Significant research interest has recently been attracted to the study of bipedal robots due to the wide variety of their potential applications. In reality, bipedal robots are often required to perform gait transitions to achieve flexible walking. In this paper, we consider the gait transition of a five-link underactuated three-dimensional (3D) bipedal robot, and propose a two-layer control strategy. The strategy consists of a unique, event-based, feedback controller whose feedback gain in each step is updated by an adaptive control law, and a transition controller that guides the robot from the current gait to a neighboring point of the target gait so that the state trajectory can smoothly converge to the target gait. Compared with previous works, the transition controller is parameterized and its control parameters are obtained by solving an optimization problem to guarantee the physical constraints in the transition process. Finally, the effectiveness of the control strategy is illustrated on the underactuated 3D bipedal robot.
Smart homes can provide complementary information to assist home service robots. We present a robotic misplaced item finding (MIF) system, which uses human historical trajectory data obtained in a smart home environment. First, a multi-sensor fusion method is developed to localize and track a resident. Second, a path-planning method is developed to generate the robot movement plan, which considers the knowledge of the human historical trajectory. Third, a real-time object detector based on a convolutional neural network is applied to detect the misplaced item. We present MIF experiments in a smart home testbed and the experimental results verify the accuracy and efficiency of our solution.
Searchable public key encryption enables a storage server to retrieve the publicly encrypted data without revealing the original data contents. It offers a perfect cryptographic solution to encrypted data retrieval in encrypted data storage systems. Certificateless cryptography (CLC) is a novel cryptographic primitive that has many merits. It overcomes the key escrow problem in identity-based cryptosystems and the cumbersome certificate problem in conventional public key cryptosystems. Motivated by the appealing features of CLC, three certificateless encryption with keyword search (CLEKS) schemes were presented in the literature. However, all of them were constructed with the costly bilinear pairing and thus are not suitable for the devices that have limited computing resources and battery power. So, it is interesting and worthwhile to design a CLEKS scheme without using bilinear pairing. In this study, we put forward a pairing-free CLEKS scheme that does not exploit bilinear pairing. We strictly prove that the scheme achieves keyword ciphertext indistinguishability against adaptive chosen-keyword attacks under the complexity assumption of the computational Diffie-Hellman problem in the random oracle model. Efficiency comparison and the simulation show that it enjoys better performance than the previous pairing-based CLEKS schemes. In addition, we briefly introduce three extensions of the proposed CLEKS scheme.
There are many bottlenecks that limit the computing power of the Mobile Web3D and they need to be solved before implementing a public fire evacuation system on this platform. In this study, we focus on three key problems: (1) The scene data for large-scale building information modeling (BIM) are huge, so it is difficult to transmit the data via the Internet and visualize them on the Web; (2) The raw fire dynamic simulator (FDS) smoke diffusion data are also very large, so it is extremely difficult to transmit the data via the Internet and visualize them on the Web; (3) A smart artificial intelligence fire evacuation app for the public should be accurate and real-time. To address these problems, the following solutions are proposed: (1) The large-scale scene model is made lightweight; (2) The amount of dynamic smoke is also made lightweight; (3) The dynamic obstacle maps established from the scene model and smoke data are used for optimal path planning using a heuristic method. We propose a real-time fire evacuation system based on the ant colony optimization (RFES-ACO) algorithm with reused dynamic pheromones. Simulation results show that the public could use Mobile Web3D devices to experience fire evacuation drills in real time smoothly. The real-time fire evacuation system (RFES) is efficient and the evacuation rate is better than those of the other two algorithms, i.e., the leader-follower fire evacuation algorithm and the random fire evacuation algorithm.
Retinal vessel segmentation is a significant problem in the analysis of fundus images. A novel deep learning structure called the Gaussian net (GNET) model combined with a saliency model is proposed for retinal vessel segmentation. A saliency image is used as the input of the GNET model replacing the original image. The GNET model adopts a bilaterally symmetrical structure. In the left structure, the first layer is upsampling and the other layers are max-pooling. In the right structure, the final layer is max-pooling and the other layers are upsampling. The proposed approach is evaluated using the DRIVE database. Experimental results indicate that the GNET model can obtain more precise features and subtle details than the UNET models. The proposed algorithm performs well in extracting vessel networks, and is more accurate than other deep learning methods. Retinal vessel segmentation can help extract vessel change characteristics and provide a basis for screening the cerebrovascular diseases.
Classifying single-trial electroencephalogram (EEG) based motor imagery (MI) tasks is extensively used to control brain-computer interface (BCI) applications, as a communication bridge between humans and computers. However, the low signal-to-noise ratio and individual differences of EEG can affect the classification results negatively. In this paper, we propose an improved common spatial pattern (B-CSP) method to extract features for alleviating these adverse effects. First, for different subjects, the method of Bhattacharyya distance is used to select the optimal frequency band of each electrode including strong event-related desynchronization (ERD) and event-related synchronization (ERS) patterns; then the signals of the optimal frequency band are decomposed into spatial patterns, and the features that can describe the maximum differences of two classes of MI are extracted from the EEG data. The proposed method is applied to the public data set and experimental data set to extract features which are input into a back propagation neural network (BPNN) classifier to classify single-trial MI EEG. Another two conventional feature extraction methods, original common spatial pattern (CSP) and autoregressive (AR), are used for comparison. An improved classification performance for both data sets (public data set: 91.25%±1.77% for left hand vs. foot and 84.50%±5.42% for left hand vs. right hand; experimental data set: 90.43%±4.26% for left hand vs. foot) verifies the advantages of the B-CSP method over conventional methods. The results demonstrate that our proposed B-CSP method can classify EEG-based MI tasks effectively, and this study provides practical and theoretical approaches to BCI applications.
Fast and accurate extraction of vascular structures from medical images is fundamental for many clinical procedures. However, most of the vessel segmentation techniques ignore the existence of the isolated and redundant points in the segmentation results. In this study, we propose a vascular segmentation method based on a prior shape and local statistics. It could efficiently eliminate outliers and accurately segment thick and thin vessels. First, an improved vesselness filter is defined. This quantifies the likelihood of each voxel belonging to a bright tubular-shaped structure. A matching and connection process is then performed to obtain a blood-vessel mask. Finally, the region-growing method based on local statistics is implemented on the vessel mask to obtain the whole vascular tree without outliers. Experiments and comparisons with Frangi’s and Yang’s models on real magneticresonance-angiography images demonstrate that the proposed method can remove outliers while preserving the connectivity of vessel branches.
Haze scatters light transmitted in the air and reduces the visibility of images. Dealing with haze is still a challenge for image processing applications nowadays. For the purpose of haze removal, we propose an accelerated dehazing method based on single pixels. Unlike other methods based on regions, our method estimates the transmission map and atmospheric light for each pixel independently, so that all parameters can be evaluated in one traverse, which is a key to acceleration. Then, the transmission map is bilaterally filtered to restore the relationship between pixels. After restoration via the linear hazy model, the restored images are tuned to improve the contrast, value, and saturation, in particular to offset the intensity errors in different channels caused by the corresponding wavelengths. The experimental results demonstrate that the proposed dehazing method outperforms the state-of-the-art dehazing methods in terms of processing speed. Comparisons with other dehazing methods and quantitative criteria (peak signal-to-noise ratio, detectable marginal rate, and information entropy difference) are introduced to verify its performance.
To simplify the transient stability analysis of a large-scale power system and realize real-time emergency control, a fast transient stability simulation algorithm based on real-time dynamic equivalence is proposed. Generator models are grouped and aggregated according to a fast numerical integration. A fast calculation method of the admittance matrix is then proposed to calculate the parameters of an equivalent system, and numerical integration is performed using the obtained equivalent system. Then, based on integral sensitivity, a new fast emergency control strategy is proposed for the equivalent system. The final emergency control strategy is obtained by mapping the control strategy for the equivalent system back to the original system. The results of a simulation on an East China Power System that includes 496 generators and 5075 buses show that the suggested algorithm can output an accurate transient stability simulation result and form an effective emergency control strategy. The proposed algorithm is much faster than the existing solutions and has the potential to be used for online pre-decision.
Frequency-hopping (FH) is one of the commonly used spread spectrum techniques that finds wide applications in communications and radar systems because of its inherent capability of low interception, good confidentiality, and strong antiinterference. However, non-cooperation FH transmitter classification is a significant and challenging issue for FH transmitter fingerprint feature recognition, since it not only is sensitive to noise but also has non-linear, non-Gaussian, and non-stability characteristics, which make it difficult to guarantee the classification in the original signal space. Some existing classifiers, such as the sparse representation classifier (SRC), generally use an individual representation rather than all the samples to classify the test data, which over-emphasizes sparsity but ignores the collaborative relationship among the given set of samples. To address these problems, we propose a novel classifier, called the kernel joint representation classifier (KJRC), for FH transmitter fingerprint feature recognition, by integrating kernel projection, collaborative feature representation, and classifier learning into a joint framework. Extensive experiments on real-world FH signals demonstrate the effectiveness of the proposed method in comparison with several state-of-the-art recognition methods.
Neighbor discovery is important for docking applications, where mobile nodes communicate with static nodes situated at various rendezvous points. Among the existing neighbor discovery protocols, the probabilistic methods perform well in average cases but they have aperiodic, unpredictable, and unbounded discovery latency. Yet, deterministic protocols can provide bounded worst-case discovery latency by sacrificing the average-case performance. In this study, we propose a mobility-assisted slot index synchronization (MASS), which is a new synchronization technique that can improve the average-case performance of deterministic neighbor discovery protocols via slot index synchronization without incurring additional energy consumption. Furthermore, we propose an optimized beacon strategy in MASS to mitigate beaconing collisions, which can lead to discovery failures in situations where multiple neighbors are in the vicinity. We evaluate MASS with theoretical analysis and simulations using real traces from a tourist tracking system deployed at the Mogao Grottoes, which is a famous cultural heritage site in China. We show that MASS can reduce the average discovery latency of state-of-the-art deterministic neighbor discovery protocols by up to two orders of magnitude.