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Home > Taxonomic Studies of Fossil Even and Odd- Toed Mammals from the Miocene Rocks of Dhok Bun Ameer Khatoon, District Chakwal, Punjab, Pakistan

Taxonomic Studies of Fossil Even and Odd- Toed Mammals from the Miocene Rocks of Dhok Bun Ameer Khatoon, District Chakwal, Punjab, Pakistan

Thesis Info

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External Link

Author

Samiullah, Khizar

Program

PhD

Institute

Quaid-I-Azam University

City

Islamabad

Province

Islamabad.

Country

Pakistan

Thesis Completing Year

2010

Thesis Completion Status

Completed

Subject

Natural Sciences

Language

English

Link

http://prr.hec.gov.pk/jspui/bitstream/123456789/2448/1/2555S.pdf

Added

2021-02-17 19:49:13

Modified

2024-03-24 20:25:49

ARI ID

1676727544228

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Fossil site Dhok Bun Ameer Khatoon (32o 47'' 26.4" N, 72° 55'' 35.7" E) yielded a significant amount of mammalian assemblage including four families of even-toed fossil mammal (Suidae, Tragulidae, Giraffidae, and Bovidae) and one family of odd-toed (Rhinocerotidae) of the Late Miocene. This newly discovered site has well exposed Chinji and Nagri formation and has dated approximately 14.2 Ma-9.5 Ma. This age agrees with the divergence of different mammalian genera and is important Palaeoecologically, Palaeogeographically and Palaeoclimatologically. Sedimentological evidence of the site supports that this is deposited in locustrine or fluvial environment, as Chinji formation is composed primarily of mud-stone while the Nagri formation is sand dominated. Palaeoenvironmental data indicates that Miocene climate of Pakistan was probably be monsoonal as there is now a days. Mostly the genera recovered from this site resemble with the overlying younger Dhok Pathan formation of the Siwaliks while the size variation in dentition is taxonomically important for vertebrate evolutionary point of view and this is the main reason to conduct this study at this specific site to add additional information in the field of Palaeontology. A detailed study of fossils even and odd-toed mammals found in Miocene rocks exposed at Dhok Bun Ameer Khatoon was carried out. Over all one hundred and twenty specimens were collected during field trips from which forty two specimens are well preserved and identified and are described in this thesis. Two specimens belonging to Gaindatherium browni, five specimens belonging to Listriodon pentapotamiae, four specimens belonging to Dorcatherium majus, one specimen belonging to Dorcatherium cf minus, two specimens belonging to Giraffa priscilla, twenty one specimens belonging to Giraffokeryx punjabiensis, three specimens belonging to Gazella sp. and three specimens belonging to Eotragus sp. Dhok Bun Ameer Khatoon is the new locality which is discovered in detail first time in Pakistan by the present author. The collection comprises isolated upper and lower teeth and fragments of maxillae and mandibular ramii.
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شہرِ سخن میں ایسا خزانہ نہیں ملا

شہرِ سخن کو ایسا خزانہ نہیں ملا
صلِّ علیٰ سے بڑھ کے ترانہ نہیں ملا

میرے رسولؐ! تو کہ شہہِ ہست وبود ہے
کوئی بھی تیرا لمحہ شہانہ نہیں ملا

اُٹھا جو تیرے در سے بصد نخوت و غرور
اُس شخص کو کہیں بھی ٹھکانہ نہیں ملا

اک ذرہ تیرے نقشِ کفِ پا کا پا گئی
دستِ ہوا کو ایسا خزانہ نہیں ملا

چُن لیتے تیری راہ کے کانٹے پلک پلک
افسوس! ہم کو تیرا زمانہ نہیں ملا

کیسے کروں میں آیۂ تطہیر کا بیاں
دھرتی کو پھر سے ویسا گھرانا نہیں ملا

Development and Evaluation of an Audio System Trainer

Guided by the desire to contribute a little in the development of an innovative instructional technology for the teaching of electronics, this researcher constructed a prototype audio system trainer. Electronics teachers will find the instructional tool convenient and practical to use for visual instruction, laboratory activities and troubleshooting works in electronics. To support the functionality of the audio system trainer, the researcher developed a supplementary laboratory manual. The project whose production costs totaled fourteen thousand two hundred seventy two pesos and fifty centavos (Php 14,272.50) was finished in two weeks’ time. However, this amount is small if compared to the prices of commercially available instructional device developed for the same purpose. The completed project and the supplementary laboratory exercises were evaluated by selected technical faculty members of the College of Industrial Technology of the Bulacan State University, Bulacan Polytechnic College, University of Rizal System, and Lyceum of the Philippines University. Sampling technique as used in the study is both purposive and incidental. The prototype audio system trainer received an over-all mean rating of 4.62 which means that the project is highly acceptable on a set of criteria which includes – physical features, cost, function / operations, and durability. Furthermore, the supplementary laboratory exercises obtained a mean of 4.70 which could be interpreted that the respondents strongly agree on the validity of the manual.

Human Action Recognition and Localization in Videos

Human action localization and recognition in videos is one of the most studied and active research area in computer vision. In this thesis we elaborate two main questions; First when and where is the action performed in the video and what type of action is performed. When and where localize the action spatially in a time series visual data, while what type of action determine the action category/class. The output of action localization is a sub-volume consists of the action of interest. Action localization is more challenging as compared to the action classi cation, because it is the process of extracting a speci c part of the spatio-temporal volume in a visual data. We address the problem of automatic extraction of foreground objects in videos and then determine the category of action performed in the localized region. Action localization and recognition deal with understanding when, where and what happens in a video sequence. In the last decade, some of the proposed methods addressed the problem of simultaneous recognition and localization of actions. Action recognition addresses the questionWhat type of action is performed in the video? , while action localization aims to answer the questionWhere in the video? . These methods are termedaction detectionoraction localization and recognition . The human action recognition and localization is greatly motivated by the wide range of applications in variouselds of computer vision like human perceptual segmentation, tracking human in a video sequence, recovering the body structure, medical diagnosis, monitoring the human activities in security-sensitive areas like airports, buildings (universities, hospitals, schools), border crossings and elderly daily activity recognition (related to elderly health issues). It is one of the hardest problems due to enormous variations in visual data, appearance of actors, motion patterns, changes in camera viewpoints, illumination variations, moving and cluttered backgrounds, occlusions of actors, intra-and inter-class variations, noise, moving cameras and the availability of extensive amount of visual data. Local features based action recognition methods have been extensively studied in the last two decades. These systems have numerous limitations and far enough from the real time scenario. Every phase of the system has its own importance for the next phase, such as the success and accuracy of local feature based methods depend on the accurate encoding of visual data i.e. feature extraction method, dimensionality reduction of the extracted features and compact representation, localizing the action and training a learning model (classi er) for the classi cation of action sequences (Main parts of the system should be: (1) Feature extraction, (2) Feature representation, (3) localization of the region of interest, and (4) classi cation of the action video). First of all we study, investigate, evaluate and compare the well known state-of-the-art and prominent approaches proposed for action recognition and localization. The methods proposed for action localization are too complex and computationally expensive. We have proposed a novel saliency map computation based on local and global features toll the gap between the two types of features and hence provide promising results very e ciently for salient object detection. Then the motion features are fused intelligently with the detected salient object to extract the moving object in each frame of the sequence. The object proposal algorithms normally use computationally expensive segmentation methodologies to extract di erent non-overlapping objects/regions in a frame. Our proposed methods exploit very limited spatio-temporal neighborhood to extract a compact action region based on the compensated motion information. Finally, classi er is trained on the local features to recognize/label the action sequence. We have evaluated two types of learning models, extreme learning machine (ELM) and deep neural networks (DNNs). ELM is fast, while the computationally intensive classi ers such as Deep Neural Networks (DNNs) produce comparatively better action recognition accuracy. The experimental evaluation reveals that our local features based human action recognition and localization system improves the existing systems in many aspects such as computational complexity and performance. Finally it is concluded that the proposed algorithms obtain better or very similar action recognition and localization performance/accuracy as compared to the state-of-the-art approaches on realistic, unconstrained and challenging human action recognition and localization datasets such as KTH, MSR-II, JHMDB21 and UCF Sports. Besides, to evaluate the e ectiveness of localization of proposed algorithms a number of segmentation data sets have been used such as MOViCs, I2R, SegTrack v 1 & 2, ObMiC and Wall owers. Though the approaches proposed in the thesis obtain promising and impressive results as compared to the prominent state-of-the-art methods, further research and investigations are required to get enhanced or comparable results on more challenging realistic videos encountered in practical life. The future directions are discussed in conclusions and future work section of the thesis.