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ہجر فراق سوہنے یار دے وچ: ۳

سی حرفی ۔۳
(ہجر فراق سوہنے یار دے وچ)
الف
اللہ دی آس تے رکھ تقویٰ، بیڑے ٹھیل دتے سوہنے یار ولّے
دنیا جگ جہان بھلا بیٹھے، رُجھ گئے ہاں یار دی کار ولّے
دن رات پئے تڑفدے وانگ مچھی، پھیرا گھت کھاں کدی بیمار ولّے
نیوں لگیاں دی لج پال یارا، ہوندا رب حنیف دکھیار ولّے

ب
بس ماہی اساں بھل گئے ہاں، مٹھے بول تے جان وکا بیٹھے
لٹے گئے ہاں تیرے خلوص اتے، کر جان جہان فدا بیٹھے
دل والڑے دلاں دا حال جانن، نال سادگی حال ونجا بیٹھے
دیویں آ دیدار حنیف تائیں، ہاڑے گھت کے رو کرلا بیٹھے

ت
تاہنگ پیاریاں جانیاں دی، ہک پل نہ صبر قرار مینوں
دل چیکدا نت اڈیکدا اے، چھڈ گئے نیں یاراں دے یار مینوں
منہ سامنے یار جواب دے گئے، آئی وڈی نصیباں دی ہار مینوں
وڈا دکھ اکلاپا حنیف یارا، جیہڑا کر گیا آن لاچار مینوں
ث
ثمر محبتاں میریاں دا، دیویں پا جھولی گنہگار تائیں
مزا اوسی محبت دا اوس ویلے، ملن آوسن یار جاں یار تائیں
تیرا ہسنا دم مسیح دا اے، دیویں ہس کے صحت بیمار تائیں
پچھے ہٹ دے نہیں حنیف عاشق، توڑ چاہڑ دے قول قرار تائیں
ج
جنت ،حور ، قصور سبھے ، دیواں وار محبوب دی خاک اتوں
خاطر جس دی کل کائنات ہوئی، کراں صدقے جان اس پاک اتوں
کرسی مہر شفاعت شفیع سرور، چھٹ جان گے غم ،غم ناک اتوں
بھیجے آپ درود حنیف مولا، سوہنا رب رحمت لولاک اتوں

ح
حوصلہ عاشقاں صادقاں دا، ہوندا نت ہے دون سوا میاں
ویکھ حوصلہ ہار دے لوک دانے، دیوے حوصلہ شوق ودھا میاں
رکھ حوصلہ لٹکدے سولیاں تے، نعرہ انالحق دا لا میاں
ملے حوصلہ تدوں حنیف تائیں، جدوں لواں محبوب نوں پا میاں

Terrorism in Karachi, Sindh Pakistan: A Case Study of Safoora-Incident

Terrorism is contentious issue. It has affected the lives of people across the country. This paper analyses the factors for incidence of terrorism in Karachi city which has great economic importance for country. The city represents all communities belonging to various parts of the country. Migration of people from other provinces has significally changed demographic profile of the city. This study presents statistics about various offences committed in Hyderabad and Karachi. It describes important facts about the militant groups involved in the acts of terrorism in Karachi. It has been argued in the study that in some cases the acts of violence were politically motivated and in other cases terrorism acts were sponsored by religious groups. In order to understand the complex phenomenon of terrorism in Karachi, this study investigates the root causes of terrorism including economic deprivation. In most cases marginal sections of society have been found involved in the acts of terrorism. Thus, this study creates need for social reforms, poverty alleviation and provision of quality education. Further capacity-building of Law Enforcement Agencies to cope with this situation has been emphasized in this study. This paper also recommends some proposals for dealing with the issue of terrorism.

Deep Learning for Improved Myoelectric Control

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.
Asian Research Index Whatsapp Chanel
Asian Research Index Whatsapp Chanel

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