Parameter Prediction for Lorenz Attractor by using Deep Neural Network

Nowadays, most modern deep learning models are based on artificial neural networks. This research presents Deep Neural Network to learn the database, which consists of high precision, a strange Lorenz attractor. Lorenz system is one of the simple chaotic systems, which is a nonlinear and characterized by an unstable dynamic behavior. The research aims to predict the parameter of a strange Lorenz attractor either yes or not. The primary method implemented in this paper is the Deep Neural Network by using Phyton Keras library. For the neural network, the different number of hidden layers are used to compare the accuracy of the system prediction. A set of data is used as the input of the neural network, while for the output part, the accuracy of prediction data is expected. As a result, the accuracy of the testing result shows that 100% correct prediction can be achieved when using the training data. Meanwhile, only 60% correct prediction is achieved for the new random data

Quadrotor UAV indoor localization using embedded stereo camera

Localization of Small-Size Unmanned Air Vehicles (UAVs) such as the Quadrotors in Global Positioning System (GPS)-denied environment such as indoors has been done using various techniques. Most of the experiment indoors that requires localization of UAVs, used cameras or ultrasonic sensors installed indoor or applied indoor environment modification such as patching (Infra Red) IR and visual markers. While these systems have high accuracy for the UAV localization, they are expensive and have less practicality in real situations. In this paper a system consisting of a stereo camera embedded on a quadrotor UAV (QUAV) for indoor localization was proposed. The optical flow data from the stereo camera then are fused with attitude and acceleration data from our sensors to get better estimation of the quadrotor location. The quadrotor altitude is estimated using Scale Invariant Feature Transform (SIFT) Feature Stereo Matching in addition to the one computed using optical flow. To avoid latency due to computational time, image processing and the quadrotor control are processed threads and core allocation. The performance of our QUAV altitude estimation is better compared to single-camera embedded QUAVs due to the stereo camera triangulation, where it leads to better estimation of the x-y position using optical flow when fused together

Robot Local Network Using TQS Protocol for Land-to-Underwater Communications, Journal of Telecommunications and Information Technology, 2019, nr 1

This paper presents a model and an analysis of the Tag QoS switching (TQS) protocol proposed for heterogeneous robots operating in different environments. Collaborative control is topic that is widely discussed in multirobot task allocation (MRTA) – an area which includes establishing network communication between each of the connected robots. Therefore, this research focuses on classifying, prioritizing and analyzing performance of the robot local network (RLN) model which comprises a point-to-point topology network between robot peers (nodes) in the air, on land, and under water. The proposed TQS protocol was inspired by multiprotocol label switching (MPLS), achieving a quality of service (QoS) where swapping and labeling operations involving the data packet header were applied. The OMNET++ discrete event simulator was used to analyze the percentage of losses, average access delay, and throughput of the transmitted data in different classes of service (CoS), in a line of transmission between underwater and land environments. The results show that inferior data transmission performance has the lowest priority with low bitrates and extremely high data packet loss rates when the network traffic was busy. On the other hand, simulation results for the highest CoS data forwarding show that its performance was not affected by different data transmission rates characterizing different mediums and environments

Elevator‘s External Button Recognition and Detection for Vision-based System

Recently, autonomous transporter offers the assistance and delivery for user but they are only focusing on single floor environment. To widen up fields of robotic, they teach robot to use an elevator because elevator provides an essential means of faster movement across level. However, most of the mobile service robot failed to detect elevator’s position due to the complex background and reflection on the elevator door and button panel itself. This paper presents a new strategy for recognition method to detect elevator by detecting their external button efficiently. Sobel is use as edge detection operator to find the estimated absolute gradient magnitude at each point in an input grayscale image. Then, but we enhanced the technique by combining it with wiener filter to reduce the amount of noise present in a signal by comparing the signal with an estimation of the desired noiseless signal. This filter helps to eliminate the reflection image on elevator’s button panel before it can be converted to black and white image (binarization). The process followed by some morphological and structuring elements process. Tests have been done and the results shown that elevator’s external button can be recognized and detected by those entire framework

Automated-tuned hyper-parameter deep neural network by using arithmetic optimization algorithm for Lorenz chaotic system

Deep neural networks (DNNs) are very dependent on their parameterization and require experts to determine which method to implement and modify the hyper-parameters value. This study proposes an automated-tuned hyper-parameter for DNN using a metaheuristic optimization algorithm, arithmetic optimization algorithm (AOA). AOA makes use of the distribution properties of mathematics’ primary arithmetic operators, including multiplication, division, addition, and subtraction. AOA is mathematically modeled and implemented to optimize processes across a broad range of search spaces. The performance of AOA is evaluated against 29 benchmark functions, and several real-world engineering design problems are to demonstrate AOA’s applicability. The hyper-parameter tuning framework consists of a set of Lorenz chaotic system datasets, hybrid DNN architecture, and AOA that works automatically. As a result, AOA produced the highest accuracy in the test dataset with a combination of optimized hyper-parameters for DNN architecture. The boxplot analysis also produced the ten AOA particles that are the most accurately chosen. Hence, AOA with ten particles had the smallest size of boxplot for all hyper-parameters, which concluded the best solution. In particular, the result for the proposed system is outperformed compared to the architecture tested with particle swarm optimization

A New Hybrid Image Encryption Technique Using Lorenz Chaotic System and Simulated Kalman Filter (SKF) Algorithm

Nowadays, encryption is one of the most popular and effective security methods used by company and organizations. A new hybrid technique, Lorenz chaotic system and an optimization algorithm, Simulated Kalman Filter (SKF) had been proposed to solve image encryption problem. The objectives of the hybrid technique are to improve the security and add noise from the optimization algorithm and generate chaotic secret key. To achieve that, Lorenz chaotic system is implemented to this method and produce secret key sequence. SKF is one of the optimization methods that had been proved to have great performance in engineering applications from prediction, measurement, and estimation process. Thus, the proposed method is outperformed the results and analysis compared to literature as benchmarks. In short, the proposed hybrid approach is agile and efficient to apply in image encryption proble

Evaluating IoT based passive water catchment monitoring system data acquisition and analysis

Water quality is the main aspect to determine the quality of aquatic systems. Poor water quality will pose a health risk for people and ecosystems. The old methods such as collecting samples of water manually and testing and analysing at lab will cause the time consuming, wastage of man power and not economical. A system is needed to provide a real-time data for environmental protection and tracking pollution sources. This paper aims to describe on how to monitor water quality continuously through IoT platform. Water Quality Catchment Monitoring System was introduced to check and monitor water quality continuously. It’s features five sensors which are temperature sensor, light intensity sensor, pH sensor, GPS tracker and Inertia Movement Unit (IMU). IMU is a new feature in the system where the direction of x and y is determined for planning and find out where a water quality problem exists by determining the flow of water. The system uses an internet wireless connection using the ESP8266 Wi-Fi Shield Module as a connection between Arduino Mega2560 and laptop. ThingSpeak application acts as an IoT platform used for real-time data monitorin

U-slot microstrip patch array antenna for UHF RFID reader

This paper aims to design and analyze a microstrip patch array antenna for the application of Radio Frequency Identification (RFID). Array antennas are widely used in the RFID applications as it offers high gain and directivity to allow long distance read range. The microstrip patch is arranged in 2 × 2 array and is printed on FR-4 materials. In compliance with the Malaysian RFID regulated range of frequency of 919 to 923 MHz, the antenna is designed to meet its specifications. The operating frequency of the microstrip patch antenna array is 921 MHz. The FR-4 substrate with a dielectric constant of 4.7 and height of 0.16 cm. Theoretical studies and calculations on this topic have been done in order to design the microstrip patch antenna array with the correct dimensions. By using the CST Microwave Studio 2014 as the primary software to model and simulate the results, there are a few parameters that are going to be analyze which includes reflection coefficient, Voltage Standing Ratio (VSWR), gain, directivity, radiation pattern and bandwidth

Optimization of radial distribution network with distributed generation using particle swarm optimization considering load growth

This article presents a combination of particle swarm optimization (PSO) algorithm and the backward/forward sweep power flow (BFSPF) approach to determine the optimal bus location and size of distributed generation (DG) in a radial distribution network (RDN) considering the load growth. The analysis of the proposed optimization framework is performed using MATLAB and tested on the 33–bus RDN subject to minimize the power losses. The solutions accomplished through the experiments considering four case studies show significant reductions in the system’s total power loss and improvement in desired bus voltage profiles. With the installation of DG, the percentage of reduction in power loss is 47.38% compared to the system’s power loss without DG. The DG size and location to be installed are determined at the 6th bus location with 2.59 MW. The results show that power losses will increase with the increase in load demand. The findings reveal that load growth does not influence the optimal location of the DG. However, the sizes of DGs need to be revised when considering growth in load conditions