Micro-computed tomography (micro-CT) is a widely used state-of-the-art instrument employed to study the morphological structures of objects in various fields. However, its small field-of-view (FOV) cannot meet the pressing demand for imaging relatively large objects at high spatial resolutions. Recently, we devised a novel scanning mode called multiple source translation CT (mSTCT) that effectively enlarges the FOV of the micro-CT and correspondingly developed a virtual projection-based filtered backprojection (V-FBP) algorithm for reconstruction. Although V-FBP skillfully solves the truncation problem in mSTCT, it requires densely sampled projections to arrive at high-resolution reconstruction, which reduces imaging efficiency. In this paper, we develop two backprojection-filtration (BPF)-based algorithms for mSTCT—S-BPF (derivatives along source) and D-BPF (derivatives along detector). D-BPF can achieve high-resolution reconstruction with fewer projections than V-FBP and S-BPF. Through simulated and physical experiments conducted in this paper, we demonstrate that D-BPF can dramatically reduce the number of projection views compared with V-FBP at the same spatial resolution, which makes mSTCT more feasible in practice. Meanwhile, S-BPF can yield more stable results than D-BPF, which is similar to V-FBP.

Contactless respiration monitoring using wireless signals has drawn much attention in recent years. Many approaches have been proposed, however, they may not work when there is a lack of signals directly reflected from target's chest, e.g., a target faces away from the transceiver or a target is blocked by furniture. In this paper, we design and implement a novel omnimonitoring system for human respiration, OmniRespMonitor , using a pair of speaker and microphone. Different from Radio Frequency (RF) signal, acoustic signals cannot penetrate through walls and furniture. The multipath reflection in an indoor environment will result in highly abundant acoustic signals. In this case, even though there are lack of acoustic signals directly reflected by a target's chest, indirectly-reflected acoustic signals can still be received by the microphone. We can therefore monitor the target's respiration by extracting this subtle variation of indirectly reflected signals. To achieve this, we model chest movement using truncated System Frequency Response (SFR). We then develop a global search method based on the autocorrelation function to extract minute chest movement from SFR sequences. Finally, we dynamically synthesize the chest movement information to recover the breathing wave in real time. We conduct extensive experiments with both humans and animals (goat), the results show that OmniResMonitor is able to monitor single target's respiration within 5 meters in indoor environments in various challenging scenarios there are lack of directly-reflected acoustic signals.

Recently, in some special situations, linear computed tomography (LCT) systems have provided better imaging solutions for internal structures compared with conventional CT instruments. To image the region of interest (ROI) and weaken projection truncation in the LCT reconstruction, the backprojection filtration (BPF) algorithm is an effective solution; however, in BPF for LCT, it is difficult to achieve stable interior reconstruction, and for differentiated backprojection (DBP) images of LCT. To satisfy multiple reconstruction scenarios for LCT, including interior ROI, complete object, and exterior region, we propose two types of reconstruction architectures. The first overlays multiple DBP images to obtain a complete DBP image and then uses a network to learn the overlying or composite Hilbert filtering function, referred to as the overlay-single network (OSNet). The second uses multiple networks to train different directional Hilbert filtering models for DBP images of multiple linear scanning trajectories, respectively, and then overlays the reconstructed results, i.e., multiple networks overlaying (MNetO). In two architectures, we introduce a Swin transformer (ST) block to the generator of pix2pixGAN to extract both local and global features from DBP images at the same time. We investigate the proposed architectures from different networks, FOV sizes, pixel sizes, number of projections, geometric magnification, and computation time. The two architectures are proven to recover images. OSNet outperforms BPF in multiple scenarios. In OSNet, ST-pix2pixGAN is superior to pix2pixGAN and CycleGAN for the different networks. The average peak signal-to-noise ratio (PSNR) value reaches 41.1949 dB, which improves by 31% compared to BPF. The average structural similarity index measure (SSIM) and root-mean-square error (RMSE) are improved at 0.9996 and 0.0093, respectively. In MNetO, one model in a certain direction can increase the PSNR by about 18.4% compared to BPF, enabling high-quality imaging of the exterior edge. Our architectures can provide great solutions or references for LCT high-resolution imaging and nondestructive measuring.

Wireless sensing technologies have exploited various methods for contactless respiration monitoring. One of the main methods is to use an acoustic beam to cover the chest and abdomen with real-time and high-resolution ranging. The system with high ranging resolution can detect small distance transformations, further affecting the precision of respiratory monitoring. However, the traditional range-based respiration monitoring systems are limited by an insufficient sampling rate and poor synchronization. The ranging resolution is between millimeters and centimeters. In this study, we introduced a novel, high-precision ultrasound-based system for contactless respiration monitoring. First, we developed a convenient and high-performance ultrasonic transceiver platform with universal serial bus audio architecture to eliminate the above limitations. The developed platform provides a foundation for acoustic sensing. Furthermore, based on this platform, we designed a cross correlation sine-frequency modulation continuous wave ranging method and a real-time respiration wave recovery method. The median error in monitoring respiration for 12 participants in three different sleep postures is 0 breaths/min, and the maximum error is 0.58 breaths/min. The proposed system has strong robustness concerning different respiration rates, long-term continuous respiration monitoring, and simulated apnea.

The sphericity error is a critical form and position tolerance for spheres. We explored the distribution of sphericity errors within the solution space to achieve a high-precision evaluation using the minimum zone criteria. Within local solution spaces, we propose treating the evaluation of sphericity errors as a unimodal function optimization task. And computational geometric methods are employed to achieve highly accurate solutions within the local solution spaces. Subsequently, we integrated the computational geometric method with the differential evolution algorithm (DE algorithm). By centering on individual population members of the DE algorithm, we partitioned the local solution spaces and utilized the best solutions within them to optimize the population. With the gradual convergence of the DE algorithm, we successfully achieved the high-precision resolution of sphericity errors. The experimental results demonstrate a significant order-of-magnitude improvement in precision compared to traditional algorithms in the field of sphericity error evaluation, with uncertainty levels reaching magnitudes of 10⁻¹⁴ mm. Moreover, this method enhances the accuracy of sphericity error evaluation by approximately 10% for three-coordinate measuring machines. Additionally, we conducted ablation experiments to validate the effectiveness of the proposed computational geometric method. In summary, this approach enables the high-precision evaluation of sphericity errors and provides a practical methodology for applying ultra-precision spheres in precision engineering.

For a linear scanning mode—multiple Source Translation Computed Tomography (mSTCT)—when extending a large field-of-view (FOV) with a high geometry magnification, we propose some practical filtered backprojection type reconstruction algorithms. More specially, we formulate a fan-beam Differentiation, Hilbert transform, and Backprojection reconstruction formula for STCT, whose filter is along the detector rather than the source (referred to as D-DHB) to avoid acquiring large amounts of projections, and then generalize it to the cone-beam scene. On the one hand, we apply D-DHB in the full-scan mSTCT (F-mSTCT) mode. On the other hand, we eliminate the unstable position-dependent backprojection weight in the D-DHB formula by introducing an additional weight function. In fact, the backprojection weight of analytical algorithms with filtering along the detector implies the distance between the reconstructed point and the X-ray source focus. Especially where the extended FOV is very close to the source trajectory, removing the position-dependent backprojection weight can avoid some intolerable artifacts around the reconstructed image and improve computation efficiency without losing more accuracy. To meet the requirement of extremely extended FOV imaging, we fully evaluated our two methods from different aspects of the experiments. The results, overall, reveal that our methods are the most practical for mSTCT.