Visualizing Quaternion Multiplication

Quaternion rotation is a powerful tool for rotating vectors in 3-D; as a result, it has been used in various engineering fields, such as navigations, robotics, and computer graphics. However, understanding it geometrically remains challenging, because it requires visualizing 4-D spaces, which makes exploiting its physical meaning intractable. In this paper, we provide a new geometric interpretation of quaternion multiplication using a movable 3-D space model, which is useful for describing quaternion algebra in a visual way. By interpreting the axis for the scalar part of quaternion as a 1-D translation axis of 3-D vector space, we visualize quaternion multiplication and describe it as a combined effect of translation, scaling, and rotation of a 3-D vector space. We then present how quaternion rotation formulas and the derivative of quaternions can be formulated and described under the proposed approach.112sciescopu

Deep Reinforcement Learning-Driven Scheduling in Multijob Serial Lines: A Case Study in Automotive Parts Assembly

Multijob production (MJP) is a class of flexible manufacturing systems, which produces different products within the same production system. MJP is widely used in product assembly, and efficient MJP scheduling is crucial for productivity. Most of the existing MJP scheduling methods are inefficient for multijob serial lines with practical constraints. We propose a deep reinforcement learning (DRL)-driven scheduling framework for multijob serial lines by properly considering the practical constraints of identical machines, finite buffers, machine breakdown, and delayed reward. We analyze the starvation and the blockage time, and derive a DRL-driven scheduling strategy to reduce the blockage time and balance the loads. We validate the proposed framework by using real-world factory data collected over six months from a tier-one vendor of a world top-three automobile company. Our case study shows that the proposed scheduling framework improves the average throughput by 24.2% compared with the conventional approach.TRU

Simultaneous Video Retrieval and Alignment

With the growth of the video streaming industry, video retrieval and video alignment are facing high levels of demand. Several studies have demonstrated the feasibility of these methods for various problems related to video retrieval and alignment independently, but testing in a unified framework has never been done. However, in real-world applications, it is also simultaneously necessary not only to find which video pairs are similar (video retrieval), but also to align the positions of the pairs that are related (video alignment). In this paper, we present a novel task: simultaneous video retrieval and alignment. As a solution to this task, a Simultaneous video Retrieval and Alignment framework, abbreviated as SRA, is proposed, which is a two-stage approach consisting of a foreground proposal stage and a downstream stage to efficiently process untrimmed videos. Furthermore, two criteria are suggested to support the new task: a metric mAP@J assessing how highly related videos are ranked and how well relevant positions are assigned in those videos, and a dataset FIVR+A that includes video-level relationships and hierarchical segment-level annotations. Finally, we conduct multi-pronged analyses to assess how our approach handles the new task in various experiments

Manufacturable 32-Channel Cochlear Electrode Array and Preliminary Assessment of Its Feasibility for Clinical Use

(1) Background: In this study, we introduce a manufacturable 32-channel cochlear electrode array. In contrast to conventional cochlear electrode arrays manufactured by manual processes that consist of electrode-wire welding, the placement of each electrode, and silicone molding over wired structures, the proposed cochlear electrode array is manufactured by semi-automated laser micro-structuring and a mass-produced layer-by-layer silicone deposition scheme similar to the semiconductor fabrication process. (2) Methods: The proposed 32-channel electrode array has 32 electrode contacts with a length of 24 mm and 0.75 mm spacing between contacts. The width of the electrode array is 0.45 mm at its apex and 0.8 mm at its base, and it has a three-layered arrangement consisting of a 32-channel electrode layer and two 16-lead wire layers. To assess its feasibility, we conducted an electrochemical evaluation, stiffness measurements, and insertion force measurements. (3) Results: The electrochemical impedance and charge storage capacity are 3.11 ± 0.89 kOhm at 1 kHz and 5.09 mC/cm2, respectively. The V/H ratio, which indicates how large the vertical stiffness is compared to the horizontal stiffness, is 1.26. The insertion force is 17.4 mN at 8 mm from the round window, and the maximum extraction force is 61.4 mN. (4) Conclusions: The results of the preliminary feasibility assessment of the proposed 32-channel cochlear electrode array are presented. After further assessments are performed, a 32-channel cochlear implant system consisting of the proposed 32-channel electrode array, 32-channel neural stimulation and recording IC, titanium-based hermetic package, and sound processor with wireless power and signal transmission coil will be completed

Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages.

Cerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to analyze histological sections (5-40 μm) stained with Prussian blue. Currently, users manually and subjectively identify and quantify Prussian blue-stained regions of interest, which is prone to inter-individual variability and can lead to significant delays in data analysis. To improve this labor-intensive process, we developed and compared three digital pathology approaches to identify and quantify CMHs from Prussian blue-stained brain sections: (1) ratiometric analysis of RGB pixel values, (2) phasor analysis of RGB images, and (3) deep learning using a mask region-based convolutional neural network. We applied these approaches to a preclinical mouse model of inflammation-induced CMHs. One-hundred CMHs were imaged using a 20 × objective and RGB color camera. To determine the ground truth, four users independently annotated Prussian blue-labeled CMHs. The deep learning and ratiometric approaches performed better than the phasor analysis approach compared to the ground truth. The deep learning approach had the most precision of the three methods. The ratiometric approach has the most versatility and maintained accuracy, albeit with less precision. Our data suggest that implementing these methods to analyze CMH images can drastically increase the processing speed while maintaining precision and accuracy

Spectroscopic and deep learning-based approaches to identify and quantify cerebral microhemorrhages.

Cerebral microhemorrhages (CMHs) are associated with cerebrovascular disease, cognitive impairment, and normal aging. One method to study CMHs is to analyze histological sections (5-40 μm) stained with Prussian blue. Currently, users manually and subjectively identify and quantify Prussian blue-stained regions of interest, which is prone to inter-individual variability and can lead to significant delays in data analysis. To improve this labor-intensive process, we developed and compared three digital pathology approaches to identify and quantify CMHs from Prussian blue-stained brain sections: (1) ratiometric analysis of RGB pixel values, (2) phasor analysis of RGB images, and (3) deep learning using a mask region-based convolutional neural network. We applied these approaches to a preclinical mouse model of inflammation-induced CMHs. One-hundred CMHs were imaged using a 20 × objective and RGB color camera. To determine the ground truth, four users independently annotated Prussian blue-labeled CMHs. The deep learning and ratiometric approaches performed better than the phasor analysis approach compared to the ground truth. The deep learning approach had the most precision of the three methods. The ratiometric approach has the most versatility and maintained accuracy, albeit with less precision. Our data suggest that implementing these methods to analyze CMH images can drastically increase the processing speed while maintaining precision and accuracy