مولانا سید مرتضیٰ حسن
افسوس ہے کہ گزشتہ ماہ میں جناب سیدمرتضیٰ حسن صاحب کم و بیش پچانوے سال کی عمر میں اپنے وطن چاندپور ضلع مرادآباد میں اورجناب نہال سیوہاروی نے کراچی میں وفات پائی۔ مولانا مرحوم اکابر علمائے دیوبند میں سے تھے۔ علاوہ علم و فضل کے بڑے خوش بیان مقرر، کامیاب مناظر اور واعظ تھے۔ تحریکِ خلافت کے زمانہ میں مرحوم کی تقریروں کی جن میں حقیقت وظرافت دونوں کاخوش گوار امتزاج ہوتاتھا ملک بھر میں دھوم تھی۔حضرت مولانا شاہ رفیع الدین صاحبؒ مہتمم اوّل دارالعلوم دیوبند سے نسبتِ روحانی تھی اور اس تقریب سے قطبِ وقت حضرت مفتی اعظم مولانا عزیز الرحمن صاحب سے تعلق ِخاص رکھتے تھے اورقطب ِعالم حضرت مولانا گنگوہیؒ کی مجلس علمی وروحانی کے مخصوص ہم نشینوں میں داخل تھے، اس لیے ذکرومراقبہ کاشغل بھی رکھتے تھے۔ ایک عرصہ تک مدرسۂ امدادیہ مرادآباد کے روح رواں رہے۔۱۹۲۰ء میں پھر دارالعلوم دیوبند کے ناظم تعلیمات ہوکرچلے گئے۔ اب ادھر پندرہ سولہ سال سے عملاً خانہ نشین ہوگئے تھے۔ خود بزرگ تھے اوربزرگوں کی نشانی تھے، سینکڑوں ہزاروں علما جن میں مولانا سید سلیمان ندوی ایسے بلند پایہ عالم بھی شامل ہیں، ان کے فیض تلمذ سے مستفید ہوئے۔حق تعالیٰ انھیں جنت الفردوس میں مقام جلیل عطافرمائے۔آمین ثم آمین۔ [جنوری۱۹۵۲ء]
Tujuan penelitian ini adalah untuk mengetahui peningkatan kinerja guru di MTS Negeri 2 Banggai Semester 1 Tahun Pelajaran 2018/2019 setelah mengikuti supervisi akademik. Penelitian ini adalah penelitian tindakan sekolah. Subjek penelitian adalah guru di MTS Negeri 2 Banggai yang berjumlah 8 orang guru. Data kinerja guru pada penelitian ini dikumpulkan menggunakan kuesioner. Teknik analisis data menggunakan analisis deskriptif. Indikator keberhasilan penelitian ini adalah: apabila rata-rata kinerja guru minimal pada kategori Tinggi, dan ketuntasan klasikal sebesar 90%. Berdasarkan penelitian yang telah dilakukan dapat disimpulkan bahwa: penerapan supervisi akademik secara efektif dapat meningkatkan kinerja guru di MTS Negeri 2 Banggai Semester 1 Tahun Pelajaran 2018/2019. Hal ini ditunjukkan dari rerata kinerja guru sebelum penelitian sebesar 130, 50 berada pada kategori sedang, meningkat pada siklus I menjadi 150, 13 berada pada kategori tinggi dan meningkat lagi pada siklus II menjadi 167 berada pada kategori sangat tinggi
Advancement in the myoelectric interfaces have increased the use of myoelectric controlled robotic arms for partial-hand amputees as compared to body-powered arms. Current clinical approaches based on conventional (on/off and direct) control are limited to few degree of freedom (DoF) movements which are being better addressed with pattern recognition (PR) based control schemes. Performance of any PR based scheme heavily relies on optimal features set. Although, such schemes have shown to be very effective in short-term laboratory recordings, but they are limited by unsatisfactory robustness to non-stationarities (e.g. changes in electrode positions and skin-electrode interface). Moreover, electromyographic (EMG) signals are stochastic in nature and recent studies have shown that their classification accuracies vary significantly over time. Hence, the key challenge is not the laboratory short term conditions but the daily use. Thus, this work makes use of the longitudinal approaches with deep learning in comparison to classical machine learning techniques to myoelectric control and explores the real potential of both surface and intramuscular EMG in classifying different hand movements recorded over multiple days. To the best of our knowledge, for the first time, it also explores the feasibility of using raw (bipolar) EMG as input to deep networks. Task are completed with two different studies that were performed with different datasets. In the first study, surface and intramuscular EMG data of eleven wrist movements were recorded concurrently over six channels (each) from ten able-bodied and six amputee subjects for consecutive seven days. Performance of stacked sparse autoencoders (SSAE), an emerging deep learning technique, was evaluated in comparison with state of art LDA using offline classification error as performance matric. Further, performance of surface and intramuscular EMG was also compared with respect to time. Results of different analyses showed that SSAE outperformed LDA. Although there was no significant difference found between surface and intramuscular EMG in within day analysis but surface EMG significantly outperformed intramuscular EMG in long-term assessment. In the second study, surface EMG data of seven able-bodied were recorded over eight channels using Myo armband (wearable EMG sensors). The protocol was set such that each subject performed seven movements with ten repetitions per session. Data was recorded for consecutive fifteen days with two sessions per day. Performance of convolutional neural network (CNN with raw EMG), SSAE (both with raw data and features) and LDA were evaluated offline using classification error as performance matric. Results of both the short and long-term analyses showed that CNN and SSAE-f outperformed the others while there was no difference found between the two. Overall, this dissertation concludes that deep learning techniques are promising approaches in improving myoelectric control schemes. SSAE generalizes well with hand-crafted features but fails to generalize with raw data. CNN based approach is more promising as it achieved optimal performance without the need to select features.