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Modeling simulation and analysis of sheet metal forming process

Thesis Info

Author

Syed Danish Quyyum

Supervisor

Muhammad Rizwan

Department

Department of Mechanical Engineering

Program

BS

Institute

International Islamic University

Institute Type

Public

City

Islamabad

Province

Islamabad

Country

Pakistan

Thesis Completing Year

2017

Thesis Completion Status

Completed

Page

xv, 43

Subject

Mechanical Engineering

Language

English

Other

BS 671.823 QUM

Added

2021-02-17 19:49:13

Modified

2023-01-06 19:20:37

ARI ID

1676724186741

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مولانا لطف اﷲ

مولانا لطف اﷲ صاحب کی وفات

در روزگار عشق تو ماہم فدا شدیم               افسوس کز قبیلۂ مجنون کسے نماند

          قدیم عربی مدارس کے در و دیوار اگرچہ ظاہری شان و شوکت کے لحاظ سے روز بروز بلند ہوتے جاتے ہیں، لیکن جھک کے دیکھتے ہیں تو سنگ بنیاد متزلزل نظر آتا ہے، ہماری قدیم تعلیم و تربیت کی جو یادگاریں، ان مدارس کا اساس تھیں، ایک ایک کرکے مٹ گئیں، ایک مولوی لطف اﷲ صاحب مرحوم رہ گئے تھے لیکن ۱۸؍ اکتوبر ۱۹۱۶؁ء کو صرصرفنانے ہماری علمی انجمن کے اس چراغ کو بھی گل کردیا، اناﷲ وانا الیہ راجعون۔

          مولوی لطف اﷲ صاحب مرحوم میں قدیم تعلیم و تربیت کی تمام خصوصیات باکمل وجوہ موجود تھیں، علم اخلاق، اور مذہب قدیم تعلیم و تربیت کا مایہ خمیر تھا، اور انہی محاسن کی بناء پر ہمارے علماء قوم میں عزت، رسوخ اور اثر پیدا کرتے تھے، مولوی لطف اﷲ صاحب مرحوم کی ذات میں نہ صرف یہ محاسن جمع ہوگئے تھے، بلکہ وہ ان اوصاف میں عموماً اپنے اقران و اماثل میں ممتاز کیے جاتے تھے۔

          اشاعت علم خالصۃً لوجہ اﷲ ہمیشہ ہمارے علماء کا تمغۂ امتیاز رہا ہے اور مولوی لطف اﷲ صاحب مرحوم نے اپنی عمر کا ایک کافی حصہ اس نیک کام میں صرف کیا، ہندوستان میں آج جس قدر علمی سلسلے قائم ہیں، اور جو علماء آج مسندنشین درس و تدریس ہیں، ان میں اکثر ایسے ہیں جنھوں نے مولوی لطف اﷲ صاحب مرحوم کے خرمن فیض کی خوشہ چینی کی ہے۔لیکن اﷲ تعالیٰ نے دولت دنیا سے بھی مولوی صاحب مرحوم کو کافی حصہ عطا فرمایا تھا، وہ ریاست حیدرآباد میں بمشاہرہ ایک ہزار مدتوں افتاء کی خدمت انجام دیتے رہے، لیکن...

Professional Standards Training and Understanding Pre-School Teachers’ Knowledge About Professional Standards

Theimprovement of the teacher’s quality, including teachers in general and preschool teachers in particular, hasgained interested in many countries around the world. Currently, most countries in the world have issued a framework of competency or professional standards for teachers as a basis for preschool teachers to self-assess and be assessed for their qualities and competenciet. On that basis, preschool teachers can implement the plan of quality training, strengthen and improve professional expertise. This article, the author conducts research to evaluate the implementation of the professional standards manual of schools and the teacher's understanding of professional standards. This is considered an important factor that will contribute to improving the effectiveness of teacher ratings according to professional standards. In this study, the author uses mainly quantitative research methods (survey, descriptive statistics and inference statistics) to clarify the problems that the research has posed. Research results show that there is a relationship between standard manual training and preschool teachers' understanding of professional standards. Standards instructors have a good understanding of professional standards. Especially the training is organized by the school and the education and training department, so the training classes are small, with a small number of participants and therefore higher quality.

Improved Face Recognition Using Image Resolution Reduction and Optimization of Feature Vector

Face recognition is a difficult problem that involves automated matching of a given face image with corresponding person’s image(s) in a database. Face recognition finds application in areas like surveillance & security, digital libraries and human computer interactions. Successful, speedy and practically feasible face recognition method depends heavily on the choice of feature vector used for classification and addressing the curse of image dimension. The dimension reduction and the skill to acquire minimum size of feature vector required for face recognition for diverse facial expressions is a challenging task in face recognition. Dimension reduction results in removal of irrelevant variables alongwith noise therein and a lower computation complexity of subsequent processing. This dissertation addresses the challenges of dimension reduction, choice of minimum size feature vector for face recognition and minimization of adverse effects of varying facial expressions on the recognition through reduction in image resolution. In preprocessing of face images, scale normalization is carried out through a novel scale normalization algorithm to retain only the facial part of images. This helps in reducing computational complexity by restricting dimensions of image to face region only. Tilt of face images is removed by calculating the gradient between the two eyes and applying the reverse rotation. The issue of dimensionality is addressed first by gradually reducing image resolution through spatial domain low pass filtering followed by decimation. The second method involves novel coefficient selection strategies to choose the minimum dimension of feature vector required for recognition with maximum recognition rate and reduced computational complexity. Face images with varying image resolution are obtained by varying the decimation factor. The effects of variation in image resolution on face recognition have been evaluated using template matching and Principle Components Analysis (PCA) based face recognition techniques. Classical PCA technique has been modified into sub-holistic PCA. Better recognition rate is achieved using modified PCA method with reduced image resolution. Improved recognition rate results are reported using novel coefficients selection and optimization methods in Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Discrete wavelets Transform (DWT) based face recognition methods. The experiments are carried out for various image resolutions using five different datasets. Improved recognition rate of 97.2% (template matching), 87% (PCA), 94% (Sub-holistic PCA), 100% (DFT), 95.75% (DCT) and 99.25% (DWT) is achieved at a specific image resolution for different datasets. The resolution reduction method used with square images is then extended to hexagonal images. A new technique based on Diagonal grow and Butterfly structure methodology has been developed for sampling and indexing hexagonal structure in hexagonal image processing frame work. Proposed strategy offer less pixel redundancy as compared to existing techniques. Reduction in pixel redundancy varies according to size of square image.