Dual-band circularly-polarized shared-aperture array for C/X-Band satellite communications

A novel method of achieving a single-feed circularly-polarized (CP) microstrip antenna with both broad impedance bandwidth and axial ratio (AR) bandwidth is presented. The CP characteristics are generated by employing a resonator to excite the two orthogonal modes of the patch via two coupling paths and the required 90 o phase difference is achieved by using the different orders of the two paths. The presented method, instead of conventional methods that power dividers and phase delay lines are usually required, not only significantly enhances the bandwidths of the antenna, but also results in a compact feed, reduced loss and high gain. Based on this method, a dual-band shared-aperture CP array antenna is implemented for C/X-band satellite communications. The antenna aperture includes a 2 × 2 array at C-band and a 4 ×4 array at X-band. To accommodate the C/X-band elements into the same aperture while achieving a good isolation between them, the C-band circular patches are etched at the four corners. The measured results agree well with the simulations, showing a wide impedance bandwidth of 21% and 21.2% at C-and X-band, respectively. The C-and X-band 3-dB AR bandwidths are 13.2% and 12.8%. The array also exhibits a high aperture efficiency of over 55%, low side-lobe (C-band: −12.5 dB; X-band: −15 dB) and high gain (C-band: 14.5 dBic; X-band: 17.5 dBic)

Bayesian Nested Neural Networks for Uncertainty Calibration and Adaptive Compression

Nested networks or slimmable networks are neural networks whose architectures can be adjusted instantly during testing time, e.g., based on computational constraints. Recent studies have focused on a "nested dropout" layer, which is able to order the nodes of a layer by importance during training, thus generating a nested set of sub-networks that are optimal for different configurations of resources. However, the dropout rate is fixed as a hyper-parameter over different layers during the whole training process. Therefore, when nodes are removed, the performance decays in a human-specified trajectory rather than in a trajectory learned from data. Another drawback is the generated sub-networks are deterministic networks without well-calibrated uncertainty. To address these two problems, we develop a Bayesian approach to nested neural networks. We propose a variational ordering unit that draws samples for nested dropout at a low cost, from a proposed Downhill distribution, which provides useful gradients to the parameters of nested dropout. Based on this approach, we design a Bayesian nested neural network that learns the order knowledge of the node distributions. In experiments, we show that the proposed approach outperforms the nested network in terms of accuracy, calibration, and out-of-domain detection in classification tasks. It also outperforms the related approach on uncertainty-critical tasks in computer vision.Comment: 16 pages, 10 figure

Moby: Empowering 2D Models for Efficient Point Cloud Analytics on the Edge

3D object detection plays a pivotal role in many applications, most notably autonomous driving and robotics. These applications are commonly deployed on edge devices to promptly interact with the environment, and often require near real-time response. With limited computation power, it is challenging to execute 3D detection on the edge using highly complex neural networks. Common approaches such as offloading to the cloud induce significant latency overheads due to the large amount of point cloud data during transmission. To resolve the tension between wimpy edge devices and compute-intensive inference workloads, we explore the possibility of empowering fast 2D detection to extrapolate 3D bounding boxes. To this end, we present Moby, a novel system that demonstrates the feasibility and potential of our approach. We design a transformation pipeline for Moby that generates 3D bounding boxes efficiently and accurately based on 2D detection results without running 3D detectors. Further, we devise a frame offloading scheduler that decides when to launch the 3D detector judiciously in the cloud to avoid the errors from accumulating. Extensive evaluations on NVIDIA Jetson TX2 with real-world autonomous driving datasets demonstrate that Moby offers up to 91.9% latency improvement with modest accuracy loss over state of the art.Comment: Accepted to ACM International Conference on Multimedia (MM) 202

Integrated Dual-Band Filtering/Duplexing Antennas

In this paper, the state-of-the-art integrated filtering antennas with dual-band operation are first reviewed. Then, two designs of dual-band microstrip filtering antennas with a low frequency-ratio are presented. The 1st design is a dual-band dual-polarization (DBDP) antenna with a frequency ratio of 1.2 on a single patch, by employing the coupled resonator technique. Two bands at each polarization are achieved by vertically coupling a hairpin resonator with a patch through a slot in the ground plane and then coupled to a dual-mode stub loaded resonator (SLR). Each band exhibits a 2nd-order filtering performance with improved bandwidth and out-of-band rejection. Such an integration technique could significantly reduce the dimension and complexity of traditional DBDP antennas/arrays. In the 2nd design, a novel dual-port dual-band antenna (with a frequency ratio of 1.38) with the integrated filtering and duplexing functions is proposed. The frequency duplexing function is implemented by coupling a single patch with two sets of resonator-based filtering channels via a U-slot resonator inserted in the ground. This device seamlessly integrates the functions of duplexers, filters and antennas in a very compact structure

A simulation study on the measurement of D0-D0bar mixing parameter y at BES-III

We established a method on measuring the \dzdzb mixing parameter $y$ for BESIII experiment at the BEPCII $e^+e^-$ collider. In this method, the doubly tagged $\psi(3770) \to D^0 \overline{D^0}$ events, with one $D$ decays to CP-eigenstates and the other $D$ decays semileptonically, are used to reconstruct the signals. Since this analysis requires good $e/\pi$ separation, a likelihood approach, which combines the $dE/dx$, time of flight and the electromagnetic shower detectors information, is used for particle identification. We estimate the sensitivity of the measurement of $y$ to be 0.007 based on a $20fb^{-1}$ fully simulated MC sample.Comment: 6 pages, 7 figure