Technology node scaling is challenged in many aspects, including pitch reduction, patterning flexibility, and lithography process variability during manufacturing. Without exception, layout hotspot detection, one of the critical steps to achieving design closure, also requires upgrading the associated techniques. With the rapid development of deep learning techniques, the detector exploiting convolutional neural network (CNN) is superior to ones based on pattern matching and classical machine learning algorithms. However, due to the local nature of CNN, the traditional CNN-based detector fails to model the relationship between the patterns in a large-sized layout, resulting in ignoring the impact of light propagation and some optical effects during photolithography. Even worse, another challenge arises from the fact that engineers cannot fully trust the results of learning model-based detectors, especially when handling some complicated layout patterns in practice. This makes it very difficult to deploy the detectors. Observing the facts, we propose a vision transformer (ViT) model-based layout hotspot detector with a deformed attention mechanism, where the training paradigm is inspired by the large pretrained foundation model (e.g., OpenAI's GPT-n series) and fine-tuning. Considering the light diffraction during photolithography, the hybrid domain (i.e., spatial and spectral domains) layout inputs via multichannel are leveraged. Besides, our proposed detector integrates a selective option where the model can choose to do prediction or send to engineers based on the misclassification risk level. Experimental results on the ICCAD2012 metal layer benchmarks and ICCAD2020 via layer benchmarks demonstrate the effectiveness and efficiency of our approach. We have made the ICCAD2020 dataset publicly available to support further research in hotspot detection, enable benchmarking across different process nodes and layout types, and facilitate reproducibility in the field. The dataset is accessible at https://github.com/shadowior/ICCAD2020.
Unmanned aerial vehicles (UAVs) have become important platforms for multi-object tracking. In this paper, we proposed Quick-UAV OCSORT, a fast and robust tracking-by-detection (TBD) algorithm that extends the Observation-centric SORT (OC-SORT) framework. Our method introduced a lightweight LightMBN network for enhanced re-identification, a pre-matching strategy to reduce redundant ReID calls, an improved B-IoU + algorithm for robust small object matching, and a Camera motion compensation (CMC) module to mitigate camera jitter. Experiments on the VisDrone2019 dataset demonstrate improvements of 0.88
School bus route optimization plays a crucial role in minimizing transportation costs, improving operational efficiency, and ensuring student safety. Traditional routing methods often fail to account for real-world constraints such as traffic conditions, time windows, and capacity limitations. This paper presents a comprehensive approach to optimizing school bus routes, focusing on improving safety, fuel efficiency, and overall travel time. The proposed system integrates real-time GPS tracking with geospatial mapping technologies, allowing for continuous monitoring of bus locations and traffic conditions. The system incorporates real-time traffic data, weather forecasts, and dynamic route adjustments to avoid congestion, accidents, and adverse weather conditions. Additionally, the integration of safety data ensures that bus routes and stops are adjusted to avoid high-risk areas, further enhancing student safety. The system also optimizes bus stop locations by eliminating unnecessary stops, reducing both fuel consumption and travel time. This approach not only ensures timely and safe school transportation but also provides a scalable solution for urban areas. The methodology, results, and performance of the system are discussed, demonstrating the efficacy of the solution in optimizing school bus operations.
The development of wireless communication technology has facilitated data interaction among the various components of networked wastewater treatment systems (NWTSs). However, the introduction of communication networks also brings potential risks of data injection attacks, which can compromise security control problems of NWTSs. To solve this problem, a reinforcement $Q$-learning-based security control method is proposed to maintain the control performance of NWTSs in the presence of false data injection attacks. A $Q$-table is employed in this paper to store the $Q$-values of control signals corresponding to different system states. A Softmax algorithm is designed to select the optimal control policy to control systems affected by false data injection. Unlike traditional offline training methods, the data collected during the operation of the system is also stored in the $Q$-table to update the training data. Besides, the convergence and stability of the reinforcement $Q$-learning method are theoretically proven. Finally, the proposed method is tested on the benchmark simulation model no. 1 (BSM1), simulation results validated the effectiveness of the proposed method.
The demand to acquire improved efficiency, agility, and adaptability led to rapid evolution in mobile app development (MAD). Agile approaches are recognized for being cooperative and iterative, but there are still issues in handling a range of MAD necessities. The objective here is to blend the best practices of several prominent agile approaches and non-agile approaches to form an innovative and improved MAD approach, which we refer to as the improved Agile and Lean-based MAD Approach (iALBMAD), and this approach was the improved upon our previous work, ALBMAD. Here, three aspects of improvement concerning the discovery of suitable app attributes and best practices at various MAD activities and strengthening requirement gathering activities are exploited. For this to be accomplished, first we determined different app attributes that affect the MAD, agile and non-agile best practices, and machine learning (ML) functioning in MAD from the accessible literature. Now, we have equipped ALBMAD with all these gained aspects as per their applicability and offered it to 18 MAD experts to obtain suggestions for its improvement. Considering the experts’ opinions, a three-layered approach, namely, iALBMAD, was developed. In iALBMAD, automation and an iterative cycle are established to meet finished needs; these revisions may boost the quality of requirements and minimize time. Specific and experts validated best practices and app attributes suitable for each activity of iALBMAD are offered, which will assist less-skilled developers. Thirteen users verified the usability of six teams’ apps created using three different approaches, and the results show that the iALBMAD performs better than other approaches. The suggested approach and the discoveries will provide insightful information for individuals and MAD firms aiming to improve the way of MAD.
