Fractionally sampled decorrelating detectors for time-varying rayleigh fading CDMA channels

In this dissertation, we propose novel decorrelating multiuser detectors in DSCDMA time-varying frequency-nonselective and frequency-selective fading channels and analyze their performance. We address the common shortcomings of existing multiuser detectors in a mobile environment, such as detector complexity and the error floor. An analytical approach is employed almost exclusively and Monte Carlo simulation is used to confirm the theoretical results. Practical channel models, such as Jakes\u27 and Markovian, are adopted in the numerical examples. The proposed detectors are of the decorrelating type and utilize fractional sampling to simultaneously achieve two goals: (1) the novel realization of a decorrelator with lower computational complexity and shorter processing latency; and (2) the significant reduction of the probability of error floor associated with time-varying fading. The analysis of the impact of imperfect power control on IS-95 multiple access interference is carried out first and the ineffectiveness of IS-95 power control in a mobile radio environment is demonstrated. Fractionally-spaced bit-by-bit decorrelator structures for the frequency-nonselective and frequency-selective channels are then proposed. The matrix singularity problem associated with decorrelation is also addressed, and its solution is suggested. A decorrelating receiver employing differentially coherent detection for an asynchronous CDMA, frequency-nonselective time-varying Rayleigh fading channel is proposed. A maximum likelihood detection principle is applied at the fractionally spaced decorrelator output, resulting in a significantly reduced error floor. For coherent detection, a novel single-stage and two-stage decision feedback (DF) maximum a posteriori (MAP) channel estimator is proposed. These estimators are applicable to a channel with an arbitrary spaced-time correlation function. The fractionally-spaced decorrelating detector is then modified and extended to a frequency-selective time-varying fading channel, and is shown to be capable of simultaneously eliminating MAI, ISI, and path cross-correlation interference. The implicit equivalent frequency diversity is exploited through multipath combining, and the effective time diversity is achieved by fractional sampling for significant performance improvement. The significance of the outcome of this research is in the design of new lower complexity multiuser detectors that do not exhibit the usual deficiencies and limitations associated with a time-varying fading and multipath CDMA mobile environment

Task Transfer by Preference-Based Cost Learning

The goal of task transfer in reinforcement learning is migrating the action policy of an agent to the target task from the source task. Given their successes on robotic action planning, current methods mostly rely on two requirements: exactly-relevant expert demonstrations or the explicitly-coded cost function on target task, both of which, however, are inconvenient to obtain in practice. In this paper, we relax these two strong conditions by developing a novel task transfer framework where the expert preference is applied as a guidance. In particular, we alternate the following two steps: Firstly, letting experts apply pre-defined preference rules to select related expert demonstrates for the target task. Secondly, based on the selection result, we learn the target cost function and trajectory distribution simultaneously via enhanced Adversarial MaxEnt IRL and generate more trajectories by the learned target distribution for the next preference selection. The theoretical analysis on the distribution learning and convergence of the proposed algorithm are provided. Extensive simulations on several benchmarks have been conducted for further verifying the effectiveness of the proposed method.Comment: Accepted to AAAI 2019. Mingxuan Jing and Xiaojian Ma contributed equally to this wor

FoveaBox: Beyond Anchor-based Object Detector

We present FoveaBox, an accurate, flexible, and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors. Instead, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. The scales of target boxes are naturally associated with feature pyramid representations. In FoveaBox, an instance is assigned to adjacent feature levels to make the model more accurate.We demonstrate its effectiveness on standard benchmarks and report extensive experimental analysis. Without bells and whistles, FoveaBox achieves state-of-the-art single model performance on the standard COCO and Pascal VOC object detection benchmark. More importantly, FoveaBox avoids all computation and hyper-parameters related to anchor boxes, which are often sensitive to the final detection performance. We believe the simple and effective approach will serve as a solid baseline and help ease future research for object detection. The code has been made publicly available at https://github.com/taokong/FoveaBox .Comment: IEEE Transactions on Image Processing, code at: https://github.com/taokong/FoveaBo

Text-to-3D using Gaussian Splatting

In this paper, we present Gaussian Splatting based text-to-3D generation (GSGEN), a novel approach for generating high-quality 3D objects. Previous methods suffer from inaccurate geometry and limited fidelity due to the absence of 3D prior and proper representation. We leverage 3D Gaussian Splatting, a recent state-of-the-art representation, to address existing shortcomings by exploiting the explicit nature that enables the incorporation of 3D prior. Specifically, our method adopts a progressive optimization strategy, which includes a geometry optimization stage and an appearance refinement stage. In geometry optimization, a coarse representation is established under a 3D geometry prior along with the ordinary 2D SDS loss, ensuring a sensible and 3D-consistent rough shape. Subsequently, the obtained Gaussians undergo an iterative refinement to enrich details. In this stage, we increase the number of Gaussians by compactness-based densification to enhance continuity and improve fidelity. With these designs, our approach can generate 3D content with delicate details and more accurate geometry. Extensive evaluations demonstrate the effectiveness of our method, especially for capturing high-frequency components. Video results are provided at https://gsgen3d.github.io. Our code is available at https://github.com/gsgen3d/gsgenComment: Project page: https://gsgen3d.github.io. Code: https://github.com/gsgen3d/gsge

RON: Reverse Connection with Objectness Prior Networks for Object Detection

We present RON, an efficient and effective framework for generic object detection. Our motivation is to smartly associate the best of the region-based (e.g., Faster R-CNN) and region-free (e.g., SSD) methodologies. Under fully convolutional architecture, RON mainly focuses on two fundamental problems: (a) multi-scale object localization and (b) negative sample mining. To address (a), we design the reverse connection, which enables the network to detect objects on multi-levels of CNNs. To deal with (b), we propose the objectness prior to significantly reduce the searching space of objects. We optimize the reverse connection, objectness prior and object detector jointly by a multi-task loss function, thus RON can directly predict final detection results from all locations of various feature maps. Extensive experiments on the challenging PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO benchmarks demonstrate the competitive performance of RON. Specifically, with VGG-16 and low resolution 384X384 input size, the network gets 81.3% mAP on PASCAL VOC 2007, 80.7% mAP on PASCAL VOC 2012 datasets. Its superiority increases when datasets become larger and more difficult, as demonstrated by the results on the MS COCO dataset. With 1.5G GPU memory at test phase, the speed of the network is 15 FPS, 3X faster than the Faster R-CNN counterpart.Comment: Project page will be available at https://github.com/taokong/RON, and formal paper will appear in CVPR 201