. In this context, the present work proposes the development of a high-performance multichannel broadband monitoring system with real-time analysis capabilities, designed on an SDR architecture based on USRP with three acquisition channels: two broadband (160 MHz and 80 MHz) and one narrowband (1 MHz) channel, for simultaneous, of extended spectrum segments, aligned with current requirements for analyzing emissions from drones in the 2.4 GHz and 5.8 GHz ISM bands. The processing system was configured to support cumulative bandwidths of over 200 MHz through a high-performance hardware platform (powerful CPU, fast storage, GPU acceleration) and fiber optic interconnection, ensuring stable and lossless transfer of large volumes of data. The proposed spectrum monitoring system proved to be extremely sensitive, flexible, and extensible, achieving a reception sensitivity of −130 dBm, thus exceeding the values commonly reported in the literature. Additionally, the parallel multichannel architecture facilitates real-time detection of signals from different frequency ranges and provides a foundation for advanced signal classification. Its reconfigurable design enables rapid adaptation to various signal types beyond unmanned aerial systems. Full article

The proliferation of data-intensive IoT applications has created unprecedented demand for wireless spectrum, necessitating more efficient bandwidth management. Spectrum sensing allows unlicensed secondary users to dynamically access idle channels assigned to primary users. However, traditional sensing techniques are hindered by their sensitivity to noise and reliance on prior knowledge of primary user signals. This limitation has propelled research into machine learning (ML) and deep learning (DL) solutions, which operate without such constraints. This study presents a comprehensive performance assessment of prominent ML models: random forest (RF), K-nearest neighbor (KNN), and support vector machine (SVM) against DL architectures, namely a convolutional neural network (CNN) and an Autoencoder. Evaluated using a robust suite of metrics (probability of detection, false alarm, missed detection, accuracy, and F1-score), the results reveal the clear and consistent superiority of RF. Notably, RF achieved a probability of detection of 95.7%, accuracy of 97.17%, and an F1-score of 96.93%, while maintaining excellent performance in low signal-to-noise ratio (SNR) conditions, even surpassing existing hybrid DL models. These findings underscore RF’s exceptional noise resilience and establish it as an ideal, high-performance candidate for practical spectrum sensing in wireless networks. Full article

This paper presents an all-digital fractional-N phase-locked loop (ADPLL) operating in the 2.86–3.2 GHz range, optimized for IoT and high-frequency RF transceiver applications demanding stringent phase noise performance, fast settling time, and high integration capability. The key innovation lies in the introduction of a bandpass delta-sigma time-to-digital converter (BPDSTDC) that achieves high-resolution phase detection, an extended detection range of ±2$\pi$, and superior noise-shaping characteristics, completely eliminating the complex calibration procedures typically required in conventional TDC designs. The proposed architecture synergistically combines the BPDSTDC with digital down-conversion blocks to extract phase error at baseband, a divider chain integrated with phase interpolators achieving 1/4 fractional resolution to suppress in-band quantization noise, and a wide-bandwidth digital loop filter (>1 MHz) ensuring fast dynamic response and robust stability. The bandpass delta-sigma modulator is implemented with compact resonator structures and a flash quantizer, achieving an optimal balance among resolution, power consumption, and silicon area. The incorporation of highly linear phase interpolators extends fractional frequency synthesis capability without requiring complex digital-to-time converters (DTCs), significantly reducing design complexity and calibration overhead. Fabricated in a 180-nm CMOS technology, the proposed chip demonstrates robust measured performance. The band-pass delta-sigma TDC achieves a low integrated rms timing noise of 183 fs within a 1-MHz bandwidth. Leveraging this low TDC noise, the complete ADPLL exhibits a measured in-band phase noise of −120 dBc/Hz at a 1-MHz offset for a 3.2-GHz output frequency while operating with a loop bandwidth exceeding 1 MHz. This corresponds to a normalized phase noise of −216 dBc/Hz. The system operates from a 1.8-V supply and consumes 10 mW, achieving competitive performance compared with prior noise-shaping TDC-based all-digital PLLs. Full article

This work proposes new architecture, supported by analytical modelling and computer-aided design (CAD) simulations, for a highly sensitive monolayer graphene-gated AlGaN/GaN HEMT terahertz (THz) detector operating at room temperature (RT). The monolayer graphene gate acts as a surface plasmon absorber for the incident THz radiation. The carrier density perturbation caused by incident THz energy on the monolayer graphene surface is then capacitively coupled to the two-dimensional electron gas (2DEG) channel of the HEMT structure underneath. The channel is partially depleted for increased mobility and nonlinearity with potential asymmetry across the channel for consistent photogeneration. The Drude absorption of THz radiation initiates intraband transitions in monolayer graphene, thereby reducing phonon losses. These reduced phonon losses enable RT THz detection. Based on our simulations, the proposed detector architecture can generate a responsivity of 2.12 × 10⁶ V/W at 1 THz with a broadband bandwidth of 2 THz. Full article

Understanding the activities of hospitalized patients is important for hospital administrators in terms of preventing accidents and improving the efficiency of nursing care. To solve this problem, we have developed a technology to detect 10 types of patient activities from images. Since this image recognition technology operates on edge devices, it simultaneously understands the activities of several hundred beds of patients in a large hospital without being limited by network bandwidth due to video transmission, as is the case with server-based AI. In the experiment, 10 different behaviors of residents of an elderly care facility were detected, and logs of the residents’ behaviors were collected. Analysis and utilization of the logs will be considered in future research. Full article