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The goal of fusing medical images is to integrate the diverse information that multimodal medical images hold. However, the challenges lie in the limitations of imaging sensors and the issue of incomplete modal information retention, which make it difficult to produce images encompassing both functional and anatomical information. To overcome these obstacles, several medical image fusion techniques based on CNN or transformer architectures have been presented. Nevertheless, CNN technique struggles to establish extensive dependencies between the fused and source images, and transformer architecture often overlooks shallow complementary features. To augment both the feature extraction capacity and the stability of the model, we introduce a framework, called dual‐branch complementary feature injection fusion (CFIFusion) technique, a for multimodal medical image fusion framework that combines unsupervised models of CNN model and transformer techniques. Specifically, in our framework, the entire source image and segmented source image are input into an adaptive backbone network to learn global and local features, respectively. To further retain the source images' complementary information, we design a multi‐scale complementary feature extraction framework as an auxiliary module, focusing on calculating feature differences at each level to capture the shallow complementary information. Then, we design a shallow information preservation module tailored for sliced image characteristics. Experimental results on the Harvard whole brain atlas dataset demonstrate that CFIFusion shows greater benefits than recent state‐of‐the‐art algorithms in terms of both subjective and objective evaluations. [ABSTRACT FROM AUTHOR]

Compact slow light devices are essential components for performing data caching and signal processing in photonic integrated loops. In this article, an integrated ultra-low-dispersion slow light device with a novel method is proposed. The device consists of three parts (hexagonal resonator with elliptical core, stub cavity, and tooth cavities) coupled to the waveguide, respectively. Dual Fano resonances occur in the structure, and transmission characteristics of the structure are investigated in detail by temporal coupled-mode theory. Finite-difference time-domain simulations reveal that the transmission bandwidth, group index, and delay time can be manipulated by adjusting the separation between two Fano resonances, which is related to the eccentricity of the oval core. At the 850.7 nm window, transmission bandwidth and average group index are optimized to 21.1 nm and 12.19, respectively. Moreover, multiple dispersionless wavelengths within the slow light bandwidth are obtained based on dual Fano resonances. Furthermore, feasibility of the device to perform slow light function in different channels is researched, and device performance is presented and analyzed. This device has a great impact on improving the quality of signals on chips, and the method introduced is of great significance for designing other photonic devices. [ABSTRACT FROM AUTHOR]

In this paper, a novel plasmonic all-optical half-subtractor and full-subtractor are designed for optical computing. The structure of plasmonic subtractors consists of a metal–insulator–metal (MIM) waveguide and rectangular ring resonators covered by a graphene layer. Due to the nonlinear optical properties of graphene, the states of the plasmonic resonators can be controlled by the pump intensity of a pump beam focused on the graphene layer. The resonators can work as all-optical switches with an ultra-fast response time to constitute optical logic devices according to the directed logic mechanism. A finite-difference time-domain method is utilized to numerically investigate the transmission of the output signals which represent the results of subtraction operations. Simulation results obtained indicate that the proposed plasmonic devices have the ability to implement half-subtraction and full-subtraction with a small feature size and fast response time, and provide a new concept and method for the design and realization of optical computing devices. [ABSTRACT FROM AUTHOR]

The Graphene@PtSe2 heterostructure is an excellent long-wave infrared detection material. However, the expensive cost of PtSe2 prevents its widespread use in infrared detection. In this paper, Hf was used to partially replace Pt to form Graphene@(PtSe2)n(HfSe2)4−n (n = 1, 2, and 3) bidirectional heterostructures consisting of graphene and lateral PtSe2/HfSe2 composites based on first-principles calculations. Then, the new bidirectional heterostructures were compared with heterostructures formed by graphene with pure MSe2 (M = Pt, Hf). It was found that the band gaps of the bidirectional heterostructures were between those of Graphene@PtSe2 and Graphene@HfSe2. Among these heterostructures, the Graphene@(PtSe2)3(HfSe2)1 bidirectional heterostructure has almost the same optical absorption properties in the infrared wavelength region of 1.33~40 µm as the Graphene@PtSe2 heterostructure, and it improves the absorption in the near-infrared wavelength region of 0.75~1.33 µm. Such a designment may bring the material costs down (since PtSe2 costs approximately five times more than HfSe2). This study on the designment of the bidirectional Graphene@(PtSe2)3(HfSe2)1 heterostructure also illustrates a cost-effective design method for Pt-based IR detectors. [ABSTRACT FROM AUTHOR]

SMART (Single-cycle Multi-hop Asynchronous Repeated Traversal) Network-on-Chip (NoC), a recently proposed dynamically reconfigurable NoC, enables single-cycle long-distance communication by building single-bypass paths directly between distant communication pairs. However, such a single-cycle single-bypass path will be readily broken when contention occurs. Thus, packets will be buffered at intermediate routers with blocking latency from other contending packets, and extra router-stage latency to rebuild the remaining path when available, reducing the bypassing benefits that SMART NoC offers. In this article, we for the first time propose an effective contention-minimized routing algorithm to achieve maximal bypassing in SMART NoCs. Specifically, we identify two potential routes for packets: direct route, with which packets can reach the destination in a single bypass; and indirect route, with which packets can reach the destination in multiple bypasses via a (multiple) intermediate router(s). The novel feature of the proposed routing strategy is that, contrary to an intuitive approach, not the routes with minimal distance but the indirect routes via the arbitrary intermediate routers (even if they may be non-minimal) that avoid contentions yield the minimized end-to-end latency. Our new routing strategy can greatly enrich the path diversity, effectively minimize the conflicts between communication pairs, greatly balance the workloads and fully utilize bypass paths. Evaluation on realistic benchmarks demonstrates the effectiveness of the proposed routing strategy, which achieves average performance improvement by 35.48 percent in communication latency, 28.31 percent in application schedule length, and 37.59 percent in network throughput, compared with the current routing in SMART NoCs. [ABSTRACT FROM AUTHOR]