Early detection of retinal lesions is critical for preventing vision loss. While supervised learning has shown promise, existing methods often rely on extensive labeled data, which is costly and difficult to obtain in medical applications. Unsupervised anomaly detection provides an attractive alternative by requiring only healthy retinal images and no abnormal annotations. However, current methods face significant challenges in modeling the complex structures of normal retinal anatomy, learning discriminative features for detecting subtle lesions, and capturing multi-scale features to handle anomalies of varying sizes – highlighting the need for holistic feature modeling that comprehensively represents both retinal anatomy and pathology. To address these challenges, we propose HolistAno, a novel unsupervised anomaly detection framework with holistic retinal modeling. HolistAno adopts a two-stage network architecture, incorporating a novel anomaly generator and a Balanced Mamba Scale Fusion (BMSF) module to effectively learn comprehensive retinal feature representations. This enables accurate detection of subtle lesions, diverse lesion types, and anomalies across multiple scales. Extensive experiments on five benchmark datasets demonstrate that HolistAno achieves state-of-the-art performance in both anomaly classification and localization tasks, with superior generalization and robustness across multiple datasets and cross-dataset scenarios compared to existing methods.

Modern multi-FPGA systems featuring multi-die devices connected through time-division multiplexing (TDM) techniques have become increasingly common as the scale of designs increases rapidly. FPGA designs are meticulously partitioned at die-level for prototyping in modern emulation systems. Conventional FPGA-level routers often result in a large critical connection delay that impacts the whole design’s frequency. Additionally, the excessive use of super long lines (SLLs) between neighboring dies leads to substantial routing congestion, causing the failure of the routing progress. To tackle these issues, we propose an effective and efficient die-level router for multi-FPGA systems, optimizing routing topology and the TDM ratio. Experimental results on the benchmarks from the die-level routing contest 2023 demonstrate 7.6% better critical connection delay with a $5.761 \times$ speed-up compared to the state-of-the-art router.

The paper reports on measurements performed with a simultaneous L and X band, fully polarimetric airborne SAR system, the MetaSAR-XL model produced by MetaSensing. One of those systems is owned and operated by Five Stars Electronic Technology and Tianjin SARruide Technology in China. A number of results of campaigns performed in Europe and People's Republic of China are reported, including interferometric acquisitions and polarimetric results with simultaneous multiband acquisitions. The MetaSAR XL is exclusively operated for civil and commercial mapping applications.

Light detection and ranging is a relatively new technology, developed to complement traditional remote sensing technology. Digital aerial image provides reflectance information for land cover while LIDAR data provide more accurate geometrical information. There is much potential for fusing these two sources to achieve increased accuracy and utility for feature detection and classification. In order to assist Guangxi Electric Power Design Institute to improve their efficiency in power transmission line selection and design, field survey work, and reduce the cost, we have recently carried out the first power line project by using LIDAR and high resolution digital camera in China. The use of coloring LIDAR points by RGB of digital images makes it possible for obtaining high quality of in-room surveying, feature detection and classification. In addition, high quality and high resolution of DEM, DSM, DOM and visualization provides our customer not only with better selection and design of power line route, and reduced survey work, but also significant savings by 10 percent reduction of power line construction.