Hussein R. Al-Zoubi

Performance evaluation of cache replacement policies for the SPEC CPU2000 benchmark suite

Replacement policy, one of the key factors determining the effectiveness of a cache, becomes even more important with latest technological trends toward highly associative caches. The state-of-the-art processors employ various policies such as Random, Least Recently Used (LRU), Round-Robin, and PLRU (Pseudo LRU), indicating that there is no common wisdom about the best one. Optimal yet unattainable policy would replace cache memory block whose next reference is the farthest away in the future, among all memory blocks present in the set.In our quest for replacement policy as close to optimal as possible, we thoroughly explored the design space of existing replacement mechanisms using SimpleScalar toolset and SPEC CPU2000 benchmark suite, across wide range of cache sizes and organizations. In order to better understand the behavior of different policies, we introduced new measures, such as cumulative distribution of cache hits in the LRU stack. We also dynamically monitored the number of cache misses, per each 100000 instructions.Our results show that the PLRU techniques can approximate and even outperform LRU with much lower complexity, for a wide range of cache organizations. However, a relatively large gap between LRU and optimal replacement policy, of up to 50%, indicates that new research aimed to close the gap is necessary. The cumulative distribution of cache hits in the LRU stack indicates a very good potential for way prediction using LRU information, since the percentage of hits to the bottom of the LRU stack is relatively high.

Efficient Global Motion Estimation using Fixed and Random Subsampling Patterns

Global motion generally describes the motion of the camera, although it may comprise large object motion. The region of support for global motion representation consists of the entire image frame. Therefore, estimating global motion parameters tends to be computationally costly due to the involvement of all the pixels in the calculation. Efficient global motion estimation (GME) techniques are sought after in many applications such as video coding, image stabilization and super-resolution. In this paper, we propose to select only a small subset of the pixels in estimating the global motion parameters, based on a combination of fixed and random subsampling patterns. Simulation results demonstrate that the proposed method was able to speed up the conventional all-pixel GME approach by up to 7 times, without significant loss in the estimation accuracy. The combined subsampling patterns were also found to provide better motion estimation accuracy/complexity tradeoffs than those achievable by using either fixed or random patterns alone.

Very Fast Global Motion Estimation using Partial Data

The minimization process of the Levenberg-Marquardt algorithm (LMA) used in estimating the global motion parameters tends to be very expensive computationally due to the involvement of all the pixels of an image frame. We propose to reduce the computational complexity of the LMA by using only a small portion of the image data in two stages. In the first stage, we seek to reduce the complexity of the initial guess of the transformation parameters, which is critical to the final convergence of the algorithm. The complexity of computing the initial guess can be lowered by using just a small subset of the pixels in the calculation of the translational components. The second stage of the LMA algorithm is to find the final motion parameters in an iterative fashion, based on the coarse estimate of the motion parameters obtained in the previous stage. The LMA in this stage again operates on a subset of the pixels to further reduce the overall computational complexity. Both analytical and simulation results showed that the proposed partial-data algorithm could achieve a speedup factor of over 25 for global motion estimation (GME) with an eight-parameter perspective motion model on several video sequences, without significant loss in the estimation accuracy compared with the conventional LMA on the full image data.

Fast and accurate global motion estimation algorithm using pixel subsampling

Global motion generally describes the motion of a camera, although it may comprise motions of large objects. Global motions are often modeled by parametric transformations of two-dimensional images. The process of estimating the motions parameters is called global motion estimation (GME). GME is widely employed in many applications such as video coding, image stabilization and super-resolution. To estimate global motion parameters, the Levenburg-Marquardt algorithm (LMA) is typically used to minimize an objective function iteratively. Since the region of support for the global motion representation consists of the entire image frame, the minimization process tends to be very expensive computationally by involving all the pixels within an image frame. In order to significantly reduce the computational complexity of the LMA, we proposed to select only a small subset of the pixels for estimating the motion parameters, based on several subsampling patterns and their combinations. Simulation results demonstrated that the proposed method could speed up the conventional GME approach by over ten times, with only a very slight loss (less than 0.1dB) in estimation accuracy. The proposed method was also found to outperform several state-of-the-art fast GME methods in terms of the speed/accuracy tradeoffs.

Efficient coin recognition using a statistical approach

In this paper we propose a coin recognition system using a statistical approach and apply it to the recognition of Jordanian coins. The proposed method depends on two features in the recognition process: the color of the coin, and its area. The recognition process consists of several steps. Firstly, a gray-level image is extracted from the original colored image. The image is then segmented into two regions, coin and background, based on the histogram drawn from the gray-level image. To reduce the noise, the segmented image is then cleaned by opening and closing through several erosion and dilation operations. After that, four parameters are calculated, the area, the average red, blue, and green colors of the coin to be recognized. Based on these parameters, the decision to which category the coin belongs is obtained. The results provided illustrate that the proposed approach is both simple and accurate. Although the proposed recognition approach is applied to Jordanian coins, it can be applied to the recognition of any coins.

Offline Machine-Print Hindi Digit Recognition Using Translational Motion Estimation

In this paper we propose a new method of using motion estimation for the purpose of offline recognition of machine-print Hindi digits. The recognition process can be summarized as follows: an image of each of the ten numerals (numbers 0 to 9) is stored, these are called reference images. The differences between the image of the numeral to be recognized and the reference image are considered motions. The motions are estimated and then compensated. The resultant image after motion compensation is compared to the reference image and the difference between the two (treated as error) is calculated. The process of motion estimation and compensation is done for each of the ten numerals. The numeral with the minimum error is chosen as the recognized digit. While this proposed recognition system is applied to Hindi digits in this paper, it can be generalized to the recognition of any numerals in any language and can be extended to text and voice recognition. The proposed method is simple, fast, accurate, and reliable.

Precise and accurate decimal number recognition using Global Motion Estimation

Precise and accurate automatic recognition of decimal numbers is essential for many applications. Motion Estimation (ME) is a basic component in any video compression technique used to account for the temporal redundancy in image sequences. Global Motions are often modelled by parametric transformations of two dimensional images. The process of estimating the motion parameters is called Global Motion Estimation (GME). In this paper, we propose a new way of using GME for the purpose of off-line machine-print decimal digit. We show that the proposed approach is able to achieve very high recognition rates.

Efficient k-class approach for face recognition

Display Omitted A novel k-class approach system for accurate face recognition is introduced.The system is fully automatic: every step in the system is automatically performed.The system does not take long time to recognize a picture from a large database.The system achieves high recognition rates of 96.9% for Rank 1 and 99.5% for Rank 10. In this research work, a new k-class approach for efficient and accurate face recognition called kCAFRe is established. The kCAFRe system has three stages: preprocessing, training, and testing. In the training phase, four reference pictures (classes) are constructed for each person. During testing, the correlation coefficient (CC) is calculated between the picture under test and each of the reference pictures. For each person, one accredited class is chosen. Subsequently, the accredited class for the person with the highest CC is selected. Results were given for four image databases. A recognition ratio of 97% has been obtained for the Libor Spaceks set.

Fully automated smart wireless frost prediction and protection system using a fuzzy logic controller

A smart fuzzy logic controller system is presented to protect the crops from frost damage that occurs every year. The system is a fully automated system to predict the frost and to protect the crops using wireless sensor network technology. The sensors are used to collect crops data and transmit these data to the fuzzy controller. After that, the fuzzy controller will decide the proper action to be taken in order to protect the crops. The frost protection mechanism used here is a solid fuel burner that generates an artificial smoke cloud. The conducted simulations have shown that the system can successfully handle the frost problem at critical weather situations. This is achieved by keeping the ground surface temperature above the freezing temperature, and therefore saving the crops from being injured. In order to reduce the system energy consumption requirements, different approaches are presented during the simulation process.

Recognition of Handwritten Arabic Characters using Histograms of Oriented Gradient (HOG)

Optical Character Recognition (OCR) is the process of recognizing printed or handwritten text on paper documents. This paper proposes an OCR system for Arabic characters. In addition to the preprocessing phase, the proposed recognition system consists mainly of three phases. In the first phase, we employ word segmentation to extract characters. In the second phase, Histograms of Oriented Gradient (HOG) are used for feature extraction. The final phase employs Support Vector Machine (SVM) for classifying characters. We have applied the proposed method for the recognition of Jordanian city, town, and village names as a case study, in addition to many other words that offers the characters shapes that are not covered with Jordan cites. The set has carefully been selected to include every Arabic character in its all four forms. To this end, we have built our own dataset consisting of more than 43.000 handwritten Arabic words (30000 used in the training stage and 13000 used in the testing stage). Experimental results showed a great success of our recognition method compared to the state of the art techniques, where we could achieve very high recognition rates exceeding 99%.