Kshama Parakh seeks a challenging position in SoC and IP design utilizing her skills in synthesis, DFT, UPF and VLSI. She has experience from an internship at Intel working on synthesis flow, regression and flow testing, and automation. She developed scripts to parse SCANDEF files, compare directories, and check port attributes. Her M.Tech project analyzed UPF impact on DFT parameters. Course projects included priority encoder design and fault modeling. She holds an M.Tech from Nirma University and B.E from Bhilai Institute of Technology.
Toward Transparent Coexistence for Multihop Secondary Cognitive Radio Networkskitechsolutions
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Toward Transparent Coexistence for Multihop Secondary Cognitive Radio Networkskitechsolutions
Ki-Tech Solutions IEEE PROJECTS DEVELOPMENTS WE OFFER IEEE PROJECTS MCA FINAL YEAR STUDENT PROJECTS, ENGINEERING PROJECTS AND TRAINING, PHP PROJECTS, JAVA AND J2EE PROJECTS, ASP.NET PROJECTS, NS2 PROJECTS, MATLAB PROJECTS AND IPT TRAINING IN RAJAPALAYAM, VIRUDHUNAGAR DISTRICTS, AND TAMILNADU. Mail to: kitechsolutions.in@gmail.com
Continual/Lifelong Learning with Deep Architectures, Vincenzo LomonacoData Science Milan
Humans have the extraordinary ability to learn continually from experience. Not only can we apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of AI is building an artificial continually learning agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex skills and knowledge.
"Continual Learning" (CL) is indeed a fast emerging topic in AI concerning the ability to efficiently improve the performance of a deep model over time, dealing with a long (and possibly unlimited) sequence of data/tasks. In this workshop, after a brief introduction of the topic, we’ll implement different Continual Learning strategies and assess them on common vision benchmarks. We’ll conclude the workshop with a look at possible real world applications of CL.
Vincenzo Lomonaco is a Deep Learning PhD student at the University of Bologna and founder of ContinualAI.org. He is also the PhD students representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses “Machine Learning” and “Computer Architectures” in the same department. Previously, he was a Machine Learning software engineer at IDL in-line Devices and a Master Student at the University of Bologna where he graduated cum laude in 2015 with the dissertation “Deep Learning for Computer Vision: a Comparison Between CNNs and HTMs on Object Recognition Tasks".
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Blind prediction of natural video ...IEEEBEBTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
CupCarbon simulator: Simulating the D-LPCN algorithm to find the boundary nodes of a WSN by Ahcene Bounceur, University of Bretagne Occidentale, Brest, France
Semantic Concept Detection in Video Using Hybrid Model of CNN and SVM Classif...CSCJournals
In today's era of digitization and fast internet, many video are uploaded on websites, a mechanism is required to access this video accurately and efficiently. Semantic concept detection achieve this task accurately and is used in many application like multimedia annotation, video summarization, annotation, indexing and retrieval. Video retrieval based on semantic concept is efficient and challenging research area. Semantic concept detection bridges the semantic gap between low level extraction of features from key-frame or shot of video and high level interpretation of the same as semantics. Semantic Concept detection automatically assigns labels to video from predefined vocabulary. This task is considered as supervised machine learning problem. Support vector machine (SVM) emerged as default classifier choice for this task. But recently Deep Convolutional Neural Network (CNN) has shown exceptional performance in this area. CNN requires large dataset for training. In this paper, we present framework for semantic concept detection using hybrid model of SVM and CNN. Global features like color moment, HSV histogram, wavelet transform, grey level co-occurrence matrix and edge orientation histogram are selected as low level features extracted from annotated groundtruth video dataset of TRECVID. In second pipeline, deep features are extracted using pretrained CNN. Dataset is partitioned in three segments to deal with data imbalance issue. Two classifiers are separately trained on all segments and fusion of scores is performed to detect the concepts in test dataset. The system performance is evaluated using Mean Average Precision for multi-label dataset. The performance of the proposed framework using hybrid model of SVM and CNN is comparable to existing approaches.
Continual/Lifelong Learning with Deep Architectures, Vincenzo LomonacoData Science Milan
Humans have the extraordinary ability to learn continually from experience. Not only can we apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of AI is building an artificial continually learning agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex skills and knowledge.
"Continual Learning" (CL) is indeed a fast emerging topic in AI concerning the ability to efficiently improve the performance of a deep model over time, dealing with a long (and possibly unlimited) sequence of data/tasks. In this workshop, after a brief introduction of the topic, we’ll implement different Continual Learning strategies and assess them on common vision benchmarks. We’ll conclude the workshop with a look at possible real world applications of CL.
Vincenzo Lomonaco is a Deep Learning PhD student at the University of Bologna and founder of ContinualAI.org. He is also the PhD students representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses “Machine Learning” and “Computer Architectures” in the same department. Previously, he was a Machine Learning software engineer at IDL in-line Devices and a Master Student at the University of Bologna where he graduated cum laude in 2015 with the dissertation “Deep Learning for Computer Vision: a Comparison Between CNNs and HTMs on Object Recognition Tasks".
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Blind prediction of natural video ...IEEEBEBTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
CupCarbon simulator: Simulating the D-LPCN algorithm to find the boundary nodes of a WSN by Ahcene Bounceur, University of Bretagne Occidentale, Brest, France
Semantic Concept Detection in Video Using Hybrid Model of CNN and SVM Classif...CSCJournals
In today's era of digitization and fast internet, many video are uploaded on websites, a mechanism is required to access this video accurately and efficiently. Semantic concept detection achieve this task accurately and is used in many application like multimedia annotation, video summarization, annotation, indexing and retrieval. Video retrieval based on semantic concept is efficient and challenging research area. Semantic concept detection bridges the semantic gap between low level extraction of features from key-frame or shot of video and high level interpretation of the same as semantics. Semantic Concept detection automatically assigns labels to video from predefined vocabulary. This task is considered as supervised machine learning problem. Support vector machine (SVM) emerged as default classifier choice for this task. But recently Deep Convolutional Neural Network (CNN) has shown exceptional performance in this area. CNN requires large dataset for training. In this paper, we present framework for semantic concept detection using hybrid model of SVM and CNN. Global features like color moment, HSV histogram, wavelet transform, grey level co-occurrence matrix and edge orientation histogram are selected as low level features extracted from annotated groundtruth video dataset of TRECVID. In second pipeline, deep features are extracted using pretrained CNN. Dataset is partitioned in three segments to deal with data imbalance issue. Two classifiers are separately trained on all segments and fusion of scores is performed to detect the concepts in test dataset. The system performance is evaluated using Mean Average Precision for multi-label dataset. The performance of the proposed framework using hybrid model of SVM and CNN is comparable to existing approaches.
1. KSHAMA PARAKH
Parakh Computers, Kampthee line, Rajnandgaon (Chhattisgarh)
Contact: +9176970-93000 Email: kshama.parakh@gmail.com
Career objective: To get a challenging position to design leading edge SoCs, IPs utilizing my
knowledge of Design compiler (Synthesis), DFT, UPF and VLSI.
Experience: Internship at Intel, Bengaluru Duration: June 2015-June 2016
Worked on Synthesis Flow
Analyzed Impact of UPF on DFT by writing Dummy RTL, UPF and Scan configuration.
Addressed various tool bugs by writing small test cases for reproducing and fixing the issue.
Worked on Sequential Vectoring to validate some commands.
Enabled Scan for a design by writing Scan configuration for all its partitions.
Worked on Regression and Flow Testing
Added various test cases to regression, analyzed and debugged degradation of results.
Enhanced a PERL script to capture metrics related to DFT.
Enabled Cronjob for weekly regression.
Developed a TCL utility to backup regression area.
Ported several test cases from project environment to Regression suite.
Automation Work carried out during Internship
Developed a TCL SCANDEF parser to extract various information like number of Scan
chains, elements per chain, average chain count from the SCANDEF file.
Developed a PERL script to compare contents of two directories.
Developed a PERL utility to check missing attributes on the port in design.
Skill Sets
Operating Systems Windows 7, LINUX (Ubuntu)
Programming/Scripting Language C, PERL and TCL
Hardware Description Language VHDL ,Verilog
Tools Design Compiler , Xilinx ISE Design Suite , Visual TCAD
Major Projects and Seminars
lM.Tech Project: Impact of UPF on Design for Testability (DFT)
Guide: Dr. N.M Devashrayee, Nirma University
Aim: To analyze the impact of UPF on DFT and observe the parameters like power, performance, area.
Studied the effect of different strategies like Isolation, Level Shifter and Retention on Scan-Stitching.
Compared the SCANDEF file with or without UPF and observed the differences.
M.Tech Seminar: Multi-Threshold CMOS (MTCMOS) for Low Power VLSI Circuits.
Guide: Dr. N.M Devashrayee, Nirma University
Studied MTCMOS technology which provides solution to the high performance and low power design.
Detailed study of MTCMOS, its operating modes, problems associated and Design issues, Design of
special cells, Design flow and its applications.
MTCMOS is used in Mobile Computing and other high performance applications.
2. Course Projects (M.Tech)
Specification to Layout development of 8:3 Priority Encoder
Aim: To design an 8:3 priority encoder using 4:2 priority encoder.
It’s VHDL Coding, Functional Simulation and Synthesis is done using Xilinx ISE.
Synthesized design is implemented on Spartan 6 FPGA board. Layout is developed using Microwind.
Implemented Fault Modelling algorithm for verification of VLSI Chip.
Aim: To implement a fault equivalence algorithm to get the reduced single stuck at faults.
Its takes ISCAS netlist as its input and has been developed using PERL.
Collapse ratio and minimum set of stuck-at-faults are its outputs.
Designed TUNNEL DIODE using Visual TCAD
Aim: To design a Tunnel Diode in nanometer (nm) technology and observe its characteristics using
Visual TCAD and to study its applications.
Compared the characteristics of Tunnel Diode for different doping densities.
B.E. Project: Ultrasonic Security System
Guide: Prof. Kiran Dewangan, BIT Durg
Designed a security system that generates an alarm and sends message to owner via GSM module.
Implemented motion detection algorithm to capture image of an intruder and saves it.
Enhanced security by incorporating features like high computational speed and password protection.
Academic Profile
Degree Institute/School University/Board Year Percentage/CPI
M.Tech Institute of Technology Nirma University 2016 8.95
B.E. Bhilai Institute of Technology, Durg CSVTU 2014 9.16
HSCE Yugantar Public School CBSE 2010 93.6%
SSCE Royal Kids Covent ICSE 2008 89.8%
Training and workshops
Participated in TEQIP II sponsored three days’ workshop on “An Intellectual insight into Analog and
Digital VLSI Design, GEC Gandhinagar.
Attended a workshop on “Electronic System Design and Manufacturing”.
Vocational training in Basic Telecomm: Bharat Sanchar Nigam Limited (BSNL), Durg.
Declaration
I do hereby declare that all the above information is true to the best of my knowledge and I bear the
responsibility for the correctness of the above mentioned particulars.
Kshama Parakh