مفتی عبدالقادر
افسوس ہے کہ گذشتہ مہینہ ۲۴؍ اگست کو فرنگی محل کے نامور عالم مفتی عبدالقادر صاحب نے وفات پائی، مرحوم علم و عمل میں اپنے اسلاف کرام کا نمونہ اور طبعاً نہایت خاموش اور عزلت پسند تھے، پوری زندگی خاموشی اور قناعت کے ساتھ درس و تدریس اور علم و افتاد کی خدمت میں گذاری، ان کی موت سے فرنگی محل کی ایک اہم یادگار مٹ گئی، نئی نسل جدید تعلیم یافتہ ہے، اس کو اپنے اسلاف کے علوم اور روایات سے بہت کم علاقہ رہ گیا ہے اس لئے جو ایک دو پرانے بزرگ باقی رہ گئے ہیں ان کے بعد فرنگی محل میں سناٹا نظر آتا ہے۔
اس خاندان میں جتنی طویل مدت تک علم رہا اور اس سے پورے ہندوستان کو جو فیض پہنچا اس کی مثال دوسرے علمی خاندانوں میں کم ملے گی، عموماً دو چار پشتوں سے زیادہ کسی خاندان میں علم نہیں چلتا، مگر فرنگی محل تقریباً تین صدیوں تک دینی علوم اور اس کی تعلیم کا مرکز رہا اور اس مدت میں ملا نظام الدین بانی درس نظامیہ ، ملا حیدر ، ملا حسن، مولانا بحرالعلوم، مولانا عبدالحئی اور مولانا عبدالباری رحہم اﷲ جیسے بڑے بڑے علماء پیدا ہوئے مگر اب بظاہر اس سلسلۃ الذہب کا خاتمہ نظر آتا ہے۔
مفتی صاحب مرحوم علم و عمل کے ساتھ اخلاق فاضلہ اور اوصاف حمیدہ سے بھی آراستہ نہایت خاموش متواضع، نرم خور، خندہ جبیں، شگفتہ مزاج اور خوش خلق تھے، ملنے والوں پر ان کے علم سے زیادہ ان کے اخلاق کا اثر پڑتا تھا، ان اوصاف کی بنا پر وہ ہر طبقے میں بڑے مقبول تھے۔ راقم نے ان سے مختصر المعانی پڑھی تھی، اس زمانہ میں ان کے اخلاق اور مہرومحبت کا جو نقش دل پر قائم ہوا تھا وہ اب تک باقی ہے، اﷲ تعالیٰ اس...
Allah selected Muhammad ﷺ trained by wahi provided it with all the knowledge required for any creation. Either it is any kind of Science, engineering, medical, war strategy, defense plan or any known/Unknown direction of human guidance. At last one must have to say that any precise or authentic yield of the research/effort just turn the page of Hadith or a verse of Quran no more than this. The war strategy of Muhammad ﷺ is wondering throughout the world even in such an advance time, mostly is depends upon. Initially Muhammadا started journey with the preaching of Islam, people were expecting it is too poor. How will be fruitful. It is help of Allah, constant efforts & strategy that prove whole story. This world became more stay able and more secure, was never before in the history of the mankind. Now in this age deviation from the way of Muhammadا will bring the world closer to an irreversible explosion, all the Muslim/Non-Muslim collectively believe in.
This research work addresses a major denoising problem in Magnetic Resonance (MR) Images. Magnetic Resonance Imaging (MRI) is a powerful and e ective di- agnostic tool in basic research, clinical investigation, and disease diagnosis since it provides both chemical and physiological information about the tissue. MR Images are a ected by Rician noise during acquisition phase which decreases the image quality, image analysis and becomes di cult to diagnose it accurately. This thesis is an attempt to suppress low and high categories of Rician noise from MR data in such a manner to enhance the diagnostically relevant image content. Supervised and unsupervisedltering techniques are applied to suppress the Rician noise hence improving its quality for diagnostic process. A new supervisedltering model, based on genetic programming (GP), is proposed that evolves an optimal composite mor- phological supervisedlter (FOCMSF ) by combining the gray-scale mathematical morphological operators. (FOCMSF ) is evolved through evaluating thetness of sev- eral individuals over certain number of generations. The proposed method does not need any prior information about the noise variance. In the domain of unsupervisedltering, three techniques are proposed. These are collaborative techniques based on statistical and fuzzy logic. Fuzzy similarity based non local meanslter (FSNLM) is designed to non-locally search out similar and non-similar regions of a noisy pixel. Fuzzy weights are assigned to these regions on the base of similarity. Then the noisy pixel is replaced with the fuzzy weighted average of these regions. Another hybridlter is proposed that combines FSNLM and local order statisticallters to suppress Rician noise. This hybridlter uses the strengths of non-local and locallters and adaptively calculates the fuzzy weighted estimation of the noisy pixels. Another non local fuzzy weighted Enhanced LMMSE (Linear Minimum Mean Square Estimator) is designed. The aim of this approach is to handle adaptively the low and high levels of variation of Rician noise and to estimate a closed-form of Rician distributed signal. It estimates the noise free pixel value based on similarity of the non-local neighborhood pixels around a window of certain prede ned radius. Similarity is computed using fuzzy logic approach which is served as fuzzy weights in enhanced LMMSE module for accurate estimation of noise free pixel value. The proposed schemes handle the problem with better accuracy than several well knownltering schemes NLM, LMMSE, Wavelet based techniques etc. and therefore can be considered as original contribution of this research work. The pro- posed schemes handle the problem of Rician noise at low and high noise variances on smooth as well as detailed regions where existing methods fail due to multifar- ious nature of this noise. The improved performance of the developedlters are investigated using the standard MRI dataset and its performance is compared with previously proposed state-of-the art methods. Detailed experimentation has been performed using simulated and real datasets based on well known quantitative mea- sures. Comparative analysis demonstrates the superiority of the proposed schemes over the existing techniques.