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Simultaneous localization and mapping (SLAM) is the major solution for constructing or updating a map of an unknown environment while simultaneously keeping track of a mobile robot's location. Correlative Scan Matching (CSM) is a scan matching algorithm for obtaining the posterior distribution probability for the robot's pose in SLAM. This paper combines the non-linear optimization algorithm and CSM algorithm into an NLO-CSM (Non-linear Optimization CSM) algorithm for reducing the computation resources and the amount of computation while ensuring high calculation accuracy, and it presents an efficient hardware accelerator design of the NLO-CSM algorithm for the scan matching in 2D LiDAR SLAM. The proposed NLO-CSM hardware accelerator utilizes pipeline processing and module reusing techniques to achieve low hardware overhead, fast matching, and high energy efficiency. FPGA implementation results show that, at 100 MHz clock, the power consumption of the proposed hardware accelerator is as low as 0.79 W, while it performs a scan match at 8.98 ms and 7.15 mJ per frame. The proposed design outperforms the ARM-A9 dual-core CPU implementation with a 92.74% increase and 90.71% saving in computing speed and energy consumption, respectively. It has also achieved 80.3% LUTs, 84.13% FFs, and 20.83% DSPs saving, as well as an 8.17× increase in frame rate and 96.22% improvement in energy efficiency over a state-of-the-art hardware accelerator design in the literature. ASIC implementation in 65 nm can further reduce the computing time and energy consumption per scan to 5.94 ms and 0.06 mJ, respectively, which shows that the proposed NLO-CSM hardware accelerator design is suitable for resource-limited and energy-constrained mobile and micro robot applications. [ABSTRACT FROM AUTHOR]

The constructions and algorithms of summary index models to determine unknown weight coefficients using latent variable regression with constrained parameters are presented. The summary index model with a single category can be seen as a structural equations model with a latent variable without latent equations. This article gives a new suitable algorithm for it based on factor analysis, path analysis, and prescription constraint. Another summary index model is one in which all samples have been divided into multiple categories. This article also gives a suitable algorithm by alternating projection between two convex sets and prescription constraint. [ABSTRACT FROM AUTHOR]

Abstract: A definite linear algorithm for structural equation model (SEM) is proposed that is developed from modular constraint (the length of vector is 1) least squares (MCLS) solution of SEM which may be used as the best iterative initial value of partial least square (PLS) algorithm. Based on MCLS, prescription regression (all regression coefficients are nonnegative and their sum is 1), and generalized linear regression model (the dependent variable is unknown), a definite linear algorithm is constructed. The data example of SEM with multilayer construction is provided to illustrate the algorithm. [Copyright &y& Elsevier]

This paper presents a new method based on a sensor network for designing a lighting control system for an industrial building. An illumination mapping between the work plane and the horizontal plane was derived for sensor placement to generate rules for sensor layout. Online sensor data sampling and a control center database with a real-time update algorithm were applied to lessen the effects of interference and luminous decay. We implemented the method for the lighting system of an industrial building in Xi’an, China. Skylights combined with artificial lighting were designed for daytime lighting. The optimized placement and a scene-control strategy for the artificial lighting system were generated by simulations with DIALux and Autodesk Ecotect Analysis. Daily lighting consumption data for August, 2014, demonstrated that the control system strategy realized lighting energy savings of up to an average of 80% on cloudy days. The design can be used as a reference for new industrial construction or retrofit applications. [ABSTRACT FROM AUTHOR]

Abstract: This paper develops a novel dynamic framed slotted ALOHA (DFSA) algorithm based on Bayesian estimation to improve the throughput of the radio frequency identification (RFID) system. At first, four types of anti-collision algorithms for tag identification are analyzed. Then, the proposed DFSA based on Bayesian estimation is deduced and introduced. Compared with the conventional DFSA algorithms, this algorithm takes advantage of the evidence in previous frames as the a priori information of the current frame which can end up with more precise estimation of tag number and rational frame length adjustment. Finally, the common simulation tool from Matlab is used to demonstrate the effectiveness of the proposed new algorithm for the average throughput improvements of the RFID system. [Copyright &y& Elsevier]