15,580 research outputs found
Pioneers of Library Movement in Pakistan
No full textThe paper aims to describe in brief the contribution of seven leaders of Pakistan librarianship, viz. K.B. Khalifa M. Asadullah, Prof. Dr. Abdul Moid, Dr. Abdus Subuh Qasimi, Muhammad Shafi, Fazal Elahi, Khawaja Nur Elahi and S. V. Hussain. The early library developments are given for better understanding of the role of these leaders
Skills for Building Personal Credibility and Influencing Others - Associate prof. Muhammad Shahid Khan, Ph.D
No full textGuest Lecture EM 936 - Skills for Building Personal Credibility and Influencing Others By Associate prof. Muhammad Shahid Khan, Ph.
Multiview Video Coding Accelerated on Multicore Architectures
No full textThis thesis deals with the design and implementation of extremely parallel fast motion / disparity estimation algorithm for multicore architectures. Currently, H.264/AVC is the most widely used commercial video compression standard and is based on single view. Recently, Multi-view Video Coding (MVC) has also been standardized as an extension to H.264/AVC for supporting 3D and Free Viewpoint video. As MVC is an extension to H.264/AVC, so it achieves compression not only by exploiting temporal and spatial prediction but also exploits inter-view redundancies using motion estimation tool. In H.264/AVC, motion estimation is the most important tool employed by the video encoder to mitigate temporal redundancies but it is also the most time consuming. Consequently, in MVC, the time consumed for efficient encoding is even higher as the encoder has to perform temporal as well as inter view predictions. This thesis proposes a parallel low-complexity rate-distortion optimized motion/disparity estimation algorithm that can be implemented on multicore architectures such as Graphical Processing Unit (GPU). Recently, GPU has emerged as a commercially viable multicore platform for accel-
erating computationally extensive applications and has also been applied for improving video encoder performance. Generally, the bit rate cost during motion vector calculation is ignored while implementing parallel motion estimation algorithms on GPU, due to the unavailability of the spatially predicted motion vectors, which leads to rate-distortion performance degradation. The proposed approach is able to perform the complex prediction task by means of an efficient distribution of all the computations over the GPU by mitigating the spatial dependencies. The experimental results show that the proposed scheme achieves significant speedup and has comparable rate-distortion performance with respect
to sequential fast motion estimation algorithm. The proposed algorithm is also used for exploiting inter-view prediction in MVC and is implemented on the GPU exploiting view and block level parallelism simultaneously. The results for MVC suggest a significant speedup with negligible loss in coding efficiency
Expression of Alpha-1-Antitrypsin in T-cell lymphocytes
No full textI have studied immunoblastic T-cell lymphoma and enteropathy associated T-cell lymphoma using immunohistochemical techniques for the demonstration of AlAT. Cytoplasmic staining for AlAT is present in the malignant cells of both types of T-cell neoplasm which also express CD30 and CD25. Peripheral blood T-lymphocytes on stimulation with mitogen also show granular cytoplasmic expression of AlAT. Time course studies show that this parallels the expression of CD30 and CD25, markers of lymphoid activation. AlAT expression appears therefore to be associated with activation in T-cells. Further studies of subfractionated T-lymphocytes suggest that the expression of AlAT on activation is not restricted to an individual lymphocyte subset. This study demonstrates AlAT mRNA in monocytes, granulocytes and lumphocytes stimulated with con-A, using synthetic oligonucleotide gene probes. Our results confirm AlAT synthesis by these cells. These cells also express exons A and B, exons not expressed in the hepatocyte. Using probes for the individual exons we have demonstrated alternative splicing of exon B in monocytes and lymphocytes.</p
Geoinformatic and Hydrologic Analysis using Open Source Data for Floods Management in Pakistan
No full textThere is being observed high variability in the spatial and temporal rainfall patterns under changing climate, enhancing both the intensity and frequency of the natural disasters like floods. Pakistan, a country which is highly prone to climate change, is recently facing the challenges of both flooding and severe water shortage as the surface water storage capacity is too limited to cope with heavy flows during rainy months. Thus, an effective and timely predication and management of high flows is a dire need to address both flooding and long term water shortage issues. The work of this thesis was aimed at developing and evaluating different open source data based methodologies for floods detection and analysis in Pakistan. Specifically, the research work was conducted for developing and evaluating a hydrologic model being able to run in real time based on satellite rainfall data, as well as to perform flood hazard mapping by analyzing seasonality of flooded areas using MODIS classification approach. In the first phase, TRMM monthly rainfall data (TMPA 3B43) was evaluated for Pakistan by comparison with rain gauge data, as well as by further focusing on its analysis and evaluation for different time periods and climatic zones of Pakistan. In the next phase, TRMM rainfall data and other open source datasets like digital soil map and global land cover map were utilized to develop and evaluate an event-based hydrologic model using HEC-HMS, which may be able to be run in real time for predicting peak flows due to any extreme rainfall event. Finally, to broaden the study canvas from a river catchment to the whole country scale, MODIS automated water bodies classification approach with MODIS daily surface reflectance products was utilized to develop a historical archive of reference water bodies and perform seasonal analysis of flooded areas for Pakistan. The approach was found well capable for its application for floods detection in plain areas of Pakistan. The open source data based hydrologic modeling approach devised in this study can be helpful for conducting similar rainfall-runoff modeling studies for the other river catchments and predicting peak flows at a river catchment scale, particularly in mountainous topography. Similarly, the outcomes of MODIS classification analysis regarding reference and seasonal water and flood hazard maps may be helpful for planning any management interventions in the flood prone areas of Pakista
Social Interactions Analysis through Deep Visual Nonverbal Features
No full textHuman social interaction is as common as complex to understand. It is a part of our routine life ranging from houses to communal places either in direct face-to-face interaction or through digital media. During these interactions, humans exchange their thoughts, intentions, and emotions effectively. They use verbal language along with non-verbal social signals such as variation in voice tune, hand gestures, facial expressions, and body posture. This non-verbal part of communication is still less understood despite the fact of recent huge research progression and computational advancement. Recently, social interactions in groups such as meetings, standing conversations, interviewing, and discussions have become popular areas of research for the social computing domain. In this thesis, we propose and investigate novel computational approaches for the application of emergent leadership detection, leadership style prediction,
personality traits classification, and visual voice activity detection in the context of small group interactions.
First of all, we investigated emergent leadership detection in small group meeting environments. The leaders are key players in making the decision, facing problems, and as a result, playing an important role in an organization. In organizational behavioral research, the detection of an emergent leader is an important task. From the computing perspective, we propose visual activity-based nonverbal feature extraction from video streams by applying a deep learning approach along with the feature encoding for low dimensional representation. Our method shows improved results even as compared to multi-modal non-verbal features extracted from audio and visual. These novel features also performed well for the application of autocratic or democratic leadership style prediction and the discrimination of high/low extraversion.
Afterwards, we explored the problem of voice activity detection (VAD) extensively. VAD is defined as Who is Speaking and When". Usually, VAD is accomplished using audio features only. But, due to some physical or privacy-related constraints, the audio modality is not always accessible which increases the importance of VAD based on visual modality only. Visual VAD is also a very useful for several social interactions analysis-related applications. We performed a detailed analysis to find out an efficient way of representing the raw video streams for this task. A full upper body-based holistic approach is adopted instead of using only lips motion or facial visual features as mostly suggested by the literature. Motivated from psychology literature, gesticulating style while speaking varies from person to person depending upon ethnic background or type of personality. An unsupervised domain adaptation
is also adapted and gives a good boost in VAD performance. We introduce the new RealVAD dataset, which is used to benchmark the VAD methods in real-life situations. Lastly, we performed body motion cues based VAD learning in conjunction
with a weakly supervised segmentation scheme
Data-Driven Approach based on Deep Learning and Probabilistic Models for PHY-Layer Security in AI-enabled Cognitive Radio IoT.
No full textPhD Theses.Cognitive Radio Internet of Things (CR-IoT) has revolutionized almost every eld of life
and reshaped the technological world. Several tiny devices are seamlessly connected in
a CR-IoT network to perform various tasks in many applications. Nevertheless, CR-IoT
su ers from malicious attacks that pulverize communication and perturb network performance.
Therefore, recently it is envisaged to introduce higher-level Arti cial Intelligence
(AI) by incorporating Self-Awareness (SA) capabilities into CR-IoT objects to facilitate
CR-IoT networks to establish secure transmission against vicious attacks autonomously.
In this context, sub-band information from the Orthogonal Frequency Division Multiplexing
(OFDM) modulated transmission in the spectrum has been extracted from the
radio device receiver terminal, and a generalized state vector (GS) is formed containing
low dimension in-phase and quadrature components. Accordingly, a probabilistic method
based on learning a switching Dynamic Bayesian Network (DBN) from OFDM transmission
with no abnormalities has been proposed to statistically model signal behaviors
inside the CR-IoT spectrum. A Bayesian lter, Markov Jump Particle Filter (MJPF),
is implemented to perform state estimation and capture malicious attacks.
Subsequently, GS containing a higher number of subcarriers has been investigated. In
this connection, Variational autoencoders (VAE) is used as a deep learning technique
to extract features from high dimension radio signals into low dimension latent space
z, and DBN is learned based on GS containing latent space data. Afterward, to perform
state estimation and capture abnormalities in a spectrum, Adapted-Markov Jump
Particle Filter (A-MJPF) is deployed. The proposed method can capture anomaly that
appears due to either jammer attacks in transmission or cognitive devices in a network
experiencing di erent transmission sources that have not been observed previously. The
performance is assessed using the receiver
Introducing Iqbal the Economist
No full textThe Iqbal Memorial Lecture was instituted in 1994 when the Pakistan Society of Development Economists (PSDE) celebrated the completion of a decade of steady progress. A brief announcement stated: “The Iqbal Memorial Lecture attributed to the national poet [Emphasis added], Allama Muhammad Iqbal has been included in the programme for the first time. Professor Ian M. D. Little is delivering that lecture” [Secretary’s Report (1994), p. 1472]. Iqbal, the poet and philosopher par excellence, has made incisive remarks or comments on economic and social issues in his poetry, philosophical writings, and in the course of his discourses as well as some famous letters, particularly those written to the Quaid-i-Azam, Muhammad Ali Jinnah, the founder of Pakistan. But these do not make Iqbal an economist. The Secretary of the PSDE was, therefore, careful in observing that the lecture commemorates our “national poet”. However, it will be of great interest to this largest national congregation of economists and other scholars concerned with development to know that the very first published book of Iqbal related neither to poetry nor philosophy, but economics. It was written in Urdu. He also taught the subject at undergraduate and Master’s level, even though he had not studied it as a student. At the Government College, Lahore, Iqbal studied English, Philosophy and Arabic for his B.A. and then completed the M.A. in Philosophy.
CCDC 654596: Experimental Crystal Structure Determination
No full textRelated Article: Muhammad Shahid, Mazhar Hamid, Asif A. Tahir, Muhammad Mazhar, Mohammad A. Malik, Madeleine Helliwell|2012|Ind.Eng.Chem.Res.|51|16361|doi:10.1021/ie302398
Floricane Blackberry Pruning Guide for Florida
No full textThis article aims to provide a general overview of growth habits, fruiting patterns, and pruning requirements/timing for county and state Extension faculty, growers, homeowners, and students interested in growing floricane blackberries in Florida. Written by Muhammad A. Shahid and Ali Sarkhosh, and published by the UF/IFAS Horticultural Sciences Department, March 2023
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