Contrast enhancement algorithms have been evolved through last decades to meet the requirement of its objectives. Actually, there are two main objectives while enhancing the contrast of an image: (i) improve its appearance for visual interpretation and (ii) facilitate/increase the performance of subsequent tasks (e.g., image analysis, object detection, and image segmentation). Most of the contrast enhancement techniques are based on histogram modifications, which can be performed globally or locally. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is a method which can overcome the limitations of global approaches by performing local contrast enhancement. However, this method relies on two essential hyperparameters: the number of tiles and the clip limit. An improper hyperparameter selection may heavily decrease the image quality toward its degradation. Considering the lack of methods to efficiently determine these hyperparameters, this article presents a learning-based hyperparameter selection method for the CLAHE technique. The proposed supervised method was built and evaluated using contrast distortions from well-known image quality assessment datasets. Also, we introduce a more challenging dataset containing over 6200 images with a large range of contrast and intensity variations. The results show the efficiency of the proposed approach in predicting CLAHE hyperparameters with up to 0.014 RMSE and 0.935 R 2 values. Also, our method overcomes both experimented baselines by enhancing image contrast while keeping its natural aspect.

Visible light communication (VLC) based localization is a potential candidate for wide range indoor localization applications. In this paper, we propose a VLC architecture based on orthogonal frequency division multiplexing (OFDM) with multiple functionalities integrated in the same system, i.e., the 3-D receiver location, the control of the room illumination intensity, as well as the data transmission capability. Herein we propose an original methodology for LED power discrimination applying spatial optical OFDM (SO-OFDM) structure for position estimation. The hybrid locator initially makes a first estimate using a weighted angle-of-arrival (WAoA)-based locator which is then used as the starting point of the recursive estimator based on the strength of the received signal (RSS). Hence, the first stage is deployed to increase convergence probability, reducing the root-mean-square error (RMSE) and the number of iterations of the second stage. Also, a performance vs computational complexity comparative analysis is carried out with parameter variations of these estimators. The numerical results indicate a decade improvement in the RMSE for each two decades of decrement of power noise on the receiver photodiode. The best clipping factor is obtained through the analysis of locator accuracy and transmission capacity for each simulated system. Finally, the numerical results also demonstrate effectiveness, robustness, and efficiency of the proposed architecture.

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In the mobile robotic systems, a precise estimate of the robot pose with the intention of the optimization in the path planning is essential for the correct performance, on the part of the robots, for tasks that are destined to it. This paper describes the use of RF digital signal interacting with beacons for computational triangulation in the way to provide a pose estimative at bidimensional indoor environment, where GPS system is out of range. This methodology takes advantage of high-performance multicore DSP processors to calculate ToF of the order about ns. Sensors data like odometry, compass, and the result of triangulation Cartesian estimative, are fused for better position estimative. It uses a mathematical and computational tool for nonlinear systems with time-discrete sampling for pose estimative calculation of mobile robots, with the utilization of extended Kalman filter (EKF). A mobile robot platform with differential drive and nonholonomic constraints is used as a base for state space, plants and measurements models that are used in the simulations and validation of the experiments.

With the fast innovation of the hardware and software technologies in embedded systems area, with application in the robotics and automation, more and more it becomes necessary the development of applications based on methodologies that facilitate future modifications, updates and increments in the original projected system. In this way, this article presents a system of opened architecture, distributing the several control actions in growing levels of complexity and using resources of reconfigurable computing proposal oriented to embedded systems implementation. Software and the hardware are structuralized in independents blocks, with connection through common bus. Is presented the functional blocks where the use of DSP processor board is illustrated for local control level. The supervisory control level is implemented in an IBM PC platform and is connected with the local control level, in the robot, through Ethernet WI-FI link. Also are seen the control blocks that use reprogrammable logic components (FPGA) hardware projected for sensors fusion control interface and actuators controllers and the study and applications of new structures control that possibility good performance in relation to the parameter variations. The sensors, actuators, RF transceiver unit, and others necessary peripheral components for the project implementation with their implementation blocks are listed in the article.