More advanced IDS model for drones.

Developed a comprehensive ML-based IDS tailored for drone networks, enhancing real-time threat identification. Achieved 95% accuracy in detecting malicious activities, reducing false positives by 40% by using Python, Scikit-learn, and TensorFlow. Enhancing the security of drone operations through proactive threat detection and response mechanisms, ensuring safe and reliable deployment.

Conducted a detailed analysis of energy consumption in swarm drone operations using advanced path-planning algorithms. Increased operational efficiency by 20% through optimized routing strategies by using Python and Matplotlib. Benefits industries like agriculture and disaster response, enabling more sustainable and cost-effective drone deployments.

Designed a novel community detection algorithm with a time complexity of O(nlogn), enhancing the speed and accuracy of social network analysis. Improved detection accuracy by 15%, aiding in a better understanding of social dynamics by using Python and NetworkX. Facilitates more effective interventions in public health and marketing by identifying key community structures in large networks.

Implemented network peers and smart contracts for a permissioned blockchain using React API and JavaScript for the graphical user interface. Permissioned blockchains can revolutionize industries like supply chain management, finance, and healthcare by enhancing transparency, security, and efficiency.

Developed an algorithm in MATLAB for more accurate pre-sale pricing in the energy market. Accurate pricing algorithms can lead to cost savings for energy providers and consumers, promoting sustainability and efficient resource allocation.

Created a voice-controlled home automation system to manage lighting, heat, and appliances. Reduced manual task time leading to significant improvements in user convenience and energy savings by using Raspberry Pi, Python, Google Assistant SDK. Enhanced home accessibility and energy efficiency, promoting smart living solutions.

Developed a system for real-time traffic monitoring and prediction using historical and live data. Achieved 90\% accuracy in predicting traffic conditions by using Python, Scikit-learn, TensorFlow. Provides insights for urban planners and commuters, leading to better traffic management.

Developed an RTL implementation of a high-speed Ethernet Media Access Controller (MAC), enabling 10Gbps data transmission with minimal latency by using Verilog and Zynq 7020.  Ethernet MAC enhances data throughput for high-performance networking, reduces latency for real-time applications, increases market penetration for high-speed data interfaces, and supports versatile applications across FPGA and ASIC platforms.

Designed a low-power digital signal processing accelerator for a wearable health monitoring device, achieving a 15\% performance improvement over previous designs by using Verilog and Zynq 7000. The DSP accelerator extends battery life for wearable devices, enhances user experience through faster health data processing, boosts the company’s competitiveness in the wearable tech market, and advances healthcare by providing more efficient and accurate health monitoring.

Developed an automated test case generator with 5000 lines of C code for an application-specific C compiler, streamlining the testing process. This tool enhances compiler reliability by automating test case creation, reducing manual effort, and improving overall code quality and stability.

Designed a simple CPU using Verilog and Python for educational and embedded system applications. Simple CPUs can find applications in IoT devices, educational tools, and embedded systems, contributing to technological advancements and accessibility.

Designed a vehicle control system using FPGA and Bluetooth communication for autonomous driving. FPGA-based vehicle control systems have applications in autonomous vehicles, robotics, and remote operation, advancing transportation and automation technologies.