Sagar Dahanuwala Email & Phone Number

Noida, Uttar Pradesh, India

I focus on standardizing and optimizing project processes from a controls perspective, eliminating the dependency on specific PLCs. My expertise lies in developing integration standards that enable seamless coordination between robots, ASRS, conveyors, vision systems, and tracking systems.

Noida, Uttar Pradesh, India

I possess deep expertise in Programmable Logic Controllers (PLCs), VFDs, scanners, vision systems, weighing systems, safety controllers, conveyor systems, high-speed sortation systems, and ASRS. My focus is on understanding the core principles of communication and designing logic that seamlessly integrates hardware with cloud-based software using asynchronous communication methods. Developing modular programs that enable the smooth integration of new hardware into any system is a process I find both challenging and highly rewarding.

Noida, Uttar Pradesh, India

I have a comprehensive understanding of supply chain management (SCM), material management, mechanical and electrical design, and control flow. My ability to efficiently manage teams, engineers, and clients under tight deadlines ensures the delivery of high-quality projects. I focus on understanding customer requirements, collaborating with colleagues, and providing guidance to meet client demands, ensuring the successful completion of projects with high customer satisfaction.With a multidisciplinary background across design, controls, software, and execution, I excel at analyzing root causes and delivering well-rounded, effective solutions.

- **India Patent No. 408104** for the research and development of advanced power grid stability algorithms.- Addresses key drawbacks of existing methods with the following features: - **Zero False Negatives** in fault detection. - **Adaptable Thresholds** for very large systems. - **High Information Content** in detection data. - **No reliance on SSFPM methodology,** allowing application across large geographical distances. - **Probabilistic Framework** to distinguish between major and minor events. - **Dynamic System Learning** to incorporate behavior changes over time. - **Fault Ranking Framework** to identify the largest to nth largest faults in the system. - **Robustness** even with incorrect hyper-parameter selection. - **Elimination of False Positives** even in high-noise environments. - **Precise Event Detection** with pinpointed data location. - **Fast Major Fault Detection** with exact location markings. - **Weighted Event Detection** for probabilistic intensity comparison across multiple events, improving system stability insights.

- **IEEE Publication** on the research, development, and comparison of advanced power grid stability algorithms.- Utilizes **Phasor Measurement Units (PMUs)** and **Wide Area Measurement Systems (WAMS)** for real-time insights into power system events based on PMU data.- Critical disturbances in power systems are classified as **Events**.- Analysis performed using PMU data and various **signal processing methods**.- Conducted testing across **30 PMU stations** nationwide.- Evaluated under diverse environmental and load conditions, including during the **Amphan Cyclone**.

1. **Cyclic Learning Rate (CLR) Implementation**: Developed a CLR approach on the NAdam optimizer, integrating Exponential Gradient (EG) gradients to achieve faster convergence. 2. **Custom Backpropagation and Optimizers**: Created self-written codes for backpropagation and various optimizers, providing flexibility for diverse network designs without relying on libraries such as TensorFlow or Keras.3. **Solar Radiation Forecasting**: Implemented a Recurrent Neural Network (RNN) for solar radiation forecasting, involving data pre-processing and Principal Component Analysis (PCA) to identify key features, followed by the application of standard RNN implementation rules.

**IoT Home Automation App Development**I developed an IoT home automation app, encompassing server-side and hardware coding with the following key features:1. **Google Authentication**: Integrated Google Authentication APIs for secure user login.2. **User Privileges**: Implemented tiered access for admins, users, and non-users, each with distinct control levels over home devices.3. **Data Security**: Established multiple layers of data security, including built-in shared preference compartmentalization to protect sensitive information.4. **Dynamic Pin Numbering**: Allowed users to define custom pin numbering for the interface between the control board and the server.5. **Device Control**: Enabled control of both analog and digital devices, each assigned a unique ID (UID) for data protection and access level management.6. **Efficient Data Management**: Utilized JSON to create file structures, minimizing server load.7. **Automatic Node-MCU Configuration**: Facilitated automatic web page creation on the Node-MCU, enabling users to enter SSID, password, and authentication details via mobile without requiring a laptop interface. The Node-MCU automatically connects to the database upon powering on.8. **EEPROM Storage**: Implemented an EEPROM storage mechanism to retain critical information, ensuring correct reconnection to the server after power loss.9. **User Feedback**: Integrated a feedback mechanism in the app to inform users about the status of their requests, including whether requests were sent, received, and executed correctly.10. **Scalability and Ready Deployment**: The app is designed for immediate deployment and can be scaled to accommodate various user needs.

Brain Tumor Segmentation of Tumor core, enhancing Tumor and Edema • Implemented Transfer leaning and a refined optimizer (R-Adam with Cyclic Learning Rate) which helped reduce the computation time and we also did Occlusion Experiments on the results figuring out whether the Neurons were learning different things or learning redundant features.• Following the works on Guided Back propagation and its effectiveness in getting a better picture of what’s going on, we used it for gradient plotting on how much the neuron changes with respect to a particular pixel in the input to get a better picture of what the neuron was doing and also it helped the neurons to specialize in particular feature selection.

Bachelor Of Technology - Btech, Electrical And Electronics Engineering

12Th Boards, 95.5(Maths,Physics , Chemistry,Computer Science)

Scored 95.5% in 12th BOARDS

10Th Board, 9.6 Cgpa

Scored 9.6 CGPA in 10th Board