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The article proposes a real‐time apple picking method based on an improved YOLOv5. This method accurately recognizes different apple targets on fruit trees for robots and helps them adjust their position to avoid obstructions during fruit picking. Firstly, the original BottleneckCSP module in the YOLOv5 backbone network is enhanced to extract deeper features from images while maintaining lightweight. Secondly, the ECA module is embedded into the improved backbone network to better extract features of different apple targets. Lastly, the initial anchor frame size of the network is adjusted to avoid recognizing apples in distant planting rows. The results demonstrate that this improved model achieves high accuracy rates and recall rates for recognizing various types of apple picking methods with an average recognition time of 0.025s per image. Compared with other models tested on six types of apple picking methods, including the original YOLOv5 model as well as YOLOv3 and EfficientDet‐D0 algorithms, our improved model shows significant improvements in mAP by 1.95%, 17.6%, and 12.7% respectively. This method provides technical support for robots' picking hands to actively avoid obstructions caused by branches during fruit harvesting, effectively reducing apple loss. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth exploration of adaptive filters, indispensable tools in signal processing for their ability to dynamically adjust parameters and optimize performance in varying environments. Delving into prominent methodologies such as the Least Mean Squares (LMS), Normalized Least Mean Squares (NLMS), Recursive Least Squares (RLS), Gradient Adaptive Lattice (GAL), and Fractional Tap-Length (FTL) algorithms, the study elucidates their unique characteristics, advantages, and considerations. LMS is known for its simplicity and computational efficiency, while NLMS improves convergence speed and robustness to input signal power variations. RLS offers rapid convergence and robustness through recursive estimation, distinct from the iterative approaches of LMS and NLMS. GAL employs a lattice structure for efficient parameter estimation and numerical stability, and FTL dynamically adjusts tap-lengths for enhanced performance. The paper underscores the significance of adaptive filters in diverse applications, from telecommunications and audio processing to biomedical signal analysis and control systems, highlighting their role in driving innovation and advancement in signal processing technology. [ABSTRACT FROM AUTHOR]

The line of sight (LoS) probability is a key factor for the channel modeling of air‐to‐ground (A2G) communication. However, the existing LoS probability models do not account for the effects of airframe shadowing (AS) and building density, which can cause serious link obstruction and performance loss due to the six‐dimensional (6D) mobility and self‐body of unmanned aerial vehicle (UAV). In this article, a new LoS probability model is proposed that considers the AS and building density for different UAV heights. Adding to this, the AS is derived in terms of UAV framework and 6D mobility. Next, the machine learning (ML) based graph neural network (GNN) method is developed to learn the features and structure of the urban environment and predict the LoS probability. Then, the GNN model is trained and evaluated based on the ray tracing (RT) data to establish the relationship between model parameters and UAV heights under the building density and AS factors. The interpretation and explanation of the proposed GNN model and prediction are also discussed in this article. It is shown from the simulation analysis that the GNN model accurately captures the effects of AS, building height distributions, and UAV heights, with high accuracy compared to the baseline 3GPP, GCM and NYU models. [ABSTRACT FROM AUTHOR]

The ability of piezoelectric materials to convert mechanical energy into electric energy and vice versa has made them desirable in the wide range of applications that oscillate from medicine to the energetics industry. Their implementation in optical communication is often connected with the modulation or other manipulations of the light signals. In this article, the recent advancements in the field of piezoelectrics‐based devices and their promising benefits in optical communication are explored. The application of piezoelectrics‐based devices in optical communication allows dynamic control, modulation, and manipulation of optical signals that lead to a more reliable transmission. It turns out that a combination of artificial‐intelligence‐based algorithms with piezoelectrics can enhance the performance of these devices, including optimization of piezoelectric modulation, adaptive signal processing, control of optical components, and increase the level of energy efficiency. It can enhance signal quality, mitigate interference, and reduce noise‐connected issues. Moreover, this technological fusion can increase the security of optical communication systems. Finally, the potential future research lines are determined. [ABSTRACT FROM AUTHOR]

Applying the sparse recovery (SR) technique to airborne radar space‐time adaptive processing (STAP) can greatly reduce the number of required training samples, which is advantageous in detecting targets in non‐homogeneous and non‐stationary clutter environments. However, the poor performance, the slow convergence speed or the high computational complexity of the traditional SR STAP algorithms limit their practical application. To tackle this problem, a novel efficient SR STAP algorithm is proposed. The newly proposed SR STAP algorithm utilises the log‐sum penalty to approximate the ℓ0−norm ${\ell }_{0}-\text{norm}$ penalty, which exhibits improved convergence performance and clutter suppression performance compared to the traditional SR STAP algorithms. Besides, the proposed algorithm can ensure the convergence in each iteration by offering a closed‐form analytic solution. Furthermore, the result of the mathematical derivation demonstrates the essential equivalence between our method and the iterative reweighted ℓ2 ${\ell }_{2}$ method. By utilising this equivalence, two additional methods are proposed that incorporate the knowledge of the clutter Capon spectrum and the clutter spectrum of the iterative adaptive approach (IAA) as the components of weighted values, resulting in further performance improvement of the proposed algorithm. Finally, simulation results with both simulated data and Mountain‐Top data demonstrate the high effectiveness and performance of the proposed methods. [ABSTRACT FROM AUTHOR]

The advent of 5G communication technology has revolutionized wireless communication with its high bandwidth, ultra-low latency, and massive connectivity features. However, the dynamic nature of user behavior and environmental changes poses significant challenges in optimizing signal processing for context awareness. Adaptive signal processing (ASP) offers a promising solution, but traditional methods struggle to effectively handle real-time, context-sensitive demands. In this research, we propose a novel reinforcement learning (RL)-based framework for adaptive signal processing that enhances context awareness in 5G networks. The problem addressed involves the optimization of signal parameters, such as power, frequency, and modulation schemes, to meet varying user demands and environmental conditions without compromising Quality of Service (QoS). The proposed method employs RL to adaptively optimize these parameters in real time. Specifically, a Q-learning algorithm is applied to learn the optimal policies for signal adaptation based on feedback from the environment, such as user mobility, interference levels, and network traffic. Simulation results demonstrate that the RL-based approach outperforms traditional static models, achieving up to a 30% reduction in latency and a 20% improvement in overall network throughput, while maintaining a 95% success rate in meeting user QoS requirements. This demonstrates the potential of RL for enhancing ASP in 5G systems. [ABSTRACT FROM AUTHOR]

Abstract The article proposes a real‐time apple picking method based on an improved YOLOv5. This method accurately recognizes different apple targets on fruit trees for robots and helps them adjust their position to avoid obstructions during fruit picking. Firstly, the original BottleneckCSP module in the YOLOv5 backbone network is enhanced to extract deeper features from images while maintaining lightweight. Secondly, the ECA module is embedded into the improved backbone network to better extract features of different apple targets. Lastly, the initial anchor frame size of the network is adjusted to avoid recognizing apples in distant planting rows. The results demonstrate that this improved model achieves high accuracy rates and recall rates for recognizing various types of apple picking methods with an average recognition time of 0.025s per image. Compared with other models tested on six types of apple picking methods, including the original YOLOv5 model as well as YOLOv3 and EfficientDet‐D0 algorithms, our improved model shows significant improvements in mAP by 1.95%, 17.6%, and 12.7% respectively. This method provides technical support for robots' picking hands to actively avoid obstructions caused by branches during fruit harvesting, effectively reducing apple loss.

Abstract Applying the sparse recovery (SR) technique to airborne radar space‐time adaptive processing (STAP) can greatly reduce the number of required training samples, which is advantageous in detecting targets in non‐homogeneous and non‐stationary clutter environments. However, the poor performance, the slow convergence speed or the high computational complexity of the traditional SR STAP algorithms limit their practical application. To tackle this problem, a novel efficient SR STAP algorithm is proposed. The newly proposed SR STAP algorithm utilises the log‐sum penalty to approximate the ℓ0−norm penalty, which exhibits improved convergence performance and clutter suppression performance compared to the traditional SR STAP algorithms. Besides, the proposed algorithm can ensure the convergence in each iteration by offering a closed‐form analytic solution. Furthermore, the result of the mathematical derivation demonstrates the essential equivalence between our method and the iterative reweighted ℓ2 method. By utilising this equivalence, two additional methods are proposed that incorporate the knowledge of the clutter Capon spectrum and the clutter spectrum of the iterative adaptive approach (IAA) as the components of weighted values, resulting in further performance improvement of the proposed algorithm. Finally, simulation results with both simulated data and Mountain‐Top data demonstrate the high effectiveness and performance of the proposed methods.

Abstract In the anti‐interference of broadband communication radio, interference cancellation performance is seriously affected by the uncertainty of the working frequency selection of the communication system and the direction of hostile interference. The closed‐form solution of the interference cancellation weight value and the signal‐to‐interference noise ratio after the cancellation are derived and simplified to clarify the factors affecting interference cancellation quality. Then, the efficient blind area criterion is established for the low computational complexity in view of cancellation quality, which combines the radio communication index and anti‐interference demand index. Based on this criterion, the minimum blind area model is proposed for robust performance. The experimental results in the auxiliary antenna spacing optimisation are consistent with the theoretical analysis, verifying the effectiveness of the proposed model in dealing with uncertain communication frequency and interference direction. It is very promising in communication interference cancellation.

The rapid development of wireless technology has driven a high level of informatization in modern warfare, opening a new era in electronic countermeasures (ECM). With the continuous innovation of advanced radar technology, the survival capability of weapons and equipment such as missiles and aircraft is facing unprecedented challenges. The development and application of electromagnetic stealth technology can effectively enhance the survivability of modern weapons, which becomes a crucial direction for military technology development. However, traditional electromagnetic stealth technologies are often constrained by practical conditions and fail to meet the stealth requirements of military operations. To address this issue, intelligent reflecting surface (IRS)-enabled electromagnetic stealth technology was proposed. This technology utilized IRS to suppress or redirect reflected signals, thus achieving adaptive transparent or deceptive electromagnetic stealth. To fully explore the great potential of IRS in the field of electromagnetic stealth, the basic principles, system models, design challenges, and open issues of IRS-enabled electromagnetic stealth were discussed, aiming to provide directional guidance for the future development of electromagnetic stealth technology.

In the anti‐interference of broadband communication radio, interference cancellation performance is seriously affected by the uncertainty of the working frequency selection of the communication system and the direction of hostile interference. The closed‐form solution of the interference cancellation weight value and the signal‐to‐interference noise ratio after the cancellation are derived and simplified to clarify the factors affecting interference cancellation quality. Then, the efficient blind area criterion is established for the low computational complexity in view of cancellation quality, which combines the radio communication index and anti‐interference demand index. Based on this criterion, the minimum blind area model is proposed for robust performance. The experimental results in the auxiliary antenna spacing optimisation are consistent with the theoretical analysis, verifying the effectiveness of the proposed model in dealing with uncertain communication frequency and interference direction. It is very promising in communication interference cancellation. [ABSTRACT FROM AUTHOR]

The periodical impulses caused by localized defects of components are the vital characteristic information for fault detection and diagnosis of rotating machines. In recent years, multitudinous spectrum analysis-based signal processing methods have been developed and authenticated as the powerful tools for excavating fault-related repetitive transients from the measured complex signals. Nonetheless, in practice, their applications can be severely confined by the constraints of limited system signal availability and incomplete information extraction under intricate noise interferences. To tackle the aforementioned issues, this paper proposes a periodic-modulation-oriented noise resistant correlation (PMONRC) method for target period detection and fault diagnosis of rotating machinery. Firstly, the envelope of raw signal is obtained via a novel sequential procedure of signal element-wise squaring, spectral Gini index-guided adaptive low-pass filtering, and signal element-wise square root computation, to highlight the modulated wave component that is more likely to be related to the potential fault-induced periods. Subsequently, a series of sub-signals, which can encode the fault-related repetitive information and enhance noise resistance, are constructed utilizing the envelope signal. Based upon the envelope signal and the obtained sub-signals, a weighted envelope noise resistant correlation function can be derived with the assistance of the L-moment ratio-based indicator and Sigmoid transformation. Finally, the specific fault type of the rotating machinery can be identified and affirmed accordingly. The proposed PMONRC method, which is nonparametric and completely adaptive to the signal being processed itself, overcomes the deficiencies of spectral analysis-based approaches, and is applicable for the engineering circumstances of system signal limitation and low signal-to-noise ratio (SNR), possessing immense practical merit. Both simulation analyses and experimental validations profoundly demonstrate that the proposed method is superior to other existing state-of-the-art time-domain correlation methods. Moreover, as an attempt as well as exemplar to apply this method, the PMONRC-based incipient fault diagnostic results of rolling bearing data from the well-known experimental platform PRONOSTIA are presented and discussed as well, to further elucidate the effectiveness and practical engineering significance of the proposed method. • A weighted envelope noise-resistant correlation approach coined PMONRC is developed. • An adaptive enveloping-demodulation method is proposed for AM-FM signature enhancing. • Fault information of sub-signals are appraised for more rational weights allocation. • The PMONRC is effective for fault-induced hidden periods detection and extraction. • The PMONRC is applicative for the scenarios of limited system signal availability. [ABSTRACT FROM AUTHOR]

Electrocardiogram is the most essential diagnostic test for heart disease detection in this era, where it has low frequency and small amplitude, making it vulnerable to a variety of stimuli, including high/low-frequency noises resulting in diagnostic quality suffers. Implementation of adaptive filter removes noise from the signal in a better way. Adaptive filters in signal processing plays a major role in biomedical applications for denoising different types of noises such as Power Line Interference noise which is generated by the power line electromagnetic field and it exhibits its peak from 50 to 60 Hz, Baseline Wander noise which occurs due to the variation of electrode skin impedance, Motion Artifacts which is generated by the electrode motions away from the contact zone on the skins and Muscle Noise, this is due to the Electromyographic signals, originating from the skeletal muscle contractions. In order to eliminate the above-mentioned noises, the most common adaptive filters such as Least Mean Square (LMS) and Recursive Least Squares (RLS) are used. In existing method, they used Cascaded Least Mean Square adaptive filters to reduce noises at a much higher rate than the Least Mean Square Adaptive filter and to achieve the higher Signal to Noise Ratio (SNR). The Cascaded RLS filter can be used to obtain faster range of convergence than the Cascaded LMS. The filter is designed with adaptive variable step size which calculates the step size according to the input signal of the filter, this is used to increase the stability of the filter. The systolic architecture and associativity of the VLLMS filter is implemented to reduce the number of adders and multipliers along the critical path of the filter. This work comprises of designing the 4 tap, 8 tap and 16 tap cascaded systolic associative variable leaky least mean square adaptive filters using Simulink and the SNR comparisons have been made between normal LMS cascaded filter and cascaded VLLMS filter. The area and delay between normal filter and systolic associative filter are compared by converting the Simulink design into Verilog and by implementing in Xilinx. [ABSTRACT FROM AUTHOR]

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Aiming at the problem of low quality of coherent demodulation transmission signal of digital signal in the background of Levy noise, this paper proposes a new method of 4FSK signal reception based on adaptive tristable stochastic resonance (SR) system. The bit error rate (BER) of output signals of adaptive tristable SR system model and the traditional modulation model are compared by simulation. The numerical simulation results show that compared with the traditional model, the output BER of the 4FSK signal after demodulation of the adaptive tristable SR system model is reduced by 4% compared with the traditional model. The time domain image of 4FSK signal is clearer, the burrs are significantly reduced and the spectral amplitude of 4FSK signal is greatly improved. The success of this experiment suggests a strong promise of SR systems for digital signal detection in impact noise. [ABSTRACT FROM AUTHOR]

This paper provides the parallel distributed Kalman filter (PDKF) with non-aligned time indexes, which uses four processing nodes to run the linear Kalman filter in parallel. To provide outputs that are in sync with the time indexes that are now in effect, each processing node aggregates prior time indexes utilizing non-aligned time indexes. This method increases system efficiency by enabling simultaneous data sharing among all nodes and parallel processing with earlier time indexes. Moreover, the consistent acknowledgment of each block's processing time by all nodes ensures smooth functioning. With the help of the suggested PDKF, computing-intensive processes can be carried out in a distributed fashion, improving computation efficiency without sacrificing estimation accuracy. Simulation results show that while achieving nearly equivalent convergence performance to the sequentially operated Kalman filter, the proposed PDKF architecture outperforms both the sequential recursive least squares (RLS) and parallel distributed adaptive signal processing RLS algorithms. This study highlights the algorithm's strong overall performance through a thorough discussion, revealing its capacity to lower computing costs without sacrificing estimation accuracy inside the network. The book delves deeper into the algorithm's overall performance, emphasizing its effectiveness beyond simple estimating accuracy. [ABSTRACT FROM AUTHOR]

Abstract The line of sight (LoS) probability is a key factor for the channel modeling of air‐to‐ground (A2G) communication. However, the existing LoS probability models do not account for the effects of airframe shadowing (AS) and building density, which can cause serious link obstruction and performance loss due to the six‐dimensional (6D) mobility and self‐body of unmanned aerial vehicle (UAV). In this article, a new LoS probability model is proposed that considers the AS and building density for different UAV heights. Adding to this, the AS is derived in terms of UAV framework and 6D mobility. Next, the machine learning (ML) based graph neural network (GNN) method is developed to learn the features and structure of the urban environment and predict the LoS probability. Then, the GNN model is trained and evaluated based on the ray tracing (RT) data to establish the relationship between model parameters and UAV heights under the building density and AS factors. The interpretation and explanation of the proposed GNN model and prediction are also discussed in this article. It is shown from the simulation analysis that the GNN model accurately captures the effects of AS, building height distributions, and UAV heights, with high accuracy compared to the baseline 3GPP, GCM and NYU models.

Abstract Space‐time adaptive processing (STAP) array has found extensive use in the global navigation satellite system for interference suppression. Nevertheless, the conventional space‐time array necessitates multiple antennas and time delay taps, resulting in a heavy burden on both hardware and computation resources. Thus, lowering the cost and complexity of STAP becomes a considerable problem demanding prompt solutions. This work designs a space‐time array reconfiguration scheme based on the joint selection strategy of the antenna and delay tap to address this problem. The reconstructed array can induce a high signal to interference plus noise power ratio (SINR) when using fewer antennas and delay taps. The space‐time correlation coefficient (STCC) is first presented to manifest the influence of reconfiguration on output SINR. Then, we formulate the space‐time array reconfiguration mathematical model, aiming to minimise STCC. Two reconfiguration methods are provided to solve the optimal array configuration of antennas and delay taps that can reach the minimum STCC and maximum SINR. The optimal antenna‐delay tap pairs are finally selected to comprise the reconstructed array. Substantial simulation experiments verify that the presented scheme and strategy are effective and reliable in space‐time array reconfiguration.

Abstract Rolling bearings are essential parts in machine equipment and detecting damage in the early stage is crucial for ensuring the safe production and machine life. However, it is difficult to extract weak fault features under strong background noise, discrete harmonic frequency interference and non‐stationary service conditions. This investigation proposes a hybrid fault diagnosis approach utilizing transient structure‐optimal variational mode decomposition (TS‐OVMD) and adaptive group sparse coding (AGSC) for addressing the formidable problem. According to the singular value structure between transient signal and the interference signal, this work investigates the singular value shrinkage (SVS) technique to adaptively obtain the independent components number. Then, we present a transient structure measure (TSM) to adaptively optimize the balance factor. This measure index systematically quantifies the typical characteristics of the bearing fault signal, which can maximize the fault information representation and effectively reduces the useful information loss caused by improper selection of VMD parameters. Finally, a sparse coding model called AGSC is furthermore designed to enhance the fault impulses readability and suppress residual noise based on the sparsity within group property and the TSM. The proposed approach is verified using experimental data and is found to be superiority comparison with the state‐of‐the‐art method.

Transmembrane proteins transmit chemical signals as well as mechanical cues. The latter is often achieved by coupling to the cytoskeleton. The incorporation of fully engineerable membrane‐spanning structures for the transduction of chemical and, in particular, mechanical signals is therefore a critical aim for bottom‐up synthetic biology. Here, a membrane‐spanning DNA origami signaling units (DOSUs) is designed and mechanically coupled to DNA cytoskeletons encapsulated within giant unilamellar vesicles (GUVs). The incorporation of the DOSUs into the GUV membranes is verified and clustering upon external stimulation is achieved. Dye‐influx assays reveal that clustering increases the insertion efficiency. The transmembrane‐spanning DOSUs act as pores to allow for the transport of single‐stranded DNA into the GUVs. This is employed to trigger the reconfiguration of DNA cytoskeletons within GUVs. In addition to chemical signaling, mechanical coupling of the DOSUs to the internal DNA cytoskeletons is induced. With chemical cues from the environment, clustering of the DOSUs is induced, which triggers a symmetry break in the organization of the DNA cytoskeleton inside of the GUV. DNA‐based transmembrane structures are engineered that transduce signals without transporting the signaling molecule itself—providing a route toward signal processing and adaptive synthetic cells. [ABSTRACT FROM AUTHOR]

A novel time-domain algorithm is proposed in this paper for the iterative estimation of drive files. A drive file is a synchronized batch of dynamic time series commands that are simultaneously sent to one or more actuators in a test rig that is designed for service environment replication (SER). When drive file commands are input to an SER test rig, the response of the article under test is similar to what was measured in a service environment. The proposed Pulse Train Filtered-X Least Mean Square (PT-Fx-LMS) algorithm is based on methods developed for active noise and vibration control (ANVC). A time-domain PT-Fx-LMS algorithm is shown through several simulation studies to rapidly converge to a dynamic solution in a small number of iterations for a one degree-of-freedom nonlinear suspension. The PT-Fx-LMS algorithm is also shown to enable targeted iteration over isolated time slices within the data set, which challenges conventional frequency-domain techniques. [ABSTRACT FROM AUTHOR]

In recent years, advances in materials engineering based on adaptive electronics have found a new paradigm to optimize drawbacks in signal processing. A two-layer MnO/ZnO:Zn heterostructure envisioned for frequency adaptive electronic signal processing is synthesized by sputtering, where the use of internal states allows reconfigurability to obtain new operating modes at different frequency input signals. X-ray diffraction (XRD) analysis is performed on each layer, revealing a cubic structure for MnO and a hexagonal structure for ZnO:Zn with preferential growth in [111] and [002] directions, respectively. Scanning electron microscope (SEM) micrographs show that the surface of both materials is homogeneous and smooth. The thickness for each layer is determined to be approximately 106.3 nm for MnO, 119.3 nm for ZnO:Zn and 224.1 nm for the MnO/ZnO:Zn structure. An electrical characterisation with an oscilloscope and signal generator was carried out to obtain the time-response signals and current-voltage (I–V) curves, where no degradation is detected when changing frequencies within the range of 100 Hz to 1 MHz. An equivalent circuit is proposed to explain the effects in the interface. Measurements of switching speeds from high resistance state (HRS) to low resistance state (LRS) at approximately 17 ns, highlight the device's rapid adaptability, and an estimated switching ratio of approximately 2 × 104 indicates its efficiency as a memristive component. Finally, the MnO/ZnO:Zn heterojunction delivers states that are stable, repeatable, and reproducible, demonstrating how the interaction of the materials can be utilised in adaptive device applications by applying frequencies and internal states to create new and innovative design schematics, thus reducing the number of components/connections in a system for future sustainable electronics. [ABSTRACT FROM AUTHOR]

Real-time DOA (direction of arrival) estimation of surface or underwater targets is of great significance to the research of marine environment and national security protection. When conducting real-time DOA estimation of underwater targets, it can be difficult to extract the prior characteristics of noise due to the complexity and variability of the marine environment. Therefore, the accuracy of target orientation in the absence of a known noise is significantly reduced, thereby presenting an additional challenge for the DOA estimation of the marine targets in real-time. Aiming at the problem of real-time DOA estimation of acoustic targets in complex environments, this paper applies the MEMS vector hydrophone with a small size and high sensitivity to sense the conditions of the ocean environment and change the structural parameters in the adaptive adjustments system itself to obtain the desired target signal, proposes a signal processing method when the prior characteristics of noise are unknown. Theoretical analysis and experimental verification show that the method can achieve accurate real-time DOA estimation of the target, achieve an error within 3.1° under the SNR (signal-to-noise ratio) of the X channel of −17 dB, and maintain a stable value when the SNR continues to decrease. The results show that this method has a very broad application prospect in the field of ocean monitoring. [ABSTRACT FROM AUTHOR]

This paper proposes a new secure distributed estimation for cyber‐physical systems against adversarial attacks with noisy input. To mitigate the effect of attacks, a novel distributed attack detection based on a reliable reference estimation obtained by temporal convex combination is proposed. Furthermore, for more effective and robust performance, an adaptation rule to adjust convex combination weights is presented, in which the generalized correntropy method with nonlinear loss function and stochastic gradient descent are utilized. Besides, to eliminate input noise, a bias‐compensation method in local adaptation of the secure distributed estimation is proposed. Simulations show superior dynamic and real‐time adaptability of the proposed algorithm under complex attacking scenarios. [ABSTRACT FROM AUTHOR]

Rolling bearings are essential parts in machine equipment and detecting damage in the early stage is crucial for ensuring the safe production and machine life. However, it is difficult to extract weak fault features under strong background noise, discrete harmonic frequency interference and non‐stationary service conditions. This investigation proposes a hybrid fault diagnosis approach utilizing transient structure‐optimal variational mode decomposition (TS‐OVMD) and adaptive group sparse coding (AGSC) for addressing the formidable problem. According to the singular value structure between transient signal and the interference signal, this work investigates the singular value shrinkage (SVS) technique to adaptively obtain the independent components number. Then, we present a transient structure measure (TSM) to adaptively optimize the balance factor. This measure index systematically quantifies the typical characteristics of the bearing fault signal, which can maximize the fault information representation and effectively reduces the useful information loss caused by improper selection of VMD parameters. Finally, a sparse coding model called AGSC is furthermore designed to enhance the fault impulses readability and suppress residual noise based on the sparsity within group property and the TSM. The proposed approach is verified using experimental data and is found to be superiority comparison with the state‐of‐the‐art method. [ABSTRACT FROM AUTHOR]

Space‐time adaptive processing (STAP) array has found extensive use in the global navigation satellite system for interference suppression. Nevertheless, the conventional space‐time array necessitates multiple antennas and time delay taps, resulting in a heavy burden on both hardware and computation resources. Thus, lowering the cost and complexity of STAP becomes a considerable problem demanding prompt solutions. This work designs a space‐time array reconfiguration scheme based on the joint selection strategy of the antenna and delay tap to address this problem. The reconstructed array can induce a high signal to interference plus noise power ratio (SINR) when using fewer antennas and delay taps. The space‐time correlation coefficient (STCC) is first presented to manifest the influence of reconfiguration on output SINR. Then, we formulate the space‐time array reconfiguration mathematical model, aiming to minimise STCC. Two reconfiguration methods are provided to solve the optimal array configuration of antennas and delay taps that can reach the minimum STCC and maximum SINR. The optimal antenna‐delay tap pairs are finally selected to comprise the reconstructed array. Substantial simulation experiments verify that the presented scheme and strategy are effective and reliable in space‐time array reconfiguration. [ABSTRACT FROM AUTHOR]

Abstract This article proposes a cooperative jamming (CJ) architecture based on the coded signal of interest (SOI). The CJ is superimposed on the coded SOI directly at the transmitter, and the authorized user reconstructs the received signal and cancels the CJ based on the prior knowledge of the coding information, then different radiation patterns after cancellation are created for SOI and CJ, which effectively cancels CJ while retaining SOI. Eavesdropper, however, is unaware of the coding information and will cause significant loss to the SOI while cancelling the CJ, therefore unable to achieve eavesdropping on communication signals. Numerical results verify the feasibility of the proposed method.

Abstract Deep learning‐based‐image denoising methods have recently achieved excellent performance by learning non‐linear mapping in the spatial domain. However, these methods fail to address the noise without specific distribution because they only use features of the spatial domain. Meanwhile, existing methods that utilize features in the frequency domain fail to combine the detailed information of both domains properly for effective reconstruction and demonstrate poor generalization. Therefore, a novel adaptive fusion dual‐domain network (AFDN) is introduced for single image restoration. Different from deep learning‐based methods, which operate on the spatial or dual‐domains in a certain order, the proposed AFDN combine the spatial‐domain image and corresponding frequency‐domain image as the input and use the interlacing dual‐domain module with flexible adaptability to learn the relationship between spatial and frequency domains. In experimental results, the AFDN is compared with several state‐of‐the‐art restoration methods. Quantitative results showed that the AFDN achieves enhanced effects and high index values. The code of this paper will be released at https://github.com/jzw0707/AFDN

This article proposes a cooperative jamming (CJ) architecture based on the coded signal of interest (SOI). The CJ is superimposed on the coded SOI directly at the transmitter, and the authorized user reconstructs the received signal and cancels the CJ based on the prior knowledge of the coding information, then different radiation patterns after cancellation are created for SOI and CJ, which effectively cancels CJ while retaining SOI. Eavesdropper, however, is unaware of the coding information and will cause significant loss to the SOI while cancelling the CJ, therefore unable to achieve eavesdropping on communication signals. Numerical results verify the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Abstract This paper presents an investigation into the theoretical performance of diffusion adaptive networks within a distributed beam coordination application in a cell‐free communication network that utilizes coarsely quantized data. This analysis assumed data pulses with 1, 2, and 3 bits, and based on this assumption, simulations and theoretical analyses of distributed beamforming are conducted. The consistency between the simulation results and theoretical findings validated the accuracy of the calculations. Additionally, the distributed quantization aware least mean square (DQA‐LMS) algorithm was introduced for distributed beam coordination, demonstrating its superior performance in scenarios involving coarsely quantized data. Convergence conditions were provided and the results through various simulations were presented, including the array network mean square deviation (MSD), mean square error (MSE), and beamforming diagrams.

Abstract Due to the controllable degrees of freedom of frequency diverse array in the distance dimension, the frequency diverse multiple‐input multiple‐output (FDA‐MIMO) radar can easily deal with mainlobe interference, which is often difficult to solve in traditional phased arrays. Nevertheless, the performance of FDA‐MIMO radar will suffer from degradation in the scenario where spectrum interferences and signal‐dependent interferences coexist. In order to solve this problem, a novel FDA‐MIMO radar framework via antenna switching is proposed. Based on this framework, an effective method for jointly optimising antenna selection and beamforming against spectrum interferences and signal‐dependent interferences is developed. The resulting optimization problem is nonconvex and NP‐hard owing to the integer constraints caused by antenna selection. By relaxing the integer constraints, the original problem can be transformed into a convex optimization form and an iterative reweighting strategy is used to force the obtained solution to satisfy the integer constraints. Simulation examples show that the proposed algorithm has better performance in output signal‐to‐interference‐plus‐noise ratio (SINR) and beampattern radiation performance than the existing competitive methods.

Deep learning‐based‐image denoising methods have recently achieved excellent performance by learning non‐linear mapping in the spatial domain. However, these methods fail to address the noise without specific distribution because they only use features of the spatial domain. Meanwhile, existing methods that utilize features in the frequency domain fail to combine the detailed information of both domains properly for effective reconstruction and demonstrate poor generalization. Therefore, a novel adaptive fusion dual‐domain network (AFDN) is introduced for single image restoration. Different from deep learning‐based methods, which operate on the spatial or dual‐domains in a certain order, the proposed AFDN combine the spatial‐domain image and corresponding frequency‐domain image as the input and use the interlacing dual‐domain module with flexible adaptability to learn the relationship between spatial and frequency domains. In experimental results, the AFDN is compared with several state‐of‐the‐art restoration methods. Quantitative results showed that the AFDN achieves enhanced effects and high index values. The code of this paper will be released at https://github.com/jzw0707/AFDN [ABSTRACT FROM AUTHOR]

Gesture recognition technology has been quickly developed in the field of human–computer interaction. The multiple-input multiple-output (MIMO) radar is popular in gesture recognition because of its notable spatial resolution. This work proposes a MIMO radar-based hand gesture recognition algorithm with low complexity. We leverage low-complexity adaptive signal processing to extract trajectory information and minimize noise to create a system that can be applied in real-world applications with small training datasets. First, a spectrum analysis is utilized on range-Doppler maps (RDMs), and a cell-averaging constant false alarm rate (CA-CFAR) with mirror filters is applied to improve the robustness of noise. Then, the features related to the distance, speed, direction, and elevation angle of the moving object are determined using the proposed adaptive signal analysis techniques. For classification, the random forest algorithm is implemented. The proposed system can precisely distinguish and identify eight gestures, including waving, moving to the left or right, patting, pushing, pulling, and rotating clockwise or anti-clockwise, with an accuracy of 95%. Experiments demonstrate the capability of the proposed hand gesture recognition system to classify different movements precisely. [ABSTRACT FROM AUTHOR]

Light‐field (LF) images offer the potential to improve feature capture in live scenes from multiple perspectives, and also generate additional normal vectors for performing super‐resolution (SR) image processing. With the benefit of machine learning, established AI‐based deep CNN models for LF image SR often individualize the models for various resolutions. However, the rigidity of these approaches for actual LF applications stems from the considerable diversity in angular resolution among LF instruments. Therefore, an advanced neural network proposal is required to utilize a CNN‐based model for super‐resolving LF images with different resolutions obtained from provided features. In this work, a preprocessing to calculate the depth channel from given LF information is first presented, and then a multiple‐decouple and fusion module is introduced to integrate the VGGreNet for the LF image SR, which collects global‐to‐local information according to the CNN kernel size and dynamically constructs each view through a global view module. Besides, the generated features are transformed to a uniform space to perform final fusion, achieving global alignment for precise extraction of angular information. Experimental results show that the proposed method can handle benchmark LF datasets with various angular and different resolutions, reporting the effectiveness and potential performance of the method. [ABSTRACT FROM AUTHOR]

The article presents a dual‐optimized adaptive algorithm based on continuous wavelet transform and derivative precise peak capture is developed for fiber cavity ring‐down spectroscopy. Adaptive spectral signal processing is achieved so that peak selection blindness can be effectively solved. To verify the performance of the dual‐optimized adaptive algorithm, a highly sensitive refractive index sensor using a bandgap fiber‐based linear fiber cavity is demonstrated. The developed dual‐optimized adaptive algorithm is compared with the conventional maximum extraction algorithm, and the results show that the dual‐optimized adaptive algorithm has a better performance in suppressing noise and processing superimposed peaks. A stable enhancement factor of 5 is obtained, indicating that the newly developed dual‐optimized adaptive algorithm can generate a high‐quality cavity ring‐down spectrum for applications including refractive index sensors, biosensors, strain gauges, and pressure sensors. [ABSTRACT FROM AUTHOR]

This paper presents a review of publications on methods for estimating the dynamic measurement error and methods for its correction. The error components are due to the dynamic properties (inertia) of the sensor and the additive noise at its output. A method is proposed for estimating and reducing the dynamic measurement error based on the principle of adaptive linear prediction or adaptive linear signal amplification. This approach generates an error estimation signal based on the result of comparing a delayed copy of the recovered signal with the same reconstructed signal passed through an adaptive nonrecursive filter with a linear phase characteristic. Based on this approach, a measurement system structure has been developed to estimate the dynamic measurement error and reduce it by correcting the dynamic characteristics of the sensor and adaptive filtering of measurement noise. A computer simulation of the proposed measurement system for a second-order sensor was performed. Optimal (in the sense of the mean squared deviation of the error) orders of the restoring adaptive filter were obtained in the presence of additive harmonic noise with variable frequency at the sensor output. The properties of the proposed measurement system structure with a dynamic measurement error estimator adaptive to the noise parameter are determined. The application field of the results obtained is the processing of measurement results of rapidly varying processes (including in real time), when the component of the dynamic measurement error caused by the dynamic properties (inertia) of the sensor, as well as the additive noise at its output, is dominant. The solution to such a problem is relevant, for example, when processing the results of ground tests of space technology. [ABSTRACT FROM AUTHOR]

The development of modern technologies has fundamentally transformed the field of radar signal and data processing. With the use of advanced algorithms and computational power, radars are now capable of extracting crucial information from received signals, facilitating improved target identification and tracking. This article presents some of the advanced technologies employed in radar signal and data processing and their impact on adaptability of radar systems. It traces the evolution of radar technology from old systems to the present, emphasizing the benefits of adaptive radar signal processing, which includes algorithms such as adaptive beamforming, Space-Time Adaptive Processing, and the integration of Machine Learning and Artificial Intelligence. In conclusion, challenges, and future prospects in the field of radar systems are discussed, with a focus on the potential integration of Artificial Intelligence methods, Cognitive radars, and Multiple Input Multiple Output technologies. Despite technical obstacles, opportunities emerge to enhance the performance of radar systems and achieve new levels of efficiency. [ABSTRACT FROM AUTHOR]

This paper presents an investigation into the theoretical performance of diffusion adaptive networks within a distributed beam coordination application in a cell‐free communication network that utilizes coarsely quantized data. This analysis assumed data pulses with 1, 2, and 3 bits, and based on this assumption, simulations and theoretical analyses of distributed beamforming are conducted. The consistency between the simulation results and theoretical findings validated the accuracy of the calculations. Additionally, the distributed quantization aware least mean square (DQA‐LMS) algorithm was introduced for distributed beam coordination, demonstrating its superior performance in scenarios involving coarsely quantized data. Convergence conditions were provided and the results through various simulations were presented, including the array network mean square deviation (MSD), mean square error (MSE), and beamforming diagrams. [ABSTRACT FROM AUTHOR]

Due to the controllable degrees of freedom of frequency diverse array in the distance dimension, the frequency diverse multiple‐input multiple‐output (FDA‐MIMO) radar can easily deal with mainlobe interference, which is often difficult to solve in traditional phased arrays. Nevertheless, the performance of FDA‐MIMO radar will suffer from degradation in the scenario where spectrum interferences and signal‐dependent interferences coexist. In order to solve this problem, a novel FDA‐MIMO radar framework via antenna switching is proposed. Based on this framework, an effective method for jointly optimising antenna selection and beamforming against spectrum interferences and signal‐dependent interferences is developed. The resulting optimization problem is nonconvex and NP‐hard owing to the integer constraints caused by antenna selection. By relaxing the integer constraints, the original problem can be transformed into a convex optimization form and an iterative reweighting strategy is used to force the obtained solution to satisfy the integer constraints. Simulation examples show that the proposed algorithm has better performance in output signal‐to‐interference‐plus‐noise ratio (SINR) and beampattern radiation performance than the existing competitive methods. [ABSTRACT FROM AUTHOR]

We propose and demonstrate a hybrid communication architecture that combines millimeter-wave (MMW) in the radio frequency (RF) domain and free-space-optics (FSO) technologies using adaptive combining and hybrid automatic repeat request (HARQ) techniques. At the receiving end, we employed joint signal processing with an adaptive diversity combining technique (ADCT) based on a maximum ratio combining (MRC) algorithm. We derived closed-form expressions for the outage probability and throughput of the hybrid RF and FSO (RF/FSO) system, considering various characteristics of atmospheric turbulence in the FSO link. Experimental testing with 10-Gbaud quadrature phase shift keying (QPSK) data was conducted under different simulated atmospheric turbulence intensities, FSO and MMW speed-ratios, and forward error correction (FEC) overheads. Additionally, we validated improvements in terms of bit error ratio (BER), outage probability, and throughput performance. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a new secure distributed estimation for cyber‐physical systems against adversarial attacks with noisy input. To mitigate the effect of attacks, a novel distributed attack detection based on a reliable reference estimation obtained by temporal convex combination is proposed. Furthermore, for more effective and robust performance, an adaptation rule to adjust convex combination weights is presented, in which the generalized correntropy method with nonlinear loss function and stochastic gradient descent are utilized. Besides, to eliminate input noise, a bias‐compensation method in local adaptation of the secure distributed estimation is proposed. Simulations show superior dynamic and real‐time adaptability of the proposed algorithm under complex attacking scenarios.

Abstract The jamming effect on radar is dominated by the design of the jamming waveform directly. Traditional jamming waveforms are generated using the template‐based method and are not environmentally resilient. Without the precise radar centre frequency support, using the direct guidance of the radar signal power spectrum density (PSD), a novel jamming waveform design method with an online unsupervised generative learning framework is proposed. The proposed framework consists of two modules: the waveform online generation module and the waveform unsupervised optimisation module. For the waveform online generation module, the well‐designed neural network (NN) is used to generate the jamming waveform. While for the waveform unsupervised iterative optimisation module, two loss functions are developed as two learning tasks to guide back‐propagation online. Given transmit power, the jamming PSD is designed adaptively to focus on the radar band and fit the radar PSD well, which improves the receiver in‐band jamming‐to‐signal ratio (JSR). It is worth noting that NN is end‐to‐end trained online without extensive prior training data. The framework can generate jamming waveforms within reasonable physical parameter ranges, and numerical experiments have shown that the designed jamming waveform performed better jamming effects on radar target detection in comparison with the traditional one.

Abstract Non‐sidelooking airborne radar encounters significant non‐stationary and heterogeneous clutter environments, resulting in a severe shortage of samples. Sparse recovery‐based space‐time adaptive processing (SR‐STAP) methods can achieve good clutter suppression performance with limited samples. Nonetheless, grid‐based SR‐STAP algorithms encounter off‐grid effects in non‐sidelooking arrays, which can severely degrade the clutter suppression performance. In this study, the authors propose a novel gridless SR‐STAP method in the continuous spatial‐temporal domain to address the issue of off‐grid effects. Inspired by the fact that sparse Bayesian learning (SBL) framework implicitly performs a structured covariance matrix estimation, the authors reparameterise its cost function to directly estimate the block‐Toeplitz structured matrix from the measurements in a gridless manner. Since the proposed cost function is non‐convex, we utilise a majorisation‐minimisation‐based iterative procedure to estimate the clutter covariance matrix. Finally, using the standard concept of semidefinite programming, the authors derive a convex gridless implementation of the SBL cost function for uniformly sampled radar systems. Extensive simulation experiments demonstrate the exceptional clutter suppression and target detection performance of the proposed algorithm.

Abstract This article deals with the source localisation problem when the receiver is moving over time, and the sources are sparse across the area. In addition, only one of the sources is active at each time instant, means that the sources are pulse transmitters. The problem was first formulated such as a dictionary learning problem for sparse representation. Then, an alternating minimisation approach to estimate the position of the sources and their signals iteratively is proposed. Simulation results in different scenarios demonstrate the effectiveness of the proposed method relative to state‐of‐the‐art methods.

Abstract The trajectory prediction of the hypersonic glide vehicle (HGV) can provide hit point information for early warning systems, which is of great significance for near‐space defence operations. However, the heavy‐tailed noise caused by abnormal environmental disturbance and abrupt changes in vehicle trajectory seriously affects the accuracy of HGV trajectory prediction. To solve the problem of trajectory prediction for HGV under heavy‐tailed noise, we propose an adaptive multivariate Student's t‐process regression method called aEM‐MVTPR. Firstly, a heavy‐tailed noise model is developed using the Student's t‐distribution. Secondly, a multivariate Student's t‐process regression (MVTPR) for HGV trajectory prediction is derived, and the method can learn the HGV trajectory time series features and mine the correlations among the trajectory variables. Finally, to further improve the method's robustness, we use the accelerated expectation maximisation algorithm and Pearson correlation analysis to adaptively estimate and adjust the initial values of the MVTPR parameters. The simulation experimental results show that the proposed method has more accurate predicted values of trajectory variables than multivariate Gaussian process regression (MVGPR) under heavy‐tailed noise conditions. In addition, the ability of the aEM‐MVTPR to adaptively adjust the parameter makes it more robust than the MVTPR under different noise environments.

Abstract Visibility graph methods allow time series to mine non‐Euclidean spatial features of sequences by using graph neural network algorithms. Unlike the traditional fixed‐rule‐based univariate time series visibility graph methods, a symmetric adaptive visibility graph method is proposed using orthogonal signals, a method applicable to in‐phase and quadrature (I/Q) orthogonal signals for adaptive graph mapping for radio modulated signals in automatic modulation classification tasks. The method directly models the intra‐channel and inter‐channel graph relations of I/Q signals using two different types of convolutional kernels. It captures non‐Euclidean spatial feature information of I/Q signals using a graph neural network combining graph sampling aggregation and graph differentiable pooling as a feature extractor. Extensive experimental results on two benchmark datasets and a simulated dataset containing channel fading show that the proposed Quadrature Signal Symmetric Adaptive Visibility Graph (QSSAVG) method in this paper outperforms the benchmark method in terms of classification accuracy and is also more robust against channel fading and noise variations.

Abstract Light‐field (LF) images offer the potential to improve feature capture in live scenes from multiple perspectives, and also generate additional normal vectors for performing super‐resolution (SR) image processing. With the benefit of machine learning, established AI‐based deep CNN models for LF image SR often individualize the models for various resolutions. However, the rigidity of these approaches for actual LF applications stems from the considerable diversity in angular resolution among LF instruments. Therefore, an advanced neural network proposal is required to utilize a CNN‐based model for super‐resolving LF images with different resolutions obtained from provided features. In this work, a preprocessing to calculate the depth channel from given LF information is first presented, and then a multiple‐decouple and fusion module is introduced to integrate the VGGreNet for the LF image SR, which collects global‐to‐local information according to the CNN kernel size and dynamically constructs each view through a global view module. Besides, the generated features are transformed to a uniform space to perform final fusion, achieving global alignment for precise extraction of angular information. Experimental results show that the proposed method can handle benchmark LF datasets with various angular and different resolutions, reporting the effectiveness and potential performance of the method.

Massive MIMO (multiple-input multiple-output) is a multi-user MIMO technology that can provide high-speed multimedia services in 5G wireless networks using sub-6 GHz and millimeter wave bands. The massive MIMO (MMIMO) installs array antennas in the base stations, using hundreds of transceivers with other RF modules. One of the drawbacks of the MMIMO system is its huge power consumption, and the beamforming network with RF modules for a large number of antennas is the main contributor to the power consumption. In this paper, a novel beamforming method is proposed for the low power consumption of an MMIMO system. The proposed thinned smart antenna (TSA) of a semi-circular array produces a secure beam toward the user’s terminal with reduced interference. By thinning the antenna array, some of the antenna elements are kept off, resulting in less power consumption, while the array pattern remains the same as a fully populated array with a reduced side lobe level (SLL). The sub-6 GHz band of 5 GHz is used for the design of thinned array antennas. The genetic algorithm (GA) is used to determine the array sequence in thinning, and the adaptive signal processing algorithms least mean square (LMS), recursive least square (RLS), and sample matrix inversion (SMI) are used for the beamforming of the TSA, and the corresponding algorithms are GA-LMS, GA-RLS, and GA-SMI. The power saving of 40% to 55% is achieved using TSA. The maximum SLL reductions of 13 dB, 12 dB, and 14 dB are achieved for TSA using GA-LMS, GA-RLS, and GA-SMI algorithms, respectively.

Amidst the evolving landscape of non-cooperative communication, automatic modulation classification (AMC) stands as an essential pillar, enabling adaptive and reliable signal processing. Due to the advancement of deep learning (DL) technology, neural networks have found application in AMC. However, the previous DL models face the inter-class confusion problem in high-order modulations. To address this issue, we propose a multitask-learning-empowered hybrid neural network, named CrossTLNet. Specifically, after the signal enters the model, it is first transformed into two task components: in-phase/quadrature (I/Q) form and amplitude/phase (A/P) form. For each task, we design a method that combines a temporal convolutional network (TCN) with a long short-term memory (LSTM) network to effectively capture long-term dependency features in high-order modulations. To enable interaction between these two different dimensional features, we innovatively introduce a cross-attention method, thereby further enhancing the model's ability to distinguish signal features. Moreover, we also design a simple and efficient knowledge distillation method to reduce the size of CrossTLNet, making it easier to deploy in real-time or resource-limited scenarios. The experimental results indicate that the suggested method exhibits exceptional performance in AMC on public benchmarks, especially in high-order modulations. [ABSTRACT FROM AUTHOR]

This paper considers problems related to output error models for output data estimation and parameter identification in missing output data systems. In this regard, a new sequentially parallel distributed adaptive signal processing method with the implementation of a low complexity least squares algorithm is introduced to estimate the parameters of an auxiliary model relative to the original system, as well as handling irregularly missing output data in a stochastic framework. The validation of the proposed distributed architecture using the low complexity least squares algorithm is presented in terms of computational complexity and processing time. Measurement results show that the proposed architecture using the low complexity least squares approach provides fast convergence, parallel linear computational complexity, and significantly reduced processing time compared to the sequentially operated recursive least square (RLS) algorithm for parameter identification in missing output data systems. Finally, the effectiveness of the low complexity least squares algorithm is tested with a system example [ABSTRACT FROM AUTHOR]