Inside prototype 16-113
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The document summarizes the progress and future work of a video searching application that uses automatic annotation. It analyzes video structure by fragmenting videos into frames and identifying duplicates. Deep learning is used to detect objects in images by training a model on categorized image data. Semantic textual searching tokenizes user queries and matches them to an ontology and database to return relevant video results. Future work includes optimizing code, improving techniques for frame analysis, transferring models to proto buffers, automatically categorizing videos, and analyzing query relationships.
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Inside proposal 16 113
This document proposes a video searching application called INSIDE that uses automatic annotation to provide semantic video searching. It will analyze video structure, detect objects using deep learning with Tensorflow, and perform textual searching. The objectives are to allow semantic searching by analyzing video structure and content objects, classify videos automatically, and provide an accurate and user-friendly experience. Each group member is assigned a task, such as shot boundary detection or neural network configuration. Benefits for users include accurate search results from anywhere, and automatic annotation. Commercially, it could be used in search engines, automatic categorization, and rating of videos.
PENGENALAN ERT
Pasukan Tindakan Kecemasan (ERT) ditubuhkan oleh premis untuk memantau program pencegahan kebakaran dan keselamatan. ERT bertanggungjawab menyelaras pelan kontinjensi, peralatan keselamatan, dan latihan kakitangan untuk memastikan kesiapsiagaan menghadapi kecemasan.
Strategi for bruk av sosial medier i spare og spinkebanken
Denne presentasjonen er lagd høsten 2006 i forbindelse med faget "IINI2004 Sosiale medier" ved NTNU
Inside proposal 16 113 - version 01
This document proposes a video searching application called INSIDE that uses automatic annotation to provide semantic video searching. It will analyze video structure, detect objects using deep learning with Tensorflow, and perform textual searching. The objectives are to allow semantic searching by analyzing video structure and content objects, classify videos automatically, and provide an accurate and user-friendly experience. Each group member is assigned a task, such as shot boundary detection or neural network configuration. Benefits for users include accurate search results from anywhere, and automatic annotation. Commercially, it could be used in search engines, automatic categorization, and rating of videos.
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Inside proposal 16 113
This document proposes a video searching application called INSIDE that uses automatic annotation to provide semantic video searching. It will analyze video structure, detect objects using deep learning with Tensorflow, and perform textual searching. The objectives are to allow semantic searching by analyzing video structure and content objects, classify videos automatically, and provide an accurate and user-friendly experience. Each group member is assigned a task, such as shot boundary detection or neural network configuration. Benefits for users include accurate search results from anywhere, and automatic annotation. Commercially, it could be used in search engines, automatic categorization, and rating of videos.
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Pasukan Tindakan Kecemasan (ERT) ditubuhkan oleh premis untuk memantau program pencegahan kebakaran dan keselamatan. ERT bertanggungjawab menyelaras pelan kontinjensi, peralatan keselamatan, dan latihan kakitangan untuk memastikan kesiapsiagaan menghadapi kecemasan.
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Denne presentasjonen er lagd høsten 2006 i forbindelse med faget "IINI2004 Sosiale medier" ved NTNU
Inside proposal 16 113 - version 01
This document proposes a video searching application called INSIDE that uses automatic annotation to provide semantic video searching. It will analyze video structure, detect objects using deep learning with Tensorflow, and perform textual searching. The objectives are to allow semantic searching by analyzing video structure and content objects, classify videos automatically, and provide an accurate and user-friendly experience. Each group member is assigned a task, such as shot boundary detection or neural network configuration. Benefits for users include accurate search results from anywhere, and automatic annotation. Commercially, it could be used in search engines, automatic categorization, and rating of videos.
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For further details contact:
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IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
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Prokaryotic transcription
Prokaryotic transcriptionMarudhar Kesari Jain College for Women Vaniyambadi - 635 751, Tamil Nadu, INDIA.
Transcription in prokaryotes involves RNA polymerase producing messenger RNA transcripts of genetic material in the cytoplasm. Unlike in eukaryotes, transcription and translation can occur simultaneously. Transcription is controlled by transcription factors and involves three main steps: initiation, elongation, and termination. Initiation requires RNA polymerase binding to promoter regions with sigma factors. Elongation adds complementary bases to the DNA template. Termination can occur via intrinsic terminator sequences forming stem-loop structures or with rho-dependent termination using a rho factor protein.Viewers also liked (9)
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The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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Inside prototype 16-113
- 1. VIDEO SEARCHING BY AUTOMATIC ANNOTATION GROUP NUMBER : 16 - 113 INSIDEPROTOTYPE
- 2. “INSIDE” a smart semantic video searching server side web application with automatic annotation which support easy, quick user friendly querying for an accurate result
- 3. Video structure analysis Analyze the video in order to identify the shot boundaries separately by fragmenting the video into frames and identify duplicate frames
- 4. Video structure analysis (done so far) • Fragment the video into frames • Identify duplicate images using hashing algorithm
- 5. Video structure analysis (to be done) • Optimize the code to increase the accuracy • Use better techniques with hashing algorithm to identify duplicates
- 6. Deep Learning Developing a model, containing a Neural Network to detect the objects in an image
- 7. Deep Learning (done so far) • Creation of the model using Tensorflow machine learning framework • Classify the objects into categories using the model • Storing the identified objects in a specific video in to a file
- 8. Deep Learning (to be done) • Transfer the model into a proto buffer
- 9. Data set preparation Categorizing a set of images in manner that support training an artificial model
- 10. Data set preparation and Background Identification (done so far) • Categorizing the images into classes • Creation of the file that map the classes string and the UID(unique id used by the model) • Creation of the file that map the UID and the labels of the images
- 11. Data set preparation and Background Identification (done so far) • Identify the background of the fragmented frames of the video clip
- 12. Semantic textual searching Tokenize the user query and identify a relationship among the tokens and return an accurate output
- 13. Semantic textual searching (done so far) • Ontology creation • Tokenizing the user query and match the tokens with the ontology and get a relationship • Match the relationship with the database objects and rank the videos
- 14. Semantic textual searching (to be done) • Analyzing the relationships and categorize videos automatically
- 15. Thank You