Project Reports

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0.5 V Multi Standard Filter For Biomedical Applications
(2017-08-11T11:58:36Z) Omprakash, G; Vignesh, N
The modern medical industry is dependent on electronic devices and systems to accomplish the treatment for various illness in the most efficient manner. To achieve this, accurate diagnosis of the disease/illness is mandatory. This can be done with electronic devices of high accuracy and efficiency. In this project an integrated mixed notch and low pass filter has been identified and its corresponding layout has been designed using complementary metal oxide semiconductor CMOS 180nm technology in cadence virtuoso EDA. In order to achieve a transconductance value of 1nS a bulk driven operational tranconductance amplifier (OTA) has been used with 0.5v supply voltage to design the filters. In this project, a sixth order notch filter and a fourth order low pass filter has been used to achieve high accuracy and reliability of the filters in the bio medical field. This integrated filter using the bulk driven OTA can be employed for multiple systems like ECG,EEG etc. The project focuses on error free layout design of the bulk driven OTA and hence the filters using the bulk driven OTA.
90nm Standard Library Cell Design Using GNU/Electric
(2018-06-27T11:21:19Z) Kandati Praveen, Kumar; Harish, P; Rigil, Gracious; Rohith, Prasad K M
In this project, we are designing a standard library cell with 90nm technology using GNU/Electric with MOCMOS technology. The library cell consists of 24 cells with each cell having the following views: Icon, Schematic and Layout. The standard library cell includes Basic Gates, Adder circuits, Flip-flops, Latches and Multiplexers. The parameters: Rise Time, Fall Time and Area for all these cells are calculated and listed in a table. Area of each cell in this library is compared with 180nm (CMOS Cells) and 300nm (MUDD Library) technologies. Simulation of each cell is done using LT-SPICE software. GDS file is generated for these cells and 3D view of the layouts is also shown.
A Futuristic Agricultural Robot with IOT And Machine Learning Intelligence
(NHCE, 2024) Kotagiri siva sai 1NH20EC068; Kuppam Lakshmi Prasanna 1NH20EC069; Nitish Niranjan Birasam 1NH20EC100
A Futuristic Agricultural Robot with IOT And Machine Learning Intelligence
Accident Avoidance Based on Alcohol Detection
(2016-08-08T08:50:59Z) Surya Kiran, D V; Milen S, Parameshwar; Jefson E, George
This system is aimed at making vehicle driving safer than before. This is implemented using Arduino. We have derived the driver’s condition in real time environment and we propose the detection of alcohol using alcohol detector connected to Arduino such that when the level of alcohol crosses a permissible limit, and the GPS module will capture the present location of the vehicle. Also the GSM module will automatically send distress message to police or family members.
Accident shield: automated vehicle crash alert using raspberry PI
(NHCE, 2024) C. Tharun Sai Yadav 1NH20EC035; Vimarsha Rudresh 1NH20EC183; Konduru Silpa 1NH20EC066
Accident shield: automated vehicle crash alert using raspberry PI.
Advance fire and smoke detection in trains
(NHCE, 2025) Manu M 1NH21EC090; Nandeesh Gowda B 1NH21EC102; P Hemanth Kumar Reddy 1NH21AI127; D Rahul 1NH21AI128
Fire and smoke detection systems play a vital role in ensuring safety and reducing losses caused by fire-related incidents. However, conventional systems that rely on heat and smoke sensors are often susceptible to false alarms and may fail to detect fires at their earliest stages. This paper presents a modern, cost-effective, and intelligent approach to fire and smoke detection, utilizing the ESP32-CAM microcontroller combined with a Convolutional Neural Network (CNN). The ESP32-CAM, a compact and affordable IoT-enabled microcontroller with an integrated camera, serves as a reliable platform for real-time image capture and processing. When paired with CNNs, a cutting-edge deep learning architecture for image recognition, the system provides a robust and highly accurate solution for detecting fire and smoke. By integrating IoT and AI technologies, the proposed system bridges the gap between affordability and advanced functionality, ensuring timely and precise alerts. The system architecture comprises three core components: the ESP32-CAM for image acquisition, the CNN for image-based analysis, and a communication interface for transmitting alerts. The ESP32-CAM continuously captures images or video streams, which are then preprocessed to enhance detection accuracy. Preprocessing techniques, such as resizing, normalization, and noise reduction, prepare the visual inputs for the CNN. The trained CNN identifies critical features in the data to detect fire and smoke reliably. At the heart of the system lies the CNN, optimized specifically for deployment on resource-constrained devices. Its lightweight design ensures compatibility with the ESP32-CAM’s limited computational capabilities while maintaining high accuracy. Optimization techniques like model pruning, quantization, and transfer learning help reduce computational demands without compromising performance. Moreover, the modular nature of the system allows for easy integration with existing fire safety setups and future upgrades. Enhancements such as incorporating additional sensors (e.g., temperature or gas sensors) or adopting more advanced machine learning models can further improve detection capabilities.
Advanced Footstep Power Generation System
(2018-06-27T12:37:50Z) Anu.R., Reddy; Varsha, A; Sirisha, S
Energy consumption represents the development of the universe. Modern world requires a large amount of electrical energy to meet the current demand. But the conventional energy resources are diminishing steadily as a result of vast consumption of energy. So, alternate energy sources are required not to fill up the gap between demand and supply of electricity but also they should be clean, Eco-friendly and sustainable. The main aim of this project is to meet the Energy crisis. This paper is about the generation of electricity through foot steps. The idea is to utilize the force (ie weight energy) exerted on the floor when a person walks. The power generating floor intends to translate the mechanical stress applied on the floor to electrical power using piezo sensors. This technique utilizes peizoelectric components where deformations produced by different means are directly converted into electrical energy via piezoelectric effect. In this project we are generating electrical power as a non conventional method by simply walking or running on the peizo plates. Non conventional energy system is very essential at this time to our nation.
Advanced home automation and security system
(NHCE, 2023) Y Sai Kumar Reddy1NH19EC125; Mithun M 1NH19EC147; M Yaswanth 1NH19EC079; M Sai Vignesh 1NH19EC078
Advanced home automation and security system
Advanced vehicle safety and Management system (AVSMS)
(NHCE, 2025) Manoj GR 1NH21EC089; Nikhil H L 1NH21EC109; Rohan H Savanth 1NH21CS199; Tharun A C 1NH21CS247
The Advanced Vehicle Safety and Management System (AVSMS) is an innovative project aimed at revolutionizing autonomous driving by enhancing vehicle safety, operational efficiency, and user experience. The system integrates cutting-edge hardware and software, including Raspberry Pi 4, ESP32-CAM, ultrasonic sensors, Neo 6M GPS modules, OpenCV, and TensorFlow, to deliver intelligent and reliable functionality. Key features include lane detection, obstacle avoidance, traffic signal and sign recognition, geo-boundary enforcement, and driver behavior analysis. By leveraging sensor fusion and real-time data processing, the system ensures accurate navigation, compliance with traffic regulations, and proactive collision prevention. Lane detection and obstacle avoidance use advanced cameras and deep learning models to maintain the vehicle's position and avoid hazards dynamically. Geo- boundary functionality defines virtual limits to ensure vehicles operate within designated areas, ideal for fleet management and restricted zones. Traffic signal and sign recognition utilize high-resolution cameras and machine learning to ensure adherence to road rules, while real-time speed monitoring dynamically adjusts vehicle speed based on road and traffic conditions for optimal performance and safety. The AVSMS system also features a 360-degree camera for comprehensive situational awareness, stitching real-time feeds to eliminate blind spots and aid in parking or lane changes. Range estimation calculates the vehicle's remaining travel capacity using battery levels and energy consumption patterns, ensuring uninterrupted operation. The integrated chatbot provides an intuitive interface for voice or text commands, enhancing user interaction and control. Auto light functionality dynamically adjusts vehicle lighting to ensure optimal visibility in various conditions.. By reducing road accidents, enhancing traffic flow, and promoting sustainability, AVSMS sets a benchmark for autonomous vehicle technology, paving the way for smarter and safer transportation systems.
Aerial Reconnaissance Teresstrial Surveillance System (ARTSS)
(2015-12-22T06:15:05Z) Ankit, Mishra; Santhosh, Kumar S; Sreejith R, Warrier
The project involves design and development of algorithms for an autonomous robot system deployed for surveillance activities. The work entails design of routing algorithm, communication system, application server and PID controller using MATLAB and Open Source platforms. Routing algorithm design includes feature matching, mapping of images to grid based graphs and deriving routing meta-data. The communication system is responsible for transferring routing meta-data from server to the client and application data from client to server over the internet backbone using protocol suited for deployment requirements. Communication system entails detailed study and design of buffer size requirements and read/write cycle to buffer (to synchronize bytes that are in buffer and application processing rate). The Client is an ARM based computer responsible for acquisition of data and act as liaison between routing algorithm process at server end and the PID process at the client end that is responsible for movement of robot. Application server is designed to aid surveillance buy monitoring the application data (live video stream) transmitted by the client over HTTP protocol and performing facial recognition using PYTHON and OPEN CV libraries. The design of ground bot entails design of PID controller, placement of power source, routing of signals/power lines to components and packaging within the robot chassis Modelling of routing algorithm is one of the key aspects in the project. An aerial acquired image usually contains a lot of detail in a cluttered environment. The ability to match features of the robot and obstacle to database is vital for autonomous nature of the system. Dijkstra's algorithm and its derivatives are used for finding the shortest paths between nodes in an environment. Currently, autonomous robot systems are at their infancy. Development of algorithms are key to sustainability of these systems and it’s our aim to contribute to this development through this project.
Agricultural Ecosystem Monitoring Based on Autonomous Sensor Systems
(2018-06-28T05:56:34Z) Abhishek, N; Punith Kumar, P.H; Pavan Kumar, B.G
More than two-thirds of freshwater consumed worldwide are used for irrigation, and large quantities of freshwater can be saved by improving the efficiency of irrigation systems. Irrigation control systems deployed in agriculture can substantially be enhanced by implementing intelligent monitoring techniques enabling automated sensing and continuous analyses of actual soil parameters. Automatically scheduling irrigation events based on soil moisture measurements has been proven an effective means to reduce freshwater consumption and irrigation costs, while maximizing the crop yield. Focusing on decentralized autonomous soil moisture monitoring, this project presents the design, the implementation, and the validation of a low-cost remote monitoring system for agricultural ecosystems. The prototype monitoring system consists of a number of intelligent wireless sensor nodes that are distributed in the observed environment. The sensor nodes are connected to an Internet-enabled computer system, which is installed on site for disseminating relevant soil information and providing remote access to the monitoring system. Autonomous software programs, labeled “mobile software agents”, are embedded into the wireless sensor nodes to continuously analyze the soil parameters and to autonomously trigger irrigation events based on the actual soil conditions and on weather data integrated from external sources.
Agricultural Robot
(2015-12-21T09:47:14Z) Shameek, Das; Naveen, .M; Yachamaneni, Srikar
Developed agriculture needs to find new ways to improve efficiency. One approach is to utilize available technologies in the form of more intelligent machines to reduce and target energy inputs in more effective ways than in the past. Precision Farming has shown benefits of this approach but we can now move towards a new generation of equipment. The advent of autonomous system architectures gives us the opportunity to develop a complete new range of agricultural equipment based on small smart machines that can do the right thing, in the right place, at the right time in the right way.The application of agricultural machinery in precision agriculture has experienced an increase in investment and research due to the use of robotics applications in the machinery design and task executions. Precision autonomous farming is the operation, guidance, and control of autonomous machines to carry out agricultural tasks. It motivates agricultural robotics. It is expected that, in the near future, autonomous vehicles will be at the heart of all precision agriculture applications . The goal of agricultural robotics is more than just the application of robotics technologies to agriculture. Currently, most of the automatic agricultural vehicles used for weed detection, agrochemical dispersal, terrain leveling, irrigation, etc. are manned. An autonomous performance of such vehicles will allow for the continuous supervision of the field, since information regarding the environment can be autonomously acquired, and the vehicle can then perform its task accordingly
Agrismart Robospider for Spraying and Weed Cutting
(NHCE, 2025) Samuel W S 1NH21EE101; Sandeep R Naikar 1NH21EE103; Sanketh Sheshannanavar 1NH21EC138; Soham Bag 1NH21EC153
It is amazing that animals can travel across uneven terrain at speeds that are far faster than those that are practically achievable for wheeled vehicles. Indeed, a person may travel or climb across terrain that is impassable to a wheeled or propelled vehicle by lowering themselves to all eight legs if needed. Realizing what land locomotion devices may accomplish if they are designed to mimic nature is therefore really exciting. Autonomous legged robots have a lot of promise since they can be used for space missions on alien worlds and in dangerous environments like atomic reactors. Walking robots also have the benefit of being lightweight and low power consumption, thus it's critical to employ as few actuators as possible. Learning about and creating a prototype of the Theo-Jansen eight-leg walking robot is the project's goal in this regard. The objective is to use an eight bar link system to create a new mechanical automated walker. A 13 bar structure that strolls when a crank is turned is the fundamental Theo Jansen gadget. Therefore, we tried to mimic nature by using links to assemble a specific off-road walking robot.
AI Based Smart Plant Watering System
(NHCE, 2024) Abhijeeth Talari 1NH20EC004; Gokul Mohan 1NH20EC051; Mukthapuram Divya 1NH20EC086; Cheekuru Meghana 1NH20AI021
AI Based Smart Plant Watering System
Ai based weather monitoring and prediction
(NHCE, 2025) A Chaitanya 1NH21EC009; A Mahesh 1NH21EC014; Joga Thejeswar Reddy 1NH21CS113; Adaka Bala Vamsi 1NH21CS286
The Weather Monitoring System is a comprehensive platform designed to enhance real-time weather forecasting and environmental monitoring using IoT, machine learning, and cloud technologies. This innovative system integrates a suite of sensors to measure critical environmental parameters, including temperature, humidity, atmospheric pressure, gas levels, and light intensity. These sensors are connected to a central processing unit powered by a Raspberry Pi, ensuring efficient data collection and preprocessing to remove noise and inconsistencies. Data is transmitted to the ThingSpeak cloud platform, enabling centralized storage, visualization, and real-time access. Machine learning models play a pivotal role in the system: the Random Forest algorithm is employed for binary classification, such as predicting rain occurrences, while the LSTM (Long Short-Term Memory) model analyzes sequential data to generate probabilistic weather forecasts. These predictive models are critical for identifying short-term and long-term weather trends with high accuracy. The system's outputs are made accessible to users via an intuitive web application, which provides dynamic graphs, historical data visualization, and real-time weather insights. Additionally, a notification mechanism powered by a GSM module sends SMS alerts to users, ensuring timely dissemination of critical weather information, such as rain warnings. This feature is particularly beneficial for agriculture, disaster management, and other weather- dependent domains. The Weather Monitoring System demonstrates scalability and adaptability by incorporating cloud-based storage and a modular design that allows for the integration of additional sensors and advanced predictive models. By combining IoT technology, machine learning, and cloud computing, this project addresses key challenges in modern weather monitoring, such as noise in sensor data, communication delays, and prediction reliability. With its focus on accuracy, user-friendliness, and real-time responsiveness, the system offers significant advancements in environmental monitoring and decision-making, empowering users to adapt effectively to changing weather conditions.
AI Driven Tool Wear and Product Quality monitoring System
(NHCE, 2025) V M Kheshav 1NH21EC166; Shiva Kumar.P 1NH22EC412; Tarun N 1NH21ME075; Satish 1NH21ME066
Lathe machining is a popular production technique where a fixed cutting tool shapes a revolving workpiece. Manufacturing cylindrical components like shafts, gears, and rods, which are vital in many different industries requires this procedure. The state of the cutting tool, which wears down and becomes blunt over time from constant contact with the workpiece, has a significant impact on the effectiveness and calibre of lathe machining. Reduced machining performance from this tool wear results in dimensional errors, poor surface finishes, and most importantly long downtime, all of which raise operating expenses. Early tool wear detection is crucial to preventing these detrimental effects. Visual inspections and trial-and-error testing are two time-consuming and sometimes inaccurate traditional techniques of checking tool condition. In order to promptly identify tool failure or bluntness, there is a rising demand for more effective, real-time, and data-driven methods. Using sound decibel sensors, which can record the noise produced throughout the cutting process, is one viable alternative. Since variations in sound levels are frequently associated with tool wear, these sensors can offer important information on the state of the cutting tool. The goal of this research is to create a prediction model that uses sound decibel data to estimate the bluntness or failure of cutting tools in lathe machines. Decibel sensors will be positioned close to the tool post to record and evaluate sound levels in real time, allowing for the tracking of the tool's condition. A variety of cutting variables, including feed rate, cutting speed, and the materials of the tool and workpiece, will be included in the data that is gathered. Regression models will be created using statistical analysis tools like Minitab in order to forecast bluntness or tool failure based on the sound data and additional cutting characteristics. By facilitating preventive tool maintenance and reducing expensive downtimes, this predictive model seeks to improve machining productivity and eventually contribute to more economical and environmentally friendly production processes.
Air Traffic Control using Amplitude Modulation
(NHCE, 2024) Pateel Suhas Gowd 1NH20EC106; Praveen Kumar N 1NH20EC111; Lingala Daivik Reddy 1NH20EC074
Air Traffic Control using Amplitude Modulation
Alcohol Detection and Accident Detection
(2020-10-09T12:20:16Z) S, Vijay; s, Nishant; Kumar, Rakesh
Accident detection systems help to reduce fatalities stemming from car accident by decreasing the response time of emergency responders. The symbiosis between communication technologies and vehicles offers opportunity to improve assistance to people injured in traffic accidents, by providing information about the accident to reduce the response time of emergency assistance services. The proposed design of the application can detect accidents, pin-point the location of accident and initiate emergency communications automatically. The use of this application can significantly shorten the time taken for determination of the accident site and warning the concerned authorities. A hardware kit is used to detect the accident, get the GPS location that consists of accelerometer, Node MCU and GPS receiver. And the information about the accident location is sent to the hospitals and the relatives of the user. The message is sent through Firebase Cloud Messaging (FCM). Keywords: Firebase cloud messaging, GPS, Node MCU.
Alcohol Detection and Accident Detection
(2020-10-09T12:08:35Z) S, VIJAY.; S, NISHANT; KUMAR, RAKESH; KUMAR, NAVEEN
Accident detection systems help to reduce fatalities stemming from car accident by decreasing the response time of emergency responders. The symbiosis between communication technologies and vehicles offers opportunity to improve assistance to people injured in traffic accidents, by providing information about the accident to reduce the response time of emergency assistance services. The proposed design of the application can detect accidents, pin-point the location of accident and initiate emergency communications automatically. The use of this application can significantly shorten the time taken for determination of the accident site and warning the concerned authorities. A hardware kit is used to detect the accident, get the GPS location that consists of accelerometer, Node MCU and GPS receiver. And the information about the accident location is sent to the hospitals and the relatives of the user. The message is sent through Firebase Cloud Messaging (FCM). Keywords: Firebase cloud messaging, GPS, Node MCU.
ALCOHOL DETECTION AND CAR IGNITION LOCKING SYSTEM”
(2019-06-17T10:26:41Z) S. SUHAS; NAMRATHA B.R; SANJEEV JAYASURYA. S
This system is aimed at making vehicle driving safer than before. The main purpose behind this project is “Drunken driving detection”. Now days, many accidents are happening because of the alcohol consumption of the driver or the person who is driving the vehicle. Thus, drunk driving is a major reason of accidents in almost all countries all over the world. We have proposed the detection of alcohol using alcohol detector connected to ARM such that when the level of alcohol crosses a permissible limit, the vehicle ignition system will turn off. Alcohol Detector in Car project is designed for the safety of the people seating inside the car. Alcohol breath analyzer project should be fitted / installed inside the vehicle. And there is auto theft detection of the system is also added which will detect whether the system is present or not in the car. If any of these conditions match then the concerned person will get a msg through GSM along with GPS coordinates.

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