Vehicles report-stop fraud is an important factor of highway fees loss, which is more than the proportion of 20% of the total loss in statistics. The paper uses data mining and data warehouse technology to resolve the deficiencies of traditional data management information system on the analysis of vehicles report-stop fraud. The significance of the issue and mining goal were described, and the correlational database used to integrate was analysed. what's moer, the analysis model and arithmetic of data mining on the report-stop fraud of vehicles were expatiated. Chaperonaging the process, a practical example was demonstrated by using the data mining technology, which shows it has a better application value in the analysis of vehicles report-stop fraud.
The vinyl chloride monomer (VCM), a common raw material in the plastics industry, is one of the environmental pollutants to which humans are mostly exposed. Thiodiglycolic acid (TDGA) in human urine is a specific biomarker of its exposure. TDGA plays an important role in understanding the relationships between exposure to the VCM and the identification of subgroups that are at increased risk for disease diagnosis. Therefore, its detection is of great significance. Here, we designed and established a ratiometric fluorescent sensor for TDGA by using Eu3+ as a bridge connecting the covalent organic framework (COF) and the energy donor molecule 2,6-dipicolinic acid (DPA) and named it DPA/Eu@PY-DHPB-COF-COOH. The sensor not only possesses the advantages of a ratiometric fluorescent sensor that can provide built-in self-calibration to correct a variety of target-independent factors but also presents high selectivity and high sensitivity. Currently, there are only a few reports on the detection of TDGA, and to the extent of our knowledge, this report is the first work on the detection of TDGA based on a COF system; so, it has an important reference value and lays a solid foundation for designing advanced sensors of TDGA.
The uplink achievable rate of massive multiple- input-multiple-output (MIMO) systems, where the low-resolution analog-to-digital converters (ADCs) are assumed to equip at the base station (BS), is investigated in this paper. We assume that only imperfect channel station information is known at the BS. Then a new MMSE receiver is designed by taking not only the Gaussian noise, but also the channel estimation error and quantizer noise into account. By using the Stieltjes transform of random matrix, we further derive a tight asymptotic equivalent for the uplink achievable rate with proposed MMSE receiver. We present a detailed analysis for the number of BS antennas through the expression of the achievable rates and validate the results using numerical simulations. It is also shown that we can compensate the performance loss due to the low-resolution quantization by increasing the number of antennas at the BS.
This paper deals with channel estimation in time-varying channels for orthogonal frequency division multiplexing (OFDM) uplink transmission. By modeling the uplink channel properly, the time-varying channel response can be determined by estimating the channel parameters. These unknown channel parameters are coupled with each other due to multipath propagation. By exploiting the orthogonality of the training symbol, the channel parameters can be easily separated. Therefore, we can estimate the unknown channel parameters path by path, instead of complex joint estimation. This leads to great reduction of computational complexity. Moreover, an order-recursive algorithm is proposed, which can approximate the nonlinear nature of Doppler shifts with any-order Taylor expansion rather than only the second-order one in the literature. The proposed algorithm can outperform the existing algorithms due to the employment of higher order Taylor expansion. Theoretical analysis and simulations are also given, demonstrating the efficiency of the proposed algorithm.
Realizing efficient solid‐state fluorescence in covalent organic frameworks (COFs) represents a persistent and fundamental challenge in materials science, hindering their application in next‐generation optoelectronics. Herein, a transformative strategy leveraging σ‐π hyperconjugation is introduced to enhance the fluorescence properties of COFs. The incorporation of a methyl group into a pyrene‐based COF monomer yields an unprecedented fluorescence quantum yield increase of more than 100‐fold, accompanied by significantly enhanced optoelectronic performance. This enhancement arises from σ‐π hyperconjugation, which redistributes electron density, converting pyrene from an electron donor to an electron acceptor and facilitating efficient electronic transitions. Additionally, a COF‐based fluorescent acoustic sensor, fabricated via electrospinning, demonstrates real‐time speech pattern recognition when integrated with machine learning algorithms, showcasing potential in language‐assistive technologies. This study pioneers the exploration of hyperconjugation in COFs, offering a new paradigm for the precise modulation of optoelectronic properties and bridging fundamental research with real‐world applications.
Multiple signal classification (MUSIC) has been widely applied in wireless communications for direction-of-arrival (DOA) estimation. For massive multiple-input multiple-output (MIMO) systems operating at millimeter-wave bands, hybrid analog-digital structure has been adopted in transceiver design to reduce the cost of radio frequency chains. In hybrid massive MIMO systems, the received signals at the antennas are not sent to the receiver directly, and spatial covariance matrix, which is essential in MUSIC algorithm, is thus unavailable. As a consequence, MUSIC algorithm cannot be directly used in hybrid massive MIMO systems. In this letter, we propose a beam sweeping approach for spatial covariance matrix reconstruction in hybrid massive MIMO systems. In particular, analog beamformer switches the beam direction to a group of predetermined DOA angles in turn, and then the spatial covariance matrix can be reconstructed by solving a set of linear equations. Insightful analysis on the reconstruction accuracy is also presented in this letter. Simulation results show that the proposed approach can reconstruct the spatial covariance matrix accurately so that MUSIC algorithm can be well used for DOA estimation in hybrid massive MIMO systems.
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