Pinned Repositories
cva6
The CORE-V CVA6 is an Application class 6-stage RISC-V CPU capable of booting Linux
or1ksim
The OpenRISC 1000 architectural simulator
tutorials
OpenRISC Tutorials
-Adaptive-Filtering-Technique-for-Error-Detection-and-Correction-of-Precision-Welding-RobotAdaptive-
Robotics and Automation have played a major role in the field of automobile manufacturing, space research, logistics, agriculture and many more. One such robot is a welding robot which is programmed to weld a product in the automotive industry. These robots are very accurate and don’t have any errors. But, sometimes due to some vibrations in the motors or due to any external factors, the robot may deviate from its specified position which leads to defective welding of a product. The robot arm is subjected to object tracking. The position of the robot arm is tracked by mounting an accelerometer on the robot arm. This position deviation can be corrected by using Kalman filtering technique. Kalman filters are used in the field of robotics motion planning, control and trajectory optimization. A com-mon application is for state prediction and estimation, object tracking. This paper is about applying Kalman filtering technique to a three-axis accelerometer which is mounted on the robotic arm of a welding robot. The voltage values of the accelerometer sensor are taken for state prediction and by recursive iterations the values are optimized such that error becomes minimum when the robot has deviated from its desired position.
-Hardware-Assisted-QR-Code-Generation-Using-Fault-Tolerant-TRNG
True random number generator (TRNG) is used to generate a purely random sequence in key generation. Real-world applications use key bits or strings as a passcode to secure systems. The security of a system depends on a robust design and the ambiguity of the keys that are used so that they are unpredictable. In this proposal, TRNGs are designed using reversible gates and fault-tolerant circuits. So the chance of the TRNG hardware produces faulty output is avoided. The inputs for this system are obtained from CPU usage. The generated true random number sequence is used in generating QR codes due to the uniqueness of the generated sequence. The proposed TRNG design highlights the effectiveness of using reversible fault-tolerant gates for TRNG application over the conventional logic implementation and reversible gate design in terms of power, area, and randomness. The proposed design in 90 nm technology consumes only 25.26 µW of power.
-Low-Power-Binary-Square-Rooter-using-Reversible-Logic
Calculating square root is an important mathematical operation which has wide applications. The design of square rooter in hardware needs to achieve low power, low area and high speed. Often there can be a trade-off among the three metrics. As the current technology aims for low power, designs require major architectural modification. This paper presents a low power binary square rooter using reversible logic. It uses reversible logic to achieve low power. The binary square rooter is designed and implemented using RCSM (Reversible Controlled Subtract Multiplexer).For further development such as number of quantum cost, garbage outputs and the constant inputs , binary square rooter is implemented using SRG (Samiur Rahman Gate).Binary square rooter using non-restoring algorithm is designed using both SRG and conventional approach. Simulations are carried out using ModelSim software and the power is obtained using Synopsys Design Compiler The power obtained for SRG and conventional technique are compared. The gate count has been reduced to 35 from 75. Power improvement of 20% is obtained.
-Microcontroller-based-ANN-for-Pick-and-Place-Robot-Coordinate-Monitoring-System
Industrial Robots have captivated the manufacturing process of a product in the present assembly lines. The pick and place robot plays a vital role in this process for handling the products. But sometimes it may deviate from its desired position due to vibrations in the motors or due to external factors such as the impact on the robotic arm by the nearby robotic arm in an assembly line, resulting in aberrant gripping of the product. The resulting product either becomes unusable or gets damaged. As a solution to this a microcontroller- based machine learning coordinate monitoring design is proposed. A Feed-Forward neural network is used to determine whether the robot can pick the product or not. Before the robot picks the product the position of the robot arm is tracked by the three-axis angle sensor. The simple design of the system makes it easier to implement. The output of the feed forward neural network in microcontroller will determine whether the robot arm can grip the product. The network is trained through an iterative process with the training data which consists of both accepted and rejected values. The performance of the network is tested by exposing the outputs of the sensor (i.e. test data) to the network. The accuracy and the performance of the network are achieved by modeling the network architecture with the required number of neurons in the hidden layers. The accuracy of the neural network designed is observed to be around 98% from the respective accuracy graphs at different training process. The simple design procedure makes this system compact and reprogrammable.
al-folio
A beautiful, simple, clean, and responsive Jekyll theme for academics
rsa.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
TRNG-based-Scrambler
In the modern era where huge amounts of information are being produced every second, still many loopholes are being found in the secured way of transferring data which risks the privacy of IP. Moreover, access to sensitive/confidential IoT sensor Data is vulnerable while transferring. A way to mitigate the issue that may cause serious data breach opportunities in the transferring of information from the IoT Device to a Remote Desktop/user device is being handled in this work. A new system has been described that uses multiple True Random Number Generators(TRNG’s) designed with the help of Fault-tolerant gates to generate OTP as a single-step authentication, which will then be used to transfer the files. Thus TRNG finds an effective application in the avenue of information security. The original IoT Sensor Data/scrambled IoT Sensor Data will be transferred depending upon the authentication. This ensures that only authorized users are accessible to the original data of the sensor values. The hamming distance of the original information file and the scrambled information file has been determined to verify the same and found to around 50%. Likewise, the time taken during original and scrambled file transfer has also been calculated to analyze the effectiveness of the proposal.
rsar97's Repositories
rsar97/-Adaptive-Filtering-Technique-for-Error-Detection-and-Correction-of-Precision-Welding-RobotAdaptive-
Robotics and Automation have played a major role in the field of automobile manufacturing, space research, logistics, agriculture and many more. One such robot is a welding robot which is programmed to weld a product in the automotive industry. These robots are very accurate and don’t have any errors. But, sometimes due to some vibrations in the motors or due to any external factors, the robot may deviate from its specified position which leads to defective welding of a product. The robot arm is subjected to object tracking. The position of the robot arm is tracked by mounting an accelerometer on the robot arm. This position deviation can be corrected by using Kalman filtering technique. Kalman filters are used in the field of robotics motion planning, control and trajectory optimization. A com-mon application is for state prediction and estimation, object tracking. This paper is about applying Kalman filtering technique to a three-axis accelerometer which is mounted on the robotic arm of a welding robot. The voltage values of the accelerometer sensor are taken for state prediction and by recursive iterations the values are optimized such that error becomes minimum when the robot has deviated from its desired position.
rsar97/-Microcontroller-based-ANN-for-Pick-and-Place-Robot-Coordinate-Monitoring-System
Industrial Robots have captivated the manufacturing process of a product in the present assembly lines. The pick and place robot plays a vital role in this process for handling the products. But sometimes it may deviate from its desired position due to vibrations in the motors or due to external factors such as the impact on the robotic arm by the nearby robotic arm in an assembly line, resulting in aberrant gripping of the product. The resulting product either becomes unusable or gets damaged. As a solution to this a microcontroller- based machine learning coordinate monitoring design is proposed. A Feed-Forward neural network is used to determine whether the robot can pick the product or not. Before the robot picks the product the position of the robot arm is tracked by the three-axis angle sensor. The simple design of the system makes it easier to implement. The output of the feed forward neural network in microcontroller will determine whether the robot arm can grip the product. The network is trained through an iterative process with the training data which consists of both accepted and rejected values. The performance of the network is tested by exposing the outputs of the sensor (i.e. test data) to the network. The accuracy and the performance of the network are achieved by modeling the network architecture with the required number of neurons in the hidden layers. The accuracy of the neural network designed is observed to be around 98% from the respective accuracy graphs at different training process. The simple design procedure makes this system compact and reprogrammable.
rsar97/TRNG-based-Scrambler
In the modern era where huge amounts of information are being produced every second, still many loopholes are being found in the secured way of transferring data which risks the privacy of IP. Moreover, access to sensitive/confidential IoT sensor Data is vulnerable while transferring. A way to mitigate the issue that may cause serious data breach opportunities in the transferring of information from the IoT Device to a Remote Desktop/user device is being handled in this work. A new system has been described that uses multiple True Random Number Generators(TRNG’s) designed with the help of Fault-tolerant gates to generate OTP as a single-step authentication, which will then be used to transfer the files. Thus TRNG finds an effective application in the avenue of information security. The original IoT Sensor Data/scrambled IoT Sensor Data will be transferred depending upon the authentication. This ensures that only authorized users are accessible to the original data of the sensor values. The hamming distance of the original information file and the scrambled information file has been determined to verify the same and found to around 50%. Likewise, the time taken during original and scrambled file transfer has also been calculated to analyze the effectiveness of the proposal.
rsar97/-Hardware-Assisted-QR-Code-Generation-Using-Fault-Tolerant-TRNG
True random number generator (TRNG) is used to generate a purely random sequence in key generation. Real-world applications use key bits or strings as a passcode to secure systems. The security of a system depends on a robust design and the ambiguity of the keys that are used so that they are unpredictable. In this proposal, TRNGs are designed using reversible gates and fault-tolerant circuits. So the chance of the TRNG hardware produces faulty output is avoided. The inputs for this system are obtained from CPU usage. The generated true random number sequence is used in generating QR codes due to the uniqueness of the generated sequence. The proposed TRNG design highlights the effectiveness of using reversible fault-tolerant gates for TRNG application over the conventional logic implementation and reversible gate design in terms of power, area, and randomness. The proposed design in 90 nm technology consumes only 25.26 µW of power.
rsar97/-Low-Power-Binary-Square-Rooter-using-Reversible-Logic
Calculating square root is an important mathematical operation which has wide applications. The design of square rooter in hardware needs to achieve low power, low area and high speed. Often there can be a trade-off among the three metrics. As the current technology aims for low power, designs require major architectural modification. This paper presents a low power binary square rooter using reversible logic. It uses reversible logic to achieve low power. The binary square rooter is designed and implemented using RCSM (Reversible Controlled Subtract Multiplexer).For further development such as number of quantum cost, garbage outputs and the constant inputs , binary square rooter is implemented using SRG (Samiur Rahman Gate).Binary square rooter using non-restoring algorithm is designed using both SRG and conventional approach. Simulations are carried out using ModelSim software and the power is obtained using Synopsys Design Compiler The power obtained for SRG and conventional technique are compared. The gate count has been reduced to 35 from 75. Power improvement of 20% is obtained.
rsar97/al-folio
A beautiful, simple, clean, and responsive Jekyll theme for academics
rsar97/rsa.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes