Unsupervised Light Field Depth Estimation via Multi-view Feature Matching with Occlusion Prediction

Depth estimation from light field (LF) images is a fundamental step for some applications. Recently, learning-based methods have achieved higher accuracy and efficiency than the traditional methods. However, it is costly to obtain sufficient depth labels for supervised training. In this paper, we propose an unsupervised framework to estimate depth from LF images. First, we design a disparity estimation network (DispNet) with a coarse-to-fine structure to predict disparity maps from different view combinations by performing multi-view feature matching to learn the correspondences more effectively. As occlusions may cause the violation of photo-consistency, we design an occlusion prediction network (OccNet) to predict the occlusion maps, which are used as the element-wise weights of photometric loss to solve the occlusion issue and assist the disparity learning. With the disparity maps estimated by multiple input combinations, we propose a disparity fusion strategy based on the estimated errors with effective occlusion handling to obtain the final disparity map. Experimental results demonstrate that our method achieves superior performance on both the dense and sparse LF images, and also has better generalization ability to the real-world LF images

LRT: An Efficient Low-Light Restoration Transformer for Dark Light Field Images

Light field (LF) images containing information for multiple views have numerous applications, which can be severely affected by low-light imaging. Recent learning-based methods for low-light enhancement have some disadvantages, such as a lack of noise suppression, complex training process and poor performance in extremely low-light conditions. To tackle these deficiencies while fully utilizing the multi-view information, we propose an efficient Low-light Restoration Transformer (LRT) for LF images, with multiple heads to perform intermediate tasks within a single network, including denoising, luminance adjustment, refinement and detail enhancement, achieving progressive restoration from small scale to full scale. Moreover, we design an angular transformer block with an efficient view-token scheme to model the global angular dependencies, and a multi-scale spatial transformer block to encode the multi-scale local and global information within each view. To address the issue of insufficient training data, we formulate a synthesis pipeline by simulating the major noise sources with the estimated noise parameters of LF camera. Experimental results demonstrate that our method achieves the state-of-the-art performance on low-light LF restoration with high efficiency

Network analysis of cold cognition and depression in middle-aged and elder population: the moderation of grandparenting

BackgroundCognitive decline and negative emotions are common in aging, especially decline in cold cognition which often co-occurred with depression in middle-aged and older adults. This study analyzed the interactions between cold cognition and depression in the middle-aged and elder populations using network analysis and explored the effects of grandparenting on the cold cognition-depression network.MethodsThe data of 6,900 individuals (≥ 45 years) from the China Health and Retirement Longitudinal Study (CHARLS) were used. The Minimum Mental State Examination (MMSE) and the Epidemiology Research Center Depression Scale-10 (CESD-10) were used to assess cold cognition and depressive symptoms, respectively. Centrality indices and bridge centrality indices were used to identify central nodes and bridge nodes, respectively.ResultsNetwork analysis showed that nodes “language ability” and “depressed mood” were more central nodes in the network of cold cognition and depression in all participants. Meantime, nodes “attention,” “language ability” and “hopeless” were three key bridge nodes connecting cold cognition and depressive symptoms. Additionally, the global connectivity of the cold cognition and depression network was stronger in the non-grandparenting than the grandparenting.ConclusionThe findings shed a light on the complex interactions between cold cognition and depression in the middle-aged and elder populations. Decline in language ability and depressed mood can serve as predictors for the emergence of cold cognitive dysfunction and depression in individuals during aging. Attention, language ability and hopelessness are potential targets for psychosocial interventions. Furthermore, grandparenting is effective in alleviating cold cognitive dysfunction and depression that occur during individual aging

Continuous control for robot based on deep reinforcement learning

One of the main targets of artificial intelligence is to solve the complex control problems which have high-dimensional observation spaces. Recently, the combination of deep learning and reinforcement learning has made remarkable progress, including the high-level performance in the video and board games, 3D navigations and robotic control. In this thesis, deep reinforcement learning algorithms are studied to perform some robotic tasks with continuous action spaces. Firstly, we use deep deterministic policy gradient (DDPG) and hindsight experience replay (HER) with a simple binary reward to achieve a multi-goal reinforcement learning task, making the redundant manipulator learn the policy of reaching any given position. Then we use DDPG with a shaped reward to train the redundant manipulator to complete the same task. By referring to the idea of HER, we propose a $future$ and $random$ strategy to obtain some additional goals combined with the shaped reward to generate some new transitions, which can help to improve the sample efficiency. After that, we use DDPG with prioritized experience replay to realize the trajectory tracking task of a SCARA robot and a mobile robot. Two training strategies, random referenced state initialization and early termination, are introduced to enable the robots to learn effectively from the referenced trajectories. Secondly, we focus on the distributed deep reinforcement learning. We use asynchronous advantage actor-critic (A3C) and synchronous advantage actor-critic (A2C) algorithms, both of which have multiple workers to collect the transitions and compute the gradients, to train the redundant manipulator to complete the multi-goal task. We propose a new reward function to optimize the reaching path of the end-effector. The performances of agents trained by different algorithms and reward functions are compared. Next, we propose a distributed framework of DDPG, where the synchronous workers generate transitions and compute gradients for the global network and the collecting workers only produce transitions for the shared replay memory with different policies and exploration noises. We use this proposed distributed DDPG with prioritized experience replay to train the SCARA robot and mobile robot to track the same trajectories, which presents a faster learning speed and smaller tracking errors compared with the single-worker DDPG. Next, we study on the proximal policy optimization algorithm (PPO) with generalized advantage estimation (GAE). We propose a distributed framework of PPO by running multiple workers to collect transitions for the global network at the same time. Then we use this distributed PPO with GAE to train the redundant manipulator to achieve the multi-goal task and make comparison with the previous methods. After that, we use distributed PPO with GAE and the improved training strategies to train the mobile robot to track the trajectories. In order to improve the training and sample efficiency, a two-stage training strategy which consists of the supervised pre-training and fine-training by distributed PPO is proposed. This two-stage training strategy can also obtain a better tracking performance. Then we introduce LSTM to represent the actor and critic, and use buffers to store the cell state and hidden state of LSTM used for the initialization of each episode to solve the problem of inaccurate initial LSTM states. By introducing LSTM, the tracking performance of mobile robot can be improved compared with distributed PPO with fully-connected networks. Finally, we utilize deep reinforcement learning to train a autonomous vehicle to learn the driving behaviors. Deep reinforcement learning provides an end-to-end method for the autonomous driving by directly mapping the high-dimensional raw sensory input to the control command output. We design a reward function which encourages the vehicle to drive along the road smoothly and overtake other vehicles. We adopt a two-stage training strategy which consists of the imitation learning stage and deep reinforcement learning stage. The imitation learning stage could help to solve the exploration and sample efficiency problem of reinforcement learning. We use DDPG and the improved algorithm, TD3 to train the autonomous vehicle in the second training stage, respectively. We find that TD3 could improve the driving performance of autonoumous vehicle.Master of Engineerin

Design and Analysis of Wave Sensing Scheduling Protocols for Object-Tracking Applications

Abstract. Many sensor network applications demand tightly-bounded object detection quality. To meet such stringent requirements, we develop three sensing scheduling protocols to guarantee worst-case detection quality in a sensor network while reducing sensing power consumption. Our protocols emulate a line sweeping through all points in the sensing field periodically. Nodes wake up when the sweeping line comes close, and then go to sleep when the line moves forward. In this way, any object can be detected within a certain period. We prove the correctness of the protocols and evaluate their performances by theoretical analyses and simulation.

SCOPE: Scalable Consistency Maintenance in Structured P2P Systems

While current Peer-to-Peer (P2P) systems facilitate static file sharing, newly-developed applications demand that P2P systems be able to manage dynamically-changing files. Maintaining consistency between frequently-updated files and their replicas is a fundamental reliability requirement for a P2P system. In this paper, we present SCOPE, a structured P2P system supporting consistency among a large number of replicas. By building a replica-partition-tree (RPT) for each key, SCOPE keeps track of the locations of replicas and then propagates update notifications. Our theoretical analyses and experimental results demonstrate that SCOPE can effectively maintain replica consistency while preventing hot-spot and node-failure problems. Its efficiency in maintenance and failure-recovery is particularly attractive to the deployment of large-scale P2P systems

Analyzing object detection quality under probabilistic coverage in sensor networks

Abstract. Object detection quality and network lifetime are two conflicting aspects of a sensor network, but both are critical to many sensor applications such as military surveillance. Probabilistic coverage is an appropriate approach to balancing the conflicting design requirements of monitoring applications. Under probabilistic coverage, we present an analytical model to analyze object detection quality with respect to different network conditions and sensor scheduling schemes. Our analytical model facilitates performance evaluation of a sensing schedule, network deployment, and sensing scheduling protocol design. Applying the model to real sensor networks, we design a set of sensing scheduling protocols to achieve targeted object detection quality while minimizing power consumption. The correctness of our model and the effectiveness of the proposed protocols are validated through extensive simulation experiments.

A Study on Object Tracking Quality under Probabilistic Coverage in Sensor Networks

Introduction Sensor networks are used for a wide range of object tracking applications, such as vehicle tracking in military surveillance and wild animal tracking in habitat monitoring [1]. These applications, by their nature, enforce certain tracking quality and lifetime requirements. These two requirements, however, are two conflicting optimization goals due to the stringent energy constraints of sensor nodes. Full sensing coverage [2] is mandatory for sensor monitoring applications that require either immediate response to detected events or information of all points in the sensing field. Full sensing coverage, however, is too restricted and expensive to support long-time monitoring applications. It gives little leverage to tune object-tracking quality and battery power consumption. A relaxed sensing coverage--- probabilistic coverage where any point in a sensing field is sensed with a certain probability at any time--- is a more appropriate approach to balancing objecttracking qu