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Thesis Info

Author

Awais Mohammad

Supervisor

Shahbaz Ahmed

Department

Department of Computer Science and Software Engineering

Program

BS

Institute

International Islamic University

Institute Type

Public

City

Islamabad

Province

Islamabad

Country

Pakistan

Thesis Completing Year

2011

Thesis Completion Status

Completed

Page

x, 132

Subject

Computer Science

Language

English

Other

BS 005.1 AWO

Added

2021-02-17 19:49:13

Modified

2023-01-06 19:20:37

ARI ID

1676723901582

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اسلم عارف

اسلم عارف (۱۹۲۷ء۔۱۹۹۰ء) پسرور کے ایک نواحی گاؤں بن باجوہ میں پیدا ہوئے۔(۷۵۹) آپ نظم ،غزل اور قطعہ گو شاعر تھے ۔عارف کی شاعری مقامی وملکی سطح کے رسائل وجرائد میں شائع ہوئی ۔ا ن کی شاعری میں روایتی موضوعات کے ساتھ ساتھ جدت بھی پائی جاتی ہے ۔حقیقت پسندی اور رومانیت بھی عارف کی شاعری کے اہم موضوعات ہیں ۔ نمونہ کلام ملاحظہ ہو:

اے حسینہ سنوارلے گیسو

 

%اشک کب تک بہائے گی

 

3قبر کے پھول سوکھ جائیں گے

 

-زندگی لوٹ کر نہ آئے گی

(۷۶۰)

 

ایک مفلس بندہ مزدور پر روئے گا کون؟

 

زندگی میں بھی اگرچہ بے سرو سامان تھا

 

مر کے لیکن رہ گیا اس طرح بے گور کفن

 

جیسے اس دنیا کا بس یہ آخری انسان تھا

(۷۶۱)

۷۶۰۔اسلم عارف،مشمولہ’’ماہنامہ سیارہ ‘‘،لاہور،اگست ۲۰۰۱ء ،ص:۱۵

۷۶۱۔ایضاً،ص :۱۵

 

 

 

 

Awareness about Workplace Harassment among Female Nursing Students and Nursing Staff of a Teaching Hospital in Lahore

Background: Harassment of working women is a common social and human rights problem and healthcare settings are no exception. This unacceptable situation has many avoidable determinants that need preventive measures to ensure, safe and secure working environment for nurses. Objectives: To assess the knowledge and understanding among female nursing students and nursing staff about workplace harassment in a tertiary healthcare facility. Methods: It was a cross-sectional study conducted at Tertiary care hospital Lahore from July to September 2018. Convenient sampling technique was used to collect data. Verbal informed consent of voluntary participants was taken. Anonymity and confidentiality was assured. SPSS version 21 was used for data analysis. The participants (n=80) comprised of senior female nurses students (62.5%) and senior nursing staff (37.5%), aged 18 to 46 years. Results: Awareness about common harassment forms was adequate (81.3%). Adverse effects included mental health problems (83.8%) and low self-esteem (72.5%). A large percentage of nurses (62.5%) intended to report such personal incident to institutional head and 72.5% were willing to accompany victims for such reporting. 77.5% desired arranging informative sessions and holding counseling for victims (78.8%) at workplace. Only 22.5% had vague idea about official protective legal frameworks. Conclusions: Workplace harassment of female workers is quite prevalent but under-reported phenomena in Pakistan. All organizations and institutions must have a harassment policy to protect the females.  

Development of New Image Fusion Techniques

Image fusion techniques merge the complementary information of several images (multi-focus, multi-exposure and multi-modal). Each of these scenarios poses different challenges for image fusion techniques, which are being extensively researched. However, most of these works assume that source images are preregistered, which is a less practical scenario. Both registered and unregistered image fusion algorithms are considered in this thesis. The registration involves the geometrical / spatial alignment of source images taken using different sensors or a sensor in different operating conditions. This research is concerned with the reliable fusion schemes of several scenario images (including muti-focus, Infra Red (IR) and visible, Computed Tomography (CT) and Magnetic Resonance (MR), and multi-exposure images) demonstrating high quality fused results without loss of useful information. The first scheme is a textural registration based multi-focus scheme involving the Gabor filtering (with specific frequency and orientation) for extracting texture features from the images. The filtered images are aligned/registered using affine transformation. Noise and blur play an important role in image fusion and need to be classified and treated for quality image fusion. The next two fusion schemes deal with multi-exposure noisy (real and synthetic both) and blur images. In the first algorithm, the noisy, blurry and clean images are classified using Laplacian filter and histogram spread. The noise is reduced in the frequency domain. Heavy weights are assigned to noise free pixels and the blur images are passed through the Wiener filter. In the second algorithm, a noise resistant image fusion scheme for multi-exposure sensors using color dissimilarity (for motion detection and removal), median and noise maps is proposed. A well exposed image is obtained as a result of weighted average of multi-exposure source images. Higher valued weights are assigned to pixels containing low values of noises, high values of color dissimilarity and median maps. The next work (two schemes) involve pre-registered visible and IR images. In the first one, a three stage image fusion scheme using Genetic Algorithm (GA) is presented. In the first stage, it segments the image into homogeneous regions and generates segmentation maps. In the second stage, the segmentation maps are combined by an adaptive weight adjustment procedure. The third stage fuses the input images and segmentation maps via GA based multi- objective optimization strategy. The second image fusion scheme uses Un-Decimated Dual Tree Complex Wavelet Transform (UDTCWT) for astronomical images. The UDTCWT reduces noise effects and improves object classification due to its inherited shift invariance property. Local standard deviation and distance transforms are used to extract useful information, especially small objects. In the medical (CT and MR) image fusion scheme, the source images are contrast enhanced using histogram equalization. It is a sparse decomposition based fusion technique that uses the dictionary learnt from input images and k-mean singular value decomposition algorithm. The scheme splits CT and MR images into texture and gradient images. The texture decomposition improves the overall performance of the sparse representation based fusion. The quantitative analysis performed using mutual information, structural similarity measure and edge dependent based performance metrics, yields improved results for proposed schemes, as compared to existing schemes.