رومانیت:
فیض اپنے کلام سے اپنے خشک زاہد اور ناصح ہونے کا تصور نہیں دیتے بلکہ پھول اور تلوار کے ساتھ ساتھ چشم و لب کی کاٹ کی باتیں بھی کرتے ہیں۔ ان کی اْفتاد ِ طبع رومانی ہے ان کے سینے میں پیار بھرا دل دھڑکتا ہے ان کے کلام میں لطافت اور نزاکت کی روحانی فضائ چھائی رہتی ہے وہ تصورِ جاناں پر سب کچھ نچھاور کرتے نظر آتے ہیں۔
حسن ادا اور ندرت ِ بیان:
فیض ندرت بیان سے قاری کواپنے سحر میں جکڑ لیتے ہیں اور ان کے کلام کا جتنا زیادہ مطالعہ کیا جائے نئے نئے خیالات و تصورات و ا ہوتے جاتے ہیں کیونکہ ان کے ہاں ندرت بیاں اور حسن ادا کی دلکش مثالیں پائی جاتی ہیں۔
جذبات کی ترجمانی:
شاعری میں جذبات کی ترجمانی کے لئے صدق و خلوص انتہائی ضروری ہوتے ہیں صرف احساسات ، محسوسات اور جذبات کے بیان کا نام شاعری نہیں ہوا کرتا۔ فیض کے ہاں ہمیں پر خلوص جذبہ صداقت اور اْسلوب ِ اظہار پر کامل قدرت ہمیں بھر پور انداز میں ملتے ہیں۔
عشقیہ شاعری:
فیض کی شاعری حقیقی جذبات کی عکاسی کرتی ہے ان کی شاعری میں عشق و مستی اور چاہت ومحبت کا بھی کثرت سے ذکر ملتا ہے ان کی شاعری محبت کا ایک دل آویز نمونہ ہے اس اظہار میں چاند کی چاندنی کی سی ٹھنڈک اور سکون ، باد نسیم سی نازک خرامی کے علاوہ محبت کا لوچ اور رس ہمیں دلکش پیرایے میں نظر آتا ہے۔
وطن پرستی:
فیض کو اپنی مٹی سے پیا ر ہے۔ اس مٹی کو وہ محبوبہ کی طرح چاہتے ہیں۔ محبوبہ اور وطن میں وہ فرق نہیں کرتے
اسلوب:
دراصل فیض کا مخصوص لہجہ اور اسلوب ہی وہ جادو ہے جو قاری کو اپنا اسیر کر لیتا ہے اور ہر بار...
Islamic literature is a term referring to the school of thought who believes that a good literary work should view God, man and the world through the lens of Islam. It is conceived that the style of such literature must be of high quality with the Qur’ān, Ḥadith and the legacy of the Islamic scholars being its model. Islamic literature is a universal literature and can be written in any language. However, most of what has been written on the theory and practice of Islamic literature is in Arabic. This study discusses the model of Islamic literature in era of Islam, Umayyad period, Abbasid period, and Modern world. Topics of Islamic literature in modern times are dealing with the moral values in the Qur’ān and the Sunnah of the Prophet, peace be upon him. It discusses Jurisprudence in worship Biography of the Prophet and Praise of the Prophet and his companions God bless them all. The deep knowledge of Arabic language and Islamic literature solves the social and cultural problems around the world.
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.