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The document summarizes a seminar project on real-time recognition of ambulation mode in transfemoral prostheses. Sensors including IMUs and pressure sensors will be used to collect locomotion data from different ambulation modes. An ESP 32 device will transmit the sensor data, which will be stored in Excel. IMUs will be placed on the thigh, shank, and foot to measure acceleration and gyroscopic data. Pressure sensors on the heel and toes will also record data. Various machine learning algorithms will be tested and compared to accurately classify ambulation modes such as walking on flat ground versus up/down ramps or stairs.
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A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
22CYT12-Unit-V-E Waste and its Management.ppt
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Low power architecture of logic gates using adiabatic techniques
The growing significance of portable systems to limit power consumption in ultra-large-scale-integration chips of very high density, has recently led to rapid and inventive progresses in low-power design. The most effective technique is adiabatic logic circuit design in energy-efficient hardware. This paper presents two adiabatic approaches for the design of low power circuits, modified positive feedback adiabatic logic (modified PFAL) and the other is direct current diode based positive feedback adiabatic logic (DC-DB PFAL). Logic gates are the preliminary components in any digital circuit design. By improving the performance of basic gates, one can improvise the whole system performance. In this paper proposed circuit design of the low power architecture of OR/NOR, AND/NAND, and XOR/XNOR gates are presented using the said approaches and their results are analyzed for powerdissipation, delay, power-delay-product and rise time and compared with the other adiabatic techniques along with the conventional complementary metal oxide semiconductor (CMOS) designs reported in the literature. It has been found that the designs with DC-DB PFAL technique outperform with the percentage improvement of 65% for NOR gate and 7% for NAND gate and 34% for XNOR gate over the modified PFAL techniques at 10 MHz respectively.
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DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
As digital technology becomes more deeply embedded in power systems, protecting the communication
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accuracy rate of 99.50%.
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A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMS
Low power architecture of logic gates using adiabatic techniques
Low power architecture of logic gates using adiabatic techniques
ML Based Model for NIDS MSc Updated Presentation.v2.pptx
ML Based Model for NIDS MSc Updated Presentation.v2.pptx
Literature Review Basics and Understanding Reference Management.pptx
Literature Review Basics and Understanding Reference Management.pptx
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODEL
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...
6th International Conference on Machine Learning & Applications (CMLA 2024)
6th International Conference on Machine Learning & Applications (CMLA 2024)
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
Harnessing WebAssembly for Real-time Stateless Streaming Pipelines
International Conference on NLP, Artificial Intelligence, Machine Learning an...
International Conference on NLP, Artificial Intelligence, Machine Learning an...
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
IEEE Aerospace and Electronic Systems Society as a Graduate Student Member
1708027_1708037.pptx
- 1. Seminar MTE 4110 Heaven’s light is our guide Rajshahi University of Engineering & Technology Prepared by: Md. Mahfuz Rayhan Roll: 1708027 Md. Junayed Roll: 1708037 Supervised by: Md. Mehedi Hasan Lecturer, Department of Mechatronics Engineering, RUET Real Time Recognition of Ambulation Mode in Transfemoral Prostheses.
- 2. 13/12/2022 Real Time Recognition of Ambulation Mode in Transfemoral Prostheses. 2 Components to be Used for Data Acquisition • IMU (Adafruit BNO055) • Pressure Sensor (Piezo- electric) Sensors • ESP 32 NodeMCU Data Transmitting Device
- 3. 13/12/2022 Real Time Recognition of Ambulation Mode in Transfemoral Prostheses. 3 Cost Estimation Item Name Item Quantity Per Item Cost Sub Total IMU- Adafruit-BNO055 3 2000 6000 Pressure Sensor 2 100 200 ESP 32 NodeMCU 1 1200 1200 Utilities 1000 Total 8400
- 4. 13/12/2022 Real Time Recognition of Ambulation Mode in Transfemoral Prostheses. 4 Data Acquisition Process Sensors • IMU • Pressure Sensor Data Transmitting Device • ESP 32 NodeMCU Data Storage • Excel Sheet
- 5. Sensor Positioning IMU • Above knee joint • Below knee joint • Ankle Piezo-electric • Heel of the foot • Toe (Hallux) Real Time Recognition of Ambulation Mode in Transfemoral Prostheses. 13/12/2022 Figure 1: Sensor Positions
- 6. 1/12/2023 Real Time Recognition of Ambulation Mode in Transfemoral Prostheses. 6 Dataset Description Mechanical Locomotion Data: • IMU sensors to use for collecting data. • Sensors placed at thigh, shank and foot. • 6 Channels per IMU: 3-axis gyration and 3-axis acceleration. • Cycle Definition: Toe Off to Heel Contact • Features: Minimum, Maximum, Mean, Median, Standard Deviation of cycles for every channels. • 3 IMUs × 6 Channels × 5 Features = 90 Features • Sampling Rate: 100 Hz Figure 01: Ambulation Circuit. Locomotion modes: • Plain Surface • Upward Ramp • Downward Ramp • Upward Stair and • Downward Stair Figure 01: Cycle Definition.
- 7. 1/12/2023 Real Time Recognition of Ambulation Mode in Transfemoral Prostheses. 7 Methodology • Collect data as per dataset description and perform • Post processing to remove anomalies, out of class ambulation like tripping and such moves. • Sample into cycles. • Calculate features. • Prepare a csv file for training purpose. Data Collection • Perform all algorithms found in literature review to compare results with. Those be: LDA, ANN, DT, RF, TH, SVM, DBN etc and verify the results using ANOVA. • Perform classification on other available datasets and compare results with those. • Reach a conclusion as to which algorithm is preferable and find rationalization behind it. Algorithms
- 8. Thank You