Angelo R. dela Cruz

Obstacle avoidance of hybrid mobile-quadrotor vehicle with range sensors using fuzzy logic control

This study presents a fuzzy logic based approach to a hybrid mobile quadrotor vehicle that is able to perform goal seeking and obstacle avoidance, given that the obstacles are nonmoving and are along a fixed path. Two range sensors will be used to construct the input variable of the fuzzy logic control. The algorithms are developed to achieve goal position while avoiding obstacles. Simulations are conducted and the efficiency of the results using the method is proved using MATLAB.

A multiple level MIMO fuzzy logic based intelligence for multiple agent cooperative robot system

Fuzzy Logic is a many valued logic and it is very similar to human reasoning which is not binary. It uses approximate measures rather than exact, making it suitable for linguistic variable and analysis. It has been applied to many applications in artificial intelligence, control and robotics. In this paper, the authors develop an artificial intelligence using multiple fuzzy logic for a dynamic multiple agent robot system. The system is made up of multiple robots with multiple identity assignment; which means that each robot will have its distinct behavior. In order to design pure fuzzy logic artificial intelligence, we used fuzzy logic block in different parallel and series configuration making giving it multiple fuzzy logic levels. Furthermore, there is multiple input - multiple output (MIMO) fuzzy logic implementation in one of our several fuzzy logic blocks, this is necessary in order to utilize pure fuzzy logic control in the whole artificial intelligence. The multi agent cooperative robot platform we choose to test our artificial intelligence is a multiple robot system for FIRA Micro-Robot World Soccer Tournament (MiroSot). In our setup, there are three robots to be assigned dynamically with three different identities; the Forward, the Back and the Goal-keeper. Robot identity assignment depends on the position of each robot with respect to the position of the ball. To tune each fuzzy logic block individually isolation is done. Some tuning procedures are performed in a simulator while most of them are tuned in the actual platform. Although tuning procedures are rigorous, the linguistic approach and human reasoning nature of fuzzy logic made it possible to achieve its completion. Overall, the proposed artificial intelligence produced favorable response based on the expected outcome and experimentations.

Joint Source-Channel Rate Allocation for Wireless Video Transmission Based on Distortion Estimation

Joint source channel coding provides an efficient framework in minimizing end-to-end distortion. In this paper, we propose a method of dynamically allocating the available bit rate between video encoder and channel encoder. The rate allocation algorithm is dependent on the estimated quantization and transmission distortion. We propose a quantization distortion model that is based on residual information and quantization parameter. Transmission distortion is estimated based on error propagation and error concealment distortion. Results show good estimate of the actual distortion and able to estimate the distortion before encoding the frame. The proposed distortion models are used to implement a joint source-channel video coding scheme using standard H.264/AVC encoder. The proposed scheme provides significant improvement in decoded video quality which can adapt to varying channel condition by proper allocation of available bit rate between video and channel encoder. The proposed distortion model can be extended in cross-layer optimization between channel encoder code rate and intra-refresh rate selection.

Low complexity adaptive intra-refresh rate for real-time wireless video transmission

Real-time wireless video transmission systems must consider both error resiliency and low complexity. However, most error resilient features of recent video coding standards tend to increase computational complexity of the encoder. In this paper, we propose a low complexity error resilient joint source-channel adaptive intra-refresh rate scheme where the optimum number of intra-coded macroblocks is determined at frame level based on the minimum estimated end-to-end distortion. In this work, we propose source and transmission distortion models whose parameters are independent on sequence type which allows real-time video encoding. The source distortion model is based on residual information and quantization step using linear least square method. The residual information is estimated using the mean-absolute difference (MAD) prediction model based on the linear relationship between intra-refresh rate and MAD. The transmission distortion model is based on recursive model using reliable feedback channel. The proposed models are used to implement a joint source-channel video coding scheme using standard H.264/AVC encoder. Accurate estimate of the actual distortion at various refresh rates are achieved and able to estimate the distortion before encoding the frame. The proposed scheme is compared with random and periodic intra refresh schemes under wireless fading channel. Improvements in PSNR quality are measured which verifies the effectiveness of the proposed scheme especially in time varying channel conditions.

Acquisition and analysis of electrocardiogram waveforms with diagnosis transmission through short message service communication system

In this study, we integrated the electrocardiogram waveform acquisition, analysis and diagnosis transmission into a single monitoring system. The electrocardiogram monitoring system is divided into 3 stages, namely the data acquisition stage, the decision making stage and the alerting system stage. The integration of the electrocardiogram waveforms acquisition, analysis and diagnosis transmission into a single monitoring system has been successfully implemented. This monitoring system has demonstrated the capability to measure and detect heart rate and heart rhythm respectively. In a bigger picture, an individual can implement this improvised monitoring system using a personal computer interfaced with a mobile phone.

A comparative study between DWT-ANFIS and DWT-SVM in ECG classification

Electrocardiogram (ECG) signal shows the different electrical activities of the heart. Since manual analysis is tedious and time-consuming, an offline automated ECG analysis and classification scheme is proposed in this study. Using Discrete Wavelet Transform (DWT) as feature extraction, this comparative study focused on ECG classification between Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM). It aims to classify normal and abnormal heartbeats with the addition of non-ECG signals. Abnormal heartbeats include ECG signals with atrial fibrillation and ventricular tachycardia. The ECG signals that were used as basis in comparing the results of the two pairings came from MIT-BIH Arrhythmia Database that is found on PhysioNet while the non-ECG signals came from another database which is related to stress recognition. ECG signal analysis in this study comprises three stages: the acquisition of signal from database, the feature extraction and the classification for determining the signal. In this study, SVM using the kernel function Radial Basis Function (RBF) paired with the mother wavelet Daubechies proved to be better than ANFIS paired with Haar mother wavelet.

Development of intelligent transportation system for Philippine license plate recognition

Due to the growing need for surveillance and license plate identification in the country, a Philippine license plate recognition system was proposed by adopting accurate and suitable algorithms for each phase, namely, license plate preprocessing, plate localization, character segmentation and character recognition, in recognizing old and new license plates of both private and public vehicles and motorcycles. Preprocessing is based on improved Bernsen algorithm which is a shadow removal method. This algorithm performs local thresholding of a two-dimensional array image in which the grayscale input image is converted into a binary image. For plate localization, Connected Component Analysis is utilized for labelling regions and preserving the characters by removing the unwanted information. Prior to character segmentation, Hough transform is used for tilt correction of the extracted plate. From the corrected plate, the horizontal and vertical projections are obtained to locate the characters and the boundaries will be used as basis for segmentation. Character recognition involves two phases, namely, feature extraction using Dual-Tree Complex Wavelet Transform and classification using Artificial Neural Networks. The proposed algorithms are adjusted with parameters suitable for detecting and recognizing the format and specifications of Philippine vehicular license plates, resulting to practical plate character detection accuracy of 85.6667%, character recognition accuracy of 94.0183% and a practical license plate recognition accuracy of 72.83% (i.e. in the actual test run). However, the proposed system has some restrictions, which will be studied in the future. To make the techniques applicable in less restrictive working conditions, further efforts will be concentrated on the adaptivity of the set parameters on the working environment.

Power management system for Multi-Input Multi-Output energy harvesters using fuzzy logic

With the increase in energy consumption, the concept of harvesting energy in the surroundings awaken a renewed interest. Scarcity of resources has been a great matter to all and researchers have been innovating new methods in harvesting energy. A power management system (PMS) is needed to manage and maximize the output energy coming from the piezoelectric and solar photovoltaic harvesters. However, there is no existing PMS for Multi-Input Multi-Output (MIMO) specified for the said harvesters. Hence, this paper focuses on the development of a fuzzy logic controller implemented in the PMS with the use of Fuzzy Interface System of MATLAB in order to achieve a better overall performance and utilization of the distribution of the generated energy from the harvester. In addition, the simulation and behavior of the decision making for the PMS was obtained.

Depth map characterization of RGB-D sensor for obstacle detection system

This paper presents a characterization of the depth map from an RGB-D sensor, Kinect 360, to be used as an input to an obstacle detection system. Since the accuracy, reliability and timeliness of the data are crucial in the system, there is a need to characterize it. Sensor repeatability tests are done to characterize the depth map. The mean and standard deviation of measured depths and mean error were computed to describe its detection accuracy and precision. Evident noises during tests were defined.

Video transmission for broadband power line channel

This paper introduces a simulated model of broadband intrabuilding power line noise. We obtained a periodic burst error caused by cyclostationary noise inherent in a typical intrabuilding powerline channel. The simulated model is used to locate erroneous video packets and its periodicity of occurrence. The PSNR quality of decoded video sequence subjected to the noise model is compared with the lossless decoded sequence. Results show that periodic burst error can be exploited to introduce various error resilient techniques that will improve the quality of the decoded video sequences.