Real-scene 3D digital campuses are essential for improving the accuracy and effectiveness of spatial data representation, facilitating informed decision-making for university administrators, optimizing resource management, and enriching user engagement for students and faculty. However, current approaches to constructing these digital environments face several challenges. They often rely on costly commercial platforms, struggle with integrating heterogeneous datasets, and require complex workflows to achieve both high precision and comprehensive campus coverage. This paper addresses these issues by proposing a systematic multi-source data fusion approach that employs open-source technologies to generate a real-scene 3D digital campus. A case study of Lanzhou Jiaotong University is presented to demonstrate the feasibility of this approach. Firstly, oblique photography based on unmanned aerial vehicles (UAVs) is used to capture large-scale, high-resolution images of the campus area, which are then processed using open-source software to generate an initial 3D model. Afterward, a high-resolution model of the campus buildings is then created by integrating the UAV data, while 3D Digital Elevation Model (DEM) and OpenStreetMap (OSM) building data provide a 3D overview of the surrounding campus area, resulting in a comprehensive 3D model for a real-scene digital campus. Finally, the 3D model is visualized on the web using Cesium, which enables functionalities such as real-time data loading, perspective switching, and spatial data querying. Results indicate that the proposed approach can effectively get rid of reliance on expensive proprietary systems, while rapidly and accurately reconstructing a real-scene digital campus. This framework not only streamlines data harmonization but also offers an open-source, practical, cost-effective solution for real-scene 3D digital campus construction, promoting further research and applications in twin city, Virtual Reality (VR), and Geographic Information Systems (GIS).

Specific emitter identification (SEI) is a technique that identifies the emitter through physical layer features contained in radio signals, and it is widely used in tasks such as identifying illegal transmitters and authentication. Thanks to the development of deep learning, SEI tasks based on deep learning have achieved significant improvements in recognition performance. However, illegal transmitters often broadcast for short durations and at low frequencies, resulting in very limited available training samples. In such cases, directly training deep learning models may lead to underfitting issues, thereby reducing recognition accuracy. In this paper, to address the issue of traditional methods struggling to classify transmitters when there is a scarcity of emitter samples and limited training data, we propose a method based on TFC-CNN. We propose a TFC-CNN method. Specifically, we first use continuous wavelet transform (CWT) as a data augmentation method to construct time–wavelet spectrum sample pairs. Then, we use complex-valued neural networks (CVNNs) and deep convolutional neural networks (DCNNs) to extract the hidden emitter identity features from the time and wavelet spectrum samples. We train the model using the normalized temperature-scaled cross-entropy (NT-Xent) loss and cross-entropy (CE) loss, ensuring the consistency of feature vector distributions across the two modalities with cosine loss. Finally, we fine-tune the model to achieve SEI tasks with few samples. Experimental results on open-source WiFi datasets and automatic dependent surveillance–broadcast (ADS-B) datasets show that our proposed method outperforms existing state-of-the-art methods. With only 5% of the training samples, the recognition accuracy for the ADS-B test dataset is 84.10%, and for the WiFi test dataset, it is 96.99%.

Spectrum sensing is a fundamental aspect of cognitive radio technology, tasked with detecting spectrum holes and unknown signal sources to improve spectrum management and resource utilization. While the energy detection (ED) method is widely used for spectrum sensing, it struggles with noise uncertainty (NU) and low signal-to-noise ratio (SNR) environments. To address these challenges, we present a novel spectrum sensing approach called FDED-DBSCAN, which combines frequency domain energy detection (FDED), frequency domain noise estimation (FDNE), and the density-based spatial clustering of applications with noise (DBSCAN) algorithm, which clusters signals based on their time-frequency two-dimensional feature space. The proposed method utilizes the GNU Radio toolkit for efficient acquisition and processing of in-phase and quadrature (I/Q) data from software-defined radio (SDR) hardware, and transforming this data into power spectral density (PSD) for analysis. The FDED algorithm detects signal occupancy in subchannels, while FDNE calculates an adaptive threshold based on noise variance estimates. Multiple rounds of FDED detection are compiled into a two-dimensional signal detection matrix, which is subsequently analyzed by the DBSCAN algorithm to identify signal clusters. This integrated approach facilitates accurate detection of multiple unknown signals and their bandwidths over a given frequency range, even under low SNR conditions. Experimental validation across three scenarios—single-signal comparison with ED (Scenario I), Orthogonal Frequency Division Multiplexing (OFDM)/Narrowband signal detection (Scenario II), and real-world multi-signal acquisition including FM bands (Scenario III)—confirms FDED-DBSCAN’s superiority over conventional ED, particularly in blind detection of unknown signals. This method demonstrates robust potential for practical applications in complex radio environments.

To reduce the complex communication problem that arise as the number of on-chip component increases, the use of Network-on-Chip (NoC) as interconnection architectures have become more promising to solve complex on-chip communication problems. However, providing a suitable test base to measure and verify functionality of any NoC is a compulsory. Universal Verification Methodology (UVM) is introduced as a standardized and reusable methodology for verifying integrated circuit design. In this research, a scalable and reconfigurable verification and benchmark environment for NoC is proposed.