Microwave quantum illumination: enhanced azimuth detection with cavity magnonics.

Quantum illumination can utilize entangled light to detect the low-reflectivity target that is hidden in a bright thermal background. This technique is applied to the detection of an object in the curved spacetime of the Earth, in order to explore how the curvature of spacetime affects quantum illumination. It is found that the spatial quantum illumination with entangled state transmitter outperforms that with coherent-state transmitter in the near-Earth curved spacetime. Moreover, either the quantum illumination system or the coherent-state system is employed, and gravity can enhance the spacetime target detection by reducing the thermal signal at the receiver. Besides, our model in principle can be applied to microwave quantum illumination and thus provides, to some degree, a theoretical foundation for the upcoming spatial quantum radar technologies.

Quantum illumination is one of the most basic ways to realize the quantum radar for detection of low-reflectivity targets embedded in a thermal-noise bath. It preserves a strong advantage over the classical counterparts even though its fragile quantum entanglement is completely destroyed by a large amount of noise and losses. In this paper, we describe and analyze a discrete-variable quantum illumination system with ancillary degrees of freedom of entangled photon pairs. Comparing the error probability bound, i.e., the quantum Chernoff bound, we identify that this system indeed offers higher accuracy than the single-photon illumination and quantum illumination with single degree of freedom of entangled photon pairs. Furthermore, the utilization of ancillary degrees of freedom exponentially enhances signal-to-noise ratio over quantum illumination without ancillary degrees of freedom. It means that this system reduces the necessary amount of signal photons to accurately determine whether the object is present or absent in the target region, which is absolutely crucial to the stealthiness of the radar system.

Nonreciprocal double-carrier frequency combs in cavity magnonics

Unconventional magnon and photon blockade have attracted significant research interest for their ability to generate single‐particle sources in hybrid quantum systems, particularly in cavity magnonics. In this work, a scheme is presented to investigate the effect of Barnett effects in the nonreciprocal blocked of magnons and photons within a spinning microwave magnomechanical system with a squeezed input drive of the magnonic mode. The effects of thermal noise, the amplitude of a probe field, and the magnetic‐dipole coupling strength are realized using the weak‐coupling regime. Using the Mandel parameter, the nonclassicality of the system is discussed. Additionally, the time evolution of the second‐order correlation function is examined.

With the rapid development of the low-altitude economy and smart city construction, the demand for small target detection has been growing significantly. This paper proposes a deep learning model, MAE-ResTransFPN, for radar target detection tasks in clutter-rich environments. First, real-world measured data is acquired using holographic staring radar, and the Range-Doppler plane is used as the input to the neural network to complete dataset preparation and annotation. Second, the proposed MAE-ResTransFPN model combines the pre-training capabilities of the masked autoencoder (MAE), the feature extraction capabilities of ResNet50, and the multi-scale feature fusion capabilities of Transformer-FPN, aiming to improve the accuracy and robustness of radar target detection in complex environments. Experimental results show that the proposed model achieves 78.2% in precision, 71.4% in recall, and 73.4% in mAP50, demonstrating its effectiveness in radar target detection tasks.

Microwave amplification in a [formula omitted]-symmetric-like cavity magnomechanical system

Nonreciprocal electromagnetic wave transmission is essential for wireless communication, quantum computing, and radar systems, traditionally relying on breaking time-reversal symmetry through static magnetic fields or structural modifications, which face limitations in tunability and integration. Recent advancements in cavity magnonics, particularly the use of bound states in the continuum (BIC) and pump-induced magnon mode (PIM), have enhanced the nonreciprocal isolation and dynamic control of magnon dynamics. In this study, a novel method to achieve broadband-tunable microwave nonreciprocal isolation is presented by introducing multiple modulated pump signals, thereby extending traditional single-mode magnon-based nonreciprocal transmission to multi-channel and broadband regimes. The core method involves exciting multiple PIMs in a cavity magnonics system and strongly coupling them with BIC to generate hybrid modes with pronounced nonreciprocal characteristics. The experimental setup is comprised of a 1-millimeter-diameter yttrium iron garnet (YIG) sphere positioned at the node of a microwave resonator (central frequency: 2.92 GHz), with pump signals injected through a microwave patch antenna. By dynamically tuning the frequency, power, and number of pump signals, the precise control over the number of nonreciprocal isolation channels and their spectral positions is realized. Notably, the continuous tuning of the nonreciprocal bandwidth is achieved by increasing the number of pump signals from 2 to 5, expanding the isolation bandwidth from 6 MHz to 14 MHz. Furthermore, by tailoring the spectral distribution of pump signals, the system realizes flexible switching between bandpass and band-stop isolation states. Importantly, this method eliminates the need of static magnetic field adjustments or structural reconfiguration, relying solely on coherent microwave-photon interactions to modulate PIM-BIC coupling. Experimental results highlight two key physical outcomes: 1) Extending conventional single-mode magnonic nonreciprocal transmission to multi-channel and broadband-tunable regimes; 2) achieving microwave nonreciprocal control without the need of static magnetic field adjustments or structural reconfiguration. These advances establish a robust platform for designing reconfigurable multi-channel isolators and circulators, which can be directly applied to microwave communication systems, quantum information processing, and radar technologies.

In this paper, we propose a dimension apart network (DANet) for radar object detection task. A Dimension Apart Module (DAM) is first designed to be lightweight and capable of extracting temporal-spatial information from the RAMap sequences. To fully utilize the hierarchical features from the RAMaps, we propose a multi-scale U-Net style network architecture termed DANet. Extensive experiments demonstrate that our proposed DANet achieves superior performance on the radar detection task at much less computational cost, compared to previous pioneer works. In addition to the proposed novel network, we also utilize a vast amount of data augmentation techniques. To further improve the robustness of our model, we ensemble the predicted results from a bunch of lightweight DANet variants. Finally, we achieve 82.2% on average precision and 90% on average recall of object detection performance and rank at 1st place in the ROD2021 radar detection challenge. Our code is available at: \urlhttps://github.com/jb892/ROD2021_Radar_Detection_Challenge_Baidu.

Software was developed in Python for the digital processing of object images before their detection using the You Only Look Once (YOLO) artificial neural network. Detection was carried out on object images, specifically vehicles and other road users. The preprocessing of images was performed through histogram equalization and local contrast enhancement. The original images were read from video cameras or graphic files. Image detection was performed using the medium-sized YOLO convolutional neural network model, YOLOv8m. As a result of detection, the studied objects, particularly cars, were highlighted with rectangular bounding boxes. To evaluate the accuracy of object detection, several metrics (parameters) were used: Precision, Recall, Intersection over Union (IoU), F1, and Average Precision (AP). The YOLO network was pre-trained on the Common Objects in Context dataset. The YOLO neural network returns the object recognition confidence, the coordinates of its center (x, y) on the image, and its width and height (w, h). The detected objects are marked with rectangular bounding boxes. The positioning of objects in the images was manually performed using the Roboflow tool, which allows for accurate spatial positioning of each object. The study of the impact of image preprocessing on the accuracy of object detection by the YOLO network was conducted using a typical traffic scene image. After histogram equalization, the histogram becomes more symmetrical and uniform, improving the visual quality of both dark and bright areas. This led to improved object detection accuracy across almost all metrics. The increase in detection accuracy by the IoU parameter is 0.169. The summary improvement in detection accuracy by the IoU, F1, AP parameters is 0.502. It was shown that after image preprocessing, detection accuracy increased, and all vehicles were correctly detected. The developed software can be used in various computer vision systems as well as in Internet of Things systems for remote monitoring and control.

Quantum illumination uses entangled light that consists of signal and idler modes to achieve higher detection rate of a low-reflective object in noisy environments. The best performance of quantum illumination can be achieved by measuring the returned signal mode together with the idler mode. Thus, it is necessary to prepare a quantum memory that can keep the idler mode ideal. To send a signal towards a long-distance target, entangled light in the microwave regime is used. There was a recent demonstration of a microwave quantum memory using microwave cavities coupled with a transmon qubit. We propose an ordering of bosonic operators to efficiently compute the Schrieffer–Wolff transformation generator to analyze the quantum memory. Our proposed method is applicable to a wide class of systems described by bosonic operators whose interaction part represents a definite number of transfer in quanta.

In this study, we propose an algorithm that enables the active learning of a manipulation skill that is robust to the estimation error of an object's position. First, we propose the concept of a feasible error region (FER) unique to each manipulation parameter. As the name implies, FER is a region of estimation errors even with which the manipulation still succeeds. We introduce the volume of FER as a robustness measure for manipulation parameters. Then, we develop an active learning algorithm capable of simultaneously realizing the volume maximization and estimating the FER corresponding to the optimal parameter. The obtained FER can be directly used to assess the success probability of the manipulation. As well as numerical experiments using toy models, we apply the algorithm to a robotic task in the physics simulator and two real world tasks. We demonstrate the validity of our approach, and discuss its limitations.

We present a proposal to produce bipartite and tripartite entanglement in a hybrid magnon-cavity QED system. Two macroscopic yttrium iron garnet (YIG) spheres are coupled to a single-mode microwave cavity via magnetic dipole interaction, while the cavity photons are generated via the two photon process induced by a pump field. Using the mean field theory, we show that the second order nonlinearity can result in strong bipartite entanglement between cavity photons and magnonic modes under two conditions, i.e., $\delta_c \delta_{m}=2g^2$ and $\delta_c=-\delta_{m}$. For the later one, we also show the possibility for producing the bipartite entanglement between two magnonic modes and tripartite entanglement among the cavity photons and two magnonic modes. Combining these two conditions, we further derive a third condition, i.e., $\delta_m^2-\phi^2+2g^2=0$, where the tripartite entanglement can be achieved when two magnonic modes have different resonant frequencies.

Detection and recognition of LPI radar signals using visibility graphs

Salient object detection in a real-time environment demands high accuracy with less computation time. It is a changeling task to investigate a saliency model which improves saliency detection accuracy as well as reduces computation time. In this paper, we propose a hybrid model that improves detection accuracy with low computational time. The input image is simplified and clustered at multiple scales using SLIC and K-means clustering algorithms respectively. Gaussian Mixture Model (GMM) is developed on various color components of the digital image at multiple scales. The parameters of GMM are learnt using Expectation Maximization (EM) algorithm. The spatial variance of each color component is determined using GMM parameters and hence object position is estimated. Further, spatial variance of color components and object position is exploited to compute saliency map at a scale level. Afterwards, all the saliency maps generated across various scales are linearly combined to produce the final saliency map. The performance of the proposed model is compared in terms of Precision, Recall, F-Measure, Area under the Curve (AUC), Receiver Operating Characteristics (ROC) and Mean Absolute Error (MAE). Extensive experiments on six publicly available datasets viz. MSRA10K, DUT-OMRON, ECSSD, PASCAL-S, SED2, and THUR15K show that the proposed model outperforms or comparable against 11 state-of-the-art methods of the last decade. The key features of the proposed method are object completeness and efficiency in terms of computational time.

Ship detection in synthetic aperture radar (SAR) images is crucial for maritime security and operations. However, existing methods face significant challenges when dealing with complex backgrounds and small ships. Ship targets are often misclassified as background clutter, and small ships are particularly susceptible to noise interference. To address these challenges, this paper proposes FFC-YOLO, an enhanced ship detection network based on YOLOv8. FFC-YOLO incorporates several novel modules to improve detection accuracy. Firstly, the Feature Attention Aggregation Module (FAAM) employs multi-scale fusion and Context Anchor Attention (CAA) to enhance detection in complex backgrounds by extracting features from different scales and integrating contextual information. Secondly, the Feature Augmentation Unit (FAU) enhances small ship detection accuracy by employing an improved deformable convolution (A-Defconv) for shape adaptation and an adaptive weighted fusion mechanism to refine feature extraction. Additionally, the Cross-Scale Dynamic Fusion Module (CSDF) dynamically integrates features from different scales, addressing information conflicts that arise when handling multi-scale ship targets, further enhancing detection accuracy. Finally, a novel bounding box loss function, CNW-loss, optimizes the overall network performance. Evaluated on the SSDD and HRSID datasets, FFC-YOLO achieves a significant performance advantage over existing methods, with mAP0.5 scores of 98.5% and 89.7%, respectively. The proposed FFC-YOLO network effectively enhances ship detection accuracy in SAR images, making it highly applicable to various maritime safety and operations, and providing valuable insights for a broader range of measurement applications.