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Evaluation on the Performance of Deep Excavation by Using PIV Technique
Qaisar Abbas,송주상,유충식 한국지반신소재학회 2017 한국지반신소재학회 논문집 Vol.16 No.4
The concern study, present the results of experimental study on the performance of deep excavation by using image processing technique particle image velocimetry (PIV). The purpose of present study is to check the application of PIV for the successive ground deformation during deep excavation. To meet the objectives of concern study, a series of reduce scale model test box experiments are performed by considering the wall stiffness, ground water table effect and ground relative density. The results are presented in form of contour and vector plots and further based on PIV analysis wall and ground displacement profile are drawn. The results of present study, indicate that, the PIV technique is useful to demonstrate the ground deformation zone during the successive ground excavation as the degree of accuracy in PIV analysis and measured results with LVDT are within 1%. Further the vector and contours plot effectively demonstrate the ground behavior under different conditions and the PIV analysis results fully support the measured results.
Compressive Sensing: From Theory to Applications, a Survey
Qaisar, Saad,Bilal, Rana Muhammad,Iqbal, Wafa,Naureen, Muqaddas,Lee, Sungyoung The Korea Institute of Information and Commucation 2013 Journal of communications and networks Vol.15 No.5
Compressive sensing (CS) is a novel sampling paradigm that samples signals in a much more efficient way than the established Nyquist sampling theorem. CS has recently gained a lot of attention due to its exploitation of signal sparsity. Sparsity, an inherent characteristic of many natural signals, enables the signal to be stored in few samples and subsequently be recovered accurately, courtesy of CS. This article gives a brief background on the origins of this idea, reviews the basic mathematical foundation of the theory and then goes on to highlight different areas of its application with a major emphasis on communications and network domain. Finally, the survey concludes by identifying new areas of research where CS could be beneficial.
Connection Frequency Buffer Aware Routing Protocol for Delay Tolerant Network
Qaisar Ayub,M soperi Mohd Zahid,Abdul Hanan Abdullah,Sulma Rashid 대한전기학회 2013 Journal of Electrical Engineering & Technology Vol.8 No.3
DTN flooding based routing protocol replicate the message copy to increase the delivery like hood that overloads the network resources. The probabilistic routing protocols reduce replication cost by forwarding the message to a node that holds high predictability value to meet its destination. However, the network traffic converges to high probable nodes and produce congestion that triggers the drop of previously stored messages. In this paper, we have proposed a routing protocol called as Connection frequency Buffer Aware Routing Protocol (CFBARP) that uses an adaptive method to maintain the information about the available buffer space at the receiver before message transmission. Furthermore, a frequency based method has been employed to determine the connection recurrence among nodes. The proposed strategy has performed well in terms of reducing message drop, message relay while increases the delivery probability.
FRS-OCC: Face Recognition System for Surveillance Based on Occlusion Invariant Technique
Abbas, Qaisar International Journal of Computer ScienceNetwork S 2021 International journal of computer science and netw Vol.21 No.8
Automated face recognition in a runtime environment is gaining more and more important in the fields of surveillance and urban security. This is a difficult task keeping in mind the constantly volatile image landscape with varying features and attributes. For a system to be beneficial in industrial settings, it is pertinent that its efficiency isn't compromised when running on roads, intersections, and busy streets. However, recognition in such uncontrolled circumstances is a major problem in real-life applications. In this paper, the main problem of face recognition in which full face is not visible (Occlusion). This is a common occurrence as any person can change his features by wearing a scarf, sunglass or by merely growing a mustache or beard. Such types of discrepancies in facial appearance are frequently stumbled upon in an uncontrolled circumstance and possibly will be a reason to the security systems which are based upon face recognition. These types of variations are very common in a real-life environment. It has been analyzed that it has been studied less in literature but now researchers have a major focus on this type of variation. Existing state-of-the-art techniques suffer from several limitations. Most significant amongst them are low level of usability and poor response time in case of any calamity. In this paper, an improved face recognition system is developed to solve the problem of occlusion known as FRS-OCC. To build the FRS-OCC system, the color and texture features are used and then an incremental learning algorithm (Learn++) to select more informative features. Afterward, the trained stack-based autoencoder (SAE) deep learning algorithm is used to recognize a human face. Overall, the FRS-OCC system is used to introduce such algorithms which enhance the response time to guarantee a benchmark quality of service in any situation. To test and evaluate the performance of the proposed FRS-OCC system, the AR face dataset is utilized. On average, the FRS-OCC system is outperformed and achieved SE of 98.82%, SP of 98.49%, AC of 98.76% and AUC of 0.9995 compared to other state-of-the-art methods. The obtained results indicate that the FRS-OCC system can be used in any surveillance application.
Compressive Sensing: From Theory to Applications, a Survey
Saad Qaisar,Rana Muhammad Bilal,Wafa Iqbal,Muqaddas Naureen,이승룡 한국통신학회 2013 Journal of communications and networks Vol.15 No.5
Compressive sensing (CS) is a novel sampling paradigmthat samples signals in a much more efficient way than the establishedNyquist sampling theorem. CS has recently gained a lot ofattention due to its exploitation of signal sparsity. Sparsity, an inherentcharacteristic of many natural signals, enables the signalto be stored in few samples and subsequently be recovered accurately,courtesy of CS. This article gives a brief background on theorigins of this idea, reviews the basic mathematical foundation ofthe theory and then goes on to highlight different areas of its applicationwith a major emphasis on communications and networkdomain. Finally, the survey concludes by identifying new areas ofresearch where CS could be beneficial.