Search

In this letter, a localization algorithm for mixed‐field sources based on fourth‐order cumulant matrix rank reduction is proposed. First, a cumulant matrix is constructed, and a spectral peak search function associated with the direction‐of‐arrival (DOA) of the sources only is constructed using the rank reduction principle to achieve DOA estimation. Then the range estimation is also obtained by a spectral peak search. Compared to existing algorithms, the algorithm is able to utilize all the elements of the cumulant matrix and has a higher accuracy and degrees‐of‐freedom. The simulation results verify the excellence of the algorithm. [ABSTRACT FROM AUTHOR]

The degree of freedom (DOF) is an important performance metric for evaluating the design of a sparse array structure. Designing novel sparse arrays with higher degrees of freedom, while ensuring that the array structure can be mathematically represented, is a crucial research direction in the field of direction of arrival (DOA) estimation. In this paper, we propose a novel L-shaped sparse sensor array by adjusting the physical placement of the sensors in the sparse array. The proposed L-shaped sparse array consists of two sets of three-level and single-element sparse arrays (TSESAs), which estimate the azimuth and elevation angles, respectively, through one-dimensional (1-D) spatial spectrum search. Each TSESA is composed of a uniform linear subarray and two sparse subarrays, with one single common element in the two sparse subarrays. Compared to existing L-shaped sparse arrays, the proposed array achieves higher degrees of freedom, up to 4 Q 1 Q 2 + 8 Q 1 − 5 , when estimating DOA using the received signal covariance. To facilitate the correct matching of azimuth and elevation angles, the cross-covariance between the two TSESA arrays is utilized for estimation. By comparing and analyzing performance parameters with commonly used L-shaped and other sparse arrays, it is found that the proposed L-shaped TSESA has higher degrees of freedom and array aperture, leading to improved two-dimensional (2-D) DOA estimation results. Finally, simulation experiments validate the excellent performance of the L-shaped TSESA in 2-D DOA estimation. [ABSTRACT FROM AUTHOR]

In passive localization techniques, as the scale of the array of the sensors used increases, the source distribution may be a coexistence of near-field (NF) and far-field (FF) sources. Most of the existing algorithms dedicated to the localization of mixed-field sources are based on a simplified model, which has model errors and cannot make good use of non-circular properties when non-circular signals are present in the sources. In this paper, we present a mixed-field circular and non-circular source localization algorithm based on exact spatial propagation geometry. First, we make an initial estimate of the source parameters using exact spatial geometry relations. The MUSIC algorithm is then used in combination with the non-circular properties of the signal to achieve an accurate estimate. The algorithm does not lose performance due to model mismatch and is able to make good use of the non-circular properties of the sources to improve the estimation accuracy. The simulation results show that the proposed algorithm can effectively distinguish between sources and that the algorithm performs satisfactorily. [ABSTRACT FROM AUTHOR]

At present, there are few sparse arrays used in the mixed near‐field (NF) and far‐field (FF) localization based on non‐circular (NC) signals. Inspired by the symmetric flipped nested array (SFNA) used in the existing mixed NF and FF NC source, in order to further improve the parameter estimation accuracy of the mixed NF and FF NC signal, an improved symmetric flipped nested array (ISFNA) for mixed NF and FF NC sources localization was developed. First, the uniform subarrays in the SFNA are rearranged, ⌈N22⌉−1${\lceil \frac{N_2}{2}\rceil }-1$ elements are extracted from the uniform subarrays and rearranged into ISFNA. ISFNA is more sparse, the array aperture is larger, and the array degree of freedom (DOF) is higher; second, the formula of the maximum consecutive lags of ISFNA is given; third, a special fourth‐order cumulant is used to eliminate the range parameter and then use a one‐dimensional (1‐D) spectral peak search to obtain all Directions of Arrival (DOAs). By defining the range search, the range can be obtained by bringing in estimated DOAs. Finally, the superiority of the proposed array is proved by simulation. [ABSTRACT FROM AUTHOR]

Direction of arrival (DOA) estimation of mixed near‐field (NF) and far‐field (FF) signals is one of the key issues in the study of array signal processing. For more precise localisation performance, sparse arrays have drawn the attention of an increasing number of academics. Excitingly, sparse arrays enable larger array apertures and higher degrees of freedom (DOFs) than traditionally used uniform linear arrays. Consequently, this study proposes a transformed symmetric nested array (TSNA) for the passive localisation application regarding mixed NF and FF. There are three processes that go into building the proposed array. In the first step, we swap the positions of the uniform linear arrays and the sparse linear arrays in the nested array. In the second step, we place the corresponding array sensors extracted from the uniform linear array behind the uniform linear array to form a new subarray. In the last step, the TSNA is constructed by folding the formed array in half. Furthermore, we give the formula of the largest consecutive lags of the TSNA and its corresponding constructor. Finally, through theoretical analysis and computer performance simulation, this study illustrates the proposed array's numerous advantages over currently used symmetric sparse arrays. [ABSTRACT FROM AUTHOR]

In order to solve the problems of high complexity and absence of adaptability with the joint estimation for time delay (TD) and angle of arrival (AOA) in coherent multipath environments, a fast estimation algorithm based on orthogonal frequency division multiplexing (OFDM) technique is proposed. First, we combine OFDM signal characteristics with array features to obtain extended arrays. Then, we obtain the channel frequency domain response covariance matrices for TD and AOA estimation separately by smoothing preprocessing in the spatial and frequency domains, respectively. Finally, we estimate the TD values by a one‐dimensional (1‐D) spectral peak search, as well as determine the closed‐form solution for AOA by Unitary‐ESPRIT. In comparison with previous work, the proposed algorithm not only improves the adaptability in correlated or mixed multipath environments but also significantly reduces the complexity. The simulation results show the effectiveness and robustness of the proposed joint estimation algorithm. [ABSTRACT FROM AUTHOR]

As it is known, existing mixed near‐field and far‐field non‐circular source localisation rely on the uniform linear array (ULA). Compared with the ULA, the sparse array can obtain more consecutive lags and a larger physical array aperture. So a novel symmetric flipped nested array (SFNA) is proposed for mixed near‐field and far‐field non‐circular source localisation. At the same time, according to the special geometry of the array, the non‐circular phase information can be removed and the existing algorithm can be used to solve the problem of sparse array estimating the mixed near‐field and far‐field non‐circular signals. First, by exchanging the positions of the subarrays in the traditional nested array and then flipping them, a new SFNA is formed; second, the authors present the maximum consecutive lags and composition method of SFNA. For example, 4MN +4M −3 consecutive lags can be obtained by using a 2M + 2N −1 array element; third, in the estimation of mixed near‐field and far‐field non‐circular sources, a special cumulant is used to eliminate the range of the mixed near‐field and far‐field non‐circular sources and a one‐dimensional (1‐D) spectral search is used to obtain all the directions of arrival, and the range can be obtained by defining the range spectrum; finally, numerical simulation results demonstrate the superiority of the proposed array. [ABSTRACT FROM AUTHOR]

Recently, the joint estimation for time delay (TD) and direction of arrival (DOA) has suffered from the high complexity of processing multi-dimensional signal models and the ineffectiveness of correlated/coherent signals. In order to improve this situation, a joint estimation method using orthogonal frequency division multiplexing (OFDM) and a uniform planar array composed of reconfigurable intelligent surface (RIS) is proposed. First, the time-domain coding function of the RIS is combined with the multi-carrier characteristic of the OFDM signal to construct the coded channel frequency response in tensor form. Then, the coded channel frequency response covariance matrix is decomposed by CANDECOMP/PARAFAC (CPD) to separate the signal subspaces of TD and DOA. Finally, we perform a one-dimensional (1D) spectral search for TD values and a two-dimensional (2D) spectral search for DOA values. Compared to previous efforts, this algorithm not only enhances the adaptability of coherent signals, but also greatly decreases the complexity. Simulation results indicate the robustness and effectiveness for the proposed algorithm in independent, coherent, and mixed multipath environments and low signal-to-noise ratio (SNR) conditions. [ABSTRACT FROM AUTHOR]

In order to solve the problem of severely decreased performance under the situation of rapid moving sources and unstable array platforms, a null broadening robust adaptive beamforming algorithm based on power estimation is proposed in this paper. First of all, we estimate the interference signal power according to the characteristic subspace theory. Then, the correspondence between the signal power and steering vector (SV) is obtained based on the orthogonal property, and the interference covariance matrix (ICM) is reconstructed. Finally, with the aim of setting virtual interference sources, null broadening can be carried out. The proposed algorithm results in a deeper null, lower side lobes and higher tolerance of the desired signal steering vector mismatch under the condition of low complexity. The simulation results show that the algorithm also has stronger robustness. [ABSTRACT FROM AUTHOR]

Non-invasive contactless simultaneous sensing and heating of individual droplets would allow droplet microfluidics to empower a wide range of applications. However, it is challenging to realize simultaneous sensing and heating of individual droplets as the resonance frequency of the droplet fluid, which is decided by its permittivity, must be known so that energy is only supplied at this frequency for droplet heating with one resonator. To tailor the energy transfer in real-life heating applications, the droplet has to be sensed first to identify its corresponding resonance frequency, which is used to dynamically tune the frequency for supplying the required energy for heating this particular droplet. To achieve this goal, two resonators are needed, with one for sensing and one for heating. Integrating multiple resonators into one typical microfluidic device limits placement of the resonators to be as close as possible, which would raise the concern of crosstalk between them. The crosstalk would result in inaccurate sensing and heating. This study focuses on numerically and experimentally investigating the effect of influencing parameters on the crosstalk between two adjacent resonators with the ultimate goal of providing guidance for multiplexing the resonators in a typical microfluidic device. ANSYS HFSS is used to perform the electromagnetic analysis based on the finite element method. Experimental studies are conducted on a microfluidic chip integrated with two resonators to validate the numerical results. An optimal distance between two resonators is suggested, with the recommendation for the resonator size and heating power towards simultaneous sensing and heating of individual droplets. [ABSTRACT FROM AUTHOR]

In the coprime planar array (CPA), the existing tensor DOA estimation has the problem that the statistics are not fully utilized. We propose a two-dimensional DOA estimation method based on tensor self-correlation, which realizes the high-resolution and high-precision joint estimation of elevation angle and azimuth angle. Firstly, we represent the received signals of two subarrays with tensors and then obtain the self-correlation covariance tensor of the subarrays themselves and the cross-correlation covariance tensor of the two subarrays. Then, we extract the covariance tensor corresponding to the maximum continuous virtual array and prove the expression of the maximum continuous virtual array aperture of the proposed method. Compared with the existing methods, the proposed method effectively improves the maximum aperture of the continuous virtual array. Finally, the signal subspace is solved by tensor expansion and tensor decomposition. Simulation results show that under the same conditions, the proposed method has higher estimation accuracy and degree of freedom than the cross-correlation tensor method, and the resolution is also improved significantly. [ABSTRACT FROM AUTHOR]

As we all know, the non‐uniform array is widely used in the mixed near‐field (NF) and far‐field sources localization. The nested array contains a uniform linear array, it is easy to be affected by mutual coupling, so we propose a symmetric thinned coprime array (STCA) to reduce the mutual coupling effect of mixed source localization. In this paper, the STCA configuration consists of two sparse uniform linear arrays, the first subarray is composed of 2N − 1 sensors, and the sensor element spacing is Md. The second subarray is composed of 2M+2⌊M2⌋ $2M+2\lfloor \frac{M}{2}\rfloor $ sensors, and the sensor element spacing are Nd and Md respectively. The two subarrays form the symmetric coprime array, which shares a reference sensor. Under the same number of physical array sensors, compared with the latest symmetric nested array, although STCA has lower consecutive lags, the array sensors have larger inter‐sensor spacing and larger physical array aperture, which can significantly reduce the mutual coupling effect between physical sensors, and better estimation performance of direction‐of‐arrival (DOA) estimation can be achieved. Finally, simulation results show that STCA can achieve better performance than other symmetric nested arrays under the same array sensors and mutual coupling effects. [ABSTRACT FROM AUTHOR]

The global COVID-19 pandemic caused by SARS-CoV-2 has resulted in an unprecedented economic and societal impact. Developing simple and accurate testing methods for point-of-care (POC) diagnosis is crucial not only for the control of COVID-19, but also for better response to similar outbreaks in the future. In this work, we present a novel proof-of-concept of a microfluidic microwave sensing method for POC diagnosis of the SARS-CoV-2 virus. This method relies on the antibody immobilized on the microwave sensor to selectively capture and concentrate the SARS-CoV-2 antigen or virus present in a buffer solution flowing through the sensor region in a microchannel. The capturing of the SARS-CoV-2 antigen or virus results in a change in the permittivity of the medium near the sensor region reflected by the resonance frequency shift which is used for detection. The use of microchannels offers precise control of the sample volume and the continuous flow nature also offers the potential to monitor the dynamic capturing process. The microwave-microfluidic device shows a good sensitivity of 0.1 ng ml−1 for the SARS-CoV-2 antigen and 4000 copies per ml for the SARS-CoV-2 virus. The resonance frequency shift presents a linear relationship with the logarithm of antigen or virus concentration, respectively. This detection method is able to distinguish SARS-CoV-2 from the antigen of human CD4 and two human coronaviruses (MERS and HKU1), which presents a new pathway towards POC diagnosis of the COVID-19 at the community level. It presents the potential to detect other viruses by functionalizing the microwave sensor with respective antibodies. [ABSTRACT FROM AUTHOR]

As we all know, the model mismatch, primarily when the desired signal exists in the training data, or when the sample data is used for training, will seriously affect algorithm performance. This paper combines the subspace algorithm based on direction of arrival (DOA) estimation with the adaptive beamforming. It proposes a reconstruction algorithm based on the interference plus noise covariance matrix (INCM). Firstly, the eigenvector of the desired signal is obtained according to the eigenvalue decomposition of the subspace algorithm, and the eigenvector is used as the estimated value of the desired signal steering vector (SV). Then the INCM is reconstructed according to the estimated parameters to remove the adverse effect of the desired signal component on the beamformer. Finally, the estimated desired signal SV and the reconstructed INCM are used to calculate the weight. Compared with the previous work, the proposed algorithm not only improves the performance of the adaptive beamformer but also dramatically reduces the complexity. Simulation experiment results show the effectiveness and robustness of the proposed beamforming algorithm. [ABSTRACT FROM AUTHOR]

Lead contamination in drinking water has become an increasingly serious global risk because a small concentration of lead can cause serious health problems, particularly for children. It is critical to frequently monitor lead concentration in drinking water, which can be challenging when using traditional centralized systems. In this study, we present an inexpensive, portable detection system for point-of-care (POC) monitoring of lead concentration in drinking water. The sensing mechanism is based on the interaction between the water sample flowing through a microchannel and a planar microwave resonator-based sensor that is integrated with the microfluidic chip. The microwave sensor has a double-T structure with a gap in between through which the microchannel is aligned and can be coated with gold nanoparticles to enhance its sensing performance. For proof-of-concept, the sample under test (SUT) was a small volume of deionized (DI) water or tap water spiked with lead ions at different concentrations. Results show that the gold nanoparticle-coated microwave sensor presents a much higher sensitivity than bare sensors with a detectable frequency shift of 5 MHz for a Pb2+ solution with a concentration of 1 ppb. The success of the system for testing lead ions in typical tap water which contains many different mineral ions confirms its real-world application. To highlight the potential for POC applications, a low-cost, portable vector network analyzer is used to capture the frequency shift of the sensor. The developed method offers a promising approach for POC monitoring of lead contamination in drinking water impactful for environmental and public health protection. [Display omitted] • Introduction of the importance to be aware that lead contamination in drinking water is a serious health problem. • Existing methods are not suitable for frequent monitoring, especially point-of-care (POC) testing. • The proposed microwave sensing integrated with microfluidics is promising for POC testing of lead concentration in water. • Coating nanomaterials on microwave sensors can enhance the sensing performance. [ABSTRACT FROM AUTHOR]

Abstract: Understanding the impacts of the internal cohesion and surface chemistry of supramolecular systems on the collective behaviors in the contacts between the systems and biomolecules can greatly expand the functional diversity and adaptivity of supramolecular nanostructures. Here we show how the tuned molecular interactions modulate the morphologies and internal cohesion of peptide amphiphile (PA) self‐assemblies and their resultant functions. Circular dichroism spectroscopy, fluorescence probing, atomic force and electron microscopy, along with molecular dynamics simulations, revealed that the PA self‐assembly formed compact long fibers when surface charge repulsion was screened, but formed loose short fibers or micelle‐like assemblies when hydrogen bonding was disrupted or hydrophobic core was enhanced. More importantly, depending on the strength of the phospholipid affinity for the cationic segment of the PA, the same internal cohesion of PA nanostructures can lead to either cell death or cell survival, providing unique insights into the design of supramolecular materials. [ABSTRACT FROM AUTHOR]

In this article, an effective algorithm is proposed for direction‐of‐arrival (DOA) estimation of coherent signals using the l2‐norm with moving coprime arrays. The proposed algorithm exhibits high accuracy and robustness in decorrelation of coherent signals compared to existing methods. Firstly, the received signal is filtered on the moving coprime array and a signal model is constructed that is compatible with the moving coprime array. Next, virtual sensors is inserted into the holes of the moving coprime array and the received signals are initialised. Then, the Toeplitz matrix is reconstructed based on the covariance matrix of the initial signal and perform low‐rank matrix recovery to eliminate signal coherence by compensating the matrix for zero. Finally, the DOA estimation for the signal is performed using spectral peak search. Simulation results verify that the estimation accuracy of our proposed algorithm outperforms various existing algorithms. [ABSTRACT FROM AUTHOR]

The flower pollination algorithm (FPA) is a novel optimization technique derived from the pollination behavior of flowers. However, the shortcomings of the FPA, such as a tendency towards premature convergence and poor exploitation ability, confine its application in engineering problems. To further strengthen FPA optimization performance, an orthogonal learning (OL) strategy based on orthogonal experiment design (OED) is embedded into the local pollination operator. OED can predict the optimal factor level combination by constructing a smaller but representative test set based on an orthogonal array. Using this characteristic of OED, the OL strategy can extract a promising solution from various sources of experience information, which leads the population to a potentially reasonable search direction. Moreover, the catfish effect mechanism is introduced to focus on the worst individuals during the iteration process. This mechanism explores new valuable information and maintains superior population diversity. The experimental results on benchmark functions show that our proposed algorithm significantly enhances the performance of the basic FPA and offers stronger competitiveness than several state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Heavy metal pollution on earth has evolved into a global issue causing serious risks to human health and other living entities and having an impact on sustainability. Accurate identification of metal contamination is often carried out in centralized facilities involving sampling, transportation, and the need for highly trained personnel, which becomes expensive, often causes delays in response to potential tragedies, and is prone to sample properties changes. Rapid, affordable methods for point-of-care (POC) detection of heavy metals with reasonable accuracy are ideal to address these challenges enabling diligent monitoring of metal pollution. There have been many POC systems reported, however, the systems that could work with real samples in which heavy metals are present in a complex form at a low concentration are limited. Sample preparation is often needed for the accurate identification of metal ions. Microfluidics offers tremendous potential for sample preparation and integration with various detection methods such as optical and electrochemical methods for POC detection of heavy metals. This review is limited to reviewing the reported microfluidic-based POC devices for heavy metal sensing and providing a brief perspective on the integration of microwave sensing methods with microfluidic devices for heavy metal detection. This review starts with introducing microfluidic-based heavy metal sensing using optical and electrochemical methods and then focuses on briefly discussing the development and potential of integrating microwave sensing with microfluidic devices for heavy metal sensing. The principle of each method and the limit of detection are briefly discussed. [Display omitted] • Heavy metal source and damage introduced. • Optical methods integrated with microfluidics for point-of-care heavy metal sensing introduced. • Electrochemical methods integrated with microfluidics for point-of-care heavy metal sensing introduced. • Microwave sensing and its potential to be integrated with microfluidics for point-of-care heavy metal sensing introduced. [ABSTRACT FROM AUTHOR]

In this paper, a high-accuracy localization problem under a complex non-line-of-sight (NLOS) condition is addressed by a new method that utilizes multiple NLOS paths to improve localization accuracy, as opposed to the traditional method of suppressing them. The spatial multi-path array fusion localization model is presented and analyzed, followed by an angle-of-arrival (AOA) and time-of-arrival (TOA) algorithm based on spatial multi-information fusion that seeks to improve localization accuracy. Multi-path of spatial signals, measurement of the multi-element antenna, and geographic environment information are integrated into the proposed method for localization optimization. Simulation experiments were carried out, and the results revealed that the proposed algorithm is capable of making full use of spatial multi-location information for localization, thus improving the accuracy of localization in a the NLOS environment effectively and increasing the locatable probability of complex environment localization applications. [ABSTRACT FROM AUTHOR]

Large‐scale arrays are widely used due to their advantages of high gain, high resolution, and strong beam control capability. The large number of antennas leads to a sharp increase in computational complexity, and thus, it is necessary to reduce the complexity of signal processing. A signal separation method is proposed based on subarray beam synthesis. This method can improve signal reception and separation performance through zero‐forcing calculation and realise system gain and phase error corrections in the front end of an antenna. Compared with back‐end processing, it has the advantage of low implementation difficulty. The feasibility and performance of the method are verified by simulations. [ABSTRACT FROM AUTHOR]

Owing to increased degrees of freedom (DOFs) and reduced mutual coupling effect, sparse linear arrays can improve the performance of direction‐of‐arrival estimation compared with uniform linear arrays. In this study, a novel sparse linear array is presented based on fourth‐order cumulants, which consists of five sparse sub‐arrays. The sensor locations of the five sub‐arrays are determined by a closed‐form expression. Closed‐form expressions of DOFs for the proposed array with any number of sensors are also derived. By optimising the allocation of sensors in each sub‐array, the DOFs of the proposed array with M sensors achieve OM4 $\mathcal{O}\left({M}^{4}\right)$. Because each sub‐array is sparse, the number of pairwise sensors with small separation is significantly alleviated. Theoretical analysis shows that the proposed array can provide a higher number of DOFs and is more robust in scenarios with heavy levels of mutual coupling than most sparse linear arrays. Numerical simulations are conducted to demonstrate the merits of the proposed array over other sparse linear arrays. [ABSTRACT FROM AUTHOR]

Recently, sparse linear arrays have aroused significant attention because they can provide increased degrees of freedom (DOFs) and enlarged inter‐element spacing, which are the critical factors to improve the accuracy of direction of arrival (DOA) estimation. In this paper, to alleviate the high mutual coupling caused by the dense part of transformed nested arrays (TNAs), we present a super transformed nested array (STNA) for the DOA estimation of non‐circular signals. It can be obtained by removing some sensors from the dense part of transformed nested arrays and relocating them appropriately. The proposed array structure possesses a higher DOF and a sparser physical structure, resulting in less mutual coupling compared to TNA. For the proposed array structure, we provide theoretical proof of the DOFs and deduce the weight functions. Numerical simulations are conducted to demonstrate the advantages of the STNA over existing sparse arrays. [ABSTRACT FROM AUTHOR]

• Mechanism of microwave heating induced mixing in droplets is revealed numerically. • Interfacial tension plays a key role in microwave heating induced droplet mixing. • The revealed mechanism contributes on exploiting microwave heating for rapid mixing. Microwave resonator offers simultaneous sensing and heating of individual droplets in microchannels presenting unique advantages for applications requiring well-controlled reaction time. This study numerically investigates the effects of the temperature-dependent fluid properties including viscosity, density, diffusivity, and interfacial tension, on microwave heating induced mixing. A system consisting of a spiral resonator integrated with a microchannel with aqueous droplets moving with a carrier oil is considered. The electromagnetic field which provides heating to the droplets, and the flow and tracer concentration fields inside the droplet are obtained using commercial software, Ansys HFSS and Fluent, respectively. It is found that the mixing index can be increased from 0.4 to 0.84 within 320[ms] with microwave heating and it would be slightly increased to 0.47 when only the interfacial tension was made independent on temperature, which suggests that the temperature-dependent interfacial tension is the dominant factor for microwave heating induced mixing. [ABSTRACT FROM AUTHOR]

Compared with the uniform linear array (ULA) under the same physical array element, the coprime array can obtain a higher degree of freedom and a larger array aperture, so it has received extensive attention from scholars in recent years. Based on the idea of interference plus noise covariance matrix (INCM) reconstruction, this study proposed a robust adaptive beamforming algorithm using the coprime array. In the virtual array generated by the coprime array, the authors prove from the perspective of virtual signals that the constructed Toeplitz matrix is equivalent to a virtual ULA with more array elements. Subsequently, a high‐resolution subspace algorithm is used to estimate the direction of arrival (DOA) of the signals, and the estimated DOA is used to reconstruct the direction vector (DV) of the coprime array. Through the eigenvalue decomposition of the reconstructed Toeplitz matrix, the corresponding relationship between the eigenvalue and the power is deduced, and the signals and noise power are estimated using the eigenvalue. Finally, combine the DV of the desired signal and the reconstructed INCM to calculate the weight vector of the proposed algorithm. The simulation results prove the superiority and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

When there is the coexistence of uncorrelated and coherent signals in sparse arrays, the conventional algorithms using coarray are fail. In order to solve this problem, the letter proposes a novel method based on compressed sensing. Firstly, the authors vectorize the covariance matrix and establish a sparse representation model through constructing a two‐dimensional redundant dictionary. Then, the authors use an improved orthogonal matching pursuit algorithm for off‐grid sources to recover the sparse vector. Through analysing location of non‐zero elements in sparse vector, the direction‐of‐arrivals of both uncorrelated and coherent signals can be obtained. The proposed method has no strict limitation by the structure of the existing sparse arrays. Moreover, it makes full use of vectorized data and can estimate more number of signals than that of sensors. Numerical experiments prove the effectiveness and favourable performance of the proposed method. [ABSTRACT FROM AUTHOR]

Modulation signal intelligent recognition model based on deep learning is widely used in the field of radio signal intelligent processing, but the adversarial attack has become a huge security threat. In order to promote the safe and reliable application of the modulation recognition intelligent model, it is necessary to study its adversarial defense technology. An adversarial defense method based on ensemble learning for modulation signal intelligent recognition model is proposed in this paper. Specifically, this method is achieved by combining multiple defense models such as adversarial training, defensive distillation, and noise smoothing. Variety of attack algorithms in both the white-box and black-box scenarios under different intensities of perturbation and different signal-to-noise ratios are carried out to verify the robustness performance of the proposed method. Strikingly, the accuracy of the model is improved to over 80% when the SNR is above 0 dB under Carlini and Wagner attack. [ABSTRACT FROM AUTHOR]

BACKGROUND: Muscle strength in the upper and lower limbs is a major contributing factor to punch force and is one of the keys to success in boxing. OBJECTIVE: This study aimed to investigate the relationship between knee and shoulder strength and punching force in boxers. METHODS: Twenty-one boxers completed knee flexion, knee extension, shoulder external rotation and internal rotation isokinetic concentric contractions at slow (60 ∘ /s) and fast (180 ∘ /s) angular velocities. Maximal punch force tests included lead and rear straight arm punching were assessed using a force platform. RESULTS: Pearson's correlation coefficients revealed that knee extension (r = 0.646–0.848) and knee flexion (r = 0.470–0.646) peak torques were moderately to very strongly correlated with maximal punching force in lead and rear arms. The shoulder internal rotation (r = 0.492–0.634) and shoulder external rotation (r = 0.441–0.588) peak torques were moderate to strongly correlated with maximal punching force. Moreover, knee extension peak torques at 60 ∘ /s had higher correlation with maximal punching force. In contrast, shoulder internal rotation peak torques at 180 ∘ /s had a larger correlation with maximal punching force. CONCLUSIONS: The main findings indicated the importance of the capacity to generate maximum knee extension and rapid shoulder internal rotation strength, contributing to punch force production. [ABSTRACT FROM AUTHOR]

Simple Summary: Black-billed capercaillie (Tetrao parvirostris) were listed as least concern (LC) class by the International Union for Conservation of Nature (IUCN). However, black-billed capercaillie was an endangered species in China and classified as first-class state protection animal (category I). Only a few studies have focused on the feeding habits of black-billed capercaillie and there is less research content at the molecular level, such as genomics, microbiome, and molecular markers. To improve our understanding of black-billed capercaillie, the fecal microbiome was characterized. This study provides reasonable scientific data for the understanding of black-billed capercaillie and may provide important insights for protecting this endangered species in China. Black-billed capercaillie (Tetrao parvirostris) was listed as a first-class state-protected animal because it was endangered in China (Category I). This study is the first to examine the diversity and composition of T. parvirostris gut microbiome in the wild. We collected fecal samples from five black-billed capercaillie flock roosting sites (each 20 km apart) in one day. Thirty fecal samples were sequenced with 16S rRNA gene amplicons on the Illumina HiSeq platform. This study is the first to analyze the fecal microbiome composition and diversity of black-billed capercaillie in the wild. At the phylum level, Camplyobacterota, Bacillota, Cyanobacteria, Actinomycetota, and Bacteroidota were the most abundant in the fecal microbiome of black-billed capercaillie. At the genus level, unidentified Chloroplast, Escherichia−Shigella, Faecalitalea, Bifidobacterium, and Halomonas were the dominant genera. Based on alpha and beta diversity analyses, we found no significant differences in the fecal microbiome between five flocks of black-billed capercaillie. Protein families: genetic information processing; protein families: signaling and cellular processes, carbohydrate metabolism; protein families: metabolism and energy metabolism are the main predicted functions of the black-billed capercaillie gut microbiome through the PICRUSt2 method. This study reveals the composition and structure of the fecal microbiome of the black-billed capercaillie under wild survival conditions, and this study provides scientific data for the comprehensive conservation of the black-billed capercaillie. [ABSTRACT FROM AUTHOR]

In the rapidly evolving landscape of wireless communication systems, the vulnerability of automatic modulation classification (AMC) models to adversarial attacks presents a significant security challenge. This study introduces a pruning and training methodology tailored to address the nuances of signal processing within these systems. Leveraging a pruning method based on channel activation contributions, our approach optimizes adversarial training potential, enhancing the model’s capacity to improve robustness against attacks. Additionally, the approach constructs a resilient training method based on a composite strategy, integrating balanced adversarial training, soft target regularization, and gradient masking. This combination effectively broadens the model’s uncertainty space and obfuscates gradients, thereby enhancing the model’s defenses against a wide spectrum of adversarial tactics. The training regimen is carefully adjusted to retain sensitivity to adversarial inputs while maintaining accuracy on original data. Comprehensive evaluations conducted on the RML2016.10A dataset demonstrate the effectiveness of our method in defending against both gradient-based and optimization-based attacks within the realm of wireless communication. This research offers insightful and practical approaches to improving the security and performance of AMC models against the complex and evolving threats present in modern wireless communication environments. [ABSTRACT FROM AUTHOR]

Recently, the design of sparse linear array for direction of arrival (DOA) estimation of non-Gaussian signals has attracted considerable interest due to the fact that the fourth-order difference coarray offered by non-Gaussian significantly increases the aperture of a virtual linear array, which improves the performance of DOA estimation. In this paper, a super four-level nested array (S-FL-NA) configuration based on fourth-order cumulants (FOC) is proposed. The S-FL-NA consists of uniform linear arrays which have different interelement spacing. The proposed array configuration is designed based on interelement spacing, which, for a given number of sensors, is uniquely determined by a closed-form expression. We also derive the closed-form expression for the degrees of freedom (DOFs) of the proposed array. The optimal distribution of the number of sensors in each uniform linear array of the proposed array is given for an arbitrary number of sensors. Compared with the existing sparse arrays, the proposed array can provide a higher number of degrees of freedom and a larger physical array aperture. In addition, to improve the calculation speed of the fourth-order cumulant matrix, we simplify the FOC matrix by removing some redundancy. Numerical simulations are conducted to verify the superiority of the S-FL-NA over other sparse arrays. [ABSTRACT FROM AUTHOR]

It is challenging to use traditional remote sensing techniques to accurately determine the extent and thickness of ice in the Bohai Sea, on account of the presence of sea impurities (i.e. mud, salt bubbles and sand) and shape irregularities. Accordingly, we performed a series of reflectance spectra experiments to empirically link remote measurements of surface reflectance with in situ sea ice thickness measurements in the Bohai Sea. Two years of Thematic Mapper (TM) band 2 and Moderate Resolution Imaging Spectroradiometer (MODIS) band 4 data were used to distinguish between the following sea ice types, using spectral reflectance thresholds of 6.4, 9.6, 10.3 and 12.1%: (a) clean nilas ice (a thin elastic crust of ice up to 10 cm thick that, under pressure, may deform by finger rafting; (b) nilas ice and pancake ice (roughly circular accumulations of frazil ice, usually less than about 3 m in diameter, with raised rims caused by collisions); (c) grey and grey-white ice; and (d) cumulative ice (<30 cm). By establishing a relationship between sea ice type and ice thickness, a novel, practical and low-cost remote sensing technique is introduced to estimate the extent and distribution of sea ice thickness over a large spatial scale. The results obtained by remote sensing are validated with in situ ice shape measurements. The MODIS and TM data are used to distinguish between three ice thickness grades (6-9, 10-20 and 20-30 cm). [ABSTRACT FROM AUTHOR]

Sample series of clean regular‐shaped ice representative of thermal interaction processes only were gathered in a controlled experiment on ice floes in China's Bohai Sea. Based on these samples, spectral characteristics for one‐year‐old sea ice were measured and analyzed. The same experiments were then performed for ice samples with two normal impurities and the results compared against those of clean samples. Along with increasing thickness, the reflectance of clean regular‐shaped ice rises in the form of a ladder within the visible light range. While flat regular‐shaped ice is covered by thaws snow and/or mixed with mud and sand, the trend of increasing reflection with thickness of clean regular‐shaped ice is disordered. [ABSTRACT FROM AUTHOR]