Automated Inflation and Pressure Regulation for Recreational and Professional Cyclists
Cycling is a very popular mode of transport as well as a famous sport around the world. Many people enjoy this sport either professionally or recreationally. Cycling in the UK alone has grown up to 200% since lockdown in 2020. (Chandler, 2020) Cyclists make use of a broad selection of products to enhance their performance. Those products range from wireless gear shifting, advanced geometry, smart suspension. This project is aimed to indicate the importance of tire pressure and to introduce a product which will be able to adjust tire pressure while cycling. This product will give cyclist an advantage on different terrains as well as eliminate some common problems amongst cyclists. Flat tires are one of these problems. It occurs commonly amongst cyclists and can happen due to a variety of reasons. Another problem is wrongly inflated tires. This causes unnecessary loss in a cyclist’s power and speeds due to the high rolling resistance between the tires and the surface. This then results in losing time whether racing or commuting. In an article published in 2014 in Velonews.com, Lennard Zinn states: “Whether on tarmac or singletrack, a tire with lower rolling resistance reduces the power required to move forward while also providing a better quality ride. The tire absorbs small bumps by not transferring them into the bicycle and rider, resulting in a smoother ride, faster speeds, and better cornering." (Zinn, 2014) Taking this in consideration it becomes clear that it is important to develop a system which is able to control tire pressure.
Designing a LiDAR topographic navigation system: A novel approach to aid the visually impaired
The WHO reports 2.2 billion people internationally have a form of visual impairment, with Perkins School of Blind adding that 4 to 8 percent (8.8 - 17.6 million people) solely rely on a white cane for navigation. In an interview by Stephen Yin for NPR, visually impaired interviewees claimed that a white cane was ineffective as it failed to detect moving obstacles (ex. bikes), aerial obstacles (ex. falling objects), and it became physically demanding after a prolonged period. This problem can be solved with a headset that integrates LiDAR technology and haptic feedback to provide a real-time assessment of their environment. Theoretically, the device will determine how far an object is from the user and place it into one of three conditionals based on distance (0- 290mm, 310-500mm, 510-1200mm). As the user gets closer to the object, the haptic will vibrate more frequently. The device has 11 LIDAR sensors, beetle processors, and ERM motors so that when the LiDAR detects an object, the device will send a haptic signal in that area. It not only identifies the existence of an object but it tells the user its relative position with a latency period of approximately 2 milliseconds. When testing the device, a simulated walking environment was made. Ten obstacles were included: five below the waist (72”, 28”, 35” and 8.5” tall sticks) and five above the waist (paper suspended 6”, 10”, 48” and 28” from the ceiling). The white cane detected 4.1 obstacles, whereas the device detected 7.3 on average. The LiDAR navigation system is 178% more effective at detecting objects comparatively. Visually impaired individuals no longer must rely on the white cane; rather, using this device, they can detect small, moving, and aerial objects at a much faster, and more accurate speed.
朽木生花-初探以中藥萃取液對木材染色之防蟲抑菌效果
In our experiment, we used traditional Chinese medicine to dye on cheap wood, in addition to avoiding the impact of chemical paint on human body; After dyeing, the color and texture quality of the wood are improved, which makes cheap wood have higher price and improves the value of wood; At the same time, it can reduce the felling of slow growing precious wood, which has the functions of environmental protection, earth love and carbon saving. The test material was pretreated with hydrogen peroxide and surfactant, and the bleaching effect was obvious. After dyed with different Chinese medicinal, soak in strong acid and alkali solution for 15 minutes, which shows that strong acid and acid treatment is not allowed. On the other hand, after 15 minutes of immersion in detergent, the color difference value is less than 2, and the rubbing fastness is above grade 4. In the bacteriostasis experiment, no fungus grew in the first 3 days, and it did not grow in the 12th day. In the anti-termite experiment, the mortality rate on the fifth day was 65% for Lithospermum and 83.8% for Wolfberry, and the other groups had a good effect of total elimination. While plastic products have a great impact on the environment, wood that is dyed or modified with natural colored dye, its environmental value far exceeds the human visual perception.
DEVELOPMENT AND USE OF LASER 3D SCANNER OF PREMISES
This research work is devoted to the stages of development and creation of a prototype of a laser 3D scanner model, programming of a controlling microcontroller, construction of 3D models of a scanned object. In the course of the work, the market of 3D scanners, which are used to build three-dimensional models of premises, was analyzed, the equipment necessary for the development and creation of the prototype was analyzed, as well as the software necessary for the operation of the prototype. The result of the work was the creation of a laser 3D scanner based on an Arduino microcontroller using a Lidar type sensor that scans and builds 3D models of objects. This working model of the 3D scanner demonstrates good capabilities and turned out to be easy to use.
An Efficient and Accurate Super-Resolution Approach to Low-Field MRI via U-Net Architecture With Logarithmic Loss and L2 Regularization
Low-field (LF) MRI scanners have the power to revolutionize medical imaging by provid- 27 ing a portable and cheaper alternative to high-field MRI scanners. However, such scanners are usu- 28 ally significantly noisier and lower quality than their high-field counterparts. This prevents them 29 from appealing to global markets. The aim of this paper is to improve the SNR and overall image quality of low-field MRI scans (called super-resolution) to improve diagnostic capability and, as a result, make it more accessible. To address this issue, we propose a Nested U-Net neural network architecture super-resolution algorithm that outperforms previously suggested super-resolution deep learning methods with an average PSNR of 78.83 ± 0.01 and SSIM of 0.9551 ± 0.01. Our ANOVA paired t-test and Post-Hoc Tukey test demonstrate significance with a p-value < 0.0001 and no other network demonstrating significance higher than 0.1. We tested our network on artificial noisy downsampled synthetic data from 1500 T1 weighted MRI images through the dataset called the T1- mix. Four board-certified radiologists scored 25 images (100 image ratings total) on the Likert scale (1-5) assessing overall image quality, anatomical structure, and diagnostic confidence across our architecture and other published works (SR DenseNet, Generator Block, SRCNN, etc.). Our algo- rithm outperformed all other works with the highest MOS, 4.4 ± 0.3. We also introduce a new type of loss function called natural log mean squared error (NLMSE), outperforming MSE, MAE, and MSLE on this specific SR task. Additionally, we ran inference on actual Hyperfine scan images with successful qualitative results using a Generator RRDB block. In conclusion, we present a more ac- curate deep learning method for single image super-resolution applied to low-field MRI via a 45 Nested U-Net architecture.