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The knowledge of quantities and types of traffic participants at specific nodes dedicates crucial information to an intelligent transportation system (ITS) of a smart city. A single-layer laser scanner was employed on a static platform on the roadside to recognize pedestrians, bicycles, motorcycles, trucks, sedans and buses. The selected features were extracted and investigated for optimized classification rate. The classifier support vector machine (SVM) is configured to classify the types of traffic participants using multiclass classification approach. To improve the recognition correction rate, the K-folder cross-validation and grid search are applied in the training stage of the machine learning. The hierarchical division of the output space is proposed where five classifiers were applied to deal with the specific difficult classes. The overall accuracy of the final experiment is 85.5% where the multiclass accuracies are 91.9, 99.6, 97.8, 96.6 and 80.4%, respectively, which shows that the laser scanner used is a feasible and practical solution of this application. [ABSTRACT FROM AUTHOR]

Lidar has long been used in three-dimensional (3-D) reconstruction tasks. Herein a novel method using single layer Lidar and an inertial measurement unit (IMU) is proposed, which is expected to replace the multiple layer lidar in perceiving surrounding environment. The reconstruction is achieved in three steps: (i) Coordinate transformation and timestamp synchronization between the two sensors are first performed,(ii) The quaternion attitude is then calculated to generate the scanned plane by using the original range, roll, pitch, and yaw data measured,and (iii) The registrations between different frames of scanned planes are finally achieved by the iterative closest point (ICP) algorithm. Together, the 3-D spatial reconstruction can be obtained by using an IMU and a single layer, rather than multiple layer, lidar, which is more economical and flexible. [ABSTRACT FROM AUTHOR]

Since the strength of a trapped person often declines with time in urgent and dangerous circumstances, adopting a robot to rescue as many survivors as possible in limited time is of considerable significance. However, as one key issue in robot navigation, how to plan an optimal rescue path of a robot has not yet been fully solved. This paper studies robot path planning formultisurvivor rescue in limited survival time using a representative heuristic, particle swarmoptimization (PSO). First, the robot path planning problem includingmultiple survivors is formulated as a discrete optimization one with high constraint, where the number of rescued persons is taken as the unique objective function, and the strength of a trapped person is used to constrain the feasibility of a path. Then, a new integer PSO algorithm is presented to solve the mathematical model, and several new operations, such as the update of a particle, the insertion and inversion operators, and the rapidly local search method, are incorporated into the proposed algorithm to improve its effectiveness. Finally, the simulation results demonstrate the capacity of ourmethod in generating optimal paths with high quality. [ABSTRACT FROM AUTHOR]

Localising an odour source is of considerable importance in real-world applications. A modified particle swarm optimisation (PSO) algorithm was presented in this study as an effective cooperative strategy among robots to guide them to search for an odour source. In this algorithm, the whole search space is divided into several grid cells with each cell saving positions that have been detected by robots. In the phase of searching for an odour plume, each robot locating at a cell obtains a repulsive force proportional to the number of positions saved by this cell. To improve the efficiency of particles in traversing the plume, two cognitive factors of PSO are dynamically adjusted according to the effect of wind on self-cognition and social cognition of a particle. In addition, simulated annealing is incorporated into the update of a particle's local leader to prevent premature convergence of the swarm. The proposed algorithm, implemented using MATLAB, was applied to localise the odour source in four typical simulation scenes formed by Fluent, and compared with previous methods. The experimental results show that the proposed algorithm can guide a multi-robot system to localise the odour source rapidly and accurately. [ABSTRACT FROM AUTHOR]

The role of the thyroid gland in the regulation of metabolic rate has been known since the last century. The knowledge that thyroid hormones increase energy expenditure, in part by lowering metabolic efficiency, dates from the 1950s. Presumably thyroid hormones regulate energy expenditure and efficiency by controlling the rate of transcription of specific genes. However, the number, identity, and relative contributions of these genes are not known. The uncoupling proteins (UCPs) are obvious candidates to mediate thyroid thermogenesis. UCP1 is not a major contributor, since thyrotoxicosis decreases UCP1 expression and inactivates brown fat. Discovery of UCP3 and its regulation by T3 in muscle is an exciting observation, consistent with a role for UCP3 in thyroid thermogenesis. Since free fatty acids appear to regulate UCP3 expression and T3 stimulates lipolysis, further experiments are required to determine if T3 regulation of UCP3 expression is direct or not. [ABSTRACT FROM AUTHOR]