The design of a remotely operated vehicle (ROV) with a size of 18.41 cm × 29.50 cm × 33.50 cm, and a weight of 15.64 kg, is introduced herein. The main goal is to capture underwater video by remote control communication in real time via Ethernet protocol. The ROV moves under the six brushless motors governed through a smart PID controller (Proportional + Integral + Derivative) and by using pulse-wide modulation with short pulses of 1 μs to improve the stability of the position in relation to the translational, ascent or descent, and rotational movements on three axes to capture images of 800 × 640 pixels on a video graphic array standard. The motion control, 3D position, temperature sensing, and video capture are performed at the same time, exploiting the four cores of the Raspberry Pi 3, using the threading library for parallel computing. In such a way, experimental results show that the video capture stage can process up to 42 frames per second on a Raspberry Pi 3. The remote control of the ROV is executed under a graphical user interface developed in Python, which is suitable for different operating systems, such as GNU/Linux, Windows, Android, and OS X. The proposed ROV can reach up to 100 m underwater, thus solving the issue of divers who can only reach 30 m depth. In addition, the proposed ROV can be useful in underwater applications such as surveillance, operations, maintenance, and measurement.
A new embedded chaotic cryptosystem is introduced herein with the aim to encrypt digital images and performing speech recognition as an external access key. The proposed cryptosystem consists of three technologies: (i) a Spartan 3E-1600 FPGA from Xilinx; (ii) a 64-bit Raspberry Pi 3 single board computer; and (iii) a voice recognition chip manufactured by Sunplus. The cryptosystem operates with four embedded algorithms: (1) a graphical user interface developed in Python language for the Raspberry Pi platform, which allows friendly management of the system; (2) an internal control entity that entails the start-up of the embedded system based on the identification of the key access, the pixels-entry of the image to the FPGA to be encrypted or unraveled from the Raspberry Pi, and the self-execution of the encryption/decryption of the information; (3) a chaotic pseudo-random binary generator whose decimal numerical values are converted to an 8-bit binary scale under the VHDL description of m o d ( 255 ) ; and (4) two UART communication algorithms by using the RS-232 protocol, all of them described in VHDL for the FPGA implementation. We provide a security analysis to demonstrate that the proposed cryptosystem is highly secure and robust against known attacks.
Nowadays, many cities have problems with traffic congestion at certain peak hours, which produces more pollution, noise and stress for citizens. Neural networks (NN) and machine-learning (ML) approaches are increasingly used to solve real-world problems, overcoming analytical and statistical methods, due to their ability to deal with dynamic behavior over time and with a large number of parameters in massive data. In this paper, machine-learning (ML) and deep-learning (DL) algorithms are proposed for predicting traffic flow at an intersection, thus laying the groundwork for adaptive traffic control, either by remote control of traffic lights or by applying an algorithm that adjusts the timing according to the predicted flow. Therefore, this work only focuses on traffic flow prediction. Two public datasets are used to train, validate and test the proposed ML and DL models. The first one contains the number of vehicles sampled every five minutes at six intersections for 56 days using different sensors. For this research, four of the six intersections are used to train the ML and DL models. The Multilayer Perceptron Neural Network (MLP-NN) obtained better results (R-Squared and EV score of 0.93) and took less training time, followed closely by Gradient Boosting then Recurrent Neural Networks (RNNs), with good metrics results but the longer training time, and finally Random Forest, Linear Regression and Stochastic Gradient. All ML and DL algorithms scored good performance metrics, indicating that they are feasible for implementation on smart traffic light controllers.
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