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To solve the problem that the hydrophone arrays are disturbed by ocean noise when collecting signals in shallow seas, resulting in reduced accuracy and resolution of target orientation estimation, a direction-of-arrival (DOA) estimation algorithm based on iterative EMD interval thresholding (EMD-IIT) and off-grid sparse Bayesian learning is proposed. Firstly, the noisy signal acquired by the hydrophone array is denoised by the EMD-IIT algorithm. Secondly, the singular value decomposition is performed on the denoised signal, and then an off-grid sparse reconstruction model is established. Finally, the maximum a posteriori probability of the target signal is obtained by the Bayesian learning algorithm, and the DOA estimate of the target is derived to achieve the orientation estimation of the target. Simulation analysis and sea trial data results show that the algorithm achieves a resolution probability of 100% at an azimuthal separation of 8° between adjacent signal sources. At a low signal-to-noise ratio of −9 dB, the resolution probability reaches 100%. Compared with the conventional MUSIC-like and OGSBI-SVD algorithms, this algorithm can effectively eliminate noise interference and provides better performance in terms of localization accuracy, algorithm runtime, and algorithm robustness. [ABSTRACT FROM AUTHOR]

Summary Multiple‐input multiple‐output (MIMO) radar, employing multiple transmitters and receivers, enhances radar capabilities. It detects and tracks objects like aircraft and ships using radio waves. Compared with traditional phased‐array radar, MIMO systems offer greater flexibility, improving angular resolution and target detection. Researchers focus on direction of arrival (DoA) evaluation for closely spaced targets. Effective beamforming and accurate DoA estimation are crucial for MIMO radar performance. This study explores two methods: Capon beamforming with Dolph–Chebyshev windowing and the MUSIC algorithm with Eigenvalue thresholding. Tested under low signal‐to‐noise ratio (SNR) and fewer snapshots, these techniques notably reduce side lobes and enhance angular resolution, validated by experiments. Additionally, the suppression of side lobes significantly improves the clarity and accuracy of target detection, minimizing potential interference and false targets. This enhancement in side lobe suppression facilitates a more precise spatial differentiation between multiple targets, thus contributing to the overall effectiveness and reliability of MIMO radar systems. [ABSTRACT FROM AUTHOR]

How to enhance the desired signal with low signal-to-noise ratio (SNR) is a difficult problem in the estimation process of the direction-of-arrival (DOA) of the target scattering wave signal. In this paper, the feasibility of spatial spectrum estimation in the Range-Doppler (RD) domain is analyzed in principle, and the SNR gain expression of weak scattering wave signal is derived when constructing multi-snapshots virtual array data. On this basis, the mutual eigenvector singular value decomposition (MESVD) method based on RD domain mode excitation is proposed, which can robustly and effectively estimate the direction of the coherent weak signals. Simulation experiments verify that the RD domain spectral estimation method has the ability to simultaneously obtain the direction of multiple weak target scattering waves, and the direction-finding accuracy can reach the Cramer–Rao bound (CRB) of conventional spectral estimation method. The results of Monte Carlo experiments show that the root-mean-square-error (RMSE) of azimuth estimation of RD domain spatial spectrum estimation method is 5.76° lower than that of a conventional multiple signal classification (MUSIC) method. In addition, the practicability of the proposed method is demonstrated by comparing the DOA estimation results of a set of real data with Automatic Dependent Surveillance-Broadcast (ADS-B) data. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation using sparse signal representation has gained significant attention in recent decades. The spatial signals utilized for DOA estimation are reconstructed through a novel compressive sensing (CS) approach. In this study, we used a CS approach to ascertain the adaptive underdetermined DOA for multiple inputs and multiple output (MIMO) sparse media. Accurate DOA estimation through the CS approach contributes to enhanced signal investigation and the suppression of undesired noise in a sparse channel. The method proposed herein encompasses the development and execution of a novel multiplicative basis function known as the multi-kernel supported non-negative sparse Bayesian learning (NNSBL) algorithm, which is implemented on predefined grid values. Concurrently, stochastic grey wolf optimization (GWO) is virtually applied to increase the degrees of freedom (DOF) on a minimum redundancy array (MRA) while considering an optimal antenna reconfiguration model. The advantages of this proposed approach include the generation of a distinct manifold matrix for precise beam steering, resulting in improved DOA estimation accuracy. Additionally, an augmentation of DOF using GWO was observed, with low computational complexity. The simulated result of proposed algorithm leads to the attainment of optimal minimum root mean square error (RMSE) across various optimal wavelengths of the stochastic sources. Finally, a comparative analysis of RMSE and convergence plots confirms the superior performance and high potentiality of the proposed method in comparison to existing techniques across different SNR values. Notably, there is significant reduction in RMSE which is suitable for precise DOA estimation in signal processing applications. [ABSTRACT FROM AUTHOR]

In this paper, a novel weighted block sparse method based on the signal subspace is proposed to realize the Direction-of-Arrival (DOA) estimation under unknown mutual coupling in the uniform linear array. Firstly, the signal subspace is obtained by decomposing the eigenvalues of the sampling covariance matrix. Then, a block sparse model is established based on the deformation of the product of the mutual coupling matrix and the steering vector. Secondly, a suitable set of weighted coefficients is calculated to enhance sparsity. Finally, the optimization problem is transformed into a second-order cone (SOC) problem and solved. Compared with other algorithms, the simulation results of this paper have better performance on DOA accuracy estimation. [ABSTRACT FROM AUTHOR]

This paper proposes a dynamic phase comparison algorithm for planar direction finding on a high-speed moving satellite radio receiver, treating the moving antenna as equivalent to single-baseline array antennas. Based on a phase interferometer algorithm, this algorithm adjusts the baseline length according to the frequency measurement module and the satellite's high-speed motion to avoid phase ambiguity indirectly. By integrating the traditional amplitude comparison algorithm based on orthogonal dipole antennas, a dynamic fusion direction-finding method is proposed. Simulations demonstrate that this approach method not only covers a broader range of direction finding but also achieves higher accuracy, providing valuable insights for acquiring three-dimensional plasmagrams with space-borne plasma imagers. [ABSTRACT FROM AUTHOR]

The paper outlines three types of phase shifter networks utilized in phased array antennas for direction-of-arrival estimation. It compares the mechanical rotation method of antenna array with Butler matrix-based and software-controlled phase shifter networks. Performance metrics such as phase shift accuracy and response time were considered. Comparisons were made between simulation and measurement results of Butler matrix and software-controlled networks. The mechanical rotation system operates as an open-loop system, while the other two can function as closed-loop systems. [ABSTRACT FROM AUTHOR]

Since the software‐defined radio digital beamforming (DBF) system is the newest tendency in fifth‐generation (5G) development and based on its flexibility applied to antenna arrays, this research paper presents a smart antenna array architecture operating in C‐band (3.48 GHz). In developing DBF for 5G access points with beamforming capacities, it is first necessary to perform an accurate approach that identifies the position of the desired user and affects the beamforming processing. In this article, a feasible approach is represented to evaluate and test three angle‐of‐arrival algorithms. To ensure the robustness of the system, the system structure with its four main components, the transmit subsystem, the synchronization subsystem, the receive subsystem, and data processing are described in detail. This article presents a reasonably inexpensive prototype DBF in conjunction with a Universal Software Radio Peripheral B210 board. Experimental measurements and results confirm the suggested methodology. [ABSTRACT FROM AUTHOR]

Hereditary optic neuropathies (HONs) such as dominant optic atrophy (DOA) and Leber Hereditary Optic Neuropathy (LHON) are mitochondrial diseases characterized by a degenerative loss of retinal ganglion cells (RGCs) and are a cause of blindness worldwide. To date, there are only limited disease-modifying treatments for these disorders. The discovery of induced pluripotent stem cell (iPSC) technology has opened several promising opportunities in the field of HON research and the search for therapeutic approaches. This systematic review is focused on the two most frequent HONs (LHON and DOA) and on the recent studies related to the application of human iPSC technology in combination with biomaterials technology for their potential use in the development of RGC replacement therapies with the final aim of the improvement or even the restoration of the vision of HON patients. To this purpose, the combination of natural and synthetic biomaterials modified with peptides, neurotrophic factors, and other low- to medium-molecular weight compounds, mimicking the ocular extracellular matrices, with human iPSC or iPSC-derived cell retinal progenitors holds enormous potential to be exploited in the near future for the generation of transplantable RGC populations. [ABSTRACT FROM AUTHOR]

Due to a high spatial angle resolution and low circuit cost of massive hybrid analog and digital (HAD) multiple-input multiple-output (MIMO), it is viewed as a valuable green communication technology for future wireless networks. Integrating the massive HAD-MIMO with direction of arrival (DOA) will provide an even ultra-high performance of DOA measurement, which can the fully-digital (FD) MIMO. However, phase ambiguity is a challenge issue for a massive HAD-MIMO DOA estimation. In this paper, we consider three parts: detection, estimation, and Cramer-Rao lower bound (CRLB). First, a multi-layer-neural-network (MLNN) detector is proposed to infer the existence of emitters. Then, a two-layer HAD (TLHAD) MIMO structure is proposed to estimate the DOA and eliminate phase ambiguity using only one time block. Simulation results show that the proposed MLNN detector is much better than both the existing generalized likelihood ratio test (GRLT) and the ratio of maximum eigen-value (Max-EV) to minimum eigen-value (R-MaxEV-MinEV) in terms of detection probability. Additionally, the proposed TLHAD structure can achieve the corresponding CRLB.

To solve the problem that the hydrophone arrays are disturbed by ocean noise when collecting signals in shallow seas, resulting in reduced accuracy and resolution of target orientation estimation, a direction-of-arrival (DOA) estimation algorithm based on iterative EMD interval thresholding (EMD-IIT) and off-grid sparse Bayesian learning is proposed. Firstly, the noisy signal acquired by the hydrophone array is denoised by the EMD-IIT algorithm. Secondly, the singular value decomposition is performed on the denoised signal, and then an off-grid sparse reconstruction model is established. Finally, the maximum a posteriori probability of the target signal is obtained by the Bayesian learning algorithm, and the DOA estimate of the target is derived to achieve the orientation estimation of the target. Simulation analysis and sea trial data results show that the algorithm achieves a resolution probability of 100% at an azimuthal separation of 8° between adjacent signal sources. At a low signal-to-noise ratio of −9 dB, the resolution probability reaches 100%. Compared with the conventional MUSIC-like and OGSBI-SVD algorithms, this algorithm can effectively eliminate noise interference and provides better performance in terms of localization accuracy, algorithm runtime, and algorithm robustness.

Due to a high spatial angle resolution and low circuit cost of massive hybrid analog and digital (HAD) multiple-input multiple-output (MIMO), it is viewed as a valuable green communication technology for future wireless networks. Integrating the massive HAD-MIMO with direction of arrival (DOA) will provide an even ultra-high performance of DOA measurement, which can the fullydigital (FD) MIMO. However, phase ambiguity is a challenge issue for a massive HAD-MIMO DOA estimation. In this paper, we consider three parts: detection, estimation, and Cramer-Rao lower bound (CRLB). First, a multi-layerneural-network (MLNN) detector is proposed to infer the existence of emitters. Then, a two-layer HAD (TLHAD) MIMO structure is proposed to estimate the DOA and eliminate phase ambiguity using only one time block. Simulation results show that the proposed MLNN detector is much better than both the existing generalized likelihood ratio test (GRLT) and the ratio of maximum eigen-value (Max-EV) to minimum eigen-value (R-MaxEV-MinEV) in terms of detection probability. Additionally, the proposed TLHAD structure can achieve the corresponding CRLB. [ABSTRACT FROM AUTHOR]

The direction estimation of the coherent source in a uniform circular array is an essential part of the signal processing area of the array, but the traditional uniform circular array algorithm has a low localization accuracy and a poor localization effect on the coherent source. To solve this problem, this paper proposes a two-dimensional direction of arrival (DOA) estimation for the coherent source in broadband. Firstly, the central frequency of the coherent sound source is estimated using the frequency estimation method of the delayed data, and a real-valued beamformer is constructed using the concept of the multiloop phase mode. Then, the cost function in the beam space is obtained. Finally, the cost function is searched in two dimensions to locate the sound source. In this paper, we simulate the DOA of the sound source at different frequencies and signal-to-noise ratios and analyze the resolution of the circular array. The simulation results show that the proposed algorithm can estimate the direction of arrival with high precision and achieve the desired results. [ABSTRACT FROM AUTHOR]

The Toeplitz matrix reconstruction methods are capable of resolving coherent signals, playing a crucial role in the direction-of-arrival (DOA) estimation of acoustic sources. However, the decoherence processing sacrifices the array aperture and further results in a reduced resolution capability for the number of identifiable sources. To solve this issue, we propose an enhanced method using the Khatri–Rao subspace to resolve more coherent sources than that of the existing Toeplitz matrix reconstruction methods. Firstly, a full set of Toeplitz matrices with full rank is obtained. Then, the virtual array aperture can be obtained using the Khatri–Rao product of the array response, and the degree of freedom provided inherently in the virtual array structure is about twice the size of that of the existing Toeplitz methods. Next, linear processing is further used to achieve complexity reduction without losing the effective degree of freedom. Finally, the DOA estimation for more coherent sources can be achieved by combining it with conventional methods. Numerical simulations verify the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

In this study, we focused on using microarray gene data from pancreatic sources to detect diabetes mellitus. Dimensionality reduction (DR) techniques were used to reduce the dimensionally high microarray gene data. DR methods like the Bessel function, Discrete Cosine Transform (DCT), Least Squares Linear Regression (LSLR), and Artificial Algae Algorithm (AAA) are used. Subsequently, we applied meta-heuristic algorithms like the Dragonfly Optimization Algorithm (DOA) and Elephant Herding Optimization Algorithm (EHO) for feature selection. Classifiers such as Nonlinear Regression (NLR), Linear Regression (LR), Gaussian Mixture Model (GMM), Expectation Maximum (EM), Bayesian Linear Discriminant Classifier (BLDC), Logistic Regression (LoR), Softmax Discriminant Classifier (SDC), and Support Vector Machine (SVM) with three types of kernels, Linear, Polynomial, and Radial Basis Function (RBF), were utilized to detect diabetes. The classifier's performance was analyzed based on parameters like accuracy, F1 score, MCC, error rate, FM metric, and Kappa. Without feature selection, the SVM (RBF) classifier achieved a high accuracy of 90% using the AAA DR methods. The SVM (RBF) classifier using the AAA DR method for EHO feature selection outperformed the other classifiers with an accuracy of 95.714%. This improvement in the accuracy of the classifier's performance emphasizes the role of feature selection methods. [ABSTRACT FROM AUTHOR]

In this paper, a novel algorithm is presented for estimating three-dimensional (3-D) parameters of near-field (NF) sources with a centro-symmetric cross array, where the sub-array x and the sub-array y are uniform linear arrays equipped with the same number of array elements. By exploiting the time-delay-based spatial correlation of the received signal, the proper array elements are selected, respectively, from the two sub-arrays to define two sets of variables, and then two time-delay-based vectors are constructed. Further, a series of delay lags are uniformly sampled to form pseudo snapshots so as to obtain two corresponding time delay matrices, which facilitates the formation of the parallel factor (PARAFAC) model in time domain. Finally, trilinear alternating least squares decomposition is utilized to jointly estimate the two-dimensional (2-D) direction-of arrival and range parameters of NF sources from the PARAFAC model. In the case of low signal-to-noise ratio and small snapshots conditions, the estimation performance in the 3-D parameters of the proposed algorithm is superior to that of the subspace-based algorithm. In addition, the proposed algorithm only involves simple parameter pairing, which requires no eigenvalue decomposition and spectral peak searching. [ABSTRACT FROM AUTHOR]

Pembentukan Spiritual adalah subjek praktis di dalam kehidupan seorang Kristen yang sering kali tidak dipikirkan secara reflektif, jika bukan sepenuhnya diabaikan, bahkan bagi mereka yang di dalam tradisi Reformed. Artikel ini berargumen akan keunikan pembentukan rohani Kristen dari agama-agama Abrahamik lainnya berdasarkan argumen-argumen biblika dan fenomena sosial-agama. Reformasi berkontribusi terhadap pembentukan spiritual kristen berdasarkan pada prinsip-prinsip umumnya dan pemahaman antropologis Calvin. Artikel ini menyimpulkan bahwa pembentukan rohani Kristen tidak lain dari menghidupi kehidupan Tritunggal, yaitu dibentuk menyerupai Kristus dan dipenuhi Roh Kudus di dalam kehidupan kita sebagai anak-anak Allah.

In total, around 631 million broilers were slaughtered in Germany in 2022. This evaluation included data of approx. 198 million broilers of different ages and breeds that were transported in Germany in 2022 (31% of all cases of broiler chicken transport in 2022). The aim of this study was to analyze German broiler chicken transport (n = 14,054) to the slaughterhouse between January 2022 and May 2023 with regard to the dead-on-arrival (DOA) rate and the possible influencing factors. Therefore, the relation between the total amount of animals per transport, the duration and distance of the transport, the planned stocking density in the transport cages, the average daily temperature and time of day and season of the transport as well as the DOA rate were statistically evaluated. The results showed a mean DOA rate of 0.09% (SD 0.09). Transport conducted at midday (11:00 to 17:00) showed higher DOA rates (p < 0.05) than transport at other times (day split into 6 h intervals). The highest mean DOA rate (0.10%) was found in the fall, followed by the winter, while transport in the spring and summer resulted in the lowest DOA rate (p < 0.05). All in all, the relatively low DOA rate (%) in Germany indicates the good standard of their broiler transport compared to available data from research in other European countries.

How to enhance the desired signal with low signal-to-noise ratio (SNR) is a difficult problem in the estimation process of the direction-of-arrival (DOA) of the target scattering wave signal. In this paper, the feasibility of spatial spectrum estimation in the Range-Doppler (RD) domain is analyzed in principle, and the SNR gain expression of weak scattering wave signal is derived when constructing multi-snapshots virtual array data. On this basis, the mutual eigenvector singular value decomposition (MESVD) method based on RD domain mode excitation is proposed, which can robustly and effectively estimate the direction of the coherent weak signals. Simulation experiments verify that the RD domain spectral estimation method has the ability to simultaneously obtain the direction of multiple weak target scattering waves, and the direction-finding accuracy can reach the Cramer–Rao bound (CRB) of conventional spectral estimation method. The results of Monte Carlo experiments show that the root-mean-square-error (RMSE) of azimuth estimation of RD domain spatial spectrum estimation method is 5.76° lower than that of a conventional multiple signal classification (MUSIC) method. In addition, the practicability of the proposed method is demonstrated by comparing the DOA estimation results of a set of real data with Automatic Dependent Surveillance-Broadcast (ADS-B) data.

This paper proposes a dynamic phase comparison algorithm for planar direction finding on a high-speed moving satellite radio receiver, treating the moving antenna as equivalent to single-baseline array antennas. Based on a phase interferometer algorithm, this algorithm adjusts the baseline length according to the frequency measurement module and the satellite’s high-speed motion to avoid phase ambiguity indirectly. By integrating the traditional amplitude comparison algorithm based on orthogonal dipole antennas, a dynamic fusion direction-finding method is proposed. Simulations demonstrate that this approach method not only covers a broader range of direction finding but also achieves higher accuracy, providing valuable insights for acquiring three-dimensional plasmagrams with space-borne plasma imagers.

Abstract Background Meat inspection data is commonly used to monitor health and welfare in commercial broiler production; however, less used in layers. Slaughterhouse records can provide insight into animal and herd health and identify important health and welfare challenges. To gain knowledge of health issues in commercial aviary housed laying hens, the aim of this repeated cross-sectional study was to describe the occurrence and causes of carcass condemnation, including dead-on-arrivals (DOA), in commercial aviary housed layers in Norway, and to explore seasonal patterns and correlation between DOA and number of carcass condemnations. Results Data from January 2018 to December 2020 were collected from one poultry abattoir in Norway. In total, 759,584 layers were slaughtered during this period in 101 slaughter batches from 98 flocks and 56 farms. In total, 33,754 (4.4%) layers were condemned, including the DOA. The most common carcass condemnation causes were (percent of all slaughtered layers): abscess/cellulitis (2.03%), peritonitis (0.38%), DOA (0.22%), emaciation (0.22%), discoloration/smell (0.21%), acute skin lesions (0.21%) and ascites (0.17%). Regression analysis showed an estimated higher prevalence of total carcass condemnation during winter compared to the other seasons. Conclusions The three most common condemnation causes found in the present study were: abscess/cellulitis, peritonitis, and DOA. We found a large between-batch variation in causes of condemnation and DOA indicating that prevention might be possible. The results can be used to inform and guide further studies on layer health and welfare.

Dominant optic atrophy (DOA) is an inherited disease that leads to the loss of retinal ganglion cells (RGCs), the projection neurons that relay visual information from the retina to the brain through the optic nerve. The majority of DOA cases can be attributed to mutations in optic atrophy 1 (OPA1), a nuclear gene encoding a mitochondrial-targeted protein that plays important roles in maintaining mitochondrial structure, dynamics, and bioenergetics. Although OPA1 is ubiquitously expressed in all human tissues, RGCs appear to be the primary cell type affected by OPA1 mutations. DOA has not been extensively studied in human RGCs due to the general unavailability of retinal tissues. However, recent advances in stem cell biology have made it possible to produce human RGCs from pluripotent stem cells (PSCs). To aid in establishing DOA disease models based on human PSC-derived RGCs, we have generated iPSC lines from two DOA patients who carry distinct OPA1 mutations and present very different disease symptoms. Studies using these OPA1 mutant RGCs can be correlated with clinical features in the patients to provide insights into DOA disease mechanisms. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation is an effective method for detecting various active interference signals during the satellite navigation process. It can be utilized for both interference detection and anti-interference applications. This paper proposes a DOA estimation algorithm for satellite interference sources based on frequency domain covariance matrix reconstruction (FDCMR) to address various types of active interference that may occur in the satellite navigation positioning process. This algorithm can estimate the DOA of coherent signals from multiple frequency points under low signal-to-noise ratio (SNR) conditions. The signals received from the array are transformed from the time domain to the frequency domain using a fast Fourier transform (FFT). The data corresponding to the frequency point of the target signal is extracted from the signal in the frequency domain. The frequency domain covariance matrix of the received array signals is reconstructed by utilizing its covariance matrix property. The spatial spectrum search method is used for the final DOA estimation. Simulation experiments have shown that the proposed algorithm performs well in the DOA estimation under low SNR conditions and also resolves coherency. Moreover, the algorithm's effectiveness is verified through comparison with three other algorithms. Finally, the algorithm's applicability is validated through simulations of various interference scenarios. [ABSTRACT FROM AUTHOR]

Coherently Distributed (CD) sources are suitable for practical direction-of-arrival (DOA) estimation in massive Multiple-Input Multiple-Output (MIMO) communication systems. However, massive MIMO systems employ a large number of antennas to improve performance, which significantly raises the complexity of conventional DOA estimation algorithms for CD sources. To reduce the complexity, a novel low-complexity CD unitary estimation of signal parameters via the rotational invariance technique (U-ESPRIT) algorithm based on discrete cosine transformation (DCT) beamspace transformation and multistage Wiener filter (BU-ESPRIT) is proposed in this paper. To concentrate the signal energy and make better use of beamspace features, the DCT transformation is used to extract the signal. For the sake of reducing the complexity and achieving accurate angle estimation, U-ESPRIT is performed in beamspace. Moreover, the covariance matrix matching process and eigenvalue decomposition of the covariance matrix are substituted by forward recursion of the multistage Wiener filter to further reduce the complexity of the proposed algorithm. Theoretical analysis indicates the computational superiority of the proposed algorithm over that of some existing algorithms. Simulation results demonstrate that the proposed algorithm can be utilized to efficiently estimate angles of CD sources in massive MIMO systems. [ABSTRACT FROM AUTHOR]

Sensor arrays are gradually becoming a current research hotspot due to their flexible beam control, high signal gain, robustness against extreme interference, and high spatial resolution. Among them, spherical microphone arrays with complex rotational symmetry can capture more sound field information than planar arrays and can convert the collected multiple speech signals into the spherical harmonic domain for processing through spherical modal decomposition. The subspace class direction of arrival (DOA) estimation algorithm is sensitive to noise and reverberation, and its performance can be improved by introducing relative sound pressure and frequency-smoothing techniques. The introduction of the relative sound pressure can increase the difference between the eigenvalues corresponding to the signal subspace and the noise subspace, which is helpful to estimate the number of active sound sources. The eigenbeam estimation of signal parameters via the rotational invariance technique (EB-ESPRIT) is a well-known subspace-based algorithm for a spherical microphone array. The EB-ESPRIT cannot estimate the DOA when the elevation angle approaches 90°. Huang et al. proposed a two-step ESPRIT (TS-ESPRIT) algorithm to solve this problem. The TS-ESPRIT algorithm estimates the elevation and azimuth angles of the signal independently, so there is a problem with DOA parameter pairing. In this paper, the DOA parameter pairing problem of the TS-ESPRIT algorithm is solved by introducing generalized eigenvalue decomposition without increasing the computation of the algorithm. At the same time, the estimation of the elevation angle is given by the arctan function, which increases the estimation accuracy of the elevation angle of the algorithm. The robustness of the algorithm in a noisy environment is also enhanced by introducing the relative sound pressure into the algorithm. Finally, the simulation and field-testing results show that the proposed method not only solves the problem of DOA parameter pairing, but also outperforms the traditional methods in DOA estimation accuracy. [ABSTRACT FROM AUTHOR]

In this research article, a concept for a secondary RAdio Direction And Ranging (RADAR) angle of arrival based system with cooperative targets transmitting at 2.4 GHz and using Multiple Signal Classification (MuSiC) to determine the angles of incidence is investigated. In addition to introducing common algorithms and presenting thorough derivations, the system is first examined through simulations. To prove the concept, hardware, firmware, and software are developed. For MuSiC, we propose three novel methods to obtain the correct incident angle from the spectrum, especially in strong multipath environments. These methods work either for a single spectrum or for a combination recorded at multiple times. Together with the estimated angles of incidence, our methods determine measures on the respective likelihoods. Based on this, we additionally propose two algorithms for computing the final position. Our system is characterized in both a simple 20 m × 15 m outdoor and a 17 m × 13 m multipath indoor environment, where we achieve a mean angular error of 3◦ and a mean positioning error of 0.67 m for the former using only four base stations with four antennas each. Our novel approach shows position accuracy improvements of 15% outdoors and 25% indoors compared to classical MuSiC estimation. [ABSTRACT FROM AUTHOR]

In comparison to orthogonal frequency division multiplexing (OFDM), the OFDM linear frequency modulation (OFDM-LFM) signal has similar time-bandwidth product, lower peak-to-average power ratio, and higher sensitivity to Doppler frequency shift, making it a promising candidate waveform for multiple-in multiple-out (MIMO) radar applications. However, the OFDM-LFM signal is unsatisfactory in practical applications due to the challenges in waveform design and its non-stationary property, which significantly degrades the performance of current high-resolution subspace techniques for direction of departure (DoD) and direction of arrival (DoA) angle estimation. In this paper, we first proposed a novel OFDM-LFM waveform design method based on the scale discrete Fresnel transform (SDFnT), which efficiently generates orthogonal LFM signals with unlimited number of subcarriers and can be convieniently implemented using the fast Fourier transform (FFT). Then, for angle estimation in OFDM-LFM MIMO radars, we presented a new scale discrete Fresnel transform multiple signal classification (SDFnT-MUSIC) method. By converting the steering vector into the chirp domain and making it non-time-vary, high resolution angle estimation for OFDM-LFM wideband signals can be achieved. Simulation results shows that the proposed waveform design method requires less computational time than conventional method based based on the fractional Fourier transform (FrFT), whereas the proposed DoA-DoD estimation method outperform the FrFT-MUSIC method in terms of computational complexity and target angle estimate performance.

Dominant optic atrophy (DOA) is an inherited disease that leads to the loss of retinal ganglion cells (RGCs), the projection neurons that relay visual information from the retina to the brain through the optic nerve. The majority of DOA cases can be attributed to mutations in optic atrophy 1 (OPA1), a nuclear gene encoding a mitochondrial-targeted protein that plays important roles in maintaining mitochondrial structure, dynamics, and bioenergetics. Although OPA1 is ubiquitously expressed in all human tissues, RGCs appear to be the primary cell type affected by OPA1 mutations. DOA has not been extensively studied in human RGCs due to the general unavailability of retinal tissues. However, recent advances in stem cell biology have made it possible to produce human RGCs from pluripotent stem cells (PSCs). To aid in establishing DOA disease models based on human PSC-derived RGCs, we have generated iPSC lines from two DOA patients who carry distinct OPA1 mutations and present very different disease symptoms. Studies using these OPA1 mutant RGCs can be correlated with clinical features in the patients to provide insights into DOA disease mechanisms.

The Global Navigation Satellite System (GNSS) is vulnerable to deliberate spoofing signal attacks. Once the user wrongly locks on the spoofing signal, the wrong position, velocity, and time (PVT) information will be calculated, which will harm the user. GNSS spoofing signals are difficult to carry out spoofing attacks in the direction of arrival (DOA) of the real signal, so the spoofing detection method based on DOA is very effective. On the basis of identifying spoofing signals, accurate DOA information of the signal can be further used to locate the spoofer. At present, the existing DOA monitoring methods for spoofing signals are mainly based on dedicated antenna arrays and receivers, which are costly and difficult to upgrade and are not conducive to large-scale deployment, upgrade, and maintenance. This paper proposes a spoofing detection and direction-finding method based on a low-cost commercial GNSS board component (including an antenna). Based on the traditional principle of using a multi-antenna carrier phase to solve DOA, this paper innovatively solves the following problems: the poor direction-finding accuracy caused by the unstable phase center of low-cost commercial antennas, the low success rate of spoofing detection in a multipath environment, and the inconsistent sampling time among multiple low-cost commercial GNSS boards. Moreover, the corresponding prototype equipment for spoofing detection and direction-finding is developed. The measured results show that it can effectively detect spoofing signals in open environments. Under a certain false alarm rate, the detection success rate can reach 100%, and the typical direction-finding accuracy can reach 5 ° . [ABSTRACT FROM AUTHOR]

In Structural Health Monitoring (SHM) applications, the Direction of Arrival (DoA) estimation of Guided Waves (GW) on sensor arrays is often used as a fundamental means to locate Acoustic Sources (AS) generated by damages growth or undesired impacts in thin-wall structures (e.g., plates or shells). In this paper, we consider the problem of designing the arrangement and shape of piezo-sensors in planar clusters in order to optimize the DoA estimation performance in noise-affected measurements. We assume that: (i) the wave propagation velocity is unknown, (ii) the DoA is estimated via the time delays of wavefronts between sensors, and (iii) the maximum value of the time delays is limited. The optimality criterion is derived basing on the Theory of Measurements. The sensor array design is so that the DoA variance is minimized in an average sense by exploiting the Calculus of Variations. In this way, considering a three-sensor cluster and a monitored angles sector of 90°, the optimal time delays–DoA relations are derived. A suitable re-shaping procedure is used to impose such relations and, at the same time, to induce the same spatial filtering effect between sensors so that the sensor acquired signals are equal except for a time-shift. In order to achieve the last aim, the sensors shape is realized by exploiting a technique called Error Diffusion, which is able to emulate piezo-load functions with continuously modulated values. In this way, the Shaped Sensors Optimal Cluster (SS-OC) is derived. A numerical assessment via Green's functions simulations shows improved performance in DoA estimation by means of the SS-OC when compared to clusters realized with conventional piezo-disk transducers. [ABSTRACT FROM AUTHOR]

Background: Meat inspection data is commonly used to monitor health and welfare in commercial broiler production; however, less used in layers. Slaughterhouse records can provide insight into animal and herd health and identify important health and welfare challenges. To gain knowledge of health issues in commercial aviary housed laying hens, the aim of this repeated cross-sectional study was to describe the occurrence and causes of carcass condemnation, including dead-on-arrivals (DOA), in commercial aviary housed layers in Norway, and to explore seasonal patterns and correlation between DOA and number of carcass condemnations. Results: Data from January 2018 to December 2020 were collected from one poultry abattoir in Norway. In total, 759,584 layers were slaughtered during this period in 101 slaughter batches from 98 flocks and 56 farms. In total, 33,754 (4.4%) layers were condemned, including the DOA. The most common carcass condemnation causes were (percent of all slaughtered layers): abscess/cellulitis (2.03%), peritonitis (0.38%), DOA (0.22%), emaciation (0.22%), discoloration/smell (0.21%), acute skin lesions (0.21%) and ascites (0.17%). Regression analysis showed an estimated higher prevalence of total carcass condemnation during winter compared to the other seasons. Conclusions: The three most common condemnation causes found in the present study were: abscess/cellulitis, peritonitis, and DOA. We found a large between-batch variation in causes of condemnation and DOA indicating that prevention might be possible. The results can be used to inform and guide further studies on layer health and welfare. [ABSTRACT FROM AUTHOR]

The classical least mean square (LMS) algorithm is a widely studied method for adaptive beamforming. It is well known for its lower computational complexity. However, fast and robust beamforming is not possible with the classical LMS method since it uses a constant step size. This nature hinders its applications in many advanced communication systems. Furthermore, this method degrades when the signal-to-noise ratio is rapidly changing. To circumvent these issues posed by the classical LMS method, two modified LMS beamformers are presented in this paper. We name these methods as M-LMS-1 and M-LMS-2. We present two new complex array weights to accelerate the rate of convergence. Computer simulations show that both methods present fast and robust beamforming. That is these algorithms have convergence improvement of about 37.5 % and 50 % over the standard LMS algorithm. [ABSTRACT FROM AUTHOR]

This work provides an explanatory analysis of the influence of input parameters on the performance of subspace-based Direction of Arrival (DoA) estimation algorithms. The objective of this work is twofold. First, to drive a Steering Vector (SV) that works for arbitrary array configuration rather than just Uniform Linear Array (ULA) geometry. Second, to identify how the performance of the subspace-based algorithms is affected by tuning the input parameters. The later objective is crucial as it allows optimizing the algorithm through selecting optimum parameters to set an appropriate tradeoff between complexity and performance based on the intended applications. Toward that end, we firstly drive an SV for arbitrary array configuration followed by revealing the working principle of subspace based DoA techniques. Secondly, we evaluate the impact of several parameters namely Signal to Noise Ratio (SNR), number of snapshots, number of array elements, separation between array elements, number of available sources, and dependency between sources to conduct our analysis. Numerical simulations over a wide range of scenarios along with intensive Monte Carlo simulations are conducted to show the influence of these parameters on the resolution, accuracy, and complexity of the subspace based DoA estimation algorithm. As demonstrated by the obtained results, the performance of this class of DoA estimation method is determined mostly by the values of the input parameters. Furthermore, the simulation results show that tradeoff between performance and computational complexity needs to be considered when the system parameters are chosen for DoA estimation algorithms.

The direction of arrival (DOA) and number of sound sources is usually estimated by short-time Fourier transform and the conjugate cross-spectrum. However, the ability of a single AVS to distinguish between multiple sources will decrease as the number of sources increases. To solve this problem, this paper presents a multimodal fusion method based on a single acoustic vector sensor (AVS). First, the output of the AVS is decomposed into multiple modes by intrinsic time-scale decomposition (ITD). The number of sources in each mode decreases after decomposition. Then, the DOAs and source number in each mode are estimated by density peak clustering (DPC). Finally, the density-based spatial clustering of applications with the noise (DBSCAN) algorithm is employed to obtain the final source counting results from the DOAs of all modes. Experiments showed that the multimodal fusion method could significantly improve the ability of a single AVS to distinguish multiple sources when compared to methods without multimodal fusion. [ABSTRACT FROM AUTHOR]

Study Objectives: Disorder of arousal (DOA) and sleep-related hypermotor epilepsy (SHE) are complex, often bizarre, involuntary sleep behaviors, whose differential diagnosis may be challenging because they share some clinical features, such as sleep fragmentation. Mounting evidence highlights the critical role of sleep in cognitive functions. Controversial findings are raised about the cognitive profile in SHE; however, no studies have investigated the cognitive profile in DOA. This study aimed to assess whether sleep instability affects cognitive functions in patients with SHE or DOA. Methods: This study analyzed 11 patients with DOA, 11 patients with SHE, and 22 healthy controls (HC). They underwent full-night video polysomnography (vPSG) and comprehensive neuropsychological and behavioral evaluation. Differences in the variables of interest among the SHE group, DOA group, and their respective control groups were evaluated. The auto-contractive map (auto-CM) system was used to evaluate the strength of association across the collected data. Results: The SHE group had reduced sleep efficiency and increased wake after sleep onset (WASO); both the SHE and DOA groups showed increased % of N2 and REM sleep compared to the HC group. Neuropsychological and behavioral evaluations showed a different cognitive profile in the SHE group with respect to the HC group. The auto-CM showed that Pittsburgh Sleep Quality Index (PSQI), Beck depression inventory (BDI), MWCST_PE, Epworth sleepiness scale (ESS), WASO, N1, and % REM were strictly correlated with SHE, whereas the SE and arousal index (AI) were strictly related to DOA. Conclusions: Patients with SHE and DOA present different cognitive and psychiatric profiles, with subtle and selective cognitive impairments only in those with SHE, supporting the discriminative power of cognitive and psychiatric assessment in these two conditions. [ABSTRACT FROM AUTHOR]

The Swiss Association as a Legal Wrapper for a Global DAO and vis-à-vis the MiCA Regime: The article explores the utilization of decentralized autonomous organizations (DAOs) and the legal framework of Swiss associations as a possible legal wrapper – particularly for globally operating DAOs. All against the backdrop of a steadily growing regulation to the likes of the EU's MiCA framework. The discussion encompasses the essential characteristics of DAOs, emphasizing their decentralized nature, shared purpose, autonomy, and predefined rules. The article highlights the legal complexities around DAOs. Considering many DAOs operating without a deliberately chosen legal structure, the argument is made that even though DAOs might be set up with the idea to not be regulated at law, the contrary might be the case. Legal concepts to the effect of 'joint ventures' or 'simple partnership' may lead to the inadvertent formation of legal entities. Thus, legal risks may very likely loom over participants of DAOs whether they may desire that outcome or not. The article emphasizes liability as a driving force behind the need of a quest for legal wrappers. To mitigate unintended liabilities, selected solutions are being proposed. The text discusses further benefits of a legal wrapper. The argument is made that DAOs should deliberately chose a legal wrapper mirroring their nature and fitting their needs. The discussion then turns to the Swiss association as an attractive choice for globally operating DAOs. The autonomy granted to Swiss associations under Swiss Law providing for freedom, self-governance, and flexibility mirror the nature of a DAO. The features of the Swiss association and its benefits as a legal wrapper for DAOs are then being scrutinized. The text posits the establishment of a Swiss association as a solution to limit liability for DAOs participants. Ultimately, the Swiss association is presented as a wrapper offering not only liability protection but also additional benefits for DAOs, which are further explored. [ABSTRACT FROM AUTHOR]

The direction estimation of the coherent source in a uniform circular array is an essential part of the signal processing area of the array, but the traditional uniform circular array algorithm has a low localization accuracy and a poor localization effect on the coherent source. To solve this problem, this paper proposes a two-dimensional direction of arrival (DOA) estimation for the coherent source in broadband. Firstly, the central frequency of the coherent sound source is estimated using the frequency estimation method of the delayed data, and a real-valued beamformer is constructed using the concept of the multiloop phase mode. Then, the cost function in the beam space is obtained. Finally, the cost function is searched in two dimensions to locate the sound source. In this paper, we simulate the DOA of the sound source at different frequencies and signal-to-noise ratios and analyze the resolution of the circular array. The simulation results show that the proposed algorithm can estimate the direction of arrival with high precision and achieve the desired results.

Direction-of-arrival (DOA) information is vital for multiple-input-multiple-output (MIMO) systems to complete localization and beamforming tasks. Switched antenna arrays have recently emerged as an effective solution to reduce the cost and power consumption of MIMO systems. Switch-based array architectures connect a limited number of radio frequency chains to a subset of the antenna elements forming a subarray. This paper addresses the problem of antenna selection to optimize DOA estimation performance. We first perform a subarray layout alignment process to remove subarrays with identical beampatterns and create a unique subarray set. By using this set, and based on a DOA threshold region performance approximation, we propose two antenna selection algorithms; a greedy algorithm and a deep-learning-based algorithm. The performance of the proposed algorithms is evaluated numerically. The results show a significant performance improvement over selected benchmark approaches in terms of DOA estimation in the threshold region and computational complexity. [ABSTRACT FROM AUTHOR]

The paper addresses the narrowband direction of arrival estimation problem in the presence of multiplicative noise, namely, the local scatterers affect the propagation channel characteristics. Deep learning-based neural network, composed of autoencoders and softmax layer, has been trained to spatially filter the wave with desired reception angle. When the array receives at the desired angle, the filter warns, its output rises to one; otherwise it falls to zero. The filter's response in different scenarios has been investigated by performing various simulations. It has been observed that for the vast majority of scenarios, the filter can detect the direction, regardless of the exception for channels with severe fading. [ABSTRACT FROM AUTHOR]

Within a complex sea or ground surface background, multipath signals are strongly correlated or even completely coherent, which leads to signal cancellation when conventional optimal beamforming is performed. Aiming at the above problem, a coherent signal-receiving algorithm is proposed based on oblique projection technology in this paper. The direction of arrival (DOA) of incident signals is estimated firstly by the space smoothing-based MUSIC method. The composite steering vector of multipath coherent signals is then obtained utilizing the oblique projection matrix constructed with the estimated angles. The weight vector is thereby derived with the minimum variance distortionless response criteria. The proposed oblique projection-based beamformer can receive the multipath coherent signals effectively. Moreover, the proposed beamformer is more robust and converges to optimal beamformer rapidly without aperture loss. The theoretical analysis and simulation verify the validity and superiority of the proposed coherent signal beamformer. [ABSTRACT FROM AUTHOR]

In a channel state information (CSI) based indoor positioning system, the positioning performance becomes susceptible to multipath fading effects especially in non-line-of-sight environments. We propose a transformer-based indoor positioning system (TIPS) to address this challenge. The proposed TIPS utilizes a self-attention mechanism to process the continuous WiFi CSI observed from predetermined routes as fingerprints in a given indoor environment. Each route is then considered a sentence, whereas the position along the route is treated as a word in terms of natural language processing. Consequently, the problem of predicting the position with the fingerprints can then be considered the task of predicting the current word with previous words, which can be efficiently solved using the proposed TIPS. In order to fully exploit the relations among positions, we propose embedding the information of the direction of arrival (DoA) on top of the collected CSI as inputs to the TIPS. Thus, the transformer of the proposed TIPS can better capture the dependencies of the positions in the route and significantly boost positioning accuracy. To exhibit the superiority of the proposed TIPS in a radio frequency (RF) environment, we demonstrate a hardware implementation of an RF testbed consisting of an emulator of WiFi access point and user equipment. Through extensive computer simulations and experimental tests, it is demonstrated that the proposed TIPS can reduce the positioning error down to 20 cm, which is a significant improvement compared to the current state-of-the-art models.

As one of the most critical technology in array signal processing, direction of arrival (DoA) estimation has received a great deal of attention in many areas. Traditional methods perform well when the signal-to-noise ratio (SNR) is high and the receiving array is perfect, which are quite different from the situation in some real applications (e.g., the marine communication scenario). To get satisfying performance of DoA estimation when SNR is low and the array is inaccurate (mutual coupling exist), this paper introduces a scheme consisting of denoising autoencoder (DAE) and deep neural networks (DNN), referred to as DAE-DNN scheme. DAE is used to reconstruct a clean “repaired” input from its corrupted version to increase the robustness, and then divide the input into multiple parts in different sub-areas. DNN is used to learn the mapping between the received signals and the refined grids of angle in each sub-areas, then the outputs of each sub-areas are concatenated to perform the final DoA estimation. By simulations in different SNR regimes, we study the performance of DAE-DNN in terms of the different snapshots, batch size, learning rate, and epoch. Our results demonstrate that the proposed DAE-DNN scheme outperforms traditional methods in accuracy and robustness.

Direction of arrival (DOA) estimation is an effective method for detecting various active interference signals during the satellite navigation process. It can be utilized for both interference detection and anti-interference applications. This paper proposes a DOA estimation algorithm for satellite interference sources based on frequency domain covariance matrix reconstruction (FDCMR) to address various types of active interference that may occur in the satellite navigation positioning process. This algorithm can estimate the DOA of coherent signals from multiple frequency points under low signal-to-noise ratio (SNR) conditions. The signals received from the array are transformed from the time domain to the frequency domain using a fast Fourier transform (FFT). The data corresponding to the frequency point of the target signal is extracted from the signal in the frequency domain. The frequency domain covariance matrix of the received array signals is reconstructed by utilizing its covariance matrix property. The spatial spectrum search method is used for the final DOA estimation. Simulation experiments have shown that the proposed algorithm performs well in the DOA estimation under low SNR conditions and also resolves coherency. Moreover, the algorithm’s effectiveness is verified through comparison with three other algorithms. Finally, the algorithm’s applicability is validated through simulations of various interference scenarios.

Sensor arrays are gradually becoming a current research hotspot due to their flexible beam control, high signal gain, robustness against extreme interference, and high spatial resolution. Among them, spherical microphone arrays with complex rotational symmetry can capture more sound field information than planar arrays and can convert the collected multiple speech signals into the spherical harmonic domain for processing through spherical modal decomposition. The subspace class direction of arrival (DOA) estimation algorithm is sensitive to noise and reverberation, and its performance can be improved by introducing relative sound pressure and frequency-smoothing techniques. The introduction of the relative sound pressure can increase the difference between the eigenvalues corresponding to the signal subspace and the noise subspace, which is helpful to estimate the number of active sound sources. The eigenbeam estimation of signal parameters via the rotational invariance technique (EB-ESPRIT) is a well-known subspace-based algorithm for a spherical microphone array. The EB-ESPRIT cannot estimate the DOA when the elevation angle approaches 90°. Huang et al. proposed a two-step ESPRIT (TS-ESPRIT) algorithm to solve this problem. The TS-ESPRIT algorithm estimates the elevation and azimuth angles of the signal independently, so there is a problem with DOA parameter pairing. In this paper, the DOA parameter pairing problem of the TS-ESPRIT algorithm is solved by introducing generalized eigenvalue decomposition without increasing the computation of the algorithm. At the same time, the estimation of the elevation angle is given by the arctan function, which increases the estimation accuracy of the elevation angle of the algorithm. The robustness of the algorithm in a noisy environment is also enhanced by introducing the relative sound pressure into the algorithm. Finally, the simulation and field-testing results show that the proposed method not only solves the problem of DOA parameter pairing, but also outperforms the traditional methods in DOA estimation accuracy.

Autosomal dominant optic atrophy (ADOA) is frequently caused by mutations in the optic atrophy 1 (OPA1) gene, with haploinsufficiency being the major genetic pathomechanism. Almost 30% of the OPA1-associated cases suffer from splice defects. We identified a novel OPA1 mutation, c.1065+5G>A, in patients with ADOA. In patient-derived fibroblasts, the mutation led to skipping of OPA1 exon 10, reducing the OPA1 protein expression by approximately 50%. We developed a molecular treatment to correct the splice defect in OPA1 using engineered U1 splice factors retargeted to different locations in OPA1 exon 10 or intron 10. The strongest therapeutic effect was detected when U1 binding was engineered to bind to intron 10 at position +18, a position predicted by bioinformatics to be a promising binding site. We were able to significantly silence the effect of the mutation (skipping of exon 10) and simultaneously increase the expression level of normal transcripts. Retargeting U1 to the canonical splice donor site did not lead to a detectable splice correction. This proof-of-concept study indicates for the first time the feasibility of splice mutation correction as a treatment option for ADOA. Increasing the amount of correctly spliced OPA1 transcripts may suffice to overcome the haploinsufficiency.

Vibration-based damage detection tools are frequently employed because of their advantages over other non-destructive techniques. This paper presents an improved Frequency Response Function (FRF) indicator for damage identification in complex structures. To verify the effectiveness of the improved damage indicator, different structures are used, namely a 20-Bar Planar Truss (2D), a 28-Bar Space Truss (3D), a 72-Bar Space Truss, and a 600-bar Space Truss. The numerical models of all these structures are built in MATLAB using Finite Element Method (FEM). The improved indicator detects and localises single and multiple damages in the first stage. Next, after eliminating the healthy elements, it is used in a new objective function to solve the damage quantification problem using recent optimization techniques, including Artificial Gorilla Troops Optimizer (GTO), Dingo Optimization Algorithm (DOA), African Vulture Optimization algorithm (AVOA), and Gradient-Based Optimizer (GBO). The results demonstrate that all optimization techniques can accurately predict the exact level of damage. However, GTO is the most efficient in terms of convergence. To study the effectiveness of this indicator in case of noisy data, different levels of noise are considered in the damage assessment exercises.