Mohammad A. Al-Jarrah

Query by image and video content: a colored-based stochastic model approach

For efficient image retrieval, the image database should be processed to extract a representing feature vector for each member image in the database. A reliable and robust statistical image indexing technique based on a stochastic model of an image color content has been developed. Based on the developed stochastic model, a compact 12-dimensional feature vector was defined to tag images in the database system. The entries of the defined feature vector are the mean, variance, and skewness of the image color histogram distributions as well as correlation factors between color components of the RGB color space. It was shown using statistical analysis that the feature vector provides sufficient knowledge about the histogram distribution. The reliability and robustness of the proposed technique against common intensity artifacts and noise was validated through several experiments conducted for that purpose. The proposed technique outperforms traditional and other histogram based techniques in terms of feature vector size and properties, as well as performance.

A Thin Security Layer Protocol over IP Protocol on TCP/IP Suite for Security Enhancement

In this paper, we proposed a security enhancement for TCP/IP suite. This enhancement adds three modules to TCP/IP. These are security policy, security control, and data security layer. Unlike IPsec, which plugs all security enforcements into IP layer, the proposed architecture distributes the proposed module into their relevant layer. The security policy belongs to application layer, and the security control and management located in the transport layer. The data security layer is located between the transport layer and the IP layer. Security policy interacts with system administrator to define the policies and roles of security to be applied in data communication. Security control module provides the means to apply the security policy defined in security policy module and establishes a secure channel, it uses four-way handshaking and public key cryptography (PKC) to create virtual secure connection and security entity (SE). SE holds the secret key cryptography (SKC), addresses of two hosts that share this SKC, and other vital information necessary to carry out a secure data communication. For data security, we proposed a thin security protocol (TSP) over IP protocol. TSP protocol encrypts and encapsulates the coming transport layer packet into TSP packets. The TSP packet header consists only of two fields each of them is one bytes. The first field identifies the TSP packet types such as public key request, public key acknowledgement (ACK), and secret key and secret key ACK, The second field carries information about the transport layer protocol. In TSP design and implementation, our concern was to minimize the overhead added to IP including traffic volume and transmission delay. In term of data size, TSP adds only two bytes as TSP header

Recursive digital sine wave oscillators using the TMS32010 DSP

Digital sine wave oscillators are implemented in this paper using the TMS32010 digital signal processor (DSP), specifically the TMS32010 Evaluation Module kit (EVM) and the Analog Interface Board (AIB). These oscillators are based on solutions of the second-order difference equation and its modifications. the considered oscillator structures are: the direct form, modified direct form, first-order error feedback, and second-order error feedback. The parameters of these oscillators are implemented in 32 bits to reduce the harmonic distortion, improve the frequency resolution, and generate very low frequencies while maintaining a large number of samples per cycle. the performance of the implemented oscillators is evaluated and compared with that of the look-up table (LUT) methods. The numerical results obtained indicate the superiority of our oscillator structures over other oscillators using LUT, or TMS320 series.<<ETX>>

Image-based discriminating morphological features for gastric atrophy assessment: a step to go further.

The aim of this study is to establish a basis for automated assessment of gastric atrophy according to the Updated Sydney System. We sought to minimize inter- and intra-observer variations in the application of the Sydney System. A total of 160 biopsies were examined by three pathologists and graded using the visual scale of the Updated Sydney System. A consensus was reached on 135 biopsies. Digital images were captured for the studied biopsies. Image processing techniques were used to extract four morphological features that uniquely discriminate each atrophy grade. The features are related to gland density and shape. To validate the reproducibility of these features, the K-Means clustering technique was used. We were able to grade the atrophy with an overall precision of 95.6%. Furthermore, the proposed features were able to distinguish four discrete grades without any significant overlap. This has not been achieved by previous studies.

Non-proliferative diabetic retinopathy symptoms detection and classification using neural network

Abstract Diabetic retinopathy (DR) causes blindness in the working age for people with diabetes in most countries. The increasing number of people with diabetes worldwide suggests that DR will continue to be major contributors to vision loss. Early detection of retinopathy progress in individuals with diabetes is critical for preventing visual loss. Non-proliferative DR (NPDR) is an early stage of DR. Moreover, NPDR can be classified into mild, moderate and severe. This paper proposes a novel morphology-based algorithm for detecting retinal lesions and classifying each case. First, the proposed algorithm detects the three DR lesions, namely haemorrhages, microaneurysms and exudates. Second, we defined and extracted a set of features from detected lesions. The set of selected feature emulates what physicians looked for in classifying NPDR case. Finally, we designed an artificial neural network (ANN) classifier with three layers to classify NPDR to normal, mild, moderate and severe. Bayesian regularisation and resilient backpropagation algorithms are used to train ANN. The accuracy for the proposed classifiers based on Bayesian regularisation and resilient backpropagation algorithms are 96.6 and 89.9, respectively. The obtained results are compared with results of the recent published classifier. Our proposed classifier outperforms the best in terms of sensitivity and specificity.

Image Segmentation Utilizing Color-Space Feature

In this paper, the authors introduced a stochastic model for color images. Utilizing this model, they proposed a new method for color image segmentation. The proposed method consists of three stages; the first stage considers the red, green, and blue color component of the image as a gray image. One of the known gray image Thresholding algorithm is applied on the three color components. The second stage segments the image based on the results of first stage. This stage produces eight color segments. The third stage identifies the segments through color-space correlation. Color-space correlation algorithm assumes that a set of pixels are considered to belong to one region if and only if they belong to the same color cluster and all connected using neighborhood filters. The last stage may produce very small segments. These small segments are merged with their closed neighbors based on color features. Finally, Conducted experiments achieved perceptually accepted segments and compare favorably to other segmentation methods.

An intelligent decision support system for quantitative assessment of gastric atrophy

Aims To build an automated decision support system to assist pathologists in grading gastric atrophy according to the updated Sydney system. Methods A database of 143 biopsies was used to train and examine the proposed system. A panel of three experienced pathologists reached a consensus regarding the grading of the studied biopsies using the visual scale of the updated Sydney system. Digital imaging techniques were utilised to extract a set of discriminating morphological features that describe each atrophy grade sufficiently and uniquely. A probabilistic neural networks structure was used to build a grading system. To evaluate the performance of the proposed system, 66% of the biopsies (94 biopsy images) were used for training purposes and 34% (49 biopsy images) were used for testing and validation purposes. Results During the training phase, a 98.9% precision was achieved, whereas during testing, a precision of 95.9% was achieved. The overall precision achieved was 97.9%. Conclusions A fully automated decision support system to grade gastric atrophy according to the updated Sydney system is proposed. The system utilises advanced image processing techniques and probabilistic neural networks in conducting the assessment. The proposed system eliminates inter- and intra-observer variations with high reproducibility.

Development of a CAD/CAM system for simulating closed forging process using finite‐element method

Purpose – The purpose of this paper is to study the effect of the height and diameter of the dies as well as work‐piece dimensions, on stresses and strains on dies in the forging process. This helps in developing a better understanding of the effect of process parameters. As a result, the manufacturing task could be accomplished with minimal number of trials.Design/methodology/approach – After determining the most influencing parameters on the forging process, the mechanical part is drawn, size of initial billet and shape of punch and die are also determined to build a finite‐element model to represent the process. Several outputs are taken as an indication for die wear and process performance. Finally, a computer numerical control (CNC) code to manufacture the selected die is generated.Findings – It was found that when the die diameter increases, the effective stress decreases. On other hand, it was found that the work required to finish the forging process is highly affected by the dimensions of work‐pi...

Developing 3D model for mobile robot environment using mono-vision system

Mobile robot system will be an important asset in our future. Mobile robot not only has to execute predefined tasks programmed with, but also it must explore the unknown environment that might be pushed to work in. In this paper we propose, implement and test a new model for mobile robot environment using mono-vision system. This proposed system develops 3D environment model utilizing mono-vision system. The model is developed through capturing multiple shots from different locations. The 3D model describes the distance and the angle of objects with respect to the robot. Finally, the mobile robot will utilize this model to navigate its environment. This is achieved through projecting the 3D model into the motion floor and identifying the obstacles surrounding the robot. Then, the robot will avoid any object in its motion line. Most importantly, the model is updated continuously based on the changes in the environment and the location of the robot.

Fast Video Shot Boundary Detection Technique based on Stochastic Model

A video is composed of set of shots, where shot is defined as a sequence of consecutive frames captured by one camera without interruption. In video shot transition could be a prompt hard cut or gradual fade, dissolve, and wipe. Shot boundary detection is an essential component of video processing. These boundaries are utilized on many aspect of video processing such as video indexing, and video in demand. In this paper, the authors proposed a new shot boundary detection algorithm. The proposed algorithm detects all type of shot boundaries in a high accuracy. The algorithm is developed based on a global stochastic model for video stream. The proposed stochastic model utilizes the joined characteristic function and consequently the joined momentum to model the video stream. The proposed algorithm is implemented and tested against different types of categorized videos. The proposed algorithm detects cuts fades, dissolves, and wipes transitions. Experimental results show that the algorithm has high performance. The computed precision and recall rates validated its performance.