Asian Journal of Engineering, Sciences & Technology

Abbreviated key title: AJEST
ISSN: 2077-1142
Published by: Iqra University
Periodicity: Bi-Annualy
Months: March & September
Start year: 2011

Title: On the Design of Electromagnetically Coupled Microstrip Antenna Author(s): Ikram-e-Khuda, Kamran Raza, Shamim Akhter, Syed Haider Abbas Naqvi

Abstract: In this paper, unique distributive idea in the design methodology of antenna considering a constrained environment is presented. The design is carried out for aperture coupled microstrip antennas. The system parameters are evaluated and modeled to support the communication in extreme temperature constraints. The polarization is linear and working frequency is 2.4 GHz. The model is simulated on PCAAD. Design for aperture coupled patch antennas involve, ten design parameters under the constraints of bandwidth of operation. Resulting antenna structures are then simulated to find directivity and gain. Theoretical and practical values show good match. The simulated antenna can be put at the front end of an UWB transmitter and receiver.


Title: State Space Least Mean Fourth Algorithm for State Estimation of Synchronous Motor Author(s): Muhammad Moinuddin, Arif Ahmed, Ubaid M. Al-Saggaf

Abstract: The most common estimation algorithms used today for power system static and dynamic state estimation are the variants of Kalman filter (KF) like Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). These model based estimation algorithms are well known for their accuracies. However, it is a well known fact that EKF requires fine tuning and good initial guess for optimum performance. Moreover, these adaptive filters generally employed for estimation purposes require high computational power when it comes to real time estimation. Therefore, in this paper we propose a computationally light yet effective estimation algorithm based on state space model which have not yet been applied to the problem of power system dynamic state estimation. We derive and propose the use of state space least mean fourth algorithm for the purpose of dynamic state estimation considering the problem of a two phase permanent magnet synchronous motor. The algorithm has been employed successfully in this paper in the dynamic state estimation of the highly non linear synchronous motor. The problem has been investigated in the presence of Gaussian noise to show the effectiveness of the algorithm. Moreover, the algorithm is also compared with the performance of the EKF.


Title: Automatic Segmentation of Chest Radiographs Author(s): Anum Memon, Syeda Munazza, Sheeraz Memon

Abstract: In this paper, an automatic segmentation method that detects lung boundaries and bypass the irrelevant information is presented. The automatic screening of chest radiographs is crucial for the rural areas where timely presence of expert radiologists is difficult. An automatic system can help save many lives. In digital image processing of chest radiographs, to detect abnormal indications, the system must be able to perform an accurate segmentation of lung area from an image because the major abnormal symptoms lies in the lung region. The designed system perform an automatic segmentation of lung region from chest radiographs through various stages such as image acquisition ,filtering , enhancement, morphological image processing, edge detection and segmentation. The system is tested on the Japanese Society of Radiological Technology (JSRT) and acquired deep information from the Chest Specialist and Radiologist for the better results.


Title: Human Detection and Counting in Crowded Scene Author(s): Pirah Noor Soomro, Ufra Memon, Sheeraz Memon

Abstract: Crowd surveillance is an active topic ofresearch nowadays. Increased law and order situations have greatly alarmed the security organizations to improve security services in all the fields especially when security of crowd is brought under consideration. Crowd surveillance is different. In crowd there is congestion, noise and large number of people which are distributed in random manner which cause much difficulty for security officers. Therefore now crowds are scanned by cameras and are processed in computer for surveillance and for getting information of the scene. Many approaches have been proposed in this context. Keeping in view the complexity of scene in the domain of computer vision we come across the big problem of occlusion. The ultimate solution needed is the technique which overcomes the problem of occlusion. In this paper we have proposed a technique, which provide good hand in surveillance. Our approach has two folds, we detect the heads of the people in crowd and then we count the people present in the crowd. Detection of heads is done by the famous Viola and Jones Method and counting is done based on the output of detection.


Title: Bank Security System based on Weapon Detection using HOG Features Author(s): S. Asnani, A. Ahmed, A. A. Manjotho

Abstract: Banks play the most important role in currency circulations almost everywhere in the world. Due to this reason, banks have become the target places of criminals. This paper proposes a solution for providing safe and healthy work environment to the personnel. The proposed security system is based on Digital Image Processing techniques. The idea is to detect the visual unconcealed weapons whenever they appear inside banks. The major problem in training any classifier is to select the appropriate features from the images so that the classifier can work with greater accuracy and efficiency. Through various research work and experiments, we have found that Histogram of Oriented Gradients (HOG) features have proved to be a robust feature for detecting many different objects with high detection accuracy. HOG was introduced by Dalal for detecting humans, but it can also be used for detecting any other type of object equally well. A boosted cascade classifier has been used for training purpose using the HOG features. A large collection of training dataset is used and the final detector is tested on larger testing data.


Title: A Trend in Global Steganography and Steganalysis Approaches Author(s): Syed Owais Ahmed Zaidi, Tamoor Ali Khan, Syed Sajjad Hussain Rizvi, Manzoor Ahmed Hashmani

Abstract: In the past decade significant growth has been observed in security and assurance of information. Initially, cryptography was considered as the only appropriate approach for information security over the media. This is because at that time not very significant development occurred in Information and Communication Technology (ICT). But nowadays a tremendous growth of ICT is being observed and reported in literature. This opens a new era of steganography and steganalysis for information security and assurance. Steganography is the process of hiding data in an image. It significantly reduces the risk of information being stolen or sniffed. Steganalysis is the capability of detecting and retrieving the hidden messages in multimedia document. Researchers have proposed different methods of steganography and steganalysis. These techniques have been reported with certain performance parameters and issues. The researcher initiating research in this area gets caught in the labyrinth of issues and their solution before coming up with his/her own solution and thus loses significant time and effort. In order to provide a roadmap to researchers starting research in this area, we have in this paper presented a comprehensive survey of steganography and steganalysis methods with their performance measures and reported issues.


Title: Unconcealed Gun Detection using Haar-like and HOG Features - A Comparative Approach Author(s): Sorath Asnani, Syed Danial Waseem, Ali Asghar Manjotho

Abstract: Due to its wide variety of applications, object detection has been the center of attention for researchers in the field of digital image processing and computer vision. When trained with the sample training dataset, various object classifiers can detect and classify the objects with prominent accuracy and precision. The major step in any of the object classification algorithm is feature selection. Performance of the classifier depends on robustness of the feature vector selected. This paper presents unconcealed gun detection method by using Boosted Cascade Classifier. The classifier was trained with two of the widely known feature types: Haar-like features and Histogram of Oriented Gradients (HOG) features. The paper also presents a comparative study between the two of the feature types under the consideration of unconcealed gun detection. The classifier was trained with the dataset of 11,257 number of images using both the types of features separately and tested with dataset of 700 number of images. Using the Haar-like features the classifier attained the accuracy of 42.14% with the precision of 45.73%. While using the HOG features, the classifier gained the accuracy of 88.57% with the precision of 95.30%. The evaluation metrics clearly depicts the superiority of HOG features over the Haar-like features in unconcealed gun detection.


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