Advanced Driver Assistance System Based on IoT V2V and V2I for Vision Enabled Lane Changing with Futuristic Drivability.

In conventional modern vehicles, the Internet of Things-based automotive embedded systems are used to collect various data from real-time sensors and store it in the cloud platform to perform visualization and analytics. The proposed work is to implement computer vision-aided vehicle intercommunication V2V (vehicle-to-vehicle) implemented using the Internet of Things for an autonomous vehicle. Computer vision-based driver assistance supports the vehicle to perform efficiently in critical transitions such as lane change or collision avoidance during the autonomous driving mode. In addition to this, the main work emphasizes observing multiple parameters of the In-Vehicle system such as speed, distance covered, idle time, and fuel economy by the electronic control unit are evaluated in this process. Electronic control unit through brake control module, powertrain control module, transmission control module, suspension control module, and battery management system helps to predict the nature of drive-in different terrains and also can suggest effective custom driving modes for advanced driver assistance systems. These features are implemented with the help of the vehicle-to-infrastructure protocol, which collects data through gateway nodes that can be visualized in the IoT data frame. The proposed work involves the process of analyzing and visualizing the driver-influencing factors of a modern vehicle that is in connection with the IoT cloud platform. The custom drive mode suggestion and improvisation had been completed with help of computational analytics that leads to the deployment of an over-the-air update to the vehicle embedded system upgradation for betterment in drivability. These operations are progressed through a cloud server which is the prime factor proposed in this work.

Review of Semantic Segmentation of Medical Images Using Modified Architectures of UNET.

In biomedical image analysis, information about the location and appearance of tumors and lesions is indispensable to aid doctors in treating and identifying the severity of diseases. Therefore, it is essential to segment the tumors and lesions. MRI, CT, PET, ultrasound, and X-ray are the different imaging systems to obtain this information. The well-known semantic segmentation technique is used in medical image analysis to identify and label regions of images. The semantic segmentation aims to divide the images into regions with comparable characteristics, including intensity, homogeneity, and texture. UNET is the deep learning network that segments the critical features. However, UNETs basic architecture cannot accurately segment complex MRI images. This review introduces the modified and improved models of UNET suitable for increasing segmentation accuracy.

Anterior Cruciate Ligament Tear Detection Based on Deep Convolutional Neural Network.

Anterior cruciate ligament (ACL) tear is very common in football players, volleyball players, sprinters, runners, etc. It occurs frequently due to extra stretching and sudden movement and causes extreme pain to the patient. Various computer vision-based techniques have been employed for ACL tear detection, but the performance of most of these systems is challenging because of the complex structure of knee ligaments. This paper presents a three-layered compact parallel deep convolutional neural network (CPDCNN) to enhance the feature distinctiveness of the knee MRI images for anterior cruciate ligament (ACL) tear detection in knee MRI images. The performance of the proposed approach is evaluated for the MRNet knee images dataset using accuracy, recall, precision, and the F1 score. The proposed CPDCNN offers an overall accuracy of 96.60%, a recall rate of 0.9668, a precision of 0.9654, and an F1 score of 0.9582, which shows superiority over the existing state-of-the-art methods for knee tear detection.

Complex Mullerian Malformation: A Rare Case of Hypoplastic Noncavitated Uterus in the Middle with Two Rudimentary Horns on Either Side.

Mullerian anomalies which cause infertility in women were described by different classification systems. We report a rare case of uterine anomaly in a 16-year-old patient presented with primary amenorrhea. Her diagnostic laparoscopy findings revealed two uterine rudimentary horns on either side of the upper pelvis with a hypoplastic noncavitated central uterus. The pathogenesis of this anomaly may not be clearly defined but it was stated that these occur due to the developmental defects in embryo. This case report is one of the rarest cases presented and may signify the Mullerian duct anomaly.

Preparation and Thermo-Physical Properties of Fe2O3-Propylene Glycol Nanofluids.

Iron oxide (Fe2O3) nanoparticles were prepared from ferric chloride and ferrous sulphate by precipitation reaction. Fe2O3-propylene glycol nanofluid was prepared by dispersing Fe2O3 nanoparticles in propylene glycol through stirred bead milling, shear homogenization and probe ultrasonication. The nanofluid was characterized through measurement of viscosity, particle size distribution and thermal conductivity. The interactions between Fe2O3 nanoparticles and propylene glycol on the nanoparticle surfaces lead to reduction in viscosity, the magnitude of which increases with nanoparticle concentration (0-2 vol%) at room temperature. The thermal conductivity enhancement for 2 vol% nanofluid was about 21% at room temperature, with liquid layering being the major contributor for thermal conductivity enhancement.