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Registration between catadioptric omni-images and orthorectified aerial images is the key step to integrate them to achieve three-dimensional urban construction. This problem becomes very challenging because of the non-linearity of the imaging model of catadioptric omni-cameras. In this study, the authors attempt to address this problem. The authors first study the properties of horizontal line structure under catadioptric omni-cameras to prove and extend the theorem of catadioptric distance, and then present angle consistency of horizontal lines between a catadioptric omni-image and an orthorectified aerial image. The authors further employ them to achieve registration between catadioptric omni-images and orthorectified aerial images. To the best of authors’ knowledge, this study has not been done before. Experimental results on both simulated data and real scene images confirm the effectiveness of this approach.

This paper presents a region-adaptive clutter rejection method for small target detection in sea-based infrared search and track. In the real world, clutter normally generates many false detections that impede the deployment of such detection systems. Incoming targets (missiles, boats, etc.) can be located in the sky, horizon and sea regions, which have different types of clutters, such as clouds, a horizontal line and sea-glint. The characteristics of regional clutter were analyzed after the geometrical analysis-based region segmentation. The false detections caused by cloud clutter were removed by the spatial attribute-based classification. Those by the horizontal line were removed using the heterogeneous background removal filter. False alarms by sun-glint were rejected using the temporal consistency filter, which is the most difficult part. The experimental results of the various cluttered background sequences show that the proposed region adaptive clutter rejection method produces fewer false alarms than that of the mean subtraction filter (MSF) with an acceptable degradation detection rate.

This paper studies a distributed coverage algorithm of a bounded rectangular region in the presence of horizontal line obstacles by an autonomous swarm of asynchronous mobile robots. They follow the basic Look-Compute-Move model, formally known as the CORDA model. The robot has no prior knowledge about the internal environment of the target region, especially the number and location of the robots, as well as obstacles. Robots are assumed to be anonymous, small, identical, simple, oblivious, inexpensive, and non-communicating in nature. The robots have a limited range of visibility. The robots unanimously decompose the whole region into several non-overlapping horizontal strips, where each robot is responsible for painting at most two strips based on its initial position. The painting of the entire region is achieved within finite time without any collision and repetition.

BackgroundA new hyaluronic acid (HA) was appraised to improve forehead horizontal lines (FHL). Histological analysis correlated the gel distribution with clinical findings and also with wrinkle depth, 48 h and 12 months after HA application.MethodsThe new filler composed by 24 mg/ml of HA cross-linked with BDDE 2.0 ppm was injected into the subcutaneous fat, just beneath the dermis, through a retrograde backflow injection technique applied in sequence. Biopsies were taken with a 3.0-mm-diameter skin punch before HA application, 48 h and 12 months after HA application for histological evaluation.ResultsSubcutaneous application created a compact cylindrical filament as pattern of gel distribution, which acted as strut raising the FHL to the level of the surrounding tissues. Diameter average of the filament 48 h after HA application was 1.18 mm and at month twelve 0.34 mm. The residual amount of the gel at month twelve ensured the long acting of the HA into the subcutaneous fat. FHL depth of 251.83 µm before HA application and 190.20 µm after 12 months indicated that the residual amount of gel at month twelve still projected FHL.ConclusionHigh cohesivity of HA and low density of forehead subcutaneous fat caused the gel to take the form of a compact cylindrical filament. The small amount of gel still presenting into the subcutaneous fat 12 months after application validated the long acting of the HA. Evidence-based analysis showed that this new filler might be considered a safe alternative for improvement in the FHL.Level of Evidence IVThis journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

Depth discrimination is a key procedure in acoustic detection or target classification for low-frequency underwater sources. Conventional depth-discrimination methods use a vertical line array, which has disadvantage of poor mobility due to the size of the sensor array. In this paper, we propose a depth-discrimination method for low-frequency sources using a horizontal line array (HLA) of acoustic vector sensors based on mode extraction. First, we establish linear equations related to the modal amplitudes based on modal beamforming in the vector mode space. Second, we solve the linear equations by introducing the total least square algorithm and estimate modal amplitudes. Third, we select the power percentage of the low-order modes as the decision metric and construct testing hypotheses based on the modal amplitude estimation. Compared with a scalar sensor, a vector sensor improves the depth discrimination, because the mode weights are more appropriate for doing so. The presented linear equations and the solution algorithm allow the method to maintain good performance even using a relatively short HLA. The constructed testing hypotheses are highly robust against mismatched environments. Note that the method is not appropriate for the winter typical sound speed waveguide, because the characteristics of the modes differ from those in downward-refracting sound speed waveguide. Robustness analysis and simulation results validate the effectiveness of the proposed method.

European Space Agency (ESA) launched its first space-based Doppler Wind Lidar (DWL) mission called Atmospheric Dynamic Mission (ADM) – Aeolus. Onboard the Aeolus mission is the Atmospheric LAser Doppler Instrument (ALADIN) which measures the horizontal line-of-sight (HLOS) winds. Aeolus Level-2B wind observations in Rayleigh clear and Mie cloudy channels are evaluated for implementation in the NCMRWF (National Centre for Medium Range Weather Forecasting) Global Forecast System (NGFS). The GSI (grid-point statistical interpolation) analysis scheme has been modified and updated to assimilate the HLOS wind information. Quality control criteria are applied during observation processing and during minimization of cost function for computation of the initial condition. An observation system experiment (OSE) is performed by employing the GSI-3DVar (3-Dimensional Variation) approach and involving HLOS data. In addition to assimilation and forecast diagnostics, two case studies of very severe cyclonic storms are investigated to assess the impact of this new wind information on a severe weather event. Statistically, significant improvement is observed mostly over the Southern Hemisphere, Tropics, and RSMC (Regional Specialized Meteorological Centre, 29°–120°E and 21°S–46°N) region in terms of reduction in wind root mean square error. Assimilation of HLOS winds shows a reduction in direct positional error (DPE) for both cyclonic systems. Improvement in the 6-hourly analysis of minimum sea level pressure and maximum 10 m wind speed is also observed.

The aperture of the towed horizontal line array is limited and the received signal is unstable in a complex ocean environment, making it difficult to distinguish the location of the sound source. To address this challenge, this paper presents a MoELocNet (Mixture of Experts Localization Network) for deep-sea sound source localization, leveraging interference structures in range-frequency domain signals from a towed horizontal line array. Unlike traditional correlation-based methods constrained by time-varying ocean environments and low signal-to-noise ratios, the model employs multi-expert and multi-task learning to extract interference periods from single-frame data, enabling robust estimation of source range and depth. Simulation results demonstrate its superior performance in the deep-sea shadow zone, achieving a range localization error of 0.029 km and a depth error of 0.072 m. The method exhibits strong noise robustness and delivers satisfactory results across diverse deep-sea zones, with optimal performance in shadow zones and secondary effectiveness in the direct arrival zone.

The electromagnetic data observed with the CSES (China Seismo-Electromagnetic Satellite, also known as Zhangheng-1 satellite) contain numerous spatial disturbances. These disturbances exhibit various shapes on the spectrogram, and constant frequency electromagnetic disturbances (CFEDs), such as artificially transmitted very-low-frequency (VLF) radio waves, power line harmonics, and interference from the satellite platform itself, appear as horizontal lines. To exploit this feature, we proposed an algorithm based on computer vision technology that automatically recognizes these lines on the spectrogram and extracts the frequencies from the CFEDs. First, the VLF waveform data collected with the CSES electric field detector (EFD) are converted into a time–frequency spectrogram using short-time Fourier Transform (STFT). Next, the CFED automatic recognition algorithm is used to identify horizontal lines on the spectrogram. The third step is to determine the line frequency range based on the proportional relationship between the frequency domain of the satellite’s VLF and the height of the time–frequency spectrogram. Finally, we used the CSES power spectrogram to confirm the presence of CFEDs in the line frequency range and extract their true frequencies. We statistically analyzed 1034 orbit time–frequency spectrograms and power spectrograms from 8 periods (5 days per period) and identified approximately 200 CFEDs. Among them, two CFEDs with strong signals persisted throughout an entire orbit. This study establishes a foundation for detecting anomalies due to artificial sources, particularly in the study of short-term strong earthquake prediction. Additionally, it contributes to research on other aspects of spatial electromagnetic interference and the suppression and cleaning of electromagnetic waves.

The element–frequency acoustic intensity of a horizontal line array with a sufficient aperture exhibits interference striation patterns, which can be used for source range estimation without prior environmental information. Under multisource scenarios, the interference striations of the sources overlap with each other, leading to great difficulty in utilizing the information of striations. In this paper, the wavenumber filtering method is applied to each sensor of the horizontal line array to extract the surface-reflected–bottom-reflected modes and reconstruct the recognizable interference spectrogram for each source. Then, via beamforming along the striations, the source ranges can be estimated individually with little prior environmental information and without the long-time observation of moving sources. The required sensor spacing is analyzed, and the spatial filtering capabilities for a single source from different bearings and two sources for which azimuth angles are close to each other have also been investigated. The simulation results indicate that the proposed algorithm can estimate ranges of multiple sources within 25 km, with relative errors of less than 4%.

Considering that the information contained in the interference structure of the “target-receiver” path in active sonar is crucial for remote sensing of the target position or the environmental information, this paper studies the method for coherent extraction and enhancement of the interference structure of the scattered sound field using a monostatic horizontal line array (HLA) in deep water. The HLA element–frequency domain sound intensity interference pattern of the monostatic scattered sound field is numerically simulated, and the “cutting” effect on the pattern is explained by combining the scattered sound pressure expression. Then, the mechanism of the sound propagation effect of the “source-target” path on the interference structure of the “target-receiver” path is clarified. In deep water, the phase relationship of the HLA scattered sound pressure is derived based on the ray theory, and its similarity with the phase relationship of the array passive received signals affected by the source spectrum is researched. The method for the coherent enhancement of the interference structure between the target and the reference array element for the deep-water active sonar is proposed, which uses the phase information of the single-element (SE) signal to generate the array cross-correlation data and then performs striation-based beamforming on it (i.e., the striation–correlation-based beamforming with single element, SCBF-SE). The results of numerical simulation and sea trial data analysis show the effectiveness of this method for interference structure enhancement. The performance differences between SCBF-SE and the incoherent accumulation of the striation energy (IASE) method in interference structure enhancement are compared. The results indicate that SCBF-SE has better performance under the conditions of the same received signal-to-noise ratio and the number of array elements.