YOLO-BEV: Generating Bird's-Eye View in the Same Way as 2D Object Detection

Vehicle perception systems strive to achieve comprehensive and rapid visual interpretation of their surroundings for improved safety and navigation. We introduce YOLO-BEV, an efficient framework that harnesses a unique surrounding cameras setup to generate a 2D bird's-eye view of the vehicular environment. By strategically positioning eight cameras, each at a 45-degree interval, our system captures and integrates imagery into a coherent 3x3 grid format, leaving the center blank, providing an enriched spatial representation that facilitates efficient processing. In our approach, we employ YOLO's detection mechanism, favoring its inherent advantages of swift response and compact model structure. Instead of leveraging the conventional YOLO detection head, we augment it with a custom-designed detection head, translating the panoramically captured data into a unified bird's-eye view map of ego car. Preliminary results validate the feasibility of YOLO-BEV in real-time vehicular perception tasks. With its streamlined architecture and potential for rapid deployment due to minimized parameters, YOLO-BEV poses as a promising tool that may reshape future perspectives in autonomous driving systems

Path Planning based on 2D Object Bounding-box

The implementation of Autonomous Driving (AD) technologies within urban environments presents significant challenges. These challenges necessitate the development of advanced perception systems and motion planning algorithms capable of managing situations of considerable complexity. Although the end-to-end AD method utilizing LiDAR sensors has achieved significant success in this scenario, we argue that its drawbacks may hinder its practical application. Instead, we propose the vision-centric AD as a promising alternative offering a streamlined model without compromising performance. In this study, we present a path planning method that utilizes 2D bounding boxes of objects, developed through imitation learning in urban driving scenarios. This is achieved by integrating high-definition (HD) map data with images captured by surrounding cameras. Subsequent perception tasks involve bounding-box detection and tracking, while the planning phase employs both local embeddings via Graph Neural Network (GNN) and global embeddings via Transformer for temporal-spatial feature aggregation, ultimately producing optimal path planning information. We evaluated our model on the nuPlan planning task and observed that it performs competitively in comparison to existing vision-centric methods

Design and Fabrication of a MEMS Flow Sensor and Its Application in Precise Liquid Dispensing

A high speed MEMS flow sensor to enhance the reliability and accuracy of a liquid dispensing system is proposed. Benefitting from the sensor information feedback, the system can self-adjust the open time of the solenoid valve to accurately dispense desired volumes of reagent without any pre-calibration. First, an integrated high-speed liquid flow sensor based on the measurement of the pressure difference across a flow channel is presented. Dimensions of the micro-flow channel and two pressure sensors have been appropriately designed to meet the static and dynamic requirements of the liquid dispensing system. Experiments results show that the full scale (FS) flow measurement ranges up to 80 μL/s, with a nonlinearity better than 0.51% FS. Secondly, a novel closed-loop control strategy is proposed to calculate the valve open time in each dispensing cycle, which makes the system immune to liquid viscosity, pressure fluctuation, and other sources of error. Finally, dispensing results show that the system can achieve better dispensing performance, and the coefficient of variance (CV) for liquid dispensing is below 3% at 1 μL and below 4% at 100 nL

Critical success criteria for B2B E-commerce systems in Chinese medical supply chain.

The paper presents an exploratory investigation to determine and prioritise the critical success criteria, which can measure and guide the successful application and performance improvement of business to business e-commerce system (BBECS) in a medical supply chain's selling and buying functions, in the context of global business expansion. The research reveals that the buying and the selling functions have different prioritisations on the majority of the determined critical success measuring criteria. These criteria are categorised into three Critical Success Measuring Criteria Groups, for the selling and the buying functions, respectively, guiding medical supply chain members in harnessing the full advantage of a BBECS. For the selling function, the top critical success measuring criteria are as follows: integrating information searching/transmission and application processes, ensuring the reliability and timeliness of technical support, ensuring recognition and acceptance of e-commerce processes, displaying the organisation's business focus and product/service provisions online, securing a large scale/amount of business transactions, adjusting production outputs and inventory levels and having more registered users than competitors do. The top critical success measuring criteria for the buying function are as follows: securing the establishment of business relationships between businesses, displaying the measures ensuring mutual trust and cooperation online, ensuring employees' recognition of the benefit of e-commerce in increasing revenue, ensuring the contribution to the development and realisation of corporate strategy, achieving cost reduction for the organisation, making the purchase of famous brand products available/doable, securing a large scale/amount of business transactions, and ensuring the attainability of products/services at a lower price

Ticagrelor vs Clopidogrel in CYP2C19 loss-of-function carriers with Stroke or TIA

BACKGROUNDComparisons between ticagrelor- aspirin and clopidogrel-aspirin in CYP2C19 loss-of-function carriers have not been well studied for secondary stroke prevention.METHODSWe conducted a randomized, double-blind, placebo-controlled trial of 6,412 patients with a minor ischemic stroke or TIA who carried CYP2C19 LOF alleles determined by point-of-care testing. Patients were randomly assigned within 24 hours after symptom onset, in a 1:1 ratio to receive ticagrelor (180 mg loading dose on day 1 followed by 90 mg twice daily for days 2 through 90) or clopidogrel (300 mg loading dose on day 1 followed by 75 mg per day for days 2 through 90), plus aspirin (75-300 mg loading dose followed by 75 mg daily for 21 days). The primary efficacy outcome was stroke and the primary safety outcome was severe or moderate bleeding, both within 90 days. RESULTSStroke occurred within 90 days in 191 (6.0%) versus 243 (7.6%), respectively (hazard ratio, 0.77; 95% confidence interval, 0.64 to 0.94; P=0.008). Moderate or severe bleeding occurred in 9 patients (0.3%) in the ticagrelor-aspirin group and in 11 patients (0.3%) in the clopidogrel-aspirin group; any bleeding event occurred in 170 patients (5.3%) vs 80 (2.5%), respectively. CONCLUSIONSAmong Chinese patients with minor ischemic stroke or TIA within 24 hours after symptoms onset who were carriers of CYP2C19 loss-of-function alleles, ticagrelor- aspirin was modestly better than clopidogrel-aspirin for reducing the risk of stroke but was associated with more total bleeding events at 90 days. (CHANCE-2 ClinicalTrials.gov number, NCT04078737.

SC-AOF: A Sliding Camera and Asymmetric Optical-Flow-Based Blending Method for Image Stitching

Parallax processing and structure preservation have long been important and challenging tasks in image stitching. In this paper, an image stitching method based on sliding camera to eliminate perspective deformation and asymmetric optical flow to solve parallax is proposed. By maintaining the viewpoint of two input images in the mosaic non-overlapping area and creating a virtual camera by interpolation in the overlapping area, the viewpoint is gradually transformed from one to another so as to complete the smooth transition of the two image viewpoints and reduce perspective deformation. Two coarsely aligned warped images are generated with the help of a global projection plane. After that, the optical flow propagation and gradient descent method are used to quickly calculate the bidirectional asymmetric optical flow between the two warped images, and the optical-flow-based method is used to further align the two warped images to reduce parallax. In the image blending, the softmax function and registration error are used to adjust the width of the blending area, further eliminating ghosting and reducing parallax. Finally, by comparing our method with APAP, AANAP, SPHP, SPW, TFT, and REW, it has been proven that our method can not only effectively solve perspective deformation, but also gives more natural transitions between images. At the same time, our method can robustly reduce local misalignment in various scenarios, with higher structural similarity index. A scoring method combining subjective and objective evaluations of perspective deformation, local alignment and runtime is defined and used to rate all methods, where our method ranks first

Using Product Network Analysis to Optimize Product-to-Shelf Assignment Problems

A good product-to-shelf assignment strategy not only helps customers easily find desired product items but also increases retailer profit. Recent research has attempted to solve product-to-shelf problems using product association analysis, a powerful data mining tool that can detect significant co-purchase rules underlying a large amount of purchase transaction data. While some studies have developed efficient approaches for this task, they largely overlook important factors related to optimizing product-to-shelf assignment, including product characteristics, physical proximity, and category constraints. This paper proposes a three-stage product-to-shelf assignment method to address this shortcoming. The first stage constructs a product relationship network that represents the purchase association among product items. The second stage derives the centrality value of each product item through network analysis. Based on the centrality of each product, an item is classified as an attraction item, an opportunity item, or a trivial item. The third stage considers purchase association, physical relationship, and category constraint when evaluating the location preference of each product. Based on the location preference values, a product assignment algorithm is then developed to optimize locations for opportunity items. A series of analyses and comparisons on the performance of different network types are conducted. It is found that the two network types provide variant managerial meanings for store managers. In addition, the implementation and experimental results show the proposed method is feasible and helpful