The use of unmanned aerial vehicles (UAVs) for a variety of commercial, civilian, and defense applications has increased many folds in recent years.While UAVs are expected to transform future air operations, there are instances where they can be used for malicious purposes.In this context, the detection, classification, and tracking (DCT) of UAVs (DCT-U) for safety and surveillance of national air space is a challenging task when compared to DCT of manned aerial vehicles.In this survey, we discuss the threats and challenges from malicious UAVs and we subsequently study three radio frequency (RF)-based systems for DCT-U.These RF-based systems include radars, communication systems, and RF analyzers.Radar systems are further divided into conventional and modern radar systems, while communication systems can be used for joint communications and sensing (JC&S) in active mode and act as a source of illumination to passive radars for DCT-U.The limitations of the three RF-based systems are also provided.The survey briefly discusses non-RF systems for DCT-U and their limitations.Future directions based on the lessons learned are provided at the end of the survey.
SUMMARY This article presents a basic cost-income model for electronically delivered instruction (EDI) (Jewett, 1999; Karelis, 1999). Systematic elaborations on the basic model are presented that explain the financial problems associated with EDI, and suggest strategies for making EDI financially viable. The basic model presented in the first figure includes dollars, enrollment, and hypothetical delivery systems and start-up costs. The second figure adds income and crossover points. The third figure adds the market share concept. The fourth figure simplifies the third. Seven solutions for moving the crossover to the left of the market share are presented and discussed: Increase income per enrollment, decrease the start-up costs, decrease the marginal cost, improve market share, change mode of delivery, and reduce fixed costs.
The goal of this paper is to investigate the importance of providing visual "big pictures" in the teaching of economics. The plurality and variety of concepts, variables, diagrams, and models involved in economics can be a source of confusion for many economics students. However, reviewing the existing literature on the importance of providing visual "big pictures" in the process of learning suggests that furnishing students with a visual "big picture" that illustrates the ways through which those numerous, diverse concepts are connected to each other could be an effective solution to clear up the mentioned mental chaos. As a practical example, this paper introduces a "big picture" that can be used as a good resource in intermediate macroeconomics classes. This figure presents twenty-seven commonly-discussed macroeconomic diagrams in the intermediate macroeconomics course, and gives little detail on some of these diagrams, aiming at helping students to get the whole picture at once on a single piece of paper. This macroeconomics big picture mostly focuses on the routes through which common diagrams in macroeconomics are connected to each other, and finally introduces the general macroeconomic equilibrium that is graphically derived through those connections.
Healthcare organizations have a high volume of sensitive data and traditional technologies have limited storage capacity and computational resources. The prospect of sharing healthcare data for machine learning is more arduous due to firm regulations related to patient privacy. In recent years, federated learning has offered a solution to accelerate distributed machine learning addressing concerns related to data privacy and governance. Currently, the blend of quantum computing and machine learning has experienced significant attention from academic institutions and research communities. The ultimate objective of this work is to develop a federated quantum machine learning framework (FQML) to tackle the optimization, security, and privacy challenges in the healthcare industry for medical imaging tasks. In this work, we proposed federated quantum convolutional neural networks (QCNNs) with distributed training across edge devices. To demonstrate the feasibility of the proposed FQML framework, we performed extensive experiments on two benchmark medical datasets (Pneumonia MNIST, and CT kidney disease analysis), which are non-independently and non-identically partitioned among the healthcare institutions/clients. The proposed framework is validated and assessed via large-scale simulations. Based on our results, the quantum simulation experiments achieve performance levels on par with well-known classical CNN models, 86.3% accuracy on the pneumonia dataset and 92.8% on the CT-kidney dataset, while requiring fewer model parameters and consuming less data. Moreover, the client selection mechanism is proposed to reduce the computation overhead at each communication round, which effectively improves the convergence rate.
The integration of multi-omics data presents a promising frontier in cancer diagnosis and biomarker discovery, especially for complex diseases like lung cancer. However, challenges such as high dimensionality, low sample sizes, and inherent data noise hinder traditional machine-learning approaches. Quantum Machine Learning (QML) is a cutting-edge field that bridges quantum computing and machine learning to address computational challenges more effectively. This study explores the application of QML to address these limitations, offering a novel framework-Multi-Omic QML Lung Subtype Classification (MQML-LungSC)-for classifying lung cancer subtypes: lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). Leveraging Quantum Neural Networks with multi-dimensional feature encoding, our model efficiently integrates genomic, epigenomic, and transcriptomic data from TCGA. The model not only achieves high classification accuracy (training: 0.95; testing: 0.90) using 256 encoded features, but also demonstrates enhanced efficiency by outperforming classical machine learning methods and other quantum models with a significantly reduced architectural complexity. Notably, QNN-64,delivers performance comparable to CNN-64 while maintaining a more compact and resource-efficient design. By identifying key differentiating features, this approach advances early diagnostic capabilities and supports personalized treatment strategies. This study provides strong empirical support for the future potential of unconventional computing approaches in advancing biomedical research and applications.
