The beamforming-based direction of arrival (DOA) estimation method is widely used because of its advantages of robustness, which requires a large array aperture to ensure high spatial resolution. The deconvolution beam-forming method has high-resolution DOA estimation without increasing the array aperture. However, it is based on the assumption of incoherent narrowband signals. When the signals are coherent, the high cross-term sidelobes result in large estimation errors. In this paper, a new sparse deconvolution beamforming is proposed to estimate the DOA of wideband coherent signals. First, extend the deconvolution beamforming method to the wideband case by matrix transformation. Second, solve the problem that the traditional deconvolution algorithm is not applicable to coherent signals by sparse representation. Numerical simulations and underwater experiments are presented to verify the effectiveness of the proposed method. The results show that even at low signal-to-noise ratio and blind environment parameters, the proposed method can obtain better performance compared with other coherent signals DOA estimation algorithms.

Weak target detection is a great challenging in radar field. To detect the weak targets with beam migration, a novel tri-dimensional time model (i.e. fast time, slow time, and beam time) and a novel tri-dimensional signal model which based on the time model are set up firstly. Then, according to the presented models, we propose two multi-beam associated (MBA) coherent integration algorithms based on time-shared multi-beam (TSMB) and space-shared multi-beam (SSMB), respectively. The two proposed algorithms could both eliminate beam migration via associating multi-beam and realize coherent integration via discrete Fourier transform. According to different beam scanning modes, the subsequent analyses show that the MBA coherent integration algorithm based on SSMB (MBACIA-SSMB) may have a better detection performance than that based on TSMB (MBACIA-TSMB). Moreover, the capabilities to estimate the target's radial velocity and tangency velocity are analyzed. Finally, some numerical experiments are given to verify the performances of MBACIA-TSMB and MBACIA-SSMB.

Gravitational-wave (GW) observations provide unique information about compact objects. As detector sensitivity increases, new astrophysical sources of GWs could emerge. Close hyperbolic encounters are one such source class: Scattering of stellar mass compact objects is expected to manifest as GW burst signals in the frequency band of current detectors. We present the search for GWs from hyperbolic encounters in the second half of the third Advanced LIGO-Virgo observing run (O3b). We perform a model-informed search with a machine-learning enhanced Coherent WaveBurst algorithm. No significant event has been identified in addition to known detections of compact binary coalescences. We inject in the O3b data nonspinning third post-Newtonian order accurate hyperbolic encounter model with component masses between $[2,100]{M}_{\ensuremath{\bigodot}}$, impact parameter in $[60,100]GM/{c}^{2}$, and eccentricity in [1.05, 1.6]. We further discuss the properties of the simulation recovered. For the first time, we report the sensitivity volume achieved for such sources, which for O3b data reaches up to $3.9\ifmmode\pm\else\textpm\fi{}1.4\ifmmode\times\else\texttimes\fi{}{10}^{5}\text{ }\text{ }{\mathrm{Mpc}}^{3}\text{ }\mathrm{yr}$ for compact objects with masses in the range $[20,40]{M}_{\ensuremath{\bigodot}}$, corresponding to a rate density upper limit of $0.589\ifmmode\pm\else\textpm\fi{}0.094\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}5}\text{ }\text{ }{\mathrm{Mpc}}^{\ensuremath{-}3}\text{ }{\mathrm{yr}}^{\ensuremath{-}1}$. Finally, we present a projected sensitive volume for the next observing runs of current detectors, namely, O4 and O5.

Objective. This study aims to develop and assess a tumor contraction model, enhancing the precision of ablative margin (AM) evaluation after microwave ablation (MWA) treatment for hepatocellular carcinomas (HCCs). Approach. We utilize a probabilistic method called the coherent point drift algorithm to align pre-and post-ablation MRI images. Subsequently, a nonlinear regression method quantifies local tumor contraction induced by MWA, utilizing data from 47 HCC with viable ablated tumors in post-ablation MRI. After automatic non-rigid registration, correction for tumor contraction involves contracting the 3D contour of the warped tumor towards its center in all orientations. Main results. We evaluate the performance of our proposed method on 30 HCC patients who underwent MWA. The Dice similarity coefficient between the post-ablation liver and the warped pre-ablation livers is found to be 0.95 ± 0.01, with a mean corresponding distance between the corresponding landmarks measured at 3.25 ± 0.62 mm. Additionally, we conduct a comparative analysis of clinical outcomes assessed through MRI over a 3 month follow-up period, noting that the AM, as evaluated by our proposed method, accurately detects residual tumor after MWA. Significance. Our proposed method showcases a high level of accuracy in MRI liver registration and AM assessment following ablation treatment. It introduces a potentially approach for predicting incomplete ablations and gauging treatment success.

Prolonging radar observation time is capable of enhancing noise robustness for weak target detection, whereas the range migration phenomenon induced by the target's high-speed movement will deteriorate the energy accumulation ability of the traditional moving target detection method. This article concerns long-time coherent integration for radar high-speed target and proposes an efficient detection algorithm based upon azimuth resampling. The proposed algorithm can be implemented via azimuth nonuniform fast Fourier transform and further developed into the sparse representation mechanism utilizing 2-D sparse Fourier transform. Furthermore, the dictionary dynamic updating strategy is presented to improve the weak target detectability in the multitarget observation environment. The processed results of both simulated and measured radar data have been displayed to validate the effectiveness of the presented algorithm.

Bone functional adaptation is routinely invoked to interpret skeletal morphology despite ongoing debate regarding the limits of the bone response to mechanical stimuli. The wide variation in human body mass presents an opportunity to explore the relationship between mechanical load and skeletal response in weight-bearing elements. Here, we examine variation in femoral macroscopic morphology as a function of body mass index (BMI), which is used as a metric of load history. A sample of 80 femora (40 female; 40 male) from recent modern humans was selected from the Texas State University Donated Skeletal Collection. Femora were imaged using x-ray computed tomography (voxel size ~0.5 mm), and segmented to produce surface models. Landmark-based geometric morphometric analyses based on the Coherent Point Drift algorithm were conducted to quantify shape. Principal components analyses were used to summarize shape variation, and component scores were regressed on BMI. Within the male sample, increased BMI was associated with a mediolaterally expanded femoral shaft, as well as increased neck-shaft angle and decreased femoral neck anteversion angle. No statistically significant relationships between shape and BMI were found in the female sample. While mechanical stimulus has traditionally been applied to changes in long bong diaphyseal shape it appears that bone functional adaptation may also result in fundamental changes in the shape of skeletal elements.

Copper and aluminum foils serve as predominant materials in fluid collectors, and defects within them can significantly impact the electrochemical performance of cells. However, existing methods for detecting defects within non-ferromagnetic thin metals, such as copper and aluminum foils, have several limitations. This study aims to address the need for detecting micrometer-scale defects on 0.1 mm copper foils, aligning with industrial field requirements. We devised an inspection device based on the induced magnetic field detection principle and explored the impact of copper foil undulations on micrometer-scale defect detection using COMSOL modeling. Subsequently, we introduced a coherent cumulative-differential algorithm to effectively mitigate the influences of circuit noise and sampling heave noise on defect signals. Consequently, the signal-to-noise ratios of 100- and 200-micron defect signals were significantly improved by 157% and 234%, respectively. This approach shows promise for detecting micrometer-scale defects in non-ferromagnetic thin metals and lays a robust foundation for future defect identification and inversion endeavors.

The network traffic of data centers (DCs) has increased unprecedentedly with the rapid development of digital economy. However, the data transmission faces security threats in the distributed optical interconnection and intensive interaction of DC networks. In this paper, we propose a chaotic phase noise-like encryption algorithm using geometric shaping (GS) for coherent DC interconnections (DCIs). A GS constellation is used to improve transmission performance, and it is combined with coherent equalization algorithms to improve security performance. Then, a chaotic encryption is designed based on phase noise-like transformation (PNLT). The data are effectively scrambled, and the confusion level of phase can be increased. Finally, 216 Gb/s 8-quadrature amplitude modulation (8-QAM) encrypted data are successfully verified on a 240 km transmission link of DCIs. The results show that this scheme can achieve a bit error rate (BER) performance gain of 1.1 dB and provide a highly compatible solution for realizing security enhanced DCIs.

This study investigates the relationships between Intervertebral Disc (IVD) morphology and biomechanics using patient-specific (PS) finite element (FE) models and poromechanical simulations.169 3D lumbar IVD shapes from the European project MySpine (FP7-269909), spanning healthy to Pfirrmann grade 4 degeneration, were obtained from MRIs. A Bayesian Coherent Point Drift algorithm aligned meshes to a previously validated structural FE mesh of the IVD. After mesh quality analyses and Hausdorff distance measurements, mechanical simulations were performed: 8 and 16 hours of sleep and daytime, respectively, applying 0.11 and 0.54 MPa of pressure on the upper cartilage endplate (CEP). Simulation results were extracted from the anterior (ATZ) and posterior regions (PTZ) and the center of the nucleus pulposus (CNP). Data mining was performed using Linear Regression, Support Vector Machine, and eXtreme Gradient Boosting techniques. Mechanical variables of interest in DD, such as pore fluid velocity (FLVEL), water content, and swelling pressure, were examined. The morphological variables of the simulated discs were used as input features.Local morphological variables significantly impacted the local mechanical response. The local disc heights, respectively in the mid (mh), anterior (ah), and posterior (ph) regions, were key factors in general. Additionally, fluid transport, reflected by FLVEL, was greatly influenced (r2 0.69) by the shape of the upper and lower cartilage endplates (CEPs).This study suggests that disc morphology affects Mechanical variables of interest in DD. Attention should be paid to the antero-posterior distribution and local effects of disc heights. Surprisingly, the CEP morphology remotely affected the fluid transport in NP volumes around mid-height, and mechanobiological implications shall be explored. In conclusion, patient-specific IVD modeling has strong potential to unravel important correlations between IVD phenotypes and local tissue regulation.Acknowledgments: European Commission: Disc4All-MSCA-2020-ITN-ETN GA: 955735; O-Health-ERC-CoG-2021-101044828

In this paper, the possibility to improve target detection performance in passive bistatic radar by jointly exploiting multiple signals at different carrier frequencies emitted by the same illuminator is investigated, namely multi-frequency passive bistatic radar (MFPBR) coherent integration. Since the carrier frequency of each signal is agile, the MFPBR coherent integration suffers from the problems of range phase incoherence and Doppler broadening. In order to tackle these challenges, a multi-frequency coherent integration target detection algorithm for passive bistatic radar is proposed. Specifically, this scheme corrects the Doppler broadening effect via TSP. Then, low sidelobe filtering based on convex optimization is carried out to remove the range phase incoherence and obtain the MF of the target's energy. Meanwhile, the high-range-resolution profiles (HRRP) of target can be generated. The advantage of the proposed algorithm is that it can obtain superior coherent integration and detection performance for both the single and multiple targets scenarios compared to existing methods. Finally, a series of measured and simulation results are presented to demonstrate the effectiveness of the proposed algorithm.

Among the directional angle calculation models of Bluetooth 5.1, the rectangular hollow array offers the advantage of shorter sampling time due to its fewer elements compared to traditional planar arrays. However, the antimultipath algorithms suitable for traditional planar arrays cannot be applied to rectangular hollow arrays. Therefore, this study proposes a virtual array filling algorithm, wherein four virtual matrices are inserted into the hollow matrix to transform the array into a uniform rectangular array. This algorithm ensures translation invariance of the rectangular array, enabling the application of the antimultipath coherent source algorithm to a rectangular hollow array. An algorithm for reconstructing Toeplitz matrices in two‐dimensional uniform planar arrays is also proposed. Through the analysis of the spatial spectrum and angle estimation results of various algorithms, the effectiveness of the signal angle of arrival estimation theory is verified.

Global navigation satellite system (GNSS)reflected signals have been widely used in remote sensing applications. To overcome the low-power budget of GNSS reflected signals, traditional detection strategies involve extending the coherent processing interval (CPI) of a single frame or employing the multiframe detection (MFD) method in which the noncoherent integration is used to accumulate echo energy. In this article, a novel coherent dynamic-programming-based track-before-detect (C-DP-TBD) algorithm is proposed to search and compensate for the interframe phase shift to improve the performance of weak target detection using GNSS reflected signals. The proposed algorithm is included in the two-stage MFD and follows a range-Doppler (RD) map generator. The RD map generator extracts the RD map candidate list for a specific data frame, which contains all of its variants based on the potential phase value and location shifts of the target peak in the RD domain relative to a designated reference frame. Subsequently, the C-DP-TBD processor coherently integrates each variant in this list with the reference frame, removes implausible variants, and selects the most reliable one by comparing all resulting peak signal-to-noise ratios with a preset threshold. The confirmation of the target track in the RD domain is finally achieved by extracting the position of the target peak from every available frame. Compared with traditional motion target detection methods with long CPI and DP-TBD methods, the performance superiority of the proposed algorithm is demonstrated by both simulation and experimental results in which a ferry was employed as a moving target.

The paper analyzes the drawbacks of an non coherent discriminant algorithm used to detect the mono signal from broadcasting FM radio stations.It has been shown that the MATLAB implementation of this algorithm for the RTL-SDR receiver suffers from low selectivity, leading to the phenomenon of overlapping sounds from neighboring broadcasting FM radio stations, which results in interference.To eliminate this disadvantage an improved algorithm was developed.This algorithm uses a low-frequency finite impulse response (FIR) filter in the FM signal band before the decimator.The research involved three types of FIR filters: Hamming, Kaiser, and Parks-McClellan.Kaiser and Parks-McClellan FIR filters have a high order and provide an almost vertical cutoff, with slightly different levels of side lobes.Kaiser filters start at -40 dB and drop to -70 dB gradually, while Parks-McClellan filters immediately reach a constant -58 dB level.Hamming filter has a lower order, with similar levels of side lobes, but a flatter main lobe (losses at cutoff frequency are 6 dB and side lobes begin at ~220 kHz). After the FIR decimator, based on the Parks-McClellan algorithm, starting at 20 kHz, attenuation of about 40 dB is provided. To better understand the influence of various factors in the proposedprocessing method, a model was created using two broadcasting FM signals with a frequency difference of 700 kHz, simulating the situation where signal reception must take place from one radio station in the presence of another powerful radio station located nearby in frequency.In the experiments, the excess of the interference signal over the desired signal is assumed to be 20 dB.The inverse of the standard deviation of the original and filtered signals is used as a criterion to compare the FIR filters.It has been shown that over the entire range of signal-to-noise ratios (0-20 dB), the Parks-McClellan FIR filter performs best.The Hamming filter, with a relatively low order, is inferior by 5 % and the Kaiser filter is inferior by 7 %.Implementation of the proposed algorithms is provided in the MATLAB programming language.

The algorithms to detect the presence of water on land surfaces using the Global Navigation Satellite System (GNSS) reflected signals collected aboard of the future ESA Scout 2-satellite mission HydroGNSS are presented. HydroGNSS will be ready for launch in H2/2024, into polar orbits, and it will operate at dual frequency, dual-polarization, and in two simultaneous acquisition modes (low-rate power and high-rate complex signal modes). The overall strategy to generate level-2, point-by-point along track water classification using all these signals is introduced, yet currently available datasets only permit the validation of the algorithms applied to one single frequency and polarization. Two signal coherence indicators are selected per each acquisition mode, and together with geolocation, largescale surface roughness, and land cover type are the inputs of the random forest classifier, which is an ensemble learning method, with the monthly Global Surface Water (GSW) as a reference target. The algorithms are tested with NASA/CYGNSS low-rate power delay-Doppler maps (DDMs) and with CYGNSS and U.K./TechDemoSat-1 (TDS-1) raw intermediate frequency (IF) sampled signals. The results of the validation are analyzed at different scales, with results achieving the mission's 90% accuracy requirement. Final retraining and validation using actual dual-polarization and dual-frequency HydroGNSS data will be conducted once the satellites are in-orbit.

PurposeTibia plafond or pilon fractures present a high level of complexity, making their surgical management challenging. Three-Dimensional Virtual Planning (3DVP) can assist in preoperative planning to achieve optimal fracture reduction. This study aimed to assess the symmetry of the left and right tibial plafond and whether left–right mirroring can reliably be used.MethodsBilateral CT scans of the lower limbs of 75 patients without ankle problems or prior fractures of the lower limb were included. The CT images were segmented to create 3D surface models of the tibia. Subsequently, the left tibial models were mirrored and superimposed onto the right tibia models using a Coherent Point Drift surface matching algorithm. The tibias were then cut to create bone models of the distal tibia with a height of 30 mm, and correspondence points were established. The Euclidean distance was calculated between correspondence points and visualized in a boxplot and heatmaps. The articulating surface was selected as a region of interest.ResultsThe median left–right difference was 0.57 mm (IQR, 0.38 – 0.85 mm) of the entire tibial plafond and 0.53 mm (IQR, 0.37 – 0.76 mm) of the articulating surface. The area with the greatest left–right differences were the medial malleoli and the anterior tubercle of the tibial plafond.ConclusionThe tibial plafond exhibits a high degree of bilateral symmetry. Therefore, the mirrored unfractured tibial plafond may be used as a template to optimize preoperative surgical reduction using 3DVP techniques in patients with pilon fractures.

DOA estimation of coherent signals in underdetermined problems using sparse-based Toeplitz covariance reconstruction

Electromagnetic tracking (EMT) has been researched for brachytherapy applications, showing a great potential for automating implant reconstruction, and overcoming image-based limitations such as contrast and spatial resolution. One of the challenges of this technology is that it does not intrinsically share the same reference frame as the patient's medical imaging. To present a novel phantom that can be used for a comprehensive quality assurance (QA) program of brachytherapy EMT systems and use this phantom to validate a novel applicator-based registration method of EMT and image reference frames for gynecological (GYN) interstitial brachytherapy. Eleven 6F-catheters (20cm long), one 6F round tip catheter (29.4cm long) and a tandem and ring gynecological applicator (Elekta, CT/MR 60°, 40mm long tandem, 30mm diameter ring) were placed in a rigid custom-made phantom (Elekta Brachytherapy, Veenendaal, The Netherlands) to reconstruct their geometry using a five-degree of freedom EMT sensor attached to an afterloader's check cable. All EMT reconstructions were done in three different environments: disturbance free (no metal nearby), computed tomography (CT)-on-rails brachytherapy suite and magnetic resonance imaging (MRI) brachytherapy suite. Implants were placed parallel to a magnetic field generatorand were reconstructed using two different acquisition methods: step-and-record and continuous motion. In all cases, the acquisition is performed at a rate of approximately 40Hz. A CT scan of the phantom inside a water cube was obtained. In the treatment planning system (TPS), all catheters in the CT images were manually reconstructed and the applicator reconstruction was achieved by manually placing its solid 3D model, found in the applicator library of the TPS. The Iterative Closest Point and the Coherent Point Drift algorithms were used, initialized with four known points, to register both EMT and CT scan reference frames using corresponding points from the EMT and CT based reconstructions of the phantom, following three approaches: one gynecological applicator, four interstitial catheters inside four calibration plates having an S-shaped path, and four 5mm diameter ceramic marbles found in each of the four calibration plates. Once registered, the registration error (perpendicular distance) was computed. The absolute median deviation from the expected value for EMT measurements in the disturbance free environment, CT-on-rails brachytherapy suite, and MRI-brachytherapy suite are 0.41, 0.23, and 0.31mm, respectively, while for the CT scan it is 0.18mm. These values significantly lie below the sensor's expected accuracy of 0.70mm (p<0.001), suggesting that the environment did not have a significant impact on the measurements, given that care is taken in the immediate surroundings. In all three environments, the two acquisitions and three registration approaches have mean and median registration errors that lie at or below 1mm, which is lower than the clinical acceptable threshold of 2mm. The novel phantom allowed to successfully evaluate the accuracy of EMT-based reconstructions of catheters and a GYN tandem and ring applicator in different clinical environments. A registration method based only on the applicator geometry, reconstructed withan EMT sensor and the TPS solid applicator library, was validated and shows clinically acceptable accuracy, comparable to CT-based reconstruction but within a few minutes. Since the applicator is also visible in MRI, this method could potentially be used in clinics in an EMT-MR interstitial GYN brachytherapy workflow.

The ptychographic iterative engine (PIE) is a lensless coherent diffraction imaging algorithm known for its simplicity, easy to use, scalability, and fast convergence. However, practical applications often encounter interference in imaging results caused by non-static scattering media, such as dense fog, seawater target detection and medical biology diagnosis. To address this challenge, we propose a novel approach using computational deep learning for dynamic scattering medium image reconstruction, enabling lens-free coherent diffraction imaging through dynamic scattering media. Through extensive analysis, we evaluate the effectiveness of the neural network for PIE image recovery under varying scattering medium concentration conditions. We also test scattering images obtained by hybrid training with different concentrations of scattering medium to assess the generalisation ability of the neural network. The experimental results demonstrate that our proposed method achieve PIE lens-free imaging under non-static scattering media interference. This coherent diffraction imaging method, based on transmission through dynamic scattering media, opens up new possibilities for practical applications of PIE and fosters its development in complex environments. Its significance extends to fields like atmospheric pollution, seawater target detection and medical biology diagnosis, providing valuable references for research in these domains.

The Direction of Arrival (DOA) estimation of coherent signals in co-prime arrays has become a popular research topic. However, traditional spatial smoothing and subspace algorithms fail to perform well under low signal-to-noise ratio (SNR) and small snapshots. To address this issue, we have introduced an Enhanced Spatial Smoothing (ESS) algorithm that utilizes a space-time correlation matrix for de-noising and decoherence. Finally, an Estimating Signal Parameter via Rotational Invariance Techniques (ESPRIT) algorithm is used for DOA estimation. In comparison to other decoherence methods, when the SNR is −8 dB and the number of snapshots is 150, the mean square error (MSE) of the proposed algorithm approaches the Cramér–Rao bound (CRB), the probability of resolution (PoR) can reach over 88%, and, when the angular resolution is greater than 4°, the estimation accuracy can reach over 90%.

By considering time-scale separation characteristics and nonlinear node voltage equation, an improved slow coherency algorithm has been proposed, and for the first time, applied to the slow coherency model widely used for modeling three-phase droop-controlled grid-connected inverters. The method works by combining two coherency clustering results, which in turn, are computed from mode matrix, representing slow mode and damping coefficient of the slow coherency model. Subsequently, fuzzy C-means spatial clustering algorithm and aggregation method are used for identifying coherent generators and building the reduced-order model, respectively. Compared to the traditional slow coherency algorithms, the presented method can accurately identify oscillating mode between power supplies under the condition of selecting any number of areas. Its range of application can moreover be expanded to cover distributed networks with multiple droop-controlled inverters. These expectations, and accuracy and effectiveness of the proposed method have eventually been verified by simulations performed with both detailed and reduced-order models under different faults.