The paper presents an innovative method to artfully present multiple human and nonhuman viewpoints on a user’s current viewpoint. The method is built on a digital platform for capturing a large number of human and nonhuman viewpoints in the world, and enabling us to collectively share the viewpoints. Inspired by the art, the presentation method enables people to watch the world in a novel way.
In precision livestock farming, lameness detection in cattle is particularly important for breeding management. The accurate detection of lameness is crucial for delivering effective and economical treatment and for preventing future diseases. The noticeable sign of lameness is that their speed of walking, arching their backs and drop their heads during walking. Here, we emphasis on lameness of dairy cattle by implementing the intelligent visual perception system on the laneways after milking process. Employing a deep learning technique of Mask-RCNN for cattle region detection and identification. The novelty of this work noticeably implies that deep learning instance segmentation could be effectively employed as a cattle region extraction from complex background prior to using identification and tracking.
This paper presents a sensor-based data acquisition glove for gesture recognition in Japanese Sign Language (JSL), which uses five flex sensors and an inertial measurement unit (IMU) to detect finger flexion and hand motion information. The detected data is sent from the Arduino Micro to a computer. We collected data from the "A" to "Ta" lines of the Japanese (kana) syllabary and using four different machine learning algorithms: Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) to recognize them. RF and KNN have the highest average accuracy, reaching 99.75%. Also, SVM and DT had an average accuracy of 99% and 94.25% respectively. The experimental results show that the proposed system has great potential for gesture recognition in Japanese Sign Language.
Cow identification has become important in recent years due to outbreaks of diseases such as bovine spongiform encephalopathy. Conventional identification methods available are not efficient, affordable, non-invasive, and cost- effective. Among them, some methods are based on biological markers, such as muzzle point matching and facial recognition. Facial images are the most common biometric characteristics used by humans to identify individuals, and they have received much attention. In this study, we used RGB camera to identify individual cow by their faces and confirmed the effectiveness of this method.
The economic depression accompanied by COVID-19 pandemic has also had some adverse effects on people's mental health. 4 in 10 adults have symptoms of anxiety and depression, and these symptoms may continue to increase. In particular, the reported rate of mental health disorders among adolescents is as high as 56.2%. Recently, pet ownership has become widely accepted as a means of improving mental health. However, due to various reasons, the separation of owners and pets has also increased significantly. Therefore, we have developed a portable simulated pet "KEDAMA", which can be used in place of real pets, and can be customized with fur and sound. The price is relatively cheap and can be ordered easily. We conducted a statistical analysis on 20 users, and the results show that the "KEDAMA" has a positive effect on users' mental health.
