A | B | C | D | E | F | G | H | I | |
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1 | |||||||||
2 | Committee I - SSR (Chair), SDR, TKG | ||||||||
3 | 11 May 2018 (Fri) 03:00 pm-05:15 pm, Bharti School Bldg IIA-105 (Committee Room, Ground Floor) | ||||||||
4 | S.No | Entry Number | Name | Supervisor(s) | Topic | Sup | Com | Total | Grade |
5 | 1 | 2016EET2645 | SUMEET INANI | SDR | Fast Fingerprint Image Matching and Retrieval (Biometrics) | 0.00 | |||
6 | 2 | 2017EET2295 | SRIJEET CHATTERJEE | SDR | Image Super-resolution | 0.00 | |||
7 | 3 | 2017EET2839 | VARUN SOOD | SDR | Vision-Guided Feedback for Robotic Manipulation (Robotic Vision) | 0.00 | |||
8 | 4 | 2017EET2841 | PRATEEK ARORA | SDR | Layout-based Document Matching and Retrieval (Document Image Analysis) | 0.00 | |||
9 | 5 | 2017EET2304 | HAREESH KUMAWAT | SSR | Occupancy detection in classrooms (Image processing, embedded hardware and software) | 0.00 | |||
10 | 6 | 2017EET2681 | PRABHLEEN KAUR | SSR | Occupancy detection in classrooms (Image processing, embedded hardware and software) | 0.00 | |||
11 | 7 | 2017EET2306 | VISHAL KUMAR | TKG | Automated Brain Tumor Detection and Identification | 0.00 | |||
12 | 8 | 2011EE50547 | GAURAV KALYAN | TKG | 0.00 | ||||
13 | 9 | 2011EE50550 | HEMANT SAHARIA | TKG | 0.00 | ||||
14 | |||||||||
15 | |||||||||
16 | Committee 2 – RB (Chair), SRS, SMKR, SK, MSuri | ||||||||
17 | 11 May 2018 (Fri) 03:00 pm-05:15 pm, Bharti School Bldg IIA-101 (Seminar Room, Ground Floor) | ||||||||
18 | S.No | Entry Number | Name | Supervisor(s) | Topic | Sup | Com | Total | Grade |
19 | 1 | 2017EET2296 | ANKIT GOLA | SRS | Algorithm for temperature hotspot estimation in a liquid cooled chip | 0.00 | |||
20 | 2 | 2017EET2299 | ANAND SINGH | SRS | Algorithm for temperature hotspot estimation in a liquid cooled chip | 0.00 | |||
21 | 3 | 2016EET2643 | RAKESH ROSHAN BEHERA | SMKR | To develop a new fetal monitoring tool for Tocoanalysis | 0.00 | |||
22 | 4 | 2017EET2300 | RAVI SHANKAR SINGH | RB | Indianized implementation of AES encryption using FPGA/ARM and optimize it. | 0.00 | |||
23 | 5 | 2017EET2303 | HARSHIT GUPTA | RB | Using ML Algorithms to classify IoT data to be processed on device, Fog and Cloud | 0.00 | |||
24 | 6 | 2017EET2840 | RAVI SINGH THAKUR | RB | Implementing Blockchain with AI to detect malware and making system decentralized | 0.00 | |||
25 | 7 | 2017EET2294 | ISHANK GUPTA | SK | Porting a distributed OS for an loosely coupled array of processors | 0.00 | |||
26 | 8 | 2017EET2856 | VUDATHANENI DIVYA NARENDRA | SK | Smart camera based field techniques | 0.00 | |||
27 | 9 | 2015EE10045 | NARAYANI BHATIA | MSuri | (To be evaluated by a separate sub-committee, off-line) | 0.00 | |||
28 | |||||||||
29 | |||||||||
30 | Committee 3 – SA (Chair), BL, SC, APP, SB, DUP | ||||||||
31 | 11 May 2018 (Fri) 03:00 pm-05:30 pm, II-405 (Multimedia Lab, Third Floor) | ||||||||
32 | S.No | Entry Number | Name | Supervisor(s) | Topic | Sup | Com | Total | Grade |
33 | 1 | 2017EET2291 | SAKSHI AGRAWAL | BL | computer vision+machine learning | 0.00 | |||
34 | 2 | 2017EET2292 | PALAKH SHANGLE | BL | computer vision+machine learning | 0.00 | |||
35 | 3 | 2017EET2302 | MANSI GARG | BL | computer vision+machine learning | 0.00 | |||
36 | 4 | 2017EET2305 | PRIYA KUMARI | BL | Object Identification | 0.00 | |||
37 | 5 | 2017EET2680 | PUSHPENDRA SINGH DAHIYA | BL | Edge computing for IOT | 0.00 | |||
38 | 6 | 2017EET2864 | ZEEL PATIJKUMAR SHAH | BL | Object Identification | 0.00 | |||
39 | 7 | 2017EET2307 | VINAY KYATHAM | SC | Generative Adversarial Networks for Non Stationary Environment | 0.00 | |||
40 | 8 | 2017EET2297 | ARAVIND J | APP | Generative deep networks for isolated phoneme generation | 0.00 | |||
41 | 9 | 2016EET2646 | SHANTANU | APP | Web Page Classification using Deep Neural Network (presentation, if the Dean permits) | 0.00 | |||
42 | 9 | 2017EEA2664 | APEKSHA TANDON | SA | Controllability for Social Networks | 0.00 | |||
43 | |||||||||
44 | [Committee members listed in purple are invitees from other groups for specific presentations of interest.] | ||||||||
45 | |||||||||
46 | Relative Percentages: | ||||||||
47 | Sup(40%), Com(60%) | ||||||||
48 | Grades: A, A-, B, B-, C, C-, D, E, X | ||||||||
49 | Guideline for grades: | ||||||||
50 | A = Exceptional original outstanding work | ||||||||
51 | A- = Excellent original work but I have seen been better work | ||||||||
52 | B = Very good work but lacking in either originality/scope/implementation | ||||||||
53 | B- = Good work which can be improved with guidance | ||||||||
54 | C = Fair work with scope for improvement | ||||||||
55 | C- = Fair work with gaps in one or more areas | ||||||||
56 | D = Unsatisfactory | ||||||||
57 | E = Unsatisfactory, must be redone | ||||||||
58 | X = Incomplete | ||||||||
59 | |||||||||
60 | EndSem Report requirements: 1 sheet summary in the IEEE Transactions style (printout), for committee members (2-3 copies) | ||||||||
61 | Time: 15 min [incl Q&A time] | ||||||||
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64 | |||||||||
65 |