Photon-counting computed tomography (PCCT) based on photon-counting detectors (PCDs) represents a cutting-edge CT technology, offering higher spatial resolution, reduced radiation dose, and advanced material decomposition capabilities. Accurately modeling complex and nonlinear PCDs under limited calibration data becomes one of the challenges hindering the widespread accessibility of PCCT. This paper introduces a physics-ASIC architecture-driven deep learning detector model for PCDs. This model adeptly captures the comprehensive response of the PCD, encompassing both sensor and ASIC responses. We present experimental results demonstrating the model's exceptional accuracy and robustness with limited calibration data. Key advancements include reduced calibration errors, reasonable physics-ASIC parameters estimation, and high-quality and high-accuracy material decomposition images.

Medium access control (MAC) is crucial for resource allocation in wireless body area networks (WBANs). However, existing MAC protocols often suffer from transmission conflicts and inefficient channel utilization. To address these issues, this paper proposes a Node-Differentiated Resource Scheduling (NDRS) MAC protocol, which dynamically allocates access resources based on node-specific requirements. This protocol employs a superframe structure consisting of a contention-based phase and a contention-free phase for data transmission. A Mamdani fuzzy inference system is utilized to calculate continuous node priorities. These priorities achieve fine-grained differentiation of node importance and thus serve as the foundation for transmission conflict minimization. During the contention-based phase, continuous and differentiated backoff times are assigned to nodes based on their priorities. These backoff times effectively reduce transmission collisions and enhance channel utilization. In the contention-free phase, time slots are preferentially allocated to nodes with higher priority, better channel utilization, and greater transmission reliability. This allocation thereby enhances channel usage efficiency and reduce transmission delays. This protocol is characterized by three key features: precise node prioritization, low transmission collisions, and high channel utilization. Extensive experimental results demonstrate that NDRS outperforms existing protocols in terms of average delay, throughput, packet loss ratio, and average energy consumption.

With limited annotated data, the existing deep learning-based ground-penetrating radar (GPR) data inversion methods have difficulty effectively reconstructing the relative permittivity (RP) of subsurface defects. To address this issue, a GPR data inversion method based on semisupervised deep learning is proposed. A synthetic aperture radar (SAR) feature fusion branch is designed in the encoding stage of the RP generator to enrich the feature representation of the defect shape profiles. A multidimensional parallel attention module is designed to aggregate defect-related invariant features, which thereby enhances the capability for inversing new defect types with new shapes. In addition, foreground defect loss is introduced to improve the performance of the defect boundary reconstruction. The validation of the proposed method is performed using both synthetic and real data. Comparative experiments with the existing methods indicate that in scenarios with limited annotated data, the proposed method exhibits superior performance for imaging new subsurface defect types that are absent in the labeled target domain.

Spectral CT has shown promise for high-sensitivity quantitative imaging and material decomposition. This work presents a new device called a spatial-spectral filter (SSF) which consists of a tiled array of filter materials positioned near the x-ray source that is used to modulate the spectral shape of the x-ray beam. The filter is moved to obtain projection data that is sparse in each spectral channel. To process this sparse data, we employ a one-step direct model-based material decomposition (MBMD) to reconstruct basis material density images directly from the SSF CT data. To evaluate different possible SSF designs, we define a new Fisher-information-based predictive image quality metric called separability index which characterizes the ability of a spectral CT system to distinguish between the signals from two or more materials. This spectral CT performance metric can be used to optimize spectral CT system design. We conducted simulation-based design optimization study to find optimized combinations of filter materials, filter thicknesses, filter widths, and source settings. Finally, we present MBMD results using simulated SSF CT measurements from the optimized designs to demonstrate the ability to reconstruct basis material density images and to show the benefits of the optimized designs. Our results indicate that optimizing SSF CT for separability leads to high-performance at material discrimination tasks.

Spatial matching for object retrieval is often time-consuming and susceptible to viewpoint changes. To address this problem, we propose a novel spatial matching method that is robust to viewpoint changes and implement it on modern graphics processing unit (GPU) in parallel for real-time applications. Unlike previous spatial matching methods used in object retrieval, in which the affine transformation estimation is based on the gravity vector assumption, our method abandons this strong assumption by matching the affine covariant neighbors (ACNs) of corresponding local regions and estimating affine transformation from each single pair of corresponding local regions. Taking into account real-time applications, we implement the method on modern GPU in parallel to speed up the process. Computations are distributed evenly to threads with load balancing, and device memory accesses are optimized with bitmap-based parallel scan. Experimental results demonstrate that our method is more robust and more efficient than previous methods especially when the viewpoints are changed, and the parallel implementation on GPU obtains ten times speedup.

Spatial matching for visual words based object retrieval often involves generating affine transformation hypotheses and then choosing the best hypothesis to measure the spatial consistency. In existing methods, generating an affine transformation hypothesis either requires three correspondences or assumes images are taken in restricted range of viewpoints in using a single correspondence. In this paper, we propose a novel spatial matching method, in which the transformation hypothesis can be estimated from only a single correspondence without the assumption of the viewpoints from which the images are taken. Firstly, affine covariant neighborhoods(ACNs) of features are used to eliminate possible false matches. Secondly, we decompose the affine transformation into three sub-transforms and conquer each sub-transform by exploiting the shape information and the ACNs of a single pair of corresponding features. Experiment results demonstrate that this method improves the average retrieval precision evidently with less computation in comparison with the previous methods.

Spatial matching for object retrieval is often time-consuming and susceptible to viewpoint changes. To address this problem, we propose a novel spatial matching method and implement it on modern GPU in parallel. Unlike previous spatial matching methods, in which the affine transformation estimation is based on the gravity vector assumption, our method abandons this strong assumption by matching the ACNs (affine covariant neighbors) of corresponding local regions and estimating affine transformation from a single pair of corresponding local regions. To speed up the process, we implement the method on modern GPU in parallel. Computations are distributed evenly to threads with load balancing, and the memory accesses are optimized and bitmap based parallel scan is exploited. Experimental results demonstrate that our method is more robust and more efficient than previous methods especially when the viewpoints are changed, and the parallel implementation on GPU obtains ten times speedup.