Mohd Rizal Arshad

Dynamic modeling and characteristics estimation for USM underwater glider

Underwater gliders are a new class of autonomous underwater vehicles, which are energy efficient, inexpensive and can be utilised for long duration mission. They use a ballast system and moving mass to glide in the saw-tooth path pattern through the ocean water column. This paper presents the dynamic model of USM underwater glider based on slender-body theory. In this work, a moving mass implementation and hydrodynamic interaction among hull, wings, and tail are included. The simulation results illustrate the dynamic characteristics and its response over velocity and angle of attack. In the results, the equilibrium angle of attack and velocity of underwater glider at minimum drag force can be obtained based on specific dimension. In this work, USM underwater glider produced minimum drag force at 1.7° angle of attack with velocity of 2.7 m/s. In addition, the effectiveness of a moving mass actuator also presented in the simulation results. This model will be used for further works in developing an optimized USM underwater glider with six degrees of freedom, including designing an establish full mathematical model of its motions and control algorithm.

An analysis of homeostatic motion control system for a hybrid-driven underwater glider

This paper presents an analysis of homeostatic controller, which controls the motion of a hybrid-driven underwater glider. The homeostatic controller is inspired from a biological process known as homeostasis, which maintains a stable state in the face of massively dynamics conditions. Within a biological context, organism homeostasis is an emergent property of the interactions between nervous, endocrine and immune system. Artificially these three systems are presented as Artificial Neural Network (ANN), Artificial Endocrine System (AES) and Artificial Immune System (AIS). The ANN is designed as the controller backbone, the AES is designed as the weight tuner, and the AIS is designed as the optimizer of the control system. The design objective is to obtain better control performance of the motion control system which includes the disturbance from the water currents. We have simulated the algorithm by using MatlabTM, and the results demonstrated that the homeostatic controller reduced the cost function of the control system and produced better control performance than the neuroendocrine controller.

Modelling of a novel design of microfluidic based acoustic sensor

This paper reports the initial investigation on a novel acoustic sensor design based on micro fluidic technology. The report includes the proposed design structure and the simulation of key structure materials that affect the performance of such sensor. Simulation works included the analysis of acoustic response of the membrane and the damping effect when the cavity gap is filled with liquid or electrolyte material. For membrane analysis three different materials, silicon nitride (Si3N4), Teflon and Polydimethylsiloxane (PDMS) are simulated to obtain the most responsive material with respect to acoustic pressure signal. PDMS was found to be the most responsive material with the deflection sensitivity of 1.6 μm/Pa. Both Si3N4 and Teflon yielded a sensitivity of 0.034 μm/Pa and 0.67 μm/Pa respectively. In damping analysis, Propylene Carbonate electrolyte was used as a backing layer that filled the cavity gap. With the PDMS was selected as the membrane structure, harmonic analysis was performed to investigat the damping effect caused by electrolyte material on resonance frequency and deflection sensitivity. Result showed that with the proposed design structure and electrolyte backing layer, the harmonic frequency was shifted to a lower value with the maximum deflection was reduced by about 50%. The result also suggests the needs for selecting the right gap material for micro fluidic application that can compromise the damping and the response of the membrane.

Motion simulation for propeller-driven USM underwater glider with controllable wings and rudder

This paper describes the dynamic model and motion simulation for propeller-driven USM underwater glider with independently controllable wings and rudder. The underwater glider is a highly efficient autonomous underwater vehicle which glides through the ocean water column in saw-tooth pattern. It uses buoyancy for propulsion by controlling ballast pump and internal moving mass. However, in our research work, we attempt to design an underwater glider which can be driven by buoyancy or propeller, and the external actuators (wings and rudder) can be controlled independently. We have designed the mathematical model and studied the dynamic characteristics of USM underwater glider. The simulation results demonstrate the motion of propeller-driven USM underwater glider based on different angles of wings and rudder. The results show the position of glider, linear and angular velocities of the glider, angle of attack and glider speed. With the resulting glider behavior in this open loop control output, we will extend the control approach in order to create an efficient biologically inspired control algorithm for an optimized hybrid-driven USM underwater glider.

Characterization of Microfluidic-Based Acoustic Sensor for Immersion Application

This paper reported the characterization study on a new type of acoustic microsensor meant for immersion or underwater application. The new proposed device is based on microfluidic technology that is found to be able to overcome the fabrication issues associated with conventional capacitive micromachined ultrasonic transducer device. The design parameters have been explained in detail as well as their design justification. In this paper, two experiments have been setup to characterize the device performance. First, the pulse catch technique was used to investigate the devices response toward acoustic pulse or burst signal. The result shows that different number of burst cycles affects the capacitive response of the device. Providing the acoustic projector with suitable burst cycle parameter had yielded capacitive response with resolution of 1.16 pF, which is useful to be deployed in any suitable application such as for control or communication purposes. Second, the vibration effect had been studied between the ranges of 10-100 Hz of vibration. For rapid transition of the vibration frequency, the fall transition has a comparable error compared with the rise transition with an error ratio of 37:1. In terms of fluctuation behavior of the response, operating the device at static or constant frequency vibration does not have significant differences on the response, indicating its stability at single-frequency operation.

Features detection and matching for visual simultaneous localization and mapping (VSLAM)

This paper presents the feature detection method for aerial image. The image captured from the navigation was used to select the best landmarks for localization and mapping in SLAM. A robust visual detection method has contributed to better landmark and data association selection. Therefore, different feature detection algorithms were compared to evaluate the best landmark detector and descriptor for the VSLAM. The performances of the feature detectors were evaluated using dataset provided by the Robotics Research Group at University of Oxford. The local images of matching effect on the detector and descriptor have proved the correctness of key point matching. The selected method has been validated and proven efficient for the VSLAM.

Adaptive shorelines detection for autonomous surface vessel navigation

Shorelines and water surface recognition are fundamental cues for ASV navigating in a river. In this paper we present an adaptive shorelines detection approach which is combination of feature-based and edge based method. Firstly, the river boundary is extracted adaptively by color segmentation with a sampling patch. Secondly, the river boundary constrained region is defined for Hough Transform lines detection. The results display that this approach is efficient for real river environment with satisfying accuracy performance.

Design and implementation of embedded multiprocessor architecture using FPGA

Modern embedded multiprocessors are complex systems that often require years to design and verify. A significant factor is that engineers must allocate a disproportionate share of their effort to ensure that modern FPGA chips architecture behave correctly. This paper proposes a design and creation of embedded multiprocessors architecture system focusing on its design area and performance. Embedded multiprocessor design presents challenges and opportunities that stem from task coarse granularity and the large number of inputs and outputs for each task. We have therefore designed a new architecture called embedded concurrent computing (ECC), which is implemented on an FPGA chip using VHDL. We synthesized and evaluated the embedded system based on an Altera environment. The performances of a realistic application show scalable speedups comparable to that of the simulation. The results show many data is gathered with the systems, such as size 18699 logic elements and maximum frequency 212 MHz. These data have been gathered by synthesis. Implementation was achieved by the provision of low complexities in terms of FPGA resource usage and frequency. In addition, the design methodology allows scalable embedded multiprocessors for system expansion.

Evaluation of Morphological Features for Breast Cells Classification Using Neural Networks

Rapid technology advancement has contributed towards achievements in medical applications. Cancer detection in its earliest stage is definitely very important for effective treatments. Innovation in diagnostic features of tumours may play a central role in development of new treatment methods. Thus, the purpose of this study is to evaluate proposed morphological features to classify breast cancer cells. In this paper, the morphological features were evaluated using neural networks. The features were presented to several neural networks architecture to investigate the most suitable neural network type for classifying the features effectively. The performance of the networks was compared based on resulted mean squared error, accuracy, false positive, false negative, sensitivity and specificity. The optimum network for classification of breast cancer cells was found using Hybrid Multilayer Perceptron (HMLP) network. The HMLP network was then employed to investigate the diagnostic capability of the features individually and in combination. The features were found to have important diagnostic capabilities. Training the network with a larger number of dominant morphological features was found to significantly increase the diagnostic capabilities. A combination of the proposed features gave the highest accuracy of 96%.

Tracking of multiple markers based on color for visual servo control in underwater docking

Autonomous underwater vehicle can perform underwater docking to power up its battery in order to prolong operation thereby reduces maintenance and cost. Prior researches indicate visual servo technique is used for docking operation. Basically, image of markers placed on underwater station is captured and the vehicle surges towards the station. These previous works focuses on deep water environment where the view is uncluttered. It will be a challenging problem if the view has so much information to be processed likewise shallow water which inadvertently reduces the chances of successful docking operation. This paper proposed tracking method using color where the markers were placed in cluttered environment. The method shows a promising result where absolute errors between current captured images and a desired image kept on reducing as the camera closing in on the markers.