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5 UPDRS points, and the predicted score accounted for approximately 76% of the variability of the UPDRS. These results demonstrate that the ASAP protocol can measure differences for individuals who are clinically different. This indicates that the ASAP protocol may be able to measure changes with time in the motor signs of an individual with PD.We have built a wireless implantable microelectronic device for transmitting cortical signals transcutaneously. The device is aimed at interfacing a microelectrode array cortical to an external computer for neural control applications. Our implantable microsystem enables presently 16-channel broadband neural recording in a nonhuman primate brain by converting these signals to a digital stream of infrared light pulses for transmission through the skin. The implantable unit employs a flexible polymer substrate onto which we have integrated ultra-low power amplification with analog multiplexing, an analog-to-digital converter, a low power digital controller chip, and infrared telemetry. The scalable 16-channel microsystem can employ any of several modalities of power supply, including via radio frequency by induction, or infrared light via a photovoltaic converter. As of today, the implant has been tested as a sub-chronic unit in non-human primates (~ 1 month), yielding robust spike and broadband neural data on all available channels.The importance of body sensor networks to monitor patients over a prolonged period of time has increased with an advance in home healthcare applications. Sensor nodes need to operate with very low-power consumption and under the constraint of limited memory capacity. Therefore, it is wasteful to digitize the sensor signal at a constant sample rate, given that the frequency contents of the signals vary with time. Adaptive sampling is established as a practical method to reduce the sample data volume. In this paper a low-power analog system is proposed, which adjusts the converter clock rate to perform a peak-picking algorithm on the second derivative of the input signal. The presented implementation does not require an analog-to-digital converter or a digital processor in the sample selection process. The criteria for selecting a suitable detection threshold are discussed, so that the maximum sampling error can be limited. A circuit level implementation is presented. Measured results exhibit a significant reduction in the average sample frequency and data rate of over 50% and 38% respectively.Induced hypothermia has been broadly applied in neurological intensive care unit (NICU). Meanwhile, accidental hypothermia is also a threatening condition in daily life. It is meaningful to investigate the influences of temperature change on the cerebral blood flow (CBF). In the present study, temporal laser speckle image contrast analysis (tLASCA) was implemented to study the relative CBF change in cerebral artery, vein and capillary level under mild (35$\circ$C) and moderate (32$\circ$C) hypothermia. Twelve male Sprague-Dawley rats (300±50g) were anesthetized with sodium pentobarbital and randomly assigned to mild and moderate hypothermia groups (n=9 each). Laser speckle imaging trials were acquired from baseline (37$\circ$C), hypothermia (35$\circ$C or 32$\circ$C) and post-rewarming (37$\circ$C) phases. In the mild group, mean CBF in different vessels all increased throughout the hypothermic and post-rewarming phases. On the contrary, mean CBF reduced by 10% to 20% at 32$\circ$C and returned to ~95% of the baseline level during the post-rewarming session in the moderate group. Besides, in the moderate group, a CBF rebound in vein was found in the post-rewarming phase. Our results suggested that the CBF changed differently between mild and moderate hypothermia, which may worth for further study in clinical. And we demonstrated LSI as a promising method to achieve high spatiotemporal resolution CBF change with minimal invasion.Rigid registration of partial observations is a fundamental problem in various applied fields. In computer graphics, special attention has been given to the registration between two partial point clouds generated by scanning devices. State-of-the-art registration techniques still struggle when the overlap region between the two point clouds is small, and completely fail if there is no overlap between the scan pairs. In this paper, we present a learning-based technique that alleviates this problem, and allows registration between point clouds, presented in arbitrary poses, and having little or even no overlap, a setting that has been referred to as tele-registration. Our technique is based on a novel neural network design that learns a prior of a class of shapes and can complete a partial shape. The key idea is combining the registration and completion tasks in a way that reinforces each other. ALK cancer In particular, we simultaneously train the registration network and completion network using two coupled flows, one that register-and-complete, and one that complete-and-register, and encourage the two flows to produce a consistent result. We show that, compared with each separate flow, this two-flow training leads to robust and reliable tele-registration, and hence to a better point cloud prediction that completes the registered scans. It is also worth mentioning that each of the components in our neural network outperforms state-of-the-art methods in both completion and registration. We further analyze our network with several ablation studies and demonstrate its performance on a large number of partial point clouds.Region-based methods are currently achieving state-of-the-art performance for monocular 3D object tracking. However, they are still prone to fail in cases of partial occlusions and ambiguous colors. We propose a novel region-based method to tackle these problems. The key idea is to derive a pixel-wise weighted region-based cost function using contour constraints. Firstly, we propose a novel region-based cost function using search lines around the object contour, which is more efficient than previous region-based cost functions using signed distance transform, and in the meantime can deal with partial occlusions and ambiguous colors more effectively. Secondly, we propose an optimal searching strategy to search the object contour points in cluttered scenes, and then use the object contour points to detect partial occlusions and ambiguous colors. Thirdly, we propose a pixel-wise weight function based on color and distance constraints of the object contour points, and integrate it into the proposed region-based cost function to reduce the negative impact of partial occlusions and ambiguous colors.