Step-by-Step Guide for Paper Submission via Microsoft CMT

1. Connecting to Microsoft CMT

YOU CAN ONLY SUBMIT YOUR PAPER BY LOGGING ON THE FOLLOWING WEBSITE:
https://cmt3.research.microsoft.com/ISIVC2026.

• New users: Click "Register" and fill in your details. Use a professional email address (not a personal one) to avoid account issues.
• Returning users: Log in with the same email address used for previous submissions or reviews. Using multiple emails (e.g., personal/professional) may create duplicate accounts and cause delays.
• If you forget your password, use the "Forgot Password?" link. Do not create a new account.
• Check your spam folder for confirmation emails from CMT.

Web link for more information: https://cmt3.research.microsoft.com/docs/help/general/account-creation.html.

2. Submitting Your Paper

• Once logged in, click "Create new submission" and select the appropriate track (SAP, CVIP, DCN, or DSA).
• Fill in the paper title, abstract, and author list. Ensure all co-authors are added with their correct email addresses.
• Make sure to upload your submission correctly in PDF format using the IEEE template:
• ✓ Link to the IEEE template (chose “Conferences” and then “Original Research”)
  ✓ Including all figures, tables, and references, you are allowed a total of 6 pages.
  ✓ The only language permitted for papers is English.
• Confirm all details and submit. You will receive a confirmation email with a submission ID.

3. Paper Review Process

All submissions undergo a double-blind peer review:

• Each paper is assigned to at least 3 reviewers from the Technical Program Committee.
• Reviews focus on originality, technical quality, relevance, and clarity.
• Authors will be notified of acceptance or rejection by 2 February 2026 via email.
• If accepted, you will receive instructions for camera-ready submission (deadline: 1 March 2026).

For further reference, the following topics of interest are provided for each track (but not limited to):

Track 1: Signal and Audio Processing (SAP)

• • Biomedical Signal Processing
  • DSP Processors and FPGA Applications
  • Deep Learning & Machine Learning for Signal Processing
  • Intelligent Transport Systems
  • Nonlinear Signal Processing
  • Sensor Array & Multichannel Signal Processing
  • Sentiment Analysis and Emotion Recognition from Speech
  • Signal Processing Theory & Methods
  • Signal Processing for Big Data
  • Signal Processing for Brain-Machine Interface
  • Signal Processing for Communication
  • Signal Processing for Cyber Security
  • Signal Processing for Smart Systems
  • Signal Processing for Applications in Healthcare
  • Speech and Audio Processing
  • Speech Processing
  • Speech Recognition
  • Statistical Signal Processing

Track 2: Computer Vision and Image Processing (CVIP)

• • 3D Imaging, Stereovision and Virtual Reality
  • Analysis, Modeling and Understanding
  • Classification, Clustering and Segmentation
  • Color, Multi-spectral and Perceptual Processing
  • Compression, Coding and Watermarking
  • Enhancement, Restoration, Filtering and Denoising
  • Image and Video Processors Architectures
  • Image, Video and Object Indexing and Retrieval
  • Pattern Recognition and Machine Learning
  • Tracking and Real-time Imaging
  • Applications: Healthcare Imaging, Biometrics, Forensics and Remote Sensing

Track 3: Digital Communications and Networking (DCN)

• • Channel Modeling and Propagation
  • Coding for Wireless Systems
  • Detection, Estimation, and Synchronization
  • Modulation Techniques (CDMA, OFDM, etc.)
  • Multiuser and Multiple Access Schemes
  • Network Mobility and Task Coordination
  • Network Performance, Reliability, and QoS
  • Ultra-Wideband Systems
  • Vehicular Networks
  • Wireless Adhoc and Sensor Networks
  • Wireless Multimedia Communication
  • Wireless Sensors Networks
  • Wireless System Architectures and Applications
  • Wireless Traffic and Routing

Track 4: Data Science and Applications (DSA)

• • Data Mining, Web-Mining, Information Mining
  • Databases and Information Integration
  • Information Retrieval Methods
  • Information Visualization
  • Machine Learning, Deep Learning
  • Model Verification and Validation
  • Model-Driven Engineering
  • Natural Language Processing (NLP)
  • Predictive Analytics
  • Knowledge Discovery
  • Applications: Big Data, Data Analytics in Business Enterprises, Internet of Things, Smart Home, Smart City, Soft Computing, Natural Language, Sentiment Analysis