The optical rotation technique arose in the 1990s. Optical tweezer brought an ideal platform for research on the angular momentum of laser beams. For decades, the optical rotation technique has been widely applied in laboratory optical manipulation and the fields of biology and optofluidics. Recently, it has attracted much attention for its potential in the classical and quantum regimes. In this work, we review the progress of experiments and applications of optically induced rotation. First, we introduce the basic exploration of angular momentum. Then, we cover the development and application of optical rotation induced by orbital angular momentum, and the spin angular momentum is presented. Finally, we elaborate on recent applications of the optical rotation technique in high vacuum. As precise optical manipulation in a liquid medium enters its maturity, optical tweezers in high vacuum open a new path for the high-speed micro-rotor.
In constructing a smart court, to provide intelligent assistance for achieving more efficient, fair, and explainable trial proceedings, we propose a full-process intelligent trial system (FITS). In the proposed FITS, we introduce essential tasks for constructing a smart court, including information extraction, evidence classification, question generation, dialogue summarization, judgment prediction, and judgment document generation. Specifically, the preliminary work involves extracting elements from legal texts to assist the judge in identifying the gist of the case efficiently. With the extracted attributes, we can justify each piece of evidence’s validity by establishing its consistency across all evidence. During the trial process, we design an automatic questioning robot to assist the judge in presiding over the trial. It consists of a finite state machine representing procedural questioning and a deep learning model for generating factual questions by encoding the context of utterance in a court debate. Furthermore, FITS summarizes the controversy focuses that arise from a court debate in real time, constructed under a multi-task learning framework, and generates a summarized trial transcript in the dialogue inspectional summarization (DIS) module. To support the judge in making a decision, we adopt first-order logic to express legal knowledge and embed it in deep neural networks (DNNs) to predict judgments. Finally, we propose an attentional and counterfactual natural language generation (AC-NLG) to generate the court’s judgment.
For flow-related design optimization problems, e.g., aircraft and automobile aerodynamic design, computational fluid dynamics (CFD) simulations are commonly used to predict flow fields and analyze performance. While important, CFD simulations are a resource-demanding and time-consuming iterative process. The expensive simulation overhead limits the opportunities for large design space exploration and prevents interactive design. In this paper, we propose FlowDNN, a novel deep neural network (DNN) to efficiently learn flow representations from CFD results. FlowDNN saves computational time by directly predicting the expected flow fields based on given flow conditions and geometry shapes. FlowDNN is the first DNN that incorporates the underlying physical conservation laws of fluid dynamics with a carefully designed attention mechanism for steady flow prediction. This approach not only improves the prediction accuracy, but also preserves the physical consistency of the predicted flow fields, which is essential for CFD. Various metrics are derived to evaluate FlowDNN with respect to the whole flow fields or regions of interest (RoIs) (e.g., boundary layers where flow quantities change rapidly). Experiments show that FlowDNN significantly outperforms alternative methods with faster inference and more accurate results. It speeds up a graphics processing unit (GPU) accelerated CFD solver by more than 14 000×, while keeping the prediction error under 5%.
Deep learning has proven to be an effective mechanism for computer vision tasks, especially for image denoising and burst image denoising. In this paper, we focus on solving the burst image denoising problem and aim to generate a single clean image from a burst of noisy images. We propose to combine the power of block matching and 3D filtering (BM3D) and a convolutional neural network (CNN) for burst image denoising. In particular, we design a CNN with a divide-and-conquer strategy. First, we employ BM3D to preprocess the noisy burst images. Then, the preprocessed images and noisy images are fed separately into two parallel CNN branches. The two branches produce somewhat different results. Finally, we use a light CNN block to combine the two outputs. In addition, we improve the performance by optimizing the two branches using two different constraints: a signal constraint and a noise constraint. One maps a clean signal, and the other maps the noise distribution. In addition, we adopt block matching in the network to avoid frame misalignment. Experimental results on synthetic and real noisy images show that our algorithm is competitive with other algorithms.
A fundamental task for mobile robots is simultaneous localization and mapping (SLAM). Moreover, long-term robustness is an important property for SLAM. When vehicles or robots steer fast or steer in certain scenarios, such as low-texture environments, long corridors, tunnels, or other duplicated structural environments, most SLAM systems might fail. In this paper, we propose a novel robust visual inertial light detection and ranging (LiDaR) navigation (VILN) SLAM system, including stereo visual-inertial LiDaR odometry and visual-LiDaR loop closure. The proposed VILN SLAM system can perform well with low drift after long-term experiments, even when the LiDaR or visual measurements are degraded occasionally in complex scenes. Extensive experimental results show that the robustness has been greatly improved in various scenarios compared to state-of-the-art SLAM systems.
Robots need more intelligence to complete cognitive tasks in home environments. In this paper, we present a new cloud-assisted cognition adaptation mechanism for home service robots, which learns new knowledge from other robots. In this mechanism, a change detection approach is implemented in the robot to detect changes in the user’s home environment and trigger the adaptation procedure that adapts the robot’s local customized model to the environmental changes, while the adaptation is achieved by transferring knowledge from the global cloud model to the local model through model fusion. First, three different model fusion methods are proposed to carry out the adaptation procedure, and two key factors of the fusion methods are emphasized. Second, the most suitable model fusion method and its settings for the cloud–robot knowledge transfer are determined. Third, we carry out a case study of learning in a changing home environment, and the experimental results verify the efficiency and effectiveness of our solutions. The experimental results lead us to propose an empirical guideline of model fusion for the cloud–robot knowledge transfer.
Threshold proxy re-encryption (TPRE) can prevent collusion between a single proxy and a delegatee from converting arbitrary files against the wishes of the delegator through multiple proxies, and can also provide normal services even when certain proxy servers are paralyzed or damaged. A non-interactive identity-based TPRE (IB-TPRE) scheme over lattices is proposed which removes the public key certificates. To accomplish this scheme, Shamir’s secret sharing is employed twice, which not only effectively hides the delegator’s private key information, but also decentralizes the proxy power by splitting the re-encryption key. Robustness means that a combiner can detect a misbehaving proxy server that has sent an invalid transformed ciphertext share. This property is achieved by lattice-based fully homomorphic signatures. As a result, the whole scheme is thoroughly capable of resisting quantum attacks even when they are available. The security of the proposed scheme is based on the decisional learning with error hardness assumption in the standard model. Two typical application scenarios, including a file-sharing system based on a blockchain network and a robust key escrow system with threshold cryptography, are presented.
Since traditional machine learning methods are sensitive to skewed distribution and do not consider the characteristics in multiclass imbalance problems, the skewed distribution of multiclass data poses a major challenge to machine learning algorithms. To tackle such issues, we propose a new splitting criterion of the decision tree based on the one-against-all-based Hellinger distance (OAHD). Two crucial elements are included in OAHD. First, the one-against-all scheme is integrated into the process of computing the Hellinger distance in OAHD, thereby extending the Hellinger distance decision tree to cope with the multiclass imbalance problem. Second, for the multiclass imbalance problem, the distribution and the number of distinct classes are taken into account, and a modified Gini index is designed. Moreover, we give theoretical proofs for the properties of OAHD, including skew insensitivity and the ability to seek a purer node in the decision tree. Finally, we collect 20 public real-world imbalanced data sets from the Knowledge Extraction based on Evolutionary Learning (KEEL) repository and the University of California, Irvine (UCI) repository. Experimental and statistical results show that OAHD significantly improves the performance compared with the five other well-known decision trees in terms of Precision, F-measure, and multiclass area under the receiver operating characteristic curve (MAUC). Moreover, through statistical analysis, the Friedman and Nemenyi tests are used to prove the advantage of OAHD over the five other decision trees.
Active queue management (AQM) is essential to prevent the degradation of quality of service in TCP/AQM systems with round-trip time (RTT) delay. RTT delays are primarily caused by packet-propagation delays, but they can also be caused by the processing time of queuing operations and dynamically changing network situations. This study focuses on the design and analysis of an AQM digital controller under time-delay uncertainty. The controller is based on the Smith predictor algorithm and is called the SMITHPI controller. This study also demonstrates the stability of the controller and its robustness against network parameter variations such as the number of TCP connections, time delays, and user datagram protocol flows. The performance, robustness, and effectiveness of the proposed SMITHPI controller are evaluated using the NS-2 simulator. Finally, the performance of the SMITHPI controller is compared with that of a well-known queue-based AQM, called the proportional-integral controller.
To avoid Doppler ambiguity, pulse Doppler radar may operate on a high pulse repetition frequency (PRF). The use of a high PRF can, however, lead to range ambiguity in many cases. At present, the major efficient solution to solve range ambiguity is based on a waveform design scheme. It adds complexity to a radar system. However, the traditional multiple-PRF-based scheme is difficult to be applied in multiple targets because of unknown correspondence between the target range and measured range, especially using the Chinese remainder theorem (CRT) algorithm. We make a study of the CRT algorithm for multiple targets when the residue set contains noise error. In this paper, we present a symmetry polynomial aided CRT algorithm to effectively achieve range estimation of multiple targets when the measured ranges are overlapped with noise error. A closed-form and robust CRT algorithm for single target and the Aitken acceleration algorithm for finding roots of a polynomial equation are used to decrease the computational complexity of the proposed algorithm.
Because it is magnet-free and can achieve a high integration level, the switched-capacitor (SC) converter acting as a direct current transformer has many promising applications in modern electronics. However, designing an SC converter with large current capability and high power efficiency is still challenging. This paper proposes a dual-branch SC voltage divider and presents its integrated circuit (IC) implementation. The designed SC converter is capable of driving large current load, thus widening the use of SC converters to high-power applications. This SC converter has a constant conversion ratio of 1/2 and its dual-branch interleaved operation ensures a continuous input current. An effective on-chip gate-driving method using a capacitively coupled floating-voltage level shifter is proposed to drive the all-NMOS power train. Due to the self-powered structure, the flying capacitor itself is also a bootstrap capacitor for gate driving and thus reduces the number of needed components. A digital frequency modulation method is adopted and the switching frequency decreases automatically at light load to improve light load efficiency. The converter IC is implemented using a 180 nm triple-well BCD process. Experimental results verify the effectiveness of the dual-branch interleaved operation and the self-powered gate-driving method. The proposed SC divider can drive up to 4 A load current with 5–12 V input voltage and its power efficiency is as high as 96.5%. At light load, using the proposed optimization method, the power efficiency is improved by 30%.
In this paper, an intelligent fractional-order integral sliding mode control (FOISMC) strategy based on an improved cascade observer is proposed. First, an FOISMC strategy is designed to control a permanent magnet synchronous motor. It has good tracking performance, is strongly robust, and can effectively reduce chattering. The proposed FOISMC strategy associates strong points of the integral action (which can eliminate steady-state tracking errors) and the fractional calculus (which is flexible). Second, an improved cascade observer is proposed to detect the rotor information with a smaller observation error. The proposed observer combines an adaptive sliding mode observer and an extended high-gain observer. In addition, an improved variable-speed grey wolf optimization algorithm is designed to enhance controller parameters. The effectiveness of the strategy is tested using simulations and an experiment involving model uncertainty and external disturbance.