Search Results (68)

In this paper, we present a novel passive dual-tuned magnetic metasurface, which can enhance the field distribution produced by a closely placed radio-frequency coil for both ¹H and ²³Na 1.5 T MRI imaging. In particular, the proposed solution comprises a 5 × 5 capacitively loaded array, in which each unit-cell is composed of two concentric spiral coils. Specifically, the unit-cell internal spiral coil operates at the proton Larmor frequency (64 MHz), whereas the external is at the sodium one (17 MHz). Therefore, the paper aims to demonstrate the possibility of enhancing the magnetic field distribution in transmission and reception for 1.5 T MRI scanners by using the same metasurface configuration for imaging both nuclei, thus drastically simplifying the required instrumentation. We first describe the theoretical model used to design and synthetize the dual-tuned magnetic metasurface. Next, full-wave simulations are carried out to validate the approach. Finally, we report the experimental results acquired by testing the fabricated prototype at the workbench, observing a good agreement with the theoretical design and the numerical simulations. In particular, the metasurface increases the transmission efficiency T_x in presence of a biological phantom by a factor 3.5 at 17 MHz and by a factor 5 at 64 MHz, respectively. The proposed solution can pave the way for MRI multi-nuclei diagnostic technique with better images quality, simultaneously reducing the scanning time, the invasiveness on the patient and the overall costs. Full article

Medical ultrasound imaging using coherent plane-wave compounding (CPWC) for higher frame-rate applications has generated considerable interest in the research community. The adaptive Eigenspace Beamformer technique combined with a Generalized Sidelobe Canceler (GSC) provides noise and interference reduction in images, improving resolution and contrast compared to basic methods: Delay and Sum (DAS) and Minimum Variance (MV). Different filtering approaches are applied in ultrasound image processing to reduce speckle signals. This work introduces the combination of beamformer Eigenspace Based on Minimum Variance (ESBMV) associated with GSC (EGSC) and the Kuan (EGSCK), Lee (EGSCL), and Wiener (EGSCW) filters and their enhanced versions to obtain better quality of plane-wave ultrasound images. The EGSCK technique did not present significant improvements compared to other methods. However, the EGSC with enhanced Kuan (EGSCKe) showed a remarkable reduction in geometric distortion, i.e., 0.13 mm (35%) and 0.49 mm (67%) compared to the EGSC and DAS techniques, respectively. The EGSC with Enhanced Wiener (EGSCWe) showed the best improvements in contrast radio (CR) aspects, i.e., 74% compared to the DAS technique and 60% to the EGSC technique. Furthermore, our proposed method reduces geometric distortion, making it a good option for plane-wave ultrasound imaging. Full article

Radar imaging is a technology that uses radar systems to generate target images. It transmits radio waves, receives the signal reflected back by the target, and realizes imaging by analyzing the target’s position, shape, and motion information. The three-dimensional (3D) forward-looking imaging of missile-borne radar is a branch of radar imaging. However, owing to the limitation of antenna aperture, the imaging resolution of real aperture radar is restricted. By implementing the super-resolution techniques in array signal processing into missile-borne radar 3D forward-looking imaging, the resolution can be further improved. In this paper, a 3D forward-looking imaging algorithm based on the two-dimensional (2D) super-resolution algorithm is proposed for missile-borne planar array radars. In the proposed algorithm, a forward-looking planar array with scanning beams is considered, and each range-pulse cell in the received data is processed one by one using a 2D super-resolution method with the error function constructed according to the weighted least squares (WLS) criterion to generate a group of 2D spectra in the azimuth-pitch domain. Considering the lack of training samples, the super-resolution spectrum of each range-pulse cell is estimated via adaptive iteration processing only with one sample, i.e., the cell under process. After that, all the 2D super-resolution spectra in azimuth-pitch are accumulated according to the changes in instantaneous beam centers of the beam scanning. As is verified by simulation results, the proposed algorithm outperforms the real aperture imaging method in terms of azimuth-pitch resolution and can obtain 3D forward-looking images that are of a higher quality. Full article

This paper presents the mission analysis of ³Cat-8, a 6-Unit CubeSat mission being developed by the UPC NanoSat Lab for ionospheric research. The primary objective of the mission is to monitor the ionospheric scintillation of the aurora, and to perform several technological demonstrations. The satellite incorporates several novel systems, including a deployable Fresnel Zone Plate Antenna (FZPA), an integrated PocketQube deployer, a dual-receiver GNSS board for radio occultation and reflectometry experiments, and a polarimetric multi-spectral imager for auroral emission observations. The mission design, the suite of payloads, and the concept of operations are described in detail. This paper discusses the current development status of ³Cat-8, with several subsystems already developed and others in the final design phase. It is expected that the data gathered by ³Cat-8 will contribute to a better understanding of ionospheric effects on radio wave propagation and demonstrate the feasibility of compact remote sensors in a CubeSat platform. Full article

Drones have been attracting significant attention in the field of agriculture. They can be used for various tasks such as spraying pesticides, monitoring pests, and assessing crop growth. Sensors are also widely used in agriculture to monitor environmental parameters such as soil moisture and temperature. Due to the high cost of communication infrastructure and radio-wave modules, the adoption of high-density sensing systems in agriculture is limited. To address this issue, we propose an agricultural sensor network system using drones and Optical Camera Communication (OCC). The idea is to transmit sensor data from LED panels mounted on sensor nodes and receive the data using a drone-mounted camera. This enables high-density sensing at low cost and can be deployed in areas with underdeveloped infrastructure and radio silence. We propose a trajectory control algorithm for the receiving drone to efficiently collect the sensor data. From computer simulations, we confirmed that the proposed algorithm reduces total flight time by $30\%$ compared to a shortest-path algorithm. We also conducted a preliminary experiment at a leaf mustard farm in Kamitonda-cho, Wakayama, Japan, to demonstrate the effectiveness of the proposed system. We collected 5178 images of LED panels with a drone-mounted camera to train YOLOv5 for object detection. With simple On–Off Keying (OOK) modulation, we achieved sufficiently low bit error rates (BERs) under ${10}^{-3}$ in the real-world environment. The experimental results show that the proposed system is applicable for drone-based sensor data collection in agriculture. Full article

The radio frequency (RF) data generated from a single-chip millimeter-wave (mmWave) ground-based multi-input multi-output (GB-MIMO) radar can provide a highly robust, precise measurement for deformation in harsh environments, overcoming challenges such as different lighting and weather conditions. Monitoring deformation is significant for safety factors in different applications, such as detecting and monitoring the ground stability of underground mines. However, radar images can experience different types of clutter and artifacts besides the spreading effects caused by the side lobes, resulting in the foremost challenge of suppressing clutter and monitoring deformation.In the state of the art, the introduced frameworks usually include many filters proposed for different types of noise, with commercial systems typically using an amplitude threshold. This paper proposes a framework for monitoring the deformation, where the essential process is to apply a data-driven threshold to the amplitude heatmap, detect the deformation, and eliminate noise. The proposed threshold is an iterative approach based on radar imagery statistics, and it performs well for the collected dataset. The principal advantage of our proposed framework is simplicity, reducing the burden of using different filters. We can consider the dynamic threshold based on data statistics as a data-driven machine learning tool. The results show promising performance for our method in monitoring the deformation and removing clutter compared to the benchmark method. Full article

The Advanced X-ray Imaging Satellite (AXIS) promises revolutionary science in the X-ray and multi-messenger time domain. AXIS will leverage excellent spatial resolution (<1.5 arcsec), sensitivity ($80\times$ that of Swift), and a large collecting area (5–10× that of Chandra) across a 24-arcmin diameter field of view at soft X-ray energies (0.3–10.0 keV) to discover and characterize a wide range of X-ray transients from supernova-shock breakouts to tidal disruption events to highly variable supermassive black holes. The observatory’s ability to localize and monitor faint X-ray sources opens up new opportunities to hunt for counterparts to distant binary neutron star mergers, fast radio bursts, and exotic phenomena like fast X-ray transients. AXIS will offer a response time of <2 h to community alerts, enabling studies of gravitational wave sources, high-energy neutrino emitters, X-ray binaries, magnetars, and other targets of opportunity. This white paper highlights some of the discovery science that will be driven by AXIS in this burgeoning field of time domain and multi-messenger astrophysics. This White Paper is part of a series commissioned for the AXIS Probe Concept Mission; additional AXIS White Papers can be found at the AXIS website. Full article

We review the evolution of our understanding of the planetary nebulae phenomenon and their place in the scheme of stellar evolution. The historical steps leading to our current understanding of central star evolution and nebular formation are discussed. Recent optical imaging, X-ray, ultraviolet, infrared, millimeter wave, and radio observations have led to a much more complex picture of the structure of planetary nebulae. The optically bright regions have multiple shell structures (rims, shells, crowns, and haloes), which can be understood within the interacting winds framework. However, the physical mechanism responsible for bipolar and multipolar structures that emerged during the proto-planetary nebulae phase is yet to be identified. Our morphological classifications of planetary nebulae are hampered by the effects of sensitivity, orientation, and field-of-view coverage, and the fraction of bipolar or multipolar nebulae may be much higher than commonly assumed. The optically bright bipolar lobes may represent low-density, ionization-bounded cavities carved out of a neutral envelope by collimated fast winds. Planetary nebulae are sites of active synthesis of complex organic compounds, suggesting that planetary nebulae play a major role in the chemical enrichment of the Galaxy. Possible avenues of future advancement are discussed. Full article

Synthetic-aperture radar (SAR) can work in all weather conditions and at all times, and satellite-borne radar has the characteristics of short revisiting period and large imaging width. Therefore, satellite-borne synthetic-aperture radar has been widely deployed, and the SAR images have been widely used in geographic mapping, radar interpretation, ship detection, and other fields. Satellite-borne synthetic-aperture radar is also susceptible to various types of intentional or unintentional interference during the imaging process, and because the interference is a direct wave, its power is much stronger than the wave reflected by targets. As a common interference pattern, radio-frequency interference widely exists in various satellite-borne synthetic-aperture radars, which seriously deteriorates SAR image quality. In order to solve the above problems, this paper proposes a feature decomposition network to suppress interference based on regularization optimization. The contributions of this work are as follows: 1. By analyzing the performance limitations of the existing methods, this work proposes a novel regularization method for radio-frequency interference suppression tasks. From the perspective of data distribution histograms and residual components, the proposed method eliminates the variable components introduced by common regularization, greatly reduces the difficulty of data mapping, and significantly improves its robustness and performance. 2. This work proposes a feature decomposition network, where the feature decomposition module contains two parts; one part only represents the interference signal, and the other part only represents the radar signal. The neurons representing the interference signal are discarded, and the neurons representing the radar signal are used as input for the subsequent network. A cosine similarity constraint is used to separate the interference from the network as much as possible. Finally, this method is validated on the MiniSAR dataset and Sentinel-1A dataset. Full article

The main objective of the research presented in the following work was the adaptation of reflection-radar technology in a detection and navigation system using radio-tomographic imaging techniques. As key aspects of this work, the energy optimization of high-frequency transmitters can be considered for use inside buildings while maintaining user safety. The resulting building monitoring and control system using a network of intelligent sensors supported by artificial intelligence algorithms, such as logistic regression or neural networks, should be considered an outcome. This paper discusses the methodology for extracting information from signal echoes and how they were transported and aggregated. The data extracted in this way were used to support user navigation through a building, optimize energy based on presence information, and increase the facility’s overall security level. A band from 5 GHz to 6 GHz was chosen as the carrier frequency of the signals, representing a compromise between energy expenditure, range, and the properties of wave behavior in contact with different types of matter. The system includes proprietary hardware solutions that allow parameters to be adjusted over the entire range and guarantee adaptation for RTI (radio tomography imaging) technology. Full article

Radio frequency interference (RFI) significantly hampers the target detection performance of frequency-modulated continuous-wave radar. To address the problem and maintain the target echo signal, this paper proposes a priori assumption on the interference component nature in the radar received signal, as well as a method for interference estimation and mitigation via time–frequency analysis. The solution employs Fourier synchrosqueezed transform to implement the radar’s beat signal transformation from time domain to time–frequency domain, thus converting the interference mitigation to the task of time–frequency distribution image restoration. The solution proposes the use of image processing based on the dual-tree complex wavelet transform and combines it with the spatial domain-based approach, thereby establishing a dual-domain fusion interference filter for time–frequency distribution images. This paper also presents a convolutional neural network model of structurally improved UNet++, which serves as the interference estimator. The proposed solution demonstrated its capability against various forms of RFI through the simulation experiment and showed a superior interference mitigation performance over other CNN model-based approaches. Full article

Most advances in the remote sensing of floating marine plastic litter have been made using passive remote-sensing techniques in the visible (VIS) to short-wave-infrared (SWIR) parts of the electromagnetic spectrum based on the spectral absorption features of plastic surfaces. In this paper, we present developments of new and emerging remote-sensing technologies of marine plastic litter such as passive techniques: fluid lensing, multi-angle polarimetry, and thermal infrared sensing (TIS); and active techniques: light detection and ranging (LiDAR), multispectral imaging detection and active reflectance (MiDAR), and radio detection and ranging (RADAR). Our review of the detection capabilities and limitations of the different sensing technologies shows that each has their own weaknesses and strengths, and that there is not one single sensing technique that applies to all kinds of marine litter under every different condition in the aquatic environment. Rather, we should focus on the synergy between different technologies to detect marine plastic litter and potentially the use of proxies to estimate its presence. Therefore, in addition to further developing remote-sensing techniques, more research is needed in the composition of marine litter and the relationships between marine plastic litter and their proxies. In this paper, we propose a common vocabulary to help the community to translate concepts among different disciplines and techniques. Full article

Ice thickness has a significant effect on the physical and biogeochemical processes of a lake, and it is an integral focus of research in the field of ice engineering. The Qinghai–Tibetan Plateau, known as the Third Pole of the world, contains numerous lakes. Compared with some information, such as the area, water level, and ice phenology of its lakes, the ice thickness of these lakes remains poorly understood. In this study, we used an unmanned aerial vehicle (UAV) with a 400/900 MHz ice-penetrating radar to detect the ice thickness of Qinghai Lake and Gahai Lake. Two observation fields were established on the western side of Qinghai Lake and Gahai Lake in January 2019 and January 2021, respectively. Based on the in situ ice thickness and the propagation time of the radar, the accuracy of the ice thickness measurements of these two non-freshwater lakes was comprehensively assessed. The results indicate that pre-processed echo images from the UAV-borne ice-penetrating radar identified non-freshwater lake ice, and we were thus able to accurately calculate the propagation time of radar waves through the ice. The average dielectric constants of Qinghai Lake and Gahai Lake were 4.3 and 4.6, respectively. This means that the speed of the radar waves that propagated through the ice of the non-freshwater lake was lower than that of the radio waves that propagated through the freshwater lake. The antenna frequency of the radar also had an impact on the accuracy of ice thickness modeling. The RMSEs were 0.034 m using the 400 MHz radar and 0.010 m using the 900 MHz radar. The radar with a higher antenna frequency was shown to provide greater accuracy in ice thickness monitoring, but the control of the UAV’s altitude and speed should be addressed. Full article

The transition region is a very thin but most peculiar layer in the solar atmosphere located between the solar chromosphere and the corona. It is a key region for understanding coronal heating, solar eruption triggers, and the origin of solar winds. Here, almost all physical parameters (density, temperature, and magnetic fields) have the maximum gradient. Therefore, this region should be highly dynamic, including fast energy releasing and transporting, plasma heating, and particle accelerating. The physical processes can be categorized into two classes: thermal and non-thermal processes. Thermal processes can be observed at ultraviolet (UV) and extreme ultraviolet (EUV) wavelengths via multi-wavelength images. Non-thermal processes accelerate non-thermal electrons and produce radio emissions via the gyrosynchrotron mechanism resulting from the interaction between the non-thermal electrons and magnetic fields. The frequency range spans from several GHz to beyond 100 GHz, in great number of bursts with narrowband, millisecond lifetime, rapid frequency drifting rates, and being referred to as transition region small-scale microwave bursts (TR-SMBs). This work proposes a new type of Solar Ultra-wide Broadband Millimeter-wave Spectrometer (SUBMS) that can be used to observe TR-SMBs. From SUBMS observations, we can derive rich dynamic information about the transition region, such as information about non-thermal energy release and propagation, the flows of plasma and energetic particles, the magnetic fields and their variations, the generation and transportation of various waves, and the formation and evolution of the source regions of solar eruptions. Such an instrument can actually detect the non-thermal signals in the transition region during no flare as well as the eruptive high-energy processes during solar flares. Full article

In this paper, we propose a method for extracting the structure of an indoor environment using radar. When using the radar in an indoor environment, ghost targets are observed through the multipath propagation of radio waves. The presence of these ghost targets obstructs accurate mapping in the indoor environment, consequently hindering the extraction of the indoor environment. Therefore, we propose a deep learning-based method that uses image-to-image translation to extract the structure of the indoor environment by removing ghost targets from the indoor environment map. In this paper, the proposed method employs a conditional generative adversarial network (CGAN), which includes a U-Net-based generator and a patch-generative adversarial network-based discriminator. By repeating the process of determining whether the structure of the generated indoor environment is real or fake, CGAN ultimately returns a structure similar to the real environment. First, we generate a map of the indoor environment using radar, which includes ghost targets. Next, the structure of the indoor environment is extracted from the map using the proposed method. Then, we compare the proposed method, which is based on the structural similarity index and structural content, with the k-nearest neighbors algorithm, Hough transform, and density-based spatial clustering of applications with noise-based environment extraction method. When comparing the methods, our proposed method offers the advantage of extracting a more accurate environment without requiring parameter adjustments, even when the environment is changed. Full article