Vehicle-borne positioning in urban environments is a crucial application scenario of the BeiDou Real-Time Kinematic (RTK) technique. However, its performance is often affected by signal interruptions and ambiguity resolution failures. The integration of BeiDou-3 (BDS-3) and BeiDou-2 (BDS-2) will significantly increase the number of available satellites with improved ambiguity resolution, thereby enhancing high-precision positioning performance in complex urban environments. In this work, a vehicle-borne experiment with collected triple-frequency observations from BDS-2 and BDS-3 satellites were utilized to evaluate the ambiguity resolution and positioning performance of single-epoch triple-frequency RTK positioning in urban environments. The results indicate that in complex urban environments, the extra-wide-lane ambiguity of certain satellites cannot be correctly resolved by the integer rounding methods. The centimeter-level positional availability of BDS-2 alone is relatively low, whereas the ambiguity resolution success rate and centimeter-level positional availability are significantly improved when BDS-2 and BDS-3 signals are integrated.

In complex urban environments, Global Navigation Satellite System (GNSS) positioning accuracy suffers from Non-Line-of-Sight (NLOS) signals and multipath effects. In this study, we propose a method that combines visual information and machine learning to identify and remove NLOS signals to enhance localization performance in GNSS-challenged environments. We generate LOS/NLOS labels using data from an inertial navigation system (INS) and images from a fisheye camera, and then extract three key features, Signal-to-Noise Ratio (SNR), elevation and azimuth to train machine learning models for signal classification. Classification performance of seven different machine learning models was evaluated for comparison. Experimental results show that deep and ensemble learning models, especially Multilayer Perceptron (MLP) algorithm, achieve higher classification accuracy but lower computational efficiency. Also feature importance analysis highlights elevation as the most influential factor. The GNSS Single Point Positioning (SPP) accuracy is improved significantly with the removal of satellites classified as NLOS, and the positioning accuracy improvement with the MLP algorithm is 22.16% and 29.55% in the horizontal and vertical direction, respectively.

As the fundamental timing reference in integrated circuit systems, the clock signal is often required to have extremely short start-up time. Existing methods to improve startup time typically employ external signal control to drive the crystal, which evidently compromises the circuit’s integration and power efficiency. This paper presents a quick start-up 32.768 kHz crystal oscillator with adaptive dual-mode switching. During the start-up phase, the negative resistance boosting (NRB) technique is applied by a multistage amplifier to shorten the start-up time. Transition from boost-mode to steady-mode is controlled through amplitude detection, facilitating dual-mode adaptive operation without the reliance on external clocks. This approach enhances the robustness and integration of the circuit while effectively mitigating the increase in power consumption typically associated with NRB. As a validation, the proposed technique is incorporated into the design of a crystal oscillator and implemented in 180 nm CMOS technology, where experiments show that the start-up time and the power consumption of the core starting circuit are 116 ms and 1.03 µW, respectively.

In this paper, we propose a method with artificial intelligence to optimize manual astronomical observation works. For the large amount of data generated by radio astronomy monitoring, we compile 4 algorithms including VTD, WSV, MAD, and MAS in the procedure of data analysis. Then the platform can recognize the radio interference signals from radio astronomy monitoring data and analyze the spatiotemporal distribution characteristics, and generate reports automatically. Through this method, the amount of work would greatly improve work efficiency and accuracy. The distribution patterns and changes of radio frequency interference signals in the area can be grasped and analyzed efficiently and quickly by astronomy researchers.

Given the inherent challenges of measuring phase in numerous scenarios, Phase Retrieval (PR)—the task of reconstructing the original signal from phaseless measurements—stands as paramount. The absence of phase often renders prior knowledge about the signal and the structure of phaseless measurements crucial for effective solutions. This paper tackles the Fourier Transform (FT) PR problem for sparse signals. We recast the FT PR as a novel optimization problem on the Riemannian manifold by leveraging the sparsity and structural properties of the measurement. Then, an effective iterative algorithm is developed to address this problem using Riemannian optimization techniques. Numerical simulations validate the effectiveness of the proposed algorithm and demonstrate its superior accuracy compared to the existing methods.

We conducted frequency and direction of arrival estimations when more signals than sensors were present using a sub-Nyquist sampling system. We proposed a degrees of freedom (DOF) extension arrayed modulated wideband converter method based on a novel joint space-frequency sparse model, which can increase the DOF of the receiving array by appropriately increasing the sub-Nyquist sampling rate, was proposed. Our method does not require any special sparse array arrangement or an increase in the number of channels, thereby reducing cost and simplifying implementation. Coherent signals and source ambiguity were theoretically analyzed, and rigorous reconstruction conditions were provided for each case. Numerical results were provided to verify the effectiveness of the proposed method.

Finite rate of innovation (FRI) sampling is widely used in modern high-resolution range-Doppler (RD) detection systems to reduce the sampling rate. However, as the sampling rate decreases, the method becomes more noise-sensitive. This study proposes a deep atomic norm denoising network (DAND-Net) to denoise FRI samples of RD signals and estimate the noise levels (NLs). Based on the sparse common support property of the FRI samples, the denoising problem of the RD signals was modeled as an atomic norm soft thresholding problem, which jointly used multiple measurement vectors and could be unfolded into a model-driven deep network. The trainability and generalization ability of the network were improved by introducing piecewise linear functions and new trainable variables. By introducing an NL estimation operator, the NL could be better estimated during denoising, and the network training and convergence better supported. Additionally, the plug-and-play method could be used to complete denoising prior to many existing FRI methods. The simulation experiments showed that the proposed denoising network can achieve better denoising and NL estimation results with fewer iterations.

In urban informatics, traffic congestion prediction is of great importance for travel route planning and traffic management, and has received extensive attention from academia and industry. However, most previous works fail to implement a citywide traffic congestion prediction on fine-grained road segment, and without comprehensively considering strong spatial-temporal correlations. To overcome these concerns, in this paper, we propose a spatial-temporal context embedding and metric learning approach (STE-ML) to predict the traffic congestion level. In particular, our STE-ML consists of a traffic spatial-temporal context embedding component, and a metric learning component. From local and global perspectives, the context embedding component can simultaneously integrate local spatial-temporal correlation features and global traffic statistics information, and compress into an unified and abstract embedding representation. Meanwhile, metric learning component benefits from learning a more suitable distance function tuned to specific task. The combination of these models together could enhance traffic congestion prediction performance. We conduct extensive experiments on real traffic data set to evaluate the performance of our proposed STE-ML approach, and make comparison with other existing techniques. The experimental results demonstrate that the proposed STE-ML outperforms the existing methods.

As wireless communication technology is applied to various fields and the non-renewable nature of spectrum resources, spectrum resources become more and more scarce. Spectrum sensing proposed to improve spectrum efficiency requires a lot of time and energy, therefore, people pay attention to spectrum prediction, but most of existing algorithms are only used for single-step prediction. Thus, we use LSTM, Seq-to-Seq modelling and attention mechanism to design a joint multi-channel multi-step spectrum prediction algorithm. The simulation results show that the prediction performance of the algorithm is better than that of single-channel prediction algorithm.

The increasing volume of spatial-textual data generated from check-ins and reviews facilitates many practical applications. In these applications, similarity joins are the basic operation which further supports a wide range of similarity queries. However, this compute-intensive join operation calls for more efficient and effective algorithm to support real-time queries. Exploiting general purpose GPUs (GPGPUs) is one natural solution. Nevertheless, existing join algorithms utilizing GPGPU lack considerations on unique features of spatial-textual trajectories, especially the textual domain, thus resulting in poor performance. In this paper, we first propose a baseline spatial-textual trajectory similarity join algorithm, which is essentially based on a traditional algorithm for relational joins on GPGPUs. Then, by analyzing the bottleneck of this baseline, we further develop a fine-grained join algorithm which consists of memory access techniques and optimizations. We propose a strategy of flip to ensure coalesced memory accesses, and a batch scheduler for dividing the whole task into batches to fit the global memory while alleviating imbalanced throughput. Extensive experiments have been conducted over real-life datasets and the results show that our fine-grained algorithm performs the best and reduces the latency effectively.

The modulated wideband converter (MWC) is a sub‐Nyquist sampling system recently proposed for sampling sparse multi‐band signals. However, due to non‐ideal analogue components, transfer‐matrix calibration is required for the successful reconstruction. In practice, it is difficult to control the phases of the consecutive sinusoidal inputs precisely which are used to calibrate the transfer‐matrix. Here, the authors propose a method of transfer‐matrix calibration for the MWC with digital expanded channels when the phases of the sinusoidal inputs are unknown. As the oblique elements of the MWC transfer‐matrix are equal, the authors can estimate the unknown phases first. The authors then obtain the actual transfer‐matrix by phase correction. Finally, the validity of this method is verified by simulation and hardware experiment. The system performance is measured by computing the signal‐to‐noise ratio and the mean‐square error between the original and reconstructed signals.

In this letter, we propose new sub-Nyquist sampling schemes for multiple sinusoids, which require fewer number of samples than previous works. Since it is impossible to resolve the frequency ambiguity using a single sub-Nyquist sample sequence, an additional sampling channel is used to determine the correct frequencies. First, a time-staggered sampling system, with the staggered time less than or equal to the Nyquist sampling interval, is proposed. This approach requires only 3K samples to estimate the K frequency components in the signal. However, aliasing can occur when the differences between some frequencies are integer multiples of the sampling rate. Then, another sampling strategy that makes use of feedback is proposed to prevent aliasing. We demonstrate that using two sampling channels and with feedback, 4K samples suffice to resolve both frequency ambiguity and image frequency aliasing. Simulation results are provided to demonstrate the effectiveness of the proposed systems.

Multi-pulse signals are composed of finite pulse streams of arbitrary pulse shape. With the pulse shape known, a multichannel sampling scheme for multi-pulse signals can operate at the rate of innovation, which is much lower than the Nyquist rate. The sampling system is based on low-pass filters, oscillators and integrators. By now there is no hardware to practice the approach. In this paper, we design a hardware system and discover that the non-idealities of low-pass filters will lead to failing in signal reconstruction. We research how the low-pass filters affect the reconstruction and solve the problem by channel calibration. The experiments show that channel calibration compensates most of the errors induced by low-pass filters, and this approach can achieve better estimation of time-delays and amplitudes of multi-pulse signals with a known pulse shape.

In the last decade, it was shown that it is possible to reconstruct signals with finite rate of innovation (FRI signals) from the samples of their filtered versions. However, when noise is present, the present reconstruction algorithms tend to be low accuracy. In this work, a new sparsity-based reconstruction method for FRI signals is put forward. The streams of Diracs and exponential reproducing kernel are considered. Firstly, the analog time axis is quantified and aligned to grids. Secondly, selecting a finite subset of time delay parameters, the measurement vector is represented as a sparse linear combination of the amplitude parameters. Finally, the sparse solution is calculated by solving an optimization problem under L0 norm. The position of non-zero elements is approximation to the time delays, and the value of non-zero elements is the amplitude. Extensive numerical simulations demonstrate the accuracy and robustness of our method.

Compressive Sampling is an attractive way implementing analog to information conversion (AIC), of which the most successful hardware architecture is modulated wideband converter (MWC). Unfortunately, the MWC has high hardware complexity owing to high degree of freedom of the random waveforms constructing the measurement matrix. To reduce the complexity, in this letter, we present a novel Compressive Circulant Matrix based AIC (CCM-AIC) generating random waveforms by cyclic shift of a special sequence with unit amplitude and random phase in frequency domain. Theoretical analysis shows this scheme is optimal for signals sparse in frequency. CCM-AIC outperforms MWC and is more robust. Simulations classify the above analysis.

In large-scale grid applications, relationship between VOs is not simple information aggregating for autonomy and visiting security. This paper presents a virtual grid information database (VGID) which implements a hierarchical and distributed information management mechanism. An information model is presented with extensible resource metadata management and hierarchical information organization that manages storage-independent resources into a global information structure according to logical relations between resources. Local information database is built within a domain (VO), which includes information collection, storage, and query, as lots of information service has done. Based on dynamical domain information management with topology service and distributed query mechanism, local information databases are integrated to provide a user-transparent global information view. VGID supports standard XPath statement, and index-based query process is tested to be efficient. VGID has been applied to Information Service of ChinaGrid.

The shape-based routing needs a routing architecture with a geometrical computation framework on it. This paper introduces a novel routing architecture, Oct-Touched Tile (OTT), with a geometrical computation method along the horizontal- and vertical-constraints. The architecture is represented by the tiles spreading over the 2-D plane. Each tile is flexible to satisfy the constraints imposed for non-overlapping and sizing request. In this framework, any practically useful path finding and shape-based sizing technique are executed on the same architecture. Our system ex-perimentally demonstrates the performance comparable to a commercial tool.