Mohamad Sofian Abu Talip

Fuzzy Logic Based Algorithm for Disk Scheduling Policy

Hard disk is the important component in computer system as data storage device. It is being used to store large amount of information in all modern computers. Disk performance can be improved by incorporating disk scheduling optimization for satisfy pending request in the disk queue. Operating system, being the interface between the user and the hardware, has the responsibility to use hardware efficiently. Current algorithm have not knowledge about the additional information provided by operating system and not been used for improvement of the seeking and/or tracking accessing. The paper present fuzzy model knowledge based for improving the disk scheduling. We use the Matlab implementation of this algorithm to compare and contrast the output with existing algorithms. Incorporating knowledge-based scheduling policy using fuzzy logic improves overall system behavior. The performance of the new disk scheduling algorithm based on fuzzy logic shows that the seek distance movement could be decreased by applying our methodology.

Partially reconfigurable flux calculation scheme in advection term computation

Fast Aerodynamics Routines (FaSTAR) is one of the most recent fluid dynamics software package. The problem of FaSTAR is hard to be executed in parallel machines because of its irregular and unpredictable data structure. Exploiting reconfigurable hardware with their advantages to make up for the inadequacy of the existing high performance computers had gradually become the solutions. However, a single FPGA is not enough for the FaSTAR package because the whole module is very large. Instead of using many FPGAs, partially reconfigurable hardware available in recent FPGAs is explored for this application. Advection term computation module in FaSTAR is chosen as a target subroutine. We proposed a reconfigurable flux calculation scheme using partial reconfiguration technique to save hardware resources to fit in a single FPGA. We developed flux computational module and five flux calculation schemes are implemented as reconfigurable modules. This implementation has advantages of up to 62.75% resource saving and enhancing the configuration speed by 6.28 times. Performance evaluation also shows that 2.65 times acceleration is achieved compared to Intel Core 2 Duo at 2.4 GHz.

Tree species recognition system based on macroscopic image analysis

Abstract An automated wood texture recognition system of 48 tropical wood species is presented. For each wood species, 100 macroscopic texture images are captured from different timber logs where 70 images are used for training while 30 images are used for testing. In this work, a fuzzy pre-classifier is used to complement a set of support vector machines (SVM) to manage the large wood database and classify the wood species efficiently. Given a test image, a set of texture pore features is extracted from the image and used as inputs to a fuzzy pre-classifier which assigns it to one of the four broad categories. Then, another set of texture features is extracted from the image and used with the SVM dedicated to the selected category to further classify the test image to a particular wood species. The advantage of dividing the database into four smaller databases is that when a new wood species is added into the system, only the SVM classifier of one of the four databases needs to be retrained instead of those of the entire database. This shortens the training time and emulates the experts’ reasoning when expanding the wood database. The results show that the proposed model is more robust as the size of wood database is increased.

Cost effective implementation of flux limiter functions using partial reconfiguration

Computational Fluid Dynamics (CFD) is used as a common design tool in aerospace industry. UPACS, a package for CFD is convenient for users, since a customized simulator can be built just by selecting required functions. The problem is its computation speed which is hard to be enhanced by using clusters due to its complex memory access patterns. As an economical solution, accelerators using FPGAs are hopeful candidates. However, the total scale of UPACS is too large to be implemented on small numbers of FPGAs. For cost efficient implementation, partial reconfiguration which can dynamically reconfigure only required functions is proposed in this paper. Here, MUSCL algorithm used frequently in UPACS is selected as a target. Partial reconfiguration is applied to the flux limiter functions (FLF) in MUSCL. Four FLFs are implemented for Turbulence MUSCL (TMUSCL) and eight FLFs are for Convection MUSCL (CMUSCL). All FLFs are developed independently and separated from the top MUSCL module. At start-up, only required FLFs are selected and deployed to the system without interfering the other modules. This implementation has successfully reduced the resource utilization by 44% to 63%. Total power consumption also reduced by 33%. Configuration speed is improved by 34-times faster as compared to fully reconfiguration method. All implemented functions achieved at least 17 times speed-up compared with the software implementation.

A design of one-dimensional Euler equations for Fluid Dynamics on FPGA

Modeling, simulation and optimization using computing tools are the core approach nowadays in science complementary to experiment and theory. Computational Fluid Dynamics (CFD) has evolved many years ago to simulate fluid physics by solving Navier-Stokes equations, or its simple variants, Euler equations. However, most problems spend many hours to get solutions even with expensive supercomputers or clusters. The long computation time required for fluid dynamics simulations has lead the industry to look for some alternatives. Field Programmable Gate Arrays (FPGAs) are becoming more and more attractive for high precision scientific computations. FPGA holds the potential to alleviate this situations. It is possible for an FPGA to configure hundreds of multipliers working concurrently. In this paper, the authors explain the design on implementing the one-dimensional Euler equations in hardware. Two designs with single and double floating-point arithmetic are developed in an FPGA. Synthesis results show that a single floating-point arithmetic design is consumed less area and memory usage, also operating at higher frequency. However, double-precision design is crucial for give a better accuracy of the result.

Knowledge-based disk scheduling policy using fuzzy logic

Hard disk is the important component in computer system as data storage device. It is being used to store large amount of information in all modern computers. However, hard drive speed improvement slower than their capacity. Thus, long service delay for I/O bound processes may occur. Disk scheduling is an important issue for providing a quick response time in query processing. Operating System being the interface between the user and the hardware has the responsibility to use hardware efficiently. One crucial branch of this function is to decrease the response time and allow for better bandwidth through disk scheduling. Disk scheduling is used when processes running on the machine have multiple requests for data from the disks and a particular order is needed to access them most effectively. There are two major operations in the disk scheduling head-positioning of a hard disk i.e. seeking and tracking. Current algorithm have not included the system knowledge about the additional information provided by Operating System and not been used for improvement of the seeking and/or tracking accessing. This research project applies fuzzy model knowledge based on improving the disk scheduling algorithm. Incorporating knowledge-based scheduling policy using fuzzy logic improves the average response time, average seeks distance and average seeks time.

Waste level detection and HMM based collection scheduling of multiple bins

In this paper, an image-based waste collection scheduling involving a node with three waste bins is considered. First, the system locates the three bins and determines the waste level of each bin using four Laws Masks and a set of Support Vector Machine (SVM) classifiers. Next, a Hidden Markov Model (HMM) is used to decide on the number of days remaining before waste is collected from the node. This decision is based on the HMM’s previous state and current observations. The HMM waste collection scheduling seeks to maximize the number of days between collection visits while preventing waste contamination due to late collection. The proposed system was trained using 100 training images and then tested on 100 test images. Each test image contains three bins that might be shifted, rotated, occluded or toppled over. The upright bins could be empty, partially full or full of garbage of various shapes and sizes. The method achieves bin detection, waste level classification and collection day scheduling rates of 100%, 99.8% and 100% respectively.

Enhancing Ride Comfort of Quarter Car Semi-active Suspension System Through State-Feedback Controller

The objective of this study is to simulate the road disturbance toward suspension in quarter car system. Suspension consists of the system of springs, shock absorbers, and linkages that connects a vehicle to its wheel and allows relative motion between the car body and the wheel. This paper shows the mathematical modeling in order to design the quarter car suspension system using Simulink and MATLAB software. The work shows the effect of suspension travel in quarter car system toward road profile by using state-feedback controller. The state-feedback controller’s purpose is to decrease the continuous damping in suspension system. The inconsistency condition of the road is the main element that affects the ride comfort which is in this paper represented by different heights of road profile. In suspension principles, the road wheels and vehicle body produce vertical forces which are rotational motions. Therefore, state-feedback controller must be able to reduce body deflection caused by road disturbance to achieve the ride comfort of driver and passengers. The results show that the proposed controller is capable of reducing the vibration of suspension after experiencing the bumps with different heights.

HIGH SPEED NUMERICAL INTEGRATION ALGORITHM USING FPGA

Conventionally, numerical integration algorithm is executed in software and time consuming to accomplish. Field Programmable Gate Arrays (FPGAs) can be used as a much faster, very efficient and reliable alternative to implement the numerical integration algorithm. This paper proposed a hardware implementation of four numerical integration algorithms using FPGA. The computation is based on Left Riemann Sum (LRS), Right Riemann Sum (RRS), Middle Riemann Sum (MRS) and Trapezoidal Sum (TS) algorithms. The system performance is evaluated based on target chip Altera Cyclone IV FPGA in the metrics of resources utilization, clock latency, execution time, power consumption and computational error compared to the other algorithms. The result also shows execution time of the FPGA are much faster compared to the software implementation.

Dynamically reconfigurable flux limiter functions in MUSCL scheme

In aerospace application, computational fluid dynamics (CFD) is recognized as a cost effective design tool. UPACS, a package for CFD is convenient for users, which has various solvers to support large scale of complexity. The problem is its computation speed which is hard to be enhanced by using clusters due to its complex memory access patterns. As an economical solution, accelerators using FPGAs are hopeful candidates. However, the total scale of UPACS is too large to be implemented on small numbers of FPGAs. For cost efficient implementation, partial reconfiguration which can dynamically reconfigure only required functions is proposed in this paper. MUSCL scheme, a main function used frequently in UPACS is selected as a target. Partial reconfiguration is applied to the flux limiter functions in MUSCL. Two reconfigurable partitions are created for Turbulence MUSCL and Convection MUSCL. All limiter functions are developed independently and synthesized separately from the top MUSCL module. Required limiter functions for both MUSCLs are loaded dynamically without interfering each other operation. This implementation has successfully reduced the resource utilization by 60%. Total power consumption is also reduced by 29%. Configuration speed is improved by 15-times faster as compared to fully reconfiguration method. All implemented functions achieved at least 17 times speed-up compared with the software implementation.