Dissing Little (calfrecess3)

In this work, we propose a new two-view domain adaptation network named Deep-Shallow Domain Adaptation Network (DSDAN) for 3D point cloud recognition. Different from the traditional 2D image recognition task, the valuable texture information is often absent in point cloud data, making point cloud recognition a challenging task, especially in the cross-dataset scenario where the training and testing data exhibit a considerable distribution mismatch. In our DSDAN method, we tackle the challenging cross-dataset 3D point cloud recognition task from two aspects. On one hand, we propose a two-view learning framework, such that we can effectively leverage multiple feature representations to improve the recognition performance. To this end, we propose a simple and efficient Bag-of-Points feature method, as a complementary view to the deep representation. Moreover, we also propose a cross view consistency loss to boost the two-view learning framework. On the other hand, we further propose a two-level adaptation strategy to effectively address the domain distribution mismatch issue. Specifically, we apply a feature-level distribution alignment module for each view, and also propose an instance-level adaptation approach to select highly confident pseudo-labeled target samples for adapting the model to the target domain, based on which a co-training scheme is used to integrate the learning and adaptation process on the two views. Extensive experiments on the benchmark dataset show that our newly proposed DSDAN method outperforms the existing state-of-the-art methods for the cross-dataset point cloud recognition task.This paper proposes an innovative viscosity sensor based on the thickness-shear vibration of an SC-cut quartz resonator. The thickness-shear mode is firstly analyzed and further studied with fluid-structure interaction between the resonator and the viscous fluid loading. The characteristic equation is derived based on the 3D linear piezoelectric equations and solved for sensitivity analysis. Then laboratory experiment is carried out to validate the theory. To conduct the viscosity measurement, the SC-cut quartz resonator is integrated with a U-tube test fixture, which is designed and fabricated for sensor housing to avoid the influence of the mass of the fluid. The resonator is tested with various viscosities by tuning the ratio of glycerol/water mixture. Experiment results show consistency with the analytical solution, which together present an improved sensitivity of viscosity measurement by using SC-cut quartz resonator comparing to other resonator-based viscosity sensors. The proposed viscosity sensor is sensitive, accurate, and portable, and therefore can be applied to real-time, on-site measurement or sampling of fluidic samples.In this paper, we discuss the design study for a brain SPECT imaging system, referred to as the HelmetSPECT system, based on a spherical synthetic compound-eye (SCE) gamma camera design. The design utilizes a large number ( 500) of semiconductor detector modules, each coupled to an aperture with a very narrow opening for high-resolution SPECT imaging applications. In this study, we demonstrate that this novel system design could provide an excellent spatial resolution, a very high sensitivity, and a rich angular sampling without scanning motion over a clinically relevant field-of-view (FOV). These properties make the proposed HelmetSPECT system attractive for dynamic imaging of epileptic patients during seizures. In ictal SPECT, there is typically no prior information on where the seizures would happen, and both the imaging resolution and quantitative accuracy of the dynamic SPECT images would provide critical information for staging the seizures outbreak and refining the plans for subsequent surgical intervention. We report the performance evaluation and comparison among similar system geometries using non-conventional apertures, such as micro-ring and micro-slit, and traditional lofthole aper