Khairulmizam Samsudin

A highly interpretable fuzzy rule base using ordinal structure for obstacle avoidance of mobile robot

Conventional fuzzy logic controller is applicable when there are only two fuzzy inputs with usually one output. Complexity increases when there are more than one inputs and outputs making the system unrealizable. The ordinal structure model of fuzzy reasoning has an advantage of managing high-dimensional problem with multiple input and output variables ensuring the interpretability of the rule set. This is achieved by giving an associated weight to each rule in the defuzzification process. In this work, a methodology to design an ordinal fuzzy logic controller with application for obstacle avoidance of Khepera mobile robot is presented. The implementation will show that ordinal structure fuzzy is easier to design with highly interpretable rules compared to conventional fuzzy controller. In order to achieve high accuracy, a specially tailored Genetic Algorithm (GA) approach for reinforcement learning has been proposed to optimize the ordinal structure fuzzy controller. Simulation results demonstrated improved obstacle avoidance performance in comparison with conventional fuzzy controllers. Comparison of direct and incremental GA for optimization of the controller is also presented.

Evaluation of fall detection classification approaches

As we grow old, our desire for being independence does not decrease while our health needs to be monitored more frequently. Accidents such as falling can be a serious problem for the elderly. An accurate automatic fall detection system can help elderly people be safe in every situation. In this paper a waist worn fall detection system has been proposed. A tri-axial accelerometer (ADXL345) was used to capture the movement signals of human body and detect events such as walking and falling to a reasonable degree of accuracy. A set of laboratory-based falls and activities of daily living (ADL) were performed by healthy volunteers with different physical characteristics. This paper presents the comparison of different machine learning classification algorithms using Waikato Environment for Knowledge Analysis (WEKA) platform for classifying falling patterns from ADL patterns. The aim of this paper is to investigate the performance of different classification algorithms for a set of recorded acceleration data. The algorithms are Multilayer Perceptron, Naive Bayes, Decision tree, Support Vector Machine, ZeroR and OneR. The acceleration data with a total data of 6962 instances and 29 attributes were used to evaluate the performance of the different classification algorithm. Results show that the Multilayer Perceptron algorithm is the best option among other mentioned algorithms, due to its high accuracy in fall detection.

Interoperability among Heterogeneous Systems in Smart Home Environment

The smart home environment is highly characterized by heterogeneity with many systems that need to interoperate and perform their tasks efficiently. With rapid growth of services, applications and devices in smart home environment, the interoperability factor seems still elusive. This is due to the nature of smart home as distributed architecture that needs certain degree of interoperability and interoperation for managing heterogeneous systems comprising of different platforms. These heterogeneous systems are developed in isolation and consist of different operating systems, different programming platform and different tier of services. There is need for a mechanism that could make the heterogeneous systems `talk¿ each other and interoperate in an efficient manner regardless of operating platform. Web services seems to be state-of-the art technology that could be one potential solution in providing greater interoperability. In this paper we describe interoperability issues that need to be considered and we present a solution based on Simple Object Access Protocol (SOAP) technology to solve the interoperability problem in smart home environment.

Development of a Miniature Robot for Swarm Robotic Application

Biological swarm is a fascinating behavior of nature that has been successfully applied to solve human problem especially for robotics application. The high economical cost and large area required to execute swarm robotics scenarios does not permit experimentation with real robot. Model and simulation of the mass number of these robots are extremely complex and often inaccurate. This paper describes the design decision and presents the development of an autonomous miniature mobile-robot (AMiR) for swarm robotics research and education. The large number of robot in these systems allows designing an individual AMiR unit with simple perception and mobile abilities. Hence a large number of robots can be easily and economically feasible to be replicated. AMiR has been designed as a complete platform with supporting software development tools for robotics education and researches in the Department of Computer and Communication Systems Engineering, UPM. The experimental results demonstrate the feasibility of using this robot to implement swarm robotic applications.

Imitation of Honeybee Aggregation with Collective Behavior of Swarm Robots.

This paper analyzes the collective behaviors of swarm robots that play role in the aggregation scenario. Honeybee aggregation is an inspired behavior of young honeybees which tend to aggregate around an optimal zone. This aggregation is implemented based on variation of parameters values. In the second phase, two modifications on original honeybee aggregation namely dynamic velocity and comparative waiting time are proposed. Results of the performed experiments showed the significant differences in collective behavior of the swarm system for different algorithms.

A Short-Range Infrared Communication for Swarm Mobile Robots

This paper presents another short-range communication technique suitable for swarm mobile robots application. Infrared is used for transmitting and receiving data packets and obstacle detection. The infrared communication system is used for an autonomous mobile robot (UPM-AMR) that will be used as a low-cost platform for robotics research. A pulse-code modulation (PCM) digital scheme is used for transmitting data. The reflected infrared signal is also used for distance estimation for obstacle avoidance. Analysis of robot’s behaviors shows the feasibility of using infrared signals to obtain a reliable local communication between swarm mobile robots.

Evaluation of Genetic Algorithm based solar tracking system for Photovoltaic panels

The maximum power supplied by a photovoltaic (PV) panels system change over time. It depends on environmental factors such as the solar irradiation and the temperature of these panels. The average solar energy harvested by the conventional solar panels during the course of the day, is not always maximized. This is due to the static placement of the panel which limits their area of exposure to the sun. In practice, there are three possible approaches for maximizing the solar power extraction in medium and large scale PV systems are sun tracking, maximum power point (MPP) tracking or combination of both. In this paper, a genetic algorithm (GA) has been proposed utilizing sun tracking approaches to maximize the performance of PV panels. Literature suggested that the PV panels could produce maximum power if the panels have angle of inclination zero degree to the sun position. This work evaluate the best combination of GA parameters to optimize a solar tracking system for PV panels in terms of azimuth angle and tilt angle. Simulation results demonstrated the ability of the proposed GA system to search for optimal panel positions in term of consistency and convergence properties. It also has proved the ability of the GA-solar to adapt to different environmental conditions and successfully track sun positions in finding the maximum power by precisely orienting the PV panels.

COMPARATIVE STUDY ON WAVELET FILTER AND THRESHOLDING SELECTION FOR GPS/INS DATA FUSION

Navigation and guidance of an autonomous vehicle require determination of the position and velocity of the vehicle. Therefore, fusing the Inertial Navigation System (INS) and Global Positioning System (GPS) is important. Various methods have been applied to smooth and predict the INS and GPS errors. Recently, wavelet de-noising methodologies have been applied to improve the accuracy and reliability of the GPS/INS system. In this work, analysis of real data to identify the optimal wavelet filter for each GPS and INS component for high quality error estimation is presented. A comprehensive comparison of various wavelet thresholding selections with different level of decomposition is conducted to study the effect on GPS/INS error estimation while maintaining the original features of the signal. Results show that while some wavelet filters and thresholding selection algorithms perform better than others on each of the GPS and INS components, no specific parameter selection perform uniformly better than others.

Optimizing of ANFIS for estimating INS error during GPS outages

Global positioning system (GPS) has been extensively used for land vehicle navigation systems. However, GPS is incapable of providing permanent and reliable navigation solutions in the presence of signal evaporation or blockage. On the other hand, navigation systems, in particular, inertial navigation systems (INSs), have become important components in different military and civil applications due to the recent advent of micro-electro-mechanical systems (MEMS). Both INS and GPS systems are often paired together to provide a reliable navigation solution by integrating the long-term GPS accuracy with the short-term INS accuracy. This article presents an alternative method to integrate GPS and INS systems and provide a robust navigation solution. This alternative approach to Kalman filtering (KF) utilizes artificial intelligence based on adaptive neuro-fuzzy inference system (ANFIS) to fuse data from both systems and estimate position and velocity errors. The KF is usually criticized for working only under predefined models and for its observability problem of hidden state variables, sensor error models, immunity to noise, sensor dependency, and linearization dependency. The training and updating of ANFIS parameters is one of the main problems. Therefore, the challenges encountered implementing an ANFIS module in real time have been overcome using particle swarm optimization (PSO) to optimize the ANFIS learning parameters since PSO involves less complexity and has fast convergence. The proposed alternative method uses GPS with INS data and PSO to update the intelligent PANFIS navigator using GPS/INS error as a fitness function to be minimized. Three methods of optimization have been tested and compared to estimate the INS error. Finally, the performance of the proposed alternative method has been examined using real field test data of MEMS grade INS integrated with GPS for different GPS outage periods. The results obtained outperform KF, particularly during long GPS signal blockage.

A parallel XTS encryption mode of operation

Securing data stored inside the storage devices is becoming an important concern in computer security now. It is known that the most efficient techniques to protect storage devices are using cryptography. Developing newer and more secure encryption algorithms and modes of operation might be critically important to protect these devices since conventional disk encryption algorithms, such as CBC mode, have shown serious security flaws. In this paper, the newly standardized IEEE XTS encryption mode of operation for storage encryption (P1619 standard) has been implemented using parallel design. A performance comparison between the sequential and parallel algorithms of XTS mode has been presented. The parallel XTS algorithm has shown a speedup of 1.80 (with 90% efficiency) faster than the sequential algorithm. In these simulations, AES is used as encryption algorithm with 256-bit encryption key.