Track initiation algorithm based on Hough transform is an effective method on condition of strong clutter environment. But due to the influence of measurement error and the selection of parameter space threshold, the problems of clutter points mix in target tracks and multiple tracks initialized by only one target are existed. In order to solve these problems, this paper proposes a track initiation algorithm based on Hough transform and space accumulation. The algorithm firstly using Hough transform for the preliminary screening of the original data. And then the obtained primary measurement data would be accumulated on data space. Finally, the threshold detection is used to judge whether to initial track. Algorithm effectively reduces the clutter points mixed in target tracks and solves the problem of multiple tracks initialized by only one target. Simulation results show that the algorithm can initial the target track effectively.

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

In the era of digitalization, a massive amount of data has been generated from people’s online activities or use of portable/wearable devices. The data often carries rich information about people. Therefore, privacy technologies are needed, from data generation to usage and from transmission to storage, to protect people’s sensitive information. Although the research community is making great progress in addressing advanced privacy protection technologies, very few educational materials have been developed to incorporate the latest research results and engage students in learning privacy technologies, especially for younger generations. In this paper, we present our newly designed educational materials on privacy technologies, which can be used for training high quality cybersecurity professionals to meet the ever-increasing demand. The developed learning modules not only incorporate the latest research results in privacy technologies but also include effective hand-on lab activities. To help other institutions effectively teach privacy technologies, we organized a faculty training workshop in summer 2022. Twenty-nine faculty from twenty institutions nationwide participated in the training. Survey results show that the participants gained a better understanding of privacy issues and demonstrated strong interest in teaching privacy technologies after attending the workshop.

Mass-shooting events pose a significant challenge to public safety, generating large volumes of unstructured textual data that hinder effective investigations and the formulation of public policy. Despite the urgency, few prior studies have effectively automated the extraction of key information from these events to support legal and investigative efforts. This paper presented the first dataset designed for knowledge acquisition on mass-shooting events through the application of named entity recognition (NER) techniques. It focuses on identifying key entities such as offenders, victims, locations, and criminal instruments, that are vital for legal and investigative purposes. The NER process is powered by Large Language Models (LLMs) using few-shot prompting, facilitating the efficient extraction and organization of critical information from diverse sources, including news articles, police reports, and social media. Experimental results on real-world mass-shooting corpora demonstrate that GPT-4o is the most effective model for mass-shooting NER, achieving the highest Micro Precision, Micro Recall, and Micro F1-scores. Meanwhile, o1-mini delivers competitive performance, making it a resource-efficient alternative for less complex NER tasks. It is also observed that increasing the shot count enhances the performance of all models, but the gains are more substantial for GPT-4o and o1-mini, highlighting their superior adaptability to few-shot learning scenarios.

Nanopattering of silicon substrates using near-field enhancement by particle-lens arrays in a chemical solution is presented for the first time. Both experimental and theoretical investigations have been carried out to understand the properties of the nanopatterns produced. The obtained results proved that the developed nanofabrication technique is a flexible method for precise surface nanopatterning over large area surface. Novel nanostructures such as nano-bump and ring arrays, that previously not possible to be produed using the particle-lens arrays, have been demonstrated using this technique. A close-packed monolayer of SiO2 spheres (r= 250 nm) was directly formed onto the silicon surface by self-assembly, and a 248nm wavelength KrF excimer laser was used to irradiate the samples. The theoretical modeling of the optical near-field is based on extended Mie theory. The effect of the laser fluence and assisted chemicals is studied. Due to the focusing of the particle lens, a reaction between the chemical solution and substrate can be induced in the locally high energy region. As a result, a lower laser fluence than the ablation threshold of bulk silicon can be used to generate nano-scale features.