Change detection (CD) aims to identify and locate changes in the same observed surface coverage area across bitemporal images. This technique has widespread applications in urban planning, land use, and disaster damage extraction. Deep learning-based CD methods typically use learnable encoders to map bitemporal images to a common domain distribution space, allowing for the discrimination and localization of change and invariant features. However, due to differences in imaging mechanisms, seasons, and shooting angles, a large number of pseudo-changes may easily appear, affecting the accurate recognition of the domain distribution space. Additionally, binary CD focuses solely on whether scene targets have changed, resulting in change labels that encompass a variety of different objects, thus increasing the significance of intra-class differences. To address the aforementioned issues, we propose a domain distribution reconstruction learning (DDRL) framework for binary CD, which effectively mitigates the problem of pseudo-changes by detecting abnormal feature domain distributions. Specifically, DDRL first extracts multi-scale features from bitemporal images using a Siamese cross-window self-attention module, achieving feature domain transformation from the original space. Subsequently, it employs a graph attention enhanced module to improve the low-level domain distribution, enabling it to focus on change regions. Additionally, DDRL utilizes a cross-domain feature contrastive learning module for reconstructive learning of high-level fused features. This process ensures that intra-class features are compact while inter-class features are dispersed within the high-level domain distribution, thereby significantly improving the domain distribution representation to discriminate pseudo-changes. Experimental results show that the proposed DDRL performs excellently across multiple public datasets, surpassing mainstream methods and significantly improving CD performance. The source code will be made available at https://github.com/yzygit1230/DDRL.

In a beamforming circuit for a modern broadband phased-array system, high accuracy and compactness have received sufficient attention as they are directly related to side lobe level and fabrication cost, respectively. In order to meet the low phase error required, this paper proposed an ultra-broadband 6-bit digital step switched-type attenuator (STA) with capacitive/inductive compensation networks. Compared to the conventional methods, the proposed technique employs an improved simplified T-structure with capacitive compensation networks, which simultaneously achieves low insertion loss and high-accuracy amplitude/phase control. In addition, on-chip level shifting circuit is integrated to avoid complex control schemes. The strategy of prioritizing return loss is adopted to alleviate the performance degradation caused by impedance mismatch after cascade. As a proof-of-principle demonstration, a wideband 6-bit STA with core area of only 0.5 mm × 1.8 mm was designed via 0.15-micrometer GaAs pHEMT technology. It exhibits ultra-broadband operation with a 31.5 dB amplitude tuning range and a 0.5 dB tuning step. The insertion loss of the reference state is 4–5.3 dB. The return loss is better than 15 dB for all the 64 states. The RMS amplitude and phase errors are less than 0.2 dB and 2° over the 10 to 20 GHz.

Generalized broadband operation facilitates multifunction or multiband highly integrated applications, such as modern transceiver systems, where ultra-wideband bidirectional passive mixers are favored to avoid a complex up/down-conversion scheme. In this paper, a modified Ruthroff-type transmission line transformer (TLT) balun is presented to enhance the isolation of the mixer from the local oscillator (LO) to the radio frequency (RF). Compared to the conventional methods, the proposed Ruthroff-type architecture adopts a combination of shunt capacitors and parallel coupled lines to improve the return loss at the LO port, thus effectively avoiding the area consumption for the diode-to-balun impedance transformation while simultaneously providing a suitable point for IF extraction. In addition, a parallel compensation technique consisting of an inductor and resistor is applied to the RF balun to significantly improve the amplitude/phase balance performance over a wide bandwidth. Benefiting from the aforementioned operations, an isolation-enhanced 8–30 GHz passive double-balanced mixer is designed as a proof-of-principle demonstration via 0.15-micrometer GaAs p-HEMT technology. It exhibits ultra-broadband performance with 7 dB average conversion loss and 50 dB LO-to-RF isolation under 15 dBm LO power. The monolithic microwave integrated circuit area is 0.96 × 1.68 mm² including all pads.

The sea surface displays dynamic characteristics, such as waves and various formations. As a result, images of the sea surface usually have few stable feature points, with a background that is often complex and variable. Moreover, the sea surface undergoes significant changes due to variations in wind speed, lighting conditions, weather, and other environmental factors, resulting in considerable discrepancies between images. These variations present challenges for identification using traditional methods. This paper introduces an algorithm based on the phase-consistency model. We utilize image data collected from a specific maritime area with a high-frame-rate surface array infrared camera. By accurately detecting images with identical names, we focus on the subtle texture information of the sea surface and its rotational invariance, enhancing the accuracy and robustness of the matching algorithm. We begin by constructing a nonlinear scale space using a nonlinear diffusion method. Maximum and minimum moments are generated using an odd symmetric Log–Gabor filter within the two-dimensional phase-consistency model. Next, we identify extremum points in the anisotropic weighted moment space. We use the phase-consistency feature values as image gradient features and develop feature descriptors based on the Log–Gabor filter that are insensitive to scale and rotation. Finally, we employ Euclidean distance as the similarity measure for initial matching, align the feature descriptors, and remove false matches using the fast sample consensus (FSC) algorithm. Our findings indicate that the proposed algorithm significantly improves upon traditional feature-matching methods in overall efficacy. Specifically, the average number of matching points for long-wave infrared images is 1147, while for mid-wave infrared images, it increases to 8241. Additionally, the root mean square error (RMSE) fluctuations for both image types remain stable, averaging 1.5. The proposed algorithm also enhances the rotation invariance of image matching, achieving satisfactory results even at significant rotation angles.