Egocentric recognition is exciting computer vision research by acquiring images and video from the first-person overview.However,an image becomes noisy and dark under low illumination conditions,making subsequent hand...Egocentric recognition is exciting computer vision research by acquiring images and video from the first-person overview.However,an image becomes noisy and dark under low illumination conditions,making subsequent hand detection tasks difficult.Thus,image enhancement is necessary to make buried detail more visible.This article addresses the challenge of egocentric hand grasp recognition in low light conditions by utilizing the flex sensor and image enhancement algorithm based on adaptive gamma correction with weighting distribution.Initially,a flex sensor is installed to the thumb for object manipulation.The thumb placement that holds in a different position on the object of each grasp affects the voltage changing of the flex sensor circuit.The average voltages are used to configure the weighting parameter to improve images in the image enhancement stage.Moreover,the contrast and gamma function are used to adjust varies the low light condition.These grasp images are then separated to be training and testing with pretrained deep neural networks as the feature extractor in YOLOv2 detection network for the grasp recognition system.The proposed of using a flex sensor significantly improves the grasp recognition rate in low light conditions.展开更多
Sowing depth has an important impact on the performance of no-tillage planters,it is one of the key factors to ensure rapid germination.However,the consistency of sowing depth is easily affected by the complex environ...Sowing depth has an important impact on the performance of no-tillage planters,it is one of the key factors to ensure rapid germination.However,the consistency of sowing depth is easily affected by the complex environment of no-tillage operation.In order to improve the performance of no-tillage planters and improve the control precision of sowing depth,an intelligent depth regulation system was designed.Three Flex sensors installed on the inner surface of the gauge wheel at 120°intervals were used to monitor the downward force exerted by the seeding row unit against ground.The peak value of the output voltage of the sensor increased linearly with the increase of the downward force.In addition,the pneumatic spring was used as a downforce generator,and its intelligent regulation model was established by the Mamdani fuzzy algorithm,which can realize the control of the downward force exerted by the seeding row unit against ground and ensure the proper seeding depth.The working process was simulated based on MATLAB-Simulink,and the results showed that the Mamdani fuzzy model performed well in changing the pressure against ground.Field results showed that when the operating speed was 6 km/h,8 km/h and 10 km/h,the error of the system’s control of sowing depth was±9 mm,±12 mm,and±22 mm,respectively,and its sowing performance was significantly higher than that of the unadjusted passive operation.展开更多
Cable-driven soft robots exhibit complex deformations,making state estimation challenging.Hence,this paper develops a multi-sensor fusion approach using a gradient descent strategy to estimate the weighting coefficien...Cable-driven soft robots exhibit complex deformations,making state estimation challenging.Hence,this paper develops a multi-sensor fusion approach using a gradient descent strategy to estimate the weighting coefficients.These coefficients combine measurements from proprioceptive sensors,such as resistive flex sensors,to determine the bending angle.Additionally,the fusion strategy adopted provides robust state estimates,overcoming mismatches between the flex sensors and soft robot dimensions.Furthermore,a nonlinear differentiator is introduced to filter the differentiated sensor signals to address noise and irrational values generated by the Analog-to-Digital Converter.A rational polynomial equation is also introduced to compensate for temperature drift exhibited by the resistive flex sensors,which affect the accuracy of state estimation and control.The processed multi-sensor data is then utilized in an improved PD controller for closed-loop control of the soft robot.The controller incorporates the nonlinear differentiator and drift compensation,enhancing tracking performance.Experimental results validate the effectiveness of the integrated approach,demonstrating improved tracking accuracy and robustness compared to traditional PD controllers.展开更多
In this cyber era, novelty plays a prime role in the field of agriculture that majorly depends on computer-based measurements and control. Herein before, it was totally controlled and performed by the agriculturists. ...In this cyber era, novelty plays a prime role in the field of agriculture that majorly depends on computer-based measurements and control. Herein before, it was totally controlled and performed by the agriculturists. One of the technological innovative methods to measure and monitor the turmeric finger growth characteristics is the embedded system that is based on sensor array module such as flex sensor, temperature sensor and pH sensor. The experimental work has been designed and tested with five set of nodes and the growth of turmeric finger is tenuously monitored by measuring the change in flex resistance. Out of five nodes, two nodes were diseased. Deliberately, one node was left as such and the other node was treated with natural pesticides (pseudomonas and viride) to restrict the rhizome rot disease attack. After cultivation, it was found that the rhizome rot disease attack on the node which was treated with pesticides was comparatively lesser than the other node. The five different nodes have been used in the experimental work with an average flex sensor resistance of 3.962 cm/kΩ. In a nutshell, this proposed method manifests the farmers to detect the rhizome rot disease at its earlier stage and to prevent it as well by screening the growth of the turmeric fingers when it is under the soil.展开更多
People who are deaf or have difficulty speaking use sign language,which consists of hand gestures with particular motions that symbolize the“language”they are communicating.A gesture in a sign language is a particul...People who are deaf or have difficulty speaking use sign language,which consists of hand gestures with particular motions that symbolize the“language”they are communicating.A gesture in a sign language is a particular movement of the hands with a specific shape from the fingers and whole hand.In this paper,we present an Intelligent for Deaf/Dumb People approach in real time based on Deep Learning using Gloves(IDLG).The approach IDLG offers scientific contributions based deep-learning,a multimode command techniques,real-time,and effective use,and high accuracy rates.For this purpose,smart gloves working in real time were designed.The data obtained from the gloves was processed using deep-learning-based approaches and classified multi-mode commands that allow dumb people to speak with regular people via their smart phone.Internally,the glove has five flex sensors and an accelerometer using to achieve Low-Cost Control System.The flex sensor generates a proportional change in resistance for each individual move.The processing of these hand gestures is in Atmega32A Microcontroller which is an advance version of the microcontroller and the lab view software.IDLG compares the input signal to memory-stored specified voltage values.The performance of the IDLG approach was verified on a dataset created using different hand gestures from 20 different people.In the test using the IDLG approach on 10,000 data points,process time performance of milliseconds was achieved with 97%accuracy.展开更多
设计了一种基于弯曲传感器的机器人手势控制系统。该控制系统采用开放原始代码的Arduino UNO R3作为主控板,将手指的姿态变化通过弯曲传感器的电压变化输入到主控板内。利用移动平均滤波算法和阈值方法对弯曲传感器的原始输入信号进行...设计了一种基于弯曲传感器的机器人手势控制系统。该控制系统采用开放原始代码的Arduino UNO R3作为主控板,将手指的姿态变化通过弯曲传感器的电压变化输入到主控板内。利用移动平均滤波算法和阈值方法对弯曲传感器的原始输入信号进行处理后,将主控板的输出信号转换为舵机的控制信号,从而用来驱动机器人的运动,实现了利用手指动作控制机器人的目的。实验证明,该方法可以实时可靠地控制机器人的运动,并且准确度很高。展开更多
基金This research is supported by the NationalResearch Council of Thailand(NRCT).NRISS No.144276 and 2589488.
文摘Egocentric recognition is exciting computer vision research by acquiring images and video from the first-person overview.However,an image becomes noisy and dark under low illumination conditions,making subsequent hand detection tasks difficult.Thus,image enhancement is necessary to make buried detail more visible.This article addresses the challenge of egocentric hand grasp recognition in low light conditions by utilizing the flex sensor and image enhancement algorithm based on adaptive gamma correction with weighting distribution.Initially,a flex sensor is installed to the thumb for object manipulation.The thumb placement that holds in a different position on the object of each grasp affects the voltage changing of the flex sensor circuit.The average voltages are used to configure the weighting parameter to improve images in the image enhancement stage.Moreover,the contrast and gamma function are used to adjust varies the low light condition.These grasp images are then separated to be training and testing with pretrained deep neural networks as the feature extractor in YOLOv2 detection network for the grasp recognition system.The proposed of using a flex sensor significantly improves the grasp recognition rate in low light conditions.
基金by the National Key R&D Plan Project(Grant No.2016YFD070030201)。
文摘Sowing depth has an important impact on the performance of no-tillage planters,it is one of the key factors to ensure rapid germination.However,the consistency of sowing depth is easily affected by the complex environment of no-tillage operation.In order to improve the performance of no-tillage planters and improve the control precision of sowing depth,an intelligent depth regulation system was designed.Three Flex sensors installed on the inner surface of the gauge wheel at 120°intervals were used to monitor the downward force exerted by the seeding row unit against ground.The peak value of the output voltage of the sensor increased linearly with the increase of the downward force.In addition,the pneumatic spring was used as a downforce generator,and its intelligent regulation model was established by the Mamdani fuzzy algorithm,which can realize the control of the downward force exerted by the seeding row unit against ground and ensure the proper seeding depth.The working process was simulated based on MATLAB-Simulink,and the results showed that the Mamdani fuzzy model performed well in changing the pressure against ground.Field results showed that when the operating speed was 6 km/h,8 km/h and 10 km/h,the error of the system’s control of sowing depth was±9 mm,±12 mm,and±22 mm,respectively,and its sowing performance was significantly higher than that of the unadjusted passive operation.
基金financial support from the National Natural Science Foundation of China(62103039,62073030)the Joint Fund of Ministry of Education for Equipment Pre-Research(8091B03032303).
文摘Cable-driven soft robots exhibit complex deformations,making state estimation challenging.Hence,this paper develops a multi-sensor fusion approach using a gradient descent strategy to estimate the weighting coefficients.These coefficients combine measurements from proprioceptive sensors,such as resistive flex sensors,to determine the bending angle.Additionally,the fusion strategy adopted provides robust state estimates,overcoming mismatches between the flex sensors and soft robot dimensions.Furthermore,a nonlinear differentiator is introduced to filter the differentiated sensor signals to address noise and irrational values generated by the Analog-to-Digital Converter.A rational polynomial equation is also introduced to compensate for temperature drift exhibited by the resistive flex sensors,which affect the accuracy of state estimation and control.The processed multi-sensor data is then utilized in an improved PD controller for closed-loop control of the soft robot.The controller incorporates the nonlinear differentiator and drift compensation,enhancing tracking performance.Experimental results validate the effectiveness of the integrated approach,demonstrating improved tracking accuracy and robustness compared to traditional PD controllers.
文摘In this cyber era, novelty plays a prime role in the field of agriculture that majorly depends on computer-based measurements and control. Herein before, it was totally controlled and performed by the agriculturists. One of the technological innovative methods to measure and monitor the turmeric finger growth characteristics is the embedded system that is based on sensor array module such as flex sensor, temperature sensor and pH sensor. The experimental work has been designed and tested with five set of nodes and the growth of turmeric finger is tenuously monitored by measuring the change in flex resistance. Out of five nodes, two nodes were diseased. Deliberately, one node was left as such and the other node was treated with natural pesticides (pseudomonas and viride) to restrict the rhizome rot disease attack. After cultivation, it was found that the rhizome rot disease attack on the node which was treated with pesticides was comparatively lesser than the other node. The five different nodes have been used in the experimental work with an average flex sensor resistance of 3.962 cm/kΩ. In a nutshell, this proposed method manifests the farmers to detect the rhizome rot disease at its earlier stage and to prevent it as well by screening the growth of the turmeric fingers when it is under the soil.
基金This work is funded by King Saud University from Riyadh,Saudi Arabia.Project Number(RSP-2021/164),King Saud University,Riyadh,Saudi Arabia.https://ksu.edu.sa/.
文摘People who are deaf or have difficulty speaking use sign language,which consists of hand gestures with particular motions that symbolize the“language”they are communicating.A gesture in a sign language is a particular movement of the hands with a specific shape from the fingers and whole hand.In this paper,we present an Intelligent for Deaf/Dumb People approach in real time based on Deep Learning using Gloves(IDLG).The approach IDLG offers scientific contributions based deep-learning,a multimode command techniques,real-time,and effective use,and high accuracy rates.For this purpose,smart gloves working in real time were designed.The data obtained from the gloves was processed using deep-learning-based approaches and classified multi-mode commands that allow dumb people to speak with regular people via their smart phone.Internally,the glove has five flex sensors and an accelerometer using to achieve Low-Cost Control System.The flex sensor generates a proportional change in resistance for each individual move.The processing of these hand gestures is in Atmega32A Microcontroller which is an advance version of the microcontroller and the lab view software.IDLG compares the input signal to memory-stored specified voltage values.The performance of the IDLG approach was verified on a dataset created using different hand gestures from 20 different people.In the test using the IDLG approach on 10,000 data points,process time performance of milliseconds was achieved with 97%accuracy.
文摘设计了一种基于弯曲传感器的机器人手势控制系统。该控制系统采用开放原始代码的Arduino UNO R3作为主控板,将手指的姿态变化通过弯曲传感器的电压变化输入到主控板内。利用移动平均滤波算法和阈值方法对弯曲传感器的原始输入信号进行处理后,将主控板的输出信号转换为舵机的控制信号,从而用来驱动机器人的运动,实现了利用手指动作控制机器人的目的。实验证明,该方法可以实时可靠地控制机器人的运动,并且准确度很高。
