Search Results (92)

A new compact, wideband, millimeter-wave microstrip crossover—designed without vias—demonstrates effective performance with an insertion loss of 2 dB across a wide frequency range. For Path 1, the operational bandwidth spans 11 GHz (13–24 GHz), while for Path 2, it extends over 10 GHz (12–22 GHz). The overlapping bandwidth, maintaining the 2 dB insertion loss criterion, covers 9 GHz (13–22 GHz). The design introduces two transition mechanisms to achieve optimal scattering parameters for the crossover: a stair-shaped microstrip line (MST) to ground-backed coplanar waveguide (GCPW) for the initial crossed line (Path 1), and vertical coupling between microstrip and coplanar hourglass microstrip patches on a single-layer substrate for Path 2. This innovative approach ensures an insertion loss of approximately 1 dB for both paths across the bandwidth, with a slight increase beyond 20 GHz for Path 2 due to substrate losses. Both crossed lines maintain a return loss of 10 dB across the spectrum, with isolation of approximately 20 dB. This design presents a flat, compact, and via-less configuration, with physical dimensions measuring 6.5 mm × 7.6 mm. The proposed design exhibits excellent scattering parameters, which enhance the efficiency of phased array antenna systems in terms of power transfer between input and output ports, as well as improving isolation between different input ports in the feed network of these systems used in remote sensing. Consequently, this contributes to the increased sensitivity and accuracy of such systems. Full article

The multi-band atmospheric correction algorithms, now referred to as remote sensing reflectance (R_rs) algorithms, have been implemented on a NASA computing facility for global remote sensing of ocean color and atmospheric aerosol parameters from data acquired with several satellite instruments, including the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi spacecraft platform. These algorithms are based on the 2-band version of the SeaWiFS (Sea-Viewing Wide Field-of-View Sensor) algorithm. The bands centered near 0.75 and 0.865 μm are used for atmospheric corrections. In order to obtain high-quality R_rs values over Case 1 waters (deep clear ocean waters), strict masking criteria are implemented inside these algorithms to mask out thin clouds and very turbid water pixels. As a result, R_rs values are often not retrieved over bright Case 2 waters. Through our analysis of VIIRS data, we have found that spatial features of bright Case 2 waters are observed in VIIRS visible band images contaminated by thin cirrus clouds. In this article, we describe methods of combining cirrus and aerosol corrections to improve spatial coverage in R_rs retrievals over Case 2 waters. One method is to remove cirrus cloud effects using our previously developed operational VIIRS cirrus reflectance algorithm and then to perform atmospheric corrections with our updated version of the spectrum-matching algorithm, which uses shortwave IR (SWIR) bands above 1 μm for retrieving atmospheric aerosol parameters and extrapolates the aerosol parameters to the visible region to retrieve water-leaving reflectances of VIIRS visible bands. Another method is to remove the cirrus effect first and then make empirical atmospheric and sun glint corrections for water-leaving reflectance retrievals. The two methods produce comparable retrieved results, but the second method is about 20 times faster than the spectrum-matching method. We compare our retrieved results with those obtained from the NASA VIIRS R_rs algorithm. We will show that the assumption of zero water-leaving reflectance for the VIIRS band centered at 0.75 μm (M6) over Case 2 waters with the NASA R_rs algorithm can sometimes result in slight underestimates of water-leaving reflectances of visible bands over Case 2 waters, where the M6 band water-leaving reflectances are actually not equal to zero. We will also show conclusively that the assumption of thin cirrus clouds as ‘white’ aerosols during atmospheric correction processes results in overestimates of aerosol optical thicknesses and underestimates of aerosol Ångström coefficients. Full article

The backbone of the Internet of Things ecosystem relies heavily on wireless sensor networks and low-power wide area network technologies, such as LoRa modulation, to provide the long-range, energy-efficient communications essential for applications as diverse as smart homes, healthcare, agriculture, smart grids, and transportation. With the number of IoT devices expected to reach approximately 41 billion by 2034, managing radio spectrum resources becomes a critical issue. However, as these devices are deployed at an increasing rate, the limited spectral resources will result in increased interference, packet collisions, and degraded quality of service. Current methods for increasing network capacity have limitations and require advanced solutions. This paper proposes a novel hybrid spectrum sensing framework that combines traditional signal processing and artificial intelligence techniques specifically designed for LoRa spreading factor detection and communication channel analytics. Our proposed framework processes wideband signals directly from IQ samples to identify and classify multiple concurrent LoRa transmissions. The results show that the framework is highly effective, achieving a detection accuracy of 96.2%, a precision of 99.16%, and a recall of 95.4%. The proposed framework’s flexible architecture separates the AI processing pipeline from the channel analytics pipeline, ensuring adaptability to various communication protocols beyond LoRa. Full article

In order to realize robust communication in complicated interference electromagnetic environments, an intelligent transmitter design is proposed in this paper, where an auxiliary wideband receiver senses the electromagnetic distribution information in a wide bandwidth range to decide the optimal working frequency. One of the key issues is suppressing the self-interference of high-power transmitter signals to the co-platform wideband sensing receiver. Due to the multipath effect of the self-interference channel, perfect time synchronization of self-interference signals is not achievable, which reduces the interference cancelation performance of the co-platform. Therefore, this paper investigates the impact of time synchronization errors on the self-interference cancellation performance of the Nyquist folding receiver (NYFR)-based system. First, a self-interference cancellation architecture based on NYFR is proposed to support the realization of real-time wideband spectrum sensing. Secondly, closed-form expressions for the residual interference power and the self-interference cancellation performance are derived, and the impact of reference signal sampling errors on the self-interference cancellation performance is also analyzed. Theoretical analysis and simulation results show that the NYFR-based self-interference cancellation performance decreases with increasing time synchronization errors and folding multiples, and the system is especially sensitive to time synchronization errors. Moreover, frequency detection simulations show that, under an SI-to-NCS power ratio of 0 dB, the proposed interference cancellation scheme improves the frequency detection probability by approximately 80%. The research results provide a theoretical reference for the compressed sensing-aided intelligent transmitter realization. Full article

Satellite remote sensing has been widely recognized as an effective tool for estimating fire severity. Existing indies predominantly rely on broad-spectrum multispectral data, limiting the ability to elucidate the intricate relationship between fire severity and spectral response. To address this challenge, the optimal spectral bands and indices for fire severity assessment were explored using ZY-1 hyperspectral data, which captured pre- and post-fire conditions of a forest fire site in Yuxi City, Yunnan Province, China. Separability contrast and threshold segmentation methods were applied to perform a sensitivity analysis on the original spectral bands and constructed indices derived from surface reflectance of the post-fire image and the pre- and post-fire image combination, respectively. The findings indicate the following: (1) The spectral bands of the post-fire image exhibited superior spectral separability and classification capabilities compared to the pre- and post-fire difference image, with the highest forest fire severity classification accuracy of 78.99% achieved at the 800 nm central wavelength. (2) The difference of normalized difference index category for the pre- and post-fire image combination outperformed the vegetation indices of the post-fire image and the other vegetation indices using the pre- and post-fire image combination, with the highest forest fire severity classification accuracy of 83.39% achieved with the combination of 2048 nm and 1106 nm central wavelength. (3) Unburned areas exhibited strong separability, facilitating effective segmentation, but burned areas showed poor separability between fire severities, particularly between low and moderate–high severity, which remains the primary limitation in fire severity assessment. In conclusion, this study advances the understanding of fire severity and spectral response by leveraging the narrow-band advantages. It aims to enhance the accuracy of satellite-based fire severity estimation, offering valuable technical guidance and theoretical insights for assessing forest fire impacts and vegetation recovery. Full article

The present numerical study proposes a technique to extend the sensing range of tunable diode laser absorption spectroscopy (TDLAS) for flame measurement by involving physics constraints on both gas condition and spectroscopic parameters in the interpretation of spectra from multiple bands. A total of 24 major spectral lines for 2 spectral segments 4029–4031 cm⁻¹ and 7185–7186 cm⁻¹ are determined by specially designed detection function and contribution filtering. Numerical tests on uniform and complicated combustion fields prove the high accuracy, strong robustness to noise, wide sensing range, and good compatibility with tomography. The present study provides a strong technique for future complex combustion detection with advanced laser sources of broad spectrum. Full article

In cognitive radio, compressive spectrum sensing (CSS) is critical for efficient wideband spectrum sensing (WSS). However, traditional reconstruction algorithms exhibit suboptimal performance, and conventional WSS methods fail to fully capture the inherent structural information of wideband spectrum signals. Moreover, most existing deep learning-based approaches fail to effectively exploit the sparse structures of wideband spectrum signals, resulting in limited reconstruction performance. To overcome these limitations, we propose BEISTA-Net, a deep learning-based framework for reconstructing compressed wideband signals. BEISTA-Net integrates the iterative shrinkage-thresholding algorithm (ISTA) with deep learning, thereby extracting and enhancing the block sparsity features of wideband spectrum signals, which significantly improves reconstruction accuracy. Next, we propose BSWSS-Net, a lightweight network that efficiently leverages the sparse features of the reconstructed signal to enhance WSS performance. By jointly employing BEISTA-Net and BSWSS-Net, the challenges in CSS are effectively addressed. Extensive numerical experiments demonstrate that our proposed CSS method achieves state-of-the-art performance across both low and high signal-to-noise ratio scenarios. Full article

Cognitive Radio (CR) is widely acknowledged as a pivotal technology for mitigating the scarcity of spectrum resources, with Transform Domain Communication Systems (TDCSs) regarded as one of the primary candidate technologies for CR. However, conventional Wideband Spectrum Sensing (WBSS) techniques utilized in TDCS exhibit limitations and are insufficient for adapting to the current complex electromagnetic environment. This paper tackles the time–frequency reconstruction challenge in WBSS by proposing a fast time–frequency reconstruction (FTFR) algorithm. The proposed algorithm acquires sub-Nyquist samples through the introduction of a Multi-Coset Sampling structure and reconstructs the autocorrelation of signals across various windows through a series of low-complexity operations. It captures the dynamic variations of signals by integrating spectra from adjacent time windows. In comparison to existing time–frequency reconstruction algorithms in WBSS, the proposed algorithm demonstrates reduced computational complexity. Simulation experiments indicate that the FTFR algorithm can effectively reconstruct the time–frequency characteristics of signals and significantly restore the primary temporal and frequency distributions, even in low Signal-to-Noise Ratio (SNR) environments. Full article

A low-cost, wavelength-tunable single-longitudinal-mode (SLM) thulium–holmium co-doped fiber laser (THDFL) in a 2 μm band with a simple structure is described in the present paper. To obtain a stable SLM and narrow laser linewidth, a five-coupler-based three-ring (FCTR) filter is utilized in the ring cavity of the fiber laser. Tunable SLM wavelength output from THDFLs with kHz linewidths can be achieved by designing the FCTR filter with an effective free-spectral range and a 3 dB bandwidth at the main resonant peak. The measurement results show that the laser is in the SLM lasing state, with a highly stabilized optical spectrum, a linewidth of approximately 9.45 kHz, an optical signal-to-noise ratio as high as 73.6 dB, and a relative intensity noise of less than −142.66 dB/Hz. Furthermore, the wavelength can be tuned in the range of 2.6 nm. The proposed fiber laser has a wide range of applications, including coherence optical communication, optical fiber sensing, and dense wavelength-division-multiplexing. Full article

We consider the wideband spectrum sensing within a multi-path propagation environment, where a multi-antenna base station (BS) is tasked with identifying the frequency positions of multiple narrowband transmissions distributed across a broad range of frequencies. To tackle this, we propose a sub-Nyquist sampling structure that incorporates a phased array system. Specifically, each antenna is connected to two separate sampling channels, i.e., one for direct sampling and another for delayed sampling, with the latter incorporating a specified time delay factor. The cross-correlation matrices associated with the samples, which are characterized by different time lags, are calculated. These matrices are represented in tensor form, and the factor matrices are extracted through CANDECOMP/PARAFAC (CP) decomposition. By these factor matrices, the carrier frequencies and the power spectra of the far-field signals of interest are estimated. Numerical simulations are conducted to evaluate the performance of the proposed method, and the results reveal the feasibility and effectiveness of the approach, demonstrating its potential for accurate and efficient wideband spectrum sensing in complex multi-path propagation environments. Full article

This study focused on designing an ultra-wideband metamaterial absorber, consisting of layers of Mn (manganese) and MoO₃ (molybdenum trioxide) arranged in a planar interleaving pattern, with a matrix square-shaped Ti (titanium) on the top MoO₃ layer. Key features of this research included the novel use of Mn and MoO₃ in a planar interleaving configuration for designing an ultra-wideband absorber, which was rarely explored in previous studies. MoO₃ thin film served as the fundamental material, leveraging its favorable optical properties and absorption capabilities in the infrared spectrum. Alternating layers of Mn and MoO₃ were adjusted in thickness and order to optimize absorptivity across desired wavelength ranges. Another feature is that the Mn and MoO₃ materials in the investigated absorber had a planar structure, which simplified the manufacturing of the absorber. Furthermore, the topmost layer of square-shaped Ti was strategically placed to enhance the absorber’s bandwidth and efficiency. When the investigated absorber lacked a Ti layer, its absorptivity and bandwidth significantly decreased. This structural design leveraged the optical properties of Mn, MoO₃, and Ti to significantly expand the absorption range across an ultra-wideband spectrum. When the Ti height was 280 nm, the investigated absorber exhibited a bandwidth with absorptivity greater than 0.9, spanning from the near-infrared (0.80 μm) to the mid-infrared (9.07 μm). The average absorptivity in this range was 0.950 with a maximum absorptivity of 0.989. Additionally, three absorption peaks were observed at 1010, 2510, and 6580 nm. This broad absorption capability makes it suitable for a variety of optical applications, ranging from near-infrared to mid-infrared wavelengths, including thermal imaging and optical sensing. Full article

Fixed-wing unmanned aerial vehicles (UAVs) are widely considered as a vital candidate of aerial base station in beyond Fifth Generation (B5G) systems. Accurate knowledge of air-to-ground (A2G) wireless propagation is important for A2G communication system development and testing where, however, there is still a lack of A2G wideband channel models for such a purpose. In this paper, we present a wideband fixed-wing UAV-based A2G channel measurement campaign at 2.7 GHz, and consider typical flight phases, based on which a wide-sense stationary uncorrelated scattering (WSSUS)-based tapped delay line (TDL) wideband channel model is proposed. Parameters of individual channel taps are analyzed in terms of gain, amplitude distribution, Rice factor and delay-Doppler spectrum. It is shown that UAV flight phases significantly influence the channel tap parameters. Particularly, the “Bell”-type spectrum is found to be the most suitable model for the delay-Doppler spectrum under various flight scenarios for A2G propagation. The proposed channel model can provide valuable assistance and guidance for UAV communication system evaluation and network planning. Full article

In order to address the ‘capacity crisis’ caused by the narrow bandwidth of the current C band and the demand for wide-spectrum sensing sources and tunable fiber lasers, a broadband luminescence covering the C + L bands using Er³⁺/Yb³⁺ co-doped fluorotellurite glass fiber is investigated in this paper. The optimal doping concentrations in the glass host were determined based on the intensity, lifetime, and full width at half maximum (FWHM) of the fluorescence centered at 1.5 µm, which were found to be 1.5 mol% Er₂O₃ and 3 mol% Yb₂O₃. We also systematically investigated this in terms of optical absorption spectra, absorption and emission cross-sections, gain coefficients, Judd–Ofelt parameters, and up-conversion fluorescence. The energy transfer (ET) mechanism between the high concentrations of Er³⁺ and Yb³⁺ was summarized. In addition, a step-indexed fiber was prepared based on these fluorotellurite glasses, and a wide bandwidth of ~112.5 nm (covering the C + L bands from 1505.1 to 1617.6 nm) at 3 dB for the amplified spontaneous emission (ASE) spectra has been observed at a fiber length of 0.57 m, which is the widest bandwidth among all the reports based on tellurite glass. Therefore, this kind of Er³⁺/Yb³⁺ co-doped fluorotellurite glass fiber has great potential for developing broadband C + L band amplifiers, ultra-wide fiber sources for sensing, and tunable fiber lasers. Full article

To better meet communication needs, 6G proposes Integrated Satellite-Terrestrial Networks. Integrated Sensing and Communication (ISAC) is one of the key technologies of Integrated Satellite-Terrestrial Networks, which can reduce the energy consumption of the system, improve communication efficiency, and increase the utilization rate of spectrum resources. In the existing technology, the Modulated Wideband Converter (MWC) system can provide support for the miniaturization and intelligence of wireless device sensing and communication systems. Therefore, the MWC system can be used as a preliminary application of ISAC technology. However, the reconstruction effect of the conventional MWC system under the influence of noise is not stable. Therefore, we propose a signal processing optimization scheme for the MWC system based on spatial information entropy. First, the subsequent reconstruction algorithm is considered to require the dynamic and flexible processing of the sampled signals to reduce the influence of noise. Second, for the shortcomings of the original Orthogonal Matching Pursuit (OMP) algorithm, the concept of the genetic algorithm is used to optimize the algorithm by constructing the feature factor through spatial information gain and spatial information features. According to the simulation results, compared with the traditional MWC system, the scheme proposed in this paper is improved in all indicators. Full article

The radio spectrum is a scarce and extremely valuable resource that demands careful real-time monitoring and dynamic resource allocation. Dynamic spectrum access (DSA) is a new paradigm for managing the radio spectrum, which requires AI/ML-driven algorithms for optimum performance under rapidly changing channel conditions and possible cyber-attacks in the electromagnetic domain. Fast sensing across multiple directions using array processors, with subsequent AI/ML-based algorithms for the sensing and perception of waveforms that are measured from the environment is critical for providing decision support in DSA. As part of directional and wideband spectrum perception, the ability to finely channelize wideband inputs using efficient Fourier analysis is much needed. However, a fine-grain fast Fourier transform (FFT) across a large number of directions is computationally intensive and leads to a high chip area and power consumption. We address this issue by exploiting the recently proposed approximate discrete Fourier transform (ADFT), which has its own sparse factorization for real-time implementation at a low complexity and power consumption. The ADFT is used to create a wideband multibeam RF digital beamformer and temporal spectrum-based attention unit that monitors 32 discrete directions across 32 sub-bands in real-time using a multiplierless algorithm with low computational complexity. The output of this spectral attention unit is applied as a decision variable to an intelligent receiver that adapts its center frequency and frequency resolution via FFT channelizers that are custom-built for real-time monitoring at high resolution. This two-step process allows the fine-gain FFT to be applied only to directions and bands of interest as determined by the ADFT-based low-complexity 2D spacetime attention unit. The fine-grain FFT provides a spectral signature that can find future use cases in neural network engines for achieving modulation recognition, IoT device identification, and RFI identification. Beamforming and spectral channelization algorithms, a digital computer architecture, and early prototypes using a 32-element fully digital multichannel receiver and field programmable gate array (FPGA)-based high-speed software-defined radio (SDR) are presented. Full article