A novel method for camera external parameters online calibration using dotted road line

On board camera is the most important information source for driver assistant application which is based on computer vision. One problem for on board camera is that the external parameters are easy to be changed when moving on the road. Traditional method either calibrates external parameters offline or calibrates external parameters semi-online, which both need human intervention. By observation, it is found that corner connection of dotted road lane can form two groups of parallel lines. Using this geometric characteristics and through theoretical derivation, a novel online camera external parameter calibration method is proposed which focus on the situation when vehicle is moving. The pro of this method is that it maintains relatively high calculation accuracy and more important, it does not require any human intervention in whole calibration process. Experimental and comparison results show that this method is simple and have accurate results which fully meet the requirements of practical application.     

Internet Studies and Development Discourses: The Cases of China and India

This paper investigates Internet studies in two leading developing countries (i.e. China and India) and finds that the Chinese scholarly community relies on the discourse of liberation from the state as a form of critique, whereas Indian Internet studies question the discourse of modernization to contemplate about the success and failure factors of information and communication technologies in development. The difference generally reflects the academic responses to the development discourses embraced by the two governments. We suggest that Internet studies should not only respond to the realities but also transcend the contextual constraints to direct attention to the often neglected dimensions of development, which are to make actual impacts through allowing the people and the communities to define their own development discourses as well as building research institutions that are oriented to influence policy-making.     

Adaptive Neural Network Finite-Time Control for Uncertain Robotic Manipulators

An adaptive neural network finite-time controller (NNFTC) for a class of uncertain nonlinear systems is proposed by using the backstepping method, which employs an adaptive neural network (NN) system to approximate the structure uncertainties and uses a variable structure term to compensate the approximation errors, thus improving the robustness of the system to external disturbances. The controller is then applied to uncertain robotic manipulators, with a control objective of driving the system state to the original equilibrium point. It is proved that the closed-loop system is finite-time stable. Moreover, simulated and experimental results indicate that the proposed NNFTC is effective and robust.     

Unmanned Ground Vehicle Navigation in Coordinate-Free and Localization-Free Wireless Sensor and Actuator Networks

We present a set of algorithms for the navigation of Unmanned Ground Vehicles (UGVs) towards a set of pre-identified target nodes in coordinate-free and localization-free wireless sensor and actuator networks. The UGVs are equipped with a set of wireless listeners that provide sensing information about the potential field generated by the network of actuators. Two main navigation scenarios are considered: single-UGV, single-destination navigation and multi-UGV, multi-destination navigation. For the single-UGV, single-destination case, we present both centralized and distributed navigation algorithms. Both algorithms share a similar two-phase concept. In the first phase, the system assigns level numbers to individual nodes based on their hop distance from the target nodes. In the second phase, the UGV uses the potential field created by the network of actuators to move towards the target nodes, requiring cooperation between triplets of actuator nodes and the UGV. The hop distance to the target nodes is used to control the main moving direction while the potential field, which can be measured by listeners on the UGV, is used to determine the UGV’s movement. For the multi-UGV, multi-destination case, we present a decentralized allocation algorithm such that multiple UGVs avoid converging to the same destination. After each UGV determines its destination, the proposed navigation scheme is applied. The presented algorithms do not attempt to localize UGVs or sensor nodes and are therefore suitable for operating in GPS-free/denied environments. We also present a study of the communication complexity of the algorithms as well as simulation examples that verify the proposed algorithms and compare their performances.     

A new parallel algorithm for vertex priorities of data flow acyclic digraphs

Data flow acyclic directed graphs (digraph) are widely used to describe the data dependency of mesh-based scientific computing. The parallel execution of such digraphs can approximately depict the flowchart of parallel computing. During the period of parallel execution, vertex priorities are key performance factors. This paper firstly takes the distributed digraph and its resource-constrained parallel scheduling as the vertex priorities model, and then presents a new parallel algorithm for the solution of vertex priorities using the well-known technique of forward–backward iterations. Especially, in each iteration, a more efficient vertex ranking strategy is proposed. In the case of simple digraphs, both theoretical analysis and benchmarks show that the vertex priorities produced by such an algorithm will make the digraph scheduling time converge non-increasingly with the number of iterations. In other cases of non-simple digraphs, benchmarks also show that the new algorithm is superior to many traditional approaches. Embedding the new algorithm into the heuristic framework for the parallel sweeping solution of neutron transport applications, the new vertex priorities improve the performance by 20 % or so while the number of processors scales up from 32 to 2048.     

A saliency-driven robotic head with bio-inspired saccadic behaviors for social robotics

This paper presents a robotic head for social robots to attend to scene saliency with bio-inspired saccadic behaviors. Scene saliency is determined by measuring low-level static scene information, motion, and object prior knowledge. Towards the extracted saliency spots, the designed robotic head is able to turn gazes in a saccadic manner while obeying eye–head coordination laws with the proposed control scheme. The results of the simulation study and actual applications show the effectiveness of the proposed method in discovering of scene saliency and human-like head motion. The proposed techniques could possibly be applied to social robots to improve social sense and user experience in human–robot interaction.     

Multi-scale patch-based sparse appearance model for robust object tracking

When objects undergo large pose change, illumination variation or partial occlusion, most existing visual tracking algorithms tend to drift away from targets and even fail to track them. To address the issue, in this paper we propose a multi-scale patch-based appearance model with sparse representation and provide an efficient scheme involving the collaboration between multi-scale patches encoded by sparse coefficients. The key idea of our method is to model the appearance of an object by different scale patches, which are represented by sparse coefficients with different scale dictionaries. The model exploits both partial and spatial information of targets based on multi-scale patches. Afterwards, a similarity score of one candidate target is input into a particle filter framework to estimate the target state sequentially over time in visual tracking. Additionally, to decrease the visual drift caused by frequently updating model, we present a novel two-step object tracking method which exploits both the ground truth information of the target labeled in the first frame and the target obtained online with the multi-scale patch information. Experiments on some publicly available benchmarks of video sequences showed that the similarity involving complementary information can locate targets more accurately and the proposed tracker is more robust and effective than others.     

A one-shot bargaining strategy for dealing with multifarious opponents

Bargaining is an effective paradigm to solve the problem of resource allocation. The consideration of factors such as bounded rationality of negotiators, time constraints, incomplete information, and complexity of dynamic environment make the design of optimal strategy for one-shot bargaining much tougher than the situation that all bargainers are assumed to be absolutely rational. Lots of prediction-based strategies have been explored either based on assuming a finite number of models for opponents, or focusing on the prediction of opponent’s reserve price, deadline, or the probabilities of different behaviors. Following the methods of estimating opponent’s private information, this paper gives a strategy which improves the BLGAN strategy to adapt to various possible bargaining situations and deal with multifarious opponents. In addition, this paper compares the improved BLGAN strategy with related work. Experimental results show that the improved BLGAN strategy can outperform related ones when faced with various opponents, especially the agents who frequently change their strategies for anti-learning.     

A memetic algorithm for the capacitated m-ring-star problem

The Capacitated m-Ring-Star Problem (CmRSP) models a network topology design problem in the telecommunications industry. In this paper, we propose to solve this problem using a memetic algorithm that includes a crossover operation, a mutation operation, a local search involving three neighborhood operators, and a population selection strategy that maintains population diversity. Our approach generates the best known solutions for 131 out of 138 benchmark instances, improving on the previous best solutions for 24 of them, and exhibits more advantages on large benchmark instances when compared with the best existing approach. Additionally, all existing algorithms for this problem in literature assume that the underlying graph of the problem instance satisfies the triangle inequality rule; our approach does not require this assumption. We also generated a new set of 36 larger test instances based on a digital data service network price structure to serve as a new benchmark data set for future researchers.