An approach to compensation of dust effects on seed flow sensors

Optical seed mass flow sensors are widely used on seed drills and planters. An important challenge in these sensors is their malfunction in a dusty condition. Dust caused by soil and seeds may sit on the light elements and disrupt its function. In this study, an approach was developed to compensate this effect. A non-contact intelligent system with infrared diodes and a microcontroller with ARM architecture was built up to monitor the seed flow in the delivery tube of seed drills. At the hardware phase, a glass with a different radius of curvature was installed in front of the elements. The semi-cylindrical glass placement in front of the optical elements meant that the arrangement was sealed against dust. Besides, the fall of the seeds tangential to the glass during the sowing caused the glass to self-clean. However, the hardware configuration of the seed flow sensor with semi-cylindrical glass alone was not sufficient under adverse dusty conditions. A suitable algorithm was therefore developed and applied to compensate for the dust effect. In this case, instead of the level of output voltage, MS (mean of variances) of sensor outputs was calculated. The mass flow estimation model was obtained using multiple regression between the MS index of the seed flow sensor and digital scale data. Experiments were carried out using different types of seeds in several repetitions. In all tests, the correlation coefficient of the mass flow estimation model was obtained above 0.9. The results revealed that this system works correctly and precisely in dusty field conditions without having to clean the sensing elements

STR-906: COMPUTER-IMAGE-BASED LOOSENED BOLT DETECTION USING SUPPORT VECTOR MACHINES

Despite many contact-sensor-based methods have been proposed to monitor and detect structural defects, there are still difficulties compensating for environmental effects and malfunctions of attached sensors, which are main reasons for transmitting false signals. Moreover, regardless of releasing correct or incorrect signals, it eventually leads to human-conducted on-site inspections. In light of these shortcomings, vision-based inspections are considered as potential approach to overcome the explained issues. A number of vision-based methods for detecting damages from images have been developed. However, there are only a few vision-based methods for detecting loosened bolts. Thus, a computer-vision method for detecting loosened bolts is proposed. This study includes two algorithms. The first one is a preprocessing to crop bolt images from bolted-joint images. The second algorithm is a feature extraction by integrating previously proposed algorithms in computer-vision. To accomplish an automated inspection, linear support vector machine is trained and used as a classifier. The robustness of the proposed is verified by the experimental validation; 22 bolt images are used to build a classifier, and 40 bolt images are tested

Aerospace Medicine and Biology: a Continuing Bibliography with Indexes (Supplement 328)

This bibliography lists 104 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during September, 1989. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Q-PPG: Energy-Efficient PPG-Based Heart Rate Monitoring on Wearable Devices

Hearth Rate (HR) monitoring is increasingly performed in wrist-worn devices using low-cost photoplethysmography (PPG) sensors. However, Motion Artifacts (MAs) caused by movements of the subject's arm affect the performance of PPG-based HR tracking. This is typically addressed coupling the PPG signal with acceleration measurements from an inertial sensor. Unfortunately, most standard approaches of this kind rely on hand-tuned parameters, which impair their generalization capabilities and their applicability to real data in the field. In contrast, methods based on deep learning, despite their better generalization, are considered to be too complex to deploy on wearable devices.In this work, we tackle these limitations, proposing a design space exploration methodology to automatically generate a rich family of deep Temporal Convolutional Networks (TCNs) for HR monitoring, all derived from a single "seed" model. Our flow involves a cascade of two Neural Architecture Search (NAS) tools and a hardware-friendly quantizer, whose combination yields both highly accurate and extremely lightweight models. When tested on the PPG-Dalia dataset, our most accurate model sets a new state-of-the-art in Mean Absolute Error. Furthermore, we deploy our TCNs on an embedded platform featuring a STM32WB55 microcontroller, demonstrating their suitability for real-time execution. Our most accurate quantized network achieves 4.41 Beats Per Minute (BPM) of Mean Absolute Error (MAE), with an energy consumption of 47.65 mJ and a memory footprint of 412 kB. At the same time, the smallest network that obtains a MAE < 8 BPM, among those generated by our flow, has a memory footprint of 1.9 kB and consumes just 1.79 mJ per inference

Seed sensor position on seeder performance at varying speeds

ABSTRACT: The uniformity of seed distribution and sowing speed directly impact crop quality and productivity. This experiment assessed how the position of the sowing monitoring sensor influences the distribution of cotton seeds using a pneumatic meter at different operating speeds. The experiment employed a completely randomized two-factor factorial design on a static simulation bench. The first factor involved the sensor installation sites (upper, middle, and lower portions of the conductor tube and conveyor belt), while the second factor encompassed simulated speeds of 3.0, 5.0, 7.0, 9.0, and 11.0 km/h. Parameters such as frequency of double, flawed, and acceptable spacing, coefficient of variation, and precision index were measured based on five replications of 250 consecutive spacing. The results indicated that the sensor’s placement significantly influences reading accuracy. Optimal results were observed when the sensor was positioned at the final portion of the conductor tube, providing more accurate seed deposition, and facilitating decision-making

Principal Component Analysis based Image Fusion Routine with Application to Stamping Split Detection

This dissertation presents a novel thermal and visible image fusion system with application in online automotive stamping split detection. The thermal vision system scans temperature maps of high reflective steel panels to locate abnormal temperature readings indicative of high local wrinkling pressure that causes metal splitting. The visible vision system offsets the blurring effect of thermal vision system caused by heat diffusion across the surface through conduction and heat losses to the surroundings through convection. The fusion of thermal and visible images combines two separate physical channels and provides more informative result image than the original ones. Principal Component Analysis (PCA) is employed for image fusion to transform original image to its eigenspace. By retaining the principal components with influencing eigenvalues, PCA keeps the key features in the original image and reduces noise level. Then a pixel level image fusion algorithm is developed to fuse images from the thermal and visible channels, enhance the result image from low level and increase the signal to noise ratio. Finally, an automatic split detection algorithm is designed and implemented to perform online objective automotive stamping split detection. The integrated PCA based image fusion system for stamping split detection is developed and tested on an automotive press line. It is also assessed by online thermal and visible acquisitions and illustrates performance and success. Different splits with variant shape, size and amount are detected under actual operating conditions